diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index f5f556cd..9ab91421 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -225,6 +225,7 @@ set(GGML_PUBLIC_HEADERS include/ggml-cann.h include/ggml-cuda.h include/ggml-kompute.h + include/ggml-opt.h include/ggml-metal.h include/ggml-rpc.h include/ggml-sycl.h @@ -241,7 +242,7 @@ install(TARGETS ggml-base LIBRARY) if (GGML_METAL) # FIXME: does this need to be installed with GGML_METAL_EMBED_LIBRARY? install( - FILES ggml/src/ggml-metal/ggml-metal.metal + FILES src/ggml-metal/ggml-metal.metal PERMISSIONS OWNER_READ OWNER_WRITE diff --git a/ggml/src/ggml-amx.cpp b/ggml/src/ggml-amx.cpp deleted file mode 100644 index 144dc9d8..00000000 --- a/ggml/src/ggml-amx.cpp +++ /dev/null @@ -1,436 +0,0 @@ -#include "ggml-amx.h" -#include "ggml-amx/common.h" -#include "ggml-amx/mmq.h" -#include "ggml-backend-impl.h" -#include "ggml-impl.h" - -#if defined(__gnu_linux__) -#include -#include -#endif - -#include -#include -#include - -#if defined(__AMX_INT8__) - -// AMX buffer interface -static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { - free(buffer->context); -} - -static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { - return (void *)(buffer->context); -} - -static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { - memset((char *)tensor->data + offset, value, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - if (qtype_has_amx_kernels(tensor->type)) { - ggml_backend_amx_convert_weight(tensor, data, offset, size); - } else { - memcpy((char *)tensor->data + offset, data, size); - } - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); - memcpy(data, (const char *)tensor->data + offset, size); - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { - if (ggml_backend_buffer_is_host(src->buffer)) { - if (qtype_has_amx_kernels(src->type)) { - ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst)); - } else { - memcpy(dst->data, src->data, ggml_nbytes(src)); - } - return true; - } - return false; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - memset(buffer->context, value, buffer->size); -} - -static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { - /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, - /* .get_base = */ ggml_backend_amx_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, - /* .clear = */ ggml_backend_amx_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "AMX"; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * data = aligned_alloc(TENSOR_ALIGNMENT, size); - if (data == NULL) { - fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); - return NULL; - } - - return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); -} - -static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return TENSOR_ALIGNMENT; - - GGML_UNUSED(buft); -} - -static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { - return ggml_backend_amx_get_alloc_size(tensor); - - GGML_UNUSED(buft); -} - -static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - GGML_UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { - /* .iface = */ { - /* .get_name = */ ggml_backend_amx_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_amx_buffer_type_is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), - /* .context = */ NULL, - }; - - return &ggml_backend_buffer_type_amx; -} - -// backend interface - -static const char * ggml_backend_amx_name(ggml_backend_t backend) { - return "AMX"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_amx_free(ggml_backend_t backend) { - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; - delete ctx; - delete backend; -} - -static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; - - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - switch (node->op) { - case GGML_OP_MUL_MAT: - ggml_backend_amx_mul_mat(ctx, node); - break; - - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - break; - - default: - fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node)); - GGML_ASSERT(false); - } - } - - return GGML_STATUS_SUCCESS; - - GGML_UNUSED(backend); -} - -static struct ggml_backend_i ggml_backend_amx_i = { - /* .get_name = */ ggml_backend_amx_name, - /* .free = */ ggml_backend_amx_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_amx_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_amx_guid() { - static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e }; - return &guid; -} - -#define ARCH_GET_XCOMP_PERM 0x1022 -#define ARCH_REQ_XCOMP_PERM 0x1023 -#define XFEATURE_XTILECFG 17 -#define XFEATURE_XTILEDATA 18 - -static bool ggml_amx_init() { -#if defined(__gnu_linux__) - if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { - fprintf(stderr, "AMX is not ready to be used!\n"); - return false; - } - return true; -#elif defined(_WIN32) - return true; -#endif -} - -ggml_backend_t ggml_backend_amx_init() { - - // invoke a Linux system call to request access to AMX features - ggml_amx_init(); - - // backend context - ggml_backend_amx_context * ctx = new ggml_backend_amx_context; - - // ggml amx backend - ggml_backend_t backend = new ggml_backend { - /* .guid = */ ggml_backend_amx_guid(), - /* .interface = */ ggml_backend_amx_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), - /* .context = */ ctx, - }; - - return backend; -} - -bool ggml_backend_is_amx(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid()); -} - -void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { - GGML_ASSERT(ggml_backend_is_amx(backend_amx)); - - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context; - ctx->n_threads = n_threads; -} - -// device interface - -static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) { - return "AMX"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) { - return "Intel Advanced Matrix Extensions"; - - GGML_UNUSED(dev); -} - -static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_ACCEL; - - GGML_UNUSED(dev); -} - -static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_amx_device_get_name(dev); - props->description = ggml_backend_amx_device_get_description(dev); - props->type = ggml_backend_amx_device_get_type(dev); - ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total); - - // `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ false, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_amx_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_amx_buffer_type(); - - GGML_UNUSED(dev); -} - -static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - - // handle only 2d gemm for now - auto is_contiguous_2d = [](const struct ggml_tensor * t) { - return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; - }; - - switch (op->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - return true; - - case GGML_OP_MUL_MAT: { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - - const enum ggml_type type = src0->type; - const int64_t ne0 = op->ne[0]; - - bool is_training = src0->grad || src1->grad; - - // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 - // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 - bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); - - bool can_use_amx = - is_contiguous_2d(src0) && // src0 must be contiguous - is_contiguous_2d(src1) && // src1 must be contiguous - !is_training && // inference only - src1->type == GGML_TYPE_F32 && // src1 must be float32 - has_amx_kernels && // with amx kernel impls - ne0 % (TILE_N * 2) == 0; // out_features is 32x - - return can_use_amx; - } - default: - return false; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_amx_device_i = { - /* .get_name = */ ggml_backend_amx_device_get_name, - /* .get_description = */ ggml_backend_amx_device_get_description, - /* .get_memory = */ ggml_backend_amx_device_get_memory, - /* .get_type = */ ggml_backend_amx_device_get_type, - /* .get_props = */ ggml_backend_amx_device_get_props, - /* .init_backend = */ ggml_backend_amx_device_init, - /* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_amx_device_supports_op, - /* .supports_buft = */ ggml_backend_amx_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// backend reg interface - -static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) { - return "AMX"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_device ggml_backend_amx_device = { - /* .iface = */ ggml_backend_amx_device_i, - /* .reg = */ reg, - /* .context = */ nullptr, - }; - - return &ggml_backend_amx_device; - - GGML_UNUSED(reg); - GGML_UNUSED(index); -} - -static void * ggml_backend_amx_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_amx_set_n_threads; - } - return NULL; - - GGML_UNUSED(reg); - GGML_UNUSED(name); -} - -static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = { - /* .get_name = */ ggml_backend_amx_reg_get_name, - /* .get_device_count = */ ggml_backend_amx_reg_get_device_count, - /* .get_device = */ ggml_backend_amx_reg_get_device, - /* .get_proc_address = */ ggml_backend_amx_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_amx_reg(void) { - static struct ggml_backend_reg ggml_backend_amx_reg = { - /* .iface = */ ggml_backend_amx_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_amx_reg; -} - -#else // if defined(__AMX_INT8__) - -ggml_backend_t ggml_backend_amx_init(void) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - return ggml_backend_t{}; -} - -void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - - GGML_UNUSED(backend_amx); - GGML_UNUSED(n_threads); -} - -#endif diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index e30a9a00..9dcde8d1 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -525,197 +525,6 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na return reg->iface.get_proc_address(reg, name); } -// Backend registry - -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_SYCL -#include "ggml-sycl.h" -#endif - -#ifdef GGML_USE_VULKAN -#include "ggml-vulkan.h" -#endif - -#ifdef GGML_USE_BLAS -#include "ggml-blas.h" -#endif - -#ifdef GGML_USE_RPC -#include "ggml-rpc.h" -#endif - -#ifndef __AMX_INT8__ -#undef GGML_USE_AMX -#endif - -#ifdef GGML_USE_AMX -# include "ggml-amx.h" -#endif - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif - -#ifdef GGML_USE_KOMPUTE -#include "ggml-kompute.h" -#endif - -#include "ggml-cpu.h" - -struct ggml_backend_registry { - std::vector backends; - std::vector devices; - - ggml_backend_registry() { -#ifdef GGML_USE_CUDA - register_backend(ggml_backend_cuda_reg()); -#endif -#ifdef GGML_USE_METAL - register_backend(ggml_backend_metal_reg()); -#endif -#ifdef GGML_USE_SYCL - register_backend(ggml_backend_sycl_reg()); -#endif -#ifdef GGML_USE_VULKAN - register_backend(ggml_backend_vk_reg()); -#endif -#ifdef GGML_USE_CANN - register_backend(ggml_backend_cann_reg()); -#endif -#ifdef GGML_USE_BLAS - register_backend(ggml_backend_blas_reg()); -#endif -#ifdef GGML_USE_RPC - register_backend(ggml_backend_rpc_reg()); -#endif -#ifdef GGML_USE_AMX - register_backend(ggml_backend_amx_reg()); -#endif -#ifdef GGML_USE_KOMPUTE - register_backend(ggml_backend_kompute_reg()); -#endif - - register_backend(ggml_backend_cpu_reg()); - } - - void register_backend(ggml_backend_reg_t reg) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n", - __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg)); -#endif - backends.push_back(reg); - for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) { - register_device(ggml_backend_reg_dev_get(reg, i)); - } - } - - void register_device(ggml_backend_dev_t device) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device)); -#endif - devices.push_back(device); - } -}; - -static ggml_backend_registry & get_reg() { - static ggml_backend_registry reg; - return reg; -} - -// Internal API -void ggml_backend_register(ggml_backend_reg_t reg) { - get_reg().register_backend(reg); -} - -void ggml_backend_device_register(ggml_backend_dev_t device) { - get_reg().register_device(device); -} - -// Backend (reg) enumeration -size_t ggml_backend_reg_count() { - return get_reg().backends.size(); -} - -ggml_backend_reg_t ggml_backend_reg_get(size_t index) { - GGML_ASSERT(index < ggml_backend_reg_count()); - return get_reg().backends[index]; -} - -ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) { - for (size_t i = 0; i < ggml_backend_reg_count(); i++) { - ggml_backend_reg_t reg = ggml_backend_reg_get(i); - if (strcmp(ggml_backend_reg_name(reg), name) == 0) { - return reg; - } - } - return NULL; -} - -// Device enumeration -size_t ggml_backend_dev_count() { - return get_reg().devices.size(); -} - -ggml_backend_dev_t ggml_backend_dev_get(size_t index) { - GGML_ASSERT(index < ggml_backend_dev_count()); - return get_reg().devices[index]; -} - -ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) { - for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (strcmp(ggml_backend_dev_name(dev), name) == 0) { - return dev; - } - } - return NULL; -} - -ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) { - for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) == type) { - return dev; - } - } - return NULL; -} - -// Convenience functions -ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) { - ggml_backend_dev_t dev = ggml_backend_dev_by_name(name); - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, params); -} - -ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(type); - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, params); -} - -ggml_backend_t ggml_backend_init_best(void) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU); - if (!dev) { - dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); - } - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, NULL); -} - // multi-buffer buffer struct ggml_backend_multi_buffer_context { @@ -1641,7 +1450,7 @@ ggml_backend_sched_t ggml_backend_sched_new( bool parallel) { GGML_ASSERT(n_backends > 0); GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); - GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU + GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU); struct ggml_backend_sched * sched = (ggml_backend_sched *) calloc(1, sizeof(struct ggml_backend_sched)); @@ -2038,17 +1847,6 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t return true; } - - -#include "ggml-backend.h" -#include "ggml-backend-impl.h" -#include "ggml-cpu.h" -#include "ggml-impl.h" -#include -#include - -// ggml-backend interface - // CPU backend - buffer static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { @@ -2122,7 +1920,9 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { /* .reset = */ NULL, }; -// CPU backend - buffer type +// CPU backend buffer type + +// this buffer type is defined here to make it available to all backends static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { return "CPU"; @@ -2163,7 +1963,7 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), /* .context = */ NULL, }; @@ -2186,479 +1986,14 @@ static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) { /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), /* .context = */ NULL, }; return &ggml_backend_cpu_buffer_type; } -#ifdef GGML_USE_CPU_HBM - -// buffer type HBM - -#include - -static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU_HBM"; - - GGML_UNUSED(buft); -} - -static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { - hbw_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr; - int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); - if (result != 0) { - GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size); - return NULL; - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, - }, - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_buffer_type_hbm; -} -#endif - -static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backend_dev_t device) { - static ggml_backend_buffer_type_t bufts[] = { -#ifdef GGML_USE_CPU_HBM - ggml_backend_cpu_hbm_buffer_type(), -#endif - NULL - }; - - return bufts; - - GGML_UNUSED(device); -} - -// CPU backend - backend (stream) - -struct ggml_backend_cpu_context { - int n_threads; - ggml_threadpool_t threadpool; - - uint8_t * work_data; - size_t work_size; - - ggml_abort_callback abort_callback; - void * abort_callback_data; -}; - -static const char * ggml_backend_cpu_get_name(ggml_backend_t backend) { - return "CPU"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_cpu_free(ggml_backend_t backend) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - delete[] cpu_ctx->work_data; - delete cpu_ctx; - delete backend; -} - -struct ggml_backend_plan_cpu { - struct ggml_cplan cplan; - struct ggml_cgraph cgraph; -}; - -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - - struct ggml_backend_plan_cpu * cpu_plan = new ggml_backend_plan_cpu; - - cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); - cpu_plan->cgraph = *cgraph; // FIXME: deep copy - - if (cpu_plan->cplan.work_size > 0) { - cpu_plan->cplan.work_data = new uint8_t[cpu_plan->cplan.work_size]; - if (cpu_plan->cplan.work_data == NULL) { - delete cpu_plan; - return NULL; - } - } - - cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; - cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; - - return cpu_plan; -} - -static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - - delete[] cpu_plan->cplan.work_data; - delete cpu_plan; - - GGML_UNUSED(backend); -} - -static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - - return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); - - GGML_UNUSED(backend); -} - -static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - - struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); - - if (cpu_ctx->work_size < cplan.work_size) { - delete[] cpu_ctx->work_data; - cpu_ctx->work_data = new uint8_t[cplan.work_size]; - if (cpu_ctx->work_data == NULL) { - cpu_ctx->work_size = 0; - return GGML_STATUS_ALLOC_FAILED; - } - cpu_ctx->work_size = cplan.work_size; - } - cplan.work_data = (uint8_t *)cpu_ctx->work_data; - - cplan.abort_callback = cpu_ctx->abort_callback; - cplan.abort_callback_data = cpu_ctx->abort_callback_data; - - return ggml_graph_compute(cgraph, &cplan); -} - -static const struct ggml_backend_i ggml_backend_cpu_i = { - /* .get_name = */ ggml_backend_cpu_get_name, - /* .free = */ ggml_backend_cpu_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, - /* .graph_compute = */ ggml_backend_cpu_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_cpu_guid(void) { - static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; - return &guid; -} - -ggml_backend_t ggml_backend_cpu_init(void) { - // initialize CPU backend now to avoid slowing the first graph computation - ggml_cpu_init(); - - struct ggml_backend_cpu_context * ctx = new ggml_backend_cpu_context; - if (ctx == NULL) { - return NULL; - } - - ctx->n_threads = GGML_DEFAULT_N_THREADS; - ctx->threadpool = NULL; - ctx->work_data = NULL; - ctx->work_size = 0; - ctx->abort_callback = NULL; - ctx->abort_callback_data = NULL; - - ggml_backend_t cpu_backend = new ggml_backend { - /* .guid = */ ggml_backend_cpu_guid(), - /* .interface = */ ggml_backend_cpu_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), - /* .context = */ ctx, - }; - - if (cpu_backend == NULL) { - delete ctx; - return NULL; - } - - return cpu_backend; -} - -bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); -} - -void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->n_threads = n_threads; -} - -void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - - if (ctx->threadpool && ctx->threadpool != threadpool) { - // already had a different threadpool, pause/suspend it before switching - ggml_threadpool_pause(ctx->threadpool); - } - ctx->threadpool = threadpool; -} - -void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->abort_callback = abort_callback; - ctx->abort_callback_data = abort_callback_data; -} - ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); } - -// CPU backend - device - -struct ggml_backend_cpu_device_context { - std::string description = "CPU"; - - ggml_backend_cpu_device_context() { -#ifdef __APPLE__ - size_t len = 0; - if (!sysctlbyname("machdep.cpu.brand_string", NULL, &len, NULL, 0)) { - description.resize(len); - sysctlbyname("machdep.cpu.brand_string", &description[0], &len, NULL, 0); // NOLINT - } -#elif defined(__linux__) - FILE * f = fopen("/proc/cpuinfo", "r"); - if (f) { - char buf[1024]; - while (fgets(buf, sizeof(buf), f)) { - if (strncmp(buf, "model name", 10) == 0) { - char * p = strchr(buf, ':'); - if (p) { - p++; - while (std::isspace(*p)) { - p++; - } - while (std::isspace(p[strlen(p) - 1])) { - p[strlen(p) - 1] = '\0'; - } - description = p; - break; - } - } - } - fclose(f); - } -#elif defined(_WIN32) - HKEY hKey; - if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, - TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), - 0, - KEY_READ, - &hKey) == ERROR_SUCCESS) { - DWORD cpu_brand_size = 0; - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - NULL, - &cpu_brand_size) == ERROR_SUCCESS) { - description.resize(cpu_brand_size); - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - (LPBYTE)&description[0], // NOLINT - &cpu_brand_size) == ERROR_SUCCESS) { - if (description.find('\0') != std::string::npos) { - description.resize(description.find('\0')); - } - } - } - RegCloseKey(hKey); - } -#endif - } -}; - -static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) { - return "CPU"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t dev) { - struct ggml_backend_cpu_device_context * ctx = (struct ggml_backend_cpu_device_context *)dev->context; - - return ctx->description.c_str(); -} - -static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU; - - GGML_UNUSED(dev); -} - -static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_cpu_device_get_name(dev); - props->description = ggml_backend_cpu_device_get_description(dev); - props->type = ggml_backend_cpu_device_get_type(dev); - ggml_backend_cpu_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ true, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_cpu_device_init_backend(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_cpu_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(dev); -} - -static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - return ggml_backend_cpu_buffer_from_ptr(ptr, size); - - GGML_UNUSED(dev); - GGML_UNUSED(max_tensor_size); -} - -static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - switch (op->op) { - case GGML_OP_CPY: - return - op->type != GGML_TYPE_IQ2_XXS && - op->type != GGML_TYPE_IQ2_XS && - op->type != GGML_TYPE_IQ1_S && - op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float - case GGML_OP_MUL_MAT: - //return op->src[1]->type == GGML_TYPE_F32; // TMP: workaround until sync with latest ggml - return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_get_type_traits_cpu(op->src[0]->type)->vec_dot_type; - case GGML_OP_ROPE_BACK: - return op->src[2] == NULL && (op->op_params[2] & 4) == 0; - case GGML_OP_IM2COL_BACK: - return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32; - case GGML_OP_OUT_PROD: - return (op->src[0]->type == GGML_TYPE_F32 || ggml_is_quantized(op->src[0]->type)) && op->src[1]->type == GGML_TYPE_F32; - default: - return true; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_cpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return ggml_backend_buft_is_host(buft); - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { - /* .get_name = */ ggml_backend_cpu_device_get_name, - /* .get_description = */ ggml_backend_cpu_device_get_description, - /* .get_memory = */ ggml_backend_cpu_device_get_memory, - /* .get_type = */ ggml_backend_cpu_device_get_type, - /* .get_props = */ ggml_backend_cpu_device_get_props, - /* .init_backend = */ ggml_backend_cpu_device_init_backend, - /* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_host_ptr, - /* .supports_op = */ ggml_backend_cpu_device_supports_op, - /* .supports_buft = */ ggml_backend_cpu_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// CPU backend - backend (reg) - -static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) { - return "CPU"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_cpu_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_cpu_device_context ctx; - static ggml_backend_device ggml_backend_cpu_device = { - /* .iface = */ ggml_backend_cpu_device_i, - /* .reg = */ reg, - /* .context = */ &ctx, - }; - - return &ggml_backend_cpu_device; -} - -static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_cpu_set_n_threads; - } - if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { - return (void *)ggml_backend_cpu_get_extra_bufts; - } - - return NULL; - - GGML_UNUSED(reg); -} - -static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = { - /* .get_name = */ ggml_backend_cpu_reg_get_name, - /* .get_device_count = */ ggml_backend_cpu_reg_get_device_count, - /* .get_device = */ ggml_backend_cpu_reg_get_device, - /* .get_proc_address = */ ggml_backend_cpu_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_cpu_reg(void) { - static struct ggml_backend_reg ggml_backend_cpu_reg = { - /* .iface = */ ggml_backend_cpu_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_reg; -} diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas.cpp deleted file mode 100644 index 8d96220b..00000000 --- a/ggml/src/ggml-blas.cpp +++ /dev/null @@ -1,514 +0,0 @@ -#include "ggml-impl.h" -#include "ggml-blas.h" -#include "ggml-backend-impl.h" - -#include -#include -#include - -#if defined(GGML_USE_ACCELERATE) -# include -#elif defined(GGML_BLAS_USE_MKL) -# include -#elif defined(GGML_BLAS_USE_BLIS) -# include -#elif defined(GGML_BLAS_USE_NVPL) -# include -#else -# include -#endif - -struct ggml_backend_blas_context { - int n_threads = GGML_DEFAULT_N_THREADS; - std::unique_ptr work_data; - size_t work_size = 0; -#ifndef GGML_USE_OPENMP - std::vector> tasks; -#endif -}; - -static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const enum ggml_type type = src0->type; - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; - - const int64_t ne_plane = ne01*ne00; - const size_t desired_wsize = type == GGML_TYPE_F32 ? 0 : ne03*ne02*ne_plane*sizeof(float); - - if (ctx->work_size < desired_wsize) { - ctx->work_data.reset(new char[desired_wsize]); - ctx->work_size = desired_wsize; - } - void * wdata = ctx->work_data.get(); - - // convert src0 to float - if (type != GGML_TYPE_F32) { - const auto * type_traits = ggml_get_type_traits(type); - ggml_to_float_t const to_float = type_traits->to_float; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const void * x = (char *) src0->data + i02*nb02 + i03*nb03; - float * const wplane = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane; - - const int min_cols_per_thread = 4096; - const int min_rows_per_thread = std::max((int)(min_cols_per_thread/ne00), 1); - const int n_threads = std::max(std::min(ctx->n_threads, (int)(ne01/min_rows_per_thread)), 1); - -#ifdef GGML_USE_OPENMP - #pragma omp parallel for num_threads(n_threads) - for (int64_t i01 = 0; i01 < ne01; i01++) { - to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00); - } -#else - for (int i = 1; i < n_threads; i++) { - const int64_t start = i*ne01/n_threads; - const int64_t end = (i + 1)*ne01/n_threads; - if (start < end) { - ctx->tasks.push_back(std::async(std::launch::async, [=]() { - for (int64_t i01 = start; i01 < end; i01++) { - to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00); - } - })); - } - } - { - // reuse the current thread for the first task - const int64_t start = 0; - const int64_t end = ne01/n_threads; - for (int64_t i01 = start; i01 < end; i01++) { - to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00); - } - } -#endif - } - } - -#ifndef GGML_USE_OPENMP - // wait for all tasks to finish - for (auto & task : ctx->tasks) { - task.get(); - } - ctx->tasks.clear(); -#endif - } - -#if defined(OPENBLAS_VERSION) - openblas_set_num_threads(ctx->n_threads); -#endif - -#if defined(GGML_BLAS_USE_BLIS) - bli_thread_set_num_threads(ctx->n_threads); -#endif - -#if defined(GGML_BLAS_USE_NVPL) - nvpl_blas_set_num_threads(ctx->n_threads); -#endif - - for (int64_t i13 = 0; i13 < ne13; i13++) { - for (int64_t i12 = 0; i12 < ne12; i12++) { - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; - - const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03); - const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13); - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - - if (type != GGML_TYPE_F32) { - x = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane; - } - - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - ne1, ne01, ne10, - 1.0f, y, ne10, - x, ne00, - 0.0f, d, ne01); - } - } -} - -static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne3 == ne13); - GGML_ASSERT(ne03 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - // Arguments to ggml_compute_forward_out_prod (expressed as major,minor) - // src0: (k,n) - // src1: (k,m) - // dst: (m,n) - // - // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f) - // Also expressed as (major,minor) - // a: (m,k): so src1 transposed - // b: (k,n): so src0 - // c: (m,n) - // - // However, if ggml_is_transposed(src1) is true, then - // src1->data already contains a transposed version, so sgemm mustn't - // transpose it further. - - int n = src0->ne[0]; - int k = src0->ne[1]; - int m = src1->ne[0]; - - CBLAS_TRANSPOSE transposeA; - int lda; - - if (!ggml_is_transposed(src1)) { - transposeA = CblasTrans; - lda = m; - } else { - transposeA = CblasNoTrans; - lda = k; - } - - float * a = (float *) ((char *) src1->data); - float * b = (float *) ((char *) src0->data); - float * c = (float *) ((char *) dst->data); - - cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n); - - GGML_UNUSED(ctx); -} - -// backend interface - -static const char * ggml_backend_blas_get_name(ggml_backend_t backend) { - return "BLAS"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_blas_free(ggml_backend_t backend) { - ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context; - delete ctx; - delete backend; -} - -static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context; - - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - switch (node->op) { - case GGML_OP_MUL_MAT: - ggml_backend_blas_mul_mat(ctx, node); - break; - - case GGML_OP_OUT_PROD: - ggml_backend_blas_out_prod(ctx, node); - break; - - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - break; - - default: - GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node)); - } - } - - return GGML_STATUS_SUCCESS; - - GGML_UNUSED(backend); -} - -static struct ggml_backend_i blas_backend_i = { - /* .get_name = */ ggml_backend_blas_get_name, - /* .free = */ ggml_backend_blas_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_blas_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_blas_guid(void) { - static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d }; - return &guid; -} - -ggml_backend_t ggml_backend_blas_init(void) { - ggml_backend_blas_context * ctx = new ggml_backend_blas_context; - - ggml_backend_t backend = new ggml_backend { - /* .guid = */ ggml_backend_blas_guid(), - /* .interface = */ blas_backend_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_blas_reg(), 0), - /* .context = */ ctx, - }; - -#if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP) - if (openblas_get_parallel() != OPENBLAS_OPENMP) { - GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__); - } -#endif - -#if defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP) - GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__); -#endif - - return backend; -} - -bool ggml_backend_is_blas(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid()); -} - -void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads) { - GGML_ASSERT(ggml_backend_is_blas(backend_blas)); - - ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context; - ctx->n_threads = n_threads; -} - -// device interface - -static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) { - return "BLAS"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) { - #if defined(GGML_USE_ACCELERATE) - return "Accelerate"; - #elif defined(GGML_BLAS_USE_MKL) - return "MKL"; - #elif defined(GGML_BLAS_USE_BLIS) - return "BLIS"; - #elif defined(GGML_BLAS_USE_NVPL) - return "NVPL"; - #elif defined(OPENBLAS_VERSION) - return "OpenBLAS"; - #else - return "BLAS"; - #endif - - GGML_UNUSED(dev); -} - -static void ggml_backend_blas_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_blas_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_ACCEL; - - GGML_UNUSED(dev); -} - -static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_blas_device_get_name(dev); - props->description = ggml_backend_blas_device_get_description(dev); - props->type = ggml_backend_blas_device_get_type(dev); - ggml_backend_blas_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ true, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_blas_device_init_backend(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_blas_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_blas_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(dev); -} - -static ggml_backend_buffer_t ggml_backend_blas_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - return ggml_backend_cpu_buffer_from_ptr(ptr, size); - - GGML_UNUSED(dev); - GGML_UNUSED(max_tensor_size); -} - -static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - - switch (op->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - return true; - - case GGML_OP_MUL_MAT: - { - // BLAS usually is only faster for large matrices - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = op->ne[0]; - const int64_t ne1 = op->ne[1]; - - // TODO: find the optimal value - const int64_t min_batch = 32; - - return ggml_is_contiguous(src0) && - ggml_is_contiguous(src1) && - src1->type == GGML_TYPE_F32 && - (ne0 >= min_batch && ne1 >= min_batch && ne10 >= min_batch) && - (src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL); - } - - case GGML_OP_OUT_PROD: - return op->src[0]->type == GGML_TYPE_F32 && - op->src[1]->type == GGML_TYPE_F32 && - ggml_is_matrix(src0) && - ggml_is_matrix(src1) && - ggml_is_contiguous(src0) && - (ggml_is_contiguous(src1) || ggml_is_transposed(src1)) && - (src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL); - - default: - return false; - - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_blas_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return ggml_backend_buft_is_host(buft); - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_blas_device_i = { - /* .get_name = */ ggml_backend_blas_device_get_name, - /* .get_description = */ ggml_backend_blas_device_get_description, - /* .get_memory = */ ggml_backend_blas_device_get_memory, - /* .get_type = */ ggml_backend_blas_device_get_type, - /* .get_props = */ ggml_backend_blas_device_get_props, - /* .init_backend = */ ggml_backend_blas_device_init_backend, - /* .get_buffer_type = */ ggml_backend_blas_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_blas_device_buffer_from_host_ptr, - /* .supports_op = */ ggml_backend_blas_device_supports_op, - /* .supports_buft = */ ggml_backend_blas_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// backend reg interface - -static const char * ggml_backend_blas_reg_get_name(ggml_backend_reg_t reg) { - return "BLAS"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_blas_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_blas_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_device ggml_backend_blas_device = { - /* .iface = */ ggml_backend_blas_device_i, - /* .reg = */ reg, - /* .context = */ nullptr, - }; - - return &ggml_backend_blas_device; - - GGML_UNUSED(reg); - GGML_UNUSED(index); -} - -static void * ggml_backend_blas_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_blas_set_n_threads; - } - return NULL; - - GGML_UNUSED(reg); - GGML_UNUSED(name); -} - -static const struct ggml_backend_reg_i ggml_backend_blas_reg_i = { - /* .get_name = */ ggml_backend_blas_reg_get_name, - /* .get_device_count = */ ggml_backend_blas_reg_get_device_count, - /* .get_device = */ ggml_backend_blas_reg_get_device, - /* .get_proc_address = */ ggml_backend_blas_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_blas_reg(void) { - static struct ggml_backend_reg ggml_backend_blas_reg = { - /* .iface = */ ggml_backend_blas_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_blas_reg; -} diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp deleted file mode 100644 index 77634088..00000000 --- a/ggml/src/ggml-cann.cpp +++ /dev/null @@ -1,2128 +0,0 @@ -/* - * Copyright (c) 2023-2024 The ggml authors - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in - * all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING - * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS - * IN THE SOFTWARE. - */ - -#include "ggml-cann.h" - -#include -#include - -#include -#include -#include -#include - -#include "ggml-impl.h" -#include "ggml-backend-impl.h" -#include "ggml-cann/aclnn_ops.h" -#include "ggml-cann/common.h" - -#define GGML_COMMON_DECL_C - -#include "ggml-common.h" - -#define GGML_CANN_NAME "CANN" - -/** - * @brief Handles CANN errors by printing an error message and aborting. - * - * @param stmt The statement that caused the error. - * @param func The function in which the error occurred. - * @param file The file in which the error occurred. - * @param line The line number where the error occurred. - * @param msg The error message. - */ -[[noreturn]] void ggml_cann_error(const char* stmt, const char* func, - const char* file, int line, const char* msg) { - int32_t id = -1; - aclrtGetDevice(&id); - - GGML_LOG_ERROR("CANN error: %s\n", msg); - GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, - file, line); - GGML_LOG_ERROR(" %s\n", stmt); - // abort with GGML_ASSERT to get a stack trace - GGML_ABORT("CANN error"); -} - -/** - * @brief Sets the device to be used by CANN. - * - * @param device The device ID to set. - */ -void ggml_cann_set_device(const int32_t device) { - // TODO: uncomment these lines after empty context has fixed. - // int current_device; - // ACL_CHECK(aclrtGetDevice(¤t_device)); - - // if (device == current_device) { - // return; - // } - ACL_CHECK(aclrtSetDevice(device)); -} - -/** - * @brief Retrieves the current device ID. - * - * @return The current device ID. - */ -int32_t ggml_cann_get_device() { - int32_t id; - ACL_CHECK(aclrtGetDevice(&id)); - return id; -} - -/** - * @brief Initialize the CANN device information. - * - * This function initializes the CANN device information by obtaining the - * device count and setting the memory allocation granularity for each device. - * - * @return A structure containing the device information. - */ -static ggml_cann_device_info ggml_cann_init() { - ggml_cann_device_info info = {}; - - aclError err = aclrtGetDeviceCount((uint32_t*)&info.device_count); - - if (err != ACL_SUCCESS) { - GGML_LOG_ERROR("%s: failed to initialize CANN: %s\n", - __func__, aclGetRecentErrMsg()); - return info; - } - - GGML_ASSERT(info.device_count <= GGML_CANN_MAX_DEVICES); - - for (int id = 0; id < info.device_count; ++id) { - aclrtPhysicalMemProp prop = {}; - prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; - prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; - prop.memAttr = ACL_HBM_MEM_HUGE; - prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = id; - prop.reserve = 0; - ACL_CHECK(aclrtMemGetAllocationGranularity( - &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED, - &info.devices[id].vmm_granularity)); - } - - // TODO: add more device info later. - return info; -} - -/** - * @brief Retrieve the CANN device information. - * - * This function returns a reference to a structure containing the CANN device - * information. The device information is initialized once and reused on - * subsequent calls. - * - * @return A reference to the structure containing the device information. - */ -const ggml_cann_device_info& ggml_cann_info() { - static ggml_cann_device_info info = ggml_cann_init(); - return info; -} - -//#define DEBUG_CANN_MALLOC -/** - * @brief A pool of CANN buffers(legacy). - * - * This class manages a pool of CANN buffers for a specific device. - */ -struct ggml_cann_pool_leg : public ggml_cann_pool { - /** - * @brief The maximum number of buffers in the pool. - */ - static const int MAX_BUFFERS = 256; - - /** - * @brief The device ID associated with this buffer pool. - */ - int device; - - /** - * @brief Structure representing a CANN buffer. - */ - struct ggml_cann_buffer { - void* ptr = nullptr; ///< Pointer to the buffer memory. - size_t size = 0; ///< Size of the buffer. - }; - - /** - * @brief Array of CANN buffers in the pool. - */ - ggml_cann_buffer buffer_pool[MAX_BUFFERS] = {}; - - /** - * @brief Total size of all buffers in the pool. - */ - size_t pool_size = 0; - - /** - * @brief Constructor to initialize the buffer pool for a specific device. - * - * @param device The device ID to associate with this buffer pool. - */ - explicit ggml_cann_pool_leg(int device) : device(device) {} - - /** - * @brief Destructor to free all buffers in the pool. - */ - ~ggml_cann_pool_leg() { - ggml_cann_set_device(device); - for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; - if (b.ptr != nullptr) { - ACL_CHECK(aclrtFree(b.ptr)); - pool_size -= b.size; - } - } - GGML_ASSERT(pool_size == 0); - } - - /** - * @brief Allocate a buffer of the given size. - * - * @param size The size of the buffer to allocate. - * @param actual_size A pointer to a variable to receive the actual size of - * the allocated buffer. - * @return A pointer to the allocated buffer. - */ - void* alloc(size_t size, size_t* actual_size) override { -#ifdef DEBUG_CANN_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_CANN_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void* ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - } - } - } - } - if (ibest >= 0) { - ggml_cann_buffer& b = buffer_pool[ibest]; - void* ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - void* ptr; - size_t look_ahead_size = (size_t)(1.05 * size); - look_ahead_size = 256 * ((look_ahead_size + 255) / 256); - ggml_cann_set_device(device); - ACL_CHECK( - aclrtMalloc(&ptr, look_ahead_size, ACL_MEM_MALLOC_HUGE_FIRST)); - *actual_size = look_ahead_size; - pool_size += look_ahead_size; -#ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO( - "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, " - "requested %u MB\n", - __func__, device, nnz, (uint32_t)(max_size / 1024 / 1024), - (uint32_t)(pool_size / 1024 / 1024), - (uint32_t)(size / 1024 / 1024)); -#endif - return ptr; - } - - /** - * @brief Free a buffer and return it to the pool. - * - * @param ptr Pointer to the buffer to free. - * @param size Size of the buffer to free. - */ - void free(void* ptr, size_t size) override { - for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; - } - } - // memory should always buffered. these memory may still needed by - // tasks in stream. - // TODO, fix me. - GGML_ABORT("Cann buffer pool full, increase MAX_CANN_BUFFERS\n"); - } -}; - -/** - * @brief A pool of CANN buffers with virtual memory. - * - * This class manages a pool of CANN buffers with virtual memory for a specific - * device. - */ -struct ggml_cann_pool_vmm : public ggml_cann_pool { - /** - * @brief The maximum size of the virtual memory pool (32 GB). - */ - static const size_t CANN_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB - - /** - * @brief The device ID associated with this buffer pool. - */ - int device; - - /** - * @brief Pointer to the start of the virtual memory pool. - */ - void* pool_addr = 0; - - /** - * @brief Amount of virtual memory used in the pool. - */ - size_t pool_used = 0; - - /** - * @brief Total size of the virtual memory pool. - */ - size_t pool_size = 0; - - /** - * @brief Allocation granularity for the virtual memory pool. - */ - size_t granularity; - - /** - * @brief Handles for the physical memory allocated. - */ - std::vector handles; - - /** - * @brief Offsets for the mapped memory regions. - */ - std::vector map_offsets; - - /** - * @brief Constructor to initialize the buffer pool with virtual memory for - * a specific device. - * - * @param device The device ID to associate with this buffer pool. - */ - explicit ggml_cann_pool_vmm(int device) - : device(device), - granularity(ggml_cann_info().devices[device].vmm_granularity) {} - - /** - * @brief Destructor to free all buffers in the virtual memory pool. - */ - ~ggml_cann_pool_vmm() { - if (pool_addr != 0) { - for (auto& offset : map_offsets) { - ACL_CHECK(aclrtUnmapMem(offset)); - } - for (auto& handle : handles) { - ACL_CHECK(aclrtFreePhysical(handle)); - } - ACL_CHECK(aclrtReleaseMemAddress(pool_addr)); - } - } - - /** - * @brief Allocate a buffer of the given size in the virtual memory pool. - * - * @param size The size of the buffer to allocate. - * @param actual_size A pointer to a variable to receive the actual size of - * the allocated buffer. - * @return A pointer to the allocated buffer. - */ - void* alloc(size_t size, size_t* actual_size) override { - // round up the allocation size to the alignment to ensure that all - // allocations are aligned for all data types - const size_t alignment = 128; - size = alignment * ((size + alignment - 1) / alignment); - - size_t avail = pool_size - pool_used; - - if (size > avail) { - // round up to the next multiple of the granularity - size_t reserve_size = size - avail; - reserve_size = - granularity * ((reserve_size + granularity - 1) / granularity); - - GGML_ASSERT(pool_size + reserve_size <= CANN_POOL_VMM_MAX_SIZE); - - // allocate more physical memory - aclrtPhysicalMemProp prop = {}; - prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; - prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; - prop.memAttr = ACL_HBM_MEM_HUGE; - prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; - prop.reserve = 0; - aclrtDrvMemHandle handle; - ACL_CHECK(aclrtMallocPhysical(&handle, reserve_size, &prop, 0)); - - // reserve virtual address space (if not already reserved) - if (pool_addr == 0) { - ACL_CHECK(aclrtReserveMemAddress( - &pool_addr, CANN_POOL_VMM_MAX_SIZE, 0, NULL, 1)); - } - - // map at the end of the pool - ACL_CHECK(aclrtMapMem((char*)pool_addr + pool_size, reserve_size, 0, - handle, 0)); - - handles.push_back(handle); - map_offsets.push_back((char*)pool_addr + pool_size); - - // add to the pool - pool_size += reserve_size; - - // GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB ( - // reserved %llu MB)\n", - // device, (unsigned long long) (pool_size/1024/1024), - // (unsigned long long) (reserve_size/1024/1024)); - } - - GGML_ASSERT(pool_addr != 0); - - void* ptr = (void*)((char*)pool_addr + pool_used); - *actual_size = size; - pool_used += size; - -#ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device, - (unsigned long long)size, (unsigned long long)ptr); -#endif - return ptr; - } - - /** - * @brief Free a buffer and return it to the virtual memory pool. - * - * @param ptr Pointer to the buffer to free. - * @param size Size of the buffer to free. - */ - void free(void* ptr, size_t size) override { -#ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device, - (unsigned long long)size, (unsigned long long)ptr); -#endif - - pool_used -= size; - - // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void*)((char*)pool_addr + pool_used)); - } -}; - -/** - * @brief Create a new CANN pool for a specific device. - * - * Factory method to create a new CANN pool object based on the device type. - * - * @param device The device ID for which to create the pool. - * @return A unique pointer to the created CANN pool. - */ -std::unique_ptr ggml_backend_cann_context::new_pool_for_device( - int device) { - // return std::unique_ptr(new ggml_cann_pool_leg(device)); - return std::unique_ptr(new ggml_cann_pool_vmm(device)); -} - -// cann buffer -/** - * @brief Context for managing a CANN buffer associated with a specific device. - * - * This structure holds information about a CANN buffer, including the device - * ID, device pointer, and a name derived from GGML_CANN_NAME and the device ID. - */ -struct ggml_backend_cann_buffer_context { - int32_t device; ///< The device ID associated with this buffer context. - void* dev_ptr = - nullptr; ///< Pointer to the device memory allocated for the buffer. - - /** - * @brief Constructor to initialize the CANN buffer context. - * - * @param device The device ID associated with this buffer context. - * @param dev_ptr Pointer to the device memory allocated for the buffer. - */ - ggml_backend_cann_buffer_context(int32_t device, void* dev_ptr) - : device(device), - dev_ptr(dev_ptr) {} - - /** - * @brief Destructor to free the device memory allocated for the buffer. - */ - ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); } -}; - -/** - * @brief Check if a buffer is a CANN buffer. - * - * This function checks if a given buffer is a CANN buffer by comparing its - * `get_name` function pointer to `ggml_backend_cann_buffer_get_name`. - * - * @param buffer The buffer to check. - * @return true if the buffer is a CANN buffer, false otherwise. - */ -static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft); -static bool ggml_backend_buffer_is_cann( - ggml_backend_buffer_t buffer) { - return ggml_backend_buft_is_cann(buffer->buft); -} - -/** - * @brief Free resources associated with a CANN buffer. - * - * This function frees the resources associated with a CANN buffer, including - * its context. - * - * @param buffer The CANN buffer to free. - */ -static void ggml_backend_cann_buffer_free_buffer( - ggml_backend_buffer_t buffer) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; - delete ctx; -} - -/** - * @brief Retrieve the base pointer of a CANN buffer. - * - * This function returns the base pointer of a CANN buffer, which points to the - * device memory allocated for the buffer. - * - * @param buffer The CANN buffer whose base pointer is to be retrieved. - * @return A pointer to the base of the device memory allocated for the buffer. - */ -static void* ggml_backend_cann_buffer_get_base( - ggml_backend_buffer_t buffer) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; - return ctx->dev_ptr; -} - -/** - * @brief Transform quantized Q4.0 tensor data into a format suitable for CANN - * processing. - * - * This function transforms quantized Q4.0 tensor data into a format suitable - * for CANN processing. It extracts quantization values and scales from the - * source data and prepares them in a format expected by CANN operations. - * - * @param tensor Pointer to the tensor information. - * @param src Pointer to the source data in Q4.0 format. - * @param dst Pointer to the destination buffer where transformed data will be - * stored. - */ -static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, - const void* src, - void* dst) { - - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK4_0; - size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - - uint8_t* quant_offset = (uint8_t*)dst; - uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes); - - for (int i = 0; i < groups; i++) { - const block_q4_0* group = - (const block_q4_0*)((const char*)src + i * sizeof(block_q4_0)); - *scale_offset = group->d; - scale_offset++; - - // 0-15 - for (int j = 0; j < QK4_0 / 2; j += 2) { - (*quant_offset) = (group->qs[j] & 0x0F); - (*quant_offset) |= ((group->qs[j + 1] << 4)); - quant_offset++; - } - - // 16-31 - for (int j = 0; j < QK4_0 / 2; j += 2) { - (*quant_offset) = (group->qs[j] >> 4); - (*quant_offset) |= (group->qs[j + 1] & 0xF0); - quant_offset++; - } - } - - // put (uint4b_t -8) into int4b_t - for (quant_offset = (uint8_t*)dst; - quant_offset < (uint8_t*)dst + quant_bytes; quant_offset++) { - (*quant_offset) ^= 0x88; - } -} - -/** - * @brief Transform CANN processed data back into quantized Q4.0 format. - * - * This function transforms CANN processed data back into quantized Q4.0 format. - * It reverses the transformation performed by - * ggml_backend_cann_transform_q4_0(), converting the data back into its - * original quantized form. - * - * @param tensor Pointer to the tensor information. - * @param src Pointer to the source buffer containing transformed data. - * @param dst Pointer to the destination buffer where the Q4.0 formatted data - * will be stored. - */ -static void ggml_backend_cann_transform_back_q4_0( - const ggml_tensor* tensor, void* src, void* dst) { - - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK4_0; - size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - - uint8_t* quant_offset = (uint8_t*)src; - uint16_t* scale_offset = (uint16_t*)((char*)src + quant_bytes); - - for (; quant_offset < (uint8_t*)src + quant_bytes; quant_offset++) { - (*quant_offset) ^= 0x88; - } - quant_offset = (uint8_t*)src; - - for (int i = 0; i < groups; i++) { - block_q4_0* group = (block_q4_0*)((char*)dst + i * sizeof(block_q4_0)); - group->d = *scale_offset; - scale_offset++; - - // 0-15 - for (int j = 0; j < QK4_0 / 2; j += 2) { - group->qs[j] = ((*quant_offset) & 0x0F); - group->qs[j + 1] = ((*quant_offset) >> 4); - quant_offset++; - } - - // 16-31 - for (int j = 0; j < QK4_0 / 2; j += 2) { - group->qs[j] |= ((*quant_offset) << 4); - group->qs[j + 1] |= ((*quant_offset) & 0xF0); - quant_offset++; - } - } -} - -/** - * @brief Transform quantized Q8.0 tensor data into a format suitable for CANN - * processing. - * - * This function transforms quantized Q8.0 tensor data into a format suitable - * for CANN processing. It extracts quantization values and scales from the - * source data and prepares them in a format expected by CANN operations. - * - * @param tensor Pointer to the tensor information. - * @param src Pointer to the source data in Q8.0 format. - * @param dst Pointer to the destination buffer where transformed data will be - * stored. - */ -static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor, - const void* src, - void* dst) { - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK8_0; - size_t quant_bytes = n_elems * sizeof(uint8_t); - - uint8_t* quant_offset = (uint8_t*)dst; - uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes); - - for (int i = 0; i < groups; i++) { - const block_q8_0* group = - (const block_q8_0*)((const char*)src + i * sizeof(block_q8_0)); - *scale_offset = group->d; - scale_offset++; - size_t group_quant_size = QK8_0 * sizeof(uint8_t); - memcpy(quant_offset, group->qs, group_quant_size); - quant_offset += group_quant_size; - } -} - -/** - * @brief Transform CANN processed data back into quantized Q8.0 format. - * - * This function transforms CANN processed data back into quantized Q8.0 format. - * It reverses the transformation performed by - * ggml_backend_cann_transform_q8_0(), converting the data back into its - * original quantized form. - * - * @param tensor Pointer to the tensor information. - * @param src Pointer to the source buffer containing transformed data. - * @param dst Pointer to the destination buffer where the Q8.0 formatted data - * will be stored. - */ -static void ggml_backend_cann_transform_back_q8_0( - const ggml_tensor* tensor, const void* src, void* dst) { - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK8_0; - size_t quant_bytes = n_elems * sizeof(uint8_t); - - const uint8_t* quant_offset = (const uint8_t*)src; - const uint16_t* scale_offset = - (const uint16_t*)((const char*)src + quant_bytes); - - for (int i = 0; i < groups; i++) { - block_q8_0* group = (block_q8_0*)((char*)dst + i * sizeof(block_q8_0)); - group->d = *scale_offset; - scale_offset++; - size_t group_quant_size = QK8_0 * sizeof(uint8_t); - memcpy(group->qs, quant_offset, group_quant_size); - quant_offset += group_quant_size; - } -} - -/** - * @brief Transform tensor data based on its type for CANN processing. - * - * This function transforms tensor data based on its quantization type for CANN - * processing. It dispatches the transformation based on the tensor's type to - * specialized functions handling Q4.0 and Q8.0 formats. - * - * @param tensor Pointer to the tensor information. - * @param src Pointer to the source data to be transformed. - * @param dst Pointer to the destination buffer where transformed data will be - * stored. - */ -static void ggml_backend_cann_transform(ggml_tensor* tensor, - const void* src, void* dst) { - switch (tensor->type) { - case GGML_TYPE_Q4_0: - ggml_backend_cann_transform_q4_0(tensor, src, dst); - break; - case GGML_TYPE_Q8_0: - ggml_backend_cann_transform_q8_0(tensor, src, dst); - break; - default: - break; - } -} - -/** - * @brief Transform CANN processed data back into tensor data based on its type. - * - * This function transforms CANN processed data back into tensor data based on - * its quantization type for Q4.0 and Q8.0 formats. It dispatches the - * transformation based on the tensor's type to specialized functions. - * - * @param tensor Pointer to the tensor information. - * @param src Pointer to the source data containing CANN processed data. - * @param dst Pointer to the destination buffer where transformed tensor data - * will be stored. - */ -static void ggml_backend_cann_transform_back( - const ggml_tensor* tensor, void* src, void* dst) { - switch (tensor->type) { - case GGML_TYPE_Q4_0: - ggml_backend_cann_transform_back_q4_0(tensor, src, dst); - break; - case GGML_TYPE_Q8_0: - ggml_backend_cann_transform_back_q8_0(tensor, src, dst); - break; - default: - break; - } -} - -/** - * @brief Check if transformation is needed for a given tensor type. - * - * This function checks if transformation is needed for a given tensor type - * to prepare data for CANN processing. - * - * @param type The tensor type to check. - * @return true if transformation is needed, false otherwise. - */ -static bool need_transform(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q8_0: - return true; - default: - return false; - } -} - -/** - * @brief Initialize a tensor using data from a CANN buffer. - * - * This function initializes a tensor using data from a CANN buffer. - * It handles special cases such as views and quantization. - * - * @param buffer The CANN buffer from which to initialize the tensor. - * @param tensor Pointer to the tensor to be initialized. - */ -static void ggml_backend_cann_buffer_init_tensor( - ggml_backend_buffer_t buffer, ggml_tensor* tensor) { - if (tensor->view_src != NULL && tensor->view_offs == 0) { - GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); - return; - } - - // TODO: can backend doesn't support quantized yet. Just leave the code - // here. - if (ggml_is_quantized(tensor->type)) { - // Initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = - ggml_backend_buft_get_alloc_size(buffer->buft, tensor); - - if (padded_size > original_size && tensor->view_src == nullptr) { - size_t memset_size = padded_size - original_size; - ACL_CHECK(aclrtMemset((char*)tensor->data + original_size, - memset_size, 0, memset_size)); - } - } -} - -// TODO: need handle tensor which has paddings. -/** - * @brief Set tensor data in a CANN buffer. - * - * This function sets tensor data in a CANN buffer, handling transformations - * if needed based on the tensor's type. - * - * @param buffer The CANN buffer where the tensor data will be set. - * @param tensor Pointer to the tensor whose data will be set. - * @param data Pointer to the source data to be copied into the tensor. - * @param offset Offset in the source data from where to start copying. - * @param size Size of the data to be copied, in bytes. - */ -static void ggml_backend_cann_buffer_set_tensor( - ggml_backend_buffer_t buffer, ggml_tensor *tensor, const void *data, - size_t offset, size_t size) { - ggml_backend_cann_buffer_context *ctx = - (ggml_backend_cann_buffer_context *)buffer->context; - - ggml_cann_set_device(ctx->device); - // TODO: refer to cann(#6017), it use thread's default stream. - // For acl, synchronous functions use this default stream. - // Why aclrtSynchronizeDevice? - - if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size, - ACL_MEMCPY_HOST_TO_DEVICE)); - } else { - void *transform_buffer = malloc(size); - ggml_backend_cann_transform(tensor, data, transform_buffer); - - ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, - transform_buffer, size, - ACL_MEMCPY_HOST_TO_DEVICE)); - free(transform_buffer); - } -} - -/** - * @brief Get tensor data from a CANN buffer. - * - * This function retrieves tensor data from a CANN buffer, handling - * transformations if needed based on the tensor's type. - * - * @param buffer The CANN buffer from which to retrieve tensor data. - * @param tensor Pointer to the tensor whose data will be retrieved. - * @param data Pointer to the destination buffer where the tensor data will be - * copied. - * @param offset Offset in the destination buffer where to start copying. - * @param size Size of the data to be copied, in bytes. - */ -static void ggml_backend_cann_buffer_get_tensor( - ggml_backend_buffer_t buffer, const ggml_tensor* tensor, void* data, - size_t offset, size_t size) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; - - ggml_cann_set_device(ctx->device); - - if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpy(data, size, (char*)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST)); - } else { - void* transform_buffer = malloc(size); - ACL_CHECK(aclrtMemcpy(transform_buffer, size, - (char*)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST)); - ggml_backend_cann_transform_back(tensor, transform_buffer, data); - free(transform_buffer); - } -} - -/** - * @brief Copy tensor data between CANN buffers if possible. - * - * This function copies tensor data between CANN buffers if the source and - * destination buffers are CANN buffers and they meet the necessary conditions - * (same device or devices can access each other). - * - * @param buffer The destination CANN buffer where the tensor data will be - * copied. - * @param src Pointer to the source tensor whose data will be copied. - * @param dst Pointer to the destination tensor where the data will be copied. - * @return true if the copy operation succeeded, false otherwise. - */ -static bool ggml_backend_cann_buffer_cpy_tensor( - ggml_backend_buffer_t buffer, const ggml_tensor* src, ggml_tensor* dst) { - if (ggml_backend_buffer_is_cann(src->buffer)) { - ggml_backend_cann_buffer_context* src_ctx = - (ggml_backend_cann_buffer_context*)src->buffer->context; - ggml_backend_cann_buffer_context* dst_ctx = - (ggml_backend_cann_buffer_context*)buffer->context; - - size_t memcpy_size = ggml_nbytes(src); - // Same device. - if (src_ctx->device == dst_ctx->device) { - ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size, - (const char*)src->data, memcpy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE)); - return true; - } else { - // Different device but can access by peer. - int32_t canAccessPeer = 0; - ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device, - dst_ctx->device)); - if (canAccessPeer) { - ggml_cann_set_device(src_ctx->device); - ACL_CHECK(aclrtDeviceEnablePeerAccess(dst_ctx->device, 0)); - ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size, - (const char*)src->data, memcpy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE)); - return true; - } - } - } - return false; -} - -/** - * @brief Clear a CANN buffer by setting all its memory to a specified value. - * - * This function clears a CANN buffer by setting all its memory to a specified - * value. - * - * @param buffer The CANN buffer to be cleared. - * @param value The value to which each byte in the buffer will be set. - */ -static void ggml_backend_cann_buffer_clear( - ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; - - ggml_cann_set_device(ctx->device); - ACL_CHECK(aclrtMemset(ctx->dev_ptr, buffer->size, value, buffer->size)); -} - -/** - * @brief Interface for a CANN buffer in the backend. - * - * This structure defines function pointers to operations that can be performed - * on a CANN buffer within the backend. - */ -static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { - /* .free_buffer = */ ggml_backend_cann_buffer_free_buffer, - /* .get_base = */ ggml_backend_cann_buffer_get_base, - /* .init_tensor = */ ggml_backend_cann_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_cann_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cann_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cann_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cann_buffer_clear, - /* .reset = */ NULL, -}; - -// cann buffer type -/** - * @brief Structure representing context information for a specific backend - * buffer type. - */ -struct ggml_backend_cann_buffer_type_context { - int32_t - device; /**< Device identifier associated with the buffer context. */ - std::string name; /**< Name associated with the buffer context. */ -}; - -/** - * @brief Retrieves the name associated with a CANN buffer type. - * - * This function returns the descriptive name associated with the specified - * CANN buffer type context. - * - * @param buft Pointer to the buffer type context. - * @return Const pointer to the C-style string containing the name. - */ -static const char* ggml_backend_cann_buffer_type_name( - ggml_backend_buffer_type_t buft) { - ggml_backend_cann_buffer_type_context* buft_ctx = - (ggml_backend_cann_buffer_type_context*)buft->context; - - return buft_ctx->name.c_str(); -} - -/** - * @brief Allocates a new CANN buffer of the specified type and size. - * - * This function allocates a new CANN buffer on the specified device with the - * given size. - * - * @param buft Pointer to the buffer type context. - * @param size Size in bytes of the buffer to allocate. - * @return Pointer to the allocated buffer, or nullptr if allocation fails. - */ -static ggml_backend_buffer_t -ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, - size_t size) { - ggml_backend_cann_buffer_type_context* buft_ctx = - (ggml_backend_cann_buffer_type_context*)buft->context; - - ggml_cann_set_device(buft_ctx->device); - - size = std::max(size, (size_t)1); - - void* dev_ptr; - aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST); - if (err != ACL_SUCCESS) { - GGML_LOG_ERROR( - "%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n", - __func__, size / 1024.0 / 1024.0, buft_ctx->device, - aclGetRecentErrMsg()); - return nullptr; - } - - ggml_backend_cann_buffer_context* ctx = - new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr); - - return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface, - ctx, size); -} - -/** - * @brief Retrieves the memory alignment requirement for CANN buffers of this - * type. - * - * This function returns the alignment requirement in bytes for memory allocated - * by the CANN buffer type. - * - * @param buft Pointer to the buffer type context (unused in this - * implementation). - * @return The alignment requirement in bytes (fixed at 128 bytes for CANN - * buffers). - */ -static size_t ggml_backend_cann_buffer_type_get_alignment( - ggml_backend_buffer_type_t buft) { - return 128; - - GGML_UNUSED(buft); -} - -/** - * @brief Calculates the allocation size required for a tensor in a CANN buffer. - * - * Computes the total allocation size needed for storing the tensor's data in a - * CANN buffer, considering any necessary padding or adjustments for quantized - * types. - * - * @param buft Pointer to the buffer type context (unused in this - * implementation). - * @param tensor Pointer to the tensor for which the allocation size is - * calculated. - * @return The total allocation size in bytes required for the tensor in the - * CANN buffer. - */ -static size_t ggml_backend_cann_buffer_type_get_alloc_size( - ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; - - // last line must bigger than 32, because every single op deal at - // least 32 bytes. - // TODO: quantized type? - // int64_t line_size = ne0 * ggml_element_size(tensor); - // int64_t line_size_align_32 = (line_size + 31) & ~31; - // size += (line_size_align_32 - line_size); - - // TODO: not support quantized yet. - // TODO: consider un-continue tensor. - if (ggml_is_quantized(tensor->type)) { - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size( - tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return size; - - GGML_UNUSED(buft); -} - -static bool ggml_backend_cann_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - GGML_UNUSED(buft); -} - -/** - * @brief Interface for managing CANN buffer types in the GGML backend. - * - * Provides function pointers for allocating, querying properties, and managing - * memory for CANN buffer types in the GGML backend. - */ -static const ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = { - /* .get_name = */ ggml_backend_cann_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cann_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cann_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cann_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_cann_buffer_type_is_host, -}; - -/** - * @brief Retrieves the CANN buffer type for a specified device. - * - * This function initializes and returns the buffer type interface associated - * with the given device. It ensures thread-safe access using a mutex. - * - * @param device The device index for which to retrieve the buffer type. - * @return A pointer to the buffer type interface for the specified device, or - * nullptr if the device index is out of range. - */ -ggml_backend_buffer_type_t -ggml_backend_cann_buffer_type(int32_t device) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - if (device >= ggml_backend_cann_get_device_count()) { - return nullptr; - } - - static ggml_backend_buffer_type - ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES]; - - static bool ggml_backend_cann_buffer_type_initialized = false; - - if (!ggml_backend_cann_buffer_type_initialized) { - for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) { - ggml_backend_cann_buffer_types[i] = { - /* .iface = */ ggml_backend_cann_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), - /* .context = */ - new ggml_backend_cann_buffer_type_context{ - i, "CANN" + std::to_string(i)}, - }; - } - ggml_backend_cann_buffer_type_initialized = true; - } - - return &ggml_backend_cann_buffer_types[device]; -} - -/** - * @brief Retrieves the name associated with a CANN host buffer type. - * - * This function returns the descriptive name associated with the specified - * CANN host buffer type context. - * - * @param buft Pointer to the host buffer type context. - * @return Const pointer to the C-style string containing the name. - */ -static const char * ggml_backend_cann_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return "CANN_Host"; - - GGML_UNUSED(buft); -} - -/** - * @brief Retrieves the name associated with a CANN host buffer. - * - * This function returns the descriptive name associated with the specified - * CANN host buffer context. - * - * @param buft Pointer to the host buffer context. - * @return Const pointer to the C-style string containing the name. - */ -static const char * ggml_backend_cann_host_buffer_name(ggml_backend_buffer_t buffer) { - return "CANN_Host"; - - GGML_UNUSED(buffer); -} - -/** - * @brief Free resources associated with a CANN host buffer. - * - * This function frees the resources associated with a CANN host buffer, including - * its context. - * - * @param buffer The CANN host buffer to free. - */ -static void ggml_backend_cann_host_buffer_free(ggml_backend_buffer_t buffer) { - ACL_CHECK(aclrtFreeHost(buffer->context)); -} - -/** - * @brief Allocates a new CANN host buffer of the specified size. - * - * This function allocates a new CANN host buffer with the given size. - * @param size Size in bytes of the host buffer to allocate. - * @return Pointer to the allocated host buffer, or nullptr if allocation fails. - */ -static void * ggml_cann_host_malloc(size_t size) { - if (getenv("GGML_CANN_NO_PINNED") != nullptr) { - return nullptr; - } - - void * hostPtr = nullptr; - aclError err = aclrtMallocHost((void **) &hostPtr, size); - if (err != ACL_SUCCESS) { - - GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size / 1024.0 / 1024.0, aclGetRecentErrMsg()); - return nullptr; - } - return hostPtr; -} - -/** - * @brief Allocates a new CANN host buffer of the specified type and size. - * - * @param buft Pointer to the host buffer type context. - * @param size Size in bytes of the host buffer to allocate. - * @return Pointer to the allocated host buffer, or CPU buffer pointer if allocation fails. - */ -static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * hostPtr = ggml_cann_host_malloc(size); - - if (hostPtr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; - - return buffer; -} - -/** - * @brief Interface for managing CANN host buffer types in the GGML backend. - * - * Provides function pointers for allocating, querying properties, and managing - * memory for CANN buffer types in the GGML backend. - */ -ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_cann_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cann_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cann_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), - /* .context = */ nullptr, - }; - - return &ggml_backend_cann_buffer_type_host; -} - -/** - * @brief Computes the forward operation for a given tensor using CANN - * operations. - * - * This function selects the appropriate CANN operation based on the type of - * operation specified in the tensor and performs the computation. - * - * @param ctx The CANN context containing necessary resources and - * configurations. - * @param dst The destination tensor where the result of the computation will be - * stored. - * @return true if the computation was successful; false otherwise. - */ -static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, - struct ggml_tensor* dst) { - switch (dst->op) { - case GGML_OP_REPEAT: - ggml_cann_repeat(ctx, dst); - break; - case GGML_OP_GET_ROWS: - ggml_cann_get_rows(ctx, dst); - break; - case GGML_OP_DUP: - ggml_cann_dup(ctx, dst); - break; - case GGML_OP_ADD: - ggml_cann_add(ctx, dst); - break; - case GGML_OP_ACC: - ggml_cann_acc(ctx, dst); - break; - case GGML_OP_MUL: - ggml_cann_mul_div(ctx, dst); - break; - case GGML_OP_DIV: - ggml_cann_mul_div(ctx, dst); - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(dst)) { - case GGML_UNARY_OP_GELU: - ggml_cann_activation( - ctx, dst); - break; - case GGML_UNARY_OP_SILU: - ggml_cann_activation( - ctx, dst); - break; - // TODO: Use faster gelu?? - case GGML_UNARY_OP_GELU_QUICK: - ggml_cann_activation( - ctx, dst); - break; - case GGML_UNARY_OP_TANH: - ggml_cann_activation( - ctx, dst); - break; - case GGML_UNARY_OP_RELU: - ggml_cann_activation( - ctx, dst); - break; - case GGML_UNARY_OP_HARDSIGMOID: - ggml_cann_activation(ctx, dst); - break; - case GGML_UNARY_OP_HARDSWISH: - ggml_cann_activation(ctx, dst); - break; - default: - return false; - } - break; - case GGML_OP_NORM: - ggml_cann_norm(ctx, dst); - break; - case GGML_OP_GROUP_NORM: - ggml_cann_group_norm(ctx, dst); - break; - case GGML_OP_CONCAT: - ggml_cann_concat(ctx, dst); - break; - case GGML_OP_UPSCALE: - ggml_cann_upsample_nearest2d(ctx, dst); - break; - case GGML_OP_PAD: - ggml_cann_pad(ctx, dst); - break; - case GGML_OP_ARANGE: - ggml_cann_arange(ctx, dst); - break; - case GGML_OP_TIMESTEP_EMBEDDING: - ggml_cann_timestep_embedding(ctx, dst); - break; - case GGML_OP_LEAKY_RELU: - ggml_cann_leaky_relu(ctx, dst); - break; - case GGML_OP_RMS_NORM: - ggml_cann_rms_norm(ctx, dst); - break; - case GGML_OP_MUL_MAT: - ggml_cann_mul_mat(ctx, dst); - break; - case GGML_OP_MUL_MAT_ID: - return false; - case GGML_OP_SCALE: - ggml_cann_scale(ctx, dst); - break; - case GGML_OP_SQR: - ggml_cann_sqr(ctx, dst); - break; - case GGML_OP_CLAMP: - ggml_cann_clamp(ctx, dst); - break; - case GGML_OP_CPY: - ggml_cann_cpy(ctx, dst); - break; - case GGML_OP_CONT: - ggml_cann_dup(ctx, dst); - break; - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - break; - case GGML_OP_DIAG_MASK_INF: - ggml_cann_diag_mask(ctx, dst, -INFINITY); - break; - case GGML_OP_SOFT_MAX: - ggml_cann_softmax(ctx, dst); - break; - case GGML_OP_ROPE: - ggml_cann_rope(ctx, dst); - break; - case GGML_OP_IM2COL: - ggml_cann_im2col(ctx, dst); - break; - case GGML_OP_POOL_2D: - ggml_cann_pool2d(ctx, dst); - break; - case GGML_OP_SUM_ROWS: - ggml_cann_sum_rows(ctx, dst); - break; - case GGML_OP_ARGSORT: - ggml_cann_argsort(ctx, dst); - break; - default: - return false; - } - - return true; -} - -// backend -/** - * @brief Retrieves the name associated with the CANN backend. - * - * This function returns the name assigned to the CANN backend, which is stored - * in the context of the provided backend structure. - * - * @param backend Pointer to the CANN backend structure. - * @return A pointer to a constant string representing the backend name. - */ -static const char* ggml_backend_cann_name(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - return cann_ctx->name.c_str(); -} - -/** - * @brief Frees resources associated with the CANN backend. - * - * This function releases resources associated with the CANN backend context - * and resets the device associated with the backend to its initial state. - * - * @param backend Pointer to the CANN backend structure to be freed. - */ -static void ggml_backend_cann_free(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - ACL_CHECK(aclrtSynchronizeDevice()); - ACL_CHECK(aclrtResetDevice(cann_ctx->device)); - - // finalize when last backend freed. - if (cann_ctx->device == ggml_backend_cann_get_device_count() - 1) { - ACL_CHECK(aclFinalize()); - } - - delete cann_ctx; - delete backend; -} - -/** - * @brief Sets tensor data asynchronously in the CANN backend. - * - * This function asynchronously sets tensor data in the CANN backend. Depending - * on the tensor type, it may perform data transformations before copying data - * to the device. - * - * @param backend Pointer to the CANN backend structure. - * @param tensor Pointer to the tensor structure to set data for. - * @param data Pointer to the host data to copy to the tensor. - * @param offset Offset in bytes within the host data. - * @param size Size of the data to copy in bytes. - */ -static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, - ggml_tensor *tensor, - const void *data, - size_t offset, - size_t size) { - ggml_backend_cann_context *cann_ctx = - (ggml_backend_cann_context *)backend->context; - - if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpyAsync((char *)tensor->data + offset, size, data, - size, ACL_MEMCPY_HOST_TO_DEVICE, - cann_ctx->stream())); - } else { - void *transform_buffer = malloc(size); - ggml_backend_cann_transform(tensor, data, transform_buffer); - - ACL_CHECK(aclrtMemcpyAsync( - (char *)tensor->data + offset, size, transform_buffer, size, - ACL_MEMCPY_HOST_TO_DEVICE, cann_ctx->stream())); - ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream())); - free(transform_buffer); - } -} - -static void ggml_backend_cann_get_tensor_async( - ggml_backend_t backend, const ggml_tensor *tensor, void *data, - size_t offset, size_t size) { - ggml_backend_cann_context *cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_buffer_t buf = - tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - - GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && - "unsupported buffer type"); - - if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpyAsync(data, size, (char *)tensor->data + offset, - size, ACL_MEMCPY_DEVICE_TO_HOST, - cann_ctx->stream())); - } else { - void *transform_buffer = malloc(size); - ACL_CHECK(aclrtMemcpyAsync( - transform_buffer, size, (char *)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST, cann_ctx->stream())); - ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream())); - ggml_backend_cann_transform_back(tensor, transform_buffer, data); - free(transform_buffer); - } -} - -/** - * @brief Asynchronously copies tensor data between CANN backends. - * - * This function copies tensor data asynchronously between two CANN backends. It - * checks if both tensors reside in CANN buffers and whether the devices support - * peer-to-peer access for direct copying. If not, it returns false. - * - * @param backend_src Pointer to the source CANN backend structure. - * @param backend_dst Pointer to the destination CANN backend structure. - * @param src Pointer to the source tensor to copy data from. - * @param dst Pointer to the destination tensor to copy data to. - * @return true if the copy operation succeeds, false otherwise. - */ -static bool ggml_backend_cann_cpy_tensor_async( - ggml_backend_t backend_src, ggml_backend_t backend_dst, - const ggml_tensor* src, ggml_tensor* dst) { - GGML_ASSERT(ggml_backend_is_cann(backend_src) || - ggml_backend_is_cann(backend_dst)); - - if (!ggml_backend_buffer_is_cann(src->buffer) || - !ggml_backend_buffer_is_cann(dst->buffer)) { - return false; - } - - ggml_backend_buffer_t buf_src = - src->view_src ? src->view_src->buffer : src->buffer; - ggml_backend_buffer_t buf_dst = - dst->view_src ? dst->view_src->buffer : dst->buffer; - - ggml_backend_cann_context* cann_ctx_src = - (ggml_backend_cann_context*)backend_src->context; - ggml_backend_cann_context* cann_ctx_dst = - (ggml_backend_cann_context*)backend_dst->context; - - size_t copy_size = ggml_nbytes(dst); - if (backend_src != backend_dst) { - ggml_backend_cann_buffer_context* buf_ctx_src = - (ggml_backend_cann_buffer_context*)buf_src->context; - ggml_backend_cann_buffer_context* buf_ctx_dst = - (ggml_backend_cann_buffer_context*)buf_dst->context; - - GGML_ASSERT(cann_ctx_src->device == buf_ctx_src->device); - GGML_ASSERT(cann_ctx_dst->device == buf_ctx_dst->device); - - int32_t canAccessPeer = 0; - ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device, - cann_ctx_dst->device)); - if (!canAccessPeer) { - return false; - } - - // need open both directions for memcpyasync between devices. - ggml_cann_set_device(cann_ctx_dst->device); - ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_src->device, 0)); - ggml_cann_set_device(cann_ctx_src->device); - ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_dst->device, 0)); - - ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE, - cann_ctx_src->stream())); - - //TODO: workaround for Event didn`t work here. - aclrtSynchronizeStream(cann_ctx_src->stream()); - } else { - // src and dst are on the same backend - ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE, - cann_ctx_dst->stream())); - } - - return true; -} - -/** - * @brief Synchronizes a CANN backend. - * - * This function synchronizes the specified CANN backend by waiting for all - * operations in its associated stream to complete. - * - * @param backend Pointer to the CANN backend structure to synchronize. - */ -static void ggml_backend_cann_synchronize(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - ggml_cann_set_device(cann_ctx->device); - - ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream())); -} - -/** - * @brief Computes a computational graph using a CANN backend. - * - * This function computes the operations defined in the computational graph - * using the specified CANN backend. - * - * @param backend Pointer to the CANN backend structure to use for computation. - * @param cgraph Pointer to the computational graph structure containing nodes - * representing operations to be computed. - * @return enum ggml_status Returns GGML_STATUS_SUCCESS if computation - * completes successfully, otherwise an appropriate error status. - */ -static enum ggml_status ggml_backend_cann_graph_compute( - ggml_backend_t backend, ggml_cgraph* cgraph) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - - ggml_cann_set_device(cann_ctx->device); - - for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_tensor* node = cgraph->nodes[i]; - - if (ggml_is_empty(node) || node->op == GGML_OP_NONE) { - continue; - } - - bool ok = ggml_cann_compute_forward(*cann_ctx, node); - - if (!ok) { - GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__, - node->name, ggml_op_name(node->op)); - } - GGML_ASSERT(ok); - } - - return GGML_STATUS_SUCCESS; -} - -/** - * @brief Checks if the CANN backend supports a specific operation. - * - * This function checks whether the specified operation is supported by the - * CANN backend. - * - * @param backend Pointer to the CANN backend structure to check support for - * the operation. - * @param op Pointer to the tensor representing the operation to check. - * @return bool Returns true if the operation is supported by the backend, - * otherwise false. - */ -static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, - const ggml_tensor* op) { - switch (op->op) { - case GGML_OP_UNARY: - switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_TANH: - return true; - default: - return false; - } - case GGML_OP_MUL_MAT: { - switch (op->src[0]->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - case GGML_TYPE_Q8_0: - // TODO: fix me - // Current groupsize should not be greater than k-1 in - // aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(). - case GGML_TYPE_Q4_0: - return true; - default: - return false; - } - } - case GGML_OP_MUL_MAT_ID: - return false; - // embedding - case GGML_OP_GET_ROWS: { - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q8_0: - return true; - default: - return false; - } - } break; - case GGML_OP_CPY: { - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q4_0: - return true; - default: - return false; - } - } - case GGML_OP_DUP: - case GGML_OP_REPEAT: - case GGML_OP_CONCAT: - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NORM: - case GGML_OP_ADD: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_RMS_NORM: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_CLAMP: - case GGML_OP_CONT: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - case GGML_OP_IM2COL: - case GGML_OP_POOL_2D: - case GGML_OP_SUM_ROWS: - case GGML_OP_ARGSORT: - case GGML_OP_ACC: - case GGML_OP_GROUP_NORM: - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_ARANGE: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_LEAKY_RELU: - return true; - default: - return false; - } - - GGML_UNUSED(dev); -} - -/** - * @brief Checks if the backend buffer type is associated with the CANN backend. - * - * This function checks whether the provided backend buffer type is associated - * with the CANN backend based on the comparison of its name retrieval function - * pointer. - * - * @param buft Pointer to the backend buffer type to check. - * @return bool Returns true if the buffer type is associated with the CANN - * backend, otherwise false. - */ -static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_cann_buffer_type_name; -} - -/** - * @brief Determines if a tensor operation should be offloaded to the CANN - * backend. - * - * This function checks if a given tensor operation should be offloaded to the - * CANN backend based on the operation type and the size of the tensor. It - * returns true if the second dimension (ne[1]) of the tensor is greater than or - * equal to the minimum batch size and the operation is not GGML_OP_GET_ROWS. - * - * @param backend Pointer to the CANN backend. - * @param op Pointer to the tensor operation to check. - * @return bool Returns true if the operation should be offloaded, otherwise - * false. - */ -static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, - const ggml_tensor* op) { - const int min_batch_size = 32; - GGML_UNUSED(dev); - - return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; -} - -/** - * @brief Records an event on the CANN backend stream. - * - * This function records the given event on the ACL runtime stream associated - * with the backend context. - * - * @param event Pointer to the event structure to be recorded. - */ -static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream())); -} - -/** - * @brief Waits for a recorded event to complete on the CANN backend stream. - * - * This function makes the given backend wait for the event to complete on its - * ACL runtime stream. - * - * @param backend Pointer to the backend structure. - * @param event Pointer to the event structure that the backend needs to wait - * for. - */ -static void ggml_backend_cann_event_wait(ggml_backend_t backend, - ggml_backend_event_t event) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - if (ggml_backend_is_cann(backend)) { - ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), - (aclrtEvent)event->context)); - } else { - GGML_ABORT("fatal error"); - } -} - -/** - * @brief Structure defining the interface for the CANN backend. - * - * This structure contains function pointers for various operations - * supported by the CANN backend, including name retrieval, memory - * management, tensor operations, synchronization, and event handling. - */ -static const ggml_backend_i ggml_backend_cann_interface = { - /* .get_name = */ ggml_backend_cann_name, - /* .free = */ ggml_backend_cann_free, - /* .set_tensor_async = */ ggml_backend_cann_set_tensor_async, - /* .get_tensor_async = */ ggml_backend_cann_get_tensor_async, - /* .cpy_tensor_async = */ ggml_backend_cann_cpy_tensor_async, - /* .synchronize = */ ggml_backend_cann_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_cann_graph_compute, - /* .event_record = */ ggml_backend_cann_event_record, - /* .event_wait = */ ggml_backend_cann_event_wait, -}; - -/** - * @brief Return the hardcoded GUID for the CANN backend. - * - * This function returns a static GUID which uniquely identifies the CANN - * backend. - * - * @return A pointer to the static GUID. - */ -static ggml_guid_t ggml_backend_cann_guid() { - static ggml_guid guid = {0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34, - 0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64}; - return &guid; -} - -// backend device -struct ggml_backend_cann_device_context { - int device; - std::string name; - std::string description; -}; - -static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; - return ctx->name.c_str(); -} - -static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; - return ctx->description.c_str(); -} - -static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; - ggml_backend_cann_get_device_memory(ctx->device, free, total); -} - -static enum ggml_backend_dev_type ggml_backend_cann_device_get_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU; -} - -static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { - props->name = ggml_backend_cann_device_get_name(dev); - props->description = ggml_backend_cann_device_get_description(dev); - props->type = ggml_backend_cann_device_get_type(dev); - ggml_backend_cann_device_get_memory(dev, &props->memory_free, &props->memory_total); - - bool host_buffer = getenv("GGML_CANN_NO_PINNED") == nullptr; - - props->caps = { - /* .async = */ false, - /* .host_buffer = */ host_buffer, - /* .buffer_from_host_ptr = */ false, - /* .events = */ true, - }; -} - -static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) { - GGML_UNUSED(params); - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; - return ggml_backend_cann_init(ctx->device); -} - -/** - * @brief Checks if the CANN backend supports a specific backend buffer type. - * - * This function determines whether the CANN backend supports the given backend - * buffer type by comparing the device context of the backend and buffer type. - * It returns true if the devices are same between the backend context and - * buffer type context. - * - * @param backend Pointer to the CANN backend. - * @param buft Pointer to the backend buffer type to check. - * @return bool Returns true if the CANN backend supports the buffer type, - * otherwise false. - */ -static bool ggml_backend_cann_supports_buft( - ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (ggml_backend_buft_is_cann(buft)) { - ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; - ggml_backend_cann_buffer_type_context * buft_ctx = - (ggml_backend_cann_buffer_type_context *)buft->context; - return buft_ctx->device == dev_ctx->device; - } - return false; -} - -static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; - return ggml_backend_cann_buffer_type(ctx->device); -} - -static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return ggml_backend_cann_host_buffer_type(); -} - -/** - * @brief Creates a new event for the CANN backend device. - * - * This function initializes a new event for the CANN backend by setting the - * device and creating an ACL runtime event. The created event is then wrapped - * in a ggml_backend_event structure and returned. - * - * @param backend Pointer to the CANN backend. - * @return ggml_backend_event_t Returns a pointer to the new event structure. - */ -static ggml_backend_event_t ggml_backend_cann_device_event_new( - ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; - - ggml_cann_set_device(dev_ctx->device); - - aclrtEvent event; - ACL_CHECK(aclrtCreateEvent(&event)); - - return new ggml_backend_event{ - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), dev_ctx->device), - /* .context = */ event, - }; -} - -/** - * @brief Frees a CANN backend event. - * - * This function destroys the ACL runtime event associated with the given CANN - * backend event and then deletes the event structure itself. - * - * @param event Pointer to the event structure to be freed. - */ -static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { - ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); - - delete event; - GGML_UNUSED(dev); -} - -/** - * @brief Synchronizes the given event on the CANN backend. - * - * This function waits for the specified event to complete on the ACL runtime. - * - * @param event Pointer to the event structure to be synchronized. - */ -static void ggml_backend_cann_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { - ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); - - GGML_UNUSED(dev); -} - -static const ggml_backend_device_i ggml_backend_cann_device_interface = { - /* .get_name = */ ggml_backend_cann_device_get_name, - /* .get_description = */ ggml_backend_cann_device_get_description, - /* .get_memory = */ ggml_backend_cann_device_get_memory, - /* .get_type = */ ggml_backend_cann_device_get_type, - /* .get_props = */ ggml_backend_cann_device_get_props, - /* .init_backend = */ ggml_backend_cann_device_init, // called for every card - /* .get_buffer_type = */ ggml_backend_cann_device_get_buffer_type, - /* .get_host_buffer_type = */ ggml_backend_cann_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ NULL, // not supported for CANN - /* .supports_op = */ ggml_backend_cann_supports_op, - /* .supports_buft = */ ggml_backend_cann_supports_buft, - /* .offload_op = */ ggml_backend_cann_offload_op, - /* .event_new = */ ggml_backend_cann_device_event_new, - /* .event_free = */ ggml_backend_cann_device_event_free, - /* .event_synchronize = */ ggml_backend_cann_device_event_synchronize, -}; - - -// backend reg -struct ggml_backend_cann_reg_context { - std::vector devices; -}; - -static const char * ggml_backend_cann_reg_get_name(ggml_backend_reg_t reg) { - GGML_UNUSED(reg); - return GGML_CANN_NAME; -} - -static size_t ggml_backend_cann_reg_get_device_count(ggml_backend_reg_t reg) { - ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; - return ctx->devices.size(); -} - -static ggml_backend_dev_t ggml_backend_cann_reg_get_device(ggml_backend_reg_t reg, size_t index) { - ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; - GGML_ASSERT(index < ctx->devices.size()); - return ctx->devices[index]; -} - -static void * ggml_backend_cann_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) { - GGML_UNUSED(reg); - GGML_UNUSED(name); - // reserved for future use - return nullptr; -} - -static const ggml_backend_reg_i ggml_backend_cann_reg_interface = { - /* .get_name = */ ggml_backend_cann_reg_get_name, - /* .get_device_count = */ ggml_backend_cann_reg_get_device_count, - /* .get_device_get = */ ggml_backend_cann_reg_get_device, - /* .get_proc_address = */ ggml_backend_cann_reg_get_proc_address, -}; - -// backend registry, called only once for cann backend -ggml_backend_reg_t ggml_backend_cann_reg() { - static ggml_backend_reg reg; - static bool initialized = false; - - { - static std::mutex mutex; - std::lock_guard lock(mutex); - if (!initialized) { - aclInit(nullptr); - ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context; - - for (int i = 0; i < ggml_cann_info().device_count; i++) { - ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context(); - dev_ctx->description = aclrtGetSocName(); - dev_ctx->device = i; - dev_ctx->name = GGML_CANN_NAME + std::to_string(i); - ggml_cann_set_device(i); - ggml_backend_dev_t dev = new ggml_backend_device { - /* .interface = */ ggml_backend_cann_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx - }; - ctx->devices.push_back(dev); - } - - reg = ggml_backend_reg { - /* .interface = */ ggml_backend_cann_reg_interface, - /* .context = */ ctx - }; - } - - initialized = true; - } - - return ® -} - -ggml_backend_t ggml_backend_cann_init(int32_t device) { - aclInit(nullptr); - if (device < 0 || device >= ggml_backend_cann_get_device_count()) { - GGML_LOG_ERROR("%s: error: invalid device %d\n", __func__, device); - return nullptr; - } - - ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device); - if (ctx == nullptr) { - GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); - return nullptr; - } - ggml_cann_set_device(ctx->device); - ggml_backend_t cann_backend = - new ggml_backend{/* .guid = */ ggml_backend_cann_guid(), - /* .interface = */ ggml_backend_cann_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), - /* .context = */ ctx}; - - return cann_backend; -} - -bool ggml_backend_is_cann(ggml_backend_t backend) { - return backend != NULL && - ggml_guid_matches(backend->guid, ggml_backend_cann_guid()); -} - -int32_t ggml_backend_cann_get_device_count() { - return ggml_cann_info().device_count; -} - -void ggml_backend_cann_get_device_description( - int32_t device, char* description, size_t description_size) { - ggml_cann_set_device(device); - const char* soc_name = aclrtGetSocName(); - snprintf(description, description_size, "%s", soc_name); -} - -void ggml_backend_cann_get_device_memory(int32_t device, size_t* free, - size_t* total) { - ggml_cann_set_device(device); - ACL_CHECK(aclrtGetMemInfo(ACL_HBM_MEM, free, total)); -} diff --git a/ggml/src/ggml-cpu-impl.h b/ggml/src/ggml-cpu-impl.h deleted file mode 100644 index 5b45155b..00000000 --- a/ggml/src/ggml-cpu-impl.h +++ /dev/null @@ -1,614 +0,0 @@ -#pragma once - -// GGML CPU internal header - -#include "ggml.h" -#include "ggml-impl.h" -#include // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/ -//#include -#include -#include // memcpy -#include // fabsf - - -#ifdef __cplusplus -extern "C" { -#endif - -#if defined(_MSC_VER) - -#define m512bh(p) p -#define m512i(p) p - -#else - -#define m512bh(p) (__m512bh)(p) -#define m512i(p) (__m512i)(p) - -#endif - -/** - * Converts brain16 to float32. - * - * The bfloat16 floating point format has the following structure: - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌──┴───┐┌─┴───┐ - * 0b0000000000000000 brain16 - * - * Since bf16 has the same number of exponent bits as a 32bit float, - * encoding and decoding numbers becomes relatively straightforward. - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌──┴───┐┌─┴───────────────────┐ - * 0b00000000000000000000000000000000 IEEE binary32 - * - * For comparison, the standard fp16 format has fewer exponent bits. - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌─┴─┐┌─┴──────┐ - * 0b0000000000000000 IEEE binary16 - * - * @see IEEE 754-2008 - */ -static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) { - union { - float f; - uint32_t i; - } u; - u.i = (uint32_t)h.bits << 16; - return u.f; -} - -/** - * Converts float32 to brain16. - * - * This is binary identical with Google Brain float conversion. - * Floats shall round to nearest even, and NANs shall be quiet. - * Subnormals aren't flushed to zero, except perhaps when used. - * This code should vectorize nicely if using modern compilers. - */ -static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { - ggml_bf16_t h; - union { - float f; - uint32_t i; - } u; - u.f = s; - if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */ - h.bits = (u.i >> 16) | 64; /* force to quiet */ - return h; - } - h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16; - return h; -} - -#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) -#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) - -// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 -#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) -#ifndef __FMA__ -#define __FMA__ -#endif -#ifndef __F16C__ -#define __F16C__ -#endif -#endif - -// __SSE3__ and __SSSE3__ are not defined in MSVC, but SSE3/SSSE3 are present when AVX/AVX2/AVX512 are available -#if defined(_MSC_VER) && (defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)) -#ifndef __SSE3__ -#define __SSE3__ -#endif -#ifndef __SSSE3__ -#define __SSSE3__ -#endif -#endif - -#if defined(__ARM_FEATURE_SVE) -#include -#include -#endif - -// 16-bit float -// on Arm, we use __fp16 -// on x86, we use uint16_t -#if defined(__ARM_NEON) - -// if YCM cannot find , make a symbolic link to it, for example: -// -// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ -// -#include - -#ifdef _MSC_VER - -typedef uint16_t ggml_fp16_internal_t; - -#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) } - -#else - -typedef __fp16 ggml_fp16_internal_t; - -#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) } - -#endif // _MSC_VER - -#if !defined(__aarch64__) - -// 32-bit ARM compatibility - -// vaddlvq_s16 -// vpaddq_s16 -// vpaddq_s32 -// vaddvq_s32 -// vaddvq_f32 -// vmaxvq_f32 -// vcvtnq_s32_f32 -// vzip1_u8 -// vzip2_u8 - -inline static int32_t vaddlvq_s16(int16x8_t v) { - int32x4_t v0 = vreinterpretq_s32_s64(vpaddlq_s32(vpaddlq_s16(v))); - return vgetq_lane_s32(v0, 0) + vgetq_lane_s32(v0, 2); -} - -inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { - int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); - int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); - return vcombine_s16(a0, b0); -} - -inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) { - int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a)); - int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b)); - return vcombine_s32(a0, b0); -} - -inline static int32_t vaddvq_s32(int32x4_t v) { - return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); -} - -inline static float vaddvq_f32(float32x4_t v) { - return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); -} - -inline static float vmaxvq_f32(float32x4_t v) { - return - MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), - MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); -} - -inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { - int32x4_t res; - - res[0] = roundf(vgetq_lane_f32(v, 0)); - res[1] = roundf(vgetq_lane_f32(v, 1)); - res[2] = roundf(vgetq_lane_f32(v, 2)); - res[3] = roundf(vgetq_lane_f32(v, 3)); - - return res; -} - -inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) { - uint8x8_t res; - - res[0] = a[0]; res[1] = b[0]; - res[2] = a[1]; res[3] = b[1]; - res[4] = a[2]; res[5] = b[2]; - res[6] = a[3]; res[7] = b[3]; - - return res; -} - -inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { - uint8x8_t res; - - res[0] = a[4]; res[1] = b[4]; - res[2] = a[5]; res[3] = b[5]; - res[4] = a[6]; res[5] = b[6]; - res[6] = a[7]; res[7] = b[7]; - - return res; -} - -// vld1q_s16_x2 -// vld1q_u8_x2 -// vld1q_u8_x4 -// vld1q_s8_x2 -// vld1q_s8_x4 -// TODO: double-check these work correctly - -typedef struct ggml_int16x8x2_t { - int16x8_t val[2]; -} ggml_int16x8x2_t; - -inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) { - ggml_int16x8x2_t res; - - res.val[0] = vld1q_s16(ptr + 0); - res.val[1] = vld1q_s16(ptr + 8); - - return res; -} - -typedef struct ggml_uint8x16x2_t { - uint8x16_t val[2]; -} ggml_uint8x16x2_t; - -inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) { - ggml_uint8x16x2_t res; - - res.val[0] = vld1q_u8(ptr + 0); - res.val[1] = vld1q_u8(ptr + 16); - - return res; -} - -typedef struct ggml_uint8x16x4_t { - uint8x16_t val[4]; -} ggml_uint8x16x4_t; - -inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) { - ggml_uint8x16x4_t res; - - res.val[0] = vld1q_u8(ptr + 0); - res.val[1] = vld1q_u8(ptr + 16); - res.val[2] = vld1q_u8(ptr + 32); - res.val[3] = vld1q_u8(ptr + 48); - - return res; -} - -typedef struct ggml_int8x16x2_t { - int8x16_t val[2]; -} ggml_int8x16x2_t; - -inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) { - ggml_int8x16x2_t res; - - res.val[0] = vld1q_s8(ptr + 0); - res.val[1] = vld1q_s8(ptr + 16); - - return res; -} - -typedef struct ggml_int8x16x4_t { - int8x16_t val[4]; -} ggml_int8x16x4_t; - -inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { - ggml_int8x16x4_t res; - - res.val[0] = vld1q_s8(ptr + 0); - res.val[1] = vld1q_s8(ptr + 16); - res.val[2] = vld1q_s8(ptr + 32); - res.val[3] = vld1q_s8(ptr + 48); - - return res; -} - -// NOTE: not tested -inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { - int8x16_t res; - - res[ 0] = a[b[ 0]]; - res[ 1] = a[b[ 1]]; - res[ 2] = a[b[ 2]]; - res[ 3] = a[b[ 3]]; - res[ 4] = a[b[ 4]]; - res[ 5] = a[b[ 5]]; - res[ 6] = a[b[ 6]]; - res[ 7] = a[b[ 7]]; - res[ 8] = a[b[ 8]]; - res[ 9] = a[b[ 9]]; - res[10] = a[b[10]]; - res[11] = a[b[11]]; - res[12] = a[b[12]]; - res[13] = a[b[13]]; - res[14] = a[b[14]]; - res[15] = a[b[15]]; - - return res; -} - -// NOTE: not tested -inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { - uint8x16_t res; - - res[ 0] = a[b[ 0]]; - res[ 1] = a[b[ 1]]; - res[ 2] = a[b[ 2]]; - res[ 3] = a[b[ 3]]; - res[ 4] = a[b[ 4]]; - res[ 5] = a[b[ 5]]; - res[ 6] = a[b[ 6]]; - res[ 7] = a[b[ 7]]; - res[ 8] = a[b[ 8]]; - res[ 9] = a[b[ 9]]; - res[10] = a[b[10]]; - res[11] = a[b[11]]; - res[12] = a[b[12]]; - res[13] = a[b[13]]; - res[14] = a[b[14]]; - res[15] = a[b[15]]; - - return res; -} - -#else - -#define ggml_int16x8x2_t int16x8x2_t -#define ggml_uint8x16x2_t uint8x16x2_t -#define ggml_uint8x16x4_t uint8x16x4_t -#define ggml_int8x16x2_t int8x16x2_t -#define ggml_int8x16x4_t int8x16x4_t - -#define ggml_vld1q_s16_x2 vld1q_s16_x2 -#define ggml_vld1q_u8_x2 vld1q_u8_x2 -#define ggml_vld1q_u8_x4 vld1q_u8_x4 -#define ggml_vld1q_s8_x2 vld1q_s8_x2 -#define ggml_vld1q_s8_x4 vld1q_s8_x4 -#define ggml_vqtbl1q_s8 vqtbl1q_s8 -#define ggml_vqtbl1q_u8 vqtbl1q_u8 - -#endif // !defined(__aarch64__) - -#if !defined(__ARM_FEATURE_DOTPROD) - -inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { - const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b)); - const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b)); - - return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))); -} - -#else - -#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c) - -#endif // !defined(__ARM_FEATURE_DOTPROD) - -#endif // defined(__ARM_NEON) - -#if defined(__ARM_NEON) && !defined(_MSC_VER) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - ggml_fp16_internal_t tmp; - memcpy(&tmp, &h, sizeof(ggml_fp16_t)); - return (float)tmp; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - ggml_fp16_t res; - ggml_fp16_internal_t tmp = f; - memcpy(&res, &tmp, sizeof(ggml_fp16_t)); - return res; -} - -#else - -#ifdef __wasm_simd128__ -#include -#else -#ifdef __POWER9_VECTOR__ -#include -#undef bool -#define bool _Bool -#else -#if defined(_MSC_VER) || defined(__MINGW32__) -#include -#else -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__) -#if !defined(__riscv) -#include -#endif -#endif -#endif -#endif -#endif - -#ifdef __riscv_v_intrinsic -#include -#endif - -#if defined(__loongarch64) -#if defined(__loongarch_asx) -#include -#endif -#if defined(__loongarch_sx) -#include -#endif -#endif - -#if defined(__loongarch_asx) - -typedef union { - int32_t i; - float f; -} ft_union; - -/* float type data load instructions */ -static __m128 __lsx_vreplfr2vr_s(float val) { - ft_union fi_tmpval = {.f = val}; - return (__m128)__lsx_vreplgr2vr_w(fi_tmpval.i); -} - -static __m256 __lasx_xvreplfr2vr_s(float val) { - ft_union fi_tmpval = {.f = val}; - return (__m256)__lasx_xvreplgr2vr_w(fi_tmpval.i); -} -#endif - -#ifdef __F16C__ - -#ifdef _MSC_VER -#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) -#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) -#else -#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) -#endif - -#elif defined(__POWER9_VECTOR__) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) -/* the inline asm below is about 12% faster than the lookup method */ -#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - register float f; - register double d; - __asm__( - "mtfprd %0,%2\n" - "xscvhpdp %0,%0\n" - "frsp %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=f"(f): - /* in */ "r"(h)); - return f; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - register double d; - register ggml_fp16_t r; - __asm__( /* xscvdphp can work on double or single precision */ - "xscvdphp %0,%2\n" - "mffprd %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=r"(r): - /* in */ "f"(f)); - return r; -} - -#else - -// FP16 <-> FP32 -// ref: https://github.com/Maratyszcza/FP16 - -static inline float fp32_from_bits(uint32_t w) { - union { - uint32_t as_bits; - float as_value; - } fp32; - fp32.as_bits = w; - return fp32.as_value; -} - -static inline uint32_t fp32_to_bits(float f) { - union { - float as_value; - uint32_t as_bits; - } fp32; - fp32.as_value = f; - return fp32.as_bits; -} - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - const uint32_t w = (uint32_t) h << 16; - const uint32_t sign = w & UINT32_C(0x80000000); - const uint32_t two_w = w + w; - - const uint32_t exp_offset = UINT32_C(0xE0) << 23; -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float exp_scale = 0x1.0p-112f; -#else - const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); -#endif - const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; - - const uint32_t magic_mask = UINT32_C(126) << 23; - const float magic_bias = 0.5f; - const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; - - const uint32_t denormalized_cutoff = UINT32_C(1) << 27; - const uint32_t result = sign | - (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); - return fp32_from_bits(result); -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float scale_to_inf = 0x1.0p+112f; - const float scale_to_zero = 0x1.0p-110f; -#else - const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); - const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); -#endif - float base = (fabsf(f) * scale_to_inf) * scale_to_zero; - - const uint32_t w = fp32_to_bits(f); - const uint32_t shl1_w = w + w; - const uint32_t sign = w & UINT32_C(0x80000000); - uint32_t bias = shl1_w & UINT32_C(0xFF000000); - if (bias < UINT32_C(0x71000000)) { - bias = UINT32_C(0x71000000); - } - - base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; - const uint32_t bits = fp32_to_bits(base); - const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); - const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); - const uint32_t nonsign = exp_bits + mantissa_bits; - return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); -} - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#endif // __F16C__ - -#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) - -#ifdef __ARM_FEATURE_SVE -#include -#endif // __ARM_FEATURE_SVE - -// precomputed f32 table for f16 (256 KB) -// defined in ggml.c, initialized in ggml_init() -extern float ggml_table_f32_f16[1 << 16]; - -// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, -// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. -// This is also true for POWER9. -#if !defined(GGML_FP16_TO_FP32) -inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { - uint16_t s; - memcpy(&s, &f, sizeof(uint16_t)); - return ggml_table_f32_f16[s]; -} - -#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) -#endif - -#if !defined(GGML_FP32_TO_FP16) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) -#endif - -#ifdef __cplusplus -} -#endif diff --git a/ggml/src/ggml-cpu.c b/ggml/src/ggml-cpu.c deleted file mode 100644 index de1de18e..00000000 --- a/ggml/src/ggml-cpu.c +++ /dev/null @@ -1,13834 +0,0 @@ -#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows -#define _USE_MATH_DEFINES // For M_PI on MSVC - -#include "ggml-aarch64.h" -#include "ggml-backend-impl.h" -#include "ggml-backend.h" -#include "ggml-cpu-impl.h" -#include "ggml-cpu.h" -#include "ggml-impl.h" -#include "ggml-quants.h" -#include "ggml.h" - -#if defined(_MSC_VER) || defined(__MINGW32__) -#include // using malloc.h with MSC/MINGW -#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) -#include -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#if defined(__gnu_linux__) -#include -#endif - -#ifdef GGML_USE_OPENMP -#include -#endif - -#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) -#undef GGML_USE_LLAMAFILE -#endif - -#ifdef GGML_USE_LLAMAFILE -#include -#endif - -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) - -// disable POSIX deprecation warnings -// these functions are never going away, anyway -#pragma warning(disable: 4996) - -// unreachable code because of multiple instances of code after GGML_ABORT -#pragma warning(disable: 4702) -#endif - -// Note: once we move threading into a separate C++ file -// will use std::hardware_destructive_interference_size instead of hardcoding it here -// and we'll use C++ attribute syntax. -#define GGML_CACHE_LINE 64 - -#if defined(__clang__) || defined(__GNUC__) -#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE))) -#endif - -#if defined(__has_feature) -#if __has_feature(thread_sanitizer) -#define GGML_TSAN_ENABLED 1 -#endif -#else // __has_feature -#if defined(__SANITIZE_THREAD__) -#define GGML_TSAN_ENABLED 1 -#endif -#endif // __has_feature - -#define UNUSED GGML_UNUSED -#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) - -#if defined(GGML_USE_ACCELERATE) -#include -#endif - -// floating point type used to accumulate sums -typedef double ggml_float; - -#define GGML_GELU_FP16 -#define GGML_GELU_QUICK_FP16 - -#define GGML_SOFT_MAX_UNROLL 4 -#define GGML_VEC_DOT_UNROLL 2 -#define GGML_VEC_MAD_UNROLL 32 - -// -// global data -// - -// precomputed gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; - -// precomputed quick gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; - -// precomputed f32 table for f16 (256 KB) (ggml-impl.h) -float ggml_table_f32_f16[1 << 16]; - -#if defined(__ARM_ARCH) -struct ggml_arm_arch_features_type { - int has_neon; - int has_i8mm; - int has_sve; - int sve_cnt; -} ggml_arm_arch_features = {-1, -1, -1, 0}; -#endif - - -#if defined(_WIN32) - -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX - #define NOMINMAX -#endif -#include - - -#if !defined(__clang__) -#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE)) - -typedef volatile LONG atomic_int; -typedef atomic_int atomic_bool; -typedef atomic_int atomic_flag; - -#define ATOMIC_FLAG_INIT 0 - -typedef enum { - memory_order_relaxed, - memory_order_consume, - memory_order_acquire, - memory_order_release, - memory_order_acq_rel, - memory_order_seq_cst -} memory_order; - -static void atomic_store(atomic_int * ptr, LONG val) { - InterlockedExchange(ptr, val); -} -static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) { - // TODO: add support for explicit memory order - InterlockedExchange(ptr, val); -} -static LONG atomic_load(atomic_int * ptr) { - return InterlockedCompareExchange(ptr, 0, 0); -} -static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) { - // TODO: add support for explicit memory order - return InterlockedCompareExchange(ptr, 0, 0); -} -static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { - return InterlockedExchangeAdd(ptr, inc); -} -static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) { - // TODO: add support for explicit memory order - return InterlockedExchangeAdd(ptr, inc); -} -static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { - return InterlockedExchange(ptr, 1); -} -static void atomic_flag_clear(atomic_flag * ptr) { - InterlockedExchange(ptr, 0); -} -static void atomic_thread_fence(memory_order mo) { - MemoryBarrier(); -} -#else // clang -#include -#endif - -typedef HANDLE pthread_t; - -typedef DWORD thread_ret_t; -static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { - (void) unused; - HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); - if (handle == NULL) - { - return EAGAIN; - } - - *out = handle; - return 0; -} - -static int pthread_join(pthread_t thread, void * unused) { - (void) unused; - int ret = (int) WaitForSingleObject(thread, INFINITE); - CloseHandle(thread); - return ret; -} - -static int sched_yield (void) { - Sleep (0); - return 0; -} -#else - -#include -#include -#include -#if defined(__FreeBSD__) -#include -#endif - -typedef void * thread_ret_t; - -#include -#include -#include - -#endif - -typedef pthread_t ggml_thread_t; - -#ifdef GGML_USE_CPU_HBM -#include -#endif - -#if defined(__APPLE__) -#include -#include -#include -#endif - -// -// cache line -// - -#if defined(__cpp_lib_hardware_interference_size) -#define CACHE_LINE_SIZE hardware_destructive_interference_size -#else -#if defined(__POWER9_VECTOR__) -#define CACHE_LINE_SIZE 128 -#else -#define CACHE_LINE_SIZE 64 -#endif -#endif - -static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); - - -static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); -static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); -static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); - -static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { - [GGML_TYPE_F32] = { - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, - .vec_dot_type = GGML_TYPE_F32, - .nrows = 1, - }, - [GGML_TYPE_F16] = { - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, - .vec_dot_type = GGML_TYPE_F16, - .nrows = 1, - }, - [GGML_TYPE_Q4_0] = { - .vec_dot = ggml_vec_dot_q4_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif - }, - [GGML_TYPE_Q4_1] = { - .vec_dot = ggml_vec_dot_q4_1_q8_1, - .vec_dot_type = GGML_TYPE_Q8_1, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif - }, - [4] = { // GGML_TYPE_Q4_2 - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_COUNT, - .nrows = 1, - }, - [5] = { // GGML_TYPE_Q4_3 - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_COUNT, - .nrows = 1, - }, - [GGML_TYPE_Q5_0] = { - .vec_dot = ggml_vec_dot_q5_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - }, - [GGML_TYPE_Q5_1] = { - .vec_dot = ggml_vec_dot_q5_1_q8_1, - .vec_dot_type = GGML_TYPE_Q8_1, - .nrows = 1, - }, - [GGML_TYPE_Q8_0] = { - .from_float_to_mat = quantize_mat_q8_0, - .vec_dot = ggml_vec_dot_q8_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif - }, - [GGML_TYPE_Q8_1] = { - .vec_dot_type = GGML_TYPE_Q8_1, - .nrows = 1, - }, - [GGML_TYPE_Q2_K] = { - .vec_dot = ggml_vec_dot_q2_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_Q3_K] = { - .vec_dot = ggml_vec_dot_q3_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_Q4_K] = { - .vec_dot = ggml_vec_dot_q4_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_Q5_K] = { - .vec_dot = ggml_vec_dot_q5_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_Q6_K] = { - .vec_dot = ggml_vec_dot_q6_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ2_XXS] = { - .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ2_XS] = { - .vec_dot = ggml_vec_dot_iq2_xs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ3_XXS] = { - .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ3_S] = { - .vec_dot = ggml_vec_dot_iq3_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ2_S] = { - .vec_dot = ggml_vec_dot_iq2_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ1_S] = { - .vec_dot = ggml_vec_dot_iq1_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ1_M] = { - .vec_dot = ggml_vec_dot_iq1_m_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_IQ4_NL] = { - .vec_dot = ggml_vec_dot_iq4_nl_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - }, - [GGML_TYPE_IQ4_XS] = { - .vec_dot = ggml_vec_dot_iq4_xs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_BF16] = { - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, - .vec_dot_type = GGML_TYPE_BF16, - .nrows = 1, - }, - [GGML_TYPE_Q4_0_4_4] = { - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 4, - .gemv = ggml_gemv_q4_0_4x4_q8_0, - .gemm = ggml_gemm_q4_0_4x4_q8_0, - }, - [GGML_TYPE_Q4_0_4_8] = { - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 4, - .gemv = ggml_gemv_q4_0_4x8_q8_0, - .gemm = ggml_gemm_q4_0_4x8_q8_0, - }, - [GGML_TYPE_Q4_0_8_8] = { - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 8, - .gemv = ggml_gemv_q4_0_8x8_q8_0, - .gemm = ggml_gemm_q4_0_8x8_q8_0, - }, - [GGML_TYPE_TQ1_0] = { - .vec_dot = ggml_vec_dot_tq1_0_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, - [GGML_TYPE_TQ2_0] = { - .vec_dot = ggml_vec_dot_tq2_0_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, - }, -}; - -const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) { - return &type_traits_cpu[type]; -} - -// -// simd mappings -// - -// we define a common set of C macros which map to specific intrinsics based on the current architecture -// we then implement the fundamental computation operations below using only these macros -// adding support for new architectures requires to define the corresponding SIMD macros -// -// GGML_F32_STEP / GGML_F16_STEP -// number of elements to process in a single step -// -// GGML_F32_EPR / GGML_F16_EPR -// number of elements to fit in a single register -// - -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) - -#define GGML_SIMD - -// F32 NEON - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 float32x4_t -#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) -#define GGML_F32x4_SET1(x) vdupq_n_f32(x) -#define GGML_F32x4_LOAD vld1q_f32 -#define GGML_F32x4_STORE vst1q_f32 -#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) -#define GGML_F32x4_ADD vaddq_f32 -#define GGML_F32x4_MUL vmulq_f32 -#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - (res) = GGML_F32x4_REDUCE_ONE((x)[0]); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 NEON - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - #define GGML_F16_STEP 32 - #define GGML_F16_EPR 8 - - #define GGML_F16x8 float16x8_t - #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) - #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) - #define GGML_F16x8_STORE vst1q_f16 - #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) - #define GGML_F16x8_ADD vaddq_f16 - #define GGML_F16x8_MUL vmulq_f16 - #define GGML_F16x8_REDUCE(res, x) \ - do { \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ - const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ - (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ - } while (0) - - #define GGML_F16_VEC GGML_F16x8 - #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO - #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) - #define GGML_F16_VEC_FMA GGML_F16x8_FMA - #define GGML_F16_VEC_ADD GGML_F16x8_ADD - #define GGML_F16_VEC_MUL GGML_F16x8_MUL - #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE -#else - // if FP16 vector arithmetic is not supported, we use FP32 instead - // and take advantage of the vcvt_ functions to convert to/from FP16 - - #define GGML_F16_STEP 16 - #define GGML_F16_EPR 4 - - #define GGML_F32Cx4 float32x4_t - #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) - #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) - #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) - #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) - #define GGML_F32Cx4_ADD vaddq_f32 - #define GGML_F32Cx4_MUL vmulq_f32 - #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - - #define GGML_F16_VEC GGML_F32Cx4 - #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO - #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) - #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA - #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD - #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL - #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE -#endif - -#elif defined(__AVX512F__) - -#define GGML_SIMD - -// F32 AVX512 - -#define GGML_F32_STEP 64 -#define GGML_F32_EPR 16 - -#define GGML_F32x16 __m512 -#define GGML_F32x16_ZERO _mm512_setzero_ps() -#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) -#define GGML_F32x16_LOAD _mm512_loadu_ps -#define GGML_F32x16_STORE _mm512_storeu_ps -// _mm512_fmadd_ps is defined in AVX512F so no guard is required -#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32x16_ADD _mm512_add_ps -#define GGML_F32x16_MUL _mm512_mul_ps -#define GGML_F32x16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = _mm512_reduce_add_ps(x[0]); \ -} while (0) - -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x16 -#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD -#define GGML_F32_VEC_STORE GGML_F32x16_STORE -#define GGML_F32_VEC_FMA GGML_F32x16_FMA -#define GGML_F32_VEC_ADD GGML_F32x16_ADD -#define GGML_F32_VEC_MUL GGML_F32x16_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE - -// F16 AVX512 - -// F16 AVX - -#define GGML_F16_STEP 64 -#define GGML_F16_EPR 16 - -// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead - -#define GGML_F32Cx16 __m512 -#define GGML_F32Cx16_ZERO _mm512_setzero_ps() -#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) - -// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F -// so F16C guard isn't required -#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) -#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) - -#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32Cx16_ADD _mm512_add_ps -#define GGML_F32Cx16_MUL _mm512_mul_ps -#define GGML_F32Cx16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = _mm512_reduce_add_ps(x[0]); \ -} while (0) - -#define GGML_F16_VEC GGML_F32Cx16 -#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE - -#elif defined(__AVX__) - -#define GGML_SIMD - -// F32 AVX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO _mm256_setzero_ps() -#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) -#define GGML_F32x8_LOAD _mm256_loadu_ps -#define GGML_F32x8_STORE _mm256_storeu_ps -#if defined(__FMA__) - #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) -#else - #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) -#endif -#define GGML_F32x8_ADD _mm256_add_ps -#define GGML_F32x8_MUL _mm256_mul_ps -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ - _mm256_extractf128_ps(x[0], 1)); \ - const __m128 t1 = _mm_hadd_ps(t0, t0); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 AVX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO _mm256_setzero_ps() -#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) - -#if defined(__F16C__) -// the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) -#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) -#else -static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { - float arr[8]; - - _mm256_storeu_ps(arr, y); - - for (int i = 0; i < 8; i++) - x[i] = GGML_FP32_TO_FP16(arr[i]); -} -#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) -#endif - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD _mm256_add_ps -#define GGML_F32Cx8_MUL _mm256_mul_ps -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__POWER9_VECTOR__) - -#define GGML_SIMD - -// F32 POWER9 - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 vector float -#define GGML_F32x4_ZERO 0.0f -#define GGML_F32x4_SET1 vec_splats -#define GGML_F32x4_LOAD(p) vec_xl(0, p) -#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) -#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) -#define GGML_F32x4_ADD vec_add -#define GGML_F32x4_MUL vec_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - res = vec_extract(x[0], 0) + \ - vec_extract(x[0], 1) + \ - vec_extract(x[0], 2) + \ - vec_extract(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 POWER9 -#define GGML_F16_STEP GGML_F32_STEP -#define GGML_F16_EPR GGML_F32_EPR -#define GGML_F16_VEC GGML_F32x4 -#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F16_VEC_FMA GGML_F32x4_FMA -#define GGML_F16_VEC_ADD GGML_F32x4_ADD -#define GGML_F16_VEC_MUL GGML_F32x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE -// Use vec_xl, not vec_ld, in case the load address is not aligned. -#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ - vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ - vec_extract_fp32_from_shortl(vec_xl(0, p)) -#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] -#define GGML_F16_VEC_STORE(p, r, i) \ - if (i & 0x1) \ - vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ - r[i - GGML_ENDIAN_BYTE(0)]), \ - 0, p - GGML_F16_EPR) - -#elif defined(__wasm_simd128__) - -#define GGML_SIMD - -// F32 WASM - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 v128_t -#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F32x4_LOAD wasm_v128_load -#define GGML_F32x4_STORE wasm_v128_store -#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) -#define GGML_F32x4_ADD wasm_f32x4_add -#define GGML_F32x4_MUL wasm_f32x4_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 WASM - -#define GGML_F16_STEP 16 -#define GGML_F16_EPR 4 - -inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(p[0]); - tmp[1] = GGML_FP16_TO_FP32(p[1]); - tmp[2] = GGML_FP16_TO_FP32(p[2]); - tmp[3] = GGML_FP16_TO_FP32(p[3]); - - return wasm_v128_load(tmp); -} - -inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { - float tmp[4]; - - wasm_v128_store(tmp, x); - - p[0] = GGML_FP32_TO_FP16(tmp[0]); - p[1] = GGML_FP32_TO_FP16(tmp[1]); - p[2] = GGML_FP32_TO_FP16(tmp[2]); - p[3] = GGML_FP32_TO_FP16(tmp[3]); -} - -#define GGML_F16x4 v128_t -#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) -#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) -#define GGML_F16x4_FMA GGML_F32x4_FMA -#define GGML_F16x4_ADD wasm_f32x4_add -#define GGML_F16x4_MUL wasm_f32x4_mul -#define GGML_F16x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F16_VEC GGML_F16x4 -#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F16x4_FMA -#define GGML_F16_VEC_ADD GGML_F16x4_ADD -#define GGML_F16_VEC_MUL GGML_F16x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE - -#elif defined(__SSE3__) - -#define GGML_SIMD - -// F32 SSE - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO _mm_setzero_ps() -#define GGML_F32x4_SET1(x) _mm_set1_ps(x) -#define GGML_F32x4_LOAD _mm_loadu_ps -#define GGML_F32x4_STORE _mm_storeu_ps -#if defined(__FMA__) - // TODO: Does this work? - #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) -#else - #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) -#endif -#define GGML_F32x4_ADD _mm_add_ps -#define GGML_F32x4_MUL _mm_mul_ps -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ -} -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 SSE - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return _mm_loadu_ps(tmp); -} - -static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { - float arr[4]; - - _mm_storeu_ps(arr, y); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO _mm_setzero_ps() -#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) -#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD _mm_add_ps -#define GGML_F32Cx4_MUL _mm_mul_ps -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#elif defined(__loongarch_asx) - -#define GGML_SIMD - -// F32 LASX -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) -#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) -#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) -#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) -#define GGML_F32x8_ADD __lasx_xvfadd_s -#define GGML_F32x8_MUL __lasx_xvfmul_s -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - float *tmp_p = (float *)&x[0]; \ - res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 LASX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) - -static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return (__m256)__lasx_xvld(tmp, 0); -} -static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { - float arr[8]; - - __lasx_xvst(y, arr, 0); - - for (int i = 0; i < 8; i++) { - x[i] = GGML_FP32_TO_FP16(arr[i]); - } -} -#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD __lasx_xvfadd_s -#define GGML_F32Cx8_MUL __lasx_xvfmul_s -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__loongarch_sx) - -#define GGML_SIMD - -// F32 LSX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO __lsx_vldi(0) -#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) -#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) -#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) -#define GGML_F32x4_ADD __lsx_vfadd_s -#define GGML_F32x4_MUL __lsx_vfmul_s -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ - tmp = __lsx_vsrli_d((__m128i)t0, 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 LSX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return __lsx_vld(tmp, 0); -} - -static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { - float arr[4]; - - __lsx_vst(y, arr, 0); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO __lsx_vldi(0) -#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD __lsx_vfadd_s -#define GGML_F32Cx4_MUL __lsx_vfmul_s -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#endif - -// GGML_F32_ARR / GGML_F16_ARR -// number of registers to use per step -#ifdef GGML_SIMD -#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) -#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) -#endif - -// -// Threading defs -// - -typedef pthread_t ggml_thread_t; - -#if defined(_WIN32) - -typedef CONDITION_VARIABLE ggml_cond_t; -typedef SRWLOCK ggml_mutex_t; - -#define ggml_mutex_init(m) InitializeSRWLock(m) -#define ggml_mutex_destroy(m) -#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m) -#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m) -#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m) -#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m) - -#define ggml_cond_init(c) InitializeConditionVariable(c) -#define ggml_cond_destroy(c) -#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED) -#define ggml_cond_broadcast(c) WakeAllConditionVariable(c) - -#define ggml_thread_create pthread_create -#define ggml_thread_join pthread_join - -#else - -typedef pthread_cond_t ggml_cond_t; -typedef pthread_mutex_t ggml_mutex_t; - -#define ggml_mutex_init(m) pthread_mutex_init(m, NULL) -#define ggml_mutex_destroy(m) pthread_mutex_destroy(m) -#define ggml_mutex_lock(m) pthread_mutex_lock(m) -#define ggml_mutex_unlock(m) pthread_mutex_unlock(m) -#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m) -#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m) - -#define ggml_lock_init(x) UNUSED(x) -#define ggml_lock_destroy(x) UNUSED(x) -#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) -#define ggml_lock_lock(x) _mm_pause() -#else -#define ggml_lock_lock(x) UNUSED(x) -#endif -#define ggml_lock_unlock(x) UNUSED(x) - -#define GGML_LOCK_INITIALIZER 0 -#define ggml_cond_init(c) pthread_cond_init(c, NULL) -#define ggml_cond_destroy(c) pthread_cond_destroy(c) -#define ggml_cond_wait(c, m) pthread_cond_wait(c, m) -#define ggml_cond_broadcast(c) pthread_cond_broadcast(c) - -#define ggml_thread_create pthread_create -#define ggml_thread_join pthread_join - -#endif - -// Threadpool def -struct ggml_threadpool { - ggml_mutex_t mutex; // mutex for cond.var - ggml_cond_t cond; // cond.var for waiting for new work - - struct ggml_cgraph * cgraph; - struct ggml_cplan * cplan; - - // synchronization primitives - atomic_int n_graph; // incremented when there is work to be done (i.e each graph) - atomic_int GGML_CACHE_ALIGN n_barrier; - atomic_int GGML_CACHE_ALIGN n_barrier_passed; - atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. - - // these are atomic as an annotation for thread-sanitizer - atomic_bool stop; // Used for stopping the threadpool altogether - atomic_bool pause; // Used for pausing the threadpool or individual threads - atomic_bool abort; // Used for aborting processing of a graph - - struct ggml_compute_state * workers; // per thread state - int n_threads_max; // number of threads in the pool - atomic_int n_threads_cur; // number of threads used in the current graph - - int32_t prio; // Scheduling priority - uint32_t poll; // Polling level (0 - no polling) - - enum ggml_status ec; -}; - -// Per-thread state -struct ggml_compute_state { -#ifndef GGML_USE_OPENMP - ggml_thread_t thrd; - bool cpumask[GGML_MAX_N_THREADS]; - int last_graph; - bool pending; -#endif - struct ggml_threadpool * threadpool; - int ith; -}; - -struct ggml_compute_params { - // ith = thread index, nth = number of threads - int ith, nth; - - // work buffer for all threads - size_t wsize; - void * wdata; - - struct ggml_threadpool * threadpool; -}; - -// -// fundamental operations -// - -inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } -inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } -inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } -inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } -inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } -inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } -inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } -inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } -inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } - -static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - -#if defined(GGML_SIMD) - float sumf = 0.0f; - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F32_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += x[i]*y[i]; - } -#else - // scalar - ggml_float sumf = 0.0; - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(x[i]*y[i]); - } -#endif - - *s = sumf; -} - -static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - int i = 0; - ggml_float sumf = 0; - -#if defined(__AVX512BF16__) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), - m512bh(_mm512_loadu_si512((y + i)))); - c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), - m512bh(_mm512_loadu_si512((y + i + 32)))); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#elif defined(__AVX512F__) -#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#undef LOAD -#elif defined(__AVX2__) -#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) - __m256 c1 = _mm256_setzero_ps(); - __m256 c2 = _mm256_setzero_ps(); - __m256 c3 = _mm256_setzero_ps(); - __m256 c4 = _mm256_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); - c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); - c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); - } - __m128 g; - c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), - _mm256_add_ps(c2, c4)); - g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), - _mm256_castps256_ps128(c1)); - g = _mm_add_ps(g, _mm_movehl_ps(g, g)); - g = _mm_add_ss(g, _mm_movehdup_ps(g)); - sumf += (ggml_float)_mm_cvtss_f32(g); - -#undef LOAD -#endif - - for (; i < n; ++i) { - sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * - GGML_BF16_TO_FP32(y[i])); - } - *s = sumf; -} - -static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - ggml_float sumf = 0.0; - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F16_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#else - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#endif - - *s = sumf; -} - -// compute GGML_VEC_DOT_UNROLL dot products at once -// xs - x row stride in bytes -inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { - ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; - - ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); - - sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); - } - } - } - - // reduce sum0..sum3 to sum0 - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - GGML_F16_VEC_REDUCE(sumf[k], sum[k]); - } - - // leftovers - for (int i = np; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#else - for (int i = 0; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#endif - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - s[i] = sumf[i]; - } -} - -inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] += x[i]*v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] += x[i]*v; - } -#endif -} - -inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#endif -} - -// xs and vs are byte strides of x and v -inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { - - const float * restrict x[GGML_VEC_MAD_UNROLL]; - const float * restrict v[GGML_VEC_MAD_UNROLL]; - - for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { - x[i] = (const float *) ((const char *) xv + i*xs); - v[i] = (const float *) ((const char *) vv + i*vs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - vx[k] = GGML_F32_VEC_SET1(v[k][0]); - } - - GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); - } - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = np; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#else - // scalar - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = 0; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#endif -} - -//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } -inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { -#if defined(GGML_USE_ACCELERATE) - vDSP_vsmul(y, 1, &v, y, 1, n); -#elif defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_MUL(ay[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] *= v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] *= v; - } -#endif -} - -inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_MUL(ay[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#endif -} - -inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } -inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } -inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } -inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } -inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } -inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } -inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } -inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } -inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } -inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } -inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } -inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } -inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } -inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } -// TODO: optimize performance -inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } - -static const float GELU_COEF_A = 0.044715f; -static const float GELU_QUICK_COEF = -1.702f; -static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - -inline static float ggml_gelu_f32(float x) { - return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); -} - -inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - const uint16_t * i16 = (const uint16_t *) x; - for (int i = 0; i < n; ++i) { - y[i] = ggml_table_gelu_f16[i16[i]]; - } -} - -#ifdef GGML_GELU_FP16 -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - if (x[i] <= -10.0f) { - y[i] = 0.0f; - } else if (x[i] >= 10.0f) { - y[i] = x[i]; - } else { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); - } - } -} -#else -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_f32(x[i]); - } -} -#endif - -inline static float ggml_gelu_quick_f32(float x) { - return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); -} - -//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_gelu_quick_f16[i16[i]]; -// } -//} - -#ifdef GGML_GELU_QUICK_FP16 -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); - } -} -#else -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_quick_f32(x[i]); - } -} -#endif - -// Sigmoid Linear Unit (SiLU) function -inline static float ggml_silu_f32(float x) { - return x/(1.0f + expf(-x)); -} - -#if __FINITE_MATH_ONLY__ -#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" -#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" -#endif - -#if defined(__ARM_NEON) && defined(__aarch64__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static float32x4_t ggml_v_expf(float32x4_t x) { - const float32x4_t r = vdupq_n_f32(0x1.8p23f); - const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); - const float32x4_t n = vsubq_f32(z, r); - const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, - vdupq_n_f32(0x1.7f7d1cp-20f)); - const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); - const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); - const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); - const float32x4_t u = vmulq_f32(b, b); - const float32x4_t j = vfmaq_f32( - vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), - vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), - vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); - if (!vpaddd_u64(vreinterpretq_u64_u32(c))) - return vfmaq_f32(k, j, k); - const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); - const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); - const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); - return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), - vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static float32x4_t ggml_v_silu(float32x4_t x) { - const float32x4_t one = vdupq_n_f32(1.0f); - const float32x4_t zero = vdupq_n_f32(0.0f); - const float32x4_t neg_x = vsubq_f32(zero, x); - const float32x4_t exp_neg_x = ggml_v_expf(neg_x); - const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); - return vdivq_f32(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX512F__) && defined(__AVX512DQ__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m512 ggml_v_expf(__m512 x) { - const __m512 r = _mm512_set1_ps(0x1.8p23f); - const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); - const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __mmask16 d = - _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps( - _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); - const __m512 res = _mm512_scalef_ps(j, n); - if (_mm512_kortestz(d, d)) - return res; - const __m512 zero = _mm512_setzero_ps(); - const __m512 alt = _mm512_mask_blend_ps( - _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); - return _mm512_mask_blend_ps(d, res, alt); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m512 ggml_v_silu(__m512 x) { - const __m512 one = _mm512_set1_ps(1); - const __m512 zero = _mm512_setzero_ps(); - const __m512 neg_x = _mm512_sub_ps(zero, x); - const __m512 exp_neg_x = ggml_v_expf(neg_x); - const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); - return _mm512_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX2__) && defined(__FMA__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m256 ggml_v_expf(__m256 x) { - const __m256 r = _mm256_set1_ps(0x1.8p23f); - const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); - const __m256 n = _mm256_sub_ps(z, r); - const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), - _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); - const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); - const __m256 k = _mm256_castsi256_ps( - _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); - const __m256i c = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(126), _CMP_GT_OQ)); - const __m256 u = _mm256_mul_ps(b, b); - const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, - _mm256_set1_ps(0x1.573e2ep-5f)), u, - _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, - _mm256_set1_ps(0x1.fffdb6p-2f))), - u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) - return _mm256_fmadd_ps(j, k, k); - const __m256i g = _mm256_and_si256( - _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), - _mm256_set1_epi32(0x82000000u)); - const __m256 s1 = - _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); - const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); - const __m256i d = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(192), _CMP_GT_OQ)); - return _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), - _mm256_andnot_ps( - _mm256_castsi256_ps(d), - _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(c), - _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), - _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m256 ggml_v_silu(__m256 x) { - const __m256 one = _mm256_set1_ps(1); - const __m256 zero = _mm256_setzero_ps(); - const __m256 neg_x = _mm256_sub_ps(zero, x); - const __m256 exp_neg_x = ggml_v_expf(neg_x); - const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); - return _mm256_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON - -#if defined(__FMA__) -#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) -#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) -#else -#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) -#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) -#endif - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m128 ggml_v_expf(__m128 x) { - const __m128 r = _mm_set1_ps(0x1.8p23f); - const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); - const __m128 n = _mm_sub_ps(z, r); - const __m128 b = - NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); - const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); - const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); - const __m128i c = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); - const __m128 u = _mm_mul_ps(b, b); - const __m128 j = - MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, - MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), - u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm_movemask_epi8(c)) - return MADD128(j, k, k); - const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), - _mm_set1_epi32(0x82000000u)); - const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); - const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); - const __m128i d = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); - return _mm_or_ps( - _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), - _mm_andnot_ps(_mm_castsi128_ps(d), - _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), - _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m128 ggml_v_silu(__m128 x) { - const __m128 one = _mm_set1_ps(1); - const __m128 zero = _mm_setzero_ps(); - const __m128 neg_x = _mm_sub_ps(zero, x); - const __m128 exp_neg_x = ggml_v_expf(neg_x); - const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); - return _mm_div_ps(x, one_plus_exp_neg_x); -} - -#endif // __ARM_NEON / __AVX2__ / __SSE2__ - -static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - int i = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); - } -#endif - for (; i < n; ++i) { - y[i] = ggml_silu_f32(x[i]); - } -} - -static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { - int i = 0; - ggml_float sum = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), - _mm512_set1_ps(max))); - _mm512_storeu_ps(y + i, val); - sum += (ggml_float)_mm512_reduce_add_ps(val); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), - _mm256_set1_ps(max))); - _mm256_storeu_ps(y + i, val); - __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), - _mm256_castps256_ps128(val)); - val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); - val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); - sum += (ggml_float)_mm_cvtss_f32(val2); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), - _mm_set1_ps(max))); - _mm_storeu_ps(y + i, val); -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) - val = _mm_add_ps(val, _mm_movehl_ps(val, val)); - val = _mm_add_ss(val, _mm_movehdup_ps(val)); -#else - __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); - val = _mm_add_ps(val, tmp); - tmp = _mm_movehl_ps(tmp, val); - val = _mm_add_ss(val, tmp); -#endif - sum += (ggml_float)_mm_cvtss_f32(val); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), - vdupq_n_f32(max))); - vst1q_f32(y + i, val); - sum += (ggml_float)vaddvq_f32(val); - } -#endif - for (; i < n; ++i) { - float val = expf(x[i] - max); - sum += (ggml_float)val; - y[i] = val; - } - return sum; -} - -static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { - // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) - - int i = 0; - ggml_float sum = 0; - for (; i < n; ++i) { - float val = x[i] - max; - y[i] = val; - sum += (ggml_float)expf(val); - } - return sum = (ggml_float)logf(sum); -} - -inline static float ggml_silu_backward_f32(float x, float dy) { - const float s = 1.0f/(1.0f + expf(-x)); - return dy*s*(1.0f + x*(1.0f - s)); -} - -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - dx[i] = ggml_silu_backward_f32(x[i], dy[i]); - } -} - -inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -#else - vDSP_sve(x, 1, s, n); -#endif -} - -inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -} - -inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_FP16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_BF16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - float max = -INFINITY; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - } - *s = max; -#else - vDSP_maxv(x, 1, s, n); -#endif -} - -inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { - ggml_vec_norm_f32(n, s, x); - *s = 1.f/(*s); -} - -inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { - float max = -INFINITY; - int idx = 0; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - if (max == x[i]) { idx = i; } - } - *s = idx; -} - -// Helpers for polling loops -#if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) -static inline void ggml_thread_cpu_relax(void) { - __asm__ volatile("yield" ::: "memory"); -} -#elif defined(__x86_64__) -static inline void ggml_thread_cpu_relax(void) { - _mm_pause(); -} -#else -static inline void ggml_thread_cpu_relax(void) {;} -#endif - -// -// NUMA support -// - -#define GGML_NUMA_MAX_NODES 8 -#define GGML_NUMA_MAX_CPUS 512 - -struct ggml_numa_node { - uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node - uint32_t n_cpus; -}; - -struct ggml_numa_nodes { - enum ggml_numa_strategy numa_strategy; - struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; - uint32_t n_nodes; - uint32_t total_cpus; // hardware threads on system - uint32_t current_node; // node on which main process is execting -#if defined(__gnu_linux__) - cpu_set_t cpuset; // cpuset from numactl -#else - uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype -#endif -}; - -// -// ggml state -// - -struct ggml_state { - struct ggml_numa_nodes numa; -}; - -// global state -static struct ggml_state g_state = {0}; -static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; - -// TODO: move to threading file -// critical section via spin lock -void ggml_critical_section_start(void) { - while (atomic_flag_test_and_set(&g_state_critical)) { - // spin - sched_yield(); - } -} - -void ggml_critical_section_end(void) { - atomic_flag_clear(&g_state_critical); -} - -static void ggml_barrier(struct ggml_threadpool * tp) { - int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); - if (n_threads == 1) { - return; - } - -#ifdef GGML_USE_OPENMP - #pragma omp barrier -#else - int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed); - - // enter barrier (full seq-cst fence) - int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst); - - if (n_barrier == (n_threads - 1)) { - // last thread - atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); - - // exit barrier (fill seq-cst fence) - atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); - return; - } - - // wait for other threads - while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) { - ggml_thread_cpu_relax(); - } - - // exit barrier (full seq-cst fence) - // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead - #ifdef GGML_TSAN_ENABLED - atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst); - #else - atomic_thread_fence(memory_order_seq_cst); - #endif -#endif -} - -#if defined(__gnu_linux__) -static cpu_set_t ggml_get_numa_affinity(void) { - cpu_set_t cpuset; - pthread_t thread; - thread = pthread_self(); - CPU_ZERO(&cpuset); - pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); - return cpuset; -} -#else -static uint32_t ggml_get_numa_affinity(void) { - return 0; // no NUMA support -} -#endif - -void ggml_numa_init(enum ggml_numa_strategy numa_flag) { - if (g_state.numa.n_nodes > 0) { - fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); - - return; - } - -#if defined(__gnu_linux__) - struct stat st; - char path[256]; - int rv; - - // set numa scheme - g_state.numa.numa_strategy = numa_flag; - - GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); - - g_state.numa.cpuset = ggml_get_numa_affinity(); - - // enumerate nodes - while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) != 0) { break; } - ++g_state.numa.n_nodes; - } - - // enumerate CPUs - while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) != 0) { break; } - ++g_state.numa.total_cpus; - } - - GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); - - // figure out which node we're on - uint current_cpu; - int getcpu_ret = 0; -#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) || defined(__COSMOPOLITAN__) - getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); -#else - // old glibc doesn't have a wrapper for this call. Fall back on direct syscall -# if !defined(SYS_getcpu) && defined(SYS_get_cpu) -# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name -# endif - getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); -#endif - - if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { - g_state.numa.n_nodes = 0; - return; - } - - GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); - - for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { - struct ggml_numa_node * node = &g_state.numa.nodes[n]; - GGML_PRINT_DEBUG("CPUs on node %u:", n); - node->n_cpus = 0; - for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) == 0) { - node->cpus[node->n_cpus++] = c; - GGML_PRINT_DEBUG(" %u", c); - } - } - GGML_PRINT_DEBUG("\n"); - } - - if (ggml_is_numa()) { - FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); - if (fptr != NULL) { - char buf[42]; - if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { - GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); - } - fclose(fptr); - } - } -#else - UNUSED(numa_flag); - // TODO -#endif -} - -bool ggml_is_numa(void) { - return g_state.numa.n_nodes > 1; -} - -#if defined(__ARM_ARCH) - -#if defined(__linux__) && defined(__aarch64__) -#include -#elif defined(__APPLE__) -#include -#endif - -#if !defined(HWCAP2_I8MM) -#define HWCAP2_I8MM 0 -#endif - -static void ggml_init_arm_arch_features(void) { -#if defined(__linux__) && defined(__aarch64__) - uint32_t hwcap = getauxval(AT_HWCAP); - uint32_t hwcap2 = getauxval(AT_HWCAP2); - - ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); - ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); - ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); -#endif -#elif defined(__APPLE__) - int oldp = 0; - size_t size = sizeof(oldp); - if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_neon = oldp; - - if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_i8mm = oldp; - - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#else -// Run-time CPU feature detection not implemented for this platform, fallback to compile time -#if defined(__ARM_NEON) - ggml_arm_arch_features.has_neon = 1; -#else - ggml_arm_arch_features.has_neon = 0; -#endif - -#if defined(__ARM_FEATURE_MATMUL_INT8) - ggml_arm_arch_features.has_i8mm = 1; -#else - ggml_arm_arch_features.has_i8mm = 0; -#endif - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.has_sve = 1; - ggml_arm_arch_features.sve_cnt = 16; -#else - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#endif -#endif -} -#endif - -struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { - GGML_ASSERT(!ggml_get_no_alloc(ctx)); - - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); - - ggml_set_i32(result, value); - - return result; -} - -struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { - GGML_ASSERT(!ggml_get_no_alloc(ctx)); - - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); - - ggml_set_f32(result, value); - - return result; -} - -struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { - const int n = ggml_nrows(tensor); - const int nc = tensor->ne[0]; - const size_t n1 = tensor->nb[1]; - - char * const data = tensor->data; - - switch (tensor->type) { - case GGML_TYPE_I8: - { - assert(tensor->nb[0] == sizeof(int8_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I16: - { - assert(tensor->nb[0] == sizeof(int16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I32: - { - assert(tensor->nb[0] == sizeof(int32_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_F16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); - } - } break; - case GGML_TYPE_BF16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); - } - } break; - case GGML_TYPE_F32: - { - assert(tensor->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f32(nc, (float *)(data + i*n1), value); - } - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - return tensor; -} - -struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { - const int n = ggml_nrows(tensor); - const int nc = tensor->ne[0]; - const size_t n1 = tensor->nb[1]; - - char * const data = tensor->data; - - switch (tensor->type) { - case GGML_TYPE_I8: - { - assert(tensor->nb[0] == sizeof(int8_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I16: - { - assert(tensor->nb[0] == sizeof(int16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I32: - { - assert(tensor->nb[0] == sizeof(int32_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_F16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); - } - } break; - case GGML_TYPE_BF16: - { - assert(tensor->nb[0] == sizeof(ggml_bf16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); - } - } break; - case GGML_TYPE_F32: - { - assert(tensor->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f32(nc, (float *)(data + i*n1), value); - } - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - return tensor; -} - -int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]); - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); - return ((int8_t *)(tensor->data))[i]; - } - case GGML_TYPE_I16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); - return ((int16_t *)(tensor->data))[i]; - } - case GGML_TYPE_I32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); - return ((int32_t *)(tensor->data))[i]; - } - case GGML_TYPE_F16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); - return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); - } - case GGML_TYPE_BF16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); - return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); - } - case GGML_TYPE_F32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); - return ((float *)(tensor->data))[i]; - } - default: - { - GGML_ABORT("fatal error"); - } - } -} - -void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value); - return; - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); - ((int8_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); - ((int16_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); - ((int32_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); - ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); - ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); - ((float *)(tensor->data))[i] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - return ((int8_t *) data)[0]; - case GGML_TYPE_I16: - return ((int16_t *) data)[0]; - case GGML_TYPE_I32: - return ((int32_t *) data)[0]; - case GGML_TYPE_F16: - return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); - case GGML_TYPE_BF16: - return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); - case GGML_TYPE_F32: - return ((float *) data)[0]; - default: - GGML_ABORT("fatal error"); - } -} - -void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(data))[0] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(data))[0] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(data))[0] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(data))[0] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]); - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - return ((int8_t *)(tensor->data))[i]; - } - case GGML_TYPE_I16: - { - return ((int16_t *)(tensor->data))[i]; - } - case GGML_TYPE_I32: - { - return ((int32_t *)(tensor->data))[i]; - } - case GGML_TYPE_F16: - { - return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); - } - case GGML_TYPE_BF16: - { - return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); - } - case GGML_TYPE_F32: - { - return ((float *)(tensor->data))[i]; - } - default: - { - GGML_ABORT("fatal error"); - } - } -} - -void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value); - return; - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(tensor->data))[i] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - return ((int8_t *) data)[0]; - case GGML_TYPE_I16: - return ((int16_t *) data)[0]; - case GGML_TYPE_I32: - return ((int32_t *) data)[0]; - case GGML_TYPE_F16: - return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); - case GGML_TYPE_BF16: - return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); - case GGML_TYPE_F32: - return ((float *) data)[0]; - default: - GGML_ABORT("fatal error"); - } -} - -void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(data))[0] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(data))[0] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(data))[0] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(data))[0] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -//////////////////////////////////////////////////////////////////////////////// - -// ggml_compute_forward_dup - -static void ggml_compute_forward_dup_same_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - GGML_ASSERT(src0->type == dst->type); - - const size_t nb0 = ggml_type_size(src0->type); - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by elements - const int ne = ggml_nelements(dst); - const int dr = (ne + nth - 1) / nth; - const int ie0 = dr * ith; - const int ie1 = MIN(ie0 + dr, ne); - - if (ie0 < ie1) { - memcpy( - ((char *) dst->data + ie0*nb0), - ((char *) src0->data + ie0*nb0), - (ie1 - ie0) * nb0); - } -} - -static void ggml_compute_forward_dup_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_fp16_t)) { - if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (ggml_get_type_traits(dst->type)->from_float) { - ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dst->type)->from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_bf16_t)) { - if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (ggml_get_type_traits(dst->type)->from_float) { - ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dst->type)->from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - // TODO: simplify - if (nb00 == sizeof(float)) { - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (ggml_get_type_traits(dst->type)->from_float) { - ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dst->type)->from_float; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - quantize_row_q(src0_ptr, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(float)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. -static void ggml_compute_forward_dup_bytes( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(src0->type == dst->type); - - GGML_TENSOR_UNARY_OP_LOCALS; - - if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { - ggml_compute_forward_dup_same_cont(params, dst); - return; - } - - const size_t type_size = ggml_type_size(src0->type); - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == type_size && nb0 == type_size) { - // copy by rows - const size_t rs = ne00 * type_size; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - size_t id = 0; - char * dst_ptr = (char *) dst->data; - const size_t rs = ne00 * type_size; - - if (nb00 == type_size) { - // src0 is contigous on first dimension, copy by rows - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, type_size); - - id += type_size; - } - } - id += rs * (ne01 - ir1); - } - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, type_size); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } -} - -static void ggml_compute_forward_dup( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (src0->type == dst->type) { - ggml_compute_forward_dup_bytes(params, dst); - return; - } - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_dup_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_dup_bf16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_dup_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add - -static void ggml_compute_forward_add_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; - } - } - } -} - -static void ggml_compute_forward_add_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (dst->type == GGML_TYPE_F32) { - GGML_ASSERT( nb0 == sizeof(float)); - } - else { - GGML_ASSERT(dst->type == GGML_TYPE_F16); - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - } - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - if (dst->type == GGML_TYPE_F16) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); - } - } - } else { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; - } - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (dst->type == GGML_TYPE_F32) { - GGML_ASSERT( nb0 == sizeof(float)); - } - else { - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - } - - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - if (dst->type == GGML_TYPE_BF16) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); - } - } - } else { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; - } - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(ggml_fp16_t)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(ggml_bf16_t)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - const enum ggml_type dtype = dst->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - ggml_from_float_t const quantize_row_q = ggml_get_type_traits(dtype)->from_float; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - // src1 and dst are same shape as src0 => same indices - const int i13 = i03; - const int i12 = i02; - const int i11 = i01; - - const int i3 = i03; - const int i2 = i02; - const int i1 = i01; - - void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); - float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); - void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - assert(ne00 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne00); - // add src1 - ggml_vec_acc_f32(ne00, wdata, src1_row); - // quantize row to dst - if (quantize_row_q != NULL) { - quantize_row_q(wdata, dst_row, ne00); - } else { - memcpy(dst_row, wdata, ne0*nb0); - } - } -} - -static void ggml_compute_forward_add( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_add_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add1 - -static void ggml_compute_forward_add1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_add1_f32); - - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, - (float *) ((char *) src1->data), 0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, - ne0); -#else - ggml_vec_add1_f32(ne0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), - *(float *) src1->data); -#endif - } -} - -static void ggml_compute_forward_add1_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - ggml_from_float_t const quantize_row_q = ggml_get_type_traits(type)->from_float; - - // we don't support permuted src0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(dst->type == src0->type); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); - void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); - - assert(ne0 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne0); - // add src1 - ggml_vec_acc1_f32(ne0, wdata, v); - // quantize row to dst - quantize_row_q(wdata, dst_row, ne0); - } -} - -static void ggml_compute_forward_add1_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add1_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add1_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add1_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_add1_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_acc - -static void ggml_compute_forward_acc_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during acc - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during acc - const size_t nb0 = ggml_element_size(src0); - - const size_t nb00 = nb0; - const size_t nb01 = nb1; - const size_t nb02 = nb2; - const size_t nb03 = nb3; - - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - -#ifdef GGML_USE_ACCELERATE - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); -#else - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); -#endif - } -} - -static void ggml_compute_forward_acc( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_acc_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sub - -static void ggml_compute_forward_sub_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - vDSP_vsub(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_sub_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; - } - } - } -} - -static void ggml_compute_forward_sub( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sub_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mul - -static void ggml_compute_forward_mul_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (nb10 == sizeof(float)) { - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0 ; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_mul_f32); - - vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne00; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); - } - } - } -} - -static void ggml_compute_forward_mul( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mul_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_div - -static void ggml_compute_forward_div_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (nb10 == sizeof(float)) { - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_div_f32); - - vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne00; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); - } - } - } -} - -static void ggml_compute_forward_div( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_div_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sqr - -static void ggml_compute_forward_sqr_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sqr_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sqr( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sqr_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sqrt - -static void ggml_compute_forward_sqrt_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sqrt_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sqrt( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sqrt_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_log - -static void ggml_compute_forward_log_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_log_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_log( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_log_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sin - -static void ggml_compute_forward_sin_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sin_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sin( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sin_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cos - -static void ggml_compute_forward_cos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_cos_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_cos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum - -static void ggml_compute_forward_sum_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - ggml_float sum = 0; - ggml_float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32_ggf(ne00, - &row_sum, - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - sum += row_sum; - } - } - } - ((float *) dst->data)[0] = sum; -} - -static void ggml_compute_forward_sum_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_fp16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f16_ggf(ne00, - &row_sum, - (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); -} - -static void ggml_compute_forward_sum_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_bf16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_bf16_ggf(ne00, - &row_sum, - (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); -} - -static void ggml_compute_forward_sum( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_sum_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_sum_bf16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum_rows - -static void ggml_compute_forward_sum_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne0 == 1); - GGML_ASSERT(ne1 == ne01); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - for (int64_t i3 = 0; i3 < ne03; i3++) { - for (int64_t i2 = 0; i2 < ne02; i2++) { - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); - float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); - float row_sum = 0; - ggml_vec_sum_f32(ne00, &row_sum, src_row); - dst_row[0] = row_sum; - } - } - } -} - -static void ggml_compute_forward_sum_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mean - -static void ggml_compute_forward_mean_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - assert(ne0 == 1); - assert(ne1 == ne01); - assert(ne2 == ne02); - assert(ne3 == ne03); - - UNUSED(ne0); - UNUSED(ne1); - UNUSED(ne2); - UNUSED(ne3); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32(ne00, - (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - - *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; - } - } - } -} - -static void ggml_compute_forward_mean( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mean_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argmax - -static void ggml_compute_forward_argmax_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - assert(dst->nb[0] == sizeof(float)); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - - const size_t nb01 = src0->nb[1]; - const size_t nb0 = dst->nb[0]; - - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src = (float *) ((char *) src0->data + i1*nb01); - int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); - int v = 0; - ggml_vec_argmax_f32(ne00, &v, src); - dst_[0] = v; - } -} - -static void ggml_compute_forward_argmax( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argmax_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_count_equal - -static void ggml_compute_forward_count_equal_i32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(src0->type == GGML_TYPE_I32); - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_I64); - - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - int64_t * sums = (int64_t *) params->wdata; - int64_t sum_thread = 0; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir / (ne02*ne01); - const int64_t i02 = (ir - i03*ne03) / ne01; - const int64_t i01 = ir - i03*ne03 - i02*ne02; - - const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; - const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; - - for (int64_t i00 = 0; i00 < ne00; ++i00) { - const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); - const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); - - sum_thread += val0 == val1; - } - } - if (ith != 0) { - sums[ith] = sum_thread; - } - ggml_barrier(params->threadpool); - - if (ith != 0) { - return; - } - - for (int ith_other = 1; ith_other < nth; ++ith_other) { - sum_thread += sums[ith_other]; - } - *((int64_t *) dst->data) = sum_thread; -} - -static void ggml_compute_forward_count_equal( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_I32: - { - ggml_compute_forward_count_equal_i32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat - -static void ggml_compute_forward_repeat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_cpy_f32(ne00, - (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), - (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); - ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); - // ggml_vec_cpy_f16(ne00, y, x) - for (int i = 0; i < ne00; ++i) { - y[i] = x[i]; - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_I16: - { - ggml_compute_forward_repeat_f16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_repeat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat_back - -static void ggml_compute_forward_repeat_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(dst, src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne00/ne0); - const int nr1 = (int)(ne01/ne1); - const int nr2 = (int)(ne02/ne2); - const int nr3 = (int)(ne03/ne3); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (ggml_is_contiguous(dst)) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } else { - for (int k3 = 0; k3 < ne3; k3++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int k1 = 0; k1 < ne1; k1++) { - ggml_vec_set_f32(ne0, - (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), - 0); - } - } - } - } - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne3; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne1; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_acc_f32(ne0, - (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), - (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_repeat_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_concat - -static void ggml_compute_forward_concat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const float * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); - } else { - x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); - } - - float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_concat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_concat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_abs - -static void ggml_compute_forward_abs_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_abs_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_abs( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_abs_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sgn - -static void ggml_compute_forward_sgn_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_sgn_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sgn( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sgn_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_neg - -static void ggml_compute_forward_neg_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_neg_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_neg( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_neg_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_step - -static void ggml_compute_forward_step_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_step_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_step( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_step_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_tanh - -static void ggml_compute_forward_tanh_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_tanh_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_tanh( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_tanh_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_elu - -static void ggml_compute_forward_elu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_elu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_elu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_elu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_relu - -static void ggml_compute_forward_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_relu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sigmoid - -static void ggml_compute_forward_sigmoid_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_sigmoid_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sigmoid( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sigmoid_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu - -static void ggml_compute_forward_gelu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu_quick - -static void ggml_compute_forward_gelu_quick_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_quick_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu_quick( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_quick_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu - -static void ggml_compute_forward_silu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -// ggml_compute_forward_leaky_relu - -static void ggml_compute_forward_leaky_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_leaky_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); - } -} - -static void ggml_compute_forward_leaky_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_leaky_relu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu_back - -static void ggml_compute_forward_silu_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * grad = dst->src[1]; - - assert(ggml_is_contiguous_1(grad)); - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - assert(ggml_are_same_shape(src0, grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_backward_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1])), - (float *) ((char *) grad->data + i1*(grad->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -static void ggml_compute_forward_hardswish_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_hardswish_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} -static void ggml_compute_forward_hardswish( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_hardswish_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_hardsigmoid_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_hardsigmoid_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_hardsigmoid( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_hardsigmoid_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_exp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_exp_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_exp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_exp_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_norm - -static void ggml_compute_forward_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps > 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; - } - - float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_float sum2 = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sum2 += (ggml_float)(v*v); - } - - float variance = sum2/ne00; - const float scale = 1.0f/sqrtf(variance + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_rms_norm - -static void ggml_compute_forward_rms_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps > 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)(x[i00] * x[i00]); - } - - const float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - memcpy(y, x, ne00 * sizeof(float)); - // for (int i00 = 0; i00 < ne00; i00++) { - // y[i00] = x[i00]; - // } - - const float scale = 1.0f/sqrtf(mean + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_rms_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_rms_norm_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - // src1 is same shape as src0 => same indices - const int64_t i11 = i01; - const int64_t i12 = i02; - const int64_t i13 = i03; - - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); - - ggml_float sum_xx = 0.0; - ggml_float sum_xdz = 0.0; - - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum_xx += (ggml_float)(x[i00] * x[i00]); - sum_xdz += (ggml_float)(x[i00] * dz[i00]); - } - - //const float mean = (float)(sum_xx)/ne00; - const float mean_eps = (float)(sum_xx)/ne00 + eps; - const float sum_eps = (float)(sum_xx) + eps*ne00; - //const float mean_xdz = (float)(sum_xdz)/ne00; - // we could cache rms from forward pass to improve performance. - // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. - //const float rms = sqrtf(mean_eps); - const float rrms = 1.0f / sqrtf(mean_eps); - //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) - - { - // z = rms_norm(x) - // - // rms_norm(src0) = - // scale( - // src0, - // div( - // 1, - // sqrt( - // add( - // scale( - // sum( - // sqr( - // src0)), - // (1.0/N)), - // eps)))); - - // postorder: - // ## op args grad - // 00 param src0 grad[#00] - // 01 const 1 - // 02 sqr (#00) grad[#02] - // 03 sum (#02) grad[#03] - // 04 const 1/N - // 05 scale (#03, #04) grad[#05] - // 06 const eps - // 07 add (#05, #06) grad[#07] - // 08 sqrt (#07) grad[#08] - // 09 div (#01,#08) grad[#09] - // 10 scale (#00,#09) grad[#10] - // - // backward pass, given grad[#10] - // #10: scale - // grad[#00] += scale(grad[#10],#09) - // grad[#09] += sum(mul(grad[#10],#00)) - // #09: div - // grad[#08] += neg(mul(grad[#09], div(#09,#08))) - // #08: sqrt - // grad[#07] += mul(grad[#08], div(0.5, #08)) - // #07: add - // grad[#05] += grad[#07] - // #05: scale - // grad[#03] += scale(grad[#05],#04) - // #03: sum - // grad[#02] += repeat(grad[#03], #02) - // #02: - // grad[#00] += scale(mul(#00, grad[#02]), 2.0) - // - // substitute and simplify: - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#02] = repeat(grad[#03], #02) - // grad[#02] = repeat(scale(grad[#05],#04), #02) - // grad[#02] = repeat(scale(grad[#07],#04), #02) - // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) - // a = b*c + d*e - // a = b*c*f/f + d*e*f/f - // a = (b*c*f + d*e*f)*(1/f) - // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) - // a = (b + d*e/c)*c - // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms - // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms - // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms - // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms - // a = (dz + x*div(-mean_xdz,mean_eps))*rrms - // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) - // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - } - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // post-order: - // dx := x - // dx := scale(dx,-mean_xdz/mean_eps) - // dx := add(dx, dz) - // dx := scale(dx, rrms) - float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_vec_cpy_f32 (ne00, dx, x); - // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); - ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); - ggml_vec_acc_f32 (ne00, dx, dz); - ggml_vec_scale_f32(ne00, dx, rrms); - } - } - } -} - -static void ggml_compute_forward_rms_norm_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_norm - -static void ggml_compute_forward_group_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - // TODO: optimize - - float eps; - memcpy(&eps, dst->op_params + 1, sizeof(float)); - - int n_channels = src0->ne[2]; - int n_groups = dst->op_params[0]; - int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i += nth) { - int start = i * n_channels_per_group; - int end = start + n_channels_per_group; - if (end > n_channels) { - end = n_channels; - } - int step = end - start; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - ggml_float sum = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sumr += (ggml_float)x[i00]; - } - sum += sumr; - } - } - const float mean = sum / (ne00 * ne01 * step); - - ggml_float sum2 = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sumr += (ggml_float)(v * v); - } - sum2 += sumr; - } - } - const float variance = sum2 / (ne00 * ne01 * step); - const float scale = 1.0f / sqrtf(variance + eps); - - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - ggml_vec_scale_f32(ne00, y, scale); - } - } - } - } -} - -static void ggml_compute_forward_group_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_group_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mul_mat - -static void ggml_compute_forward_mul_mat_one_chunk( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const int64_t num_rows_per_vec_dot, - const int64_t ir0_start, - const int64_t ir0_end, - const int64_t ir1_start, - const int64_t ir1_end) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const enum ggml_type type = src0->type; - - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; - - // broadcast factors - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); - - // threads with no work simply yield (not sure if it helps) - if (ir0_start >= ir0_end || ir1_start >= ir1_end) { - return; - } - - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - assert(ne12 % ne02 == 0); - assert(ne13 % ne03 == 0); - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; - - const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; - - // attempt to reduce false-sharing (does not seem to make a difference) - // 16 * 2, accounting for mmla kernels - float tmp[32]; - - for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { - for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { - const int64_t i13 = (ir1 / (ne12 * ne1)); - const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; - const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); - - // broadcast src0 into src1 - const int64_t i03 = i13 / r3; - const int64_t i02 = i12 / r2; - - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; - - const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); - - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char*)wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size - : (i11 * nb11 + i12 * nb12 + i13 * nb13)); - float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); - - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} - - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { - vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); - } - - for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { - memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); - } - } - } - } -} - -static void ggml_compute_forward_mul_mat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - - enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; - ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float; - ggml_from_float_to_mat_t const from_float_to_mat = type_traits_cpu[vec_dot_type].from_float_to_mat; - int64_t const vec_dot_num_rows = type_traits_cpu[type].nrows; - int64_t const matmul_num_cols = type_traits_cpu[type].ncols; - int64_t const blck_size_interleave = ggml_get_type_traits(type)->blck_size_interleave; - ggml_gemv_t const gemv = type_traits_cpu[type].gemv; - ggml_gemm_t const gemm = type_traits_cpu[type].gemm; - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if GGML_USE_LLAMAFILE - // broadcast factors - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const bool src1_cont = ggml_is_contiguous(src1); - - if (src1_cont) { - for (int64_t i13 = 0; i13 < ne13; i13++) - for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), - (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, - nb01/ggml_type_size(src0->type), - (const char *)src1->data + i12*nb12 + i13*nb13, - nb11/ggml_type_size(src1->type), - (char *)dst->data + i12*nb2 + i13*nb3, - nb1/ggml_type_size(dst->type), - ith, nth, - src0->type, - src1->type, - dst->type)) - goto UseGgmlGemm1; - return; - } -UseGgmlGemm1:; -#endif - - if (src1->type != vec_dot_type) { - char * wdata = params->wdata; - - const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); - const size_t nbw2 = nbw1*ne11; - const size_t nbw3 = nbw2*ne12; - - assert(params->wsize >= ne13*nbw3); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - int64_t i11_processed = 0; - if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) { - for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) { - from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - 4, ne10, blck_size_interleave); - } - i11_processed = ne11 - ne11 % 4; - } - for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) { - from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - ne10); - } - } - } - } - - if (ith == 0) { - // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. - atomic_store_explicit(¶ms->threadpool->current_chunk, nth, memory_order_relaxed); - } - - ggml_barrier(params->threadpool); - -#if GGML_USE_LLAMAFILE - if (src1->type != vec_dot_type) { - const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - for (int64_t i13 = 0; i13 < ne13; i13++) - for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), - (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, - nb01/ggml_type_size(src0->type), - (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, - row_size/ggml_type_size(vec_dot_type), - (char *)dst->data + i12*nb2 + i13*nb3, - nb1/ggml_type_size(dst->type), - ith, nth, - src0->type, - vec_dot_type, - dst->type)) - goto UseGgmlGemm2; - return; - } -UseGgmlGemm2:; -#endif - - // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) - const int64_t nr0 = ne0; - - // This is the size of the rest of the dimensions of the result - const int64_t nr1 = ne1 * ne2 * ne3; - - // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t num_rows_per_vec_dot = vec_dot_num_rows; - // TODO: currently the mmla kernels support only even numbered rows/cols. - // this check can be removed once they are extended to support odd numbered rows/cols too - if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - num_rows_per_vec_dot = 1; - } - - // Now select a reasonable chunk size. - int chunk_size = 16; - - // We need to step up the size if it's small - if (nr0 == 1 || nr1 == 1) { - chunk_size = 64; - } - - // distribute the work across the inner or outer loop based on which one is larger - // The number of chunks in the 0/1 dim. - // CEIL(nr0/chunk_size) - int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; - int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; - - // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. - // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 - // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. - if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { - // distribute the thread work across the inner or outer loop based on which one is larger - nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - } - - // The number of elements in each chunk - const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; - const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; - - if ((ggml_n_dims(src0) == 2) && gemv) { - const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11; - int64_t src0_start = (ith * ne01) / nth; - int64_t src0_end = ((ith + 1) * ne01) / nth; - src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start; - src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end; - if (src0_start >= src0_end) return; - - // If there are more than three rows in src1, use gemm; otherwise, use gemv. - if (gemm && (ne11 > 3)) { - gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, - (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start); - } - for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) { - gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01, - (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1, - src0_end - src0_start); - } - return; - } - - // The first chunk comes from our thread_id, the rest will get auto-assigned. - int current_chunk = ith; - - while (current_chunk < nchunk0 * nchunk1) { - const int64_t ith0 = current_chunk % nchunk0; - const int64_t ith1 = current_chunk / nchunk0; - - const int64_t ir0_start = dr0 * ith0; - const int64_t ir0_end = MIN(ir0_start + dr0, nr0); - - const int64_t ir1_start = dr1 * ith1; - const int64_t ir1_end = MIN(ir1_start + dr1, nr1); - - ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); - - if (nth >= nchunk0 * nchunk1) { - break; - } - - current_chunk = atomic_fetch_add_explicit(¶ms->threadpool->current_chunk, 1, memory_order_relaxed); - } -} - -// ggml_compute_forward_mul_mat_id - -static void ggml_compute_forward_mul_mat_id( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * ids = dst->src[2]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; - ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float; - int64_t const matmul_num_cols = type_traits_cpu[type].ncols; - ggml_gemv_t const gemv = type_traits_cpu[type].gemv; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // row groups - const int n_ids = ids->ne[0]; // n_expert_used - const int n_as = ne02; // n_expert - - char * wdata_src1_end = (src1->type == vec_dot_type) ? - (char *) params->wdata : - (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); - - struct mmid_row_mapping { - int32_t i1; - int32_t i2; - }; - - int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] - struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] - - if (src1->type != vec_dot_type) { - char * wdata = params->wdata; - - const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); - const size_t nbw2 = nbw1*ne11; - const size_t nbw3 = nbw2*ne12; - - assert(params->wsize >= ne13*nbw3); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = ith; i11 < ne11; i11 += nth) { - from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - ne10); - } - } - } - } - -#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] - - if (ith == 0) { - // initialize matrix_row_counts - memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); - - // group rows by src0 matrix - for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { - for (int id = 0; id < n_ids; ++id) { - const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); - - assert(i02 >= 0 && i02 < n_as); - - MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; - matrix_row_counts[i02] += 1; - } - } - } - - ggml_barrier(params->threadpool); - - // compute each matrix multiplication in sequence - for (int cur_a = 0; cur_a < n_as; ++cur_a) { - const int64_t cne1 = matrix_row_counts[cur_a]; - - if (cne1 == 0) { - continue; - } - - const char * src0_cur = (const char *) src0->data + cur_a*nb02; - - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = cne1; // src1 rows - - if (((ggml_n_dims(src0) - 1) == 2) && gemv) { - int64_t src0_cur_start = (ith * ne01) / nth; - int64_t src0_cur_end = ((ith + 1) * ne01) / nth; - src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start; - src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end; - if (src0_cur_start >= src0_cur_end) return; - - for (int ir1 = 0; ir1 < nr1; ir1++) { - struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1); - const int id = row_mapping.i1; // selected expert index - - const int64_t i11 = id % ne11; - const int64_t i12 = row_mapping.i2; // row index in src1 - - const int64_t i1 = id; // selected expert index - const int64_t i2 = i12; // row - - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12 * ne11) * row_size - : (i11 * nb11 + i12 * nb12)); - - gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, - (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); - } - continue; - } - - // distribute the thread work across the inner or outer loop based on which one is larger - - const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - - const int64_t ith0 = ith % nth0; - const int64_t ith1 = ith / nth0; - - const int64_t dr0 = (nr0 + nth0 - 1)/nth0; - const int64_t dr1 = (nr1 + nth1 - 1)/nth1; - - const int64_t ir010 = dr0*ith0; - const int64_t ir011 = MIN(ir010 + dr0, nr0); - - const int64_t ir110 = dr1*ith1; - const int64_t ir111 = MIN(ir110 + dr1, nr1); - - // threads with no work simply yield (not sure if it helps) - //if (ir010 >= ir011 || ir110 >= ir111) { - // sched_yield(); - // continue; - //} - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; - - // attempt to reduce false-sharing (does not seem to make a difference) - float tmp[16]; - - for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { - for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { - const int64_t _i12 = ir1; // logical row index for this expert - - struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); - const int id = row_mapping.i1; // selected expert index - - const int64_t i11 = id % ne11; - const int64_t i12 = row_mapping.i2; // row index in src1 - - const int64_t i1 = id; // selected expert index - const int64_t i2 = i12; // row - - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11)*row_size - : (i11*nb11 + i12*nb12)); - - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); - - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} - - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); - } - - memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); - } - } - } - } - -#undef MMID_MATRIX_ROW -} - -// ggml_compute_forward_out_prod - -static void ggml_compute_forward_out_prod_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne3 == ne13); - GGML_ASSERT(ne03 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // block-tiling attempt - const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); - const int64_t blck_1 = 16; - - for (int64_t bir = ir0; bir < ir1; bir += blck_1) { - const int64_t bir1 = MIN(bir + blck_1, ir1); - for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { - const int64_t bne01 = MIN(bi01 + blck_0, ne01); - for (int64_t ir = bir; ir < bir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - -#if GGML_VEC_MAD_UNROLL > 2 - const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); - for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); - } - for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#else - for (int64_t i01 = bi01; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#endif - } - } - } -} - -static void ggml_compute_forward_out_prod_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 dim0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst dim0 cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int64_t ir = ir0; ir < ir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - - for (int64_t i01 = 0; i01 < ne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - dequantize_row_q(s0, wdata, ne0); - ggml_vec_mad_f32(ne0, d, wdata, *s1); - } - } -} - -static void ggml_compute_forward_out_prod( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_out_prod_q_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - GGML_ABORT("fatal error"); // todo - // ggml_compute_forward_out_prod_f16_f32(params, dst); - } - case GGML_TYPE_F32: - { - ggml_compute_forward_out_prod_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_scale - -static void ggml_compute_forward_scale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - // scale factor - float v; - memcpy(&v, dst->op_params, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const size_t nb01 = src0->nb[1]; - - const size_t nb1 = dst->nb[1]; - - for (int i1 = ir0; i1 < ir1; i1++) { - if (dst->data != src0->data) { - // src0 is same shape as dst => same indices - memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); - } - ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); - } -} - -static void ggml_compute_forward_scale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_scale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_set - -static void ggml_compute_forward_set_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during set - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during set - const size_t nb0 = ggml_element_size(src0); - - const int im0 = (ne10 == 0 ? 0 : ne10-1); - const int im1 = (ne11 == 0 ? 0 : ne11-1); - const int im2 = (ne12 == 0 ? 0 : ne12-1); - const int im3 = (ne13 == 0 ? 0 : ne13-1); - - GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); - } -} - -static void ggml_compute_forward_set( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_set_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cpy - -static void ggml_compute_forward_cpy( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_cont - -static void ggml_compute_forward_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_reshape - -static void ggml_compute_forward_reshape( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_view - -static void ggml_compute_forward_view( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_permute - -static void ggml_compute_forward_permute( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_transpose - -static void ggml_compute_forward_transpose( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_get_rows - -static void ggml_compute_forward_get_rows_q( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == ggml_type_size(type)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - dequantize_row_q( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_fp16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_fp16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_bf16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_bf16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(float)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), - (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); - } -} - -static void ggml_compute_forward_get_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_get_rows_q(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rows_bf16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_get_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_get_rows_back - -static void ggml_compute_forward_get_rows_back_f32_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - for (int j = 0; j < nc; ++j) { - ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; - ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); - } - } -} - -static void ggml_compute_forward_get_rows_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) src0->data + i*src0->nb[1])); - } -} - -static void ggml_compute_forward_get_rows_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_back_f32_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_get_rows_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_diag - -static void ggml_compute_forward_diag_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - // TODO: handle transposed/permuted matrices - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne00 == ne0); - GGML_ASSERT(ne00 == ne1); - GGML_ASSERT(ne01 == 1); - GGML_ASSERT(ne02 == ne2); - GGML_ASSERT(ne03 == ne3); - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb0 == sizeof(float)); - - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = 0; i1 < ne1; i1++) { - float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); - for (int i0 = 0; i0 < i1; i0++) { - d[i0] = 0; - } - d[i1] = s[i1]; - for (int i0 = i1+1; i0 < ne0; i0++) { - d[i0] = 0; - } - } - } - } -} - -static void ggml_compute_forward_diag( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_diag_mask_inf - -static void ggml_compute_forward_diag_mask_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const float value) { - - const struct ggml_tensor * src0 = dst->src[0]; - - const int ith = params->ith; - const int nth = params->nth; - - const int n_past = ((int32_t *) dst->op_params)[0]; - const bool inplace = src0->data == dst->data; - - GGML_ASSERT(n_past >= 0); - - if (!inplace) { - if (ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - // TODO: handle transposed/permuted matrices - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - const int nr = src0->ne[1]; - const int nz = n/nr; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int k = 0; k < nz; k++) { - for (int j = ith; j < nr; j += nth) { - for (int i = n_past; i < nc; i++) { - if (i > n_past + j) { - *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; - } - } - } - } -} - -static void ggml_compute_forward_diag_mask_inf( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_diag_mask_zero( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, 0); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_soft_max - -static void ggml_compute_forward_soft_max_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_is_contiguous(dst)); - assert(ggml_are_same_shape(src0, dst)); - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - //const int64_t ne11 = src1 ? src1->ne[1] : 1; - - // TODO: is this supposed to be ceil instead of floor? - // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 - const uint32_t n_head = ne02; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - - for (int i1 = ir0; i1 < ir1; i1++) { - // ALiBi - const uint32_t h = (i1/ne01)%ne02; // head - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); - float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); - - // broadcast the mask across rows - ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - - ggml_vec_cpy_f32 (nc, wp, sp); - ggml_vec_scale_f32(nc, wp, scale); - if (mp_f32) { - if (use_f16) { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); - } - } else { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*mp_f32[i]; - } - } - } - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(wp[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, wp); - - ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(nc, dp, sum); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dp[i])); - assert(!isinf(dp[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_soft_max_back - -static void ggml_compute_forward_soft_max_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_are_same_shape(src1, dst)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); - float *y = (float *)((char *) src1->data + i1*src1->nb[1]); - float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(dy[i])); - assert(!isnan(y[i])); - } -#endif - // Jii = yi - yi*yi - // Jij = -yi*yj - // J = diag(y)-y.T*y - // dx = J * dy - // dxk = sum_i(Jki * dyi) - // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*dyk - // dxk = -yk * sum_i(yi * dyi) + yk*dyk - // dxk = -yk * dot(y, dy) + yk*dyk - // dxk = yk * (- dot(y, dy) + dyk) - // dxk = yk * (dyk - dot(y, dy)) - // - // post-order: - // dot_y_dy := dot(y, dy) - // dx := dy - // dx := dx - dot_y_dy - // dx := dx * y - - // linear runtime, no additional memory - float dot_y_dy = 0; - ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); - ggml_vec_cpy_f32 (nc, dx, dy); - ggml_vec_acc1_f32(nc, dx, -dot_y_dy); - ggml_vec_mul_f32 (nc, dx, dx, y); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dx[i])); - assert(!isinf(dx[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_clamp - -static void ggml_compute_forward_clamp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - float min; - float max; - memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - for (int j = ith; j < n; j += nth) { - float * dst_ptr = (float *) ((char *) dst->data + j*nb1); - float * src0_ptr = (float *) ((char *) src0->data + j*nb01); - - for (int i = 0; i < nc; i++) { - dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); - } - } -} - -static void ggml_compute_forward_clamp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_clamp_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q8_K: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_I64: - case GGML_TYPE_F64: - case GGML_TYPE_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope - -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / MAX(0.001f, high - low); - return 1 - MIN(1, MAX(0, y)); -} - -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); - } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; -} - -// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get -// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { - return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); -} - -static void ggml_rope_cache_init( - float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, - float * cache, float sin_sign, float theta_scale) { - // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py - float theta = theta_base; - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; - rope_yarn( - theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] - ); - cache[i0 + 1] *= sin_sign; - - theta *= theta_scale; - } -} - -void ggml_rope_yarn_corr_dims( - int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] -) { - // start and end correction dims - float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); - float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); - dims[0] = MAX(0, start); - dims[1] = MIN(n_dims - 1, end); -} - -static void ggml_compute_forward_rope_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb00 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = src[0]; - const float x1 = src[1]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims/2]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } - } - - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } -} - -// TODO: deduplicate f16/f32 code -static void ggml_compute_forward_rope_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[1]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } - - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } -} - -static void ggml_compute_forward_rope( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, true); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, true); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope_back - -static void ggml_compute_forward_rope_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, false); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, false); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_conv_transpose_1d - -static void ggml_compute_forward_conv_transpose_1d_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); - ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // permute source data (src1) from (L x Cin) to (Cin x L) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - ggml_fp16_t * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne02, &v, 0, - (ggml_fp16_t *) wdata_src + i1n, 0, - (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - float * const wdata = (float *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); - float * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // prepare source data (src1) - { - float * const wdata = (float *) params->wdata + nk; - float * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = src[i10]; - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * const wdata = (float *) params->wdata + 0; - float * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - float * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f32(ne02, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_transpose_1d_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_f32 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - - -// ggml_compute_forward_im2col_f16 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_im2col( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_im2col_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_im2col_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_back_f32 - -static void ggml_compute_forward_im2col_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne3 : ne2; - const int64_t IC = is_2D ? ne2 : ne1; - const int64_t IH = is_2D ? ne1 : 1; - const int64_t IW = ne0; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne12 : 1; - const int64_t OW = ne11; - - int ofs0 = is_2D ? nb3 : nb2; - int ofs1 = is_2D ? nb2 : nb1; - - GGML_ASSERT(nb0 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - for (int64_t iih = 0; iih < IH; iih++) { - for (int64_t iiw = 0; iiw < IW; iiw++) { - - // micro kernel - float grad = 0.0f; - for (int64_t ikh = 0; ikh < KH; ikh++) { - for (int64_t ikw = 0; ikw < KW; ikw++) { - // For s0 > 1 some values were skipped over in the forward pass. - // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. - const int64_t tmpw = (iiw + p0 - ikw*d0); - if (tmpw % s0 != 0) { - continue; - } - const int64_t iow = tmpw / s0; - - // Equivalent logic as above except for s1. - int64_t ioh; - if (is_2D) { - const int64_t tmph = iih + p1 - ikh*d1; - - if (tmph % s1 != 0) { - continue; - } - - ioh = tmph / s1; - } else { - ioh = 0; - } - - if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { - continue; - } - - const float * const src_data = (const float *) src1->data - + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - grad += src_data[iic*(KH*KW) + ikh*KW + ikw]; - } - } - float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] - dst_data[iih*IW + iiw] = grad; - } - } - } - } - } -} - -// ggml_compute_forward_conv_transpose_2d - -static void ggml_compute_forward_conv_transpose_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02*ne03; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); - ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; - for (int64_t i01 = 0; i01 < ne01; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; - } - } - } - } - } - - // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - for (int i12 = 0; i12 < ne12; i12++) { - for (int i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); - ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; - for (int i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - } - - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t stride = ggml_get_op_params_i32(dst, 0); - - // total patches in dst - const int np = ne2; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i2 = ip0; i2 < ip1; i2++) { // Cout - float * dst_data = (float *)((char *) dst->data + i2*nb2); - ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; - for (int i11 = 0; i11 < ne11; i11++) { - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i11*ne10*ne12 + i10*ne12; - for (int i01 = 0; i01 < ne01; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne03, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); - dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; - } - } - } - } - } -} - -// ggml_compute_forward_pool_1d_sk_p0 - -static void ggml_compute_forward_pool_1d_sk_p0( - const struct ggml_compute_params * params, - const enum ggml_op_pool op, - const int k, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const char * cdata = (const char *)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - float * drow = (float *)dst->data; - - const int64_t rs = dst->ne[0]; - - while (cdata < data_end) { - const void * srow = (const void *)cdata; - int j = 0; - for (int64_t i = 0; i < rs; ++i) { - switch (op) { - case GGML_OP_POOL_AVG: drow[i] = 0; break; - case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - for (int ki = 0; ki < k; ++ki) { - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: drow[i] += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - ++j; - } - switch (op) { - case GGML_OP_POOL_AVG: drow[i] /= k; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - - cdata += src->nb[1]; - drow += rs; - } -} - -// ggml_compute_forward_pool_1d - -static void ggml_compute_forward_pool_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int s0 = opts[2]; - const int p0 = opts[3]; - GGML_ASSERT(p0 == 0); // padding not supported - GGML_ASSERT(k0 == s0); // only s = k supported - - ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); -} - -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - const char * cdata = (const char*)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - - const int64_t px = dst->ne[0]; - const int64_t py = dst->ne[1]; - const int64_t pa = px * py; - - float * dplane = (float *)dst->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - float * const drow = dplane + oy * px; - for (int ox = 0; ox < px; ++ox) { - float * const out = drow + ox; - switch (op) { - case GGML_OP_POOL_AVG: *out = 0; break; - case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; - const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= src->ne[0]) continue; - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: *out += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - switch (op) { - case GGML_OP_POOL_AVG: *out /= ka; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - - cdata += src->nb[2]; - dplane += pa; - } -} - -// ggml_compute_forward_pool_2d_back - -static void ggml_compute_forward_pool_2d_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst - - assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - - char * cdata = (char *) dst->data; - const char * cdataf = (const char *) dstf->data; - const char * const data_end = cdata + ggml_nbytes(dst); - - GGML_ASSERT(params->ith == 0); - memset(cdata, 0, ggml_nbytes(dst)); - - const int64_t px = src->ne[0]; - const int64_t py = src->ne[1]; - const int64_t pa = px * py; - - const float * splane = (const float *) src->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - const float * const srow = splane + oy * px; - for (int ox = 0; ox < px; ++ox) { - const float grad0 = srow[ox]; - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - if (op == GGML_OP_POOL_MAX) { - float maxval = -FLT_MAX; - int kxmax = -1; - int kymax = -1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - const float val = dst->type == GGML_TYPE_F32 ? - ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); - if (val <= maxval) { - continue; - } - - maxval = val; - kxmax = kx; - kymax = ky; - } - } - - if (kxmax == -1 || kymax == -1) { - continue; - } - - void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); - const int j = ix + kxmax; - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad0; - } else { - ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); - } - } else if (op == GGML_OP_POOL_AVG) { - const float grad = grad0 / ka; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad; - } else { - ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); - } - } - } - } else { - GGML_ASSERT(false); - } - } - } - - cdata += dst->nb[2]; - cdataf += dst->nb[2]; - splane += pa; - } -} - -// ggml_compute_forward_upscale - -static void ggml_compute_forward_upscale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const float sf0 = (float)ne0/src0->ne[0]; - const float sf1 = (float)ne1/src0->ne[1]; - const float sf2 = (float)ne2/src0->ne[2]; - const float sf3 = (float)ne3/src0->ne[3]; - - // TODO: optimize - - for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3 / sf3; - for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2 / sf2; - for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / sf1; - for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / sf0; - - const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_upscale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_upscale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_pad - -static void ggml_compute_forward_pad_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT( dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float * dst_ptr = (float *) dst->data; - - // TODO: optimize - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = ith; i1 < ne1; i1 += nth) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - for (int64_t i3 = 0; i3 < ne3; ++i3) { - const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; - - const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - dst_ptr[dst_idx] = *src_ptr; - } else { - dst_ptr[dst_idx] = 0; - } - } - } - } - } -} - -static void ggml_compute_forward_pad( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_pad_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_arange - -static void ggml_compute_forward_arange_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const float start = ggml_get_op_params_f32(dst, 0); - const float stop = ggml_get_op_params_f32(dst, 1); - const float step = ggml_get_op_params_f32(dst, 2); - - const int64_t steps = (int64_t) ceilf((stop - start) / step); - - GGML_ASSERT(ggml_nelements(dst) == steps); - - for (int64_t i = ith; i < steps; i+= nth) { - float value = start + step * i; - ((float *)dst->data)[i] = value; - } -} - -static void ggml_compute_forward_arange( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_arange_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_timestep_embedding_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const int dim = ggml_get_op_params_i32(dst, 0); - const int max_period = ggml_get_op_params_i32(dst, 1); - - int half = dim / 2; - - for (int64_t i = 0; i < ne00; i++) { - float * embed_data = (float *)((char *) dst->data + i*nb1); - for (int64_t j = ith; j < half; j += nth) { - float timestep = ((float *)src0->data)[i]; - float freq = (float)expf(-logf(max_period) * j / half); - float arg = timestep * freq; - embed_data[j] = cosf(arg); - embed_data[j + half] = sinf(arg); - } - if (dim % 2 != 0 && ith == 0) { - embed_data[dim] = 0.f; - } - } -} - -static void ggml_compute_forward_timestep_embedding( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_timestep_embedding_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argsort - -static void ggml_compute_forward_argsort_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(nb0 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); - - for (int64_t i = ith; i < nr; i += nth) { - int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); - const float * src_data = (float *)((char *) src0->data + i*nb01); - - for (int64_t j = 0; j < ne0; j++) { - dst_data[j] = j; - } - - // C doesn't have a functional sort, so we do a bubble sort instead - for (int64_t j = 0; j < ne0; j++) { - for (int64_t k = j + 1; k < ne0; k++) { - if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || - (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { - int32_t tmp = dst_data[j]; - dst_data[j] = dst_data[k]; - dst_data[k] = tmp; - } - } - } - } -} - -static void ggml_compute_forward_argsort( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argsort_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_ext - -static void ggml_compute_forward_flash_attn_ext_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne2 == N); - - // input tensor rows must be contiguous - GGML_ASSERT(nbq0 == ggml_type_size(q->type)); - GGML_ASSERT(nbk0 == ggml_type_size(k->type)); - GGML_ASSERT(nbv0 == ggml_type_size(v->type)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nev0 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // broadcast factors - const int64_t rk2 = neq2/nek2; - const int64_t rk3 = neq3/nek3; - - const int64_t rv2 = neq2/nev2; - const int64_t rv3 = neq3/nev3; - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float scale = 1.0f; - float max_bias = 0.0f; - float logit_softcap = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); - - if (logit_softcap != 0) { - scale /= logit_softcap; - } - - const uint32_t n_head = neq2; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - enum ggml_type const k_vec_dot_type = type_traits_cpu[k->type].vec_dot_type; - ggml_from_float_t const q_to_vec_dot = ggml_get_type_traits(k_vec_dot_type)->from_float; - ggml_vec_dot_t const kq_vec_dot = type_traits_cpu[k->type].vec_dot; - ggml_to_float_t const v_to_float = ggml_get_type_traits(v->type)->to_float; - - GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); - GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); - - // loop over n_batch and n_head - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - const uint32_t h = iq2; // head index - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float S = 0.0f; // sum - float M = -INFINITY; // maximum KQ value - - float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator - float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer - ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator - ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 - - if (v->type == GGML_TYPE_F16) { - memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); - } else { - memset(VKQ32, 0, D*sizeof(float)); - } - - const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; - - // k indices - const int ik3 = iq3 / rk3; - const int ik2 = iq2 / rk2; - - // v indices - const int iv3 = iq3 / rv3; - const int iv2 = iq2 / rv2; - - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); - q_to_vec_dot(pq, Q_q, D); - - // online softmax / attention - // loop over n_kv and n_head_kv - // ref: https://arxiv.org/pdf/2112.05682.pdf - for (int64_t ic = 0; ic < nek1; ++ic) { - const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; - if (mv == -INFINITY) { - continue; - } - - float s; // KQ value - - const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); - kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); - - s = s*scale; // scale KQ value - - if (logit_softcap != 0.0f) { - s = logit_softcap*tanhf(s); - } - - s += mv; // apply mask - - const float Mold = M; - - float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value - float vs = 1.0f; // post-softmax KQ value, expf(s - M) - - const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - - if (v->type == GGML_TYPE_F16) { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f16(D, VKQ16, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - // V += v*expf(s - M) - ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); - } else { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f32(D, VKQ32, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - v_to_float(v_data, V32, D); - - // V += v*expf(s - M) - ggml_vec_mad_f32(D, VKQ32, V32, vs); - } - - S = S*ms + vs; // scale and increment sum with partial sum - } - - if (v->type == GGML_TYPE_F16) { - for (int64_t d = 0; d < D; ++d) { - VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); - } - } - - // V /= S - const float S_inv = 1.0f/S; - ggml_vec_scale_f32(D, VKQ32, S_inv); - - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // original - //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); - - // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); - } -} - -static void ggml_compute_forward_flash_attn_ext( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - switch (dst->op_params[3]) { - case GGML_PREC_DEFAULT: - case GGML_PREC_F32: - { - // uses F32 accumulators - ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_back - -static void ggml_compute_forward_flash_attn_back_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - const struct ggml_tensor * d = dst->src[3]; - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ned, d, ne) - GGML_TENSOR_LOCALS(size_t, nbd, d, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - const int mxDM = MAX(D, Mup); - - // GGML_ASSERT(ne0 == D); - // GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned1 == N); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (ith == 0) { - memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); - } - ggml_barrier(params->threadpool); - - const int64_t elem_q = ggml_nelements(q); - const int64_t elem_k = ggml_nelements(k); - - enum ggml_type result_type = dst->type; - GGML_ASSERT(ggml_blck_size(result_type) == 1); - const size_t tsize = ggml_type_size(result_type); - - const size_t offs_q = 0; - const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); - const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); - - void * grad_q = (char *) dst->data; - void * grad_k = (char *) dst->data + offs_k; - void * grad_v = (char *) dst->data + offs_v; - - const size_t nbgq1 = nb0*neq0; - const size_t nbgq2 = nb0*neq0*neq1; - const size_t nbgq3 = nb0*neq0*neq1*neq2; - - const size_t nbgk1 = nb0*nek0; - const size_t nbgk2 = nb0*nek0*nek1; - const size_t nbgk3 = nb0*nek0*nek1*neq2; - - const size_t nbgv1 = nb0*nev0; - const size_t nbgv2 = nb0*nev0*nev1; - const size_t nbgv3 = nb0*nev0*nev1*neq2; - - // parallelize by k rows using ggml_vec_dot_f32 - - // total rows in k - const int nr = nek2*nek3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - // how often k2 (and v2) is repeated in q2 - int nrep = neq2/nek2; - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int ik3 = ir/(nek2); - const int ik2 = ir - ik3*nek2; - - const int iq3 = ik3; - const int id3 = ik3; - const int iv3 = ik3; - const int iv2 = ik2; - - for (int irep = 0; irep < nrep; ++irep) { - const int iq2 = ik2 + irep*nek2; - const int id2 = iq2; - - // (ik2 + irep*nek2) % nek2 == ik2 - for (int iq1 = 0; iq1 < neq1; ++iq1) { - const int id1 = iq1; - - // not sure about CACHE_LINE_SIZE_F32.. - // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? - float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); - float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SM values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(SM, 1, &max, SM, 1, Mup); - vvexpf(SM, SM, &Mup); - ggml_vec_sum_f32(Mup, &sum, SM); -#else - sum = ggml_vec_soft_max_f32(Mup, SM, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, SM, sum); - - } - - // step-by-step explanation - { - // forward-process shape grads from backward process - // parallel_for ik2,ik3: - // for irep: - // iq2 = ik2 + irep*nek2 - // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] - // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] - // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] - // for iq1: - // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur - // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur - // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 - // S0 = -Inf [D,1,1,1] - // ~S1[i] = dot(kcur[:D,i], qcur) - // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale - // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) - // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur - // ~S5[i] = dot(vcur[:,i], S4) - // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] - // ~dst[i,iq1,iq2,iq3] = S5[i] ^ - // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] - // dst backward-/ grad[dst] = d - // - // output gradients with their dependencies: - // - // grad[kcur] = grad[S1].T @ qcur - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S4] = grad[S5] @ vcur - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[qcur] = grad[S1] @ kcur - // grad[vcur] = grad[S5].T @ S4 - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // in post-order: - // - // S1 = qcur @ kcur.T - // S2 = S1 * scale - // S3 = diag_mask_inf(S2, P) - // S4 = softmax(S3) - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[qcur] = grad[S1] @ kcur - // grad[kcur] = grad[S1].T @ qcur - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // using less variables (SM=S4): - // - // S = diag_mask_inf(qcur @ kcur.T * scale, P) - // SM = softmax(S) - // S = d[:D,iq1,iq2,iq3] @ vcur - // dot_SM_gradSM = dot(SM, S) - // S = SM * (S - dot(SM, S)) - // S = diag_mask_zero(S, P) * scale - // - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[k][:D,:M,ik2,ik3] += S.T @ qcur - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - } - - // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // for ic: - // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] - // exclude known future zero S[..] values from operation - ggml_vec_set_f32(masked_begin, S, 0); - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - S, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - - // S = SM * (S - dot(SM, S)) - float dot_SM_gradSM = 0; - ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); - ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); - ggml_vec_mul_f32 (masked_begin, S, S, SM); - - // S = diag_mask_zero(S, P) * scale - // already done by above ggml_vec_set_f32 - - // exclude known zero S[..] values from operation - ggml_vec_scale_f32(masked_begin, S, scale); - - // S shape [M,1] - // SM shape [M,1] - // kcur shape [D,M] - // qcur shape [D,1] - // vcur shape [M,D] - - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] - // for ic: - // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), - (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), - S[ic]); - } - - // grad[k][:D,:M,iq2,iq3] += S.T @ qcur - // for ic: - // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] - // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), - S[ic]); - } - - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - // for ic: - // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] - // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] - // exclude known zero SM[..] values from mad - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), - SM, - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - } - } - } -} - -static void ggml_compute_forward_flash_attn_back( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - - switch (q->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_flash_attn_back_f32(params, masked, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_conv - -static void ggml_compute_forward_ssm_conv_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // conv_x - const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src1->ne[0]; // d_conv - const int ncs = src0->ne[0]; // d_conv - 1 + n_t - const int nr = src0->ne[1]; // d_inner - const int n_t = dst->ne[1]; // tokens per sequence - const int n_s = dst->ne[2]; // number of sequences in the batch - - GGML_ASSERT( dst->ne[0] == nr); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - // {d_conv - 1 + n_t, d_inner, n_seqs} - // sliding window - const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} - const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} - float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} - - // TODO: transpose the output for smaller strides for big batches? - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // rowwise dot product - // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision - float sumf = 0.0f; - - // d_conv - for (int i0 = 0; i0 < nc; ++i0) { - sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; - } - x[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_conv( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_conv_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_scan - -static void ggml_compute_forward_ssm_scan_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // s - const struct ggml_tensor * src1 = dst->src[1]; // x - const struct ggml_tensor * src2 = dst->src[2]; // dt - const struct ggml_tensor * src3 = dst->src[3]; // A - const struct ggml_tensor * src4 = dst->src[4]; // B - const struct ggml_tensor * src5 = dst->src[5]; // C - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nc = src0->ne[0]; // d_state - const int64_t nr = src0->ne[1]; // d_inner - const int64_t n_t = src1->ne[1]; // number of tokens per sequence - const int64_t n_s = src0->ne[2]; // number of sequences in the batch - - GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src2->nb[0] == sizeof(float)); - GGML_ASSERT(src3->nb[0] == sizeof(float)); - GGML_ASSERT(src4->nb[0] == sizeof(float)); - GGML_ASSERT(src5->nb[0] == sizeof(float)); - // required for the dot product between s and C - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - // required for per-sequence offsets for states - GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); - // required to get correct offset for state destination (i.e. src1->nb[3]) - GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} - const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} - const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} - const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} - const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} - float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} - - // use the output as the source for the next token-wise iterations - if (i2 > 0) { s0 = s; } - - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 - float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; - float x_dt = x[i1] * dt_soft_plus; - float sumf = 0.0f; - // d_state - for (int i0 = 0; i0 < nc; ++i0) { - int i = i0 + i1*nc; - // state = prev_state * dA + dB * x - float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); - // y = rowwise_dotprod(state, C) - sumf += state * C[i0]; - s[i] = state; - } - y[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_scan( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_scan_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_part - -static void ggml_compute_forward_win_part_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t w = ((const int32_t *)(dst->op_params))[2]; - - assert(ne00 == ne0); - assert(ne3 == nep0*nep1); - - // TODO: optimize / multi-thread - for (int py = 0; py < nep1; ++py) { - for (int px = 0; px < nep0; ++px) { - const int64_t i3 = py*nep0 + px; - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i02 = py*w + i2; - const int64_t i01 = px*w + i1; - const int64_t i00 = i0; - - const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; - const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; - - if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { - ((float *) dst->data)[i] = 0.0f; - } else { - ((float *) dst->data)[i] = ((float *) src0->data)[j]; - } - } - } - } - } - } -} - -static void ggml_compute_forward_win_part( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_part_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_unpart - -static void ggml_compute_forward_win_unpart_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t w = ((const int32_t *)(dst->op_params))[0]; - - // padding - const int px = (w - ne1%w)%w; - //const int py = (w - ne2%w)%w; - - const int npx = (px + ne1)/w; - //const int npy = (py + ne2)/w; - - assert(ne0 == ne00); - - // TODO: optimize / multi-thread - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int ip2 = i2/w; - const int ip1 = i1/w; - - const int64_t i02 = i2%w; - const int64_t i01 = i1%w; - const int64_t i00 = i0; - - const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; - const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; - - ((float *) dst->data)[j] = ((float *) src0->data)[i]; - } - } - } -} - -static void ggml_compute_forward_win_unpart( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_unpart_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -//gmml_compute_forward_unary - -static void ggml_compute_forward_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const enum ggml_unary_op op = ggml_get_unary_op(dst); - - switch (op) { - case GGML_UNARY_OP_ABS: - { - ggml_compute_forward_abs(params, dst); - } break; - case GGML_UNARY_OP_SGN: - { - ggml_compute_forward_sgn(params, dst); - } break; - case GGML_UNARY_OP_NEG: - { - ggml_compute_forward_neg(params, dst); - } break; - case GGML_UNARY_OP_STEP: - { - ggml_compute_forward_step(params, dst); - } break; - case GGML_UNARY_OP_TANH: - { - ggml_compute_forward_tanh(params, dst); - } break; - case GGML_UNARY_OP_ELU: - { - ggml_compute_forward_elu(params, dst); - } break; - case GGML_UNARY_OP_RELU: - { - ggml_compute_forward_relu(params, dst); - } break; - case GGML_UNARY_OP_SIGMOID: - { - ggml_compute_forward_sigmoid(params, dst); - } break; - case GGML_UNARY_OP_GELU: - { - ggml_compute_forward_gelu(params, dst); - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - ggml_compute_forward_gelu_quick(params, dst); - } break; - case GGML_UNARY_OP_SILU: - { - ggml_compute_forward_silu(params, dst); - } break; - case GGML_UNARY_OP_HARDSWISH: - { - ggml_compute_forward_hardswish(params, dst); - } break; - case GGML_UNARY_OP_HARDSIGMOID: - { - ggml_compute_forward_hardsigmoid(params, dst); - } break; - case GGML_UNARY_OP_EXP: - { - ggml_compute_forward_exp(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_get_rel_pos - -static void ggml_compute_forward_get_rel_pos_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 - - GGML_TENSOR_UNARY_OP_LOCALS - - const int64_t w = ne1; - - ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; - ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - const int64_t pos = (w - i1 - 1) + i2; - for (int64_t i0 = 0; i0 < ne0; ++i0) { - dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; - } - } - } -} - -static void ggml_compute_forward_get_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rel_pos_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add_rel_pos - -static void ggml_compute_forward_add_rel_pos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; - if (!inplace) { - if (params->ith == 0) { - memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 - - float * src1_data = (float *) src1->data; - float * src2_data = (float *) src2->data; - float * dst_data = (float *) dst->data; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int ith = params->ith; - const int nth = params->nth; - - // total patches in dst - const int np = ne13; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - for (int64_t i13 = ip0; i13 < ip1; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t jp0 = jp1 + i10; - const float src1_e = src1_data[jp0]; - const float src2_e = src2_data[jp0]; - - const int64_t jdh = jp0 * ne10; - const int64_t jdw = jdh - (ne10 - 1) * i10; - - for (int64_t j = 0; j < ne10; ++j) { - dst_data[jdh + j ] += src2_e; - dst_data[jdw + j*ne10] += src1_e; - } - } - } - } - } -} - -static void ggml_compute_forward_add_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add_rel_pos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rwkv_wkv6 - -static void ggml_compute_forward_rwkv_wkv6_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const int64_t T = dst->src[1]->ne[3]; - const int64_t C = dst->ne[0]; - const int64_t HEADS = dst->src[1]->ne[2]; - const int64_t n_seqs = dst->src[5]->ne[1]; - const int64_t head_size = C / HEADS; - - float * dst_data = (float *) dst->data; - float * state = ((float *) dst->data) + C * T; - - const int ith = params->ith; - const int nth = params->nth; - - if (ith >= HEADS) { - return; - } - - const int h_start = (HEADS * ith) / nth; - const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? - (HEADS * (ith + 1)) / nth : HEADS; - - float * k = (float *) dst->src[0]->data; - float * v = (float *) dst->src[1]->data; - float * r = (float *) dst->src[2]->data; - float * time_faaaa = (float *) dst->src[3]->data; - float * time_decay = (float *) dst->src[4]->data; - - size_t t_stride = HEADS * head_size; // Same to C - - size_t h_stride = C / HEADS; - GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS - size_t h_stride_2d = head_size * head_size; - - if (ith == 0) { - memset(dst_data, 0, T * C * sizeof(float)); - } - ggml_barrier(params->threadpool); - - - #if defined(__AVX__) && !defined(__AVX512F__) - #define GGML_F32X GGML_F32x8 - #define GGML_F32X_SET1 GGML_F32x8_SET1 - #define GGML_F32X_LOAD GGML_F32x8_LOAD - #define GGML_F32X_STORE GGML_F32x8_STORE - #define GGML_F32X_MUL GGML_F32x8_MUL - #define GGML_F32X_FMA GGML_F32x8_FMA - #define WKV_VECTOR_SIZE 8 - #elif defined(__AVX512F__) - #define GGML_F32X GGML_F32x16 - #define GGML_F32X_SET1 GGML_F32x16_SET1 - #define GGML_F32X_LOAD GGML_F32x16_LOAD - #define GGML_F32X_STORE GGML_F32x16_STORE - #define GGML_F32X_MUL GGML_F32x16_MUL - #define GGML_F32X_FMA GGML_F32x16_FMA - #define WKV_VECTOR_SIZE 16 - #elif defined(__ARM_NEON) && defined(__aarch64__) - #define GGML_F32X GGML_F32x4 - #define GGML_F32X_SET1 GGML_F32x4_SET1 - #define GGML_F32X_LOAD GGML_F32x4_LOAD - #define GGML_F32X_STORE GGML_F32x4_STORE - #define GGML_F32X_MUL GGML_F32x4_MUL - #define GGML_F32X_FMA GGML_F32x4_FMA - #define WKV_VECTOR_SIZE 4 - #endif - - #ifdef WKV_VECTOR_SIZE - const int64_t vec_count = head_size / WKV_VECTOR_SIZE; - - for (int64_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_i_offset = h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float r_val = r[t_h_i_offset]; - float time_faaaa_val = time_faaaa[h_i_offset]; - float time_decay_val = time_decay[t_h_i_offset]; - - // Broadcast scalar values to vectors - GGML_F32X k_vec = GGML_F32X_SET1(k_val); - GGML_F32X r_vec = GGML_F32X_SET1(r_val); - GGML_F32X time_faaaa_vec = GGML_F32X_SET1(time_faaaa_val); - GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val); - - for (int64_t j = 0; j < vec_count; j++) { - size_t base_j = j * WKV_VECTOR_SIZE; - size_t t_h_j_offset = t_h_offset + base_j; - size_t h_2d_i_j_offset = h_2d_i_offset + base_j; - - // Load x elements at once - GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); - GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); - GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); - - // Compute kv = v * k - GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); - - // Compute temp = kv * time_faaaa + prev_state - GGML_F32X temp_vec = GGML_F32X_FMA(prev_state_vec, kv_vec, time_faaaa_vec); - - // Update dst: dst += temp * r - dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, r_vec); - GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); - - // Update state: state = prev_state * time_decay + kv - GGML_F32X new_state_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, time_decay_vec); - GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], new_state_vec); - } - - // Handle remaining elements, this will not be used. - for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val * time_faaaa_val + prev_state_val; - dst_data[t_h_j_offset] += temp_val * r_val; - state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; - } - } - } - } - - #else - // basically fused operations: - // dst = r @ (time_faaaa * (k @ v) + state), - // state = time_decay * state + (k @ v), - // recursive through each token - for (int64_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = head_size * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; - - for (int64_t h = h_start; h < h_end; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (int64_t i = 0; i < head_size; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_i_offset = h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float r_val = r[t_h_i_offset]; - float time_faaaa_val = time_faaaa[h_i_offset]; - // RWKV v6: different time_decay for each token. - float time_decay_val = time_decay[t_h_i_offset]; - - for (int64_t j = 0; j < head_size; j++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val * time_faaaa_val + prev_state_val; - dst_data[t_h_j_offset] += temp_val * r_val; - state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; - } - } - } - } - #endif -} - - -static void ggml_compute_forward_rwkv_wkv6( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rwkv_wkv6_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_unary - -static void ggml_compute_forward_map_unary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_map_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_unary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_binary - -static void ggml_compute_forward_map_binary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(src1)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), - (float *) ((char *) src1->data + i*(src1->nb[1]))); - } -} - -static void ggml_compute_forward_map_binary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_binary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom1_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - - if (params->ith != 0) { - return; - } - - fun(dst, a); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom2_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom3_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b, c); -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - - struct ggml_map_custom1_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - struct ggml_map_custom2_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[2]; - - struct ggml_map_custom3_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_cross_entropy_loss - -static void ggml_compute_forward_cross_entropy_loss_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - float * sums = (float *) params->wdata; - float * st = ((float *) params->wdata) + nth + ith*nc; - float sum_thread = 0.0f; - - GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t i1 = ir0; i1 < ir1; ++i1) { - const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); - const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); - assert(sum_softmax >= 0.0); - - ggml_vec_add1_f32(nc, st, st, -sum_softmax); - ggml_vec_mul_f32(nc, st, st, s1); - - float sum_st = 0.0f; - ggml_vec_sum_f32(nc, &sum_st, st); - sum_thread += sum_st; - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(st[i])); - assert(!isinf(st[i])); - } -#endif - } - sums[ith] = sum_thread; - ggml_barrier(params->threadpool); - - if (ith == 0) { - float * dp = (float *) dst->data; - ggml_vec_sum_f32(nth, dp, sums); - dp[0] *= -1.0f / (float) nr; - } -} - -static void ggml_compute_forward_cross_entropy_loss( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cross_entropy_loss_back - -static void ggml_compute_forward_cross_entropy_loss_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * opt0 = dst->src[2]; - - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(opt0)); - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int64_t ith = params->ith; - const int64_t nth = params->nth; - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr; - - for (int64_t i1 = ir0; i1 < ir1; i1++) { - float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); - float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); - float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - // soft_max - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); - assert(sum > 0.0); - ggml_vec_scale_f32(nc, ds0, 1.0/sum); - - // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr - ggml_vec_sub_f32(nc, ds0, ds0, s1); - ggml_vec_scale_f32(nc, ds0, d_by_nr); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(ds0[i])); - assert(!isinf(ds0[i])); - } -#endif - } -} - -static void ggml_compute_forward_cross_entropy_loss_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_opt_step_adamw_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src0_grad = dst->src[1]; - const struct ggml_tensor * src0_grad_m = dst->src[2]; - const struct ggml_tensor * src0_grad_v = dst->src[3]; - GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - /* const float gnorm = 1.0f; */ - int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t)); - const float alpha = ggml_get_op_params_f32(dst, 2); - const float beta1 = ggml_get_op_params_f32(dst, 3); - const float beta2 = ggml_get_op_params_f32(dst, 4); - const float eps = ggml_get_op_params_f32(dst, 5); - const float wd = ggml_get_op_params_f32(dst, 6); - - const float beta1h = alpha/(1.0f - powf(beta1, iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, iter)); - - for (int ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; - - float * w = (float *) ((char *) src0->data + offset); // weight - const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad - float * m = (float *) ((char *) src0_grad_m->data + offset); - float * v = (float *) ((char *) src0_grad_v->data + offset); - - for (int i00 = 0; i00 < ne00; ++i00) { - m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); - v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); - - const float mh = m[i00]*beta1h; - const float vh = sqrtf(v[i00]*beta2h) + eps; - - // The weight decay is applied independently of the Adam momenta m and v. - // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. - // See: https://arxiv.org/pdf/1711.05101v3.pdf - w[i00] = w[i00]*(1.0f - alpha*wd) - mh/vh; - } - } - - ggml_barrier(params->threadpool); - if (ith != 0) { - return; - } - - iter++; - memcpy(&dst->op_params[0], &iter, sizeof(int64_t)); -} - -static void ggml_compute_forward_opt_step_adamw( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_opt_step_adamw_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -///////////////////////////////// - -static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { - GGML_ASSERT(params); - - if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { - return; - } - - switch (tensor->op) { - case GGML_OP_DUP: - { - ggml_compute_forward_dup(params, tensor); - } break; - case GGML_OP_ADD: - { - ggml_compute_forward_add(params, tensor); - } break; - case GGML_OP_ADD1: - { - ggml_compute_forward_add1(params, tensor); - } break; - case GGML_OP_ACC: - { - ggml_compute_forward_acc(params, tensor); - } break; - case GGML_OP_SUB: - { - ggml_compute_forward_sub(params, tensor); - } break; - case GGML_OP_MUL: - { - ggml_compute_forward_mul(params, tensor); - } break; - case GGML_OP_DIV: - { - ggml_compute_forward_div(params, tensor); - } break; - case GGML_OP_SQR: - { - ggml_compute_forward_sqr(params, tensor); - } break; - case GGML_OP_SQRT: - { - ggml_compute_forward_sqrt(params, tensor); - } break; - case GGML_OP_LOG: - { - ggml_compute_forward_log(params, tensor); - } break; - case GGML_OP_SIN: - { - ggml_compute_forward_sin(params, tensor); - } break; - case GGML_OP_COS: - { - ggml_compute_forward_cos(params, tensor); - } break; - case GGML_OP_SUM: - { - ggml_compute_forward_sum(params, tensor); - } break; - case GGML_OP_SUM_ROWS: - { - ggml_compute_forward_sum_rows(params, tensor); - } break; - case GGML_OP_MEAN: - { - ggml_compute_forward_mean(params, tensor); - } break; - case GGML_OP_ARGMAX: - { - ggml_compute_forward_argmax(params, tensor); - } break; - case GGML_OP_COUNT_EQUAL: - { - ggml_compute_forward_count_equal(params, tensor); - } break; - case GGML_OP_REPEAT: - { - ggml_compute_forward_repeat(params, tensor); - } break; - case GGML_OP_REPEAT_BACK: - { - ggml_compute_forward_repeat_back(params, tensor); - } break; - case GGML_OP_CONCAT: - { - ggml_compute_forward_concat(params, tensor); - } break; - case GGML_OP_SILU_BACK: - { - ggml_compute_forward_silu_back(params, tensor); - } break; - case GGML_OP_NORM: - { - ggml_compute_forward_norm(params, tensor); - } break; - case GGML_OP_RMS_NORM: - { - ggml_compute_forward_rms_norm(params, tensor); - } break; - case GGML_OP_RMS_NORM_BACK: - { - ggml_compute_forward_rms_norm_back(params, tensor); - } break; - case GGML_OP_GROUP_NORM: - { - ggml_compute_forward_group_norm(params, tensor); - } break; - case GGML_OP_MUL_MAT: - { - ggml_compute_forward_mul_mat(params, tensor); - } break; - case GGML_OP_MUL_MAT_ID: - { - ggml_compute_forward_mul_mat_id(params, tensor); - } break; - case GGML_OP_OUT_PROD: - { - ggml_compute_forward_out_prod(params, tensor); - } break; - case GGML_OP_SCALE: - { - ggml_compute_forward_scale(params, tensor); - } break; - case GGML_OP_SET: - { - ggml_compute_forward_set(params, tensor); - } break; - case GGML_OP_CPY: - { - ggml_compute_forward_cpy(params, tensor); - } break; - case GGML_OP_CONT: - { - ggml_compute_forward_cont(params, tensor); - } break; - case GGML_OP_RESHAPE: - { - ggml_compute_forward_reshape(params, tensor); - } break; - case GGML_OP_VIEW: - { - ggml_compute_forward_view(params, tensor); - } break; - case GGML_OP_PERMUTE: - { - ggml_compute_forward_permute(params, tensor); - } break; - case GGML_OP_TRANSPOSE: - { - ggml_compute_forward_transpose(params, tensor); - } break; - case GGML_OP_GET_ROWS: - { - ggml_compute_forward_get_rows(params, tensor); - } break; - case GGML_OP_GET_ROWS_BACK: - { - ggml_compute_forward_get_rows_back(params, tensor); - } break; - case GGML_OP_DIAG: - { - ggml_compute_forward_diag(params, tensor); - } break; - case GGML_OP_DIAG_MASK_INF: - { - ggml_compute_forward_diag_mask_inf(params, tensor); - } break; - case GGML_OP_DIAG_MASK_ZERO: - { - ggml_compute_forward_diag_mask_zero(params, tensor); - } break; - case GGML_OP_SOFT_MAX: - { - ggml_compute_forward_soft_max(params, tensor); - } break; - case GGML_OP_SOFT_MAX_BACK: - { - ggml_compute_forward_soft_max_back(params, tensor); - } break; - case GGML_OP_ROPE: - { - ggml_compute_forward_rope(params, tensor); - } break; - case GGML_OP_ROPE_BACK: - { - ggml_compute_forward_rope_back(params, tensor); - } break; - case GGML_OP_CLAMP: - { - ggml_compute_forward_clamp(params, tensor); - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - ggml_compute_forward_conv_transpose_1d(params, tensor); - } break; - case GGML_OP_IM2COL: - { - ggml_compute_forward_im2col(params, tensor); - } break; - case GGML_OP_IM2COL_BACK: - { - ggml_compute_forward_im2col_back_f32(params, tensor); - } break; - case GGML_OP_CONV_TRANSPOSE_2D: - { - ggml_compute_forward_conv_transpose_2d(params, tensor); - } break; - case GGML_OP_POOL_1D: - { - ggml_compute_forward_pool_1d(params, tensor); - } break; - case GGML_OP_POOL_2D: - { - ggml_compute_forward_pool_2d(params, tensor); - } break; - case GGML_OP_POOL_2D_BACK: - { - ggml_compute_forward_pool_2d_back(params, tensor); - } break; - case GGML_OP_UPSCALE: - { - ggml_compute_forward_upscale(params, tensor); - } break; - case GGML_OP_PAD: - { - ggml_compute_forward_pad(params, tensor); - } break; - case GGML_OP_ARANGE: - { - ggml_compute_forward_arange(params, tensor); - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - ggml_compute_forward_timestep_embedding(params, tensor); - } break; - case GGML_OP_ARGSORT: - { - ggml_compute_forward_argsort(params, tensor); - } break; - case GGML_OP_LEAKY_RELU: - { - ggml_compute_forward_leaky_relu(params, tensor); - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - bool masked = t != 0; - ggml_compute_forward_flash_attn_back(params, masked, tensor); - } break; - case GGML_OP_SSM_CONV: - { - ggml_compute_forward_ssm_conv(params, tensor); - } break; - case GGML_OP_SSM_SCAN: - { - ggml_compute_forward_ssm_scan(params, tensor); - } break; - case GGML_OP_WIN_PART: - { - ggml_compute_forward_win_part(params, tensor); - } break; - case GGML_OP_WIN_UNPART: - { - ggml_compute_forward_win_unpart(params, tensor); - } break; - case GGML_OP_UNARY: - { - ggml_compute_forward_unary(params, tensor); - } break; - case GGML_OP_GET_REL_POS: - { - ggml_compute_forward_get_rel_pos(params, tensor); - } break; - case GGML_OP_ADD_REL_POS: - { - ggml_compute_forward_add_rel_pos(params, tensor); - } break; - case GGML_OP_RWKV_WKV6: - { - ggml_compute_forward_rwkv_wkv6(params, tensor); - } break; - case GGML_OP_MAP_UNARY: - { - ggml_unary_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_unary(params, tensor, fun); - } - break; - case GGML_OP_MAP_BINARY: - { - ggml_binary_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_binary(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM1_F32: - { - ggml_custom1_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom1_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM2_F32: - { - ggml_custom2_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom2_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM3_F32: - { - ggml_custom3_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom3_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM1: - { - ggml_compute_forward_map_custom1(params, tensor); - } - break; - case GGML_OP_MAP_CUSTOM2: - { - ggml_compute_forward_map_custom2(params, tensor); - } - break; - case GGML_OP_MAP_CUSTOM3: - { - ggml_compute_forward_map_custom3(params, tensor); - } - break; - case GGML_OP_CROSS_ENTROPY_LOSS: - { - ggml_compute_forward_cross_entropy_loss(params, tensor); - } - break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - ggml_compute_forward_cross_entropy_loss_back(params, tensor); - } - break; - case GGML_OP_OPT_STEP_ADAMW: - { - ggml_compute_forward_opt_step_adamw(params, tensor); - } - break; - case GGML_OP_NONE: - { - // nop - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -// Android's libc implementation "bionic" does not support setting affinity -#if defined(__gnu_linux__) -static void set_numa_thread_affinity(int thread_n) { - if (!ggml_is_numa()) { - return; - } - - int node_num; - int rv; - size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); - - switch(g_state.numa.numa_strategy) { - case GGML_NUMA_STRATEGY_DISTRIBUTE: - // run thread on node_num thread_n / (threads per node) - node_num = thread_n % g_state.numa.n_nodes; - break; - case GGML_NUMA_STRATEGY_ISOLATE: - // run thread on current_node - node_num = g_state.numa.current_node; - break; - case GGML_NUMA_STRATEGY_NUMACTL: - // use the cpuset that numactl gave us - rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); - } - return; - default: - return; - } - - struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; - - cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); - CPU_ZERO_S(setsize, cpus); - for (size_t i = 0; i < node->n_cpus; ++i) { - CPU_SET_S(node->cpus[i], setsize, cpus); - } - - rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); - } - - CPU_FREE(cpus); -} - -static void clear_numa_thread_affinity(void) { - if (!ggml_is_numa()) { - return; - } - - size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); - - cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); - CPU_ZERO_S(setsize, cpus); - for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { - CPU_SET_S(i, setsize, cpus); - } - - int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); - } - - CPU_FREE(cpus); -} -#else -// TODO: Windows etc. -// (the linux implementation may also work on BSD, someone should test) -static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } -static void clear_numa_thread_affinity(void) {} -#endif - -static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { - int n_tasks = 0; - - if (ggml_is_empty(node)) { - // no need to multi-thread a no-op - n_tasks = 1; - return n_tasks; - } - - switch (node->op) { - case GGML_OP_CPY: - case GGML_OP_DUP: - case GGML_OP_CONT: - case GGML_OP_ADD: - case GGML_OP_ADD1: - case GGML_OP_ACC: - { - n_tasks = n_threads; - } break; - case GGML_OP_SUB: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_LOG: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - { - n_tasks = 1; - } break; - case GGML_OP_COUNT_EQUAL: - { - n_tasks = n_threads; - } break; - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - case GGML_OP_LEAKY_RELU: - { - n_tasks = 1; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SGN: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_ELU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_EXP: - { - n_tasks = 1; - } break; - - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - { - n_tasks = n_threads; - } break; - default: - GGML_ABORT("fatal error"); - } - break; - case GGML_OP_SILU_BACK: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_GROUP_NORM: - case GGML_OP_CONCAT: - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - case GGML_OP_OUT_PROD: - { - n_tasks = n_threads; - } break; - case GGML_OP_GET_ROWS: - { - // FIXME: get_rows can use additional threads, but the cost of launching additional threads - // decreases performance with GPU offloading - //n_tasks = n_threads; - n_tasks = 1; - } break; - case GGML_OP_SCALE: - case GGML_OP_SET: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_GET_ROWS_BACK: - case GGML_OP_DIAG: - { - n_tasks = 1; - } break; - case GGML_OP_DIAG_MASK_ZERO: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_ADD_REL_POS: - { - n_tasks = n_threads; - } break; - case GGML_OP_CLAMP: - { - n_tasks = 1; //TODO - } break; - case GGML_OP_SOFT_MAX: - { - n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); - } break; - case GGML_OP_IM2COL: - case GGML_OP_IM2COL_BACK: - case GGML_OP_CONV_TRANSPOSE_1D: - case GGML_OP_CONV_TRANSPOSE_2D: - { - n_tasks = n_threads; - } break; - case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: - case GGML_OP_POOL_2D_BACK: - { - n_tasks = 1; - } break; - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_ARANGE: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_ARGSORT: - case GGML_OP_FLASH_ATTN_EXT: - case GGML_OP_FLASH_ATTN_BACK: - case GGML_OP_SSM_CONV: - case GGML_OP_SSM_SCAN: - { - n_tasks = n_threads; - } break; - case GGML_OP_WIN_PART: - case GGML_OP_WIN_UNPART: - case GGML_OP_GET_REL_POS: - case GGML_OP_RWKV_WKV6: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - { - n_tasks = 1; - } break; - case GGML_OP_MAP_CUSTOM1: - { - struct ggml_map_custom1_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM2: - { - struct ggml_map_custom2_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM3: - { - struct ggml_map_custom3_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_CROSS_ENTROPY_LOSS: - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - case GGML_OP_OPT_STEP_ADAMW: - { - n_tasks = n_threads; - } break; - case GGML_OP_NONE: - { - n_tasks = 1; - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - default: - { - fprintf(stderr, "%s: op not implemented: ", __func__); - if (node->op < GGML_OP_COUNT) { - fprintf(stderr, "%s\n", ggml_op_name(node->op)); - } else { - fprintf(stderr, "%d\n", node->op); - } - GGML_ABORT("fatal error"); - } - } - - assert(n_tasks > 0); - - return n_tasks; -} - -static thread_ret_t ggml_graph_compute_secondary_thread(void* data); - -#if defined(_WIN32) -#include "windows.h" - -// TODO: support > 64 CPUs -bool ggml_thread_apply_affinity(bool * mask) { - HANDLE h = GetCurrentThread(); - uint64_t bitmask = 0ULL; - - assert(GGML_MAX_N_THREADS >= 64); - - for (int32_t i = 0; i < 8; i++) { - int32_t idx = i * 8; - uint8_t val = 0; - val |= mask[idx + 0] << 0; - val |= mask[idx + 1] << 1; - val |= mask[idx + 2] << 2; - val |= mask[idx + 3] << 3; - val |= mask[idx + 4] << 4; - val |= mask[idx + 5] << 5; - val |= mask[idx + 6] << 6; - val |= mask[idx + 7] << 7; - bitmask |= (uint64_t)val << idx; - } - - for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { - fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n"); - break; - } - } - - DWORD_PTR m = (DWORD_PTR)bitmask; - - m = SetThreadAffinityMask(h, m); - - return m != 0; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - // Note that on Windows the Process Priority Class must be updated in order to set Thread priority. - // This is up to the applications. - DWORD p = THREAD_PRIORITY_NORMAL; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break; - case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break; - case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break; - case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - if (!SetThreadPriority(GetCurrentThread(), p)) { - fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError()); - return false; - } - - return true; -} - -#elif defined(__APPLE__) -#include -#include - -static bool ggml_thread_apply_affinity(const bool * mask) { - // Not supported on Apple platforms - UNUSED(mask); - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - struct sched_param p; - int32_t policy = SCHED_OTHER; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; - case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; - case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; - case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - int32_t err = pthread_setschedparam(pthread_self(), policy, &p); - if (err != 0) { - fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); - return false; - } - - return true; -} - -#elif defined(__gnu_linux__) -// TODO: this may not work on BSD, to be verified - -static bool ggml_thread_apply_affinity(const bool * mask) { - cpu_set_t cpuset; - int err; - - CPU_ZERO(&cpuset); - - for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { - GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i); - CPU_SET(i, &cpuset); - } - } - -#ifdef __ANDROID__ - err = sched_setaffinity(0, sizeof(cpuset), &cpuset); - if (err < 0) { - err = errno; - } -#else - err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset); -#endif - if (err != 0) { - fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err); - return false; - } - - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - struct sched_param p; - int32_t policy = SCHED_OTHER; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; - case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; - case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; - case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - int32_t err = pthread_setschedparam(pthread_self(), policy, &p); - if (err != 0) { - fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); - return false; - } - - return true; -} - -#else // unsupported platforms - -static bool ggml_thread_apply_affinity(const bool * mask) { - UNUSED(mask); - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - UNUSED(prio); - return true; -} - -#endif - -static bool ggml_thread_cpumask_is_valid(const bool * mask) { - for (int i = 0; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { return true; } - } - return false; -} - -static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) { - if (!strict) { - memcpy(local_mask, global_mask, GGML_MAX_N_THREADS); - return; - } else { - memset(local_mask, 0, GGML_MAX_N_THREADS); - int32_t base_idx = *iter; - for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { - int32_t idx = base_idx + i; - if (idx >= GGML_MAX_N_THREADS) { - // Just a cheaper modulo - idx -= GGML_MAX_N_THREADS; - } - if (global_mask[idx]) { - local_mask[idx] = 1; - *iter = idx + 1; - return; - } - } - } -} - -void ggml_threadpool_free(struct ggml_threadpool* threadpool) { - if (!threadpool) return; - - const int n_threads = threadpool->n_threads_max; - -#ifndef GGML_USE_OPENMP - struct ggml_compute_state* workers = threadpool->workers; - - ggml_mutex_lock(&threadpool->mutex); - - threadpool->stop = true; - threadpool->pause = false; - - ggml_cond_broadcast(&threadpool->cond); - ggml_mutex_unlock(&threadpool->mutex); - - for (int j = 1; j < n_threads; j++) { - int32_t rc = ggml_thread_join(workers[j].thrd, NULL); - GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED); - UNUSED(rc); - } - - ggml_mutex_destroy(&threadpool->mutex); - ggml_cond_destroy(&threadpool->cond); -#endif // GGML_USE_OPENMP - - const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads; - ggml_aligned_free(threadpool->workers, workers_size); - ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool)); -} - -#ifndef GGML_USE_OPENMP -// pause/resume must be called under mutex -static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) { - GGML_PRINT_DEBUG("Pausing threadpool\n"); - threadpool->pause = true; - ggml_cond_broadcast(&threadpool->cond); -} - -static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) { - GGML_PRINT_DEBUG("Resuming threadpool\n"); - threadpool->pause = false; - ggml_cond_broadcast(&threadpool->cond); -} -#endif - -void ggml_threadpool_pause(struct ggml_threadpool * threadpool) { -#ifndef GGML_USE_OPENMP - ggml_mutex_lock(&threadpool->mutex); - if (!threadpool->pause) { - ggml_threadpool_pause_locked(threadpool); - } - ggml_mutex_unlock(&threadpool->mutex); -#else - UNUSED(threadpool); -#endif -} - -void ggml_threadpool_resume(struct ggml_threadpool * threadpool) { -#ifndef GGML_USE_OPENMP - ggml_mutex_lock(&threadpool->mutex); - if (threadpool->pause) { - ggml_threadpool_resume_locked(threadpool); - } - ggml_mutex_unlock(&threadpool->mutex); -#else - UNUSED(threadpool); -#endif -} - -struct ggml_cplan ggml_graph_plan( - const struct ggml_cgraph * cgraph, - int n_threads, - struct ggml_threadpool * threadpool) { - - if (threadpool == NULL) { - //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); - } - if (n_threads <= 0) { - n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; - } - - size_t work_size = 0; - - struct ggml_cplan cplan; - memset(&cplan, 0, sizeof(struct ggml_cplan)); - - int max_tasks = 1; - - // thread scheduling for the different operations + work buffer size estimation - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - const int n_tasks = ggml_get_n_tasks(node, n_threads); - - max_tasks = MAX(max_tasks, n_tasks); - - size_t cur = 0; - - switch (node->op) { - case GGML_OP_CPY: - case GGML_OP_DUP: - { - if (ggml_is_quantized(node->type) || - // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 - (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || - (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; - } - } break; - case GGML_OP_ADD: - case GGML_OP_ADD1: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; - } - } break; - case GGML_OP_ACC: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; - } - } break; - case GGML_OP_COUNT_EQUAL: - { - cur = ggml_type_size(node->type)*n_tasks; - } break; - case GGML_OP_MUL_MAT: - { - const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type; - - if (node->src[1]->type != vec_dot_type) { - cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); - } - } break; - case GGML_OP_MUL_MAT_ID: - { - cur = 0; - const struct ggml_tensor * src0 = node->src[0]; - const struct ggml_tensor * src1 = node->src[1]; - const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type; - if (src1->type != vec_dot_type) { - cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); - } - const int n_as = src0->ne[2]; - cur += GGML_PAD(cur, sizeof(int64_t)); // align - cur += n_as * sizeof(int64_t); // matrix_row_counts - cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows - } break; - case GGML_OP_OUT_PROD: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; - } - } break; - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - { - cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - GGML_ASSERT(node->src[0]->ne[3] == 1); - GGML_ASSERT(node->src[1]->ne[2] == 1); - GGML_ASSERT(node->src[1]->ne[3] == 1); - - const int64_t ne00 = node->src[0]->ne[0]; // K - const int64_t ne01 = node->src[0]->ne[1]; // Cout - const int64_t ne02 = node->src[0]->ne[2]; // Cin - - const int64_t ne10 = node->src[1]->ne[0]; // L - const int64_t ne11 = node->src[1]->ne[1]; // Cin - - if ((node->src[0]->type == GGML_TYPE_F16 || - node->src[0]->type == GGML_TYPE_BF16) && - node->src[1]->type == GGML_TYPE_F32) { - cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; - cur += sizeof(ggml_fp16_t)*ne10*ne11; - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur += sizeof(float)*ne00*ne01*ne02; - cur += sizeof(float)*ne10*ne11; - } else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_CONV_TRANSPOSE_2D: - { - const int64_t ne00 = node->src[0]->ne[0]; // W - const int64_t ne01 = node->src[0]->ne[1]; // H - const int64_t ne02 = node->src[0]->ne[2]; // Channels Out - const int64_t ne03 = node->src[0]->ne[3]; // Channels In - - const int64_t ne10 = node->src[1]->ne[0]; // W - const int64_t ne11 = node->src[1]->ne[1]; // H - const int64_t ne12 = node->src[1]->ne[2]; // Channels In - - cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; - cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - const int64_t ne00 = node->src[0]->ne[0]; // D - - cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - const int64_t D = node->src[0]->ne[0]; - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); - const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } - } break; - - case GGML_OP_CROSS_ENTROPY_LOSS: - { - cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - default: - break; - } - - work_size = MAX(work_size, cur); - } - - if (work_size > 0) { - work_size += CACHE_LINE_SIZE*(n_threads); - } - - cplan.threadpool = threadpool; - cplan.n_threads = MIN(max_tasks, n_threads); - cplan.work_size = work_size; - cplan.work_data = NULL; - - return cplan; -} - -static thread_ret_t ggml_graph_compute_thread(void * data) { - struct ggml_compute_state * state = (struct ggml_compute_state *) data; - struct ggml_threadpool * tp = state->threadpool; - - const struct ggml_cgraph * cgraph = tp->cgraph; - const struct ggml_cplan * cplan = tp->cplan; - - set_numa_thread_affinity(state->ith); - - struct ggml_compute_params params = { - /*.ith =*/ state->ith, - /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), - /*.wsize =*/ cplan->work_size, - /*.wdata =*/ cplan->work_data, - /*.threadpool=*/ tp, - }; - - for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) { - struct ggml_tensor * node = cgraph->nodes[node_n]; - - ggml_compute_forward(¶ms, node); - - if (state->ith == 0 && cplan->abort_callback && - cplan->abort_callback(cplan->abort_callback_data)) { - tp->abort = true; - tp->ec = GGML_STATUS_ABORTED; - } - - ggml_barrier(state->threadpool); - } - - return 0; -} - -#ifndef GGML_USE_OPENMP - -// check if thread is active -static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); - return (state->ith < n_threads); -} - -// check if thread is ready to proceed (exit from polling or sleeping) -static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - if (state->pending || threadpool->stop || threadpool->pause) { return true; } - - // check for new graph/work - int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); - if (new_graph != state->last_graph) { - state->pending = ggml_graph_compute_thread_active(state); - state->last_graph = new_graph; - } - - return state->pending; -} - -// sync thread state after polling -static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) { - // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead - #ifdef GGML_TSAN_ENABLED - atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst); - #else - atomic_thread_fence(memory_order_seq_cst); - #endif - UNUSED(state); -} - -static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - // Skip polling for unused threads - if (!ggml_graph_compute_thread_active(state)) { - return state->pending; - } - - // This seems to make 0 ... 100 a decent range for polling level across modern processors. - // Perhaps, we can adjust it dynamically based on load and things. - const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; - - for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) { - // No new work. Keep polling. - ggml_thread_cpu_relax(); - } - - return state->pending; -} - -static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - if (ggml_graph_compute_poll_for_work(state)) { - ggml_graph_compute_thread_sync(state); - return state->pending; - } - - ggml_mutex_lock_shared(&threadpool->mutex); - while (!ggml_graph_compute_thread_ready(state)) { - // No new work. Wait for the signal. - GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith); - ggml_cond_wait(&threadpool->cond, &threadpool->mutex); - } - ggml_mutex_unlock_shared(&threadpool->mutex); - - return state->pending; -} - -static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { - struct ggml_compute_state * state = (struct ggml_compute_state *) data; - struct ggml_threadpool * threadpool = state->threadpool; - - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(state->cpumask)) { - ggml_thread_apply_affinity(state->cpumask); - } - - while (true) { - // Check if we need to sleep - while (threadpool->pause) { - GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith); - ggml_mutex_lock_shared(&threadpool->mutex); - if (threadpool->pause) { - ggml_cond_wait(&threadpool->cond, &threadpool->mutex); - } - GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith); - ggml_mutex_unlock_shared(&threadpool->mutex); - } - - // This needs to be checked for after the cond_wait - if (threadpool->stop) break; - - // Check if there is new work - // The main thread is the only one that can dispatch new work - - ggml_graph_compute_check_for_work(state); - if (state->pending) { - state->pending = false; - - ggml_graph_compute_thread(state); - } - } - - return (thread_ret_t) 0; -} - -// Start processing new graph -static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads) -{ - // Always take the mutex here because the worker threads are doing hybrid poll/wait - - ggml_mutex_lock(&threadpool->mutex); - - GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); - - // Update the number of active threads - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); - - // Indicate the graph is ready to be processed - // We need the full seq-cst fence here because of the polling threads (used in thread_sync) - atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); - - if (threadpool->pause) { - // Update main thread prio and affinity to match the threadpool settings - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { - ggml_thread_apply_affinity(threadpool->workers[0].cpumask); - } - - // resume does cond broadcast - ggml_threadpool_resume_locked(threadpool); - } else { - ggml_cond_broadcast(&threadpool->cond); - } - - ggml_mutex_unlock(&threadpool->mutex); -} - -#endif // GGML_USE_OPENMP - -void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { - p->n_threads = n_threads; - p->prio = 0; // default priority (usually means normal or inherited) - p->poll = 50; // hybrid-polling enabled - p->strict_cpu = false; // no strict placement (all threads share same cpumask) - p->paused = false; // threads are ready to go - memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) -} - -struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { - struct ggml_threadpool_params p; - ggml_threadpool_params_init(&p, n_threads); - return p; -} - -bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { - if (p0->n_threads != p1->n_threads ) return false; - if (p0->prio != p1->prio ) return false; - if (p0->poll != p1->poll ) return false; - if (p0->strict_cpu != p1->strict_cpu ) return false; - return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; -} - -static struct ggml_threadpool * ggml_threadpool_new_impl( - struct ggml_threadpool_params * tpp, - struct ggml_cgraph * cgraph, - struct ggml_cplan * cplan) { - - struct ggml_threadpool * threadpool = - ggml_aligned_malloc(sizeof(struct ggml_threadpool)); - { - threadpool->cgraph = cgraph; - threadpool->cplan = cplan; - threadpool->n_graph = 0; - threadpool->n_barrier = 0; - threadpool->n_barrier_passed = 0; - threadpool->current_chunk = 0; - threadpool->stop = false; - threadpool->pause = tpp->paused; - threadpool->abort = false; - threadpool->workers = NULL; - threadpool->n_threads_max = tpp->n_threads; - threadpool->n_threads_cur = tpp->n_threads; - threadpool->poll = tpp->poll; - threadpool->prio = tpp->prio; - threadpool->ec = GGML_STATUS_SUCCESS; - } - - // Allocate and init workers state - const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; - struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size); - - memset(workers, 0, workers_size); - for (int j = 0; j < tpp->n_threads; j++) { - workers[j].threadpool = threadpool; - workers[j].ith = j; - } - - threadpool->workers = workers; - -#ifndef GGML_USE_OPENMP - ggml_mutex_init(&threadpool->mutex); - ggml_cond_init(&threadpool->cond); - - // Spin the threads for all workers, and update CPU placements. - // Place the main thread last (towards the higher numbered CPU cores). - - int32_t cpumask_iter = 0; - - for (int j = 1; j < tpp->n_threads; j++) { - ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter); - - int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]); - GGML_ASSERT(rc == 0); - } - - ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter); - - if (!threadpool->pause) { - // Update main thread prio and affinity at the start, otherwise we'll do it in resume - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { - ggml_thread_apply_affinity(threadpool->workers[0].cpumask); - } - } -#endif // GGML_USE_OPENMP - - return threadpool; -} - -struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) { - return ggml_threadpool_new_impl(tpp, NULL, NULL); -} - -enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { - ggml_cpu_init(); - - GGML_ASSERT(cplan); - GGML_ASSERT(cplan->n_threads > 0); - GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL); - - int n_threads = cplan->n_threads; - struct ggml_threadpool * threadpool = cplan->threadpool; - - bool disposable_threadpool = false; - - if (threadpool == NULL) { - //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); - disposable_threadpool = true; - - struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads); - threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan); - } else { - // Reset some of the parameters that need resetting - // No worker threads should be accessing the parameters below at this stage - threadpool->cgraph = cgraph; - threadpool->cplan = cplan; - threadpool->current_chunk = 0; - threadpool->abort = false; - threadpool->ec = GGML_STATUS_SUCCESS; - } - -#ifdef GGML_USE_OPENMP - if (n_threads > 1) { - #pragma omp parallel num_threads(n_threads) - { - #pragma omp single - { - // update the number of threads from the actual number of threads that we got from OpenMP - n_threads = omp_get_num_threads(); - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); - } - - ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]); - } - } else { - atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); - ggml_graph_compute_thread(&threadpool->workers[0]); - } -#else - if (n_threads > threadpool->n_threads_max) { - GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); - n_threads = threadpool->n_threads_max; - } - - // Kick all threads to start the new graph - ggml_graph_compute_kickoff(threadpool, n_threads); - - // This is a work thread too - ggml_graph_compute_thread(&threadpool->workers[0]); -#endif - - // don't leave affinity set on the main thread - clear_numa_thread_affinity(); - - enum ggml_status ret = threadpool->ec; - - if (disposable_threadpool) { - ggml_threadpool_free(threadpool); - } - - return ret; -} - -enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { - struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL); - - cplan.work_data = (uint8_t *)ggml_new_buffer(ctx, cplan.work_size); - - return ggml_graph_compute(cgraph, &cplan); -} - -int ggml_cpu_has_neon(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_neon; -#else - return 0; -#endif -} - -int ggml_cpu_has_sve(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_sve; -#else - return 0; -#endif -} - -int ggml_cpu_has_matmul_int8(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_i8mm; -#else - return 0; -#endif -} - -int ggml_cpu_get_sve_cnt(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.sve_cnt; -#else - return 0; -#endif -} - -void ggml_cpu_init(void) { - // needed to initialize f16 tables - { - struct ggml_init_params params = { 0, NULL, false }; - struct ggml_context * ctx = ggml_init(params); - ggml_free(ctx); - } - - ggml_critical_section_start(); - - static bool is_first_call = true; - - if (is_first_call) { - // initialize GELU, Quick GELU, SILU and EXP F32 tables - { - const uint64_t t_start = ggml_time_us(); UNUSED(t_start); - - for (int i = 0; i < (1 << 16); ++i) { - union { - uint16_t u16; - ggml_fp16_t fp16; - } u = {i}; - float f = GGML_FP16_TO_FP32(u.fp16); - ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); - ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - } - - const uint64_t t_end = ggml_time_us(); UNUSED(t_end); - - GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0); - } - -#if defined(__ARM_ARCH) - ggml_init_arm_arch_features(); -#endif - - is_first_call = false; - } - - ggml_critical_section_end(); -} diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu deleted file mode 100644 index 357cee66..00000000 --- a/ggml/src/ggml-cuda.cu +++ /dev/null @@ -1,3364 +0,0 @@ -#include "ggml-cuda.h" -#include "ggml-impl.h" -#include "ggml-backend-impl.h" - -#include "ggml-cuda/common.cuh" -#include "ggml-cuda/acc.cuh" -#include "ggml-cuda/arange.cuh" -#include "ggml-cuda/argmax.cuh" -#include "ggml-cuda/argsort.cuh" -#include "ggml-cuda/binbcast.cuh" -#include "ggml-cuda/clamp.cuh" -#include "ggml-cuda/concat.cuh" -#include "ggml-cuda/conv-transpose-1d.cuh" -#include "ggml-cuda/convert.cuh" -#include "ggml-cuda/count-equal.cuh" -#include "ggml-cuda/cpy.cuh" -#include "ggml-cuda/cross-entropy-loss.cuh" -#include "ggml-cuda/diagmask.cuh" -#include "ggml-cuda/dmmv.cuh" -#include "ggml-cuda/fattn.cuh" -#include "ggml-cuda/getrows.cuh" -#include "ggml-cuda/im2col.cuh" -#include "ggml-cuda/mmq.cuh" -#include "ggml-cuda/mmvq.cuh" -#include "ggml-cuda/norm.cuh" -#include "ggml-cuda/opt-step-adamw.cuh" -#include "ggml-cuda/out-prod.cuh" -#include "ggml-cuda/pad.cuh" -#include "ggml-cuda/pool2d.cuh" -#include "ggml-cuda/quantize.cuh" -#include "ggml-cuda/rope.cuh" -#include "ggml-cuda/scale.cuh" -#include "ggml-cuda/softmax.cuh" -#include "ggml-cuda/sum.cuh" -#include "ggml-cuda/sumrows.cuh" -#include "ggml-cuda/tsembd.cuh" -#include "ggml-cuda/unary.cuh" -#include "ggml-cuda/upscale.cuh" -#include "ggml-cuda/wkv6.cuh" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); - -[[noreturn]] -void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { - int id = -1; // in case cudaGetDevice fails - cudaGetDevice(&id); - - GGML_LOG_ERROR(GGML_CUDA_NAME " error: %s\n", msg); - GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line); - GGML_LOG_ERROR(" %s\n", stmt); - // abort with GGML_ABORT to get a stack trace - GGML_ABORT(GGML_CUDA_NAME " error"); -} - -// this is faster on Windows -// probably because the Windows CUDA libraries forget to make this check before invoking the drivers -void ggml_cuda_set_device(int device) { - int current_device; - CUDA_CHECK(cudaGetDevice(¤t_device)); - - if (device == current_device) { - return; - } - - CUDA_CHECK(cudaSetDevice(device)); -} - -int ggml_cuda_get_device() { - int id; - CUDA_CHECK(cudaGetDevice(&id)); - return id; -} - -static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { - ggml_cuda_set_device(device); -#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA) - auto res = hipMallocManaged(ptr, size); - if (res == hipSuccess) { - // if error we "need" to know why... - CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device)); - } - return res; -#else - -#if !defined(GGML_USE_HIPBLAS) - cudaError_t err; - if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr) - { - err = cudaMallocManaged(ptr, size); - } - else - { - err = cudaMalloc(ptr, size); - } - return err; -#else - return cudaMalloc(ptr, size); -#endif // !defined(GGML_USE_HIPBLAS) - -#endif -} - -static ggml_cuda_device_info ggml_cuda_init() { -#ifdef __HIP_PLATFORM_AMD__ - // Workaround for a rocBLAS bug when using multiple graphics cards: - // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346 - rocblas_initialize(); - CUDA_CHECK(cudaDeviceSynchronize()); -#endif - - ggml_cuda_device_info info = {}; - - cudaError_t err = cudaGetDeviceCount(&info.device_count); - if (err != cudaSuccess) { - GGML_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); - return info; - } - - GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES); - - int64_t total_vram = 0; -#ifdef GGML_CUDA_FORCE_MMQ - GGML_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); -#else - GGML_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); -#endif // GGML_CUDA_FORCE_MMQ -#ifdef GGML_CUDA_FORCE_CUBLAS - GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: yes\n", __func__); -#else - GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: no\n", __func__); -#endif // GGML_CUDA_FORCE_CUBLAS - GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); - for (int id = 0; id < info.device_count; ++id) { - int device_vmm = 0; - -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) - CUdevice device; - CU_CHECK(cuDeviceGet(&device, id)); - CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); - - if (device_vmm) { - CUmemAllocationProp alloc_prop = {}; - alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; - alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - alloc_prop.location.id = id; - CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); - } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) - info.devices[id].vmm = !!device_vmm; - - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); - - info.default_tensor_split[id] = total_vram; - total_vram += prop.totalGlobalMem; - - info.devices[id].nsm = prop.multiProcessorCount; - info.devices[id].smpb = prop.sharedMemPerBlock; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - info.devices[id].smpbo = prop.sharedMemPerBlock; - info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; -#else - info.devices[id].smpbo = prop.sharedMemPerBlockOptin; - info.devices[id].cc = 100*prop.major + 10*prop.minor; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - } - - for (int id = 0; id < info.device_count; ++id) { - info.default_tensor_split[id] /= total_vram; - } - - // configure logging to stdout - // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); - - return info; -} - -const ggml_cuda_device_info & ggml_cuda_info() { - static ggml_cuda_device_info info = ggml_cuda_init(); - return info; -} - -// #define DEBUG_CUDA_MALLOC - -// buffer pool for cuda (legacy) -struct ggml_cuda_pool_leg : public ggml_cuda_pool { - static const int MAX_BUFFERS = 256; - - int device; - struct ggml_cuda_buffer { - void * ptr = nullptr; - size_t size = 0; - }; - - ggml_cuda_buffer buffer_pool[MAX_BUFFERS] = {}; - size_t pool_size = 0; - - explicit ggml_cuda_pool_leg(int device) : - device(device) { - } - - ~ggml_cuda_pool_leg() { - ggml_cuda_set_device(device); - for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cuda_buffer & b = buffer_pool[i]; - if (b.ptr != nullptr) { - CUDA_CHECK(cudaFree(b.ptr)); - pool_size -= b.size; - } - } - GGML_ASSERT(pool_size == 0); - } - - void * alloc(size_t size, size_t * actual_size) override { -#ifdef DEBUG_CUDA_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cuda_buffer& b = buffer_pool[i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_CUDA_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - } - } - } - } - if (ibest >= 0) { - ggml_cuda_buffer& b = buffer_pool[ibest]; - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - void * ptr; - size_t look_ahead_size = (size_t) (1.05 * size); - look_ahead_size = 256 * ((look_ahead_size + 255)/256); - ggml_cuda_set_device(device); - CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device)); - *actual_size = look_ahead_size; - pool_size += look_ahead_size; -#ifdef DEBUG_CUDA_MALLOC - GGML_LOG_INFO("%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, - (uint32_t)(max_size / 1024 / 1024), (uint32_t)(pool_size / 1024 / 1024), (uint32_t)(size / 1024 / 1024)); -#endif - return ptr; - } - - void free(void * ptr, size_t size) override { - for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cuda_buffer& b = buffer_pool[i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; - } - } - GGML_LOG_DEBUG(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n"); - ggml_cuda_set_device(device); - CUDA_CHECK(cudaFree(ptr)); - pool_size -= size; - } -}; - -// pool with virtual memory -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) -struct ggml_cuda_pool_vmm : public ggml_cuda_pool { - static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB - - int device; - CUdeviceptr pool_addr = 0; - size_t pool_used = 0; - size_t pool_size = 0; - size_t granularity; - - explicit ggml_cuda_pool_vmm(int device) : - device(device), - granularity(ggml_cuda_info().devices[device].vmm_granularity) { - } - - ~ggml_cuda_pool_vmm() { - if (pool_addr != 0) { - CU_CHECK(cuMemUnmap(pool_addr, pool_size)); - CU_CHECK(cuMemAddressFree(pool_addr, CUDA_POOL_VMM_MAX_SIZE)); - } - } - - void * alloc(size_t size, size_t * actual_size) override { - // round up the allocation size to the alignment to ensure that all allocations are aligned for all data types - const size_t alignment = 128; - size = alignment * ((size + alignment - 1) / alignment); - - size_t avail = pool_size - pool_used; - - if (size > avail) { - // round up to the next multiple of the granularity - size_t reserve_size = size - avail; - reserve_size = granularity * ((reserve_size + granularity - 1) / granularity); - - GGML_ASSERT(pool_size + reserve_size <= CUDA_POOL_VMM_MAX_SIZE); - - // allocate more physical memory - CUmemAllocationProp prop = {}; - prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; - prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; - CUmemGenericAllocationHandle handle; - CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); - - // reserve virtual address space (if not already reserved) - if (pool_addr == 0) { - CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)); - } - - // map at the end of the pool - CU_CHECK(cuMemMap(pool_addr + pool_size, reserve_size, 0, handle, 0)); - - // the memory allocation handle is no longer needed after mapping - CU_CHECK(cuMemRelease(handle)); - - // set access - CUmemAccessDesc access = {}; - access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = device; - access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; - CU_CHECK(cuMemSetAccess(pool_addr + pool_size, reserve_size, &access, 1)); - - // add to the pool - pool_size += reserve_size; - - //printf("cuda pool[%d]: size increased to %llu MB (reserved %llu MB)\n", - // device, (unsigned long long) (pool_size/1024/1024), - // (unsigned long long) (reserve_size/1024/1024)); - } - - GGML_ASSERT(pool_addr != 0); - - void * ptr = (void *) (pool_addr + pool_used); - *actual_size = size; - pool_used += size; - -#ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr); -#endif - - return ptr; - } - - void free(void * ptr, size_t size) override { -#ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr); -#endif - - pool_used -= size; - - // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (pool_addr + pool_used)); - } -}; -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) - -std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(int device) { -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) - if (ggml_cuda_info().devices[device].vmm) { - return std::unique_ptr(new ggml_cuda_pool_vmm(device)); - } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) - return std::unique_ptr(new ggml_cuda_pool_leg(device)); -} - -// cuda buffer - -struct ggml_backend_cuda_buffer_context { - int device; - void * dev_ptr = nullptr; - std::string name; - - ggml_backend_cuda_buffer_context(int device, void * dev_ptr) : - device(device), dev_ptr(dev_ptr), - name(GGML_CUDA_NAME + std::to_string(device)) { - } - - ~ggml_backend_cuda_buffer_context() { - CUDA_CHECK(cudaFree(dev_ptr)); - } -}; - -static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - delete ctx; -} - -static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) { - return buffer->iface.free_buffer == ggml_backend_cuda_buffer_free_buffer; -} - -static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - return ctx->dev_ptr; -} - -static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - - if (tensor->view_src != NULL) { - assert(tensor->view_src->buffer->buft == buffer->buft); - return; - } - - if (ggml_is_quantized(tensor->type) && tensor->view_src == nullptr && ggml_backend_buffer_get_usage(buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE) { - // initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); - - if (padded_size > original_size) { - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size)); - } - } -} - -static void ggml_backend_cuda_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + offset, value, size, cudaStreamPerThread)); - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); -} - -static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread)); - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); -} - -static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); -} - -static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { - if (ggml_backend_buffer_is_cuda(src->buffer)) { - ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; - ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context; - if (src_ctx->device == dst_ctx->device) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); - } else { -#ifdef GGML_CUDA_NO_PEER_COPY - return false; -#else - CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); -#endif - } - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); - return true; - } - return false; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context; - - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaDeviceSynchronize()); - CUDA_CHECK(cudaMemset(ctx->dev_ptr, value, buffer->size)); - CUDA_CHECK(cudaDeviceSynchronize()); -} - -static const ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = { - /* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer, - /* .get_base = */ ggml_backend_cuda_buffer_get_base, - /* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor, - /* .memset_tensor = */ ggml_backend_cuda_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cuda_buffer_clear, - /* .reset = */ NULL, -}; - -// cuda buffer type -struct ggml_backend_cuda_buffer_type_context { - int device; - std::string name; -}; - -static const char * ggml_backend_cuda_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - - return ctx->name.c_str(); -} - -static bool ggml_backend_buft_is_cuda(ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_cuda_buffer_type_get_name; -} - -static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - - ggml_cuda_set_device(buft_ctx->device); - - void * dev_ptr; - cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device); - if (err != cudaSuccess) { - // clear the error - cudaGetLastError(); - GGML_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err)); - return nullptr; - } - - ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr); - - return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size); -} - -static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - - GGML_UNUSED(buft); -} - -static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; - - if (ggml_is_quantized(tensor->type)) { - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return size; - - GGML_UNUSED(buft); -} - -static const ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { - /* .get_name = */ ggml_backend_cuda_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, - /* .is_host = */ NULL, -}; - -ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - if (device >= ggml_backend_cuda_get_device_count()) { - return nullptr; - } - - static ggml_backend_buffer_type ggml_backend_cuda_buffer_types[GGML_CUDA_MAX_DEVICES]; - - static bool ggml_backend_cuda_buffer_type_initialized = false; - - if (!ggml_backend_cuda_buffer_type_initialized) { - for (int i = 0; i < ggml_backend_cuda_get_device_count(); i++) { - ggml_backend_cuda_buffer_types[i] = { - /* .iface = */ ggml_backend_cuda_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), i), - /* .context = */ new ggml_backend_cuda_buffer_type_context{i, GGML_CUDA_NAME + std::to_string(i)}, - }; - } - ggml_backend_cuda_buffer_type_initialized = true; - } - - return &ggml_backend_cuda_buffer_types[device]; -} - -// cuda split buffer - -static int64_t get_row_rounding(const std::array & tensor_split) { - int64_t row_rounding = 0; - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) { - continue; - } - - const int cc = ggml_cuda_info().devices[id].cc; - row_rounding = std::max(row_rounding, (int64_t)get_mmq_y_host(cc)); - } - return row_rounding; -} - -static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { - const int64_t nrows = ggml_nrows(tensor); - const int64_t rounding = get_row_rounding(tensor_split); - - *row_low = id == 0 ? 0 : nrows*tensor_split[id]; - *row_low -= *row_low % rounding; - - if (id == ggml_backend_cuda_get_device_count() - 1) { - *row_high = nrows; - } else { - *row_high = nrows*tensor_split[id + 1]; - *row_high -= *row_high % rounding; - } -} - -static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); -} - -struct ggml_backend_cuda_split_buffer_type_context { - int main_device; - std::array tensor_split; - std::string name; -}; - -struct ggml_backend_cuda_split_buffer_context { - ~ggml_backend_cuda_split_buffer_context() { - for (ggml_tensor_extra_gpu * extra : tensor_extras) { - for (int id = 0; id < GGML_CUDA_MAX_DEVICES; ++id) { - for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - CUDA_CHECK(cudaEventDestroy(extra->events[id][is])); - } - } - if (extra->data_device[id] != nullptr) { - CUDA_CHECK(cudaFree(extra->data_device[id])); - } - } - delete extra; - } - } - - std::vector tensor_extras; -}; - - -static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; - delete ctx; -} - -static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) { - // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced - return (void *)0x1000; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported - - ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context; - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - - ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; - ctx->tensor_extras.push_back(extra); - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - // FIXME: do not crash if cudaMalloc fails - // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first - ggml_cuda_set_device(id); - char * buf; - CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id)); - - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size)); - } - - extra->data_device[id] = buf; - - for (int64_t is = 0; is < GGML_CUDA_MAX_STREAMS; ++is) { - CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming)); - } - } - tensor->extra = extra; -} - -static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - const char * buf_host = (const char *)data + offset_split; - CUDA_CHECK(cudaMemcpyAsync(extra->data_device[id], buf_host, original_size, cudaMemcpyHostToDevice, cudaStreamPerThread)); - } - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); - } -} - -static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, id); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf_host = (char *)data + offset_split; - CUDA_CHECK(cudaMemcpyAsync(buf_host, extra->data_device[id], original_size, cudaMemcpyDeviceToHost, cudaStreamPerThread)); - } - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread)); - } -} - -static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - GGML_UNUSED(buffer); - GGML_UNUSED(value); -} - -static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = { - /* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer, - /* .get_base = */ ggml_backend_cuda_split_buffer_get_base, - /* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_cuda_split_buffer_clear, - /* .reset = */ NULL, -}; - -// cuda split buffer type - -static const char * ggml_backend_cuda_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; - - return ctx->name.c_str(); -} - -static bool ggml_backend_buft_is_cuda_split(ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_cuda_split_buffer_type_get_name; -} - -static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point - // instead, we allocate them for each tensor separately in init_tensor - // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, - // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. - ggml_backend_cuda_split_buffer_context * ctx = new ggml_backend_cuda_split_buffer_context(); - - return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size); -} - -static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - - GGML_UNUSED(buft); -} - -static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context; - - size_t total_size = 0; - - const int64_t ne0 = tensor->ne[0]; - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, id); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - total_size += ggml_nbytes_split(tensor, nrows_split); - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return total_size; -} - -static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - GGML_UNUSED(buft); -} - -static const ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface = { - /* .get_name = */ ggml_backend_cuda_split_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cuda_split_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, -}; - -ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - static std::map>, struct ggml_backend_buffer_type> buft_map; - - std::array tensor_split_arr = {}; - - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_CUDA_MAX_DEVICES, [](float x) { return x == 0.0f; }); - if (all_zero) { - tensor_split_arr = ggml_cuda_info().default_tensor_split; - } else { - float split_sum = 0.0f; - for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) { - tensor_split_arr[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) { - tensor_split_arr[i] /= split_sum; - } - } - - auto it = buft_map.find({main_device, tensor_split_arr}); - if (it != buft_map.end()) { - return &it->second; - } - auto * ctx = new ggml_backend_cuda_split_buffer_type_context{ - main_device, - tensor_split_arr, - GGML_CUDA_NAME + std::to_string(main_device) + "_Split", - }; - - struct ggml_backend_buffer_type buft { - /* .iface = */ ggml_backend_cuda_split_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), main_device), - /* .context = */ ctx, - }; - - auto result = buft_map.emplace(std::make_pair(main_device, tensor_split_arr), buft); - return &result.first->second; -} - -// host buffer type - -static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_CUDA_NAME "_Host"; - - GGML_UNUSED(buft); -} - -static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - CUDA_CHECK(cudaFreeHost(buffer->context)); -} - -static void * ggml_cuda_host_malloc(size_t size) { - if (getenv("GGML_CUDA_NO_PINNED") != nullptr) { - return nullptr; - } - - void * ptr = nullptr; - cudaError_t err = cudaMallocHost((void **) &ptr, size); - if (err != cudaSuccess) { - // clear the error - cudaGetLastError(); - GGML_LOG_DEBUG("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size / 1024.0 / 1024.0, cudaGetErrorString(err)); - return nullptr; - } - - return ptr; -} - -static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_cuda_host_malloc(size); - - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), 0), - /* .context = */ nullptr, - }; - - return &ggml_backend_cuda_buffer_type_host; -} - -//static bool ggml_backend_buffer_is_cuda_host(ggml_backend_buffer_t buffer) { -// return buffer->buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name; -//} - -/// kernels - -typedef void (*ggml_cuda_op_mul_mat_t)( - ggml_backend_cuda_context & ctx, - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream); - -#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE -#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128 -#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE - -#define MUL_MAT_SRC1_COL_STRIDE 128 - -static __global__ void mul_mat_p021_f16_f32( - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) { - - const half * x = (const half *) vx; - - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / (nchannels_y / nchannels_x); - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; - - if (col_x >= ncols_x) { - break; - } - - // x is transposed and permuted - const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; - const float xi = __half2float(x[ix]); - - const int row_y = col_x; - - // y is not transposed but permuted - const int iy = channel*nrows_y + row_y; - - tmp += xi * y[iy]; - } - - // dst is not transposed and not permuted - const int idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[idst] = tmp; - } -} - -static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, - const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) { - - const half * x = (const half *) vx; - - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / channel_x_divisor; - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - const int idst = channel*nrows_dst + row_dst; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; - - if (col_x >= ncols_x) { - break; - } - - const int row_y = col_x; - - const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; - const int iy = channel*nrows_y + row_y; - - const float xi = __half2float(x[ix]); - - tmp += xi * y[iy]; - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[idst] = tmp; - } -} - -static void ggml_mul_mat_p021_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, - const int nchannels_x, const int nchannels_y, cudaStream_t stream) { - - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_p021_f16_f32<<>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y); -} - -static void ggml_mul_mat_vec_nc_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x, - const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) { - - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_vec_nc_f16_f32<<>> - (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x); -} - -static cudaError_t ggml_cuda_cpy_tensor_2d( - void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) { - - GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer)); - const char * src_ptr = (const char *) src->data; - char * dst_ptr = (char *) dst; - - const int64_t ne0 = src->ne[0]; - const int64_t nb0 = src->nb[0]; - const int64_t nb1 = src->nb[1]; - const int64_t nb2 = src->nb[2]; - const int64_t nb3 = src->nb[3]; - const enum ggml_type type = src->type; - const int64_t ts = ggml_type_size(type); - const int64_t bs = ggml_blck_size(type); - const int64_t i1_diff = i1_high - i1_low; - - const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; - if (nb0 == ts && nb1 == ts*ne0/bs) { - return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, cudaMemcpyDeviceToDevice, stream); - } else if (nb0 == ts) { - return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, cudaMemcpyDeviceToDevice, stream); - } else { - for (int64_t i1 = 0; i1 < i1_diff; i1++) { - const void * rx = (const void *) ((const char *) x + i1*nb1); - void * rd = (void *) (dst_ptr + i1*ts*ne0/bs); - // pretend the row is a matrix with cols=1 - cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyDeviceToDevice, stream); - if (r != cudaSuccess) { - return r; - } - } - return cudaSuccess; - } -} - -static void ggml_cuda_op_mul_mat_cublas( - ggml_backend_cuda_context & ctx, - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { - - GGML_ASSERT(src0_dd_i != nullptr); - GGML_ASSERT(src1_ddf_i != nullptr); - GGML_ASSERT(dst_dd_i != nullptr); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - - const int64_t row_diff = row_high - row_low; - - int id = ggml_cuda_get_device(); - - // the main device has a larger memory buffer to hold the results from all GPUs - // ldc == nrows of the matrix that cuBLAS writes into - int64_t ldc = id == ctx.device ? ne0 : row_diff; - - const int compute_capability = ggml_cuda_info().devices[id].cc; - - if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { - // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 - ggml_cuda_pool_alloc src0_as_f16(ctx.pool(id)); - if (src0->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - size_t ne = row_diff*ne00; - src0_as_f16.alloc(ne); - to_fp16_cuda(src0_dd_i, src0_as_f16.get(), ne, stream); - } - const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get(); - - ggml_cuda_pool_alloc src1_as_f16(ctx.pool(id)); - if (src1->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - size_t ne = src1_ncols*ne10; - src1_as_f16.alloc(ne); - to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream); - } - const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get(); - ggml_cuda_pool_alloc dst_f16(ctx.pool(id), row_diff*src1_ncols); - - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; - - CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); - CUBLAS_CHECK( - cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, - row_diff, src1_ncols, ne10, - &alpha_f16, src0_ptr, CUDA_R_16F, ne00, - src1_ptr, CUDA_R_16F, ne10, - &beta_f16, dst_f16.get(), CUDA_R_16F, ldc, - CUBLAS_COMPUTE_16F, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); - } else { - ggml_cuda_pool_alloc src0_ddq_as_f32(ctx.pool(id)); - ggml_cuda_pool_alloc src1_ddq_as_f32(ctx.pool(id)); - - if (src0->type != GGML_TYPE_F32) { - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type); - GGML_ASSERT(to_fp32_cuda != nullptr); - src0_ddq_as_f32.alloc(row_diff*ne00); - to_fp32_cuda(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); - } - if (src1->type != GGML_TYPE_F32) { - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src1->type); - GGML_ASSERT(to_fp32_cuda != nullptr); - src1_ddq_as_f32.alloc(src1_ncols*ne10); - to_fp32_cuda(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); - } - - const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); - const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); - - const float alpha = 1.0f; - const float beta = 0.0f; - - CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); - CUBLAS_CHECK( - cublasSgemm(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, - row_diff, src1_ncols, ne10, - &alpha, src0_ddf_i, ne00, - src1_ddf1_i, ne10, - &beta, dst_dd_i, ldc)); - } - - GGML_UNUSED(dst); - GGML_UNUSED(src1_ddq_i); - GGML_UNUSED(src1_padded_row_size); -} - -static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { - static bool peer_access_enabled = false; - - const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE; - - if (peer_access_enabled == enable_peer_access) { - return; - } - -#ifdef NDEBUG - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - ggml_cuda_set_device(id); - CUDA_CHECK(cudaDeviceSynchronize()); - } - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - ggml_cuda_set_device(id); - - for (int id_other = 0; id_other < ggml_backend_cuda_get_device_count(); ++id_other) { - if (id == id_other) { - continue; - } - if (id != main_device && id_other != main_device) { - continue; - } - - int can_access_peer; - CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other)); - if (can_access_peer) { - if (enable_peer_access) { - cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0); - if (err != cudaErrorPeerAccessAlreadyEnabled) { - CUDA_CHECK(err); - } else { - // reset the error - cudaGetLastError(); - } - } else { - cudaError_t err = cudaDeviceDisablePeerAccess(id_other); - if (err != cudaErrorPeerAccessNotEnabled) { - CUDA_CHECK(err); - } else { - // reset the error - cudaGetLastError(); - } - } - } - } - } - - ggml_cuda_set_device(main_device); -#endif // NDEBUG - - peer_access_enabled = enable_peer_access; - - GGML_UNUSED(main_device); -} - -static cudaError_t ggml_cuda_Memcpy2DPeerAsync( - void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) { - -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) - // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices - cudaMemcpy3DPeerParms p = {}; - p.dstDevice = dstDevice; - p.dstPtr = make_cudaPitchedPtr(dst, dpitch, dpitch, height); - p.srcDevice = srcDevice; - p.srcPtr = make_cudaPitchedPtr(src, spitch, spitch, height); - p.extent = make_cudaExtent(width, height, 1); - return cudaMemcpy3DPeerAsync(&p, stream); -#else - // HIP does not support cudaMemcpy3DPeerAsync or vmm pools - GGML_UNUSED(dstDevice); - GGML_UNUSED(srcDevice); - return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, cudaMemcpyDeviceToDevice, stream); -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) -} - -static void ggml_cuda_op_mul_mat( - ggml_backend_cuda_context & ctx, - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op, - quantize_cuda_t quantize_src1) { - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - const int64_t nrows1 = ggml_nrows(src1); - - GGML_ASSERT(ne03 == ne13); - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - const int64_t nb2 = dst->nb[2]; - const int64_t nb3 = dst->nb[3]; - - GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer)); - ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context; - ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *) dst->buffer->context; - - GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); - - GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); - - const int64_t i02_divisor = ne12 / ne02; - - const size_t src0_ts = ggml_type_size(src0->type); - const size_t src0_bs = ggml_blck_size(src0->type); - const size_t q8_1_ts = sizeof(block_q8_1); - const size_t q8_1_bs = QK8_1; - - const bool src0_is_contiguous = ggml_is_contiguous(src0); - const bool src1_is_contiguous = ggml_is_contiguous(src1); - - const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - - const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); - GGML_ASSERT(!(split && ne02 > 1)); - GGML_ASSERT(!(split && ne03 > 1)); - GGML_ASSERT(!(split && ne02 < ne12)); - - ggml_tensor_extra_gpu * src0_extra = split ? (ggml_tensor_extra_gpu *) src0->extra : nullptr; - - - std::array tensor_split; - if (split) { - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; - tensor_split = buft_ctx->tensor_split; - } - - struct dev_data { - int cc; - - ggml_cuda_pool_alloc src0_dd_alloc; - ggml_cuda_pool_alloc src1_ddf_alloc; - ggml_cuda_pool_alloc src1_ddq_alloc; - ggml_cuda_pool_alloc dst_dd_alloc; - - char * src0_dd = nullptr; - float * src1_ddf = nullptr; // float - char * src1_ddq = nullptr; // q8_1 - float * dst_dd = nullptr; - - int64_t row_low; - int64_t row_high; - }; - - dev_data dev[GGML_CUDA_MAX_DEVICES]; - - int used_devices = 0; - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - dev[id].cc = ggml_cuda_info().devices[id].cc; - - // by default, use all rows - dev[id].row_low = 0; - dev[id].row_high = ne01; - - // for multi GPU, get the row boundaries from tensor split - // and round to mul_mat_q tile sizes - if (split) { - const int64_t rounding = get_row_rounding(tensor_split); - - if (id != 0) { - dev[id].row_low = ne01*tensor_split[id]; - if (dev[id].row_low < ne01) { - dev[id].row_low -= dev[id].row_low % rounding; - } - } - - if (id != ggml_backend_cuda_get_device_count() - 1) { - dev[id].row_high = ne01*tensor_split[id + 1]; - if (dev[id].row_high < ne01) { - dev[id].row_high -= dev[id].row_high % rounding; - } - } - } - } - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) { - continue; - } - - used_devices++; - - const bool src1_on_device = id == src1_ctx->device; - const bool dst_on_device = id == dst_ctx->device; - - ggml_cuda_set_device(id); - cudaStream_t stream = ctx.stream(id, 0); - - if (src0_is_contiguous) { - dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data; - } else { - // If src0 is not contiguous it will be copied to a temporary buffer. - // This buffer needs to be cleared entirely because multiple regions will function as padding. - const size_t nbytes_data = ggml_nbytes(src0); - const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); - dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding); - // TODO: remove this for MUSA once the Guilty Lockup issue is resolved -#ifndef GGML_USE_MUSA - CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream)); -#else // GGML_USE_MUSA - CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); -#endif // !GGML_USE_MUSA - } - - // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared: - if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) { - const size_t nbytes_data = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00); - const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING); - CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data, 0, nbytes_padding, stream)); - } - - if (src1_on_device && src1_is_contiguous) { - dev[id].src1_ddf = (float *) src1->data; - } else { - dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1)); - } - - if (quantize_src1) { - size_t src_1_ddq_size = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs; - if (quantize_src1 == quantize_mmq_q8_1_cuda) { - src_1_ddq_size += get_mmq_x_max_host(dev[id].cc)*sizeof(block_q8_1_mmq); - } - dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), src_1_ddq_size); - - if (src1_on_device && src1_is_contiguous) { - quantize_src1(dev[id].src1_ddf, dev[id].src1_ddq, ne10, ne11, ne12*ne13, src1_padded_col_size, src0->type, stream); - CUDA_CHECK(cudaGetLastError()); - } - } - - if (dst_on_device) { - dev[id].dst_dd = (float *) dst->data; - } else { - const size_t size_dst_ddf = split ? (dev[id].row_high - dev[id].row_low)*ne1 : ggml_nelements(dst); - dev[id].dst_dd = dev[id].dst_dd_alloc.alloc(ctx.pool(id), size_dst_ddf); - } - } - - // if multiple devices are used they need to wait for the main device - // here an event is recorded that signals that the main device has finished calculating the input data - if (split && used_devices > 1) { - ggml_cuda_set_device(ctx.device); - CUDA_CHECK(cudaEventRecord(src0_extra->events[ctx.device][0], ctx.stream())); - } - - const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; - for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { - const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_CUDA_MAX_STREAMS : 0; - const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - if ((!split && id != ctx.device) || dev[id].row_low == dev[id].row_high) { - continue; - } - - const bool src1_on_device = id == src1_ctx->device; - const bool dst_on_device = id == dst_ctx->device; - const int64_t row_diff = dev[id].row_high - dev[id].row_low; - - ggml_cuda_set_device(id); - cudaStream_t stream = ctx.stream(id, is); - - // wait for main GPU data if necessary - if (split && (id != ctx.device || is != 0)) { - CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[ctx.device][0], 0)); - } - - for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { - const int64_t i03 = i0 / ne12; - const int64_t i02 = i0 % ne12; - - size_t src1_ddq_i_offset = i0*ne11 * src1_padded_col_size*q8_1_ts/q8_1_bs; - if (quantize_src1 == quantize_mmq_q8_1_cuda) { - src1_ddq_i_offset += src1_col_0 * sizeof(block_q8_1_mmq); - } else { - src1_ddq_i_offset += src1_col_0 * src1_padded_col_size*q8_1_ts/q8_1_bs; - } - - // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = dev[id].src1_ddf + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = dev[id].src1_ddq + src1_ddq_i_offset; - float * dst_dd_i = dev[id].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); - - // the main device memory buffer can be on VRAM scratch, with space for all partial results - // in that case an offset on dst_ddf_i is needed - if (id == ctx.device) { - dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split - } - - // copy src0, src1 to device if necessary - if (src1_is_contiguous) { - if (id != ctx.device) { - if (quantize_src1) { - char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset; - if (quantize_src1 == quantize_mmq_q8_1_cuda) { - const size_t pitch = ne11*sizeof(block_q8_1_mmq); - const size_t width = src1_ncols*sizeof(block_q8_1_mmq); - const size_t height = src1_padded_col_size/(4*QK8_1); - CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync(src1_ddq_i, id, pitch, src1_ddq_i_source, ctx.device, pitch, width, height, stream)); - } else { - CUDA_CHECK(cudaMemcpyPeerAsync( - src1_ddq_i, id, src1_ddq_i_source, ctx.device, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream)); - } - } else { - float * src1_ddf_i_source = (float *) src1->data; - src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddf_i, id, src1_ddf_i_source, ctx.device, - src1_ncols*ne10*sizeof(float), stream)); - } - } - } else if (src1_on_device && !src1_is_contiguous) { - CUDA_CHECK(ggml_cuda_cpy_tensor_2d( - src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); - } else { - GGML_ABORT("fatal error"); - } - - if (quantize_src1 && !src1_is_contiguous) { - quantize_src1(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, 1, src1_padded_col_size, src0->type, stream); - CUDA_CHECK(cudaGetLastError()); - } - - if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) { - CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[id].row_low, dev[id].row_high, stream)); - } - - // do the computation - op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - dev[id].row_low, dev[id].row_high, src1_ncols, src1_padded_col_size, stream); - CUDA_CHECK(cudaGetLastError()); - - // copy dst to host or other device if necessary - if (!dst_on_device) { - void * dst_off_device = dst->data; - if (split) { - // src0 = weight matrix is saved as a transposed matrix for better memory layout. - // dst is NOT transposed. - // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU. - // Instead they need to be copied to the correct slice in ne0 = dst row index. - // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. - float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); - GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + dev[id].row_low; - CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync( - dhf_dst_i, ctx.device, ne0*sizeof(float), dst_dd_i, id, row_diff*sizeof(float), row_diff*sizeof(float), src1_ncols, stream)); - } else { - float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); - GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0; - CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), cudaMemcpyDeviceToDevice, stream)); - } - } - - // add event for the main device to wait on until other device is done - if (split && (id != ctx.device || is != 0)) { - CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream)); - } - } - } - } - - // main device waits for all other devices to be finished - if (split && ggml_backend_cuda_get_device_count() > 1) { - int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; - is_max = is_max <= GGML_CUDA_MAX_STREAMS ? is_max : GGML_CUDA_MAX_STREAMS; - - ggml_cuda_set_device(ctx.device); - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - if (dev[id].row_low == dev[id].row_high) { - continue; - } - for (int64_t is = 0; is < is_max; ++is) { - CUDA_CHECK(cudaStreamWaitEvent(ctx.stream(), src0_extra->events[id][is], 0)); - } - } - } -} - -static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation - GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t ne12 = src1->ne[2]; - - cudaStream_t main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); -} - -static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; - - const int64_t ne12 = src1->ne[2]; - - cudaStream_t main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - const int64_t row_stride_x = nb01 / sizeof(half); - const int64_t channel_stride_x = nb02 / sizeof(half); - - ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); -} - -static __global__ void k_compute_batched_ptrs( - const half * src0_as_f16, const half * src1_as_f16, char * dst, - const void ** ptrs_src, void ** ptrs_dst, - int64_t ne12, int64_t ne13, - int64_t ne23, - size_t nb02, size_t nb03, - size_t nb12, size_t nb13, - size_t nbd2, size_t nbd3, - int64_t r2, int64_t r3) { - int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x; - int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y; - - if (i13 >= ne13 || i12 >= ne12) { - return; - } - - int64_t i03 = i13 / r3; - int64_t i02 = i12 / r2; - - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; -} - -static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t ne_dst = ggml_nelements(dst); - - cudaStream_t main_stream = ctx.stream(); - - CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(), main_stream)); - - void * src0_ddq = src0->data; - half * src0_f16 = (half *) src0_ddq; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - // convert src1 to fp16 - ggml_cuda_pool_alloc src1_f16_alloc(ctx.pool()); - if (src1->type != GGML_TYPE_F16) { - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - const int64_t ne_src1 = ggml_nelements(src1); - src1_f16_alloc.alloc(ne_src1); - GGML_ASSERT(to_fp16_cuda != nullptr); - to_fp16_cuda(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); - } - half * src1_f16 = src1->type == GGML_TYPE_F16 ? (half *) src1_ddf : src1_f16_alloc.get(); - - ggml_cuda_pool_alloc dst_f16(ctx.pool()); - char * dst_t; - - cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; - cudaDataType_t cu_data_type = CUDA_R_16F; - - // dst strides - size_t nbd2 = dst->nb[2]; - size_t nbd3 = dst->nb[3]; - - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; - - const float alpha_f32 = 1.0f; - const float beta_f32 = 0.0f; - - const void * alpha = &alpha_f16; - const void * beta = &beta_f16; - - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne_dst); - - nbd2 /= sizeof(float) / sizeof(half); - nbd3 /= sizeof(float) / sizeof(half); - } else { - dst_t = (char *) dst_ddf; - - cu_compute_type = CUBLAS_COMPUTE_32F; - cu_data_type = CUDA_R_32F; - - alpha = &alpha_f32; - beta = &beta_f32; - } - - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; - -#if 0 - // use cublasGemmEx - { - for (int i13 = 0; i13 < ne13; ++i13) { - for (int i12 = 0; i12 < ne12; ++i12) { - int i03 = i13 / r3; - int i02 = i12 / r2; - - CUBLAS_CHECK( - cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half), - (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float), - beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } - } - } -#else -#ifdef GGML_USE_MUSA - GGML_ASSERT(false); -#else // !GGML_USE_MUSA - if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { - // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - // use cublasGemmStridedBatchedEx - CUBLAS_CHECK( - cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_f16, CUDA_R_16F, nb01/nb00, nb02/nb00, // strideA - (const char *) src1_f16, CUDA_R_16F, nb11/nb10, nb12/nb10, // strideB - beta, ( char *) dst_t, cu_data_type, ne01, nb2/nb0, // strideC - ne12*ne13, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } else { - // use cublasGemmBatchedEx - const int ne23 = ne12*ne13; - - ggml_cuda_pool_alloc ptrs_src(ctx.pool(), 2*ne23); - ggml_cuda_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23); - - dim3 block_dims(ne13, ne12); - k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>( - src0_f16, src1_f16, dst_t, - ptrs_src.get(), ptrs_dst.get(), - ne12, ne13, - ne23, - nb02, nb03, - src1->type == GGML_TYPE_F16 ? nb12 : nb12/2, - src1->type == GGML_TYPE_F16 ? nb13 : nb13/2, - nbd2, nbd3, - r2, r3); - CUDA_CHECK(cudaGetLastError()); - - CUBLAS_CHECK( - cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, - (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, - beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, - ne23, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } -#endif // GGML_USE_MUSA -#endif - - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); - to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream); - } -} - -static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); - - bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src0->ne[0] % (GGML_CUDA_DMMV_X*2) == 0 && src1->ne[1] == 1; - bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; - bool use_mul_mat_q = ggml_is_quantized(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - - // if mmvq is available it's a better choice than dmmv: -#ifndef GGML_CUDA_FORCE_DMMV - use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; -#endif // GGML_CUDA_FORCE_DMMV - - bool any_gpus_with_slow_fp16 = false; - - if (split) { - ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; - auto & tensor_split = buft_ctx->tensor_split; - for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - // skip devices that are not going to do any work: - if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) { - continue; - } - - const int cc = ggml_cuda_info().devices[id].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); - } - } else { - const int cc = ggml_cuda_info().devices[ctx.device].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); - } - - // debug helpers - //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); - //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); - //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); - //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); - //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); - //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - - if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // FP32 precision KQ single-batch for batch size 1 without FlashAttention - ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst); - } else if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // FP32 precision KQV single-batch for batch size 1 without FlashAttention - ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst); - } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) - && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { - // KQ + KQV multi-batch without FlashAttention - ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); - } else if (use_dequantize_mul_mat_vec) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, nullptr); - } else if (use_mul_mat_vec_q) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda); - } else if (use_mul_mat_q) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, quantize_mmq_q8_1_cuda); - } else { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, nullptr); - } -} - -struct mmid_row_mapping { - int32_t i1; - int32_t i2; -}; - -static __global__ void k_copy_src1_to_contiguous(const char * __restrict__ src1_original, char * __restrict__ src1_contiguous, - int * __restrict__ cur_src1_row, mmid_row_mapping * __restrict__ row_mapping, - const char * __restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, - int64_t ne11, int64_t ne10, - size_t nb11, size_t nb12) { - int32_t iid1 = blockIdx.x; - int32_t id = blockIdx.y; - - const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); - - if (row_id_i != i02) { - return; - } - - const int64_t i11 = id % ne11; - const int64_t i12 = iid1; - - __shared__ int src1_row; - if (threadIdx.x == 0) { - src1_row = atomicAdd(cur_src1_row, 1); - row_mapping[src1_row] = {id, iid1}; - } - __syncthreads(); - - const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); - float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); - - for (int i = threadIdx.x; i < ne10; i += blockDim.x) { - src1_row_contiguous[i] = src1_row_original[i]; - } -} - -static __global__ void k_copy_dst_from_contiguous(char * __restrict__ dst_original, const char * __restrict__ dst_contiguous, - const mmid_row_mapping * __restrict__ row_mapping, - int64_t ne0, - size_t nb1, size_t nb2) { - int32_t i = blockIdx.x; - - const int32_t i1 = row_mapping[i].i1; - const int32_t i2 = row_mapping[i].i2; - - const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); - float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); - - for (int j = threadIdx.x; j < ne0; j += blockDim.x) { - dst_row_original[j] = dst_row_contiguous[j]; - } -} - -static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - const ggml_tensor * src1 = dst->src[1]; - const ggml_tensor * ids = dst->src[2]; - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft) && "mul_mat_id does not support split buffers"); - - cudaStream_t stream = ctx.stream(); - - const int64_t n_as = ne02; - const int64_t n_ids = ids->ne[0]; - - std::vector ids_host(ggml_nbytes(ids)); - const char * ids_dev = (const char *) ids->data; - CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream)); - CUDA_CHECK(cudaStreamSynchronize(stream)); - - ggml_tensor src0_row = *src0; - ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; - - char * src0_original = (char *) src0->data; - char * src1_original = (char *) src1->data; - char * dst_original = (char *) dst->data; - - src0_row.ne[2] = 1; - src0_row.ne[3] = 1; - src0_row.nb[3] = nb02; - - src1_row.ne[1] = 1; - src1_row.ne[2] = 1; - src1_row.ne[3] = 1; - src1_row.nb[2] = nb11; - src1_row.nb[3] = nb11; - - dst_row.ne[1] = 1; - dst_row.ne[2] = 1; - dst_row.ne[3] = 1; - dst_row.nb[2] = nb1; - dst_row.nb[3] = nb1; - - if (ne12 == 1) { - for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { - for (int64_t id = 0; id < n_ids; id++) { - const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - - GGML_ASSERT(i02 >= 0 && i02 < n_as); - - const int64_t i11 = id % ne11; - const int64_t i12 = iid1; - - const int64_t i1 = id; - const int64_t i2 = i12; - - src0_row.data = src0_original + i02*nb02; - src1_row.data = src1_original + i11*nb11 + i12*nb12; - dst_row.data = dst_original + i1*nb1 + i2*nb2; - - ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - } - } - } else { - ggml_cuda_pool_alloc src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1)); - ggml_cuda_pool_alloc dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst)); - - src1_row.data = src1_contiguous.get(); - dst_row.data = dst_contiguous.get(); - - for (int64_t i02 = 0; i02 < n_as; i02++) { - int64_t num_src1_rows = 0; - - for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { - for (int64_t id = 0; id < n_ids; id++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - - GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); - - if (row_id_i != i02) { - continue; - } - - num_src1_rows++; - } - } - - if (num_src1_rows == 0) { - continue; - } - - ggml_cuda_pool_alloc dev_cur_src1_row(ctx.pool(), 1); - ggml_cuda_pool_alloc dev_row_mapping(ctx.pool(), num_src1_rows); - CUDA_CHECK(cudaMemsetAsync(dev_cur_src1_row.get(), 0, sizeof(int), stream)); - - { - dim3 block_dims(std::min((unsigned int)ne10, 768u)); - dim3 grid_dims(ids->ne[1], n_ids); - k_copy_src1_to_contiguous<<>>( - src1_original, src1_contiguous.get(), - dev_cur_src1_row.get(), dev_row_mapping.get(), - ids_dev, i02, ids->nb[1], ids->nb[0], - ne11, ne10, - nb11, nb12); - CUDA_CHECK(cudaGetLastError()); - } - - src0_row.data = src0_original + i02*nb02; - - GGML_ASSERT(nb11 == sizeof(float)*ne10); - GGML_ASSERT(nb1 == sizeof(float)*ne0); - - src1_row.ne[1] = num_src1_rows; - src1_row.nb[1] = nb11; - src1_row.nb[2] = num_src1_rows*nb11; - src1_row.nb[3] = num_src1_rows*nb11; - - dst_row.ne[1] = num_src1_rows; - dst_row.nb[1] = nb1; - dst_row.nb[2] = num_src1_rows*nb1; - dst_row.nb[3] = num_src1_rows*nb1; - - ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - - { - dim3 block_dims(std::min((unsigned int)ne0, 768u)); - dim3 grid_dims(num_src1_rows); - k_copy_dst_from_contiguous<<>>( - dst_original, dst_contiguous.get(), - dev_row_mapping.get(), - ne0, - nb1, nb2); - CUDA_CHECK(cudaGetLastError()); - } - } - } -} - -static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) { - // why is this here instead of mul_mat? - if (dst->src[0] != nullptr && ggml_backend_buft_is_cuda_split(dst->src[0]->buffer->buft)) { - ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device); - } - - switch (dst->op) { - case GGML_OP_ARGMAX: - ggml_cuda_argmax(ctx, dst); - break; - case GGML_OP_COUNT_EQUAL: - ggml_cuda_count_equal(ctx, dst); - break; - case GGML_OP_REPEAT: - ggml_cuda_op_repeat(ctx, dst); - break; - case GGML_OP_REPEAT_BACK: - ggml_cuda_op_repeat_back(ctx, dst); - break; - case GGML_OP_GET_ROWS: - ggml_cuda_op_get_rows(ctx, dst); - break; - case GGML_OP_DUP: - ggml_cuda_dup(ctx, dst); - break; - case GGML_OP_CPY: - ggml_cuda_cpy(ctx, dst->src[0], dst->src[1]); - break; - case GGML_OP_CONT: - ggml_cuda_dup(ctx, dst); - break; - case GGML_OP_ADD: - case GGML_OP_ADD1: // TODO: more efficient implementation - ggml_cuda_op_add(ctx, dst); - break; - case GGML_OP_SUB: - ggml_cuda_op_sub(ctx, dst); - break; - case GGML_OP_ACC: - ggml_cuda_op_acc(ctx, dst); - break; - case GGML_OP_MUL: - ggml_cuda_op_mul(ctx, dst); - break; - case GGML_OP_DIV: - ggml_cuda_op_div(ctx, dst); - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(dst)) { - case GGML_UNARY_OP_NEG: - ggml_cuda_op_neg(ctx, dst); - break; - case GGML_UNARY_OP_STEP: - ggml_cuda_op_step(ctx, dst); - break; - case GGML_UNARY_OP_GELU: - ggml_cuda_op_gelu(ctx, dst); - break; - case GGML_UNARY_OP_SILU: - ggml_cuda_op_silu(ctx, dst); - break; - case GGML_UNARY_OP_GELU_QUICK: - ggml_cuda_op_gelu_quick(ctx, dst); - break; - case GGML_UNARY_OP_TANH: - ggml_cuda_op_tanh(ctx, dst); - break; - case GGML_UNARY_OP_RELU: - ggml_cuda_op_relu(ctx, dst); - break; - case GGML_UNARY_OP_SIGMOID: - ggml_cuda_op_sigmoid(ctx, dst); - break; - case GGML_UNARY_OP_HARDSIGMOID: - ggml_cuda_op_hardsigmoid(ctx, dst); - break; - case GGML_UNARY_OP_HARDSWISH: - ggml_cuda_op_hardswish(ctx, dst); - break; - case GGML_UNARY_OP_EXP: - ggml_cuda_op_exp(ctx, dst); - break; - default: - return false; - } - break; - case GGML_OP_NORM: - ggml_cuda_op_norm(ctx, dst); - break; - case GGML_OP_GROUP_NORM: - ggml_cuda_op_group_norm(ctx, dst); - break; - case GGML_OP_CONCAT: - ggml_cuda_op_concat(ctx, dst); - break; - case GGML_OP_UPSCALE: - ggml_cuda_op_upscale(ctx, dst); - break; - case GGML_OP_PAD: - ggml_cuda_op_pad(ctx, dst); - break; - case GGML_OP_ARANGE: - ggml_cuda_op_arange(ctx, dst); - break; - case GGML_OP_TIMESTEP_EMBEDDING: - ggml_cuda_op_timestep_embedding(ctx, dst); - break; - case GGML_OP_LEAKY_RELU: - ggml_cuda_op_leaky_relu(ctx, dst); - break; - case GGML_OP_RMS_NORM: - ggml_cuda_op_rms_norm(ctx, dst); - break; - case GGML_OP_MUL_MAT: - if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) { - GGML_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); - return false; - } else { - ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst); - } - break; - case GGML_OP_MUL_MAT_ID: - ggml_cuda_mul_mat_id(ctx, dst); - break; - case GGML_OP_OUT_PROD: - ggml_cuda_out_prod(ctx, dst); - break; - case GGML_OP_SCALE: - ggml_cuda_op_scale(ctx, dst); - break; - case GGML_OP_SQR: - ggml_cuda_op_sqr(ctx, dst); - break; - case GGML_OP_SQRT: - ggml_cuda_op_sqrt(ctx, dst); - break; - case GGML_OP_SIN: - ggml_cuda_op_sin(ctx, dst); - break; - case GGML_OP_COS: - ggml_cuda_op_cos(ctx, dst); - break; - case GGML_OP_CLAMP: - ggml_cuda_op_clamp(ctx, dst); - break; - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - break; - case GGML_OP_DIAG_MASK_INF: - ggml_cuda_op_diag_mask_inf(ctx, dst); - break; - case GGML_OP_SOFT_MAX: - ggml_cuda_op_soft_max(ctx, dst); - break; - case GGML_OP_ROPE: - ggml_cuda_op_rope(ctx, dst); - break; - case GGML_OP_IM2COL: - ggml_cuda_op_im2col(ctx, dst); - break; - case GGML_OP_CONV_TRANSPOSE_1D: - ggml_cuda_op_conv_transpose_1d(ctx,dst); - break; - case GGML_OP_POOL_2D: - ggml_cuda_op_pool2d(ctx, dst); - break; - case GGML_OP_SUM: - ggml_cuda_op_sum(ctx, dst); - break; - case GGML_OP_SUM_ROWS: - ggml_cuda_op_sum_rows(ctx, dst); - break; - case GGML_OP_ARGSORT: - ggml_cuda_op_argsort(ctx, dst); - break; - case GGML_OP_FLASH_ATTN_EXT: - ggml_cuda_flash_attn_ext(ctx, dst); - break; - case GGML_OP_CROSS_ENTROPY_LOSS: - ggml_cuda_cross_entropy_loss(ctx, dst); - break; - case GGML_OP_RWKV_WKV6: - ggml_cuda_op_rwkv_wkv6(ctx, dst); - break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - ggml_cuda_cross_entropy_loss_back(ctx, dst); - break; - case GGML_OP_OPT_STEP_ADAMW: - ggml_cuda_opt_step_adamw(ctx, dst); - break; - default: - return false; - } - - cudaError_t err = cudaGetLastError(); - if (err != cudaSuccess) { - GGML_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst)); - CUDA_CHECK(err); - } - - return true; -} - -//////////////////////////////////////////////////////////////////////////////// - -// backend - -static const char * ggml_backend_cuda_get_name(ggml_backend_t backend) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - return cuda_ctx->name.c_str(); -} - -static void ggml_backend_cuda_free(ggml_backend_t backend) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - delete cuda_ctx; - delete backend; -} - -static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - - GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - - CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream())); -} - -static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - - GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type"); - - CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream())); -} - -static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { - ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer; - ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer; - - if (!ggml_backend_is_cuda(backend_src) || !ggml_backend_is_cuda(backend_dst)) { - return false; - } - - if (!ggml_backend_buffer_is_cuda(src->buffer) || !ggml_backend_buffer_is_cuda(dst->buffer)) { - return false; - } - - // device -> device copy - ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context; - ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context; - - ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context; - ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context; - - if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__); -#endif - return false; - } - - if (backend_src != backend_dst) { - // copy on src stream - if (cuda_ctx_src->device == cuda_ctx_dst->device) { - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream())); - } else { -#ifdef GGML_CUDA_NO_PEER_COPY - return false; -#else - CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream())); -#endif - } - - // record event on src stream after the copy - if (!cuda_ctx_src->copy_event) { - ggml_cuda_set_device(cuda_ctx_src->device); - CUDA_CHECK(cudaEventCreateWithFlags(&cuda_ctx_src->copy_event, cudaEventDisableTiming)); - } - - CUDA_CHECK(cudaEventRecord(cuda_ctx_src->copy_event, cuda_ctx_src->stream())); - - // wait on dst stream for the copy to complete - CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx_dst->stream(), cuda_ctx_src->copy_event, 0)); - } else { - // src and dst are on the same backend - CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream())); - } - return true; -} - -static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - CUDA_CHECK(cudaStreamSynchronize(cuda_ctx->stream())); - - GGML_UNUSED(backend); -} - -#ifdef USE_CUDA_GRAPH -static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { - graph_node_properties->node_address = node->data; - graph_node_properties->node_op = node->op; - for (int i = 0; i < GGML_MAX_DIMS; i++) { - graph_node_properties->ne[i] = node->ne[i]; - graph_node_properties->nb[i] = node->nb[i]; - } - for (int i = 0; i < GGML_MAX_SRC; i++) { - graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr; - } - memcpy(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS); -} - -static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { - if (node->data != graph_node_properties->node_address && - node->op != GGML_OP_CPY && - node->op != GGML_OP_VIEW) { - return false; - } - - if (node->op != graph_node_properties->node_op) { - return false; - } - - for (int i = 0; i < GGML_MAX_DIMS; i++) { - if (node->ne[i] != graph_node_properties->ne[i]) { - return false; - } - if (node->nb[i] != graph_node_properties->nb[i]) { - return false; - } - } - - for (int i = 0; i < GGML_MAX_SRC; i++) { - if (node->src[i] && - node->src[i]->data != graph_node_properties->src_address[i] && - node->op != GGML_OP_CPY && - node->op != GGML_OP_VIEW - ) { - return false; - } - } - - if (node->op == GGML_OP_SCALE && - memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) { - return false; - } - - return true; -} -#endif - -static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - ggml_cuda_set_device(cuda_ctx->device); - -#ifdef USE_CUDA_GRAPH - static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr); - - // Objects required for CUDA Graph - if (cuda_ctx->cuda_graph == nullptr) { - cuda_ctx->cuda_graph.reset(new ggml_cuda_graph()); - } - - bool use_cuda_graph = true; - bool cuda_graph_update_required = false; - // vector of pointers to CUDA cpy kernels, which are required to identify - // kernel parameters which need updated in the graph for each token - std::vector ggml_cuda_cpy_fn_ptrs; - - if (cuda_ctx->cuda_graph->graph == nullptr) { - if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { - cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true; -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__); -#endif - } - } - - // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly, - // or previous graph capture failure. - // Also disable for multi-gpu for now. TO DO investigate - if (disable_cuda_graphs_due_to_env - || cuda_ctx->cuda_graph->disable_due_to_gpu_arch - || cuda_ctx->cuda_graph->disable_due_to_too_many_updates - || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) { - use_cuda_graph = false; - } - - if (use_cuda_graph) { - if (cuda_ctx->cuda_graph->instance == nullptr) { - cuda_graph_update_required = true; - } - - // Check if the graph size has changed - if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) { - cuda_graph_update_required = true; - cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes); - } - - // Loop over nodes in GGML graph to determine if CUDA graph update is required - // and store properties to allow this comparison for the next token - for (int i = 0; i < cgraph->n_nodes; i++) { - bool has_matching_properties = true; - if (!cuda_graph_update_required) { - has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]); - } - if (!has_matching_properties) { - cuda_graph_update_required = true; - } - set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]); - } - - // Loop over nodes in GGML graph to obtain info needed for CUDA graph - cuda_ctx->cuda_graph->updated_kernel_arg.clear(); - for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_tensor * node = cgraph->nodes[i]; - - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { - continue; - } - - if (node->src[0] && node->src[0]->buffer && ggml_backend_buft_is_cuda_split(node->src[0]->buffer->buft)) { - use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__); -#endif - } - - if (node->op == GGML_OP_MUL_MAT_ID) { - use_cuda_graph = false; // This node type is not supported by CUDA graph capture -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to mul_mat_id\n", __func__); -#endif - } - - if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) { - // disable CUDA graphs for batch size > 1 for now. - // Changes in batch size or context size can cause changes to the grid size of some kernels. - use_cuda_graph = false; -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); -#endif - } - - if (node->op == GGML_OP_CPY) { - // store the copy op parameter which changes with each token. - cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data)); - // store a pointer to each copy op CUDA kernel to identify it later - void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); - if (!ptr) { - use_cuda_graph = false; -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported copy op\n", __func__); -#endif - } else { - if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) { - ggml_cuda_cpy_fn_ptrs.push_back(ptr); - } - } - } - - if (!use_cuda_graph) { - break; - } - } - - // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. - if (use_cuda_graph && cuda_graph_update_required) { - cuda_ctx->cuda_graph->number_consecutive_updates++; - } else { - cuda_ctx->cuda_graph->number_consecutive_updates = 0; - } - - if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) { - cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true; -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); -#endif - } - } - - if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture - CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed)); - } - -#else - bool use_cuda_graph = false; - bool cuda_graph_update_required = false; -#endif // USE_CUDA_GRAPH - - bool graph_evaluated_or_captured = false; - - while (!graph_evaluated_or_captured) { - // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph. - // With the use of CUDA graphs, the execution will be performed by the graph launch. - if (!use_cuda_graph || cuda_graph_update_required) { - for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_tensor * node = cgraph->nodes[i]; - - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { - continue; - } - -#ifndef NDEBUG - assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); - for (int j = 0; j < GGML_MAX_SRC; j++) { - if (node->src[j] != nullptr) { - assert(node->src[j]->buffer); - assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || - ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft)); - } - } -#endif - - bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); - if (!ok) { - GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); - } - GGML_ASSERT(ok); - } - } - -#ifdef USE_CUDA_GRAPH - if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture - if (cuda_ctx->cuda_graph->graph != nullptr) { - CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph)); - cuda_ctx->cuda_graph->graph = nullptr; - } - CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph)); - -#if 0 - if (disable_cuda_graphs_due_to_failed_capture) { - use_cuda_graph = false; - cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true; -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: disabling CUDA graphs due to failed graph capture\n", __func__); -#endif - } else { - graph_evaluated_or_captured = true; // CUDA graph has been captured - } -#endif - graph_evaluated_or_captured = true; // CUDA graph has been captured - } else { - graph_evaluated_or_captured = true; // ggml graph has been directly evaluated - } - } - - if (use_cuda_graph) { - if (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph. - CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0)); - } - - // Perform update to graph (if required for this token), and change copy parameter (required for every token) - - if (cuda_graph_update_required) { - // Extract nodes from graph - // First call with null argument gets number of nodes in graph - CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes)); - // Subsequent call with non-null argument gets nodes - cuda_ctx->cuda_graph->nodes.clear(); - cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes); - cuda_ctx->cuda_graph->params.clear(); - cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes); - if (cuda_ctx->cuda_graph->num_nodes > 0) { - CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes)); - - // Loop over nodes, and extract kernel parameters from each node - for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { - cudaGraphNodeType node_type; - CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type)); - if (node_type == cudaGraphNodeTypeKernel) { - cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime - if (stat == cudaErrorInvalidDeviceFunction) { - // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node. - // We don't need to update blas nodes, so clear error and move on. - cudaGetLastError(); - } else { - GGML_ASSERT(stat == cudaSuccess); - } - } - } - } - } - - // One of the arguments to the copy kernel is updated for each token, hence we need to - // replace that argument with the updated value in the CUDA graph - if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured - int k = 0; - for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { - if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) { - char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++); - cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr; - CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i])); - } - } - } - - // Update graph executable - cudaGraphExecUpdateResultInfo result_info; - cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); - if (stat == cudaErrorGraphExecUpdateFailure) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: CUDA graph update failed\n", __func__); -#endif - // The pre-existing graph exec cannot be updated due to violated constraints - // so instead clear error and re-instantiate - cudaGetLastError(); - CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance)); - cuda_ctx->cuda_graph->instance = nullptr; - CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0)); - } else { - GGML_ASSERT(stat == cudaSuccess); - } - // Launch graph - CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream())); -#else - graph_evaluated_or_captured = true; -#endif // USE_CUDA_GRAPH - } - - return GGML_STATUS_SUCCESS; -} - -static void ggml_backend_cuda_event_record(ggml_backend_t backend, ggml_backend_event_t event) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - CUDA_CHECK(cudaEventRecord((cudaEvent_t)event->context, cuda_ctx->stream())); -} - -static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - if (ggml_backend_is_cuda(backend)) { - CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx->stream(), (cudaEvent_t)event->context, 0)); - } else { -#if 0 - // untested - auto wait_fn = [](void * user_data) { - ggml_backend_event_t event = (ggml_backend_event_t)user_data; - ggml_backend_event_synchronize(event); - }; - - CUDA_CHECK(cudaLaunchHostFunc(cuda_ctx->stream(), wait_fn, event)); -#endif - GGML_ABORT("fatal error"); - } -} - -static const ggml_backend_i ggml_backend_cuda_interface = { - /* .get_name = */ ggml_backend_cuda_get_name, - /* .free = */ ggml_backend_cuda_free, - /* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async, - /* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async, - /* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async, - /* .synchronize = */ ggml_backend_cuda_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_cuda_graph_compute, - /* .event_record = */ ggml_backend_cuda_event_record, - /* .event_wait = */ ggml_backend_cuda_event_wait, -}; - -static ggml_guid_t ggml_backend_cuda_guid() { - static ggml_guid guid = { 0x2c, 0xdd, 0xe8, 0x1c, 0x65, 0xb3, 0x65, 0x73, 0x6a, 0x12, 0x88, 0x61, 0x1c, 0xc9, 0xdc, 0x25 }; - return &guid; -} - -bool ggml_backend_is_cuda(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cuda_guid()); -} - -int ggml_backend_cuda_get_device_count() { - return ggml_cuda_info().device_count; -} - -void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); - snprintf(description, description_size, "%s", prop.name); -} - -void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { - ggml_cuda_set_device(device); - - CUDA_CHECK(cudaMemGetInfo(free, total)); -} - -bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) { - if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) { - return false; - } - -#if CUDART_VERSION >= 11100 || defined(GGML_USE_MUSA) - cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly); - if (err != cudaSuccess) { - // clear the error - cudaGetLastError(); - - GGML_LOG_DEBUG("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__, - size / 1024.0 / 1024.0, cudaGetErrorString(err)); - return false; - } - return true; -#else - return false; -#endif -} - -void ggml_backend_cuda_unregister_host_buffer(void * buffer) { - if (getenv("GGML_CUDA_REGISTER_HOST") == nullptr) { - return; - } - - cudaError_t err = cudaHostUnregister(buffer); - if (err != cudaSuccess) { - // clear the error - cudaGetLastError(); - } -} - - -// backend device - -struct ggml_backend_cuda_device_context { - int device; - std::string name; - std::string description; -}; - -static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; - return ctx->name.c_str(); -} - -static const char * ggml_backend_cuda_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; - return ctx->description.c_str(); -} - -static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; - ggml_cuda_set_device(ctx->device); - CUDA_CHECK(cudaMemGetInfo(free, total)); -} - -static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU; -} - -static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { - props->name = ggml_backend_cuda_device_get_name(dev); - props->description = ggml_backend_cuda_device_get_description(dev); - props->type = ggml_backend_cuda_device_get_type(dev); - ggml_backend_cuda_device_get_memory(dev, &props->memory_free, &props->memory_total); - - bool host_buffer = getenv("GGML_CUDA_NO_PINNED") == nullptr; -#ifdef GGML_CUDA_NO_PEER_COPY - bool events = false; -#else - bool events = true; -#endif - - props->caps = { - /* .async = */ true, - /* .host_buffer = */ host_buffer, - /* .buffer_from_host_ptr = */ false, - /* .events = */ events, - }; -} - -static ggml_backend_t ggml_backend_cuda_device_init_backend(ggml_backend_dev_t dev, const char * params) { - GGML_UNUSED(params); - ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; - return ggml_backend_cuda_init(ctx->device); -} - -static ggml_backend_buffer_type_t ggml_backend_cuda_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context; - return ggml_backend_cuda_buffer_type(ctx->device); -} - -static ggml_backend_buffer_type_t ggml_backend_cuda_device_get_host_buffer_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return ggml_backend_cuda_host_buffer_type(); -} - -// TODO: move these functions here -static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context; - - // split buffers can only be used with GGML_OP_MUL_MAT - if (op->op != GGML_OP_MUL_MAT) { - for (int i = 0; i < GGML_MAX_SRC; i++) { - if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda_split(op->src[i]->buffer->buft)) { - return false; - } - } - } - - // check if all the sources are allocated on this device - for (int i = 0; i < GGML_MAX_SRC; i++) { - if (op->src[i] && op->src[i]->buffer && ggml_backend_buft_is_cuda(op->src[i]->buffer->buft)) { - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)op->src[i]->buffer->buft->context; - if (buft_ctx->device != dev_ctx->device) { - return false; - } - } - } - - switch (op->op) { - case GGML_OP_UNARY: - switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_EXP: - return ggml_is_contiguous(op->src[0]); - default: - return false; - } - break; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - { - struct ggml_tensor * a = op->src[0]; - struct ggml_tensor * b = op->src[1]; - if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) { - return false; - } - if (op->op == GGML_OP_MUL_MAT && a->ne[3] != b->ne[3]) { - return false; - } -#ifdef GGML_USE_MUSA - if (b->type == GGML_TYPE_F16 && b->ne[2]*b->ne[3] > 1 && - !ggml_is_transposed(a) && !ggml_is_transposed(b)) { - return false; - } -#endif // GGML_USE_MUSA - switch (a->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_Q8_K: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: -#ifdef GGML_USE_MUSA - if (a->type == GGML_TYPE_Q3_K) { - return false; - } -#endif // GGML_USE_MUSA - return true; - default: - return false; - } - } break; - case GGML_OP_OUT_PROD: - return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1; - case GGML_OP_GET_ROWS: - { - switch (op->src[0]->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return true; - default: - return false; - } - } break; - case GGML_OP_CPY: - { - ggml_type src0_type = op->src[0]->type; - ggml_type src1_type = op->src[1]->type; - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) { - return true; - } - if (src0_type == GGML_TYPE_Q8_0 && src1_type == GGML_TYPE_F32) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_0) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q5_1) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_IQ4_NL) { - return true; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return true; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { - return true; - } - if (src0_type == src1_type && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])) { - return true; - } - return false; - } break; - case GGML_OP_DUP: - { - ggml_type src0_type = op->src[0]->type; - return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; - } break; - case GGML_OP_ARGMAX: - case GGML_OP_COUNT_EQUAL: - { - return true; - } break; - case GGML_OP_REPEAT: - { - ggml_type src0_type = op->src[0]->type; - return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; - } break; - case GGML_OP_REPEAT_BACK: - return op->type == GGML_TYPE_F32 && op->src[0]->ne[3] == 1; - case GGML_OP_CONCAT: - { - ggml_type src0_type = op->src[0]->type; - return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - ggml_type src0_type = op->src[0]->type; - ggml_type src1_type = op->src[1]->type; - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return true; - } - return false; - } break; - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0; - break; - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_ADD: - case GGML_OP_ADD1: - case GGML_OP_SUB: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - return true; - case GGML_OP_CONT: - return op->src[0]->type != GGML_TYPE_BF16; - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - return true; - case GGML_OP_ROPE: - return ggml_is_contiguous(op->src[0]); - case GGML_OP_IM2COL: - case GGML_OP_POOL_2D: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_ARGSORT: - case GGML_OP_ACC: - case GGML_OP_GROUP_NORM: - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_ARANGE: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_LEAKY_RELU: - case GGML_OP_RWKV_WKV6: - return true; - case GGML_OP_FLASH_ATTN_EXT: { -#ifndef FLASH_ATTN_AVAILABLE - return false; -#endif - if (op->src[1]->type == GGML_TYPE_BF16 || op->src[2]->type == GGML_TYPE_BF16) { - return false; - } - if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) { - return true; - } - if (op->src[0]->ne[0] == 128) { - return true; - } - if (op->src[0]->ne[0] == 256 && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16) { - return true; - } - const int cc = ggml_cuda_info().devices[dev_ctx->device].cc; - return cc >= CC_VOLTA && cc < CC_OFFSET_AMD && op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16; - } - case GGML_OP_CROSS_ENTROPY_LOSS: - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - case GGML_OP_OPT_STEP_ADAMW: - return true; - default: - return false; - } -} - -static bool ggml_backend_cuda_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return (ggml_backend_buft_is_cuda(buft) || ggml_backend_buft_is_cuda_split(buft)) && buft->device == dev; -} - -static int64_t get_op_batch_size(const ggml_tensor * op) { - switch (op->op) { - case GGML_OP_GET_ROWS: - return 0; - case GGML_OP_MUL_MAT: - return op->ne[1]; - case GGML_OP_MUL_MAT_ID: - case GGML_OP_ROPE: - return op->ne[2]; - default: - return ggml_nrows(op); - } -} - -static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - const int min_batch_size = 32; - - return get_op_batch_size(op) >= min_batch_size; - - GGML_UNUSED(dev); -} - -static ggml_backend_event_t ggml_backend_cuda_device_event_new(ggml_backend_dev_t dev) { -#ifdef GGML_CUDA_NO_PEER_COPY - return nullptr; -#else - ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *)dev->context; - - ggml_cuda_set_device(dev_ctx->device); - - cudaEvent_t event; - CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming)); - - return new ggml_backend_event { - /* .device = */ dev, - /* .context = */ event, - }; -#endif -} - -static void ggml_backend_cuda_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { - GGML_UNUSED(dev); - - CUDA_CHECK(cudaEventDestroy((cudaEvent_t)event->context)); - delete event; -} - -static void ggml_backend_cuda_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { - GGML_UNUSED(dev); - CUDA_CHECK(cudaEventSynchronize((cudaEvent_t)event->context)); -} - -static const ggml_backend_device_i ggml_backend_cuda_device_interface = { - /* .get_name = */ ggml_backend_cuda_device_get_name, - /* .get_description = */ ggml_backend_cuda_device_get_description, - /* .get_memory = */ ggml_backend_cuda_device_get_memory, - /* .get_type = */ ggml_backend_cuda_device_get_type, - /* .get_props = */ ggml_backend_cuda_device_get_props, - /* .init_backend = */ ggml_backend_cuda_device_init_backend, - /* .get_buffer_type = */ ggml_backend_cuda_device_get_buffer_type, - /* .get_host_buffer_type = */ ggml_backend_cuda_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_cuda_device_supports_op, - /* .supports_buft = */ ggml_backend_cuda_device_supports_buft, - /* .offload_op = */ ggml_backend_cuda_device_offload_op, - /* .event_new = */ ggml_backend_cuda_device_event_new, - /* .event_free = */ ggml_backend_cuda_device_event_free, - /* .event_synchronize = */ ggml_backend_cuda_device_event_synchronize, -}; - -// backend reg - -struct ggml_backend_cuda_reg_context { - std::vector devices; -}; - -static const char * ggml_backend_cuda_reg_get_name(ggml_backend_reg_t reg) { - GGML_UNUSED(reg); - return GGML_CUDA_NAME; -} - -static size_t ggml_backend_cuda_reg_get_device_count(ggml_backend_reg_t reg) { - ggml_backend_cuda_reg_context * ctx = (ggml_backend_cuda_reg_context *)reg->context; - return ctx->devices.size(); -} - -static ggml_backend_dev_t ggml_backend_cuda_reg_get_device(ggml_backend_reg_t reg, size_t index) { - ggml_backend_cuda_reg_context * ctx = (ggml_backend_cuda_reg_context *)reg->context; - GGML_ASSERT(index < ctx->devices.size()); - return ctx->devices[index]; -} - -static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) { - GGML_UNUSED(reg); - if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { - return (void *)ggml_backend_cuda_split_buffer_type; - } - if (strcmp(name, "ggml_backend_register_host_buffer") == 0) { - return (void *)ggml_backend_cuda_register_host_buffer; - } - if (strcmp(name, "ggml_backend_unregister_host_buffer") == 0) { - return (void *)ggml_backend_cuda_unregister_host_buffer; - } - return nullptr; -} - -static const ggml_backend_reg_i ggml_backend_cuda_reg_interface = { - /* .get_name = */ ggml_backend_cuda_reg_get_name, - /* .get_device_count = */ ggml_backend_cuda_reg_get_device_count, - /* .get_device_get = */ ggml_backend_cuda_reg_get_device, - /* .get_proc_address = */ ggml_backend_cuda_reg_get_proc_address, -}; - -// backend registry -ggml_backend_reg_t ggml_backend_cuda_reg() { - static ggml_backend_reg reg; - static bool initialized = false; - - { - static std::mutex mutex; - std::lock_guard lock(mutex); - if (!initialized) { - ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context; - - for (int i = 0; i < ggml_cuda_info().device_count; i++) { - ggml_backend_cuda_device_context * dev_ctx = new ggml_backend_cuda_device_context; - dev_ctx->device = i; - dev_ctx->name = GGML_CUDA_NAME + std::to_string(i); - - ggml_cuda_set_device(i); - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, i)); - dev_ctx->description = prop.name; - - ggml_backend_dev_t dev = new ggml_backend_device { - /* .interface = */ ggml_backend_cuda_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx - }; - ctx->devices.push_back(dev); - } - - reg = ggml_backend_reg { - /* .interface = */ ggml_backend_cuda_reg_interface, - /* .context = */ ctx - }; - } - - initialized = true; - } - - return ® -} - -ggml_backend_t ggml_backend_cuda_init(int device) { - if (device < 0 || device >= ggml_backend_cuda_get_device_count()) { - GGML_LOG_ERROR("%s: invalid device %d\n", __func__, device); - return nullptr; - } - - ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device); - if (ctx == nullptr) { - GGML_LOG_ERROR("%s: failed to allocate context\n", __func__); - return nullptr; - } - - ggml_backend_t cuda_backend = new ggml_backend { - /* .guid = */ ggml_backend_cuda_guid(), - /* .interface = */ ggml_backend_cuda_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), device), - /* .context = */ ctx, - }; - - return cuda_backend; -} diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt new file mode 100644 index 00000000..e1482a26 --- /dev/null +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -0,0 +1,155 @@ +cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES + +find_package(CUDAToolkit) + +if (CUDAToolkit_FOUND) + message(STATUS "CUDA Toolkit found") + + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + # native == GPUs available at build time + # 52 == Maxwell, lowest CUDA 12 standard + # 60 == P100, FP16 CUDA intrinsics + # 61 == Pascal, __dp4a instruction (per-byte integer dot product) + # 70 == V100, FP16 tensor cores + # 75 == Turing, int8 tensor cores + if (GGML_NATIVE AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.6" AND CMAKE_VERSION VERSION_GREATER_EQUAL "3.24") + set(CMAKE_CUDA_ARCHITECTURES "native") + elseif(GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75") + else() + set(CMAKE_CUDA_ARCHITECTURES "52;61;70;75") + endif() + endif() + message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") + + enable_language(CUDA) + + file(GLOB GGML_HEADERS_CUDA "*.cuh") + list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h") + + file(GLOB GGML_SOURCES_CUDA "*.cu") + file(GLOB SRCS "template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + + if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + endif() + + add_library(ggml-cuda + ${GGML_HEADERS_CUDA} + ${GGML_SOURCES_CUDA} + ) + + target_link_libraries(ggml-cuda PRIVATE ggml-base) + target_include_directories(ggml-cuda PRIVATE . ..) + + add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) + + if (GGML_CUDA_GRAPHS) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) + endif() + + if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) + endif() + + if (GGML_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() + + if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_F16) + endif() + + if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + + if (GGML_STATIC) + if (WIN32) + # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas CUDA::cublasLt) + else () + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) + endif() + else() + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas CUDA::cublasLt) + endif() + + if (GGML_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so) + else() + target_link_libraries(ggml-cuda PRIVATE CUDA::cuda_driver) + endif() + + set(CUDA_CXX_FLAGS "") + + set(CUDA_FLAGS -use_fast_math) + + if (GGML_FATAL_WARNINGS) + list(APPEND CUDA_FLAGS -Werror all-warnings) + endif() + + if (GGML_ALL_WARNINGS AND NOT MSVC) + set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) + if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") + list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER}) + endif() + + execute_process( + COMMAND ${NVCC_CMD} -Xcompiler --version + OUTPUT_VARIABLE CUDA_CCFULLVER + ERROR_QUIET + ) + + if (NOT CUDA_CCFULLVER MATCHES clang) + set(CUDA_CCID "GNU") + execute_process( + COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion" + OUTPUT_VARIABLE CUDA_CCVER + ERROR_QUIET + ) + else() + if (CUDA_CCFULLVER MATCHES Apple) + set(CUDA_CCID "AppleClang") + else() + set(CUDA_CCID "Clang") + endif() + string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER}) + endif() + + message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") + + get_flags(${CUDA_CCID} ${CUDA_CCVER}) + list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later + endif() + + if (NOT MSVC) + list(APPEND CUDA_CXX_FLAGS -Wno-pedantic) + endif() + + list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument + + if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") + list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED}) + endif() + + target_compile_options(ggml-cuda PRIVATE "$<$:${CUDA_FLAGS}>") +else() + message(FATAL_ERROR "CUDA Toolkit not found") +endif() diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute.cpp deleted file mode 100644 index 2fea9e4c..00000000 --- a/ggml/src/ggml-kompute.cpp +++ /dev/null @@ -1,2184 +0,0 @@ -#include "ggml-impl.h" -#include "ggml-backend.h" -#include "ggml-backend-impl.h" -#include "ggml-kompute.h" - -// These are generated at build time by cmake custom command -#include "shaderop_scale.h" -#include "shaderop_scale_8.h" -#include "shaderop_add.h" -#include "shaderop_addrow.h" -#include "shaderop_mul.h" -#include "shaderop_silu.h" -#include "shaderop_relu.h" -#include "shaderop_gelu.h" -#include "shaderop_softmax.h" -#include "shaderop_norm.h" -#include "shaderop_rmsnorm.h" -#include "shaderop_diagmask.h" -#include "shaderop_mul_mat_f16.h" -#include "shaderop_mul_mat_q8_0.h" -#include "shaderop_mul_mat_q4_0.h" -#include "shaderop_mul_mat_q4_1.h" -#include "shaderop_mul_mat_q4_k.h" -#include "shaderop_mul_mat_q6_k.h" -#include "shaderop_mul_mat_mat_f32.h" -#include "shaderop_getrows_f32.h" -#include "shaderop_getrows_f16.h" -#include "shaderop_getrows_q4_0.h" -#include "shaderop_getrows_q4_1.h" -#include "shaderop_getrows_q6_k.h" -#include "shaderop_rope_f16.h" -#include "shaderop_rope_f32.h" -#include "shaderop_cpy_f16_f16.h" -#include "shaderop_cpy_f16_f32.h" -#include "shaderop_cpy_f32_f16.h" -#include "shaderop_cpy_f32_f32.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include - -#ifdef __linux__ -#include // for setenv -#endif - -#define QK4_0 32 -#define QR4_0 2 -#define QK4_1 32 -#define QK_NL 16 - -typedef ggml_fp16_t half; - -static std::string ggml_kompute_format_name(int device) { - return "Kompute" + std::to_string(device); -} - -struct ggml_kompute_context { - int device; - std::string name; - std::shared_ptr pool; - - ggml_kompute_context(int device) - : device(device), name(ggml_kompute_format_name(device)) {} -}; - -// FIXME: It would be good to consolidate the kompute manager and the kompute context into one object -// and consolidate the init functions and simplify object lifetime management. As it currently stands, -// we *have* to have the kompute manager no matter what for device discovery, but the kompute context -// is only created when a device is set and vulkan is explicitly turned on. -static ggml_kompute_context *s_kompute_context = nullptr; - -class kompute_manager { - kp::Manager *s_mgr = nullptr; - -public: - kp::Manager *operator()() { - if (s_mgr && !s_mgr->hasInstance()) { - destroy(); - } - if (!s_mgr) { - s_mgr = new kp::Manager; - } - return s_mgr; - } - - void destroy() { - delete s_mgr; - s_mgr = nullptr; - } -}; - -static kompute_manager komputeManager; - -struct ggml_vk_memory { - void *data = nullptr; - size_t size = 0; - vk::DeviceMemory *primaryMemory = nullptr; - vk::Buffer *primaryBuffer = nullptr; - vk::DeviceMemory *stagingMemory = nullptr; - vk::Buffer *stagingBuffer = nullptr; -}; - -#ifdef __linux__ -__attribute__((constructor)) -static void enable_sam() { - setenv("RADV_PERFTEST", "sam", false); -} -#endif - -static bool ggml_vk_checkPhysicalDeviceFeatures(vk::PhysicalDevice physical_device) { - vk::PhysicalDeviceFeatures availableFeatures; - physical_device.getFeatures(&availableFeatures); - - if (!availableFeatures.shaderInt16) - return false; - - vk::PhysicalDeviceVulkan11Features availableFeatures11; - vk::PhysicalDeviceVulkan12Features availableFeatures12; - - availableFeatures11.pNext = &availableFeatures12; - availableFeatures12.pNext = nullptr; - - vk::PhysicalDeviceFeatures2 features2; - features2.pNext = &availableFeatures11; - - physical_device.getFeatures2(&features2); - - if (!availableFeatures11.uniformAndStorageBuffer16BitAccess || - !availableFeatures11.storageBuffer16BitAccess) { - return false; - } - - if (!availableFeatures12.storageBuffer8BitAccess || - !availableFeatures12.uniformAndStorageBuffer8BitAccess || - !availableFeatures12.shaderFloat16 || - !availableFeatures12.shaderInt8) { - return false; - } - - return true; -} - -static const char * ggml_vk_getVendorName(uint32_t vendorID) { - switch (vendorID) { - case 0x10DE: - return "nvidia"; - case 0x1002: - return "amd"; - case 0x8086: - return "intel"; - default: - return "unknown"; - } -} - -static std::vector ggml_vk_available_devices_internal(size_t memoryRequired) { - std::vector results; - if (!komputeManager()->hasVulkan() || !komputeManager()->hasInstance()) - return results; - - std::vector physical_devices; - try { - physical_devices = komputeManager()->listDevices(); - } catch (vk::SystemError & err) { - std::cerr << __func__ << ": ignoring Vulkan exception: " << err.what() << "\n"; - return results; - } - - uint32_t deviceCount = physical_devices.size(); - if (deviceCount == 0) - return results; - - std::unordered_map count_by_name; - - for (uint32_t i = 0; i < deviceCount; i++) { - const auto & physical_device = physical_devices[i]; - - VkPhysicalDeviceProperties dev_props = physical_device.getProperties(); - VkPhysicalDeviceMemoryProperties memoryProperties = physical_device.getMemoryProperties(); - const uint32_t major = VK_VERSION_MAJOR(dev_props.apiVersion); - const uint32_t minor = VK_VERSION_MINOR(dev_props.apiVersion); - if (major < 1 || minor < 2) - continue; - - if (!ggml_vk_checkPhysicalDeviceFeatures(physical_device)) - continue; - - size_t heapSize = 0; - for (uint32_t j = 0; j < memoryProperties.memoryHeapCount; ++j) { - VkMemoryHeap heap = memoryProperties.memoryHeaps[j]; - if (heap.flags & VK_MEMORY_HEAP_DEVICE_LOCAL_BIT) { - heapSize = heap.size; - break; - } - } - - if (heapSize < memoryRequired) - continue; - - auto ext_props = physical_device.enumerateDeviceExtensionProperties(); - bool has_maintenance4 = false; - - // Check if maintenance4 is supported - for (const auto & properties : ext_props) { - if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { - has_maintenance4 = true; - } - } - - vk::PhysicalDeviceSubgroupProperties subgroup_props; - vk::PhysicalDeviceProperties2 dev_props2; - vk::PhysicalDeviceMaintenance3Properties dev_props3; - vk::PhysicalDeviceMaintenance4Properties dev_props4; - dev_props2.pNext = &dev_props3; - dev_props3.pNext = &subgroup_props; - if (has_maintenance4) { - subgroup_props.pNext = &dev_props4; - } - physical_device.getProperties2(&dev_props2); - - if (subgroup_props.subgroupSize < 32) - continue; - - ggml_vk_device d; - d.index = i; - d.type = dev_props.deviceType; - d.heapSize = heapSize; - d.vendor = strdup(ggml_vk_getVendorName(dev_props.vendorID)); - d.subgroupSize = subgroup_props.subgroupSize; - d.bufferAlignment = dev_props.limits.minStorageBufferOffsetAlignment; - - if (has_maintenance4) { - d.maxAlloc = std::min(dev_props3.maxMemoryAllocationSize, dev_props4.maxBufferSize); - } else { - d.maxAlloc = dev_props3.maxMemoryAllocationSize; - } - - std::string name(dev_props.deviceName); - size_t n_idx = ++count_by_name[name]; - if (n_idx > 1) { - name += " (" + std::to_string(n_idx) + ")"; - } - d.name = strdup(name.c_str()); - - results.push_back(d); - } - - std::stable_sort(results.begin(), results.end(), - [](const ggml_vk_device& lhs, const ggml_vk_device& rhs) -> bool { - if (lhs.type != rhs.type) { - if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return true; - if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return false; - - if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return true; - if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return false; - } - return lhs.heapSize < rhs.heapSize; - } - ); - - return results; -} - -static std::vector& ggml_vk_available_devices() { - static std::vector devices = ggml_vk_available_devices_internal(0); - return devices; -} - -static void ggml_vk_filterByVendor(std::vector& devices, const std::string& targetVendor) { - devices.erase( - std::remove_if(devices.begin(), devices.end(), - [&targetVendor](const ggml_vk_device& device) { - return device.vendor != targetVendor; - }), - devices.end() - ); -} - -static void ggml_vk_filterByName(std::vector& devices, const std::string& targetName) { - devices.erase( - std::remove_if(devices.begin(), devices.end(), - [&targetName](const ggml_vk_device& device) { - return device.name != targetName; - }), - devices.end() - ); -} - -static bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const std::string & name) { - if (name.empty()) - return false; - - auto devices = ggml_vk_available_devices_internal(memoryRequired); - if (name == "amd" || name == "nvidia" || name == "intel") { - ggml_vk_filterByVendor(devices, name); - } else if (name != "gpu") { - ggml_vk_filterByName(devices, name); - } - - if (devices.empty()) - return false; - - *device = devices.front(); - return true; -} - -bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const char * name) { - return ggml_vk_get_device(device, memoryRequired, std::string(name)); -} - -bool ggml_vk_has_vulkan() { - return komputeManager()->hasVulkan(); -} - -bool ggml_vk_has_device() { - return komputeManager()->hasDevice(); -} - -ggml_vk_device ggml_vk_current_device() { - if (!komputeManager()->hasDevice()) - return ggml_vk_device(); - - auto devices = ggml_vk_available_devices(); - ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data()); - GGML_ASSERT(!devices.empty()); - return devices.front(); -} - -static -void ggml_vk_allocate_descriptor_pool(struct ggml_kompute_context * ctx, size_t size) { - std::vector descriptorPoolSizes = { - vk::DescriptorPoolSize( - vk::DescriptorType::eStorageBuffer, - 3 * size // Descriptor count is number of possible tensors to pass into an algorithm - ) - }; - - vk::DescriptorPoolCreateInfo descriptorPoolInfo( - vk::DescriptorPoolCreateFlags(), - size, // Max sets - static_cast(descriptorPoolSizes.size()), - descriptorPoolSizes.data()); - - ctx->pool = std::make_shared(); - vk::Result r = komputeManager()->device()->createDescriptorPool( - &descriptorPoolInfo, nullptr, ctx->pool.get()); - if (r != vk::Result::eSuccess) - std::cerr << "Error allocating descriptor pool" << vk::to_string(r); -} - -static -void ggml_vk_free_descriptor_pool(struct ggml_kompute_context * ctx) { - if (ctx->pool) { - komputeManager()->device()->destroy( - *ctx->pool, - (vk::Optional)nullptr); - ctx->pool = nullptr; - } -} - -static -vk::Buffer *ggml_vk_allocate_buffer(size_t size) { - vk::BufferCreateInfo bufferCreateInfo; - bufferCreateInfo.size = size; - bufferCreateInfo.usage = vk::BufferUsageFlagBits::eStorageBuffer | - vk::BufferUsageFlagBits::eTransferSrc | - vk::BufferUsageFlagBits::eTransferDst; - bufferCreateInfo.sharingMode = vk::SharingMode::eExclusive; - - vk::Buffer *vkBuffer = new vk::Buffer; - vk::Result r = komputeManager()->device()->createBuffer(&bufferCreateInfo, nullptr, vkBuffer); - if (r != vk::Result::eSuccess) - std::cerr << "Error allocating buffer " << vk::to_string(r) << std::endl; - return vkBuffer; -} - -static -vk::DeviceMemory *ggml_vk_allocate(size_t size, vk::MemoryPropertyFlags flags, vk::MemoryRequirements requirements, bool *isHostVisible) { - - uint32_t memoryTypeIndex = -1; - bool memoryTypeIndexFound = false; - vk::PhysicalDeviceMemoryProperties memoryProperties = komputeManager()->physicalDevice()->getMemoryProperties(); - for (uint32_t i = 0; i < memoryProperties.memoryTypeCount; i++) { - const vk::MemoryType &memoryType = memoryProperties.memoryTypes[i]; - const vk::MemoryHeap &memoryHeap = memoryProperties.memoryHeaps[memoryType.heapIndex]; - if (memoryHeap.size < size) { - continue; - } - - if (requirements.memoryTypeBits & (1 << i)) { - if (((memoryProperties.memoryTypes[i]).propertyFlags & - flags) == flags) { - memoryTypeIndex = i; - memoryTypeIndexFound = true; - if (isHostVisible && (memoryProperties.memoryTypes[i].propertyFlags & vk::MemoryPropertyFlagBits::eHostVisible)) { - *isHostVisible = true; - } - break; - } - } - } - if (!memoryTypeIndexFound) { - throw std::runtime_error( - "Memory type index for buffer creation not found"); - } - - vk::MemoryAllocateInfo allocInfo; - allocInfo.allocationSize = size; - allocInfo.memoryTypeIndex = memoryTypeIndex; - vk::DeviceMemory *vkDeviceMemory = new vk::DeviceMemory; - vk::Result r = komputeManager()->device()->allocateMemory(&allocInfo, nullptr, vkDeviceMemory); - if (r != vk::Result::eSuccess) { - std::cerr << "Error allocating memory " << vk::to_string(r) << std::endl; - throw std::runtime_error("Error allocating vulkan memory."); - } - return vkDeviceMemory; -} - -static size_t ggml_vk_aligned_offset(ggml_backend_buffer_t buffer, size_t offset) { - size_t minStorageBufferOffsetAlignment = ggml_backend_buffer_get_alignment(buffer); - - // If offset is already aligned, return it directly - if (offset % minStorageBufferOffsetAlignment == 0) { - return offset; - } - - // Otherwise, return the largest multiple of minStorageBufferOffsetAlignment less than offset - return (offset / minStorageBufferOffsetAlignment) * minStorageBufferOffsetAlignment; -} - -static ggml_vk_memory ggml_vk_allocate(size_t size) { - ggml_vk_memory memory; - bool isHostVisible = false; - { - memory.primaryBuffer = ggml_vk_allocate_buffer(size); - vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.primaryBuffer); - vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eDeviceLocal; - memory.primaryMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible); - komputeManager()->device()->bindBufferMemory(*memory.primaryBuffer, *memory.primaryMemory, 0); - if (isHostVisible) { - vk::Result r = komputeManager()->device()->mapMemory(*memory.primaryMemory, 0, size, vk::MemoryMapFlags(), &memory.data); - if (r != vk::Result::eSuccess) - std::cerr << "Error mapping memory" << vk::to_string(r); - } - } - - if (!isHostVisible) { - memory.stagingBuffer = ggml_vk_allocate_buffer(size); - vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.stagingBuffer); - vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eHostVisible | - vk::MemoryPropertyFlagBits::eHostCoherent | - vk::MemoryPropertyFlagBits::eHostCached; - memory.stagingMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible); - komputeManager()->device()->bindBufferMemory(*memory.stagingBuffer, *memory.stagingMemory, 0); - vk::Result r = komputeManager()->device()->mapMemory(*memory.stagingMemory, 0, size, vk::MemoryMapFlags(), &memory.data); - if (r != vk::Result::eSuccess) - std::cerr << "Error mapping memory" << vk::to_string(r); - } - - memory.size = size; - return memory; -} - -static void ggml_vk_free_memory(ggml_vk_memory &memory) -{ - komputeManager()->device()->destroy( - *memory.primaryBuffer, - (vk::Optional)nullptr); - if (memory.stagingBuffer) { - komputeManager()->device()->destroy( - *memory.stagingBuffer, - (vk::Optional)nullptr); - } - komputeManager()->device()->freeMemory( - *memory.primaryMemory, - (vk::Optional)nullptr); - if (memory.stagingMemory) { - komputeManager()->device()->freeMemory( - *memory.stagingMemory, - (vk::Optional)nullptr); - } -} - -static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft); - -static -ggml_vk_memory * ggml_vk_find_tensor(const struct ggml_tensor * t, uint64_t & offset) { - ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer; - - // compatibility with ggml-backend - GGML_ASSERT(buffer && buffer->buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name); - - ggml_vk_memory * buf_ctx = static_cast(buffer->context); - - const intptr_t ioffs = intptr_t(t->data) - intptr_t(buf_ctx->data); - - GGML_ASSERT(ioffs >= 0 && ioffs + int64_t(ggml_nbytes(t)) <= int64_t(buffer->size)); - - offset = uint64_t(ioffs); - return buf_ctx; -} - -static -const std::shared_ptr ggml_vk_get_tensor(const struct ggml_tensor * t, uint32_t * alignedOffset = nullptr) { - uint64_t originalOffset = 0; - auto * res = ggml_vk_find_tensor(t, originalOffset); - if (!res) { - static std::shared_ptr nullTensor = nullptr; - return nullTensor; - } - - // Create a tensor whose memory will be composed of our buffers at the correct offset - const size_t nelements = ggml_nelements(t); - size_t nbytes = ggml_nbytes(t); - - size_t vulkanOffset = ggml_vk_aligned_offset(t->buffer, originalOffset); - if (alignedOffset) { - *alignedOffset = originalOffset - vulkanOffset; - nbytes += *alignedOffset; - } - - return komputeManager()->tensor( - t->data, - nelements, - nbytes, kp::Tensor::TensorDataTypes::eFloat, - res->primaryMemory, res->primaryBuffer, - res->stagingMemory, res->stagingBuffer, - vulkanOffset); -} - -static std::vector getSpirvShader(const unsigned char* rawData, size_t size) { - if (size % sizeof(uint32_t) != 0) { - throw std::runtime_error("Invalid size: must be divisible by sizeof(uint32_t)"); - } - - const uint32_t* data_ptr = reinterpret_cast(rawData); - size_t count = size / sizeof(uint32_t); - return std::vector(data_ptr, data_ptr + count); -} - -inline static -uint32_t safe_divide(uint32_t a, uint32_t b) { - if (b <= 1) { - return a; - } - if ((a % b) != 0) { - fprintf(stderr, "((%u %% %u) == %u) != 0\n", a, b, a % b); - GGML_ABORT("safe_divide result would've had remainder"); - } - return a / b; -} - -static void ggml_vk_add( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03, - int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03, - int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, - int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13, - int32_t ne0, - int32_t nb0, int32_t nb1, int32_t nb2, int32_t nb3 -) { - const static auto spirv = getSpirvShader(kp::shader_data::op_add_comp_spv, - kp::shader_data::op_add_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00; - int32_t nb00, nb01, nb02, nb03; - int32_t ne10, ne11, ne12, ne13; - int32_t nb10, nb11, nb12, nb13; - int32_t ne0; - int32_t nb0, nb1, nb2, nb3; - } const pushConsts { - safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, - nb00, nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb10, nb11, nb12, nb13, - ne0, - nb0, nb1, nb2, nb3 - }; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_addrow(kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - uint32_t size, uint32_t row = 0) { - - const static auto spirv = getSpirvShader(kp::shader_data::op_addrow_comp_spv, - kp::shader_data::op_addrow_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - uint32_t row; - } const pushConsts { - safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), - row - }; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts}); - else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({size}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_mul( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03, - int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03, - int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, - int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13, - int32_t ne0, - int32_t nb0, int32_t nb1, int32_t nb2, int32_t nb3 -) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_comp_spv, - kp::shader_data::op_mul_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00; - int32_t nb00, nb01, nb02, nb03; - int32_t ne10, ne11, ne12, ne13; - int32_t nb10, nb11, nb12, nb13; - int32_t ne0; - int32_t nb0, nb1, nb2, nb3; - } const pushConsts { - safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, - nb00, nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb10, nb11, nb12, nb13, - ne0, - nb0, nb1, nb2, nb3 - }; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_scale(kp::Sequence& seq, - const std::shared_ptr& in, - const std::shared_ptr& out, - uint32_t inOff, uint32_t outOff, - uint32_t size, float scale) { - const static auto spirv_1 = getSpirvShader( - kp::shader_data::op_scale_comp_spv, kp::shader_data::op_scale_comp_spv_len - ); - const static auto spirv_8 = getSpirvShader( - kp::shader_data::op_scale_8_comp_spv, kp::shader_data::op_scale_8_comp_spv_len - ); - - struct PushConstants { - uint32_t inOff, outOff; - float scale; - } const pushConsts { - safe_divide(inOff, 4), safe_divide(outOff, 4), - scale - }; - - const auto * spirv = &spirv_1; - std::string name(__func__); - if (size % 8 == 0) { - size /= 8; - name += "_8"; - spirv = &spirv_8; - } - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) { - s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, *spirv, {size}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({in, out}); - s_algo->setWorkgroup({size}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_xxlu( - const std::vector& spirv, const char * suffix, kp::Sequence& seq, - const std::shared_ptr& in, - const std::shared_ptr& out, - uint32_t inOff, uint32_t outOff, - uint32_t size -) { - struct PushConstants { - uint32_t inOff, outOff; - } const pushConsts { - safe_divide(inOff, 4), safe_divide(outOff, 4), - }; - - auto name = std::string(__func__) + "_" + suffix; - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) { - s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, spirv, {size}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({in, out}); - s_algo->setWorkgroup({size}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -template -static void ggml_vk_silu(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_silu_comp_spv, - kp::shader_data::op_silu_comp_spv_len); - - ggml_vk_xxlu(spirv, "silu", std::forward(args)...); -} - -template -static void ggml_vk_relu(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_relu_comp_spv, - kp::shader_data::op_relu_comp_spv_len); - - ggml_vk_xxlu(spirv, "relu", std::forward(args)...); -} - -template -static void ggml_vk_gelu(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_gelu_comp_spv, - kp::shader_data::op_gelu_comp_spv_len); - - ggml_vk_xxlu(spirv, "gelu", std::forward(args)...); -} - -static void ggml_vk_soft_max( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, uint32_t ne03, - float scale -) { - const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv, - kp::shader_data::op_softmax_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne01, ne02; - float scale; - int32_t mask; - } pushConsts { - safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne01, ne02, - scale, - bool(inB) - }; - - auto & inB_ = inB ? inB : inA; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - // FIXME: The softmax kernel needs to be fixed to use the subgroupsize which can vary by device - const uint32_t local_x = 32; - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB_, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {local_x}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB_, out}); - s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_norm_( - const std::vector& spirv, const char * suffix, kp::Sequence& seq, - const std::shared_ptr& in, - const std::shared_ptr& out, - uint32_t inOff, uint32_t outOff, - int32_t ne00, int32_t nb01, - int32_t nrows, float epsilon -) { - GGML_ASSERT(nb01%sizeof(float) == 0); - GGML_ASSERT(ne00%sizeof(float) == 0); - - struct PushConstants { - uint32_t inOff, outOff; - uint32_t ne00, nb01; - float eps; - } pushConsts { - safe_divide(inOff, 4), safe_divide(outOff, 4), - (uint32_t)ne00, (uint32_t)nb01, epsilon - }; - - auto name = std::string(__func__) + "_" + suffix; - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) { - s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, spirv, {(uint32_t)nrows}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({in, out}); - s_algo->setWorkgroup({(uint32_t)nrows}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -template -static void ggml_vk_norm(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_norm_comp_spv, - kp::shader_data::op_norm_comp_spv_len); - - ggml_vk_norm_(spirv, "norm", std::forward(args)...); -} - -template -static void ggml_vk_rms_norm(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_rmsnorm_comp_spv, - kp::shader_data::op_rmsnorm_comp_spv_len); - - ggml_vk_norm_(spirv, "rms", std::forward(args)...); -} - -static void ggml_vk_diag_mask_inf(kp::Sequence& seq, - const std::shared_ptr& in, - const std::shared_ptr& out, - uint32_t inOff, uint32_t outOff, - uint32_t n_past, - int32_t ne00, int32_t ne01, int32_t ne02) { - const static auto spirv = getSpirvShader(kp::shader_data::op_diagmask_comp_spv, - kp::shader_data::op_diagmask_comp_spv_len); - - struct PushConstants { - uint32_t inOff, outOff; - uint32_t n_past; - int32_t ne00, ne01; - } pushConsts { - safe_divide(inOff, 4), safe_divide(outOff, 4), - n_past, - ne00, ne01 - }; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne00), unsigned(ne01), unsigned(ne02)}, {}, {pushConsts}); - else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({in, out}); - s_algo->setWorkgroup({unsigned(ne00), unsigned(ne01), unsigned(ne02)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_mul_mat_f16( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, - uint32_t nb00, uint32_t nb01, uint32_t nb02, - int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, - uint32_t nb10, uint32_t nb11, uint32_t nb12, - int32_t ne0, int32_t ne1, - uint32_t r2, uint32_t r3 -) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_f16_comp_spv, - kp::shader_data::op_mul_mat_f16_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne01, ne02; - uint32_t nb00, nb01, nb02; - int32_t ne10, ne11, ne12; - uint32_t nb10, nb11, nb12; - int32_t ne0, ne1; - uint32_t r2, r3; - } pushConsts { - safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne01, ne02, - nb00, nb01, nb02, - ne10, ne11, ne12, - nb10, nb11, nb12, - ne0, ne1, - r2, r3 - }; - - const unsigned ny = unsigned((ne11 + 4 - 1)/4); - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), ny, unsigned(ne12*ne13)}, {local_x}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned(ne01), ny, unsigned(ne12*ne13)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_mul_mat_mat_f32(kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, - uint32_t nb01, uint32_t nb02, - int32_t ne11, int32_t ne12, - uint32_t nb11, uint32_t nb12, - uint32_t nb1, uint32_t nb2) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_mat_f32_comp_spv, - kp::shader_data::op_mul_mat_mat_f32_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne01, ne02, ne11, ne12; - uint32_t nb01, nb02; - uint32_t nb11, nb12; - uint32_t nb1, nb2; - } pushConsts { - safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne01, ne02, ne11, ne12, - nb01, nb02, nb11, nb12, - nb1, nb2 - }; - - const uint32_t local_x = ggml_vk_current_device().subgroupSize; - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), - {inA, inB, out}, spirv, - {unsigned(ne01), - unsigned(ne11), - unsigned(std::max(ne12, ne02)) - }, - {local_x}, - {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned(ne01), - unsigned(ne11), - unsigned(std::max(ne12, ne02)), - }); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_mul_mat_impl( - const std::vector& spirv, const char * suffix, uint32_t block_size, kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, - int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, - int32_t ne0, int32_t ne1, - uint32_t r2, uint32_t r3 -) { - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne01, ne02; - int32_t ne10, ne12; - int32_t ne0, ne1; - uint32_t r2, r3; - } pushConsts { - safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne01, ne02, - ne10, ne12, - ne0, ne1, - r2, r3 - }; - - auto name = std::string(__func__) + "_" + suffix; - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; - s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}, {local_x}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -template -static void ggml_vk_mul_mat_q4_0(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_0_comp_spv, - kp::shader_data::op_mul_mat_q4_0_comp_spv_len); - - ggml_vk_mul_mat_impl(spirv, "q4_0", 1/*We access blocks unaligned*/, std::forward(args)...); -} - -template -static void ggml_vk_mul_mat_q4_1(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_1_comp_spv, - kp::shader_data::op_mul_mat_q4_1_comp_spv_len); - - ggml_vk_mul_mat_impl(spirv, "q4_1", 1/*We access blocks unaligned*/, std::forward(args)...); -} - -template -static void ggml_vk_mul_mat_q8_0(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q8_0_comp_spv, - kp::shader_data::op_mul_mat_q8_0_comp_spv_len); - - ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward(args)...); -} - -static void ggml_vk_mul_mat_q4_k( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10, - int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0, - int32_t ne1, int32_t r2, int32_t r3 -) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_k_comp_spv, - kp::shader_data::op_mul_mat_q4_k_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3; - } pushConsts { - 0, 0, 0, - ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3 - }; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_mul_mat_q6_k( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne10, int32_t ne0, int32_t ne1, - int32_t ne01, int32_t ne11, int32_t ne12, int32_t ne02 -) { - const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q6_k_comp_spv, - kp::shader_data::op_mul_mat_q6_k_comp_spv_len); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne10, ne0, ne1, ne01, gqa; - } pushConsts { - inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne10, ne0, ne1, ne01, ne12/ne02 - }; - - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(__func__)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}, {local_x}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(__func__); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_get_rows( - const std::vector& spirv, - const char * suffix, - unsigned element_size, unsigned qk, - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t nb01, int32_t nb1, - uint32_t size -) { - GGML_ASSERT(nb01%element_size == 0); - GGML_ASSERT(nb1%sizeof(float) == 0); - if (qk) GGML_ASSERT(ne00%qk == 0); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t ne00, nb01, nb1; - } pushConsts { - safe_divide(inAOff, element_size), safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, nb01, nb1 - }; - - auto name = std::string(__func__) + "_" + suffix; - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) { - s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts}); - } else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({size}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -template -static void ggml_vk_get_rows_f32(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f32_comp_spv, - kp::shader_data::op_getrows_f32_comp_spv_len); - - ggml_vk_get_rows(spirv, "f32", sizeof(float), 0, std::forward(args)...); -} - -template -static void ggml_vk_get_rows_f16(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv, - kp::shader_data::op_getrows_f16_comp_spv_len); - - ggml_vk_get_rows(spirv, "f16", sizeof(half), 0, std::forward(args)...); -} - -template -static void ggml_vk_get_rows_q4_0(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_0_comp_spv, - kp::shader_data::op_getrows_q4_0_comp_spv_len); - - ggml_vk_get_rows(spirv, "q4_0", 1/*We access blocks unaligned*/, QK4_0, std::forward(args)...); -} - -template -static void ggml_vk_get_rows_q4_1(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_1_comp_spv, - kp::shader_data::op_getrows_q4_1_comp_spv_len); - - ggml_vk_get_rows(spirv, "q4_1", 1/*We access blocks unaligned*/, QK4_1, std::forward(args)...); -} - -template -static void ggml_vk_get_rows_q6_k(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q6_k_comp_spv, - kp::shader_data::op_getrows_q6_k_comp_spv_len); - ggml_vk_get_rows(spirv, "q6_k", 1/*We access blocks unaligned*/, QK_NL, std::forward(args)...); -} - -static void ggml_vk_rope( - kp::Sequence& seq, - const std::shared_ptr& inA, - const std::shared_ptr& inB, - const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_ctx_orig, - float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, - int32_t ne01, int32_t ne02, int32_t ne03, - uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, - int32_t ne0, - uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3 -) { - GGML_ASSERT(src0t == GGML_TYPE_F16 || src0t == GGML_TYPE_F32); - - static const auto spirv_f16 = getSpirvShader( - kp::shader_data::op_rope_f16_comp_spv, kp::shader_data::op_rope_f16_comp_spv_len - ); - static const auto spirv_f32 = getSpirvShader( - kp::shader_data::op_rope_f32_comp_spv, kp::shader_data::op_rope_f32_comp_spv_len - ); - - int type_size = src0t == GGML_TYPE_F16 ? 2 : 4; - - GGML_ASSERT(nb03 % type_size == 0); - GGML_ASSERT(nb02 % type_size == 0); - GGML_ASSERT(nb01 % type_size == 0); - GGML_ASSERT(nb00 % type_size == 0); - GGML_ASSERT(nb3 % type_size == 0); - GGML_ASSERT(nb2 % type_size == 0); - GGML_ASSERT(nb1 % type_size == 0); - GGML_ASSERT(nb0 % type_size == 0); - - struct PushConstants { - uint32_t inAOff, inBOff, outOff; - int32_t n_dims, mode, n_ctx_orig; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - uint32_t nb00, nb01, nb02, nb03; - int32_t ne0; - uint32_t nb0, nb1, nb2, nb3; - } pushConsts { - safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size), - n_dims, mode, n_ctx_orig, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, - nb00, nb01, nb02, nb03, - ne0, - nb0, nb1, nb2, nb3 - }; - - auto name = std::string(__func__) + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32"); - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) { - s_algo = komputeManager()->algorithm( - name, s_kompute_context->pool.get(), {inA, inB, out}, - src0t == GGML_TYPE_F16 ? spirv_f16 : spirv_f32, - {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts} - ); - } else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -static void ggml_vk_cpy( - const std::vector& spirv, - uint32_t in_element_size, uint32_t out_element_size, - kp::Sequence& seq, - const std::shared_ptr& in, - const std::shared_ptr& out, - uint32_t inOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03, - uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, - int32_t ne0, int32_t ne1, int32_t ne2, - uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3 -) { - struct PushConstants { - uint32_t inOff, outOff; - int32_t ne00, ne01, ne02; - uint32_t nb00, nb01, nb02, nb03; - int32_t ne0, ne1, ne2; - uint32_t nb0, nb1, nb2, nb3; - } pushConsts { - safe_divide(inOff, in_element_size), safe_divide(outOff, out_element_size), - ne00, ne01, ne02, - nb00, nb01, nb02, nb03, - ne0, ne1, ne2, - nb0, nb1, nb2, nb3 - }; - - std::string name = std::string(__func__) - + "_i_" + std::to_string(in_element_size) - + "_o_" + std::to_string(out_element_size); - std::shared_ptr s_algo = nullptr; - if (!komputeManager()->hasAlgorithm(name)) - s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}); - else { - s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({in, out}); - s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); - s_algo->setPushConstants({pushConsts}); - s_algo->updateDescriptors(s_kompute_context->pool.get()); - } - seq.record(s_algo); -} - -template -static void ggml_vk_cpy_f32_f16(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f16_comp_spv, - kp::shader_data::op_cpy_f32_f16_comp_spv_len); - ggml_vk_cpy(spirv, 4, 2, std::forward(args)...); -} - -template -static void ggml_vk_cpy_f32_f32(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f32_comp_spv, - kp::shader_data::op_cpy_f32_f32_comp_spv_len); - ggml_vk_cpy(spirv, 4, 4, std::forward(args)...); -} - -template -static void ggml_vk_cpy_f16_f16(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f16_comp_spv, - kp::shader_data::op_cpy_f16_f16_comp_spv_len); - ggml_vk_cpy(spirv, 2, 2, std::forward(args)...); -} - -template -static void ggml_vk_cpy_f16_f32(Args&&... args) { - const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f32_comp_spv, - kp::shader_data::op_cpy_f16_f32_comp_spv_len); - ggml_vk_cpy(spirv, 2, 4, std::forward(args)...); -} - -static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - switch (op->op) { - case GGML_OP_UNARY: - switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_SILU: - return ggml_is_contiguous(op->src[0]); - default: - ; - } - break; - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - case GGML_OP_ADD: - case GGML_OP_MUL: - case GGML_OP_SCALE: - case GGML_OP_SOFT_MAX: - case GGML_OP_RMS_NORM: - case GGML_OP_NORM: - case GGML_OP_ROPE: - return true; - case GGML_OP_DUP: - case GGML_OP_CPY: - case GGML_OP_CONT: - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - break; - default: - return false; - } - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - break; - default: - return false; - } - return true; - case GGML_OP_DIAG_MASK_INF: - return op->ne[3] == 1; - case GGML_OP_GET_ROWS: - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q6_K: - return op->ne[2] == 1 && op->ne[3] == 1; - default: - ; - } - return false; - case GGML_OP_MUL_MAT: - if (op->src[1]->type != GGML_TYPE_F32 || ggml_is_transposed(op->src[0]) || ggml_is_transposed(op->src[1])) - return false; - - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_Q6_K: - return op->ne[3] == 1; - case GGML_TYPE_F16: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q4_K: - return true; - default: - ; - } - default: - ; - } - return false; - - GGML_UNUSED(dev); -} - -static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) { - const int n_seq = 8; - - // FIXME: Figure out if we can somehow optimize the size of the pool... right now we're setting - // it to the size of the graph, but I think it can be made smaller? - ggml_vk_allocate_descriptor_pool(ctx, gf->n_nodes); - - std::vector> sequences(n_seq); - - for (auto& sequence : sequences) { - sequence = komputeManager()->sequence(); - } - for (int seq_idx = 0; seq_idx < n_seq; ++seq_idx) { - const int n_nodes_per_seq = (gf->n_nodes + n_seq - 1) / n_seq; - - auto& seq = *sequences[seq_idx]; - - const int node_start = (seq_idx + 0) * n_nodes_per_seq; - const int node_end = std::min((seq_idx == n_seq - 1) ? gf->n_nodes : (seq_idx + 1) * n_nodes_per_seq, gf->n_nodes); - - bool any_commands_recorded = false; - - for (int i = node_start; i < node_end; ++i) { - struct ggml_tensor * src0 = gf->nodes[i]->src[0]; - struct ggml_tensor * src1 = gf->nodes[i]->src[1]; - struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2); - struct ggml_tensor * dst = gf->nodes[i]; - GGML_ASSERT(dst->data != nullptr); - - if (ggml_is_empty(dst)) { - continue; - } - - switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - continue; // noop -> next node - default: - break; - } - - any_commands_recorded = true; - - const int32_t ne00 = src0 ? src0->ne[0] : 0; - const int32_t ne01 = src0 ? src0->ne[1] : 0; - const int32_t ne02 = src0 ? src0->ne[2] : 0; - const int32_t ne03 = src0 ? src0->ne[3] : 0; - - const uint32_t nb00 = src0 ? src0->nb[0] : 0; - const uint32_t nb01 = src0 ? src0->nb[1] : 0; - const uint32_t nb02 = src0 ? src0->nb[2] : 0; - const uint32_t nb03 = src0 ? src0->nb[3] : 0; - - const int32_t ne10 = src1 ? src1->ne[0] : 0; - const int32_t ne11 = src1 ? src1->ne[1] : 0; - const int32_t ne12 = src1 ? src1->ne[2] : 0; - const int32_t ne13 = src1 ? src1->ne[3] : 0; - - const uint32_t nb10 = src1 ? src1->nb[0] : 0; - const uint32_t nb11 = src1 ? src1->nb[1] : 0; - const uint32_t nb12 = src1 ? src1->nb[2] : 0; - const uint32_t nb13 = src1 ? src1->nb[3] : 0; - - const int32_t ne0 = dst ? dst->ne[0] : 0; - const int32_t ne1 = dst ? dst->ne[1] : 0; - const int32_t ne2 = dst ? dst->ne[2] : 0; -// const int32_t ne3 = dst ? dst->ne[3] : 0; - - const uint32_t nb0 = dst ? dst->nb[0] : 0; - const uint32_t nb1 = dst ? dst->nb[1] : 0; - const uint32_t nb2 = dst ? dst->nb[2] : 0; - const uint32_t nb3 = dst ? dst->nb[3] : 0; - - const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; - const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; - const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; - - const static std::shared_ptr nullTensor = nullptr; - uint32_t off_src0 = 0; - uint32_t off_src1 = 0; - uint32_t off_dst = 0; - const std::shared_ptr& id_src0 = src0 ? ggml_vk_get_tensor(src0, &off_src0) : nullTensor; - const std::shared_ptr& id_src1 = src1 ? ggml_vk_get_tensor(src1, &off_src1) : nullTensor; - const std::shared_ptr& id_dst = dst ? ggml_vk_get_tensor(dst, &off_dst) : nullTensor; - - switch (dst->op) { - case GGML_OP_ADD: - { - if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { - // src1 is a row - ggml_vk_addrow(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst)/4, ne00); - } else { - ggml_vk_add( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne03, - nb00, nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb10, nb11, nb12, nb13, - ne0, - nb0, nb1, nb2, nb3 - ); - } - } break; - case GGML_OP_MUL: - { - ggml_vk_mul( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne03, - nb00, nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb10, nb11, nb12, nb13, - ne0, - nb0, nb1, nb2, nb3 - ); - } break; - case GGML_OP_SCALE: - { - float scale; memcpy(&scale, dst->op_params, sizeof(float)); - - ggml_vk_scale(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst), scale); - } break; - case GGML_OP_UNARY: - { - int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - switch (ggml_get_unary_op(gf->nodes[i])) { - case GGML_UNARY_OP_SILU: - { - ggml_vk_silu(seq, id_src0, id_dst, off_src0, off_dst, n/4); - } break; - case GGML_UNARY_OP_RELU: - { - ggml_vk_relu(seq, id_src0, id_dst, off_src0, off_dst, n/4); - } break; - case GGML_UNARY_OP_GELU: - { - GGML_ASSERT(n % 8 == 0); - ggml_vk_gelu(seq, id_src0, id_dst, off_src0, off_dst, n/8); - } break; - default: - { - fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ABORT("fatal error"); - } - } - } break; - case GGML_OP_SOFT_MAX: - { - float scale; - float max_bias; - - memcpy(&scale, (float *)dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *)dst->op_params + 1, sizeof(float)); - -#pragma message("TODO: add ggml_vk_soft_max() F16 src1 support") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") - GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32); - -#pragma message("TODO: add ALiBi support") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") - GGML_ASSERT(max_bias == 0.0f); - - ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); - } break; - case GGML_OP_DIAG_MASK_INF: - { - const int n_past = ((int32_t *)(dst->op_params))[0]; - ggml_vk_diag_mask_inf(seq, id_src0, id_dst, off_src0, off_dst, n_past, ne00, ne01, ne02); - } break; - case GGML_OP_NORM: - { - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - ggml_vk_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps); - } break; - case GGML_OP_RMS_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - ggml_vk_rms_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps); - } break; - case GGML_OP_MUL_MAT: - { - GGML_ASSERT(ne00 == ne10); - - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - const uint32_t r2 = ne12/ne02; - const uint32_t r3 = ne13/ne03; - - if (src1t != GGML_TYPE_F32) { - fprintf(stderr, "%s: %s: Unsupported src1 type: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t); - goto not_implemented; - } - - if (ggml_is_transposed(src0) || - ggml_is_transposed(src1)) { - fprintf(stderr, "%s: %s: matmul on tranposed tensor not supported: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t); - goto not_implemented; - } - - switch (src0t) { - case GGML_TYPE_F32: - ggml_vk_mul_mat_mat_f32( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, nb01, nb02, ne11, ne12, nb11, nb12, nb1, nb2 - ); - break; - case GGML_TYPE_F16: - ggml_vk_mul_mat_f16( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, ne13, nb10, nb11, nb12, - ne0, ne1, r2, r3 - ); - break; - case GGML_TYPE_Q8_0: - ggml_vk_mul_mat_q8_0( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 - ); - break; - case GGML_TYPE_Q4_0: - ggml_vk_mul_mat_q4_0( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 - ); - break; - case GGML_TYPE_Q4_1: - ggml_vk_mul_mat_q4_1( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 - ); - break; - case GGML_TYPE_Q4_K: - ggml_vk_mul_mat_q4_k( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, ne12/ne02, ne13/ne03 - ); - break; - case GGML_TYPE_Q6_K: - ggml_vk_mul_mat_q6_k( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne10, ne0, ne1, ne01, ne11, ne12, ne02 - ); - break; - default: { - fprintf(stderr, "%s: %s: Unsupported quantization: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t); - goto not_implemented; - } - } - - } break; - case GGML_OP_GET_ROWS: - { - if (src0t == GGML_TYPE_F32) { - ggml_vk_get_rows_f32(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); - } else if (src0t == GGML_TYPE_F16) { - ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); - } else if (src0t == GGML_TYPE_Q4_0) { - ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); - } else if (src0t == GGML_TYPE_Q4_1) { - ggml_vk_get_rows_q4_1(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); - } else if (src0t == GGML_TYPE_Q6_K) { - ggml_vk_get_rows_q6_k(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); - } else { - fprintf(stderr, "%s: %s: Unsupported quantization: %u\n", __func__, ggml_op_name(dst->op), src0t); - goto not_implemented; - } - } break; - case GGML_OP_ROPE: - { -#pragma message("TODO: implement phi3 frequency factors support") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") - GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); - -#pragma message("TODO: update rope NORM mode to match NEOX mode") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634") - - GGML_ASSERT(ne10 == ne02); - GGML_ASSERT(src0t == dstt); - // const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - ggml_vk_rope( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_ctx_orig, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, - ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 - ); - } break; - case GGML_OP_DUP: - case GGML_OP_CPY: - case GGML_OP_CONT: - { - switch (src0t) { - case GGML_TYPE_F32: - { - switch (dstt) { - case GGML_TYPE_F16: ggml_vk_cpy_f32_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; - case GGML_TYPE_F32: ggml_vk_cpy_f32_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; - default: goto not_implemented; - } - } break; - case GGML_TYPE_F16: - { - switch (dstt) { - case GGML_TYPE_F16: ggml_vk_cpy_f16_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; - case GGML_TYPE_F32: ggml_vk_cpy_f16_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; - default: goto not_implemented; - } break; - default: goto not_implemented; - } - } - } break; - default: goto not_implemented; - } - continue; - not_implemented: {} - fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - //GGML_ABORT("fatal error"); - } - - // Evaluate sequence - if (any_commands_recorded) { - seq.evalAsync(); - } - } - - // Wait for all sequences to finish - for (auto& sequence : sequences) { - if (sequence->isRunning()) - sequence->evalAwait(); - } - - ggml_vk_free_descriptor_pool(ctx); -} - -template<> -kp::Tensor::TensorDataTypes -kp::TensorT::dataType() -{ - return TensorDataTypes::eFloat; -} - -template<> -kp::Tensor::TensorDataTypes -kp::TensorT::dataType() -{ - return TensorDataTypes::eUnsignedInt; -} - -//////////////////////////////////////////////////////////////////////////////// - -// backend interface - -struct ggml_backend_kompute_buffer_type_context { - int device; - int device_ref = 0; - uint64_t buffer_alignment; - uint64_t max_alloc; - std::string name; - - ggml_backend_kompute_buffer_type_context(int device, uint64_t buffer_alignment, uint64_t max_alloc) - : device(device), buffer_alignment(buffer_alignment), max_alloc(max_alloc), name(ggml_kompute_format_name(device)) {} -}; - -static void ggml_backend_kompute_device_ref(ggml_backend_buffer_type_t buft) { - auto * ctx = static_cast(buft->context); - - if (!ctx->device_ref) { - komputeManager()->initializeDevice( - ctx->device, {}, { - "VK_KHR_shader_float16_int8", "VK_KHR_8bit_storage", - "VK_KHR_16bit_storage", "VK_KHR_shader_non_semantic_info" - } - ); - } - - assert(ggml_vk_has_device()); - ctx->device_ref++; -} - -static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) { - auto * ctx = static_cast(buft->context); - - assert(ctx->device_ref > 0); - - ctx->device_ref--; - - if (!ctx->device_ref) { - komputeManager.destroy(); - } -} - -static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) { - auto * memory = (ggml_vk_memory *)buffer->context; - if (ggml_vk_has_device()) { - ggml_vk_free_memory(*memory); - } - delete memory; -} - -static void * ggml_backend_kompute_buffer_get_base(ggml_backend_buffer_t buffer) { - return ((ggml_vk_memory *)buffer->context)->data; -} - -static void ggml_backend_kompute_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_UNUSED(buffer); - - const auto res = ggml_vk_get_tensor(tensor); - GGML_ASSERT(res); - - memcpy((char *)tensor->data + offset, data, size); - - komputeManager()->sequence()->eval({res}); -} - -static void ggml_backend_kompute_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_UNUSED(buffer); - - const auto res = ggml_vk_get_tensor(tensor); - GGML_ASSERT(res); - - komputeManager()->sequence()->eval({res}); - - memcpy(data, (const char *)tensor->data + offset, size); -} - -static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - auto * memory = (ggml_vk_memory *)buffer->context; - memset(memory->data, value, buffer->size); - - if (memory->stagingBuffer) - komputeManager()->sequence()->eval(memory->primaryBuffer, memory->stagingBuffer, memory->size); -} - -static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = { - /* .free_buffer = */ ggml_backend_kompute_buffer_free_buffer, - /* .get_base = */ ggml_backend_kompute_buffer_get_base, - /* .init_tensor = */ NULL, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_kompute_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_kompute_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_kompute_buffer_clear, - /* .reset = */ NULL, -}; - -// default buffer type - -static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - auto * ctx = static_cast(buft->context); - return ctx->name.c_str(); -} - -static ggml_backend_buffer_t ggml_backend_kompute_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - ggml_backend_kompute_device_ref(buft); - auto * ctx = new ggml_vk_memory(ggml_vk_allocate(size)); - return ggml_backend_buffer_init(buft, ggml_backend_kompute_buffer_i, ctx, size); -} - -static size_t ggml_backend_kompute_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - auto * ctx = static_cast(buft->context); - return ctx->buffer_alignment; -} - -static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - auto * ctx = static_cast(buft->context); - return ctx->max_alloc; -} - -static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = { - /* .get_name = */ ggml_backend_kompute_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_kompute_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_kompute_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ NULL, -}; - -ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - auto devices = ggml_vk_available_devices(); - int32_t device_count = (int32_t) devices.size(); - GGML_ASSERT(device < device_count); - GGML_ASSERT(devices.size() <= GGML_KOMPUTE_MAX_DEVICES); - - static ggml_backend_buffer_type - ggml_backend_kompute_buffer_types[GGML_KOMPUTE_MAX_DEVICES]; - - static bool ggml_backend_kompute_buffer_type_initialized = false; - - if (!ggml_backend_kompute_buffer_type_initialized) { - for (int32_t i = 0; i < device_count; i++) { - ggml_backend_kompute_buffer_types[i] = { - /* .iface = */ ggml_backend_kompute_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), i), - /* .context = */ new ggml_backend_kompute_buffer_type_context{ i, devices[i].bufferAlignment, devices[i].maxAlloc }, - }; - } - ggml_backend_kompute_buffer_type_initialized = true; - } - - return &ggml_backend_kompute_buffer_types[device]; -} - -// backend - -static const char * ggml_backend_kompute_name(ggml_backend_t backend) { - auto * ctx = static_cast(backend->context); - return ctx->name.c_str(); -} - -static void ggml_backend_kompute_free(ggml_backend_t backend) { - auto * ctx = static_cast(backend->context); - - assert(ctx == s_kompute_context); - s_kompute_context = nullptr; - if (ctx != nullptr) { - delete ctx; - } - - delete backend; -} - -static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - auto * ctx = static_cast(backend->context); - ggml_vk_graph_compute(ctx, cgraph); - return GGML_STATUS_SUCCESS; -} - -static struct ggml_backend_i kompute_backend_i = { - /* .get_name = */ ggml_backend_kompute_name, - /* .free = */ ggml_backend_kompute_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_kompute_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_kompute_guid() { - static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49, 0xfb, 0x35, 0xfa, 0x9b, 0x18, 0x31, 0x1d, 0xca }; - return &guid; -} - -ggml_backend_t ggml_backend_kompute_init(int device) { - GGML_ASSERT(s_kompute_context == nullptr); - s_kompute_context = new ggml_kompute_context(device); - - ggml_backend_t kompute_backend = new ggml_backend { - /* .guid = */ ggml_backend_kompute_guid(), - /* .interface = */ kompute_backend_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_kompute_reg(), device), - /* .context = */ s_kompute_context, - }; - - return kompute_backend; -} - -bool ggml_backend_is_kompute(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid()); -} - -static size_t ggml_backend_kompute_get_device_count() { - auto devices = ggml_vk_available_devices(); - return devices.size(); -} - -static void ggml_backend_kompute_get_device_description(int device, char * description, size_t description_size) { - auto devices = ggml_vk_available_devices(); - GGML_ASSERT((size_t) device < devices.size()); - snprintf(description, description_size, "%s", devices[device].name); -} - -static void ggml_backend_kompute_get_device_memory(int device, size_t * free, size_t * total) { - auto devices = ggml_vk_available_devices(); - GGML_ASSERT((size_t) device < devices.size()); - *total = devices[device].heapSize; - *free = devices[device].heapSize; -} - -////////////////////////// - -struct ggml_backend_kompute_device_context { - int device; - std::string name; - std::string description; -}; - -static const char * ggml_backend_kompute_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; - return ctx->name.c_str(); -} - -static const char * ggml_backend_kompute_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; - return ctx->description.c_str(); -} - -static void ggml_backend_kompute_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; - ggml_backend_kompute_get_device_memory(ctx->device, free, total); -} - -static ggml_backend_buffer_type_t ggml_backend_kompute_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; - return ggml_backend_kompute_buffer_type(ctx->device); -} - -static bool ggml_backend_kompute_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_kompute_buffer_type_get_name) { - return false; - } - - ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; - ggml_backend_kompute_buffer_type_context * buft_ctx = (ggml_backend_kompute_buffer_type_context *)buft->context; - - return buft_ctx->device == ctx->device; -} - -static enum ggml_backend_dev_type ggml_backend_kompute_device_get_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU; -} - -static void ggml_backend_kompute_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_kompute_device_get_name(dev); - props->description = ggml_backend_kompute_device_get_description(dev); - props->type = ggml_backend_kompute_device_get_type(dev); - ggml_backend_kompute_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* async = */ false, - /* host_buffer = */ false, - /* .buffer_from_host_ptr = */ false, - /* events = */ false, - }; -} - -static ggml_backend_t ggml_backend_kompute_device_init(ggml_backend_dev_t dev, const char * params) { - GGML_UNUSED(params); - ggml_backend_kompute_device_context * ctx = (ggml_backend_kompute_device_context *)dev->context; - return ggml_backend_kompute_init(ctx->device); -} - -static bool ggml_backend_kompute_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - const int min_batch_size = 32; - - return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || - (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_kompute_device_i = { - /* .get_name = */ ggml_backend_kompute_device_get_name, - /* .get_description = */ ggml_backend_kompute_device_get_description, - /* .get_memory = */ ggml_backend_kompute_device_get_memory, - /* .get_type = */ ggml_backend_kompute_device_get_type, - /* .get_props = */ ggml_backend_kompute_device_get_props, - /* .init_backend = */ ggml_backend_kompute_device_init, - /* .get_buffer_type = */ ggml_backend_kompute_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_kompute_device_supports_op, - /* .supports_buft = */ ggml_backend_kompute_device_supports_buft, - /* .offload_op = */ ggml_backend_kompute_device_offload_op, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -static const char * ggml_backend_kompute_reg_get_name(ggml_backend_reg_t reg) { - GGML_UNUSED(reg); - return "Kompute"; -} - -static size_t ggml_backend_kompute_reg_get_device_count(ggml_backend_reg_t reg) { - GGML_UNUSED(reg); - return ggml_backend_kompute_get_device_count(); -} - -static ggml_backend_dev_t ggml_backend_kompute_reg_get_device(ggml_backend_reg_t reg, size_t device) { - static std::vector devices; - - static bool initialized = false; - - { - static std::mutex mutex; - std::lock_guard lock(mutex); - if (!initialized) { - for (size_t i = 0; i < ggml_backend_kompute_get_device_count(); i++) { - ggml_backend_kompute_device_context * ctx = new ggml_backend_kompute_device_context; - char desc[256]; - ggml_backend_kompute_get_device_description(i, desc, sizeof(desc)); - ctx->device = i; - ctx->name = "Kompute" + std::to_string(i); - ctx->description = desc; - devices.push_back(new ggml_backend_device { - /* .iface = */ ggml_backend_kompute_device_i, - /* .reg = */ reg, - /* .context = */ ctx, - }); - } - initialized = true; - } - } - - GGML_ASSERT(device < devices.size()); - return devices[device]; -} - -static const struct ggml_backend_reg_i ggml_backend_kompute_reg_i = { - /* .get_name = */ ggml_backend_kompute_reg_get_name, - /* .get_device_count = */ ggml_backend_kompute_reg_get_device_count, - /* .get_device = */ ggml_backend_kompute_reg_get_device, - /* .get_proc_address = */ NULL, -}; - -ggml_backend_reg_t ggml_backend_kompute_reg() { - static ggml_backend_reg reg = { - /* .iface = */ ggml_backend_kompute_reg_i, - /* .context = */ nullptr, - }; - - return ® -} diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m deleted file mode 100644 index 04ec5117..00000000 --- a/ggml/src/ggml-metal.m +++ /dev/null @@ -1,4290 +0,0 @@ -#import "ggml-metal.h" - -#import "ggml-impl.h" -#import "ggml-backend-impl.h" - -#import - -#import - -#undef MIN -#undef MAX -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - -// max memory buffers that can be mapped to the device -#define GGML_METAL_MAX_BUFFERS 64 - -// max number of MTLCommandBuffer used to submit a graph for processing -#define GGML_METAL_MAX_COMMAND_BUFFERS 8 - -#define UNUSED(x) (void)(x) - -// globals - -// overload of MTLGPUFamilyMetal3 (not available in some environments) -static const NSInteger MTLGPUFamilyMetal3_GGML = 5001; - -// initialized in ggml_backend_metal_reg -static struct ggml_backend_reg g_ggml_backend_metal_reg; -static struct ggml_backend_device g_ggml_backend_metal_device; - -// information about a Metal device -// note: assumes single GPU device - the default one -// TODO: support multiple GPU devices -static struct ggml_backend_metal_device_context { - id mtl_device; - int mtl_device_ref_count; - - bool has_simdgroup_reduction; - bool has_simdgroup_mm; - bool has_bfloat; - bool use_bfloat; - - char name[128]; -} g_ggml_ctx_dev_main = { - /*.mtl_device =*/ nil, - /*.mtl_device_ref_count =*/ 0, - /*.has_simdgroup_reduction =*/ false, - /*.has_simdgroup_mm =*/ false, - /*.has_bfloat =*/ false, - /*.use_bfloat =*/ false, - /*.name =*/ "", -}; - -// acquire -static id ggml_backend_metal_device_acq(struct ggml_backend_metal_device_context * ctx) { - assert(ctx != NULL); - - if (ctx->mtl_device == nil) { - ctx->mtl_device = MTLCreateSystemDefaultDevice(); - - ctx->has_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; - ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; - - ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; - - ctx->has_bfloat = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; - ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6]; - -#if defined(GGML_METAL_USE_BF16) - ctx->use_bfloat = ctx->has_bfloat; -#else - ctx->use_bfloat = false; -#endif - - strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1); - } - - ctx->mtl_device_ref_count++; - - return ctx->mtl_device; -} - -// release -static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_context * ctx) { - assert(ctx != NULL); - assert(ctx->mtl_device_ref_count > 0); - - ctx->mtl_device_ref_count--; - - if (ctx->mtl_device_ref_count == 0) { - [ctx->mtl_device release]; - ctx->mtl_device = nil; - } -} - -// kernels - -struct ggml_metal_kernel { - id pipeline; -}; - -enum ggml_metal_kernel_type { - GGML_METAL_KERNEL_TYPE_ADD, - GGML_METAL_KERNEL_TYPE_ADD_ROW, - GGML_METAL_KERNEL_TYPE_SUB, - GGML_METAL_KERNEL_TYPE_SUB_ROW, - GGML_METAL_KERNEL_TYPE_MUL, - GGML_METAL_KERNEL_TYPE_MUL_ROW, - GGML_METAL_KERNEL_TYPE_DIV, - GGML_METAL_KERNEL_TYPE_DIV_ROW, - GGML_METAL_KERNEL_TYPE_REPEAT_F32, - GGML_METAL_KERNEL_TYPE_REPEAT_F16, - GGML_METAL_KERNEL_TYPE_REPEAT_I32, - GGML_METAL_KERNEL_TYPE_REPEAT_I16, - GGML_METAL_KERNEL_TYPE_SCALE, - GGML_METAL_KERNEL_TYPE_SCALE_4, - GGML_METAL_KERNEL_TYPE_CLAMP, - GGML_METAL_KERNEL_TYPE_TANH, - GGML_METAL_KERNEL_TYPE_RELU, - GGML_METAL_KERNEL_TYPE_SIGMOID, - GGML_METAL_KERNEL_TYPE_GELU, - GGML_METAL_KERNEL_TYPE_GELU_4, - GGML_METAL_KERNEL_TYPE_GELU_QUICK, - GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, - GGML_METAL_KERNEL_TYPE_SILU, - GGML_METAL_KERNEL_TYPE_SILU_4, - GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, - GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, - GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, - GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, - GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, - GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, - GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, - GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, - GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, - GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, - GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, - GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, - GGML_METAL_KERNEL_TYPE_RMS_NORM, - GGML_METAL_KERNEL_TYPE_GROUP_NORM, - GGML_METAL_KERNEL_TYPE_NORM, - GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, - GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, - GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, - GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, - GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW, - GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4, - GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, - //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, - //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, - //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, - GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, - GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, - GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, - GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, - GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, - GGML_METAL_KERNEL_TYPE_IM2COL_F16, - GGML_METAL_KERNEL_TYPE_IM2COL_F32, - GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, - GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, - GGML_METAL_KERNEL_TYPE_UPSCALE_F32, - GGML_METAL_KERNEL_TYPE_PAD_F32, - GGML_METAL_KERNEL_TYPE_ARANGE_F32, - GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, - GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, - GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, - GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256, - GGML_METAL_KERNEL_TYPE_CPY_F32_F32, - GGML_METAL_KERNEL_TYPE_CPY_F32_F16, - GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, - GGML_METAL_KERNEL_TYPE_CPY_F16_F16, - GGML_METAL_KERNEL_TYPE_CPY_F16_F32, - GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, - GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, - GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, - GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, - GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, - GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, - GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, - GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, - GGML_METAL_KERNEL_TYPE_CONCAT, - GGML_METAL_KERNEL_TYPE_SQR, - GGML_METAL_KERNEL_TYPE_SQRT, - GGML_METAL_KERNEL_TYPE_SIN, - GGML_METAL_KERNEL_TYPE_COS, - GGML_METAL_KERNEL_TYPE_SUM_ROWS, - GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, - GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, - - GGML_METAL_KERNEL_TYPE_COUNT -}; - -struct ggml_backend_metal_context { - id queue; - - dispatch_queue_t d_queue; - - struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT]; - - // capture state - bool capture_next_compute; - bool capture_started; - - id capture_scope; - - // command buffer state - int n_cb; // number of extra threads used to submit the command buffers - int n_nodes_0; // number of nodes submitted by the main thread - int n_nodes_1; // remaining number of nodes submitted by the n_cb threads - int n_nodes_per_cb; - - struct ggml_cgraph * gf; - - // the callback given to the thread pool - void (^encode_async)(size_t ith); - - // n_cb command buffers + 1 used by the main thread - id command_buffers[GGML_METAL_MAX_COMMAND_BUFFERS + 1]; - - // abort ggml_metal_graph_compute if callback returns true - ggml_abort_callback abort_callback; - void * abort_callback_data; -}; - -// MSL code -// TODO: move the contents here when ready -// for now it is easier to work in a separate file -// static NSString * const msl_library_source = @"see metal.metal"; - -// Here to assist with NSBundle Path Hack -@interface GGMLMetalClass : NSObject -@end -@implementation GGMLMetalClass -@end - -static void * ggml_metal_host_malloc(size_t n) { - void * data = NULL; - -#if TARGET_OS_OSX - kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE); - if (err != KERN_SUCCESS) { - GGML_LOG_ERROR("%s: error: vm_allocate failed\n", __func__); - return NULL; - } -#else - const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); - if (result != 0) { - GGML_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); - return NULL; - } -#endif - - return data; -} - -static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t dev) { - GGML_LOG_INFO("%s: allocating\n", __func__); - -#if TARGET_OS_OSX && !GGML_METAL_NDEBUG - // Show all the Metal device instances in the system - NSArray * devices = MTLCopyAllDevices(); - for (id device in devices) { - GGML_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]); - } - [devices release]; // since it was created by a *Copy* C method -#endif - - // init context - struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context)); - struct ggml_backend_metal_device_context * ctx_dev = dev->context; - - id device = ggml_backend_metal_device_acq(ctx_dev); - GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]); - - ctx->queue = [device newCommandQueue]; - ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); - - id metal_library; - - // load library - // - // - first check if the library is embedded - // - then check if the library is in the bundle - // - if not found, load the source and compile it - // - if that fails, return NULL - { - NSBundle * bundle = nil; -#ifdef SWIFT_PACKAGE - bundle = SWIFTPM_MODULE_BUNDLE; -#else - bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; -#endif - - NSError * error = nil; - -#if GGML_METAL_EMBED_LIBRARY - const bool try_metallib = false; -#else - const bool try_metallib = true; -#endif - - NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"]; - if (try_metallib && path_lib != nil) { - // pre-compiled library found - NSURL * libURL = [NSURL fileURLWithPath:path_lib]; - GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]); - - metal_library = [device newLibraryWithURL:libURL error:&error]; - if (error) { - GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); - return NULL; - } - } else { -#if GGML_METAL_EMBED_LIBRARY - GGML_LOG_INFO("%s: using embedded metal library\n", __func__); - - extern const char ggml_metallib_start[]; - extern const char ggml_metallib_end[]; - - NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding]; -#else - GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); - - NSString * path_source; - NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; - - GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil"); - - if (path_resource) { - path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"]; - } else { - path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; - } - - if (path_source == nil) { - GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__); - path_source = @"ggml-metal.metal"; - } - - GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]); - - NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error]; - if (error) { - GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); - return NULL; - } -#endif // GGML_METAL_EMBED_LIBRARY - - @autoreleasepool { - // dictionary of preprocessor macros - NSMutableDictionary * prep = [NSMutableDictionary dictionary]; - - if (ctx_dev->use_bfloat) { - [prep setObject:@"1" forKey:@"GGML_METAL_USE_BF16"]; - } - - MTLCompileOptions * options = [MTLCompileOptions new]; - options.preprocessorMacros = prep; - - //[options setFastMathEnabled:false]; - - metal_library = [device newLibraryWithSource:src options:options error:&error]; - if (error) { - GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); - return NULL; - } - -#if !__has_feature(objc_arc) - [options release]; -#endif - } -#if GGML_METAL_EMBED_LIBRARY - [src release]; -#endif // GGML_METAL_EMBED_LIBRARY - } - } - - // print MTL GPU family: - GGML_LOG_INFO("%s: GPU name: %s\n", __func__, [[device name] UTF8String]); - - // determine max supported GPU family - // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf - // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf - { - for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { - if ([device supportsFamily:i]) { - GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); - break; - } - } - - for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) { - if ([device supportsFamily:i]) { - GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i); - break; - } - } - - for (int i = MTLGPUFamilyMetal3_GGML + 5; i >= MTLGPUFamilyMetal3_GGML; --i) { - if ([device supportsFamily:i]) { - GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3_GGML + 3, i); - break; - } - } - } - - GGML_LOG_INFO("%s: simdgroup reduction = %s\n", __func__, ctx_dev->has_simdgroup_reduction ? "true" : "false"); - GGML_LOG_INFO("%s: simdgroup matrix mul. = %s\n", __func__, ctx_dev->has_simdgroup_mm ? "true" : "false"); - GGML_LOG_INFO("%s: has bfloat = %s\n", __func__, ctx_dev->has_bfloat ? "true" : "false"); - GGML_LOG_INFO("%s: use bfloat = %s\n", __func__, ctx_dev->use_bfloat ? "true" : "false"); - GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false"); - - ctx->capture_next_compute = false; - ctx->capture_started = false; - ctx->capture_scope = nil; - - ctx->gf = nil; - ctx->encode_async = nil; - for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) { - ctx->command_buffers[i] = nil; - } - -#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) - if (@available(macOS 10.12, iOS 16.0, *)) { - GGML_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, device.recommendedMaxWorkingSetSize / 1e6); - } -#endif - - // load kernels - { - NSError * error = nil; - - for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) { - ctx->kernels[i].pipeline = nil; - } - -#define GGML_METAL_ADD_KERNEL(e, name, supported) \ - if (supported) { \ - struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ - id metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \ - kernel->pipeline = [device newComputePipelineStateWithFunction:metal_function error:&error]; \ - GGML_LOG_DEBUG("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ - (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ - (int) kernel->pipeline.threadExecutionWidth); \ - [metal_function release]; \ - if (error) { \ - GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ - [metal_library release]; \ - return NULL; \ - } \ - } else { \ - GGML_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \ - } - - const bool has_simdgroup_mm = ctx_dev->has_simdgroup_mm; - const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction; - const bool use_bfloat = ctx_dev->use_bfloat; - - // simd_sum and simd_max requires MTLGPUFamilyApple7 - - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB, sub, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB_ROW, sub_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32, repeat_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16, repeat_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32, repeat_i32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16, repeat_i16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID, sigmoid, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16, get_rows_bf16, use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, ssm_conv_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, ssm_scan_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32, mul_mv_bf16_f32, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW, mul_mv_bf16_f32_1row, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4, mul_mv_bf16_f32_l4, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16, mul_mv_bf16_bf16, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, has_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, has_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, has_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32, mul_mv_id_bf16_f32, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32, mul_mm_bf16_f32, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32, mul_mm_id_bf16_f32, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, rope_norm_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, rope_norm_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, rope_neox_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, im2col_ext_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, im2col_ext_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, flash_attn_ext_bf16_h64, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, flash_attn_ext_bf16_h80, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, flash_attn_ext_bf16_h96, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, flash_attn_ext_bf16_h112, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, flash_attn_ext_bf16_h128, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, flash_attn_ext_bf16_h256, has_simdgroup_mm && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, flash_attn_ext_q4_0_h64, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, flash_attn_ext_q4_0_h80, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, flash_attn_ext_q4_0_h96, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112, flash_attn_ext_q4_0_h112, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128, flash_attn_ext_q4_0_h128, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256, flash_attn_ext_q4_0_h256, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64, flash_attn_ext_q4_1_h64, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80, flash_attn_ext_q4_1_h80, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96, flash_attn_ext_q4_1_h96, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112, flash_attn_ext_q4_1_h112, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128, flash_attn_ext_q4_1_h128, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256, flash_attn_ext_q4_1_h256, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64, flash_attn_ext_q5_0_h64, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80, flash_attn_ext_q5_0_h80, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96, flash_attn_ext_q5_0_h96, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112, flash_attn_ext_q5_0_h112, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128, flash_attn_ext_q5_0_h128, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256, flash_attn_ext_q5_0_h256, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64, flash_attn_ext_q5_1_h64, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80, flash_attn_ext_q5_1_h80, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96, flash_attn_ext_q5_1_h96, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112, flash_attn_ext_q5_1_h112, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128, flash_attn_ext_q5_1_h128, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256, flash_attn_ext_q5_1_h256, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64, flash_attn_ext_q8_0_h64, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80, flash_attn_ext_q8_0_h80, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96, flash_attn_ext_q8_0_h96, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112, flash_attn_ext_q8_0_h112, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, flash_attn_ext_q8_0_h128, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, flash_attn_ext_q8_0_h256, has_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, flash_attn_ext_vec_bf16_h128, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, flash_attn_ext_vec_q4_0_h128, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, flash_attn_ext_vec_q4_1_h128, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, flash_attn_ext_vec_q5_0_h128, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, flash_attn_ext_vec_q5_1_h128, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, flash_attn_ext_vec_q8_0_h128, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, flash_attn_ext_vec_bf16_h256, has_simdgroup_reduction && use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, flash_attn_ext_vec_q4_0_h256, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, flash_attn_ext_vec_q4_1_h256, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, flash_attn_ext_vec_q5_0_h256, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256, flash_attn_ext_vec_q5_1_h256, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256, flash_attn_ext_vec_q8_0_h256, has_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, cpy_f32_bf16, use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, cpy_bf16_f32, use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, cpy_bf16_bf16, use_bfloat); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQRT, sqrt, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true); - } - - [metal_library release]; - - return ctx; -} - -static void ggml_metal_free(struct ggml_backend_metal_context * ctx) { - GGML_LOG_INFO("%s: deallocating\n", __func__); - - for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) { - [ctx->kernels[i].pipeline release]; - } - - Block_release(ctx->encode_async); - - [ctx->queue release]; - - dispatch_release(ctx->d_queue); - - free(ctx); -} - -// temporarily defined here for compatibility between ggml-backend and the old API - -struct ggml_backend_metal_buffer { - void * data; - size_t size; - - id metal; -}; - -struct ggml_backend_metal_buffer_context { - void * all_data; - size_t all_size; - bool owned; - - // multiple buffers are used only to avoid the maximum buffer size limitation when using mmap - int n_buffers; - struct ggml_backend_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; -}; - -// finds the Metal buffer that contains the tensor data on the GPU device -// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the -// Metal buffer based on the host memory pointer -// -static id ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs) { - //GGML_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach); - - const int64_t tsize = ggml_nbytes(t); - - ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer; - - struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context; - - // find the view that contains the tensor fully - for (int i = 0; i < buf_ctx->n_buffers; ++i) { - const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data; - - //GGML_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size); - if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) { - *offs = (size_t) ioffs; - - //GGML_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs); - - return buf_ctx->buffers[i].metal; - } - } - - GGML_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name); - - return nil; -} - -static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_context * ctx_dev, const struct ggml_tensor * op) { - const bool has_simdgroup_mm = ctx_dev->has_simdgroup_mm; - const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction; - const bool use_bfloat = ctx_dev->use_bfloat; - - if (!use_bfloat) { - for (size_t i = 0, n = 3; i < n; ++i) { - if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) { - return false; - } - } - } - - switch (op->op) { - case GGML_OP_UNARY: - switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - return ggml_is_contiguous(op->src[0]); - default: - return false; - } - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - case GGML_OP_CONCAT: - case GGML_OP_ADD: - case GGML_OP_SUB: - case GGML_OP_ACC: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_REPEAT: - case GGML_OP_SCALE: - case GGML_OP_CLAMP: - return true; - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_SIN: - case GGML_OP_COS: - return ggml_is_contiguous(op->src[0]); - case GGML_OP_SUM_ROWS: - case GGML_OP_SOFT_MAX: - case GGML_OP_RMS_NORM: - case GGML_OP_GROUP_NORM: - return has_simdgroup_reduction; - case GGML_OP_NORM: - case GGML_OP_ROPE: - return true; - case GGML_OP_IM2COL: - return op->src[0]->type == GGML_TYPE_F16; - case GGML_OP_POOL_1D: - return false; - case GGML_OP_POOL_2D: - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_ARANGE: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_ARGSORT: - case GGML_OP_LEAKY_RELU: - return true; - case GGML_OP_FLASH_ATTN_EXT: - if (op->src[1]->type != op->src[2]->type) { - return false; - } - return has_simdgroup_mm; // TODO: over-restricted for vec-kernels - case GGML_OP_SSM_CONV: - case GGML_OP_SSM_SCAN: - return true; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - return has_simdgroup_reduction && - (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32); - case GGML_OP_CPY: - case GGML_OP_DUP: - case GGML_OP_CONT: - { - switch (op->src[0]->type) { - case GGML_TYPE_F32: - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_IQ4_NL: - return true; - default: - return false; - } - case GGML_TYPE_F16: - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - return true; - default: - return false; - } - case GGML_TYPE_BF16: - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_BF16: - return true; - default: - return false; - } - default: - return false; - }; - } - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_GET_ROWS: - { - return op->ne[3] == 1; - } - default: - return false; - } -} - -static void ggml_metal_encode_node( - ggml_backend_t backend, - int idx, - id encoder) { - struct ggml_backend_metal_context * ctx = backend->context; - struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; - - struct ggml_cgraph * gf = ctx->gf; - - struct ggml_tensor * node = ggml_graph_node(gf, idx); - - //GGML_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, idx, ggml_op_name(node->op)); - - struct ggml_tensor * src0 = node->src[0]; - struct ggml_tensor * src1 = node->src[1]; - struct ggml_tensor * src2 = node->src[2]; - struct ggml_tensor * dst = node; - - if (ggml_is_empty(dst)) { - return; - } - - switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop -> next node - } return; - default: - { - } break; - } - - if (!ggml_metal_supports_op(ctx_dev, dst)) { - GGML_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); - GGML_ABORT("unsupported op"); - } - - const int64_t ne00 = src0 ? src0->ne[0] : 0; - const int64_t ne01 = src0 ? src0->ne[1] : 0; - const int64_t ne02 = src0 ? src0->ne[2] : 0; - const int64_t ne03 = src0 ? src0->ne[3] : 0; - - const uint64_t nb00 = src0 ? src0->nb[0] : 0; - const uint64_t nb01 = src0 ? src0->nb[1] : 0; - const uint64_t nb02 = src0 ? src0->nb[2] : 0; - const uint64_t nb03 = src0 ? src0->nb[3] : 0; - - const int64_t ne10 = src1 ? src1->ne[0] : 0; - const int64_t ne11 = src1 ? src1->ne[1] : 0; - const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; - - const uint64_t nb10 = src1 ? src1->nb[0] : 0; - const uint64_t nb11 = src1 ? src1->nb[1] : 0; - const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; - - const int64_t ne20 = src2 ? src2->ne[0] : 0; - const int64_t ne21 = src2 ? src2->ne[1] : 0; - const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); - const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); - const uint64_t nb21 = src2 ? src2->nb[1] : 0; - const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); - - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; - - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; - - const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; - const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; - const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; - - size_t offs_src0 = 0; - size_t offs_src1 = 0; - size_t offs_src2 = 0; - size_t offs_dst = 0; - - id id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil; - id id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil; - id id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; - id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; - -#if 0 - GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); - if (src0) { - GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, - ggml_is_contiguous(src0), src0->name); - } - if (src1) { - GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13, - ggml_is_contiguous(src1), src1->name); - } - if (dst) { - GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3, - dst->name); - } -#endif - - id device = ctx_dev->mtl_device; - - switch (dst->op) { - case GGML_OP_CONCAT: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; - - const int32_t dim = ((const int32_t *) dst->op_params)[0]; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; - - const int nth = MIN(1024, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ADD: - case GGML_OP_SUB: - case GGML_OP_MUL: - case GGML_OP_DIV: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - - const size_t offs = 0; - - bool bcast_row = false; - - int64_t nb = ne00; // used by the "row" kernels - - id pipeline = nil; - - if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { - GGML_ASSERT(ggml_is_contiguous(src0)); - - // src1 is a row - GGML_ASSERT(ne11 == 1); - - nb = ne00 / 4; - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; - case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; - default: GGML_ABORT("fatal error"); - } - - bcast_row = true; - } else { - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; - case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; - default: GGML_ABORT("fatal error"); - } - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; - - if (bcast_row) { - const int64_t n = ggml_nelements(dst)/4; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } else { - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - } break; - case GGML_OP_REPEAT: - { - id pipeline; - - switch (src0t) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; - case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; - case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; - default: GGML_ABORT("fatal error"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ACC: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - GGML_ASSERT(dstt == GGML_TYPE_F32); - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - - const size_t pnb1 = ((const int32_t *) dst->op_params)[0]; - const size_t pnb2 = ((const int32_t *) dst->op_params)[1]; - const size_t pnb3 = ((const int32_t *) dst->op_params)[2]; - const size_t offs = ((const int32_t *) dst->op_params)[3]; - - const bool inplace = (bool) ((const int32_t *) dst->op_params)[4]; - - if (!inplace) { - // run a separete kernel to cpy src->dst - // not sure how to avoid this - // TODO: make a simpler cpy_bytes kernel - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_SCALE: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - float scale; - memcpy(&scale, dst->op_params, sizeof(scale)); - - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - n /= 4; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_CLAMP: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; - - float min; - float max; - memcpy(&min, ((const int32_t *) dst->op_params) + 0, sizeof(float)); - memcpy(&max, ((const int32_t *) dst->op_params) + 1, sizeof(float)); - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&min length:sizeof(min) atIndex:2]; - [encoder setBytes:&max length:sizeof(max) atIndex:3]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - // we are not taking into account the strides, so for now require contiguous tensors - GGML_ASSERT(ggml_is_contiguous(src0)); - - case GGML_UNARY_OP_TANH: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_RELU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_SIGMOID: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU: - { - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline; - n /= 4; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline; - n /= 4; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_SILU: - { - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline; - n /= 4; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - default: - { - GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_SQR: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SQRT: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQRT].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SIN: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIN].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_COS: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_COS].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SUM_ROWS: - { - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SOFT_MAX: - { - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); - - int nth = 32; // SIMD width - - id pipeline = nil; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - - if (ne00%4 == 0) { - while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) { - nth *= 2; - } - if (use_f16) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; - } - } else { - while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { - nth *= 2; - } - if (use_f16) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline; - } - } - - float scale; - float max_bias; - - memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(scale)); - memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias)); - - const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src0->ne[1]; - - const uint32_t n_head = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - if (id_src1) { - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; - [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_DIAG_MASK_INF: - { - const int n_past = ((const int32_t *)(dst->op_params))[0]; - - id pipeline = nil; - - if (ne00%8 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; - - if (ne00%8 == 0) { - [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - else { - [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - } break; - case GGML_OP_SSM_CONV: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:11]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:12]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:15]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:16]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:17]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:18]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SSM_SCAN: - { - struct ggml_tensor * src3 = node->src[3]; - struct ggml_tensor * src4 = node->src[4]; - struct ggml_tensor * src5 = node->src[5]; - - GGML_ASSERT(src3); - GGML_ASSERT(src4); - GGML_ASSERT(src5); - - size_t offs_src3 = 0; - size_t offs_src4 = 0; - size_t offs_src5 = 0; - - id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; - id id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil; - id id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil; - - const int64_t ne30 = src3->ne[0]; GGML_UNUSED(ne30); - const int64_t ne31 = src3->ne[1]; GGML_UNUSED(ne31); - - const uint64_t nb30 = src3->nb[0]; - const uint64_t nb31 = src3->nb[1]; - - const int64_t ne40 = src4->ne[0]; GGML_UNUSED(ne40); - const int64_t ne41 = src4->ne[1]; GGML_UNUSED(ne41); - const int64_t ne42 = src4->ne[2]; GGML_UNUSED(ne42); - - const uint64_t nb40 = src4->nb[0]; - const uint64_t nb41 = src4->nb[1]; - const uint64_t nb42 = src4->nb[2]; - - const int64_t ne50 = src5->ne[0]; GGML_UNUSED(ne50); - const int64_t ne51 = src5->ne[1]; GGML_UNUSED(ne51); - const int64_t ne52 = src5->ne[2]; GGML_UNUSED(ne52); - - const uint64_t nb50 = src5->nb[0]; - const uint64_t nb51 = src5->nb[1]; - const uint64_t nb52 = src5->nb[2]; - - const int64_t d_state = ne00; - const int64_t d_inner = ne01; - const int64_t n_seq_tokens = ne11; - const int64_t n_seqs = ne02; - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; - [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4]; - [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:6]; - - [encoder setBytes:&d_state length:sizeof(d_state) atIndex:7]; - [encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:8]; - [encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:9]; - [encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:10]; - - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:11]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:12]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; - [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:18]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:19]; - [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:20]; - [encoder setBytes:&nb30 length:sizeof(nb30) atIndex:21]; - [encoder setBytes:&nb31 length:sizeof(nb31) atIndex:22]; - [encoder setBytes:&nb40 length:sizeof(nb40) atIndex:23]; - [encoder setBytes:&nb41 length:sizeof(nb41) atIndex:24]; - [encoder setBytes:&nb42 length:sizeof(nb42) atIndex:25]; - [encoder setBytes:&nb50 length:sizeof(nb50) atIndex:26]; - [encoder setBytes:&nb51 length:sizeof(nb51) atIndex:27]; - [encoder setBytes:&nb52 length:sizeof(nb52) atIndex:28]; - - [encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_MUL_MAT: - { - GGML_ASSERT(ne00 == ne10); - - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - const uint r2 = ne12/ne02; - const uint r3 = ne13/ne03; - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - int ne11_mm_min = 1; - -#if 0 - // the numbers below are measured on M2 Ultra for 7B and 13B models - // these numbers do not translate to other devices or model sizes - // TODO: need to find a better approach - if ([device.name isEqualToString:@"Apple M2 Ultra"]) { - switch (src0t) { - case GGML_TYPE_F16: ne11_mm_min = 2; break; - case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; - case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; - case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; - case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; - case GGML_TYPE_Q5_0: // not tested yet - case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet - case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; - default: ne11_mm_min = 1; break; - } - } -#endif - - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - if ([device supportsFamily:MTLGPUFamilyApple7] && - !ggml_is_transposed(src0) && - !ggml_is_transposed(src1) && - src1t == GGML_TYPE_F32 && - ne00 % 32 == 0 && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { - //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - // some Metal matrix data types require aligned pointers - // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; - case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; - case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8 == 0); break; - default: break; - } - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; - case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; - case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; - case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32 ].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; - case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break; - default: GGML_ABORT("MUL MAT-MAT not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:7]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:9]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:10]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:11]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:12]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:15]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:16]; - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - // use custom matrix x vector kernel - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; - nrows = 4; - } break; - case GGML_TYPE_F16: - { - nth0 = 32; - nth1 = 1; - if (src1t == GGML_TYPE_F32) { - if (ne11 * ne12 < 4) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; - nrows = ne11; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; - nrows = 4; - } - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; - nrows = 4; - } - } break; - case GGML_TYPE_BF16: - { - nth0 = 32; - nth1 = 1; - if (src1t == GGML_TYPE_F32) { - if (ne11 * ne12 < 4) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW].pipeline; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4].pipeline; - nrows = ne11; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32].pipeline; - nrows = 4; - } - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16].pipeline; - nrows = 4; - } - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; - } break; - case GGML_TYPE_IQ2_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_M: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline; - } break; - case GGML_TYPE_IQ4_NL: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; - } break; - case GGML_TYPE_IQ4_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline; - } break; - default: - { - GGML_LOG_ERROR("Asserting on type %d\n", (int)src0t); - GGML_ABORT("not implemented"); - } - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:13]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:14]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:15]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:19]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:20]; - - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { - const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { - const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { - const int mem_size = 32*sizeof(float); - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q3_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (ne11 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_MUL_MAT_ID: - { - const int n_as = src0->ne[2]; - - // src2 = ids - const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); - - GGML_ASSERT(src2t == GGML_TYPE_I32); - - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - - GGML_ASSERT(src1t == GGML_TYPE_F32); - - GGML_ASSERT(ne03 == 1); - GGML_ASSERT(ne13 == 1); - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - // ne20 = n_used_experts - // ne21 = n_rows - const int dst_rows = ne20*ne21; - const int dst_rows_min = n_as; - const int dst_rows_max = (device.maxThreadgroupMemoryLength - 32 - 8192)/4; - - // max size of the rowids array in the kernel shared buffer - GGML_ASSERT(dst_rows <= dst_rows_max); - - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - // !!! - // TODO: for now, always use mat-vec kernels until we figure out how to improve the - // indirect matrix multiplication - // !!! - if ([device supportsFamily:MTLGPUFamilyApple7] && - ne00 % 32 == 0 && ne00 >= 64 && - dst_rows > dst_rows_min) { - // some Metal matrix data types require aligned pointers - // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; - case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; - case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8 == 0); break; - default: break; - } - - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; - case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; - case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; - case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32 ].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; - case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break; - default: GGML_ABORT("MUL_MAT_ID not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; - - [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - - id pipeline = nil; - - // use custom matrix x vector kernel - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - nth0 = 32; - nth1 = 1; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; - } break; - case GGML_TYPE_BF16: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - nth0 = 32; - nth1 = 1; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32].pipeline; - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; - } break; - case GGML_TYPE_IQ2_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_M: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline; - } break; - case GGML_TYPE_IQ4_NL: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; - } break; - case GGML_TYPE_IQ4_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; - } break; - default: - { - GGML_LOG_ERROR("Asserting on type %d\n", (int)src2t); - GGML_ABORT("not implemented"); - } - }; - - if (ggml_is_quantized(src0t)) { - GGML_ASSERT(ne00 >= nth0*nth1); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; - - const int64_t _ne1 = 1; - const int tgz = dst_rows; - - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { - const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { - const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { - const int mem_size = 32*sizeof(float); - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q3_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1 - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_GET_ROWS: - { - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; - case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; - case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; - case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; - case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; - case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M ].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; - case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break; - case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; - default: GGML_ABORT("not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; - } break; - case GGML_OP_RMS_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ggml_is_contiguous_1(src0)); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - int nth = 32; // SIMD width - - while (nth < ne00/4 && nth < 1024) { - nth *= 2; - } - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_GROUP_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ggml_is_contiguous(src0)); - - float eps; - memcpy(&eps, dst->op_params + 1, sizeof(float)); - - const int32_t n_groups = ((const int32_t *) dst->op_params)[0]; - - int nth = 32; // SIMD width - - //while (nth < ne00/4 && nth < 1024) { - // nth *= 2; - //} - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&eps length:sizeof( float) atIndex:9]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_NORM: - { - GGML_ASSERT(ggml_is_contiguous_1(src0)); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - const int nth = MIN(256, ne00); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ROPE: - { - GGML_ASSERT(ne10 == ne02); - - const int nth = MIN(1024, ne00); - - const int n_past = ((const int32_t *) dst->op_params)[0]; - const int n_dims = ((const int32_t *) dst->op_params)[1]; - const int mode = ((const int32_t *) dst->op_params)[2]; - // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_ctx_orig = ((const int32_t *) dst->op_params)[4]; - - float freq_base; - float freq_scale; - float ext_factor; - float attn_factor; - float beta_fast; - float beta_slow; - - memcpy(&freq_base, (const int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (const int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (const int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (const int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (const int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (const int32_t *) dst->op_params + 10, sizeof(float)); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - id pipeline = nil; - - if (!is_neox) { - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; - default: GGML_ABORT("fatal error"); - }; - } else { - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; - default: GGML_ABORT("fatal error"); - }; - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - if (id_src2 != nil) { - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_IM2COL: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int32_t N = src1->ne[is_2D ? 3 : 2]; - const int32_t IC = src1->ne[is_2D ? 2 : 1]; - const int32_t IH = is_2D ? src1->ne[1] : 1; - const int32_t IW = src1->ne[0]; - - const int32_t KH = is_2D ? src0->ne[1] : 1; - const int32_t KW = src0->ne[0]; - - const int32_t OH = is_2D ? dst->ne[2] : 1; - const int32_t OW = dst->ne[1]; - - const int32_t CHW = IC * KH * KW; - - const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; - const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; - - const bool is_gt_mttpt = ((size_t)(N * KH * KW)) > pipeline.maxTotalThreadsPerThreadgroup; - - switch (dst->type) { - case GGML_TYPE_F32: { - pipeline = (is_gt_mttpt ? - ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32].pipeline - : - ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline); - } break; - case GGML_TYPE_F16: { - pipeline = (is_gt_mttpt ? - ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16].pipeline - : - ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline); - } break; - default: GGML_ABORT("fatal error"); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ofs0 length:sizeof(int32_t) atIndex:2]; - [encoder setBytes:&ofs1 length:sizeof(int32_t) atIndex:3]; - [encoder setBytes:&IW length:sizeof(int32_t) atIndex:4]; - [encoder setBytes:&IH length:sizeof(int32_t) atIndex:5]; - [encoder setBytes:&CHW length:sizeof(int32_t) atIndex:6]; - [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:7]; - [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:8]; - [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:9]; - [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:10]; - [encoder setBytes:&d0 length:sizeof(int32_t) atIndex:11]; - [encoder setBytes:&d1 length:sizeof(int32_t) atIndex:12]; - - if (is_gt_mttpt) { - [encoder setBytes:&N length:sizeof(int32_t) atIndex:13]; - [encoder setBytes:&KH length:sizeof(int32_t) atIndex:14]; - [encoder setBytes:&KW length:sizeof(int32_t) atIndex:15]; - - const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N); - - const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0); - - [encoder dispatchThreadgroups:MTLSizeMake(quotient * CHW, OH, OW) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; - } else { - [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; - } - } break; - case GGML_OP_UPSCALE: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const float sf0 = (float)ne0/src0->ne[0]; - const float sf1 = (float)ne1/src0->ne[1]; - const float sf2 = (float)ne2/src0->ne[2]; - const float sf3 = (float)ne3/src0->ne[3]; - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; - [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; - [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; - [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_PAD: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - - const int nth = MIN(1024, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ARANGE: - { - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - float start; - float step; - - memcpy(&start, ((const int32_t *) dst->op_params) + 0, sizeof(float)); - memcpy(&step, ((const int32_t *) dst->op_params) + 2, sizeof(float)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; - [encoder setBytes:&start length:sizeof(start) atIndex:2]; - [encoder setBytes:&step length:sizeof(step) atIndex:3]; - - const int nth = MIN(1024, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int dim = dst->op_params[0]; - const int max_period = dst->op_params[1]; - - const int half = dim / 2; - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; - [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; - [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; - - const int nth = MIN(1024, half); - - [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ARGSORT: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); - - const int nrows = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - - // bitonic sort requires the number of elements to be power of 2 - int64_t ne00_padded = 1; - while (ne00_padded < ne00) { - ne00_padded *= 2; - } - - // Metal kernels require the buffer size to be multiple of 16 bytes - // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength - const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16); - - id pipeline = nil; - - switch (order) { - case GGML_SORT_ORDER_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; - case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; - default: GGML_ABORT("fatal error"); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3]; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)]; - } break; - case GGML_OP_LEAKY_RELU: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - float slope; - memcpy(&slope, dst->op_params, sizeof(float)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ne11 % 32 == 0); - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == src2->type); - - GGML_ASSERT(ggml_are_same_shape (src1, src2)); - - struct ggml_tensor * src3 = node->src[3]; - - size_t offs_src3 = 0; - - id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; - - GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16); - GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && - "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); - - const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); - //const int64_t ne31 = src3 ? src3->ne[1] : 0; - const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); - const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); - - const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30); - const uint64_t nb31 = src3 ? src3->nb[1] : 0; - const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32); - const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33); - - const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); - - float scale; - float max_bias; - float logit_softcap; - memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(scale)); - memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias)); - memcpy(&logit_softcap, ((const int32_t *) dst->op_params) + 2, sizeof(logit_softcap)); - - if (logit_softcap != 0.0f) { - scale /= logit_softcap; - } - - const uint32_t n_head = src0->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - id pipeline = nil; - - bool use_vec_kernel = false; - - // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0) - // for now avoiding mainly to keep the number of templates/kernels a bit lower - if (ne01 >= 4 || (ne00%128 != 0)) { - switch (src1->type) { - case GGML_TYPE_F16: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - case GGML_TYPE_BF16: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - case GGML_TYPE_Q4_0: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - case GGML_TYPE_Q4_1: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - case GGML_TYPE_Q5_0: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - case GGML_TYPE_Q5_1: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - case GGML_TYPE_Q8_0: - { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } break; - default: - { - GGML_LOG_ERROR("unsupported type: %d\n", src1->type); - GGML_LOG_ERROR("add template specialization for this type\n"); - GGML_ABORT("add template specialization for this type"); - } - } - } else { - use_vec_kernel = true; - - switch (ne00) { - case 128: - { - switch (src1->type) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported type: %d\n", src1->type); - GGML_LOG_ERROR("add template specialization for this type\n"); - GGML_ABORT("add template specialization for this type"); - } - } - } break; - case 256: - { - switch (src1->type) { - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256].pipeline; break; - default: - { - GGML_LOG_ERROR("unsupported type: %d\n", src1->type); - GGML_LOG_ERROR("add template specialization for this type\n"); - GGML_ABORT("add template specialization for this type"); - } - } - } break; - default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - if (id_src3) { - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:18]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:19]; - [encoder setBytes:&scale length:sizeof( float) atIndex:20]; - [encoder setBytes:&max_bias length:sizeof( float) atIndex:21]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:22]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:23]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:24]; - [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:25]; - - if (!use_vec_kernel) { - // half8x8 kernel - const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! - const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! - - GGML_ASSERT(nqptg <= 32); - GGML_ASSERT(nqptg % 8 == 0); - GGML_ASSERT(ncpsg % 32 == 0); - - // 2*(2*ncpsg + nqptg)*(nsg) - // ncpsg soft_max values + ncpsg mask values + a diagonal scaling matrix (in float) - // - // 16*32*(nsg) - // the shared memory needed for the simdgroups to load the KV cache - // each thread loads (dequantizes) 16 head elements, there are 32 threads in th SG - // -#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + 16*32*(nsg))*(sizeof(float)/2), 16)) - - int64_t nsgmax = 2; - - while (true) { - const size_t smem = FATTN_SMEM(nsgmax); - if (smem > device.maxThreadgroupMemoryLength) { - break; - } - nsgmax *= 2; - } - nsgmax /= 2; - - // simdgroups per threadgroup (a.k.a. warps) - const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; - - const size_t smem = FATTN_SMEM(nsg); - - //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg); - GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); - [encoder setThreadgroupMemoryLength:smem atIndex:0]; -#undef FATTN_SMEM - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; - } else { - // half4x4 kernel - const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! - const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! - - GGML_ASSERT(nqptg <= 32); - GGML_ASSERT(nqptg % 1 == 0); - GGML_ASSERT(ncpsg % 32 == 0); - - // ne00 + 2*ncpsg*(nsg) - // for each query, we load it as f16 in shared memory (ne00) - // and store the soft_max values and the mask - // - // ne00*(nsg) - // each simdgroup has a full f16 head vector in shared mem to accumulate results - // -#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*ncpsg*(nsg)) + ne00*(nsg))*(sizeof(float)/2), 16)) - - int64_t nsgmax = 2; - - while (true) { - const size_t smem = FATTN_SMEM(nsgmax); - if (smem > device.maxThreadgroupMemoryLength) { - break; - } - nsgmax *= 2; - } - nsgmax /= 2; - - // simdgroups per threadgroup (a.k.a. warps) - const int64_t nsgt = MAX(2, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))); - - int64_t nsg = 1; - while (nsg <= nsgt) { - nsg *= 2; - } - nsg /= 2; - - const size_t smem = FATTN_SMEM(nsg); - - //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg); - GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); - [encoder setThreadgroupMemoryLength:smem atIndex:0]; -#undef FATTN_SMEM - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; - } - } break; - case GGML_OP_DUP: - case GGML_OP_CPY: - case GGML_OP_CONT: - { - GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); - - int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); - - id pipeline = nil; - - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); - - switch (dstt) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_BF16].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break; - default: GGML_ABORT("not implemented"); - }; - } break; - case GGML_TYPE_F16: - { - switch (dstt) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; - default: GGML_ABORT("not implemented"); - }; - } break; - case GGML_TYPE_BF16: - { - switch (dstt) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_F32].pipeline; break; - case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - }; - } break; - default: GGML_ABORT("not implemented"); - } - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_POOL_2D: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(src0t == GGML_TYPE_F32 && src0t == dstt); - - const int32_t * opts = dst->op_params; - enum ggml_op_pool op = opts[0]; - - id pipeline = nil; - switch (src0t) { - case GGML_TYPE_F32: { - switch(op) { - case GGML_OP_POOL_AVG: - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32].pipeline; break; - case GGML_OP_POOL_MAX: - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32].pipeline; break; - default: GGML_ASSERT(false && "not implemented"); - } - } break; - default: GGML_ASSERT(false && "not implemented"); - } - - const int32_t k0 = opts[1]; - const int32_t k1 = opts[2]; - const int32_t s0 = opts[3]; - const int32_t s1 = opts[4]; - const int32_t p0 = opts[5]; - const int32_t p1 = opts[6]; - - const int64_t IH = src0->ne[1]; - const int64_t IW = src0->ne[0]; - - const int64_t N = dst->ne[3]; - const int64_t OC = dst->ne[2]; - const int64_t OH = dst->ne[1]; - const int64_t OW = dst->ne[0]; - - const int64_t parallel_elements = N * OC * OH * OW; - const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements); - const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&k0 length:sizeof(int32_t) atIndex:2]; - [encoder setBytes:&k1 length:sizeof(int32_t) atIndex:3]; - [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:4]; - [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:5]; - [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:6]; - [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:7]; - [encoder setBytes:&IH length:sizeof(int64_t) atIndex:8]; - [encoder setBytes:&IW length:sizeof(int64_t) atIndex:9]; - [encoder setBytes:&OH length:sizeof(int64_t) atIndex:10]; - [encoder setBytes:&OW length:sizeof(int64_t) atIndex:11]; - [encoder setBytes:¶llel_elements length:sizeof(int64_t) atIndex:12]; - - [encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; - } break; - default: - { - GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); - GGML_ABORT("fatal error"); - } - } -} - -static enum ggml_status ggml_metal_graph_compute( - ggml_backend_t backend, - struct ggml_cgraph * gf) { - struct ggml_backend_metal_context * ctx = backend->context; - struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; - - // number of nodes encoded by the main thread (empirically determined) - const int n_main = 128; - - // number of threads in addition to the main thread - const int n_cb = ctx->n_cb; - - // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them - // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread - // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes - // each thread creates it's own command buffer and enqueues the ops in parallel - // - // tests on M1 Pro and M2 Ultra using LLaMA models, show that optimal values for n_cb are 1 or 2 - - @autoreleasepool { - ctx->gf = gf; - - ctx->n_nodes_0 = MIN(n_main, gf->n_nodes); - ctx->n_nodes_1 = gf->n_nodes - ctx->n_nodes_0; - - ctx->n_nodes_per_cb = (ctx->n_nodes_1 + ctx->n_cb - 1) / ctx->n_cb; - - const bool should_capture = ctx->capture_next_compute; - if (should_capture) { - ctx->capture_next_compute = false; - - if (!ctx->capture_started) { - // create capture scope - ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx_dev->mtl_device]; - - MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; - descriptor.captureObject = ctx->capture_scope; - descriptor.destination = MTLCaptureDestinationGPUTraceDocument; - descriptor.outputURL = [NSURL fileURLWithPath:[NSString stringWithFormat:@"/tmp/perf-metal.gputrace"]]; - - NSError * error = nil; - if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { - GGML_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]); - } else { - [ctx->capture_scope beginScope]; - ctx->capture_started = true; - } - } - } - - // the main thread commits the first few commands immediately - // command_buffer[n_cb] - { - id command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; - ctx->command_buffers[n_cb] = command_buffer; - - [command_buffer enqueue]; - ctx->encode_async(n_cb); - } - - // prepare the rest of the command buffers asynchronously - // command_buffer[0.. n_cb) - for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { - id command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; - ctx->command_buffers[cb_idx] = command_buffer; - - // always enqueue the first two command buffers - // enqueue all of the command buffers if we don't need to abort - if (cb_idx < 2 || ctx->abort_callback == NULL) { - [command_buffer enqueue]; - } - } - - dispatch_apply(n_cb, ctx->d_queue, ctx->encode_async); - - // wait for completion and check status of each command buffer - // needed to detect if the device ran out-of-memory for example (#1881) - { - id command_buffer = ctx->command_buffers[n_cb]; - [command_buffer waitUntilCompleted]; - - MTLCommandBufferStatus status = [command_buffer status]; - if (status != MTLCommandBufferStatusCompleted) { - GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status); - if (status == MTLCommandBufferStatusError) { - GGML_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]); - } - - return GGML_STATUS_FAILED; - } - } - - for (int i = 0; i < n_cb; ++i) { - id command_buffer = ctx->command_buffers[i]; - [command_buffer waitUntilCompleted]; - - MTLCommandBufferStatus status = [command_buffer status]; - if (status != MTLCommandBufferStatusCompleted) { - GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); - if (status == MTLCommandBufferStatusError) { - GGML_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]); - } - - return GGML_STATUS_FAILED; - } - - id next_buffer = (i + 1 < n_cb ? ctx->command_buffers[i + 1] : nil); - if (!next_buffer) { - continue; - } - - const bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued); - if (next_queued) { - continue; - } - - if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) { - GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i); - return GGML_STATUS_ABORTED; - } - - [next_buffer commit]; - } - - if (!should_capture && ctx->capture_started) { - [ctx->capture_scope endScope]; - [[MTLCaptureManager sharedCaptureManager] stopCapture]; - } - } - - return GGML_STATUS_SUCCESS; -} - -//////////////////////////////////////////////////////////////////////////////// - -// backend interface - -static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { - struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; - - for (int i = 0; i < ctx->n_buffers; i++) { - [ctx->buffers[i].metal release]; - } - ggml_backend_metal_device_rel(buffer->buft->device->context); - - if (ctx->owned) { -#if TARGET_OS_OSX - vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ctx->all_data, ctx->all_size); -#else - free(ctx->all_data); -#endif - } - - free(ctx); -} - -static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { - struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; - - return ctx->all_data; -} - -static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - memcpy((char *)tensor->data + offset, data, size); - - UNUSED(buffer); -} - -static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - memcpy(data, (const char *)tensor->data + offset, size); - - UNUSED(buffer); -} - -static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { - if (ggml_backend_buffer_is_host(src->buffer)) { - memcpy(dst->data, src->data, ggml_nbytes(src)); - return true; - } - return false; - - UNUSED(buffer); -} - -static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; - - memset(ctx->all_data, value, ctx->all_size); -} - -static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { - /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, - /* .get_base = */ ggml_backend_metal_buffer_get_base, - /* .init_tensor = */ NULL, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_metal_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor, - /* .clear = */ ggml_backend_metal_buffer_clear, - /* .reset = */ NULL, -}; - -// default buffer type - -static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "Metal"; - - UNUSED(buft); -} - -static void ggml_backend_metal_log_allocated_size(id device, size_t size_aligned) { -#ifndef GGML_METAL_NDEBUG -#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) - if (@available(macOS 10.12, iOS 16.0, *)) { - GGML_LOG_DEBUG("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)\n", - __func__, - size_aligned / 1024.0 / 1024.0, - device.currentAllocatedSize / 1024.0 / 1024.0, - device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - - if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { - GGML_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); - } - } else { - GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n", - __func__, - size_aligned / 1024.0 / 1024.0, - device.currentAllocatedSize / 1024.0 / 1024.0); - } -#endif -#endif - UNUSED(device); - UNUSED(size_aligned); -} - -static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); - - const size_t size_page = sysconf(_SC_PAGESIZE); - - size_t size_aligned = size; - if ((size_aligned % size_page) != 0) { - size_aligned += (size_page - (size_aligned % size_page)); - } - - id device = ggml_backend_metal_device_acq(buft->device->context); - - ctx->all_data = ggml_metal_host_malloc(size_aligned); - ctx->all_size = size_aligned; - ctx->owned = true; - ctx->n_buffers = 1; - - if (ctx->all_data != NULL) { - ctx->buffers[0].data = ctx->all_data; - ctx->buffers[0].size = size; - ctx->buffers[0].metal = nil; - - if (size_aligned > 0) { - ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data - length:size_aligned - options:MTLResourceStorageModeShared - deallocator:nil]; - } - } - - if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) { - GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); - free(ctx); - ggml_backend_metal_device_rel(buft->device->context); - return NULL; - } - - //ggml_backend_metal_log_allocated_size(device, size_aligned); - - return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); -} - -static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 32; - UNUSED(buft); -} - -static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - id device = ggml_backend_metal_device_acq(buft->device->context); - const size_t max_size = device.maxBufferLength; - ggml_backend_metal_device_rel(buft->device->context); - - return max_size; - - UNUSED(buft); -} - -static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return true; - - UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = { - /* .iface = */ { - /* .get_name = */ ggml_backend_metal_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size, - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_metal_buffer_type_is_host, - }, - /* .device = */ &g_ggml_backend_metal_device, - /* .context = */ NULL, - }; - - return &ggml_backend_buffer_type_metal; -} - -static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { - return "Metal_Mapped"; - - UNUSED(buft); -} - -static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) { - static struct ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_metal = { - /* .iface = */ { - /* .get_name = */ ggml_backend_metal_buffer_from_ptr_type_get_name, - /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size, - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_metal_buffer_type_is_host, - }, - /* .device = */ &g_ggml_backend_metal_device, - /* .context = */ NULL, - }; - - return &ggml_backend_buffer_from_ptr_type_metal; -} - -// TODO: obsoleted by ggml_backend_metal_device_buffer_from_ptr -ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) { - struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); - - ctx->all_data = data; - ctx->all_size = size; - ctx->owned = false; - ctx->n_buffers = 0; - - const size_t size_page = sysconf(_SC_PAGESIZE); - - // page-align the data ptr - { - const uintptr_t offs = (uintptr_t) data % size_page; - data = (void *) ((char *) data - offs); - size += offs; - } - - size_t size_aligned = size; - if ((size_aligned % size_page) != 0) { - size_aligned += (size_page - (size_aligned % size_page)); - } - - id device = ggml_backend_metal_device_acq(&g_ggml_ctx_dev_main); - - // the buffer fits into the max buffer size allowed by the device - if (size_aligned <= device.maxBufferLength) { - ctx->buffers[ctx->n_buffers].data = data; - ctx->buffers[ctx->n_buffers].size = size; - ctx->buffers[ctx->n_buffers].metal = nil; - - if (size_aligned > 0) { - ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); - return false; - } - } - - ggml_backend_metal_log_allocated_size(device, size_aligned); - - ++ctx->n_buffers; - } else { - // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into - // one of the views - const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case - const size_t size_step = device.maxBufferLength - size_ovlp; - const size_t size_view = device.maxBufferLength; - - for (size_t i = 0; i < size; i += size_step) { - const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); - - ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i); - ctx->buffers[ctx->n_buffers].size = size_step_aligned; - ctx->buffers[ctx->n_buffers].metal = nil; - - if (size_step_aligned > 0) { - ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0); - return false; - } - } - - ggml_backend_metal_log_allocated_size(device, size_step_aligned); - - if (i + size_step < size) { - GGML_LOG_INFO("\n"); - } - - ++ctx->n_buffers; - } - } - - return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size); -} - -// backend - -static const char * ggml_backend_metal_name(ggml_backend_t backend) { - return "Metal"; - - UNUSED(backend); -} - -static void ggml_backend_metal_free(ggml_backend_t backend) { - struct ggml_backend_metal_context * ctx = backend->context; - struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; - - ggml_backend_metal_device_rel(ctx_dev); - ggml_metal_free(ctx); - - free(backend); -} - -static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - return ggml_metal_graph_compute(backend, cgraph); -} - -static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { - GGML_ASSERT(ggml_backend_is_metal(backend)); - - struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; - - if (ctx->n_cb != n_cb) { - ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS); - - if (ctx->n_cb > 2) { - GGML_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb); - } - } - - if (ctx->encode_async) { - Block_release(ctx->encode_async); - } - - ctx->encode_async = Block_copy(^(size_t iter) { - const int cb_idx = iter; - const int n_cb_l = ctx->n_cb; - - const int n_nodes_0 = ctx->n_nodes_0; - const int n_nodes_1 = ctx->n_nodes_1; - - const int n_nodes_per_cb = ctx->n_nodes_per_cb; - - id command_buffer = ctx->command_buffers[cb_idx]; - id encoder = [command_buffer computeCommandEncoder]; - - int node_start = 0; - int node_end = n_nodes_0; - - if (cb_idx < n_cb_l) { - node_start = n_nodes_0 + ( (cb_idx + 0) * n_nodes_per_cb); - node_end = n_nodes_0 + (MIN((cb_idx == n_cb_l - 1) ? n_nodes_1 : (cb_idx + 1) * n_nodes_per_cb, n_nodes_1)); - } - - const bool should_capture = ctx->capture_next_compute; - - for (int idx = node_start; idx < node_end; ++idx) { - if (should_capture) { - [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(ggml_graph_node(ctx->gf, idx)) encoding:NSUTF8StringEncoding]]; - } - - ggml_metal_encode_node(backend, idx, encoder); - - if (should_capture) { - [encoder popDebugGroup]; - } - } - - [encoder endEncoding]; - - if (cb_idx < 2 || ctx->abort_callback == NULL) { - [command_buffer commit]; - } - }); -} - -static struct ggml_backend_i ggml_backend_metal_i = { - /* .get_name = */ ggml_backend_metal_name, - /* .free = */ ggml_backend_metal_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_metal_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_metal_guid(void) { - static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 }; - return &guid; -} - -// TODO: remove in the future -ggml_backend_t ggml_backend_metal_init(void) { - ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_metal_reg(), 0); - - struct ggml_backend_metal_context * ctx = ggml_metal_init(dev); - if (ctx == NULL) { - GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); - return NULL; - } - - ggml_backend_t backend = malloc(sizeof(struct ggml_backend)); - - *backend = (struct ggml_backend) { - /* .guid = */ ggml_backend_metal_guid(), - /* .interface = */ ggml_backend_metal_i, - /* .device = */ dev, - /* .context = */ ctx, - }; - - ggml_backend_metal_set_n_cb(backend, 1); - - return backend; -} - -bool ggml_backend_is_metal(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid()); -} - -void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data) { - GGML_ASSERT(ggml_backend_is_metal(backend)); - - struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; - - ctx->abort_callback = abort_callback; - ctx->abort_callback_data = user_data; -} - -bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { - GGML_ASSERT(ggml_backend_is_metal(backend)); - - struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; - - return [ctx_dev->mtl_device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; -} - -void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) { - GGML_ASSERT(ggml_backend_is_metal(backend)); - - struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; - ctx->capture_next_compute = true; -} - -// backend device - -static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) { - return "Metal"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) { - // acq/rel just to populate ctx->name in case it hasn't been done yet - struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; - ggml_backend_metal_device_acq(ctx_dev); - ggml_backend_metal_device_rel(ctx_dev); - - return ctx_dev->name; -} - -static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - if (@available(macOS 10.12, iOS 16.0, *)) { - struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; - id device = ggml_backend_metal_device_acq(ctx_dev); - - *total = device.recommendedMaxWorkingSetSize; - *free = *total - device.currentAllocatedSize; - - ggml_backend_metal_device_rel(ctx_dev); - } else { - *free = 1; - *total = 1; - } -} - -static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_GPU; - - GGML_UNUSED(dev); -} - -static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_metal_device_get_name(dev); - props->description = ggml_backend_metal_device_get_description(dev); - props->type = ggml_backend_metal_device_get_type(dev); - ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = (struct ggml_backend_dev_caps) { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ true, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_metal_device_init(ggml_backend_dev_t dev, const char * params) { - struct ggml_backend_metal_context * ctx = ggml_metal_init(dev); - if (ctx == NULL) { - GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); - return NULL; - } - - ggml_backend_t backend = malloc(sizeof(struct ggml_backend)); - - *backend = (struct ggml_backend) { - /* .guid = */ ggml_backend_metal_guid(), - /* .interface = */ ggml_backend_metal_i, - /* .device = */ dev, - /* .context = */ ctx, - }; - - ggml_backend_metal_set_n_cb(backend, 1); - - return backend; - - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_metal_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_metal_buffer_type(); - - GGML_UNUSED(dev); -} - -static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); - - ctx->all_data = ptr; - ctx->all_size = size; - ctx->owned = false; - ctx->n_buffers = 0; - - const size_t size_page = sysconf(_SC_PAGESIZE); - - // page-align the data ptr - { - const uintptr_t offs = (uintptr_t) ptr % size_page; - ptr = (void *) ((char *) ptr - offs); - size += offs; - } - - size_t size_aligned = size; - if ((size_aligned % size_page) != 0) { - size_aligned += (size_page - (size_aligned % size_page)); - } - - struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; - id device = ggml_backend_metal_device_acq(ctx_dev); - - // the buffer fits into the max buffer size allowed by the device - if (size_aligned <= device.maxBufferLength) { - ctx->buffers[ctx->n_buffers].data = ptr; - ctx->buffers[ctx->n_buffers].size = size; - ctx->buffers[ctx->n_buffers].metal = nil; - - if (size_aligned > 0) { - ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); - return false; - } - } - - ggml_backend_metal_log_allocated_size(device, size_aligned); - - ++ctx->n_buffers; - } else { - // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into - // one of the views - const size_t size_ovlp = ((max_tensor_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case - const size_t size_step = device.maxBufferLength - size_ovlp; - const size_t size_view = device.maxBufferLength; - - for (size_t i = 0; i < size; i += size_step) { - const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); - - ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) ptr + i); - ctx->buffers[ctx->n_buffers].size = size_step_aligned; - ctx->buffers[ctx->n_buffers].metal = nil; - - if (size_step_aligned > 0) { - ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; - - if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0); - return false; - } - } - - ggml_backend_metal_log_allocated_size(device, size_step_aligned); - - if (i + size_step < size) { - GGML_LOG_INFO("\n"); - } - - ++ctx->n_buffers; - } - } - - return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size); -} - -static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - struct ggml_backend_metal_device_context * ctx_dev = dev->context; - - return ggml_metal_supports_op(ctx_dev, op); -} - -static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name || - buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name; - - UNUSED(dev); -} - -static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - return false; - - GGML_UNUSED(dev); - GGML_UNUSED(op); -} - -static struct ggml_backend_device_i ggml_backend_metal_device_i = { - /* .get_name = */ ggml_backend_metal_device_get_name, - /* .get_description = */ ggml_backend_metal_device_get_description, - /* .get_memory = */ ggml_backend_metal_device_get_memory, - /* .get_type = */ ggml_backend_metal_device_get_type, - /* .get_props = */ ggml_backend_metal_device_get_props, - /* .init_backend = */ ggml_backend_metal_device_init, - /* .get_buffer_type = */ ggml_backend_metal_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_metal_device_buffer_from_ptr, - /* .supports_op = */ ggml_backend_metal_device_supports_op, - /* .supports_buft = */ ggml_backend_metal_device_supports_buft, - /* .offload_op = */ ggml_backend_metal_device_offload_op, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// backend registry - -static const char * ggml_backend_metal_reg_get_name(ggml_backend_reg_t reg) { - return "Metal"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_metal_reg_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - return &g_ggml_backend_metal_device; - - GGML_UNUSED(reg); - GGML_UNUSED(index); -} - -static struct ggml_backend_reg_i ggml_backend_metal_reg_i = { - /* .get_name = */ ggml_backend_metal_reg_get_name, - /* .device_count = */ ggml_backend_metal_reg_device_count, - /* .device_get = */ ggml_backend_metal_reg_device_get, - /* .get_proc_address = */ NULL, -}; - -ggml_backend_reg_t ggml_backend_metal_reg(void) { - // TODO: make this thread-safe somehow? - { - g_ggml_backend_metal_reg = (struct ggml_backend_reg) { - /* .iface = */ ggml_backend_metal_reg_i, - /* .context = */ NULL, - }; - - g_ggml_backend_metal_device = (struct ggml_backend_device) { - /* .iface = */ ggml_backend_metal_device_i, - /* .reg = */ &g_ggml_backend_metal_reg, - /* .context = */ &g_ggml_ctx_dev_main, - }; - } - - return &g_ggml_backend_metal_reg; -} diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal deleted file mode 100644 index e8b71a9f..00000000 --- a/ggml/src/ggml-metal.metal +++ /dev/null @@ -1,7064 +0,0 @@ -#define GGML_COMMON_DECL_METAL -#define GGML_COMMON_IMPL_METAL -#include "ggml-common.h" - -#include - -using namespace metal; - -#define MAX(x, y) ((x) > (y) ? (x) : (y)) -#define MIN(x, y) ((x) < (y) ? (x) : (y)) -#define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; } - -#define N_SIMDWIDTH 32 // assuming SIMD group size is 32 - -// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf -// -// cmd: -// .../usr/bin/metal -dM -E -c ggml/src/ggml-metal.metal -// .../usr/bin/metal -dM -E -c -target air64-apple-ios14.0 ggml/src/ggml-metal.metal -// -#if __METAL_VERSION__ < 310 && defined(GGML_METAL_USE_BF16) -#undef GGML_METAL_USE_BF16 -#endif - -#if defined(GGML_METAL_USE_BF16) -typedef matrix bfloat4x4; -#endif - -constexpr constant static float kvalues_iq4nl_f[16] = { - -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f -}; - -// NOTE: this is not dequantizing - we are simply fitting the template -template -void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) { - reg = (type4x4)(*src); -} - -template -void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) { - reg = (type4x4)(*src); -} - -#if defined(GGML_METAL_USE_BF16) -template -void dequantize_bf16(device const bfloat4x4 * src, short il, thread type4x4 & reg) { - reg = (type4x4)(*src); -} -#endif - -template -void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 1); - const float d1 = il ? (xb->d / 16.h) : xb->d; - const float d2 = d1 / 256.f; - const float md = -8.h * xb->d; - const ushort mask0 = il ? 0x00F0 : 0x000F; - const ushort mask1 = mask0 << 8; - - float4x4 reg_f; - - for (int i = 0; i < 8; i++) { - reg_f[i/2][2*(i%2) + 0] = d1 * (qs[i] & mask0) + md; - reg_f[i/2][2*(i%2) + 1] = d2 * (qs[i] & mask1) + md; - } - - reg = (type4x4) reg_f; -} - -template -void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 2); - const float d1 = il ? (xb->d / 16.h) : xb->d; - const float d2 = d1 / 256.f; - const float m = xb->m; - const ushort mask0 = il ? 0x00F0 : 0x000F; - const ushort mask1 = mask0 << 8; - - float4x4 reg_f; - - for (int i = 0; i < 8; i++) { - reg_f[i/2][2*(i%2) + 0] = ((qs[i] & mask0) * d1) + m; - reg_f[i/2][2*(i%2) + 1] = ((qs[i] & mask1) * d2) + m; - } - - reg = (type4x4) reg_f; -} - -template -void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 3); - const float d = xb->d; - const float md = -16.h * xb->d; - const ushort mask = il ? 0x00F0 : 0x000F; - - const uint32_t qh = *((device const uint32_t *)xb->qh); - - const int x_mv = il ? 4 : 0; - - const int gh_mv = il ? 12 : 0; - const int gh_bk = il ? 0 : 4; - - float4x4 reg_f; - - for (int i = 0; i < 8; i++) { - // extract the 5-th bits for x0 and x1 - const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; - const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; - - // combine the 4-bits from qs with the 5th bit - const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); - const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); - - reg_f[i/2][2*(i%2) + 0] = d * x0 + md; - reg_f[i/2][2*(i%2) + 1] = d * x1 + md; - } - - reg = (type4x4) reg_f; -} - -template -void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 4); - const float d = xb->d; - const float m = xb->m; - const ushort mask = il ? 0x00F0 : 0x000F; - - const uint32_t qh = *((device const uint32_t *)xb->qh); - - const int x_mv = il ? 4 : 0; - - const int gh_mv = il ? 12 : 0; - const int gh_bk = il ? 0 : 4; - - float4x4 reg_f; - - for (int i = 0; i < 8; i++) { - // extract the 5-th bits for x0 and x1 - const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; - const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; - - // combine the 4-bits from qs with the 5th bit - const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); - const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); - - reg_f[i/2][2*(i%2) + 0] = d * x0 + m; - reg_f[i/2][2*(i%2) + 1] = d * x1 + m; - } - - reg = (type4x4) reg_f; -} - -template -void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) { - device const int8_t * qs = ((device const int8_t *)xb->qs); - const half d = xb->d; - - float4x4 reg_f; - - for (int i = 0; i < 16; i++) { - reg_f[i/4][i%4] = (qs[i + 16*il] * d); - } - - reg = (type4x4) reg_f; -} - -template -void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) { - const float d = xb->d; - const float min = xb->dmin; - device const uint8_t * q = (device const uint8_t *)xb->qs; - float dl, ml; - uint8_t sc = xb->scales[il]; - - q = q + 32*(il/8) + 16*(il&1); - il = (il/2)%4; - - half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - ml; - } -} - -template -void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) { - const half d_all = xb->d; - device const uint8_t * q = (device const uint8_t *)xb->qs; - device const uint8_t * h = (device const uint8_t *)xb->hmask; - device const int8_t * scales = (device const int8_t *)xb->scales; - - q = q + 32 * (il/8) + 16 * (il&1); - h = h + 16 * (il&1); - uint8_t m = 1 << (il/2); - uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \ - ((il/4)>0 ? 12 : 3); - uint16_t kmask2 = il/8 ? 0xF0 : 0x0F; - uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4]; - int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) - : (scale_2&kmask2) | ((scale_1&kmask1) << 4); - float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f); - const float ml = 4.f * dl; - - il = (il/2) & 3; - const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - dl *= coef; - - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); - } -} - -static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { - return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)} - : uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))}; -} - -template -void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { - device const uchar * q = xb->qs; - - short is = (il/4) * 2; - q = q + (il/4) * 32 + 16 * (il&1); - il = il & 3; - const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.h; - const float min = xb->dmin; - const float dl = d * sc[0]; - const float ml = min * sc[1]; - - const ushort mask = il<2 ? 0x0F : 0xF0; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - ml; - } -} - -template -void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) { - device const uint8_t * q = xb->qs; - device const uint8_t * qh = xb->qh; - - short is = (il/4) * 2; - q = q + 32 * (il/4) + 16 * (il&1); - qh = qh + 16 * (il&1); - uint8_t ul = 1 << (il/2); - il = il & 3; - const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.f; - const float min = xb->dmin; - const float dl = d * sc[0]; - const float ml = min * sc[1]; - - const ushort mask = il<2 ? 0x0F : 0xF0; - const float qh_val = il<2 ? 16.f : 256.f; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; - } -} - -template -void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) { - const half d_all = xb->d; - device const uint8_t * ql = (device const uint8_t *)xb->ql; - device const uint8_t * qh = (device const uint8_t *)xb->qh; - device const int8_t * scales = (device const int8_t *)xb->scales; - - ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); - qh = qh + 32*(il/8) + 16*(il&1); - float sc = scales[(il%2) + 2 * ((il/2))]; - il = (il/2) & 3; - - const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; - const float coef = il>1 ? 1.f/16.f : 1.f; - const float ml = d_all * sc * 32.f; - const float dl = d_all * sc * coef; - for (int i = 0; i < 16; ++i) { - const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2)) - : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4)); - reg[i/4][i%4] = dl * q - ml; - } -} - -template -void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - // each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's. - device const uint16_t * q2 = xb->qs + 4*ib32; - const uint32_t aux32_g = q2[0] | (q2[1] << 16); - const uint32_t aux32_s = q2[2] | (q2[3] << 16); - thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; - const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]); - uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; - for (int i = 0; i < 8; ++i) { - reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } - grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]); - signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; - for (int i = 0; i < 8; ++i) { - reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } -} - -template -void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint16_t * q2 = xb->qs + 4*ib32; - const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511)); - uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9]; - for (int i = 0; i < 8; ++i) { - reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } - grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511)); - signs = ksigns_iq2xs[q2[2*il+1] >> 9]; - for (int i = 0; i < 8; ++i) { - reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } -} - -template -void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * q3 = xb->qs + 8*ib32; - device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32; - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f; - constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]); - constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]); - uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127]; - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); - reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); - } - grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]); - grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]); - signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127]; - for (int i = 0; i < 4; ++i) { - reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); - reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); - } -} - -template -void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * qs = xb->qs + 8*ib32; - device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; - const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); - constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); - constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); - reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); - } - grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); - grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); - for (int i = 0; i < 4; ++i) { - reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); - reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); - } -} - -template -void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint8_t * signs = qs + QK_K/8; - const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; - constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); - constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); - for (int i = 0; i < 8; ++i) { - reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); - reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); - } -} - -template -void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - const float d = xb->d; - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint16_t * qh = xb->qh; - const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1); - const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA); - const uint16_t h = qh[ib32] >> 6*il; - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) + ml; - reg[1][i] = dl * (grid1[i] >> 4) + ml; - reg[2][i] = dl * (grid2[i] & 0xf) + ml; - reg[3][i] = dl * (grid2[i] >> 4) + ml; - } -} - -template -void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - device const uint16_t * sc = (device const uint16_t *)xb->scales; - - iq1m_scale_t scale; - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const float d = scale.f16; - - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint8_t * qh = xb->qh + 2*ib32 + il; - - const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); - const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) + ml1; - reg[1][i] = dl * (grid1[i] >> 4) + ml1; - reg[2][i] = dl * (grid2[i] & 0xf) + ml2; - reg[3][i] = dl * (grid2[i] >> 4) + ml2; - } -} - -template -void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { - device const uint16_t * q4 = (device const uint16_t *)xb->qs; - const float d = xb->d; - uint32_t aux32; - thread const uint8_t * q8 = (thread const uint8_t *)&aux32; - for (int i = 0; i < 4; ++i) { - aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f; - reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; - reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; - reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; - reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; - } -} - -template -void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; - const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); - const float d = (float)xb->d * (ls - 32); - uint32_t aux32; - thread const uint8_t * q8 = (thread const uint8_t *)&aux32; - for (int i = 0; i < 4; ++i) { - aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; - reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; - reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; - reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; - reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; - } -} - -enum ggml_sort_order { - GGML_SORT_ORDER_ASC, - GGML_SORT_ORDER_DESC, -}; - -// general-purpose kernel for addition, subtraction, multiplication and division of two tensors -// pros: works for non-contiguous tensors, supports broadcast across all dims -// cons: not very efficient -kernel void kernel_add( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int64_t & offs, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) + *((device float *)(src1_ptr + i10*nb10)); - } -} - -kernel void kernel_sub( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int64_t & offs, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) - *((device float *)(src1_ptr + i10*nb10)); - } -} - -kernel void kernel_mul( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) * *((device float *)(src1_ptr + i10*nb10)); - } -} - -kernel void kernel_div( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) / *((device float *)(src1_ptr + i10*nb10)); - } -} - -template -kernel void kernel_repeat( - device const char * src0, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; - - const int64_t i03 = i3 % ne03; - const int64_t i02 = i2 % ne02; - const int64_t i01 = i1 % ne01; - - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i00 = i0 % ne00; - *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00)); - } -} - -typedef decltype(kernel_repeat) kernel_repeat_t; - -template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat; -template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat; -template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat; -template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat; - -// assumption: src1 is a row -// broadcast src1 into src0 -kernel void kernel_add_row( - device const float4 * src0, - device const float4 * src1, - device float4 * dst, - constant uint64_t & nb [[buffer(28)]], - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] + src1[tpig % nb]; -} - -kernel void kernel_sub_row( - device const float4 * src0, - device const float4 * src1, - device float4 * dst, - constant uint64_t & nb [[buffer(28)]], - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] - src1[tpig % nb]; -} - -kernel void kernel_mul_row( - device const float4 * src0, - device const float4 * src1, - device float4 * dst, - constant uint64_t & nb [[buffer(28)]], - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] * src1[tpig % nb]; -} - -kernel void kernel_div_row( - device const float4 * src0, - device const float4 * src1, - device float4 * dst, - constant uint64_t & nb [[buffer(28)]], - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] / src1[tpig % nb]; -} - -kernel void kernel_scale( - device const float * src0, - device float * dst, - constant float & scale, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] * scale; -} - -kernel void kernel_scale_4( - device const float4 * src0, - device float4 * dst, - constant float & scale, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] * scale; -} - -kernel void kernel_clamp( - device const float * src0, - device float * dst, - constant float & min, - constant float & max, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] < min ? min : (src0[tpig] > max ? max : src0[tpig]); -} - -kernel void kernel_relu( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = max(0.0f, src0[tpig]); -} - -kernel void kernel_sigmoid( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig])); -} - -kernel void kernel_tanh( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - device const float & x = src0[tpig]; - dst[tpig] = precise::tanh(x); -} - -constant float GELU_COEF_A = 0.044715f; -constant float GELU_QUICK_COEF = -1.702f; -constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - -kernel void kernel_gelu( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - device const float & x = src0[tpig]; - - dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); -} - -kernel void kernel_gelu_4( - device const float4 * src0, - device float4 * dst, - uint tpig[[thread_position_in_grid]]) { - device const float4 & x = src0[tpig]; - - // BEWARE !!! - // Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs! - // This was observed with Falcon 7B and 40B models - // - dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); -} - -kernel void kernel_gelu_quick( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - device const float & x = src0[tpig]; - - dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x))); -} - -kernel void kernel_gelu_quick_4( - device const float4 * src0, - device float4 * dst, - uint tpig[[thread_position_in_grid]]) { - device const float4 & x = src0[tpig]; - - dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x))); -} - -kernel void kernel_silu( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - device const float & x = src0[tpig]; - dst[tpig] = x / (1.0f + exp(-x)); -} - -kernel void kernel_silu_4( - device const float4 * src0, - device float4 * dst, - uint tpig[[thread_position_in_grid]]) { - device const float4 & x = src0[tpig]; - dst[tpig] = x / (1.0f + exp(-x)); -} - -kernel void kernel_sqr( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] * src0[tpig]; -} - -kernel void kernel_sqrt( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = sqrt(src0[tpig]); -} - -kernel void kernel_sin( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = sin(src0[tpig]); -} - -kernel void kernel_cos( - device const float * src0, - device float * dst, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = cos(src0[tpig]); -} - -kernel void kernel_sum_rows( - device const float * src0, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tpig[[thread_position_in_grid]]) { - int64_t i3 = tpig.z; - int64_t i2 = tpig.y; - int64_t i1 = tpig.x; - - if (i3 >= ne03 || i2 >= ne02 || i1 >= ne01) { - return; - } - - device const float * src_row = (device const float *) ((device const char *) src0 + i1*nb01 + i2*nb02 + i3*nb03); - device float * dst_row = (device float *) ((device char *) dst + i1*nb1 + i2*nb2 + i3*nb3); - - float row_sum = 0; - - for (int64_t i0 = 0; i0 < ne00; i0++) { - row_sum += src_row[i0]; - } - - dst_row[0] = row_sum; -} - -template -kernel void kernel_soft_max( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - threadgroup float * buf [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint ntg[[threads_per_threadgroup]]) { - const int64_t i03 = (tgpig) / (ne02*ne01); - const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01; - const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01); - - device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); - device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00 : nullptr; - device float * pdst = (device float *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); - - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const int64_t h = i02; - - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = pow(base, exp); - } - - // parallel max - float lmax = -INFINITY; - - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)); - } - - // find the max value in the block - float max_val = simd_max(lmax); - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = -INFINITY; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = max_val; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - max_val = buf[tiisg]; - max_val = simd_max(max_val); - } - - // parallel sum - float lsum = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val); - lsum += exp_psrc0; - pdst[i00] = exp_psrc0; - } - - // This barrier fixes a failing test - // ref: https://github.com/ggerganov/ggml/pull/621#discussion_r1425156335 - threadgroup_barrier(mem_flags::mem_none); - - float sum = simd_sum(lsum); - - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = 0.0f; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = sum; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - sum = buf[tiisg]; - sum = simd_sum(sum); - } - - const float inv_sum = 1.0f/sum; - - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - pdst[i00] *= inv_sum; - } -} - -template -kernel void kernel_soft_max_4( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - threadgroup float * buf [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint ntg[[threads_per_threadgroup]]) { - const int64_t i03 = (tgpig) / (ne02*ne01); - const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01; - const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01); - - device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; - device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00/4 : nullptr; - device float4 * pdst4 = (device float4 *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; - - float slope = 1.0f; - - if (max_bias > 0.0f) { - const int64_t h = i02; - - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = pow(base, exp); - } - - // parallel max - float4 lmax4 = -INFINITY; - - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))); - } - - const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); - - float max_val = simd_max(lmax); - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = -INFINITY; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = max_val; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - max_val = buf[tiisg]; - max_val = simd_max(max_val); - } - - // parallel sum - float4 lsum4 = 0.0f; - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val); - lsum4 += exp_psrc4; - pdst4[i00] = exp_psrc4; - } - - const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3]; - - // This barrier fixes a failing test - // ref: https://github.com/ggerganov/ggml/pull/621#discussion_r1425156335 - threadgroup_barrier(mem_flags::mem_none); - - float sum = simd_sum(lsum); - - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = 0.0f; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = sum; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - sum = buf[tiisg]; - sum = simd_sum(sum); - } - - const float inv_sum = 1.0f/sum; - - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - pdst4[i00] *= inv_sum; - } -} - -typedef decltype(kernel_soft_max) kernel_soft_max_t; -typedef decltype(kernel_soft_max_4) kernel_soft_max_4_t; - -template [[host_name("kernel_soft_max_f16")]] kernel kernel_soft_max_t kernel_soft_max; -template [[host_name("kernel_soft_max_f32")]] kernel kernel_soft_max_t kernel_soft_max; -template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4; -template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4; - -kernel void kernel_diag_mask_inf( - device const float * src0, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int & n_past, - uint3 tpig[[thread_position_in_grid]]) { - const int64_t i02 = tpig[2]; - const int64_t i01 = tpig[1]; - const int64_t i00 = tpig[0]; - - if (i00 > n_past + i01) { - dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY; - } else { - dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00]; - } -} - -kernel void kernel_diag_mask_inf_8( - device const float4 * src0, - device float4 * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int & n_past, - uint3 tpig[[thread_position_in_grid]]) { - - const int64_t i = 2*tpig[0]; - - dst[i+0] = src0[i+0]; - dst[i+1] = src0[i+1]; - int64_t i4 = 4*i; - const int64_t i02 = i4/(ne00*ne01); i4 -= i02*ne00*ne01; - const int64_t i01 = i4/(ne00); i4 -= i01*ne00; - const int64_t i00 = i4; - for (int k = 3; k >= 0; --k) { - if (i00 + 4 + k <= n_past + i01) { - break; - } - dst[i+1][k] = -INFINITY; - if (i00 + k > n_past + i01) { - dst[i][k] = -INFINITY; - } - } -} - -// ref: ggml.c:ggml_compute_forward_ssm_conv_f32 -// TODO: optimize -kernel void kernel_ssm_conv_f32( - device const void * src0, - device const void * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t ir = tgpig.x; - const int64_t i2 = tgpig.y; - const int64_t i3 = tgpig.z; - - const int64_t nc = ne10; - //const int64_t ncs = ne00; - //const int64_t nr = ne01; - //const int64_t n_t = ne1; - //const int64_t n_s = ne2; - - device const float * s = (device const float *) ((device const char *) src0 + ir*nb01 + i2*nb00 + i3*nb02); - device const float * c = (device const float *) ((device const char *) src1 + ir*nb11); - device float * x = (device float *) ((device char *) dst + ir*nb0 + i2*nb1 + i3*nb2); - - float sumf = 0.0f; - - for (int64_t i0 = 0; i0 < nc; ++i0) { - sumf += s[i0] * c[i0]; - } - - x[0] = sumf; -} - -// ref: ggml.c:ggml_compute_forward_ssm_scan_f32 -// TODO: optimize -kernel void kernel_ssm_scan_f32( - device const void * src0, - device const void * src1, - device const void * src2, - device const void * src3, - device const void * src4, - device const void * src5, - device float * dst, - constant int64_t & d_state, - constant int64_t & d_inner, - constant int64_t & n_seq_tokens, - constant int64_t & n_seqs, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb20, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb30, - constant uint64_t & nb31, - constant uint64_t & nb40, - constant uint64_t & nb41, - constant uint64_t & nb42, - constant uint64_t & nb50, - constant uint64_t & nb51, - constant uint64_t & nb52, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t ir = tgpig.x; - const int64_t i3 = tgpig.y; - - const int64_t nc = d_state; - //const int64_t nr = d_inner; - const int64_t n_t = n_seq_tokens; - //const int64_t n_s = n_seqs; - - for (int64_t i2 = 0; i2 < n_t; ++i2) { - device const float * s0 = (device const float *) ((device const char *) src0 + ir*nb01 + i3*nb02); - device const float * x = (device const float *) ((device const char *) src1 + ir*nb10 + i2*nb11 + i3*nb12); - device const float * dt = (device const float *) ((device const char *) src2 + ir*nb20 + i2*nb21 + i3*nb22); - device const float * A = (device const float *) ((device const char *) src3 + ir*nb31); - device const float * B = (device const float *) ((device const char *) src4 + i2*nb41 + i3*nb42); - device const float * C = (device const float *) ((device const char *) src5 + i2*nb51 + i3*nb52); - device float * y = (device float *) ((device char *) dst + ir*nb10 + i2*nb11 + i3*nb12); // TODO: do not use src1 strides - device float * s = (device float *) ((device char *) dst + ir*nb01 + i3*nb02 + nb13); - - if (i2 > 0) { - s0 = s; - } - - // i1 == 0 - float dt_soft_plus = dt[0] <= 20.0f ? log(1.0f + exp(dt[0])) : dt[0]; - float x_dt = x[0] * dt_soft_plus; - float sumf = 0.0f; - - for (int64_t i0 = 0; i0 < nc; ++i0) { - int64_t i = i0; - float state = (s0[i] * exp(dt_soft_plus * A[i])) + (B[i0] * x_dt); - sumf += state * C[i0]; - s[i] = state; - } - - y[0] = sumf; - } -} - -kernel void kernel_norm( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant float & eps, - threadgroup float * sum [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint ntg[[threads_per_threadgroup]]) { - device const float * x = (device const float *) ((device const char *) src0 + tgpig*nb01); - // MEAN - // parallel sum - sum[tpitg] = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - sum[tpitg] += x[i00]; - } - // reduce - threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = ntg/2; i > 0; i /= 2) { - if (tpitg < i) { - sum[tpitg] += sum[tpitg + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - const float mean = sum[0] / ne00; - - // recenter and VARIANCE - threadgroup_barrier(mem_flags::mem_threadgroup); - device float * y = dst + tgpig*ne00; - sum[tpitg] = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = x[i00] - mean; - sum[tpitg] += y[i00] * y[i00]; - } - - // reduce - threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = ntg/2; i > 0; i /= 2) { - if (tpitg < i) { - sum[tpitg] += sum[tpitg + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - const float variance = sum[0] / ne00; - - const float scale = 1.0f/sqrt(variance + eps); - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = y[i00] * scale; - } -} - -kernel void kernel_rms_norm( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant float & eps, - threadgroup float * buf [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint ntg[[threads_per_threadgroup]]) { - device const float4 * x = (device const float4 *) ((device const char *) src0 + tgpig*nb01); - - float4 sumf = 0; - float all_sum = 0; - - // parallel sum - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - sumf += x[i00] * x[i00]; - } - all_sum = sumf[0] + sumf[1] + sumf[2] + sumf[3]; - all_sum = simd_sum(all_sum); - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = 0.0f; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = all_sum; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - all_sum = buf[tiisg]; - all_sum = simd_sum(all_sum); - } - - const float mean = all_sum/ne00; - const float scale = 1.0f/sqrt(mean + eps); - - device float4 * y = (device float4 *) (dst + tgpig*ne00); - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - y[i00] = x[i00] * scale; - } -} - -kernel void kernel_group_norm( - device const float * src0, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int32_t & n_groups, - constant float & eps, - threadgroup float * buf [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint ntg[[threads_per_threadgroup]]) { - const int64_t ne = ne00*ne01*ne02; - const int64_t gs = ne00*ne01*((ne02 + n_groups - 1) / n_groups); - - int start = tgpig * gs; - int end = start + gs; - - start += tpitg; - - if (end >= ne) { - end = ne; - } - - float tmp = 0.0f; // partial sum for thread in warp - - for (int j = start; j < end; j += ntg) { - tmp += src0[j]; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - tmp = simd_sum(tmp); - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = 0.0f; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = tmp; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - tmp = buf[tiisg]; - tmp = simd_sum(tmp); - } - - const float mean = tmp / gs; - tmp = 0.0f; - - for (int j = start; j < end; j += ntg) { - float xi = src0[j] - mean; - dst[j] = xi; - tmp += xi * xi; - } - - tmp = simd_sum(tmp); - if (ntg > N_SIMDWIDTH) { - if (sgitg == 0) { - buf[tiisg] = 0.0f; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (tiisg == 0) { - buf[sgitg] = tmp; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - tmp = buf[tiisg]; - tmp = simd_sum(tmp); - } - - const float variance = tmp / gs; - const float scale = 1.0f/sqrt(variance + eps); - for (int j = start; j < end; j += ntg) { - dst[j] *= scale; - } -} - -// function for calculate inner product between half a q4_0 block and 16 floats (yl), sumy is SUM(yl[i]) -// il indicates where the q4 quants begin (0 or QK4_0/4) -// we assume that the yl's have been multiplied with the appropriate scale factor -// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) -inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) { - float d = qb_curr->d; - - float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - - device const uint16_t * qs = ((device const uint16_t *) qb_curr + 1 + il/2); - - for (int i = 0; i < 8; i += 2) { - acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F); - acc[1] += yl[i + 1] * (qs[i / 2] & 0x0F00); - acc[2] += yl[i + 8] * (qs[i / 2] & 0x00F0); - acc[3] += yl[i + 9] * (qs[i / 2] & 0xF000); - } - - return d * (sumy * -8.f + acc[0] + acc[1] + acc[2] + acc[3]); -} - -// function for calculate inner product between half a q4_1 block and 16 floats (yl), sumy is SUM(yl[i]) -// il indicates where the q4 quants begin (0 or QK4_0/4) -// we assume that the yl's have been multiplied with the appropriate scale factor -// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) -inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thread float * yl, int il) { - float d = qb_curr->d; - float m = qb_curr->m; - - float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - - device const uint16_t * qs = ((device const uint16_t *) qb_curr + 2 + il/2); - - for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F); - acc[1] += yl[i + 1] * (qs[i / 2] & 0x0F00); - acc[2] += yl[i + 8] * (qs[i / 2] & 0x00F0); - acc[3] += yl[i + 9] * (qs[i / 2] & 0xF000); - } - - return d * (acc[0] + acc[1] + acc[2] + acc[3]) + sumy * m; -} - -// function for calculate inner product between half a q5_0 block and 16 floats (yl), sumy is SUM(yl[i]) -// il indicates where the q5 quants begin (0 or QK5_0/4) -// we assume that the yl's have been multiplied with the appropriate scale factor -// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) -inline float block_q_n_dot_y(device const block_q5_0 * qb_curr, float sumy, thread float * yl, int il) { - float d = qb_curr->d; - - float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 3 + il/2); - const uint32_t qh = *((device const uint32_t *)qb_curr->qh); - - for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)); - acc[1] += yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); - acc[2] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)); - acc[3] += yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); - } - - return d * (sumy * -16.f + acc[0] + acc[1] + acc[2] + acc[3]); -} - -// function for calculate inner product between half a q5_1 block and 16 floats (yl), sumy is SUM(yl[i]) -// il indicates where the q5 quants begin (0 or QK5_1/4) -// we assume that the yl's have been multiplied with the appropriate scale factor -// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096) -inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thread float * yl, int il) { - float d = qb_curr->d; - float m = qb_curr->m; - - float acc[4] = { 0.0f, 0.0f, 0.0f, 0.0f }; - - device const uint16_t * qs = ((device const uint16_t *)qb_curr + 4 + il/2); - const uint32_t qh = *((device const uint32_t *)qb_curr->qh); - - for (int i = 0; i < 8; i+=2) { - acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il ) << 4 ) & 0x00010)); - acc[1] += yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il ) << 12) & 0x01000)); - acc[2] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100)); - acc[3] += yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000)); - } - - return d * (acc[0] + acc[1] + acc[2] + acc[3]) + sumy * m; -} - -// putting them in the kernel cause a significant performance penalty -#define N_DST 4 // each SIMD group works on 4 rows -#define N_SIMDGROUP 2 // number of SIMD groups in a thread group -//Note: This is a template, but strictly speaking it only applies to -// quantizations where the block size is 32. It also does not -// guard against the number of rows not being divisible by -// N_DST, so this is another explicit assumption of the implementation. -template -void mul_vec_q_n_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - const int nb = ne00/QK4_0; - - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * nsg + sgitg) * nr; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - //const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - //device const block_q_type * x = (device const block_q_type *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - // pointers to src0 rows - device const block_q_type * ax[nr]; - for (int row = 0; row < nr; ++row) { - const uint offset0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - - ax[row] = (device const block_q_type *) ((device char *) src0 + offset0); - } - - float yl[16]; // src1 vector cache - float sumf[nr] = {0.f}; - - const int ix = (tiisg/2); - const int il = (tiisg%2)*8; - - device const float * yb = y + ix * QK4_0 + il; - - // each thread in a SIMD group deals with half a block. - for (int ib = ix; ib < nb; ib += nw/2) { - float sumy[2] = { 0.f, 0.f }; - -#pragma unroll - for (int i = 0; i < 8; i += 2) { - sumy[0] += yb[i + 0] + yb[i + 1]; - yl[i + 0] = yb[i + 0]; - yl[i + 1] = yb[i + 1]/256.f; - - sumy[1] += yb[i + 16] + yb[i + 17]; - yl[i + 8] = yb[i + 16]/16.f; - yl[i + 9] = yb[i + 17]/4096.f; - } - -#pragma unroll - for (int row = 0; row < nr; row++) { - sumf[row] += block_q_n_dot_y(ax[row] + ib, sumy[0] + sumy[1], yl, il); - } - - yb += QK4_0 * 16; - } - - for (int row = 0; row < nr; ++row) { - const float tot = simd_sum(sumf[row]); - if (tiisg == 0 && first_row + row < ne01) { - dst[im*ne0*ne1 + r1*ne0 + first_row + row] = tot; - } - } -} - -kernel void kernel_mul_mv_q4_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); -} - -kernel void kernel_mul_mv_q4_1_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); -} - -kernel void kernel_mul_mv_q5_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); -} - -kernel void kernel_mul_mv_q5_1_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); -} - - -#define NB_Q8_0 8 - -void kernel_mul_mv_q8_0_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - const int nr = N_DST; - const int nsg = N_SIMDGROUP; - const int nw = N_SIMDWIDTH; - - const int nb = ne00/QK8_0; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * nsg + sgitg) * nr; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - //const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - //device const block_q8_0 * x = (device const block_q8_0 *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - // pointers to src0 rows - device const block_q8_0 * ax[nr]; - for (int row = 0; row < nr; ++row) { - const uint offset0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - - ax[row] = (device const block_q8_0 *) ((device char *) src0 + offset0); - } - - float yl[NB_Q8_0]; - float sumf[nr]={0.f}; - - const int ix = tiisg/4; - const int il = tiisg%4; - - device const float * yb = y + ix * QK8_0 + NB_Q8_0*il; - - // each thread in a SIMD group deals with NB_Q8_0 quants at a time - for (int ib = ix; ib < nb; ib += nw/4) { - for (int i = 0; i < NB_Q8_0; ++i) { - yl[i] = yb[i]; - } - - for (int row = 0; row < nr; row++) { - device const int8_t * qs = ax[row][ib].qs + NB_Q8_0*il; - float sumq = 0.f; - for (int iq = 0; iq < NB_Q8_0; ++iq) { - sumq += qs[iq] * yl[iq]; - } - sumf[row] += sumq*ax[row][ib].d; - } - - yb += NB_Q8_0 * nw; - } - - for (int row = 0; row < nr; ++row) { - const float tot = simd_sum(sumf[row]); - if (tiisg == 0 && first_row + row < ne01) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot; - } - } -} - -[[host_name("kernel_mul_mv_q8_0_f32")]] -kernel void kernel_mul_mv_q8_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); -} - -#define N_MV_T_T 4 - -template -void kernel_mul_mv_impl( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - uint3 tgpig, - uint tiisg) { - const int64_t r0 = tgpig.x; - const int64_t rb = tgpig.y*N_MV_T_T; - const int64_t im = tgpig.z; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - - device const T0 * x = (device const T0 *) (src0 + offset0); - - if (ne00 < 128) { - for (int row = 0; row < N_MV_T_T; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const T1 * y = (device const T1 *) (src1 + offset1); - - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (T0) x[i] * (T1) y[i]; - } - - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } - } else { - device const T04 * x4 = (device const T04 *) x; - for (int row = 0; row < N_MV_T_T; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const T1 * y = (device const T1 *) (src1 + offset1); - device const T14 * y4 = (device const T14 *) y; - - float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); - } - - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]); - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } - } -} - -template -kernel void kernel_mul_mv( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { - kernel_mul_mv_impl( - src0, - src1, - dst, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - nb03, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - nb13, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg); -} - -typedef decltype(kernel_mul_mv) mul_mv_t; - -template [[host_name("kernel_mul_mv_f32_f32")]] kernel mul_mv_t kernel_mul_mv; -template [[host_name("kernel_mul_mv_f16_f32")]] kernel mul_mv_t kernel_mul_mv; -template [[host_name("kernel_mul_mv_f16_f16")]] kernel mul_mv_t kernel_mul_mv; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_mul_mv_bf16_f32")]] kernel mul_mv_t kernel_mul_mv; -template [[host_name("kernel_mul_mv_bf16_bf16")]] kernel mul_mv_t kernel_mul_mv; -#endif - -template -kernel void kernel_mul_mv_1row( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const T * x = (device const T *) (src0 + offset0); - device const float * y = (device const float *) (src1 + offset1); - - float sumf = 0; - if (ne00 < 128) { - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } else { - device const T4 * x4 = (device const T4 *) x; - device const float4 * y4 = (device const float4 *) y; - - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); - } - - float all_sum = simd_sum(sumf); - - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]); - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } -} - -typedef decltype(kernel_mul_mv_1row) mul_mv_1row_t; - -template [[host_name("kernel_mul_mv_f16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_mul_mv_bf16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row; -#endif - -// Assumes row size (ne00) is a multiple of 4 -template -kernel void kernel_mul_mv_l4( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { - - const int nrows = ne11; - const int64_t r0 = tgpig.x; - const int64_t im = tgpig.z; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - - device const T4 * x4 = (device const T4 *) (src0 + offset0); - - for (int r1 = 0; r1 < nrows; ++r1) { - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const float4 * y4 = (device const float4 *) (src1 + offset1); - - float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); - } - - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } -} - -typedef decltype(kernel_mul_mv_l4) mul_mv_l4_t; - -template [[host_name("kernel_mul_mv_f16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_mul_mv_bf16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4; -#endif - -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / max(0.001f, high - low); - return 1.0f - min(1.0f, max(0.0f, y)); -} - -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - thread float * cos_theta, thread float * sin_theta) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * log(1.0f / freq_scale); - } - *cos_theta = cos(theta) * mscale; - *sin_theta = sin(theta) * mscale; -} - -// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get -// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -static float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) { - return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base)); -} - -static void rope_yarn_corr_dims( - int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] -) { - // start and end correction dims - dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base))); - dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base))); -} - -template -kernel void kernel_rope_norm( - device const void * src0, - device const int32_t * src1, - device const float * src2, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & n_ctx_orig, - constant float & freq_base, - constant float & freq_scale, - constant float & ext_factor, - constant float & attn_factor, - constant float & beta_fast, - constant float & beta_slow, - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]) { - const int64_t i3 = tgpig[2]; - const int64_t i2 = tgpig[1]; - const int64_t i1 = tgpig[0]; - - float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - device const int32_t * pos = src1; - - const float theta_base = (float) pos[i2]; - const float inv_ndims = -1.f/n_dims; - - float cos_theta; - float sin_theta; - - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - if (i0 < n_dims) { - const int64_t ic = i0/2; - - const float theta = theta_base * pow(freq_base, inv_ndims*i0); - - const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; - - rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); - - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = src[0]; - const float x1 = src[1]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; - } else { - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } -} - -template -kernel void kernel_rope_neox( - device const void * src0, - device const int32_t * src1, - device const float * src2, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & n_ctx_orig, - constant float & freq_base, - constant float & freq_scale, - constant float & ext_factor, - constant float & attn_factor, - constant float & beta_fast, - constant float & beta_slow, - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]) { - const int64_t i3 = tgpig[2]; - const int64_t i2 = tgpig[1]; - const int64_t i1 = tgpig[0]; - - float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - device const int32_t * pos = src1; - - const float theta_base = (float) pos[i2]; - const float inv_ndims = -1.f/n_dims; - - float cos_theta; - float sin_theta; - - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - if (i0 < n_dims) { - const int64_t ic = i0/2; - - const float theta = theta_base * pow(freq_base, inv_ndims*i0); - - const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; - - rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); - - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims/2]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } else { - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } -} - -typedef decltype(kernel_rope_norm) kernel_rope_norm_t; -typedef decltype(kernel_rope_neox) kernel_rope_neox_t; - -template [[host_name("kernel_rope_norm_f32")]] kernel kernel_rope_norm_t kernel_rope_norm; -template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_rope_norm; - -template [[host_name("kernel_rope_neox_f32")]] kernel kernel_rope_neox_t kernel_rope_neox; -template [[host_name("kernel_rope_neox_f16")]] kernel kernel_rope_neox_t kernel_rope_neox; - -typedef void (im2col_t)( - device const float * x, - device char * dst, - constant int32_t & ofs0, - constant int32_t & ofs1, - constant int32_t & IW, - constant int32_t & IH, - constant int32_t & CHW, - constant int32_t & s0, - constant int32_t & s1, - constant int32_t & p0, - constant int32_t & p1, - constant int32_t & d0, - constant int32_t & d1, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tgpg[[threadgroups_per_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]); - -template -kernel void kernel_im2col( - device const float * x, - device char * dst, - constant int32_t & ofs0, - constant int32_t & ofs1, - constant int32_t & IW, - constant int32_t & IH, - constant int32_t & CHW, - constant int32_t & s0, - constant int32_t & s1, - constant int32_t & p0, - constant int32_t & p1, - constant int32_t & d0, - constant int32_t & d1, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tgpg[[threadgroups_per_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int32_t iiw = tgpig[2] * s0 + tpitg[2] * d0 - p0; - const int32_t iih = tgpig[1] * s1 + tpitg[1] * d1 - p1; - - const int32_t offset_dst = - (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + - (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); - - device T * pdst = (device T *) (dst); - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - pdst[offset_dst] = 0.0f; - } else { - const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; - pdst[offset_dst] = x[offset_src + iih * IW + iiw]; - } -} - -template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; -template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; - -typedef void (im2col_ext_t)( - device const float * x, - device char * dst, - constant int32_t & ofs0, - constant int32_t & ofs1, - constant int32_t & IW, - constant int32_t & IH, - constant int32_t & CHW, - constant int32_t & s0, - constant int32_t & s1, - constant int32_t & p0, - constant int32_t & p1, - constant int32_t & d0, - constant int32_t & d1, - constant int32_t & N, - constant int32_t & KH, - constant int32_t & KW, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tgpg[[threadgroups_per_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]); - -template -kernel void kernel_im2col_ext( - device const float * x, - device char * dst, - constant int32_t & ofs0, - constant int32_t & ofs1, - constant int32_t & IW, - constant int32_t & IH, - constant int32_t & CHW, - constant int32_t & s0, - constant int32_t & s1, - constant int32_t & p0, - constant int32_t & p1, - constant int32_t & d0, - constant int32_t & d1, - constant int32_t & N, - constant int32_t & KH, - constant int32_t & KW, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1] - const int32_t KHW = KH * KW; // KHW == ntg[1] * ntg[2], KW == ntg[2] - - const int32_t d = tgpig[0] / CHW; - const int32_t chw = tgpig[0] % CHW; - const int32_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1) - const int32_t HW = tgpig[0] % KHW; - - const int32_t tpitg_0 = (d * ntg[0]) + tpitg[0]; - if (tpitg_0 >= N) { - return; - } - - const int32_t tpitg_1 = HW / KW; - const int32_t tpitg_2 = HW % KW; - - const int32_t iiw = tgpig[2] * s0 + tpitg_2 * d0 - p0; - const int32_t iih = tgpig[1] * s1 + tpitg_1 * d1 - p1; - - const int32_t offset_dst = - (tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + - (tgpig_0 * KHW + tpitg_1 * KW + tpitg_2); - - device T * pdst = (device T *) (dst); - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - pdst[offset_dst] = 0.0f; - } else { - const int32_t offset_src = tpitg_0 * ofs0 + tgpig_0 * ofs1; - pdst[offset_dst] = x[offset_src + iih * IW + iiw]; - } -} - -template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext; -template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext; - -kernel void kernel_upscale_f32( - device const char * src0, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant float & sf0, - constant float & sf1, - constant float & sf2, - constant float & sf3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; - - const int64_t i03 = i3/sf3; - const int64_t i02 = i2/sf2; - const int64_t i01 = i1/sf1; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int64_t i00 = i0/sf0; - - device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_ptr[0] = src0_ptr[0]; - } -} - -kernel void kernel_pad_f32( - device const char * src0, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; - - const int64_t i03 = i3; - const int64_t i02 = i2; - const int64_t i01 = i1; - - device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01); - device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1); - - if (i1 < ne01 && i2 < ne02 && i3 < ne03) { - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i0 < ne00) { - dst_ptr[i0] = src0_ptr[i0]; - } else { - dst_ptr[i0] = 0.0f; - } - } - - return; - } - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - dst_ptr[i0] = 0.0f; - } -} - -kernel void kernel_arange_f32( - device char * dst, - constant int64_t & ne0, - constant float & start, - constant float & step, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - - device float * dst_ptr = (device float *) dst; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - dst_ptr[i0] = start + step * i0; - } -} - -kernel void kernel_timestep_embedding_f32( - device const char * src0, - device char * dst, - constant uint64_t & nb1, - constant int & dim, - constant int & max_period, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - - int i = tgpig.x; - device float * embed_data = (device float *)(dst + i*nb1); - - int half_ = dim / 2; - for (int j = tpitg.x; j < half_; j += ntg.x) { - float timestep = ((device float *)src0)[i]; - float freq = (float)exp(-log((float)max_period) * j / half_); - float arg = timestep * freq; - embed_data[j ] = cos(arg); - embed_data[j + half_] = sin(arg); - } - - if (dim % 2 != 0 && tpitg.x == 0) { - embed_data[dim] = 0.f; - } -} - -// bitonic sort implementation following the CUDA kernels as reference -typedef void (argsort_t)( - device const float * x, - device int32_t * dst, - constant int64_t & ncols, - constant int64_t & ncols_pad, - threadgroup int32_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]]); - -template -kernel void kernel_argsort_f32_i32( - device const float * x, - device int32_t * dst, - constant int64_t & ncols, - constant int64_t & ncols_pad, - threadgroup int32_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]]) { - // bitonic sort - int col = tpitg[0]; - int row = tgpig[1]; - - if (col >= ncols_pad) return; - - device const float * x_row = x + row * ncols; - threadgroup int32_t * dst_row = shared_values; - - // initialize indices - dst_row[col] = col; - - threadgroup_barrier(mem_flags::mem_threadgroup); - - for (int k = 2; k <= ncols_pad; k *= 2) { - for (int j = k / 2; j > 0; j /= 2) { - int ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= ncols || - (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] > x_row[dst_row[ixj]] : - x_row[dst_row[col]] < x_row[dst_row[ixj]])) - ) { - SWAP(dst_row[col], dst_row[ixj]); - } - } else { - if (dst_row[ixj] >= ncols || - (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] < x_row[dst_row[ixj]] : - x_row[dst_row[col]] > x_row[dst_row[ixj]])) - ) { - SWAP(dst_row[col], dst_row[ixj]); - } - } - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - } - - // copy the result to dst without the padding - if (col < ncols) { - dst[row * ncols + col] = dst_row[col]; - } -} - -template [[host_name("kernel_argsort_f32_i32_asc")]] kernel argsort_t kernel_argsort_f32_i32; -template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32; - -kernel void kernel_leaky_relu_f32( - device const float * src0, - device float * dst, - constant float & slope, - uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope; -} - -// ref: https://arxiv.org/pdf/2307.08691.pdf -template< - typename q_t, // query types in shared memory - typename q4_t, - typename q8x8_t, - typename k_t, // key types in shared memory - typename k4x4_t, - typename k8x8_t, - typename v_t, // value types in shared memory - typename v4x4_t, - typename v8x8_t, - typename qk_t, // Q*K types - typename qk8x8_t, - typename s_t, // soft-max types - typename s8x8_t, - typename o_t, // attention accumulation types - typename o4_t, - typename o8x8_t, - typename kd4x4_t, // key type in device memory - short nl_k, - void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &), - typename vd4x4_t, // key type in device memory - short nl_v, - void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), - short D, // head size - short Q = 8, // queries per threadgroup - short KV = 8, // key/value processed per each simdgroup - short C = 32> // cache items per threadgroup -kernel void kernel_flash_attn_ext( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int32_t & ne01, - constant int32_t & ne02, - constant int32_t & ne03, - constant uint32_t & nb01, - constant uint32_t & nb02, - constant uint32_t & nb03, - constant int32_t & ne11, - constant int32_t & ne_12_2, // assume K and V are same shape - constant int32_t & ne_12_3, - constant uint32_t & nb_12_1, - constant uint32_t & nb_12_2, - constant uint32_t & nb_12_3, - constant uint32_t & nb31, - constant int32_t & ne1, - constant int32_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint16_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared [[threadgroup(0)]], - ushort3 tgpig[[threadgroup_position_in_grid]], - ushort3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]) { - const short nsg = ntg.y; // number of simdgroups - - const int iq3 = tgpig[2]; - const int iq2 = tgpig[1]; - const int iq1 = tgpig[0]*Q; - - const short D4 = D/4; - const short D8 = D/8; - const short D16 = D/16; - const short NW = N_SIMDWIDTH; - const short SH = (2*C + Q); // shared memory per simdgroup (s_t == float) - - const short TS = nsg*SH; // shared memory size per query in (s_t == float) - const short T = D + 2*TS; // shared memory size per query in (half) - - threadgroup q_t * sq = (threadgroup q_t *) (shared + 0*D); // holds the query data - threadgroup q4_t * sq4 = (threadgroup q4_t *) (shared + 0*D); // same as above but in q4_t - threadgroup o_t * so = (threadgroup o_t *) (shared + 0*D); // reuse query data for accumulation - threadgroup o4_t * so4 = (threadgroup o4_t *) (shared + 0*D); // same as above but in o4_t - threadgroup s_t * ss = (threadgroup s_t *) (shared + 2*sgitg*SH + Q*D); // scratch buffer for attention, mask and diagonal matrix - - threadgroup k_t * sk = (threadgroup k_t *) (shared + sgitg*(4*16*KV) + Q*T); // scratch buffer to load K in shared memory - threadgroup k4x4_t * sk4x4 = (threadgroup k4x4_t *) (shared + sgitg*(4*16*KV) + Q*T); // same as above but in k4x4_t - - threadgroup v_t * sv = (threadgroup v_t *) (shared + sgitg*(4*16*KV) + Q*T); // scratch buffer to load V in shared memory - threadgroup v4x4_t * sv4x4 = (threadgroup v4x4_t *) (shared + sgitg*(4*16*KV) + Q*T); // same as above but in v4x4_t - - // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) - o8x8_t lo[D8]; - - // load heads from Q to shared memory - for (short j = sgitg; j < Q; j += nsg) { - device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03)); - - for (short i = tiisg; i < D4; i += NW) { - if (iq1 + j < ne01) { - sq4[j*D4 + i] = (q4_t) q4[i]; - } else { - sq4[j*D4 + i] = (q4_t) 0.0f; - } - } - } - - // zero out lo - for (short i = 0; i < D8; ++i) { - lo[i] = make_filled_simdgroup_matrix((o_t) 0.0f); - } - - // zero out shared memory SH - for (short j = 0; j < Q; ++j) { - for (short i = tiisg; i < SH; i += NW) { - ss[j*TS + i] = 0.0f; - } - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - { - half S[Q] = { [0 ... Q-1] = 0.0f }; - half M[Q] = { [0 ... Q-1] = -__FLT16_MAX__/2 }; - - // thread indices inside the simdgroup - // TODO: see if we can utilize quad-group functions for better performance - // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (6.9.3) - const short tx = tiisg%4; - const short ty = tiisg/4; - - // broadcast kv - //const short rk2 = ne02/ne12; - //const short rk3 = ne03/ne13; - - const short ikv2 = iq2/(ne02/ne_12_2); - const short ikv3 = iq3/(ne03/ne_12_3); - - // load the queries from shared memory into local memory - q8x8_t mq[D8]; - - for (short i = 0; i < D8; ++i) { - simdgroup_load(mq[i], sq + i*8, D); - } - - const bool has_mask = mask != q; - - half slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const short h = iq2; - - const half base = h < n_head_log2 ? m0 : m1; - const short exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = pow(base, exph); - } - - // loop over the KV cache - // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { - const int ic = ic0 + C*sgitg; - if (ic >= ne11) { - break; - } - - if (has_mask) { - // used to detect blocks full of -INF - half smax = -INFINITY; - - // load the mask in shared memory - #pragma unroll(Q) - for (short j = 0; j < Q; ++j) { - device const half * pm = (device const half *) ((device const char *) mask + (iq1 + j)*nb31); - - const half m = pm[ic + tiisg]; - - ss[j*TS + C + tiisg] = m; - smax = max(smax, m); - } - - smax = simd_max(smax); - - if (smax == -INFINITY) { - continue; - } - } - - // Q*K^T - { - for (short cc = 0; cc < C/8; ++cc) { - qk8x8_t mqk = make_filled_simdgroup_matrix((qk_t) 0.0f); - - // this is compile-time check, so it does not have runtime overhead - if (is_same::value) { - // we can read directly from global memory - device const k_t * pk = (device const k_t *) ((device const char *) k + ((ic + 8*cc)*nb_12_1 + ikv2*nb_12_2 + ikv3*nb_12_3)); - - #pragma unroll(D8) - for (short i = 0; i < D8; ++i) { - k8x8_t mk; - simdgroup_load(mk, pk + i*8, nb_12_1/sizeof(k_t), 0, true); // transpose // TODO: use ne10 - - simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); - } - } else { - for (short ii = 0; ii < D16; ii += 4) { - device const kd4x4_t * pk4x4 = (device const kd4x4_t *) ((device const char *) k + ((ic + 8*cc + ty)*nb_12_1 + ikv2*nb_12_2 + ikv3*nb_12_3)); - - if (D16%4 == 0) { - // the head is evenly divisible by 4*16 = 64, so no need for bound checks - { - k4x4_t tmp; - deq_k(pk4x4 + (ii + tx)/nl_k, (ii + tx)%nl_k, tmp); - sk4x4[4*ty + tx] = tmp; - } - - simdgroup_barrier(mem_flags::mem_threadgroup); - - #pragma unroll(4) - for (short k = 0; k < 4; ++k) { - k8x8_t mk; - - simdgroup_load(mk, sk + 16*k + 0*8, 4*16, 0, true); // transpose - simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 0], mk, mqk); - - simdgroup_load(mk, sk + 16*k + 1*8, 4*16, 0, true); // transpose - simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 1], mk, mqk); - } - } else { - if (ii + tx < D16) { - k4x4_t tmp; - deq_k(pk4x4 + (ii + tx)/nl_k, (ii + tx)%nl_k, tmp); - sk4x4[4*ty + tx] = tmp; - } - - simdgroup_barrier(mem_flags::mem_threadgroup); - - for (short k = 0; k < 4 && ii + k < D16; ++k) { - k8x8_t mk; - - simdgroup_load(mk, sk + 16*k + 0*8, 4*16, 0, true); // transpose - simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 0], mk, mqk); - - simdgroup_load(mk, sk + 16*k + 1*8, 4*16, 0, true); // transpose - simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 1], mk, mqk); - } - } - } - } - - // cast qk_t -> s_t - //s8x8_t mqks(1.0f); - //simdgroup_multiply(mqks, mqk, mqks); - //simdgroup_store(mqks, ss + 8*cc, TS, 0, false); - - simdgroup_store(mqk, ss + 8*cc, TS, 0, false); - } - } - - // online softmax - { - for (ushort j = 0; j < Q; ++j) { - const half m = M[j]; - - // scale and apply the logitcap / mask - half s = ss[j*TS + tiisg]*scale; - - if (logit_softcap != 0.0f) { - s = logit_softcap*precise::tanh(s); - } - - // mqk = mqk + mask*slope - s += slope*ss[j*TS + C + tiisg]; - - M[j] = simd_max(max(M[j], s)); - - const half ms = exp(m - M[j]); - const half vs = exp(s - M[j]); - - S[j] = S[j]*ms + simd_sum(vs); - - // the P matrix from the paper (Q rows, C columns) - ss[j*TS + tiisg] = vs; - - // create a QxQ diagonal matrix for rescaling the output - if (tiisg == j) { - ss[j*TS + 2*C + j] = ms; - } - } - } - - // O = diag(ms)*O - { - s8x8_t mm; - simdgroup_load(mm, ss + 2*C, TS, 0, false); - - #pragma unroll(D8) - for (short i = 0; i < D8; ++i) { - simdgroup_multiply(lo[i], mm, lo[i]); - } - } - - // O = O + (Q*K^T)*V - { - for (short cc = 0; cc < C/8; ++cc) { - s8x8_t ms; - simdgroup_load(ms, ss + 8*cc, TS, 0, false); - - if (is_same::value) { - // we can read directly from global memory - device const v_t * pv = (device const v_t *) ((device const char *) v + ((ic + 8*cc)*nb_12_1 + ikv2*nb_12_2 + ikv3*nb_12_3)); - - #pragma unroll(D8) - for (short i = 0; i < D8; ++i) { - v8x8_t mv; - simdgroup_load(mv, pv + i*8, nb_12_1/sizeof(v_t), 0, false); // TODO: use ne20 - - simdgroup_multiply_accumulate(lo[i], ms, mv, lo[i]); - } - } else { - for (short ii = 0; ii < D16; ii += 4) { - device const vd4x4_t * pv4x4 = (device const vd4x4_t *) ((device const char *) v + ((ic + 8*cc + ty)*nb_12_1 + ikv2*nb_12_2 + ikv3*nb_12_3)); - - if (D16%4 == 0) { - // no need for bound checks - { - v4x4_t tmp; - deq_v(pv4x4 + (ii + tx)/nl_v, (ii + tx)%nl_v, tmp); - sv4x4[4*ty + tx] = tmp; - } - - simdgroup_barrier(mem_flags::mem_threadgroup); - - #pragma unroll(4) - for (short k = 0; k < 4; ++k) { - v8x8_t mv; - - simdgroup_load(mv, sv + 16*k + 0*8, 4*16, 0, false); - simdgroup_multiply_accumulate(lo[2*(ii + k) + 0], ms, mv, lo[2*(ii + k) + 0]); - - simdgroup_load(mv, sv + 16*k + 1*8, 4*16, 0, false); - simdgroup_multiply_accumulate(lo[2*(ii + k) + 1], ms, mv, lo[2*(ii + k) + 1]); - } - } else { - if (ii + tx < D16) { - v4x4_t tmp; - deq_v(pv4x4 + (ii + tx)/nl_v, (ii + tx)%nl_v, tmp); - sv4x4[4*ty + tx] = tmp; - } - - simdgroup_barrier(mem_flags::mem_threadgroup); - - for (short k = 0; k < 4 && ii + k < D16; ++k) { - v8x8_t mv; - - simdgroup_load(mv, sv + 16*k + 0*8, 4*16, 0, false); - simdgroup_multiply_accumulate(lo[2*(ii + k) + 0], ms, mv, lo[2*(ii + k) + 0]); - - simdgroup_load(mv, sv + 16*k + 1*8, 4*16, 0, false); - simdgroup_multiply_accumulate(lo[2*(ii + k) + 1], ms, mv, lo[2*(ii + k) + 1]); - } - } - } - } - } - } - } - - // these are needed for reducing the results from the simdgroups (reuse the ss buffer) - for (short j = 0; j < Q; ++j) { - if (tiisg == 0) { - ss[j*TS + 0] = S[j]; - ss[j*TS + 1] = M[j]; - } - } - } - - // reduce the warps sequentially - for (ushort sg = 1; sg < nsg; ++sg) { - half S = { 0.0f }; - half M = { -__FLT16_MAX__/2 }; - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // each simdgroup stores its output to shared memory, reusing sq - if (sgitg == sg) { - for (short i = 0; i < D8; ++i) { - simdgroup_store(lo[i], so + i*8, D, 0, false); - } - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // the first simdgroup accumulates the results from the other simdgroups - if (sgitg == 0) { - for (short j = 0; j < Q; ++j) { - const half S0 = ss[j*TS + 0]; - const half S1 = ss[j*TS + sg*SH + 0]; - - const half M0 = ss[j*TS + 1]; - const half M1 = ss[j*TS + sg*SH + 1]; - - M = max(M0, M1); - - const half ms0 = exp(M0 - M); - const half ms1 = exp(M1 - M); - - S = S0*ms0 + S1*ms1; - - if (tiisg == 0) { - ss[j*TS + 0] = S; - ss[j*TS + 1] = M; - - ss[j*TS + 2*C + j ] = ms0; - ss[j*TS + 2*C + j + sg*SH] = ms1; - } - } - - // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 - { - s8x8_t ms0; - s8x8_t ms1; - - simdgroup_load(ms0, ss + 2*C, TS, 0, false); - simdgroup_load(ms1, ss + 2*C + sg*SH, TS, 0, false); - - #pragma unroll(D8) - for (short i = 0; i < D8; ++i) { - o8x8_t t; - - simdgroup_load (t, so + i*8, D, 0, false); - simdgroup_multiply(t, ms1, t); - - simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t); - } - } - } - } - - // store result to shared memory (reuse sq) - if (sgitg == 0) { - for (short i = 0; i < D8; ++i) { - simdgroup_store(lo[i], so + i*8, D, 0, false); - } - } - - device float4 * dst4 = (device float4 *) dst; - - // final rescale with 1/S and store to global memory - if (sgitg == 0) { - for (short j = 0; j < Q && iq1 + j < ne01; ++j) { - const float S = ss[j*TS + 0]; - - for (short i = tiisg; i < D4; i += NW) { - dst4[((int64_t)iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) so4[j*D4 + i]/S; - } - } - } -} - -// TODO: this is quite ugly. in the future these types will be hardcoded in the kernel, but for now keep them as -// template to be able to explore different combinations -// -#define FA_TYPES \ - half, half4, simdgroup_half8x8, \ - half, half4x4, simdgroup_half8x8, \ - half, half4x4, simdgroup_half8x8, \ - float, simdgroup_float8x8, \ - float, simdgroup_float8x8, \ - half, half4, simdgroup_half8x8 - -typedef decltype(kernel_flash_attn_ext) flash_attn_ext_t; - -template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; - -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_flash_attn_ext_bf16_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_bf16_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_bf16_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_bf16_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_bf16_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_bf16_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -#endif - -template [[host_name("kernel_flash_attn_ext_q4_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; - -template [[host_name("kernel_flash_attn_ext_q4_1_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_1_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_1_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_1_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_1_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q4_1_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; - -template [[host_name("kernel_flash_attn_ext_q5_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; - -template [[host_name("kernel_flash_attn_ext_q5_1_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_1_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_1_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_1_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_1_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q5_1_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; - -template [[host_name("kernel_flash_attn_ext_q8_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q8_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q8_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q8_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q8_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; -template [[host_name("kernel_flash_attn_ext_q8_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; - -#undef FA_TYPES - -template< - typename q4_t, // query types in shared memory - typename q4x4_t, - typename k4x4_t, // key types in shared memory - typename v4x4_t, // value types in shared memory - typename qk_t, // Q*K types - typename s_t, // soft-max types - typename s4_t, - typename s4x4_t, - typename o4x4_t, // attention accumulation types - typename kd4x4_t, // key type in device memory - short nl_k, - void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &), - typename vd4x4_t, // key type in device memory - short nl_v, - void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), - short D, // head size - short Q = 1, // queries per threadgroup - short C = 32> // cache items per threadgroup -kernel void kernel_flash_attn_ext_vec( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int32_t & ne01, - constant int32_t & ne02, - constant int32_t & ne03, - constant uint32_t & nb01, - constant uint32_t & nb02, - constant uint32_t & nb03, - constant int32_t & ne11, - constant int32_t & ne_12_2, // assume K and V are same shape - constant int32_t & ne_12_3, - constant uint32_t & nb_12_1, - constant uint32_t & nb_12_2, - constant uint32_t & nb_12_3, - constant uint32_t & nb31, - constant int32_t & ne1, - constant int32_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint16_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared [[threadgroup(0)]], - ushort3 tgpig[[threadgroup_position_in_grid]], - ushort3 tpitg[[thread_position_in_threadgroup]], - ushort3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]) { - const short nsg = ntg.y; // number of simdgroups - - const int iq3 = tgpig[2]; - const int iq2 = tgpig[1]; - const int iq1 = tgpig[0]; - - const short D4 = D/4; - const short D16 = D/16; - const short NW = N_SIMDWIDTH; - const short NL = NW/4; // note: this can be adjusted to support D%64 == 0 and D%32 == 0 - const short SH = 2*C; // shared memory per simdgroup - - const short T = D + nsg*SH; // shared memory size per query in (half) - - //threadgroup q_t * sq = (threadgroup q_t *) (shared + 0*D); // holds the query data - threadgroup q4_t * sq4 = (threadgroup q4_t *) (shared + 0*D); // same as above but in q4_t - threadgroup q4x4_t * sq4x4 = (threadgroup q4x4_t *) (shared + 0*D); // same as above but in q4x4_t - threadgroup s_t * ss = (threadgroup s_t *) (shared + sgitg*SH + Q*D); // scratch buffer for attention - threadgroup s4_t * ss4 = (threadgroup s4_t *) (shared + sgitg*SH + Q*D); // same as above but in s4_t - threadgroup half * sm = (threadgroup half *) (shared + sgitg*SH + C + Q*D); // scratch buffer for mask - threadgroup o4x4_t * sr4x4 = (threadgroup o4x4_t *) (shared + sgitg*D + Q*T); // scratch buffer for the results - - // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) - o4x4_t lo[D16/NL]; - - // load heads from Q to shared memory - device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03)); - - for (short i = tiisg; i < D4; i += NW) { - if (iq1 < ne01) { - sq4[i] = (q4_t) q4[i]; - } else { - sq4[i] = (q4_t) 0.0f; - } - } - - // zero out lo - for (short i = 0; i < D16/NL; ++i) { - lo[i] = (o4x4_t) 0.0f; - } - - // zero out shared memory SH - for (short i = tiisg; i < SH/4; i += NW) { - ss4[i] = (s4_t) 0.0f; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - { - half S = 0.0f; - half M = -__FLT16_MAX__/2; - - // thread indices inside the simdgroup - const short tx = tiisg%NL; - const short ty = tiisg/NL; - - // broadcast kv - //const short rk2 = ne02/ne12; - //const short rk3 = ne03/ne13; - - const short ikv2 = iq2/(ne02/ne_12_2); - const short ikv3 = iq3/(ne03/ne_12_3); - - // load the queries from shared memory into local memory - q4x4_t mq[D16/NL]; - - #pragma unroll(D16/NL) - for (short ii = 0; ii < D16; ii += NL) { - mq[ii/NL] = sq4x4[ii + tx]; - } - - const bool has_mask = mask != q; - - // pointer to the mask - device const half * pm = (device const half *) (mask + iq1*nb31); - - half slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const short h = iq2; - - const half base = h < n_head_log2 ? m0 : m1; - const short exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = pow(base, exph); - } - - // loop over the KV cache - // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { - const int ic = ic0 + C*sgitg; - if (ic >= ne11) { - break; - } - - if (has_mask) { - sm[tiisg] = pm[ic + tiisg]; - } - - // Q*K^T - { - // each simdgroup processes 1 query and 4 (NW/NL) keys - for (short cc = 0; cc < C/4; ++cc) { - qk_t mqka[4] = { 0.0, 0.0, 0.0, 0.0 }; - - device const kd4x4_t * pk = (device const kd4x4_t *) ((device const char *) k + ((ic + 4*cc + ty)*nb_12_1 + ikv2*nb_12_2 + ikv3*nb_12_3)); - - #pragma unroll(D16/NL) - for (short ii = 0; ii < D16; ii += NL) { - const short i = ii + tx; - - k4x4_t mk; - deq_k(pk + i/nl_k, i%nl_k, mk); - - // note: this is less precise than the version below - //mqka[0] += dot(mq[ii/NL][0], mk[0]); - //mqka[1] += dot(mq[ii/NL][1], mk[1]); - //mqka[2] += dot(mq[ii/NL][2], mk[2]); - //mqka[3] += dot(mq[ii/NL][3], mk[3]); - - mqka[0] += dot((float4) mq[ii/NL][0], (float4) mk[0]); - mqka[1] += dot((float4) mq[ii/NL][1], (float4) mk[1]); - mqka[2] += dot((float4) mq[ii/NL][2], (float4) mk[2]); - mqka[3] += dot((float4) mq[ii/NL][3], (float4) mk[3]); - } - - qk_t mqk = mqka[0] + mqka[1] + mqka[2] + mqka[3]; - - // simdgroup reduce - // [ 0 .. 7] -> [ 0] - // [ 8 .. 15] -> [ 8] - // [16 .. 23] -> [16] - // [24 .. 31] -> [24] - //mqk += simd_shuffle_down(mqk, 16); - //mqk += simd_shuffle_down(mqk, 8); - mqk += simd_shuffle_down(mqk, 4); - mqk += simd_shuffle_down(mqk, 2); - mqk += simd_shuffle_down(mqk, 1); - - // mqk = mqk*scale + mask*slope - if (tx == 0) { - mqk *= scale; - - if (logit_softcap != 0.0f) { - mqk = logit_softcap*precise::tanh(mqk); - } - - mqk += sm[4*cc + ty]*slope; - - ss[4*cc + ty] = mqk; - } - } - } - - simdgroup_barrier(mem_flags::mem_threadgroup); - - // online softmax - { - const half m = M; - const half s = ss[tiisg]; - - M = simd_max(max(M, s)); - - const half ms = exp(m - M); - const half vs = exp(s - M); - - S = S*ms + simd_sum(vs); - - // the P matrix from the paper (Q rows, C columns) - ss[tiisg] = vs; - - // O = diag(ms)*O - #pragma unroll(D16/NL) - for (short ii = 0; ii < D16; ii += NL) { - lo[ii/NL] *= ms; - } - } - - simdgroup_barrier(mem_flags::mem_threadgroup); - - // O = O + (Q*K^T)*V - { - for (short cc = 0; cc < C/4; ++cc) { - device const vd4x4_t * pv4 = (device const vd4x4_t *) ((device const char *) v + ((ic + 4*cc + ty)*nb_12_1 + ikv2*nb_12_2 + ikv3*nb_12_3)); - - const s4x4_t ms(ss[4*cc + ty]); - - #pragma unroll(D16/NL) - for (short ii = 0; ii < D16; ii += NL) { - const short i = ii + tx; - - v4x4_t mv; - deq_v(pv4 + i/nl_v, i%nl_v, mv); - - lo[ii/NL] += mv*ms; - } - } - } - } - - // these are needed for reducing the results from the simdgroups (reuse the ss buffer) - if (tiisg == 0) { - ss[0] = (s_t) S; - ss[1] = (s_t) M; - } - } - - // simdgroup reduce - // [ 0, 8, 16, 24] -> [ 0] - // [ 1, 9, 17, 25] -> [ 1] - // [ 2, 10, 18, 26] -> [ 2] - // [ 3, 11, 19, 27] -> [ 3] - // [ 4, 12, 20, 28] -> [ 4] - // [ 5, 13, 21, 29] -> [ 5] - // [ 6, 14, 22, 30] -> [ 6] - // [ 7, 15, 23, 31] -> [ 7] - for (short ii = 0; ii < D16; ii += NL) { - lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 16); - lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 8); - //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 4); - //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 2); - //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 1); - - lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 16); - lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 8); - //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 4); - //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 2); - //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 1); - - lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 16); - lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 8); - //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 4); - //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 2); - //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 1); - - lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 16); - lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 8); - //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 4); - //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 2); - //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 1); - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // store results to shared memory - for (short i = tiisg; i < D16; i += NL) { - sr4x4[i] = lo[i/NL]; - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // parallel reduce - for (short r = nsg/2; r > 0; r >>= 1) { - if (sgitg < r) { - const half S0 = ss[ 0]; - const half S1 = ss[r*SH + 0]; - - const half M0 = ss[ 1]; - const half M1 = ss[r*SH + 1]; - - const half M = max(M0, M1); - - const half ms0 = exp(M0 - M); - const half ms1 = exp(M1 - M); - - const half S = S0*ms0 + S1*ms1; - - if (tiisg == 0) { - ss[0] = S; - ss[1] = M; - } - - // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 - for (short i = tiisg; i < D16; i += NW) { - sr4x4[i] = sr4x4[i]*ms0 + sr4x4[i + r*D16]*ms1; - } - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - } - - device float4x4 * dst44 = (device float4x4 *) dst; - - // final rescale with 1/S and store to global memory - if (sgitg == 0) { - const float S = ss[0]; - - for (short i = tiisg; i < D16; i += NW) { - dst44[((int64_t)iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D16 + i] = (float4x4) sr4x4[i]/S; - } - } -} - -// note: I think the s_t can be half instead of float, because the Q*K scaling is done before storing to shared mem -// in the other (non-vec) kernel, we need s_t to also be float because we scale during the soft_max -// -#define FA_TYPES \ - half4, half4x4, \ - half4x4, \ - half4x4, \ - float, \ - half, half4, half4x4, \ - half4x4 - -typedef decltype(kernel_flash_attn_ext_vec) flash_attn_ext_vec_t; - -template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -#endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; - -template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_flash_attn_ext_vec_bf16_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -#endif -template [[host_name("kernel_flash_attn_ext_vec_q4_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q4_1_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q5_1_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; -template [[host_name("kernel_flash_attn_ext_vec_q8_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; - -#undef FA_TYPES - -template -kernel void kernel_cpy( - device const void * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); - - device T1 * dst_data = (device T1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { - device const T0 * src = (device T0 *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - dst_data[i00] = (T1) src[0]; - } -} - -typedef decltype(kernel_cpy) kernel_cpy_t; - -template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_cpy_f32_bf16")]] kernel kernel_cpy_t kernel_cpy; -#endif -template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_cpy_bf16_f32")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_bf16_bf16")]] kernel kernel_cpy_t kernel_cpy; -#endif - -kernel void kernel_cpy_f32_q8_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK8_0; - - device block_q8_0 * dst_data = (device block_q8_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x*QK8_0; i00 < ne00; i00 += ntg.x*QK8_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - const float v = src[j]; - amax = MAX(amax, fabs(v)); - } - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - dst_data[i00/QK8_0].d = d; - - for (int j = 0; j < QK8_0; ++j) { - const float x0 = src[j]*id; - - dst_data[i00/QK8_0].qs[j] = round(x0); - } - } -} - -kernel void kernel_cpy_f32_q4_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_0; - - device block_q4_0 * dst_data = (device block_q4_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x*QK4_0; i00 < ne00; i00 += ntg.x*QK4_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - - float amax = 0.0f; // absolute max - float max = 0.0f; - - for (int j = 0; j < QK4_0; j++) { - const float v = src[j]; - if (amax < fabs(v)) { - amax = fabs(v); - max = v; - } - } - - const float d = max / -8; - const float id = d ? 1.0f/d : 0.0f; - - dst_data[i00/QK4_0].d = d; - - for (int j = 0; j < QK4_0/2; ++j) { - const float x0 = src[0 + j]*id; - const float x1 = src[QK4_0/2 + j]*id; - - const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f)); - const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f)); - - dst_data[i00/QK4_0].qs[j] = xi0; - dst_data[i00/QK4_0].qs[j] |= xi1 << 4; - } - } -} - -kernel void kernel_cpy_f32_q4_1( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_1; - - device block_q4_1 * dst_data = (device block_q4_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x*QK4_1; i00 < ne00; i00 += ntg.x*QK4_1) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - - float min = FLT_MAX; - float max = -FLT_MAX; - - for (int j = 0; j < QK4_1; j++) { - const float v = src[j]; - if (min > v) min = v; - if (max < v) max = v; - } - - const float d = (max - min) / ((1 << 4) - 1); - const float id = d ? 1.0f/d : 0.0f; - - dst_data[i00/QK4_1].d = d; - dst_data[i00/QK4_1].m = min; - - for (int j = 0; j < QK4_1/2; ++j) { - const float x0 = (src[0 + j] - min)*id; - const float x1 = (src[QK4_1/2 + j] - min)*id; - - const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f)); - const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f)); - - dst_data[i00/QK4_1].qs[j] = xi0; - dst_data[i00/QK4_1].qs[j] |= xi1 << 4; - } - } -} - -kernel void kernel_cpy_f32_q5_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_0; - - device block_q5_0 * dst_data = (device block_q5_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x*QK5_0; i00 < ne00; i00 += ntg.x*QK5_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - - float amax = 0.0f; // absolute max - float max = 0.0f; - - for (int j = 0; j < QK5_0; j++) { - const float v = src[j]; - if (amax < fabs(v)) { - amax = fabs(v); - max = v; - } - } - - const float d = max / -16; - const float id = d ? 1.0f/d : 0.0f; - - dst_data[i00/QK5_0].d = d; - - uint32_t qh = 0; - for (int j = 0; j < QK5_0/2; ++j) { - const float x0 = src[0 + j]*id; - const float x1 = src[QK5_0/2 + j]*id; - - const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f)); - const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f)); - - dst_data[i00/QK5_0].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); - qh |= ((xi0 & 0x10u) >> 4) << (j + 0); - qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2); - } - thread const uint8_t * qh8 = (thread const uint8_t *)&qh; - for (int j = 0; j < 4; ++j) { - dst_data[i00/QK5_0].qh[j] = qh8[j]; - } - } -} - -kernel void kernel_cpy_f32_q5_1( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_1; - - device block_q5_1 * dst_data = (device block_q5_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x*QK5_1; i00 < ne00; i00 += ntg.x*QK5_1) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - - float max = src[0]; - float min = src[0]; - - for (int j = 1; j < QK5_1; j++) { - const float v = src[j]; - min = v < min ? v : min; - max = v > max ? v : max; - } - - const float d = (max - min) / 31; - const float id = d ? 1.0f/d : 0.0f; - - dst_data[i00/QK5_1].d = d; - dst_data[i00/QK5_1].m = min; - - uint32_t qh = 0; - for (int j = 0; j < QK5_1/2; ++j) { - const float x0 = (src[0 + j] - min)*id; - const float x1 = (src[QK5_1/2 + j] - min)*id; - - const uint8_t xi0 = (uint8_t)(x0 + 0.5f); - const uint8_t xi1 = (uint8_t)(x1 + 0.5f); - - dst_data[i00/QK5_1].qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4); - qh |= ((xi0 & 0x10u) >> 4) << (j + 0); - qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2); - } - thread const uint8_t * qh8 = (thread const uint8_t *)&qh; - for (int j = 0; j < 4; ++j) { - dst_data[i00/QK5_1].qh[j] = qh8[j]; - } - } -} - -static inline int best_index_int8(int n, constant float * val, float x) { - if (x <= val[0]) return 0; - if (x >= val[n-1]) return n-1; - int ml = 0, mu = n-1; - while (mu-ml > 1) { - int mav = (ml+mu)/2; - if (x < val[mav]) mu = mav; else ml = mav; - } - return x - val[mu-1] < val[mu] - x ? mu-1 : mu; -} - -kernel void kernel_cpy_f32_iq4_nl( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_NL; - - device block_iq4_nl * dst_data = (device block_iq4_nl *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - for (int64_t i00 = tpitg.x*QK4_NL; i00 < ne00; i00 += ntg.x*QK4_NL) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - - float amax = 0.0f; // absolute max - float max = 0.0f; - - for (int j = 0; j < QK4_0; j++) { - const float v = src[j]; - if (amax < fabs(v)) { - amax = fabs(v); - max = v; - } - } - - const float d = max / kvalues_iq4nl_f[0]; - const float id = d ? 1.0f/d : 0.0f; - - float sumqx = 0, sumq2 = 0; - for (int j = 0; j < QK4_NL/2; ++j) { - const float x0 = src[0 + j]*id; - const float x1 = src[QK4_NL/2 + j]*id; - - const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl_f, x0); - const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl_f, x1); - - dst_data[i00/QK4_NL].qs[j] = xi0 | (xi1 << 4); - - const float v0 = kvalues_iq4nl_f[xi0]; - const float v1 = kvalues_iq4nl_f[xi1]; - const float w0 = src[0 + j]*src[0 + j]; - const float w1 = src[QK4_NL/2 + j]*src[QK4_NL/2 + j]; - sumqx += w0*v0*src[j] + w1*v1*src[QK4_NL/2 + j]; - sumq2 += w0*v0*v0 + w1*v1*v1; - - } - - dst_data[i00/QK4_NL].d = sumq2 > 0 ? sumqx/sumq2 : d; - - } -} - -kernel void kernel_concat( - device const char * src0, - device const char * src1, - device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int32_t & dim, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); - - device const float * x; - - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); - } else { - x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); - } - - device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - *y = *x; - } -} - -void kernel_mul_mv_q2_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_q2_K * x = (device const block_q2_K *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int ix = tiisg/8; // 0...3 - const int it = tiisg%8; // 0...7 - const int iq = it/4; // 0 or 1 - const int ir = it%4; // 0...3 - const int is = (8*ir)/16;// 0 or 1 - - device const float * y4 = y + ix * QK_K + 128 * iq + 8 * ir; - - for (int ib = ix; ib < nb; ib += 4) { - - float4 sumy = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0]; - yl[i+ 8] = y4[i+32]; sumy[1] += yl[i+ 8]; - yl[i+16] = y4[i+64]; sumy[2] += yl[i+16]; - yl[i+24] = y4[i+96]; sumy[3] += yl[i+24]; - } - - device const uint8_t * sc = (device const uint8_t *)x[ib].scales + 8*iq + is; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 16 * iq + 4 * ir; - device const half * dh = &x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+ 0] * (qs[i/2] & 0x0003); - acc2[0] += yl[i+ 1] * (qs[i/2] & 0x0300); - acc1[1] += yl[i+ 8] * (qs[i/2] & 0x000c); - acc2[1] += yl[i+ 9] * (qs[i/2] & 0x0c00); - acc1[2] += yl[i+16] * (qs[i/2] & 0x0030); - acc2[2] += yl[i+17] * (qs[i/2] & 0x3000); - acc1[3] += yl[i+24] * (qs[i/2] & 0x00c0); - acc2[3] += yl[i+25] * (qs[i/2] & 0xc000); - } - float dall = dh[0]; - float dmin = dh[1] * 1.f/16.f; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc2[0]) * (sc[0] & 0xF) * 1.f/ 1.f + - (acc1[1] + 1.f/256.f * acc2[1]) * (sc[2] & 0xF) * 1.f/ 4.f + - (acc1[2] + 1.f/256.f * acc2[2]) * (sc[4] & 0xF) * 1.f/16.f + - (acc1[3] + 1.f/256.f * acc2[3]) * (sc[6] & 0xF) * 1.f/64.f) - - dmin * (sumy[0] * (sc[0] & 0xF0) + sumy[1] * (sc[2] & 0xF0) + sumy[2] * (sc[4] & 0xF0) + sumy[3] * (sc[6] & 0xF0)); - - qs += nb01/2; - sc += nb01; - dh += nb01/2; - } - - y4 += 4 * QK_K; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -[[host_name("kernel_mul_mv_q2_K_f32")]] -kernel void kernel_mul_mv_q2_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_q3_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * 2; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_q3_K * x = (device const block_q3_K *) ((device char *) src0 + offset0); - device const float * yy = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - - //const uint16_t kmask1 = 0x3030; - //const uint16_t kmask2 = 0x0f0f; - - const int tid = tiisg/4; - const int ix = tiisg%4; - const int ip = tid/4; // 0 or 1 - const int il = 2*((tid%4)/2); // 0 or 2 - const int ir = tid%2; - const int n = 8; - const int l0 = n*ir; - - // One would think that the Metal compiler would figure out that ip and il can only have - // 4 possible states, and optimize accordingly. Well, no. It needs help, and we do it - // with these two tales. - // - // Possible masks for the high bit - const ushort4 mm[4] = {{0x0001, 0x0100, 0x0002, 0x0200}, // ip = 0, il = 0 - {0x0004, 0x0400, 0x0008, 0x0800}, // ip = 0, il = 2 - {0x0010, 0x1000, 0x0020, 0x2000}, // ip = 1, il = 0 - {0x0040, 0x4000, 0x0080, 0x8000}}; // ip = 1, il = 2 - - // Possible masks for the low 2 bits - const int4 qm[2] = {{0x0003, 0x0300, 0x000c, 0x0c00}, {0x0030, 0x3000, 0x00c0, 0xc000}}; - - const ushort4 hm = mm[2*ip + il/2]; - - const int shift = 2*il; - const float v1 = il == 0 ? 4.f : 64.f; - const float v2 = 4.f * v1; - - const uint16_t s_shift1 = 4*ip; - const uint16_t s_shift2 = s_shift1 + il; - - const int q_offset = 32*ip + l0; - const int y_offset = 128*ip + 32*il + l0; - - device const float * y1 = yy + ix*QK_K + y_offset; - - uint32_t scales32, aux32; - thread uint16_t * scales16 = (thread uint16_t *)&scales32; - thread const int8_t * scales = (thread const int8_t *)&scales32; - - float sumf1[2] = {0.f}; - float sumf2[2] = {0.f}; - for (int i = ix; i < nb; i += 4) { - for (int l = 0; l < 8; ++l) { - yl[l+ 0] = y1[l+ 0]; - yl[l+ 8] = y1[l+16]; - yl[l+16] = y1[l+32]; - yl[l+24] = y1[l+48]; - } - - device const uint16_t * q = (device const uint16_t *)(x[i].qs + q_offset); - device const uint16_t * h = (device const uint16_t *)(x[i].hmask + l0); - device const uint16_t * a = (device const uint16_t *)(x[i].scales); - device const half * dh = &x[i].d; - - for (int row = 0; row < 2; ++row) { - const float d_all = (float)dh[0]; - - scales16[0] = a[4]; - scales16[1] = a[5]; - aux32 = ((scales32 >> s_shift2) << 4) & 0x30303030; - scales16[0] = a[il+0]; - scales16[1] = a[il+1]; - scales32 = ((scales32 >> s_shift1) & 0x0f0f0f0f) | aux32; - - float s1 = 0, s2 = 0, s3 = 0, s4 = 0, s5 = 0, s6 = 0; - for (int l = 0; l < n; l += 2) { - const int32_t qs = q[l/2]; - s1 += yl[l+0] * (qs & qm[il/2][0]); - s2 += yl[l+1] * (qs & qm[il/2][1]); - s3 += ((h[l/2] & hm[0]) ? 0.f : yl[l+0]) + ((h[l/2] & hm[1]) ? 0.f : yl[l+1]); - s4 += yl[l+16] * (qs & qm[il/2][2]); - s5 += yl[l+17] * (qs & qm[il/2][3]); - s6 += ((h[l/2] & hm[2]) ? 0.f : yl[l+16]) + ((h[l/2] & hm[3]) ? 0.f : yl[l+17]); - } - float d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1); - float d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2); - sumf1[row] += d1 * (scales[0] - 32); - sumf2[row] += d2 * (scales[2] - 32); - - s1 = s2 = s3 = s4 = s5 = s6 = 0; - for (int l = 0; l < n; l += 2) { - const int32_t qs = q[l/2+8]; - s1 += yl[l+8] * (qs & qm[il/2][0]); - s2 += yl[l+9] * (qs & qm[il/2][1]); - s3 += ((h[l/2+8] & hm[0]) ? 0.f : yl[l+8]) + ((h[l/2+8] & hm[1]) ? 0.f : yl[l+9]); - s4 += yl[l+24] * (qs & qm[il/2][2]); - s5 += yl[l+25] * (qs & qm[il/2][3]); - s6 += ((h[l/2+8] & hm[2]) ? 0.f : yl[l+24]) + ((h[l/2+8] & hm[3]) ? 0.f : yl[l+25]); - } - d1 = d_all * (s1 + 1.f/256.f * s2 - s3*v1); - d2 = d_all * (s4 + 1.f/256.f * s5 - s6*v2); - sumf1[row] += d1 * (scales[1] - 32); - sumf2[row] += d2 * (scales[3] - 32); - - q += nb01/2; - h += nb01/2; - a += nb01/2; - dh += nb01/2; - } - - y1 += 4 * QK_K; - } - - for (int row = 0; row < 2; ++row) { - const float sumf = (sumf1[row] + 0.25f * sumf2[row]) / (1 << shift); - sumf1[row] = simd_sum(sumf); - } - if (tiisg == 0) { - for (int row = 0; row < 2; ++row) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = sumf1[row]; - } - } -} - -[[host_name("kernel_mul_mv_q3_K_f32")]] -kernel void kernel_mul_mv_q3_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_q4_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int ix = tiisg/8; // 0...3 - const int it = tiisg%8; // 0...7 - const int iq = it/4; // 0 or 1 - const int ir = it%4; // 0...3 - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const int first_row = r0 * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_q4_K * x = (device const block_q4_K *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[16]; - float yh[16]; - float sumf[N_DST]={0.f}, all_sum; - - device const float * y4 = y + ix * QK_K + 64 * iq + 8 * ir; - - uint16_t sc16[4]; - thread const uint8_t * sc8 = (thread const uint8_t *)sc16; - - for (int ib = ix; ib < nb; ib += 4) { - float4 sumy = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i+0] = y4[i+ 0]; sumy[0] += yl[i+0]; - yl[i+8] = y4[i+ 32]; sumy[1] += yl[i+8]; - yh[i+0] = y4[i+128]; sumy[2] += yh[i+0]; - yh[i+8] = y4[i+160]; sumy[3] += yh[i+8]; - } - - device const uint16_t * sc = (device const uint16_t *)x[ib].scales + iq; - device const uint16_t * q1 = (device const uint16_t *)x[ib].qs + 16 * iq + 4 * ir; - device const half * dh = &x[ib].d; - - for (int row = 0; row < N_DST; row++) { - sc16[0] = sc[0] & kmask1; - sc16[1] = sc[2] & kmask1; - sc16[2] = ((sc[4] >> 0) & kmask2) | ((sc[0] & kmask3) >> 2); - sc16[3] = ((sc[4] >> 4) & kmask2) | ((sc[2] & kmask3) >> 2); - - device const uint16_t * q2 = q1 + 32; - - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+0] * (q1[i/2] & 0x000F); - acc1[1] += yl[i+1] * (q1[i/2] & 0x0F00); - acc1[2] += yl[i+8] * (q1[i/2] & 0x00F0); - acc1[3] += yl[i+9] * (q1[i/2] & 0xF000); - acc2[0] += yh[i+0] * (q2[i/2] & 0x000F); - acc2[1] += yh[i+1] * (q2[i/2] & 0x0F00); - acc2[2] += yh[i+8] * (q2[i/2] & 0x00F0); - acc2[3] += yh[i+9] * (q2[i/2] & 0xF000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc8[0] + - (acc1[2] + 1.f/256.f * acc1[3]) * sc8[1] * 1.f/16.f + - (acc2[0] + 1.f/256.f * acc2[1]) * sc8[4] + - (acc2[2] + 1.f/256.f * acc2[3]) * sc8[5] * 1.f/16.f) - - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - - q1 += nb01/2; - sc += nb01/2; - dh += nb01/2; - } - - y4 += 4 * QK_K; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -[[host_name("kernel_mul_mv_q4_K_f32")]] -kernel void kernel_mul_mv_q4_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_q5_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * 2; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_q5_K * x = (device const block_q5_K *) ((device char *) src0 + offset0); - device const float * yy = (device const float *) ((device char *) src1 + offset1); - - float sumf[2]={0.f}; - - float yl[16], yh[16]; - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int tid = tiisg/4; - const int ix = tiisg%4; - const int iq = tid/4; - const int ir = tid%4; - const int n = 8; - - const int l0 = n*ir; - const int q_offset = 32*iq + l0; - const int y_offset = 64*iq + l0; - - const uint8_t hm1 = 1u << (2*iq); - const uint8_t hm2 = hm1 << 1; - const uint8_t hm3 = hm1 << 4; - const uint8_t hm4 = hm2 << 4; - - uint16_t sc16[4]; - thread const uint8_t * sc8 = (thread const uint8_t *)sc16; - - device const float * y1 = yy + ix*QK_K + y_offset; - - for (int i = ix; i < nb; i += 4) { - device const uint8_t * q1 = x[i].qs + q_offset; - device const uint8_t * qh = x[i].qh + l0; - device const half * dh = &x[i].d; - device const uint16_t * a = (device const uint16_t *)x[i].scales + iq; - - device const float * y2 = y1 + 128; - float4 sumy = {0.f, 0.f, 0.f, 0.f}; - for (int l = 0; l < 8; ++l) { - yl[l+0] = y1[l+ 0]; sumy[0] += yl[l+0]; - yl[l+8] = y1[l+32]; sumy[1] += yl[l+8]; - yh[l+0] = y2[l+ 0]; sumy[2] += yh[l+0]; - yh[l+8] = y2[l+32]; sumy[3] += yh[l+8]; - } - - for (int row = 0; row < 2; ++row) { - device const uint8_t * q2 = q1 + 64; - - sc16[0] = a[0] & kmask1; - sc16[1] = a[2] & kmask1; - sc16[2] = ((a[4] >> 0) & kmask2) | ((a[0] & kmask3) >> 2); - sc16[3] = ((a[4] >> 4) & kmask2) | ((a[2] & kmask3) >> 2); - - float4 acc1 = {0.f}; - float4 acc2 = {0.f}; - for (int l = 0; l < n; ++l) { - uint8_t h = qh[l]; - acc1[0] += yl[l+0] * (q1[l] & 0x0F); - acc1[1] += yl[l+8] * (q1[l] & 0xF0); - acc1[2] += yh[l+0] * (q2[l] & 0x0F); - acc1[3] += yh[l+8] * (q2[l] & 0xF0); - acc2[0] += h & hm1 ? yl[l+0] : 0.f; - acc2[1] += h & hm2 ? yl[l+8] : 0.f; - acc2[2] += h & hm3 ? yh[l+0] : 0.f; - acc2[3] += h & hm4 ? yh[l+8] : 0.f; - } - const float dall = dh[0]; - const float dmin = dh[1]; - sumf[row] += dall * (sc8[0] * (acc1[0] + 16.f*acc2[0]) + - sc8[1] * (acc1[1]/16.f + 16.f*acc2[1]) + - sc8[4] * (acc1[2] + 16.f*acc2[2]) + - sc8[5] * (acc1[3]/16.f + 16.f*acc2[3])) - - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - - q1 += nb01; - qh += nb01; - dh += nb01/2; - a += nb01/2; - } - - y1 += 4 * QK_K; - } - - for (int row = 0; row < 2; ++row) { - const float tot = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot; - } - } -} - -[[host_name("kernel_mul_mv_q5_K_f32")]] -kernel void kernel_mul_mv_q5_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_q6_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const uint8_t kmask1 = 0x03; - const uint8_t kmask2 = 0x0C; - const uint8_t kmask3 = 0x30; - const uint8_t kmask4 = 0xC0; - - const int nb = ne00/QK_K; - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int im = tgpig.z; - - const int row = 2 * r0 + sgitg; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_q6_K * x = (device const block_q6_K *) ((device char *) src0 + offset0); - device const float * yy = (device const float *) ((device char *) src1 + offset1); - - float sumf = 0; - - const int tid = tiisg/2; - const int ix = tiisg%2; - const int ip = tid/8; // 0 or 1 - const int il = tid%8; - const int n = 4; - const int l0 = n*il; - const int is = 8*ip + l0/16; - - const int y_offset = 128*ip + l0; - const int q_offset_l = 64*ip + l0; - const int q_offset_h = 32*ip + l0; - - for (int i = ix; i < nb; i += 2) { - - device const uint8_t * q1 = x[i].ql + q_offset_l; - device const uint8_t * q2 = q1 + 32; - device const uint8_t * qh = x[i].qh + q_offset_h; - device const int8_t * sc = x[i].scales + is; - - device const float * y = yy + i * QK_K + y_offset; - - const float dall = x[i].d; - - float4 sums = {0.f, 0.f, 0.f, 0.f}; - for (int l = 0; l < n; ++l) { - sums[0] += y[l+ 0] * ((int8_t)((q1[l] & 0xF) | ((qh[l] & kmask1) << 4)) - 32); - sums[1] += y[l+32] * ((int8_t)((q2[l] & 0xF) | ((qh[l] & kmask2) << 2)) - 32); - sums[2] += y[l+64] * ((int8_t)((q1[l] >> 4) | ((qh[l] & kmask3) << 0)) - 32); - sums[3] += y[l+96] * ((int8_t)((q2[l] >> 4) | ((qh[l] & kmask4) >> 2)) - 32); - } - - sumf += dall * (sums[0] * sc[0] + sums[1] * sc[2] + sums[2] * sc[4] + sums[3] * sc[6]); - - } - - const float tot = simd_sum(sumf); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + row] = tot; - } -} - -[[host_name("kernel_mul_mv_q6_K_f32")]] -kernel void kernel_mul_mv_q6_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -// ======================= "True" 2-bit - -void kernel_mul_mv_iq2_xxs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq2_xxs * x = (device const block_iq2_xxs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); - { - int nval = 4; - int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq2xxs_grid[pos + i]; - nval = 2; - pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; - threadgroup_barrier(mem_flags::mem_threadgroup); - } - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - for (int i = 0; i < 32; ++i) { - yl[i] = y4[i]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq2_xxs * xr = x + ibl; - device const uint16_t * q2 = xr->qs + 4 * ib; - device const half * dh = &xr->d; - - for (int row = 0; row < N_DST; row++) { - - const float db = dh[0]; - device const uint8_t * aux8 = (device const uint8_t *)q2; - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = db * (0.5f + (aux32 >> 28)); - - float sum = 0; - for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + aux8[l]); - const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; - for (int j = 0; j < 8; ++j) { - sum += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); - } - } - sumf[row] += d * sum; - - dh += nb01/2; - q2 += nb01/2; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; - } - } -} - -[[host_name("kernel_mul_mv_iq2_xxs_f32")]] -kernel void kernel_mul_mv_iq2_xxs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_iq2_xs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq2_xs * x = (device const block_iq2_xs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 512); - { - int nval = 8; - int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq2xs_grid[pos + i]; - nval = 2; - pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; - threadgroup_barrier(mem_flags::mem_threadgroup); - } - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - for (int i = 0; i < 32; ++i) { - yl[i] = y4[i]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq2_xs * xr = x + ibl; - device const uint16_t * q2 = xr->qs + 4 * ib; - device const uint8_t * sc = xr->scales + ib; - device const half * dh = &xr->d; - - for (int row = 0; row < N_DST; row++) { - - const float db = dh[0]; - const uint8_t ls1 = sc[0] & 0xf; - const uint8_t ls2 = sc[0] >> 4; - const float d1 = db * (0.5f + ls1); - const float d2 = db * (0.5f + ls2); - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < 2; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); - const uint8_t signs = shared_signs[(q2[l] >> 9)]; - for (int j = 0; j < 8; ++j) { - sum1 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); - } - } - for (int l = 2; l < 4; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); - const uint8_t signs = shared_signs[(q2[l] >> 9)]; - for (int j = 0; j < 8; ++j) { - sum2 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); - } - } - sumf[row] += d1 * sum1 + d2 * sum2; - - dh += nb01/2; - q2 += nb01/2; - sc += nb01; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; - } - } -} - -[[host_name("kernel_mul_mv_iq2_xs_f32")]] -kernel void kernel_mul_mv_iq2_xs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_iq3_xxs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq3_xxs * x = (device const block_iq3_xxs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); - { - int nval = 4; - int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3xxs_grid[pos + i]; - nval = 2; - pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; - threadgroup_barrier(mem_flags::mem_threadgroup); - } - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - for (int i = 0; i < 32; ++i) { - yl[i] = y4[i]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq3_xxs * xr = x + ibl; - device const uint8_t * q3 = xr->qs + 8 * ib; - device const uint16_t * gas = (device const uint16_t *)(xr->qs + QK_K/4) + 2 * ib; - device const half * dh = &xr->d; - - for (int row = 0; row < N_DST; row++) { - - const float db = dh[0]; - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float d = db * (0.5f + (aux32 >> 28)); - - float2 sum = {0}; - for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + q3[2*l+0]); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + q3[2*l+1]); - const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; - for (int j = 0; j < 4; ++j) { - sum[0] += yl[8*l + j + 0] * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); - sum[1] += yl[8*l + j + 4] * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); - } - } - sumf[row] += d * (sum[0] + sum[1]); - - dh += nb01/2; - q3 += nb01; - gas += nb01/2; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; - } - } -} - -[[host_name("kernel_mul_mv_iq3_xxs_f32")]] -kernel void kernel_mul_mv_iq3_xxs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_iq3_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq3_s * x = (device const block_iq3_s *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; - { - int nval = 8; - int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i]; - threadgroup_barrier(mem_flags::mem_threadgroup); - } - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - for (int i = 0; i < 32; ++i) { - yl[i] = y4[i]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq3_s * xr = x + ibl; - device const uint8_t * qs = xr->qs + 8 * ib; - device const uint8_t * qh = xr->qh + ib; - device const uint8_t * sc = xr->scales + (ib/2); - device const uint8_t * signs = xr->signs + 4 * ib; - device const half * dh = &xr->d; - - for (int row = 0; row < N_DST; row++) { - - const float db = dh[0]; - const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf)); - - float2 sum = {0}; - for (int l = 0; l < 4; ++l) { - const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values; - const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values; - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]); - for (int j = 0; j < 4; ++j) { - sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]); - sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]); - } - } - sumf[row] += d * (sum[0] + sum[1]); - - dh += nb01/2; - qs += nb01; - qh += nb01; - sc += nb01; - signs += nb01; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -[[host_name("kernel_mul_mv_iq3_s_f32")]] -kernel void kernel_mul_mv_iq3_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_iq2_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq2_s * x = (device const block_iq2_s *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - //{ - // int nval = 32; - // int pos = (32*sgitg + tiisg)*nval; - // for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i]; - // threadgroup_barrier(mem_flags::mem_threadgroup); - //} - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - for (int i = 0; i < 32; ++i) { - yl[i] = y4[i]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq2_s * xr = x + ibl; - device const uint8_t * qs = xr->qs + 4 * ib; - device const uint8_t * qh = xr->qh + ib; - device const uint8_t * sc = xr->scales + ib; - device const uint8_t * signs = qs + QK_K/8; - device const half * dh = &xr->d; - - for (int row = 0; row < N_DST; row++) { - - const float db = dh[0]; - const float d1 = db * (0.5f + (sc[0] & 0xf)); - const float d2 = db * (0.5f + (sc[0] >> 4)); - - float2 sum = {0}; - for (int l = 0; l < 2; ++l) { - //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); - //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); - constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); - constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); - for (int j = 0; j < 8; ++j) { - sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l+0] & kmask_iq2xs[j]); - sum[1] += yl[8*l + j + 16] * grid2[j] * select(1, -1, signs[l+2] & kmask_iq2xs[j]); - } - } - sumf[row] += d1 * sum[0] + d2 * sum[1]; - - dh += nb01/2; - qs += nb01; - qh += nb01; - sc += nb01; - signs += nb01; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; - } - } -} - -[[host_name("kernel_mul_mv_iq2_s_f32")]] -kernel void kernel_mul_mv_iq2_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -void kernel_mul_mv_iq1_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_value, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq1_s * x = (device const block_iq1_s *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - float sumy = 0; - for (int i = 0; i < 32; ++i) { - yl[i] = y4[i]; - sumy += yl[i]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq1_s * xr = x + ibl; - device const uint8_t * qs = xr->qs + 4 * ib; - device const uint16_t * qh = xr->qh + ib; - device const half * dh = &xr->d; - - for (int row = 0; row < N_DST; row++) { - - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 5) & 0x700))); - constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[0] << 2) & 0x700))); - constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[0] >> 1) & 0x700))); - - float sum = 0; - for (int j = 0; j < 4; ++j) { - sum += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4) - + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4) - + yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4) - + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4); - } - sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1); - - dh += nb01/2; - qs += nb01; - qh += nb01/2; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -void kernel_mul_mv_iq1_m_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_value, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq1_m * x = (device const block_iq1_m *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - float yl[32]; - float sumf[N_DST]={0.f}, all_sum; - - const int nb32 = nb * (QK_K / 32); - - const int ix = tiisg; - - device const float * y4 = y + 32 * ix; - - iq1m_scale_t scale; - - for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - - float4 sumy = {0.f}; - for (int i = 0; i < 8; ++i) { - yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0]; - yl[i+ 8] = y4[i+ 8]; sumy[1] += yl[i+ 8]; - yl[i+16] = y4[i+16]; sumy[2] += yl[i+16]; - yl[i+24] = y4[i+24]; sumy[3] += yl[i+24]; - } - - const int ibl = ib32 / (QK_K / 32); - const int ib = ib32 % (QK_K / 32); - - device const block_iq1_m * xr = x + ibl; - device const uint8_t * qs = xr->qs + 4 * ib; - device const uint8_t * qh = xr->qh + 2 * ib; - device const uint16_t * sc = (device const uint16_t *)xr->scales; - - for (int row = 0; row < N_DST; row++) { - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); - constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[1] << 8) & 0x700))); - constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[1] << 4) & 0x700))); - - float2 sum = {0.f}; - for (int j = 0; j < 4; ++j) { - sum[0] += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4) - + yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4); - sum[1] += yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4) - + yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4); - } - const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - - sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + - (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); - - sc += nb01/2; - qs += nb01; - qh += nb01; - } - - y4 += 32 * 32; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -void kernel_mul_mv_iq4_nl_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values_i8, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - threadgroup float * shared_values = (threadgroup float *)shared_values_i8; - const int nb = ne00/QK4_NL; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - const int first_row = (r0 * 2 + sgitg) * 2; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq4_nl * x = (device const block_iq4_nl *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - const int ix = tiisg/2; // 0...15 - const int it = tiisg%2; // 0 or 1 - - shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; - threadgroup_barrier(mem_flags::mem_threadgroup); - - float4 yl[4]; - float sumf[2]={0.f}, all_sum; - - device const float * yb = y + ix * QK4_NL + it * 8; - - uint32_t aux32[2]; - thread const uint8_t * q8 = (thread const uint8_t *)aux32; - - float4 qf1, qf2; - - for (int ib = ix; ib < nb; ib += 16) { - - device const float4 * y4 = (device const float4 *)yb; - yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - - for (int row = 0; row < 2 && first_row + row < ne01; ++row) { - - device const block_iq4_nl & xb = x[row*nb + ib]; - device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it); - - float4 acc1 = {0.f}, acc2 = {0.f}; - - aux32[0] = q4[0] | (q4[1] << 16); - aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; - aux32[0] &= 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; - acc1 += yl[0] * qf1; - acc2 += yl[1] * qf2; - - aux32[0] = q4[2] | (q4[3] << 16); - aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; - aux32[0] &= 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; - acc1 += yl[2] * qf1; - acc2 += yl[3] * qf2; - - acc1 += acc2; - - sumf[row] += (float)xb.d * (acc1[0] + acc1[1] + acc1[2] + acc1[3]); - - } - - yb += 16 * QK4_NL; - } - - for (int row = 0; row < 2 && first_row + row < ne01; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -void kernel_mul_mv_iq4_xs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values_i8, - uint3 tgpig, - uint tiisg, - uint sgitg) { - - threadgroup float * shared_values = (threadgroup float *)shared_values_i8; - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - const int first_row = (r0 * 2 + sgitg) * 2; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; - - device const block_iq4_xs * x = (device const block_iq4_xs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); - - const int ix = tiisg/16; // 0 or 1 - const int it = tiisg%16; // 0...15 - const int ib = it/2; - const int il = it%2; - - shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; - threadgroup_barrier(mem_flags::mem_threadgroup); - - float4 yl[4]; - float sumf[2]={0.f}, all_sum; - - device const float * yb = y + ix * QK_K + ib * 32 + il * 8; - - uint32_t aux32[2]; - thread const uint8_t * q8 = (thread const uint8_t *)aux32; - - float4 qf1, qf2; - - for (int ibl = ix; ibl < nb; ibl += 2) { - - device const float4 * y4 = (device const float4 *)yb; - yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - - for (int row = 0; row < 2; ++row) { - - device const block_iq4_xs & xb = x[row*nb + ibl]; - device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); - - float4 acc1 = {0.f}, acc2 = {0.f}; - - aux32[0] = q4[0] & 0x0f0f0f0f; - aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; - acc1 += yl[0] * qf1; - acc2 += yl[1] * qf2; - - aux32[0] = q4[1] & 0x0f0f0f0f; - aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; - acc1 += yl[2] * qf1; - acc2 += yl[3] * qf2; - - acc1 += acc2; - - const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32; - sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]); - - } - - yb += 2 * QK_K; - } - - for (int row = 0; row < 2; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} - -[[host_name("kernel_mul_mv_iq1_s_f32")]] -kernel void kernel_mul_mv_iq1_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -[[host_name("kernel_mul_mv_iq1_m_f32")]] -kernel void kernel_mul_mv_iq1_m_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); -} - -[[host_name("kernel_mul_mv_iq4_nl_f32")]] -kernel void kernel_mul_mv_iq4_nl_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -[[host_name("kernel_mul_mv_iq4_xs_f32")]] -kernel void kernel_mul_mv_iq4_xs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -template -kernel void kernel_get_rows_q( - device const void * src0, - device const void * src1, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb1, - constant uint64_t & nb2, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg [[threads_per_threadgroup]]) { - const int64_t i10 = tgpig.x; - const int64_t i11 = tgpig.y; - - const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0]; - - const int64_t i02 = i11; - - for (int64_t ind = tiitg; ind < ne00/16; ind += tptg.x) { - float4x4 temp; - dequantize_func(((device const block_q *) ((const device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp); - *(((device float4x4 *) ((device char *) dst + i11*nb2 + i10*nb1)) + ind) = temp; - } -} - -template -kernel void kernel_get_rows_f( - device const void * src0, - device const void * src1, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb1, - constant uint64_t & nb2, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg [[threads_per_threadgroup]]) { - const int64_t i10 = tgpig.x; - const int64_t i11 = tgpig.y; - - const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0]; - - const int64_t i02 = i11; - - for (int ind = tiitg; ind < ne00; ind += tptg.x) { - (( device float *) (( device char *) dst + i11*nb2 + i10*nb1))[ind] = - ((const device T *) ((const device char *) src0 + i02*nb02 + r*nb01))[ind]; - } -} - -kernel void kernel_get_rows_i32( - device const void * src0, - device const void * src1, - device int32_t * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb1, - constant uint64_t & nb2, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg [[threads_per_threadgroup]]) { - const int64_t i10 = tgpig.x; - const int64_t i11 = tgpig.y; - - const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0]; - - const int64_t i02 = i11; - - for (int ind = tiitg; ind < ne00; ind += tptg.x) { - (( device int32_t *) (( device char *) dst + i11*nb2 + i10*nb1))[ind] = - ((const device int32_t *) ((const device char *) src0 + i02*nb02 + r*nb01))[ind]; - } -} - - -#define BLOCK_SIZE_M 64 // 8 simdgroup matrices from matrix A -#define BLOCK_SIZE_N 32 // 4 simdgroup matrices from matrix B -#define BLOCK_SIZE_K 32 -#define THREAD_MAT_M 4 // each thread take 4 simdgroup matrices from matrix A -#define THREAD_MAT_N 2 // each thread take 2 simdgroup matrices from matrix B -#define THREAD_PER_BLOCK 128 -#define THREAD_PER_ROW 2 // 2 thread for each row in matrix A to load numbers -#define THREAD_PER_COL 4 // 4 thread for each row in matrix B to load numbers -#define SG_MAT_SIZE 64 // simdgroup matrix is of shape 8x8 -#define SG_MAT_ROW 8 - -// each block_q contains 16*nl weights -template -kernel void kernel_mul_mm(device const uchar * src0, - device const uchar * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup uchar * shared_memory [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - threadgroup T * sa = (threadgroup T *)(shared_memory); - threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); - - const uint r0 = tgpig.y; - const uint r1 = tgpig.x; - const uint im = tgpig.z; - - // if this block is of 64x32 shape or smaller - short n_rows = (ne0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M; - short n_cols = (ne1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N; - - // a thread shouldn't load data outside of the matrix - short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1; - short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1; - - simdgroup_T8x8 ma[4]; - simdgroup_float8x8 mb[2]; - simdgroup_float8x8 mc[8]; - - for (short i = 0; i < 8; i++){ - mc[i] = make_filled_simdgroup_matrix(0.f); - } - - short il = (tiitg % THREAD_PER_ROW); - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - uint offset0 = (i12/r2)*nb02 + (i13/r3)*nb03; - ushort offset1 = il/nl; - - device const block_q * x = (device const block_q *)(src0 + (r0*BLOCK_SIZE_M + thread_row)*nb01 + offset0) + offset1; - device const float * y = (device const float *)(src1 - + nb13 * i13 - + nb12 * i12 - + nb11 * (r1 * BLOCK_SIZE_N + thread_col) - + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); - - for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { - // load data and store to threadgroup memory - T4x4 temp_a; - dequantize_func(x, il, temp_a); - threadgroup_barrier(mem_flags::mem_threadgroup); - - #pragma unroll(16) - for (short i = 0; i < 16; i++) { - *(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \ - + (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \ - + (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = temp_a[i/4][i%4]; - } - - *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL)*8*32 + 8*(tiitg/THREAD_PER_COL)) = *((device float2x4 *) y); - - il = (il + 2 < nl) ? il + 2 : il % 2; - x = (il < 2) ? x + (2+nl-1)/nl : x; - y += BLOCK_SIZE_K; - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // load matrices from threadgroup memory and conduct outer products - threadgroup T * lsma = (sa + THREAD_MAT_M*SG_MAT_SIZE*(sgitg%2)); - threadgroup float * lsmb = (sb + THREAD_MAT_N*SG_MAT_SIZE*(sgitg/2)); - - #pragma unroll(4) - for (short ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { - #pragma unroll(4) - for (short i = 0; i < 4; i++) { - simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); - } - simdgroup_barrier(mem_flags::mem_none); - #pragma unroll(2) - for (short i = 0; i < 2; i++) { - simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); - } - - lsma += BLOCK_SIZE_M/SG_MAT_ROW * SG_MAT_SIZE; - lsmb += BLOCK_SIZE_N/SG_MAT_ROW * SG_MAT_SIZE; - - #pragma unroll(8) - for (short i = 0; i < 8; i++){ - simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]); - } - } - } - - if ((r0 + 1) * BLOCK_SIZE_M <= ne0 && (r1 + 1) * BLOCK_SIZE_N <= ne1) { - device float * C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \ - + (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0; - for (short i = 0; i < 8; i++) { - simdgroup_store(mc[i], C + 8 * (i%4) + 8 * ne0 * (i/4), ne0); - } - } else { - // block is smaller than 64x32, we should avoid writing data outside of the matrix - threadgroup_barrier(mem_flags::mem_threadgroup); - threadgroup float * temp_str = ((threadgroup float *) shared_memory) \ - + 32 * (sgitg&1) + (16 * (sgitg>>1))*BLOCK_SIZE_M; - for (short i = 0; i < 8; i++) { - simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M); - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - if (sgitg == 0) { - for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { - device float * D = dst + (r0*BLOCK_SIZE_M) + (r1*BLOCK_SIZE_N + j)*ne0 + im*ne1*ne0; - device float4 * D4 = (device float4 *) D; - - threadgroup float * C = temp_str + (j*BLOCK_SIZE_M); - threadgroup float4 * C4 = (threadgroup float4 *) C; - - int i = 0; - for (; i < n_rows/4; i++) { - *(D4 + i) = *(C4 + i); - } - - i *= 4; - for (; i < n_rows; i++) { - *(D + i) = *(C + i); - } - } - } - } -} - -// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in rowids -template -void kernel_mul_mm_id_impl( - device const uchar * src0, - device const uchar * src1, - threadgroup ushort2 * rowids, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - int64_t ne1, - int64_t ne0ne1, - threadgroup uchar * shared_memory, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - threadgroup half * sa = (threadgroup half *)(shared_memory); - threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); - - const uint r0 = tgpig.y; - const uint r1 = tgpig.x; - - if (r1 * BLOCK_SIZE_N >= ne1) return; - - // if this block is of 64x32 shape or smaller - short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M; - short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N; - - // a thread shouldn't load data outside of the matrix - short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1; - short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1; - - simdgroup_half8x8 ma[4]; - simdgroup_float8x8 mb[2]; - simdgroup_float8x8 c_res[8]; - for (int i = 0; i < 8; i++){ - c_res[i] = make_filled_simdgroup_matrix(0.f); - } - short il = (tiitg % THREAD_PER_ROW); - - ushort offset1 = il/nl; - - threadgroup const auto & id = rowids[r1 * BLOCK_SIZE_N + thread_col]; - - device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01) + offset1; - device const float * y = (device const float *)(src1 - + nb12 * id[1] - + nb11 * (id[0] % ne11) - + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); - - for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { - // load data and store to threadgroup memory - half4x4 temp_a; - dequantize_func(x, il, temp_a); - threadgroup_barrier(mem_flags::mem_threadgroup); - - for (int i = 0; i < 16; i++) { - *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \ - + (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \ - + (tiitg / THREAD_PER_ROW) % 8 + (i & 7) * 8) = temp_a[i/4][i%4]; - } - - *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y); - - il = (il + 2 < nl) ? il + 2 : il % 2; - x = (il < 2) ? x + (2+nl-1)/nl : x; - y += BLOCK_SIZE_K; - - threadgroup_barrier(mem_flags::mem_threadgroup); - - // load matrices from threadgroup memory and conduct outer products - threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); - threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); - - for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { - for (int i = 0; i < 4; i++) { - simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); - } - simdgroup_barrier(mem_flags::mem_none); - for (int i = 0; i < 2; i++) { - simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); - } - - lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; - lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE; - - for (int i = 0; i < 8; i++){ - simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]); - } - } - } - - { - threadgroup_barrier(mem_flags::mem_threadgroup); - threadgroup float * temp_str = ((threadgroup float *)shared_memory) \ - + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; - for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - device float * C = dst + (BLOCK_SIZE_M * r0); - if (sgitg == 0) { - for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { - threadgroup const auto & jid = rowids[r1 * BLOCK_SIZE_N + j]; - int joff = jid[0] * ne0 + jid[1] * ne0ne1; - for (int i = 0; i < n_rows; i++) { - *(C + i + joff) = *(temp_str + i + j * BLOCK_SIZE_M); - } - } - } - } -} - -template -kernel void kernel_mul_mm_id( - device const uchar * src0s, - device const uchar * src1, - device float * dst, - device const uchar * ids, - constant int64_t & nei0, - constant int64_t & nei1, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - threadgroup uchar * shared_memory [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - const int32_t i02 = tgpig.z; - tgpig.z = 0; - - device const uchar * src0 = src0s + i02*nb02; - - // row indices - threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shared_memory + 8192); - - // TODO: parallelize this loop - int64_t _ne1 = 0; - for (ushort ii1 = 0; ii1 < nei1; ii1++) { - for (ushort ii0 = 0; ii0 < nei0; ii0++) { - int32_t id = ((device int32_t *) (ids + ii1*nbi1))[ii0]; - if (id == i02) { - //if (tiitg == 0) { - rowids[_ne1] = ushort2(ii0, ii1); - //} - _ne1++; - } - } - } - - threadgroup_barrier(mem_flags::mem_threadgroup); - - kernel_mul_mm_id_impl( - src0, - src1, - rowids, - dst, - ne00, - ne02, - nb01, - nb02, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - _ne1, - ne0*ne1, - shared_memory, - tgpig, - tiitg, - sgitg); -} - -#define QK_NL 16 - -// -// get rows -// - -typedef decltype(kernel_get_rows_f) get_rows_f_t; - -template [[host_name("kernel_get_rows_f32")]] kernel get_rows_f_t kernel_get_rows_f; -template [[host_name("kernel_get_rows_f16")]] kernel get_rows_f_t kernel_get_rows_f; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f; -#endif - -typedef decltype(kernel_get_rows_q) get_rows_q_t; - -template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_q_t kernel_get_rows_q; -template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_q_t kernel_get_rows_q; - -// -// matrix-matrix multiplication -// - -typedef decltype(kernel_mul_mm) mat_mm_t; - -template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_mul_mm_bf16_f32")]] kernel mat_mm_t kernel_mul_mm; -#endif -template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; - -// -// indirect matrix-matrix multiplication -// - -typedef decltype(kernel_mul_mm_id) mat_mm_id_t; - -template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_mul_mm_id_bf16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#endif -template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; - -// -// matrix-vector multiplication -// - -typedef void (kernel_mul_mv_impl_t)( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - uint3 tgpig, - uint tiisg); - -typedef void (kernel_mul_mv2_impl_t)( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg); - -template -void mmv_fn( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - int64_t ne13, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint64_t nb1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiitg, - uint tiisg, - uint sgitg) { - impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,nb03,ne10,ne11,ne12,nb10,nb11,nb12,nb13,ne0,ne1,r2,r3,tgpig,tiisg); -} - -template -void mmv_fn( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - int64_t ne13, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint64_t nb1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiitg, - uint tiisg, - uint sgitg) { - impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); -} - -typedef decltype(mmv_fn>) mul_mv_impl_fn_t; - -template -kernel void kernel_mul_mv_id( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant int64_t & nei0, - constant int64_t & nei1, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int iid1 = tgpig.z/nei0; - const int idx = tgpig.z%nei0; - - tgpig.z = 0; - - const int32_t i02 = ((device const int32_t *) (ids + iid1*nbi1))[idx]; - - const int64_t i11 = idx % ne11; - const int64_t i12 = iid1; - - const int64_t i1 = idx; - const int64_t i2 = i12; - - device const char * src0_cur = src0s + i02*nb02; - device const char * src1_cur = src1 + i11*nb11 + i12*nb12; - device float * dst_cur = dst + i1*ne0 + i2*ne1*ne0; - - impl_fn( - /* src0 */ src0_cur, - /* src1 */ src1_cur, - /* dst */ dst_cur, - /* ne00 */ ne00, - /* ne01 */ ne01, - /* ne02 */ 1, // ne02, - /* nb00 */ nb00, - /* nb01 */ nb01, - /* nb02 */ nb02, - /* nb03 */ nb02, // ne02 == 1 - /* ne10 */ ne10, - /* ne11 */ 1, // ne11, - /* ne12 */ 1, // ne12, - /* ne13 */ 1, // ne13, - /* nb10 */ nb10, - /* nb11 */ nb11, - /* nb12 */ nb12, - /* ne13 */ nb12, // ne12 == 1 - /* ne0 */ ne0, - /* ne1 */ 1, // ne1, - /* nb1 */ nb1, - /* r2 */ 1, - /* r3 */ 1, - shared_values, - tgpig, - tiitg, - tiisg, - sgitg); -} - -typedef decltype(kernel_mul_mv_id>>) kernel_mul_mv_id_t; - -template [[host_name("kernel_mul_mv_id_f32_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -#if defined(GGML_METAL_USE_BF16) -template [[host_name("kernel_mul_mv_id_bf16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -#endif -template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; -template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq1_m_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq2_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; - -kernel void kernel_pool_2d_max_f32( - device const float * src0, - device float * dst, - constant int32_t & k0, - constant int32_t & k1, - constant int32_t & s0, - constant int32_t & s1, - constant int32_t & p0, - constant int32_t & p1, - constant int64_t & IH, - constant int64_t & IW, - constant int64_t & OH, - constant int64_t & OW, - constant int64_t & parallel_elements, - uint gid[[thread_position_in_grid]]) { - - if (gid >= parallel_elements) { - return; - } - - const int idx = gid; - const int I_HW = IH * IW; - const int O_HW = OH * OW; - const int nc = idx / O_HW; - const int cur_oh = idx % O_HW / OW; - const int cur_ow = idx % O_HW % OW; - - device const float * i_ptr = src0 + nc * I_HW; - device float * o_ptr = dst + nc * O_HW; - - const int start_h = cur_oh * s1 - p1; - const int bh = MAX(0, start_h); - const int eh = MIN(IH, start_h + k1); - const int start_w = cur_ow * s0 - p0; - const int bw = MAX(0, start_w); - const int ew = MIN(IW, start_w + k0); - - float res = -INFINITY; - - for (int i = bh; i < eh; i += 1) { - for (int j = bw; j < ew; j += 1) { - res = MAX(res, i_ptr[i * IW + j]); - } - } - - o_ptr[cur_oh * OW + cur_ow] = res; -} - -kernel void kernel_pool_2d_avg_f32( - device const float * src0, - device float * dst, - constant int32_t & k0, - constant int32_t & k1, - constant int32_t & s0, - constant int32_t & s1, - constant int32_t & p0, - constant int32_t & p1, - constant int64_t & IH, - constant int64_t & IW, - constant int64_t & OH, - constant int64_t & OW, - constant int64_t & parallel_elements, - uint gid[[thread_position_in_grid]]) { - - if (gid >= parallel_elements) { - return; - } - - const int idx = gid; - const int I_HW = IH * IW; - const int O_HW = OH * OW; - const int nc = idx / O_HW; - const int cur_oh = idx % O_HW / OW; - const int cur_ow = idx % O_HW % OW; - - device const float * i_ptr = src0 + nc * I_HW; - device float * o_ptr = dst + nc * O_HW; - - const int start_h = cur_oh * s1 - p1; - const int bh = MAX(0, start_h); - const int eh = MIN(IH, start_h + k1); - const int start_w = cur_ow * s0 - p0; - const int bw = MAX(0, start_w); - const int ew = MIN(IW, start_w + k0); - // const float scale = 1. / ((eh - bh) * (ew - bw)); - const float scale = 1. / (k0 * k1); - - float res = 0; - - for (int i = bh; i < eh; i += 1) { - for (int j = bw; j < ew; j += 1) { - float cur = i_ptr[i * IW + j]; - res += cur * scale; - } - } - - o_ptr[cur_oh * OW + cur_ow] = res; -} diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m index ee476302..d1abb3ce 100644 --- a/ggml/src/ggml-metal/ggml-metal.m +++ b/ggml/src/ggml-metal/ggml-metal.m @@ -3651,7 +3651,7 @@ static enum ggml_status ggml_metal_graph_compute( dispatch_apply(n_cb, ctx->d_queue, ctx->encode_async); // wait for completion and check status of each command buffer - // needed to detect if the device ran out-of-memory for example (ggml/1881) + // needed to detect if the device ran out-of-memory for example (#1881) { id command_buffer = ctx->command_buffers[n_cb]; [command_buffer waitUntilCompleted]; diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt new file mode 100644 index 00000000..f3c01369 --- /dev/null +++ b/ggml/src/ggml-musa/CMakeLists.txt @@ -0,0 +1,100 @@ +if (NOT EXISTS $ENV{MUSA_PATH}) + if (NOT EXISTS /opt/musa) + set(MUSA_PATH /usr/local/musa) + else() + set(MUSA_PATH /opt/musa) + endif() +else() + set(MUSA_PATH $ENV{MUSA_PATH}) +endif() + +set(CMAKE_C_COMPILER "${MUSA_PATH}/bin/clang") +set(CMAKE_C_EXTENSIONS OFF) +set(CMAKE_CXX_COMPILER "${MUSA_PATH}/bin/clang++") +set(CMAKE_CXX_EXTENSIONS OFF) + +list(APPEND CMAKE_MODULE_PATH "${MUSA_PATH}/cmake") + +find_package(MUSAToolkit) + +if (MUSAToolkit_FOUND) + message(STATUS "MUSA Toolkit found") + + file(GLOB GGML_HEADERS_MUSA "../ggml-cuda/*.cuh") + list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h") + + file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu") + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + + if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + endif() + + set_source_files_properties(${GGML_SOURCES_MUSA} PROPERTIES LANGUAGE CXX) + foreach(SOURCE ${GGML_SOURCES_MUSA}) + set_property(SOURCE ${SOURCE} PROPERTY COMPILE_FLAGS "-x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22") + endforeach() + + add_library(ggml-musa + ${GGML_HEADERS_MUSA} + ${GGML_SOURCES_MUSA}) + + target_link_libraries(ggml-musa PRIVATE ggml-base) + target_include_directories(ggml-musa PRIVATE . ..) + + # TODO: do not use CUDA definitions for MUSA + target_compile_definitions(ggml PUBLIC GGML_USE_CUDA) + + add_compile_definitions(GGML_USE_MUSA) + add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) + + if (GGML_CUDA_GRAPHS) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) + endif() + + if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) + endif() + + if (GGML_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() + + if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_F16) + endif() + + if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + + if (GGML_STATIC) + target_link_libraries(ggml-musa PRIVATE MUSA::musart_static MUSA::mublas_static) + else() + target_link_libraries(ggml-musa PRIVATE MUSA::musart MUSA::mublas) + endif() + + if (GGML_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the musa driver lib (libmusa.so) + else() + target_link_libraries(ggml-musa PRIVATE MUSA::musa_driver) + endif() +else() + message(FATAL_ERROR "MUSA Toolkit not found") +endif() diff --git a/ggml/src/ggml-rpc.cpp b/ggml/src/ggml-rpc.cpp deleted file mode 100644 index 8a772f22..00000000 --- a/ggml/src/ggml-rpc.cpp +++ /dev/null @@ -1,1403 +0,0 @@ -#include "ggml-rpc.h" -#include "ggml-impl.h" -#include "ggml-backend-impl.h" - -#include -#include -#include -#include -#include -#include -#include -#ifdef _WIN32 -# define WIN32_LEAN_AND_MEAN -# ifndef NOMINMAX -# define NOMINMAX -# endif -# include -# include -#else -# include -# include -# include -# include -# include -# include -# include -#endif -#include - -#define UNUSED GGML_UNUSED - -#define GGML_DEBUG 0 -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#ifdef _WIN32 -typedef SOCKET sockfd_t; -using ssize_t = __int64; -#else -typedef int sockfd_t; -#endif - -// cross-platform socket -struct socket_t { - sockfd_t fd; - socket_t(sockfd_t fd) : fd(fd) {} - ~socket_t() { - GGML_PRINT_DEBUG("[%s] closing socket %d\n", __func__, this->fd); -#ifdef _WIN32 - closesocket(this->fd); -#else - close(this->fd); -#endif - } -}; - -// all RPC structures must be packed -#pragma pack(push, 1) -// ggml_tensor is serialized into rpc_tensor -struct rpc_tensor { - uint64_t id; - uint32_t type; - uint64_t buffer; - uint32_t ne[GGML_MAX_DIMS]; - uint32_t nb[GGML_MAX_DIMS]; - uint32_t op; - int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; - int32_t flags; - uint64_t src[GGML_MAX_SRC]; - uint64_t view_src; - uint64_t view_offs; - uint64_t data; - char name[GGML_MAX_NAME]; - - char padding[4]; -}; - -static_assert(sizeof(rpc_tensor) % 8 == 0, "rpc_tensor size must be multiple of 8"); - -// RPC commands -enum rpc_cmd { - RPC_CMD_ALLOC_BUFFER = 0, - RPC_CMD_GET_ALIGNMENT, - RPC_CMD_GET_MAX_SIZE, - RPC_CMD_BUFFER_GET_BASE, - RPC_CMD_FREE_BUFFER, - RPC_CMD_BUFFER_CLEAR, - RPC_CMD_SET_TENSOR, - RPC_CMD_GET_TENSOR, - RPC_CMD_COPY_TENSOR, - RPC_CMD_GRAPH_COMPUTE, - RPC_CMD_GET_DEVICE_MEMORY, - RPC_CMD_COUNT, -}; - -struct rpc_msg_alloc_buffer_req { - uint64_t size; -}; - -struct rpc_msg_alloc_buffer_rsp { - uint64_t remote_ptr; - uint64_t remote_size; -}; - -struct rpc_msg_get_alignment_rsp { - uint64_t alignment; -}; - -struct rpc_msg_get_max_size_rsp { - uint64_t max_size; -}; - -struct rpc_msg_buffer_get_base_req { - uint64_t remote_ptr; -}; - -struct rpc_msg_buffer_get_base_rsp { - uint64_t base_ptr; -}; - -struct rpc_msg_free_buffer_req { - uint64_t remote_ptr; -}; - -struct rpc_msg_buffer_clear_req { - uint64_t remote_ptr; - uint8_t value; -}; - -struct rpc_msg_get_tensor_req { - rpc_tensor tensor; - uint64_t offset; - uint64_t size; -}; - -struct rpc_msg_copy_tensor_req { - rpc_tensor src; - rpc_tensor dst; -}; - -struct rpc_msg_copy_tensor_rsp { - uint8_t result; -}; - -struct rpc_msg_graph_compute_rsp { - uint8_t result; -}; - -struct rpc_msg_get_device_memory_rsp { - uint64_t free_mem; - uint64_t total_mem; -}; -#pragma pack(pop) - -// RPC data structures - -static ggml_guid_t ggml_backend_rpc_guid() { - static ggml_guid guid = {0x99, 0x68, 0x5b, 0x6c, 0xd2, 0x83, 0x3d, 0x24, 0x25, 0x36, 0x72, 0xe1, 0x5b, 0x0e, 0x14, 0x03}; - return &guid; -} - -struct ggml_backend_rpc_buffer_type_context { - std::string endpoint; - std::string name; - size_t alignment; - size_t max_size; -}; - -struct ggml_backend_rpc_context { - std::string endpoint; - std::string name; -}; - -struct ggml_backend_rpc_buffer_context { - std::shared_ptr sock; - std::unordered_map base_cache; - uint64_t remote_ptr; -}; - -// RPC helper functions - -static std::shared_ptr make_socket(sockfd_t fd) { -#ifdef _WIN32 - if (fd == INVALID_SOCKET) { - return nullptr; - } -#else - if (fd < 0) { - return nullptr; - } -#endif - return std::make_shared(fd); -} - -static bool set_no_delay(sockfd_t sockfd) { - int flag = 1; - // set TCP_NODELAY to disable Nagle's algorithm - int ret = setsockopt(sockfd, IPPROTO_TCP, TCP_NODELAY, (char *)&flag, sizeof(int)); - return ret == 0; -} - -static bool set_reuse_addr(sockfd_t sockfd) { - int flag = 1; - int ret = setsockopt(sockfd, SOL_SOCKET, SO_REUSEADDR, (char *)&flag, sizeof(int)); - return ret == 0; -} - -static std::shared_ptr socket_connect(const char * host, int port) { - struct sockaddr_in addr; - auto sockfd = socket(AF_INET, SOCK_STREAM, 0); - auto sock_ptr = make_socket(sockfd); - if (sock_ptr == nullptr) { - return nullptr; - } - if (!set_no_delay(sockfd)) { - fprintf(stderr, "Failed to set TCP_NODELAY\n"); - return nullptr; - } - addr.sin_family = AF_INET; - addr.sin_port = htons(port); - struct hostent * server = gethostbyname(host); - if (server == NULL) { - fprintf(stderr, "Cannot resolve host '%s'\n", host); - return nullptr; - } - memcpy(&addr.sin_addr.s_addr, server->h_addr, server->h_length); - if (connect(sock_ptr->fd, (struct sockaddr *)&addr, sizeof(addr)) < 0) { - return nullptr; - } - return sock_ptr; -} - -static std::shared_ptr socket_accept(sockfd_t srv_sockfd) { - auto client_socket_fd = accept(srv_sockfd, NULL, NULL); - auto client_socket = make_socket(client_socket_fd); - if (client_socket == nullptr) { - return nullptr; - } - if (!set_no_delay(client_socket_fd)) { - fprintf(stderr, "Failed to set TCP_NODELAY\n"); - return nullptr; - } - return client_socket; -} - -static std::shared_ptr create_server_socket(const char * host, int port) { - auto sockfd = socket(AF_INET, SOCK_STREAM, 0); - auto sock = make_socket(sockfd); - if (sock == nullptr) { - return nullptr; - } - if (!set_reuse_addr(sockfd)) { - fprintf(stderr, "Failed to set SO_REUSEADDR\n"); - return nullptr; - } - if (inet_addr(host) == INADDR_NONE) { - fprintf(stderr, "Invalid host address: %s\n", host); - return nullptr; - } - struct sockaddr_in serv_addr; - serv_addr.sin_family = AF_INET; - serv_addr.sin_addr.s_addr = inet_addr(host); - serv_addr.sin_port = htons(port); - - if (bind(sockfd, (struct sockaddr *) &serv_addr, sizeof(serv_addr)) < 0) { - return nullptr; - } - if (listen(sockfd, 1) < 0) { - return nullptr; - } - return sock; -} - -static bool send_data(sockfd_t sockfd, const void * data, size_t size) { - size_t bytes_sent = 0; - while (bytes_sent < size) { - ssize_t n = send(sockfd, (const char *)data + bytes_sent, size - bytes_sent, 0); - if (n < 0) { - return false; - } - bytes_sent += n; - } - return true; -} - -static bool recv_data(sockfd_t sockfd, void * data, size_t size) { - size_t bytes_recv = 0; - while (bytes_recv < size) { - ssize_t n = recv(sockfd, (char *)data + bytes_recv, size - bytes_recv, 0); - if (n <= 0) { - return false; - } - bytes_recv += n; - } - return true; -} - -static bool send_msg(sockfd_t sockfd, const void * msg, size_t msg_size) { - if (!send_data(sockfd, &msg_size, sizeof(msg_size))) { - return false; - } - return send_data(sockfd, msg, msg_size); -} - -static bool recv_msg(sockfd_t sockfd, void * msg, size_t msg_size) { - uint64_t size; - if (!recv_data(sockfd, &size, sizeof(size))) { - return false; - } - if (size != msg_size) { - return false; - } - return recv_data(sockfd, msg, msg_size); -} - -static bool recv_msg(sockfd_t sockfd, std::vector & input) { - uint64_t size; - if (!recv_data(sockfd, &size, sizeof(size))) { - return false; - } - try { - input.resize(size); - } catch (const std::bad_alloc & e) { - fprintf(stderr, "Failed to allocate input buffer of size %" PRIu64 "\n", size); - return false; - } - return recv_data(sockfd, input.data(), size); -} - -static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) { - size_t pos = endpoint.find(':'); - if (pos == std::string::npos) { - return false; - } - host = endpoint.substr(0, pos); - port = std::stoi(endpoint.substr(pos + 1)); - return true; -} - -// RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | -// RPC response: | response_size (8 bytes) | response_data (response_size bytes) | -static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const void * input, size_t input_size, void * output, size_t output_size) { - uint8_t cmd_byte = cmd; - if (!send_data(sock->fd, &cmd_byte, sizeof(cmd_byte))) { - return false; - } - if (!send_data(sock->fd, &input_size, sizeof(input_size))) { - return false; - } - if (!send_data(sock->fd, input, input_size)) { - return false; - } - // TODO: currently the output_size is always known, do we need support for commands with variable output size? - // even if we do, we can skip sending output_size from the server for commands with known output size - uint64_t out_size; - if (!recv_data(sock->fd, &out_size, sizeof(out_size))) { - return false; - } - if (out_size != output_size) { - return false; - } - if (!recv_data(sock->fd, output, output_size)) { - return false; - } - return true; -} - -// RPC client-side implementation - -static std::shared_ptr get_socket(const std::string & endpoint) { - static std::mutex mutex; - std::lock_guard lock(mutex); - static std::unordered_map> sockets; - static bool initialized = false; - - auto it = sockets.find(endpoint); - if (it != sockets.end()) { - if (auto sock = it->second.lock()) { - return sock; - } - } - std::string host; - int port; - if (!parse_endpoint(endpoint, host, port)) { - return nullptr; - } -#ifdef _WIN32 - if (!initialized) { - WSADATA wsaData; - int res = WSAStartup(MAKEWORD(2, 2), &wsaData); - if (res != 0) { - return nullptr; - } - initialized = true; - } -#else - UNUSED(initialized); -#endif - auto sock = socket_connect(host.c_str(), port); - if (sock == nullptr) { - return nullptr; - } - GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd); - sockets[endpoint] = sock; - return sock; -} - -static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - rpc_msg_free_buffer_req request = {ctx->remote_ptr}; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, &request, sizeof(request), nullptr, 0); - GGML_ASSERT(status); - delete ctx; -} - -static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - if (ctx->base_cache.find(buffer) != ctx->base_cache.end()) { - return ctx->base_cache[buffer]; - } - rpc_msg_buffer_get_base_req request = {ctx->remote_ptr}; - rpc_msg_buffer_get_base_rsp response; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_GET_BASE, &request, sizeof(request), &response, sizeof(response)); - GGML_ASSERT(status); - void * base_ptr = reinterpret_cast(response.base_ptr); - ctx->base_cache[buffer] = base_ptr; - return base_ptr; -} - -static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { - rpc_tensor result; - result.id = reinterpret_cast(tensor); - result.type = tensor->type; - if (tensor->buffer) { - ggml_backend_buffer_t buffer = tensor->buffer; - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - result.buffer = ctx->remote_ptr; - } else { - result.buffer = 0; - } - for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { - result.ne[i] = tensor->ne[i]; - result.nb[i] = tensor->nb[i]; - } - result.op = tensor->op; - for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { - result.op_params[i] = tensor->op_params[i]; - } - result.flags = tensor->flags; - for (uint32_t i = 0; i < GGML_MAX_SRC; i++) { - result.src[i] = reinterpret_cast(tensor->src[i]); - } - result.view_src = reinterpret_cast(tensor->view_src); - result.view_offs = tensor->view_offs; - result.data = reinterpret_cast(tensor->data); - snprintf(result.name, GGML_MAX_NAME, "%s", tensor->name); - return result; -} - -static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - UNUSED(buffer); - if (ggml_is_quantized(tensor->type)) { - // TODO: this check is due to MATRIX_ROW_PADDING in CUDA and should be generalized - GGML_ASSERT(tensor->ne[0] % 512 == 0 && "unsupported quantized tensor"); - } -} - -static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - // input serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | - size_t input_size = sizeof(rpc_tensor) + sizeof(uint64_t) + size; - std::vector input(input_size, 0); - rpc_tensor rpc_tensor = serialize_tensor(tensor); - memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); - memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); - memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR, input.data(), input.size(), nullptr, 0); - GGML_ASSERT(status); -} - -static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - rpc_msg_get_tensor_req request; - request.tensor = serialize_tensor(tensor); - request.offset = offset; - request.size = size; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_GET_TENSOR, &request, sizeof(request), data, size); - GGML_ASSERT(status); -} - -static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { - // check if src and dst are on the same server - ggml_backend_buffer_t src_buffer = src->buffer; - ggml_backend_rpc_buffer_context * src_ctx = (ggml_backend_rpc_buffer_context *)src_buffer->context; - ggml_backend_buffer_t dst_buffer = dst->buffer; - ggml_backend_rpc_buffer_context * dst_ctx = (ggml_backend_rpc_buffer_context *)dst_buffer->context; - if (src_ctx->sock != dst_ctx->sock) { - return false; - } - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - rpc_msg_copy_tensor_req request; - request.src = serialize_tensor(src); - request.dst = serialize_tensor(dst); - rpc_msg_copy_tensor_rsp response; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_COPY_TENSOR, &request, sizeof(request), &response, sizeof(response)); - GGML_ASSERT(status); - return response.result; -} - -static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; - rpc_msg_buffer_clear_req request = {ctx->remote_ptr, value}; - bool status = send_rpc_cmd(ctx->sock, RPC_CMD_BUFFER_CLEAR, &request, sizeof(request), nullptr, 0); - GGML_ASSERT(status); -} - -static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = { - /* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer, - /* .get_base = */ ggml_backend_rpc_buffer_get_base, - /* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_rpc_buffer_cpy_tensor, - /* .clear = */ ggml_backend_rpc_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_rpc_buffer_type_name(ggml_backend_buffer_type_t buft) { - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - return buft_ctx->name.c_str(); -} - -static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - rpc_msg_alloc_buffer_req request = {size}; - rpc_msg_alloc_buffer_rsp response; - auto sock = get_socket(buft_ctx->endpoint); - bool status = send_rpc_cmd(sock, RPC_CMD_ALLOC_BUFFER, &request, sizeof(request), &response, sizeof(response)); - GGML_ASSERT(status); - if (response.remote_ptr != 0) { - ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, - ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{sock, {}, response.remote_ptr}, - response.remote_size); - return buffer; - } else { - return nullptr; - } -} - -static size_t get_alignment(const std::shared_ptr & sock) { - rpc_msg_get_alignment_rsp response; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALIGNMENT, nullptr, 0, &response, sizeof(response)); - GGML_ASSERT(status); - return response.alignment; -} - -static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - return buft_ctx->alignment; -} - -static size_t get_max_size(const std::shared_ptr & sock) { - rpc_msg_get_max_size_rsp response; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_MAX_SIZE, nullptr, 0, &response, sizeof(response)); - GGML_ASSERT(status); - return response.max_size; -} - -static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - return buft_ctx->max_size; -} - -static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - UNUSED(buft); - return ggml_nbytes(tensor); -} - -static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { - /* .get_name = */ ggml_backend_rpc_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_rpc_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_rpc_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_rpc_get_max_size, - /* .get_alloc_size = */ ggml_backend_rpc_buffer_type_get_alloc_size, - /* .is_host = */ NULL, -}; - -static const char * ggml_backend_rpc_name(ggml_backend_t backend) { - ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - - return rpc_ctx->name.c_str(); -} - -static void ggml_backend_rpc_free(ggml_backend_t backend) { - ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - delete rpc_ctx; - delete backend; -} - -static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { - UNUSED(backend); - // this is no-op because we don't have any async operations -} - -static void add_tensor(ggml_tensor * tensor, std::vector & tensors, std::unordered_set & visited) { - if (tensor == nullptr) { - return; - } - if (visited.find(tensor) != visited.end()) { - return; - } - visited.insert(tensor); - for (int i = 0; i < GGML_MAX_SRC; i++) { - add_tensor(tensor->src[i], tensors, visited); - } - add_tensor(tensor->view_src, tensors, visited); - tensors.push_back(serialize_tensor(tensor)); -} - -static void serialize_graph(const ggml_cgraph * cgraph, std::vector & output) { - uint32_t n_nodes = cgraph->n_nodes; - std::vector tensors; - std::unordered_set visited; - for (uint32_t i = 0; i < n_nodes; i++) { - add_tensor(cgraph->nodes[i], tensors, visited); - } - // serialization format: - // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | - uint32_t n_tensors = tensors.size(); - int output_size = sizeof(uint32_t) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t) + n_tensors * sizeof(rpc_tensor); - output.resize(output_size, 0); - memcpy(output.data(), &n_nodes, sizeof(n_nodes)); - for (uint32_t i = 0; i < n_nodes; i++) { - memcpy(output.data() + sizeof(n_nodes) + i * sizeof(uint64_t), &cgraph->nodes[i], sizeof(uint64_t)); - } - uint32_t * out_ntensors = (uint32_t *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t)); - *out_ntensors = n_tensors; - rpc_tensor * out_tensors = (rpc_tensor *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t)); - memcpy(out_tensors, tensors.data(), n_tensors * sizeof(rpc_tensor)); -} - -static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - std::vector input; - serialize_graph(cgraph, input); - rpc_msg_graph_compute_rsp response; - auto sock = get_socket(rpc_ctx->endpoint); - bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input.data(), input.size(), &response, sizeof(response)); - GGML_ASSERT(status); - return (enum ggml_status)response.result; -} - -static ggml_backend_i ggml_backend_rpc_interface = { - /* .get_name = */ ggml_backend_rpc_name, - /* .free = */ ggml_backend_rpc_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ ggml_backend_rpc_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_rpc_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { - static std::mutex mutex; - std::lock_guard lock(mutex); - // NOTE: buffer types are allocated and never freed; this is by design - static std::unordered_map buft_map; - auto it = buft_map.find(endpoint); - if (it != buft_map.end()) { - return it->second; - } - auto sock = get_socket(endpoint); - if (sock == nullptr) { - fprintf(stderr, "Failed to connect to %s\n", endpoint); - return nullptr; - } - size_t alignment = get_alignment(sock); - size_t max_size = get_max_size(sock); - ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { - /* .endpoint = */ endpoint, - /* .name = */ "RPC[" + std::string(endpoint) + "]", - /* .alignment = */ alignment, - /* .max_size = */ max_size - }; - - ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type { - /* .iface = */ ggml_backend_rpc_buffer_type_interface, - /* .device = */ ggml_backend_rpc_add_device(endpoint), - /* .context = */ buft_ctx - }; - buft_map[endpoint] = buft; - return buft; -} - -ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { - ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { - /* .endpoint = */ endpoint, - /* .name = */ "RPC[" + std::string(endpoint) + "]", - }; - - ggml_backend_t backend = new ggml_backend { - /* .guid = */ ggml_backend_rpc_guid(), - /* .interface = */ ggml_backend_rpc_interface, - /* .device = */ ggml_backend_rpc_add_device(endpoint), - /* .context = */ ctx - }; - return backend; -} - -GGML_API bool ggml_backend_is_rpc(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_rpc_guid()); -} - -static void get_device_memory(const std::shared_ptr & sock, size_t * free, size_t * total) { - rpc_msg_get_device_memory_rsp response; - bool status = send_rpc_cmd(sock, RPC_CMD_GET_DEVICE_MEMORY, nullptr, 0, &response, sizeof(response)); - GGML_ASSERT(status); - *free = response.free_mem; - *total = response.total_mem; -} - -GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { - auto sock = get_socket(endpoint); - if (sock == nullptr) { - *free = 0; - *total = 0; - return; - } - get_device_memory(sock, free, total); -} - -// RPC server-side implementation - -class rpc_server { -public: - rpc_server(ggml_backend_t backend) : backend(backend) {} - ~rpc_server(); - - void alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response); - void get_alignment(rpc_msg_get_alignment_rsp & response); - void get_max_size(rpc_msg_get_max_size_rsp & response); - bool buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response); - bool free_buffer(const rpc_msg_free_buffer_req & request); - bool buffer_clear(const rpc_msg_buffer_clear_req & request); - bool set_tensor(const std::vector & input); - bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response); - bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response); - bool graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response); - -private: - ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor); - ggml_tensor * create_node(uint64_t id, - struct ggml_context * ctx, - const std::unordered_map & tensor_ptrs, - std::unordered_map & tensor_map); - - - ggml_backend_t backend; - std::unordered_set buffers; -}; - -void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response) { - ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); - ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, request.size); - response.remote_ptr = 0; - response.remote_size = 0; - if (buffer != nullptr) { - response.remote_ptr = reinterpret_cast(buffer); - response.remote_size = buffer->size; - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size); - buffers.insert(buffer); - } else { - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size); - } -} - -void rpc_server::get_alignment(rpc_msg_get_alignment_rsp & response) { - ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); - size_t alignment = ggml_backend_buft_get_alignment(buft); - GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment); - response.alignment = alignment; -} - -void rpc_server::get_max_size(rpc_msg_get_max_size_rsp & response) { - ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); - size_t max_size = ggml_backend_buft_get_max_size(buft); - GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size); - response.max_size = max_size; -} - -bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response) { - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr); - ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); - if (buffers.find(buffer) == buffers.end()) { - GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); - return false; - } - void * base = ggml_backend_buffer_get_base(buffer); - response.base_ptr = reinterpret_cast(base); - return true; -} - -bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) { - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr); - ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); - if (buffers.find(buffer) == buffers.end()) { - GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); - return false; - } - ggml_backend_buffer_free(buffer); - buffers.erase(buffer); - return true; -} - -bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) { - GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value); - ggml_backend_buffer_t buffer = reinterpret_cast(request.remote_ptr); - if (buffers.find(buffer) == buffers.end()) { - GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); - return false; - } - ggml_backend_buffer_clear(buffer, request.value); - return true; -} - -ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { - ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, - tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); - for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { - result->nb[i] = tensor->nb[i]; - } - result->buffer = reinterpret_cast(tensor->buffer); - if (result->buffer && buffers.find(result->buffer) == buffers.end()) { - result->buffer = nullptr; - } - - if (result->buffer) { - // require that the tensor data does not go beyond the buffer end - uint64_t tensor_size = (uint64_t) ggml_nbytes(result); - uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer); - uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer); - GGML_ASSERT(tensor->data + tensor_size >= tensor->data); // check for overflow - GGML_ASSERT(tensor->data >= buffer_start && tensor->data + tensor_size <= buffer_start + buffer_size); - } - - result->op = (ggml_op) tensor->op; - for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { - result->op_params[i] = tensor->op_params[i]; - } - result->flags = tensor->flags; - result->data = reinterpret_cast(tensor->data); - ggml_set_name(result, tensor->name); - return result; -} - - -bool rpc_server::set_tensor(const std::vector & input) { - // serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | - if (input.size() < sizeof(rpc_tensor) + sizeof(uint64_t)) { - return false; - } - const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); - uint64_t offset; - memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); - const size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset); - - struct ggml_init_params params { - /*.mem_size =*/ ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - struct ggml_context * ctx = ggml_init(params); - ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); - if (tensor == nullptr) { - GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); - ggml_free(ctx); - return false; - } - GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); - - // sanitize tensor->data - { - const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer); - const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer); - - if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); - } - } - - const void * data = input.data() + sizeof(rpc_tensor) + sizeof(offset); - ggml_backend_tensor_set(tensor, data, offset, size); - ggml_free(ctx); - return true; -} - -bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response) { - struct ggml_init_params params { - /*.mem_size =*/ ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - struct ggml_context * ctx = ggml_init(params); - ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); - if (tensor == nullptr) { - GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); - ggml_free(ctx); - return false; - } - GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, request.offset, request.size); - - // sanitize tensor->data - { - const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer); - const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer); - - if (request.tensor.data + request.offset < p0 || - request.tensor.data + request.offset >= p1 || - request.size > (p1 - request.tensor.data - request.offset)) { - GGML_ABORT("[%s] tensor->data out of bounds\n", __func__); - } - } - - response.resize(request.size, 0); - ggml_backend_tensor_get(tensor, response.data(), request.offset, request.size); - ggml_free(ctx); - return true; -} - -bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response) { - struct ggml_init_params params { - /*.mem_size =*/ 2*ggml_tensor_overhead(), - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - struct ggml_context * ctx = ggml_init(params); - ggml_tensor * src = deserialize_tensor(ctx, &request.src); - ggml_tensor * dst = deserialize_tensor(ctx, &request.dst); - if (src == nullptr || dst == nullptr) { - GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__); - ggml_free(ctx); - return false; - } - GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer); - response.result = ggml_backend_buffer_copy_tensor(src, dst); - ggml_free(ctx); - return true; -} - -ggml_tensor * rpc_server::create_node(uint64_t id, - struct ggml_context * ctx, - const std::unordered_map & tensor_ptrs, - std::unordered_map & tensor_map) { - if (id == 0) { - return nullptr; - } - if (tensor_map.find(id) != tensor_map.end()) { - return tensor_map[id]; - } - const rpc_tensor * tensor = tensor_ptrs.at(id); - struct ggml_tensor * result = deserialize_tensor(ctx, tensor); - if (result == nullptr) { - return nullptr; - } - tensor_map[id] = result; - for (int i = 0; i < GGML_MAX_SRC; i++) { - result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); - } - result->view_src = create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); - result->view_offs = tensor->view_offs; - return result; -} - -bool rpc_server::graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response) { - // serialization format: - // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | - if (input.size() < sizeof(uint32_t)) { - return false; - } - uint32_t n_nodes; - memcpy(&n_nodes, input.data(), sizeof(n_nodes)); - if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t)) { - return false; - } - const uint64_t * nodes = (const uint64_t *)(input.data() + sizeof(n_nodes)); - uint32_t n_tensors; - memcpy(&n_tensors, input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t), sizeof(n_tensors)); - if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t) + n_tensors*sizeof(rpc_tensor)) { - return false; - } - const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors)); - GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors); - - size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false); - struct ggml_init_params params = { - /*.mem_size =*/ buf_size, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - struct ggml_context * ctx = ggml_init(params); - struct ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false); - graph->n_nodes = n_nodes; - std::unordered_map tensor_ptrs; - for (uint32_t i = 0; i < n_tensors; i++) { - tensor_ptrs[tensors[i].id] = &tensors[i]; - } - std::unordered_map tensor_map; - for (uint32_t i = 0; i < n_nodes; i++) { - int64_t id; - memcpy(&id, &nodes[i], sizeof(id)); - graph->nodes[i] = create_node(id, ctx, tensor_ptrs, tensor_map); - } - ggml_status status = ggml_backend_graph_compute(backend, graph); - response.result = status; - ggml_free(ctx); - return true; -} - -rpc_server::~rpc_server() { - for (auto buffer : buffers) { - ggml_backend_buffer_free(buffer); - } -} - -static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t free_mem, size_t total_mem) { - rpc_server server(backend); - while (true) { - uint8_t cmd; - if (!recv_data(sockfd, &cmd, 1)) { - break; - } - if (cmd >= RPC_CMD_COUNT) { - // fail fast if the command is invalid - fprintf(stderr, "Unknown command: %d\n", cmd); - break; - } - switch (cmd) { - case RPC_CMD_ALLOC_BUFFER: { - rpc_msg_alloc_buffer_req request; - if (!recv_msg(sockfd, &request, sizeof(request))) { - return; - } - rpc_msg_alloc_buffer_rsp response; - server.alloc_buffer(request, response); - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - case RPC_CMD_GET_ALIGNMENT: { - if (!recv_msg(sockfd, nullptr, 0)) { - return; - } - rpc_msg_get_alignment_rsp response; - server.get_alignment(response); - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - case RPC_CMD_GET_MAX_SIZE: { - if (!recv_msg(sockfd, nullptr, 0)) { - return; - } - rpc_msg_get_max_size_rsp response; - server.get_max_size(response); - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - case RPC_CMD_BUFFER_GET_BASE: { - rpc_msg_buffer_get_base_req request; - if (!recv_msg(sockfd, &request, sizeof(request))) { - return; - } - rpc_msg_buffer_get_base_rsp response; - if (!server.buffer_get_base(request, response)) { - return; - } - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - case RPC_CMD_FREE_BUFFER: { - rpc_msg_free_buffer_req request; - if (!recv_msg(sockfd, &request, sizeof(request))) { - return; - } - if (!server.free_buffer(request)) { - return; - } - if (!send_msg(sockfd, nullptr, 0)) { - return; - } - break; - } - case RPC_CMD_BUFFER_CLEAR: { - rpc_msg_buffer_clear_req request; - if (!recv_msg(sockfd, &request, sizeof(request))) { - return; - } - if (!server.buffer_clear(request)) { - return; - } - if (!send_msg(sockfd, nullptr, 0)) { - return; - } - break; - } - case RPC_CMD_SET_TENSOR: { - std::vector input; - if (!recv_msg(sockfd, input)) { - return; - } - if (!server.set_tensor(input)) { - return; - } - if (!send_msg(sockfd, nullptr, 0)) { - return; - } - break; - } - case RPC_CMD_GET_TENSOR: { - rpc_msg_get_tensor_req request; - if (!recv_msg(sockfd, &request, sizeof(request))) { - return; - } - std::vector response; - if (!server.get_tensor(request, response)) { - return; - } - if (!send_msg(sockfd, response.data(), response.size())) { - return; - } - break; - } - case RPC_CMD_COPY_TENSOR: { - rpc_msg_copy_tensor_req request; - if (!recv_msg(sockfd, &request, sizeof(request))) { - return; - } - rpc_msg_copy_tensor_rsp response; - if (!server.copy_tensor(request, response)) { - return; - } - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - case RPC_CMD_GRAPH_COMPUTE: { - std::vector input; - if (!recv_msg(sockfd, input)) { - return; - } - rpc_msg_graph_compute_rsp response; - if (!server.graph_compute(input, response)) { - return; - } - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - case RPC_CMD_GET_DEVICE_MEMORY: { - if (!recv_msg(sockfd, nullptr, 0)) { - return; - } - rpc_msg_get_device_memory_rsp response; - response.free_mem = free_mem; - response.total_mem = total_mem; - if (!send_msg(sockfd, &response, sizeof(response))) { - return; - } - break; - } - default: { - fprintf(stderr, "Unknown command: %d\n", cmd); - return; - } - } - } -} - -void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem) { - std::string host; - int port; - if (!parse_endpoint(endpoint, host, port)) { - return; - } -#ifdef _WIN32 - { - WSADATA wsaData; - int res = WSAStartup(MAKEWORD(2, 2), &wsaData); - if (res != 0) { - fprintf(stderr, "WSAStartup failed: %d\n", res); - return; - } - } -#endif - auto server_socket = create_server_socket(host.c_str(), port); - if (server_socket == nullptr) { - fprintf(stderr, "Failed to create server socket\n"); - return; - } - while (true) { - auto client_socket = socket_accept(server_socket->fd); - if (client_socket == nullptr) { - fprintf(stderr, "Failed to accept client connection\n"); - return; - } - printf("Accepted client connection, free_mem=%zu, total_mem=%zu\n", free_mem, total_mem); - fflush(stdout); - rpc_serve_client(backend, client_socket->fd, free_mem, total_mem); - printf("Client connection closed\n"); - fflush(stdout); - } -#ifdef _WIN32 - WSACleanup(); -#endif -} - -// device interface - -struct ggml_backend_rpc_device_context { - std::string endpoint; - std::string name; -}; - -static const char * ggml_backend_rpc_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context; - - return ctx->name.c_str(); -} - -static const char * ggml_backend_rpc_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context; - - return ctx->name.c_str(); -} - -static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context; - - ggml_backend_rpc_get_device_memory(ctx->endpoint.c_str(), free, total); - - UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) { - // TODO: obtain value from the server - return GGML_BACKEND_DEVICE_TYPE_GPU; - - UNUSED(dev); -} - -static void ggml_backend_rpc_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_rpc_device_get_name(dev); - props->description = ggml_backend_rpc_device_get_description(dev); - props->type = ggml_backend_rpc_device_get_type(dev); - ggml_backend_rpc_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ false, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_rpc_device_init(ggml_backend_dev_t dev, const char * params) { - ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context; - - return ggml_backend_rpc_init(ctx->endpoint.c_str()); - - UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_rpc_device_context * ctx = (ggml_backend_rpc_device_context *)dev->context; - - return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str()); - - UNUSED(dev); -} - -static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - UNUSED(dev); - UNUSED(op); - //TODO: call the remote backend and cache the results - return true; -} - -static bool ggml_backend_rpc_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (!buft || buft->iface.get_name != ggml_backend_rpc_buffer_type_name) { - return false; - } - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - ggml_backend_rpc_device_context * dev_ctx = (ggml_backend_rpc_device_context *)dev->context; - return buft_ctx->endpoint == dev_ctx->endpoint; -} - -static const struct ggml_backend_device_i ggml_backend_rpc_device_i = { - /* .get_name = */ ggml_backend_rpc_device_get_name, - /* .get_description = */ ggml_backend_rpc_device_get_description, - /* .get_memory = */ ggml_backend_rpc_device_get_memory, - /* .get_type = */ ggml_backend_rpc_device_get_type, - /* .get_props = */ ggml_backend_rpc_device_get_props, - /* .init_backend = */ ggml_backend_rpc_device_init, - /* .get_buffer_type = */ ggml_backend_rpc_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_rpc_device_supports_op, - /* .supports_buft = */ ggml_backend_rpc_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// backend reg interface - -static const char * ggml_backend_rpc_reg_get_name(ggml_backend_reg_t reg) { - return "RPC"; - - UNUSED(reg); -} - -static size_t ggml_backend_rpc_reg_get_device_count(ggml_backend_reg_t reg) { - return 0; - - UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_rpc_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ABORT("The RPC backend does not have enumerated devices - use ggml_backend_add_device instead"); - - UNUSED(reg); - UNUSED(index); -} - -static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (std::strcmp(name, "ggml_backend_rpc_add_device") == 0) { - return (void *)ggml_backend_rpc_add_device; - } - return NULL; - - UNUSED(reg); -} - -static const struct ggml_backend_reg_i ggml_backend_rpc_reg_i = { - /* .get_name = */ ggml_backend_rpc_reg_get_name, - /* .get_device_count = */ ggml_backend_rpc_reg_get_device_count, - /* .get_device = */ ggml_backend_rpc_reg_get_device, - /* .get_proc_address = */ ggml_backend_rpc_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_rpc_reg(void) { - static struct ggml_backend_reg ggml_backend_rpc_reg = { - /* .iface = */ ggml_backend_rpc_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_rpc_reg; -} - -ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint) { - static std::unordered_map dev_map; - - static std::mutex mutex; - std::lock_guard lock(mutex); - - if (dev_map.find(endpoint) != dev_map.end()) { - return dev_map[endpoint]; - } - - ggml_backend_rpc_device_context * ctx = new ggml_backend_rpc_device_context { - /* .endpoint = */ endpoint, - /* .name = */ "RPC[" + std::string(endpoint) + "]", - }; - - ggml_backend_dev_t dev = new ggml_backend_device { - /* .iface = */ ggml_backend_rpc_device_i, - /* .reg = */ ggml_backend_rpc_reg(), - /* .context = */ ctx, - }; - - dev_map[endpoint] = dev; - - return dev; -} diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp deleted file mode 100644 index 2dba15d2..00000000 --- a/ggml/src/ggml-sycl.cpp +++ /dev/null @@ -1,4684 +0,0 @@ -// -// MIT license -// Copyright (C) 2024 Intel Corporation -// SPDX-License-Identifier: MIT -// - -// -// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. -// See https://llvm.org/LICENSE.txt for license information. -// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception -// - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include - -#include "ggml-sycl.h" -#include "ggml-impl.h" -#include "ggml-backend-impl.h" - -#include "ggml-sycl/backend.hpp" -#include "ggml-sycl/presets.hpp" -#include "ggml-sycl/gemm.hpp" - -static bool g_sycl_loaded = false; - -static ggml_sycl_device_info ggml_sycl_init() { - ggml_sycl_device_info info = {}; - - info.device_count = dpct::dev_mgr::instance().device_count(); - if (info.device_count == 0) { - fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); - return info; - } - - GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES); - - int64_t total_vram = 0; -#if defined(GGML_SYCL_FORCE_MMQ) - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); -#endif -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif - fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); - - for (int i = 0; i < info.device_count; ++i) { - info.devices[i].vmm = 0; - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(i)))); - - info.default_tensor_split[i] = total_vram; - total_vram += prop.get_global_mem_size(); - - info.devices[i].cc = - 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - - info.max_work_group_sizes[i] = prop.get_max_work_group_size(); - } - - for (int id = 0; id < info.device_count; ++id) { - info.default_tensor_split[id] /= total_vram; - } - return info; -} - -const ggml_sycl_device_info & ggml_sycl_info() { - static ggml_sycl_device_info info = ggml_sycl_init(); - return info; -} - -void print_device_detail(int id, sycl::device &device, std::string device_type) { - - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::get_device_info(prop, device))); - - std::string version; - version += std::to_string(prop.get_major_version()); - version += "."; - version += std::to_string(prop.get_minor_version()); - - device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); - std::string name = std::string(prop.get_name()); - name = std::regex_replace(name, std::regex("\\(R\\)"), ""); - name = std::regex_replace(name, std::regex("\\(TM\\)"), ""); - - auto global_mem_size = prop.get_global_mem_size()/1000000; - - fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), - name.c_str(), version.c_str(), prop.get_max_compute_units(), - prop.get_max_work_group_size(), prop.get_max_sub_group_size(), - global_mem_size, device.get_info().c_str()); -} - -void ggml_backend_sycl_print_sycl_devices() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n"); - int device_count = dpct::dev_mgr::instance().device_count(); - std::map DeviceNums; - fprintf(stderr, "found %d SYCL devices:\n", device_count); - fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); - fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); - fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); - fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); - for (int id = 0; id < device_count; ++id) { - sycl::device device = dpct::dev_mgr::instance().get_device(id); - sycl::backend backend = device.get_backend(); - std::string backend_type = get_device_backend_and_type(device); - int type_id=DeviceNums[backend_type]++; - std::stringstream device_type; - device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; - print_device_detail(id, device, device_type.str()); - } -} - -static inline int get_sycl_env(const char *env_name, int default_val) { - char *user_device_string = getenv(env_name); - int user_number = default_val; - - unsigned n; - if (user_device_string != NULL && - sscanf(user_device_string, " %u", &n) == 1) { - user_number = (int)n; - } else { - user_number = default_val; - } - return user_number; -} - -static void ggml_check_sycl() try { - static bool initialized = false; - - if (!initialized) { - fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); - g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); - - fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); - -#if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); -#endif - -/* NOT REMOVE, keep it for next optimize for XMX. -#if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); -#else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); -#endif -*/ - - if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != 0) { - initialized = true; - g_sycl_loaded = false; - return; - } - GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - ggml_backend_sycl_print_sycl_devices(); - initialized = true; - g_sycl_loaded = true; - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -/* -device_index: device index from 0 to n (continue numbers). - It is used for device select/set in SYCL backend internal data structure. -*/ -inline void check_allow_gpu_index(const int device_index) { - if (device_index >= ggml_sycl_info().device_count) { - char error_buf[256]; - snprintf( - error_buf, - sizeof(error_buf), - "%s error: device_index:%d is out of range: [0-%d]", - __func__, - device_index, - ggml_sycl_info().device_count - 1); - fprintf(stderr, "%s\n", error_buf); - assert(false); - } -} - -GGML_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_gpu_list\n"); - for(int i=0;i=max_len) break; - id_list[i] = i; - } - return; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -// sycl buffer - -struct ggml_backend_sycl_buffer_context { - int device; - void * dev_ptr = nullptr; - queue_ptr stream; - std::string name; - - ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) : - device(device), dev_ptr(dev_ptr), stream(stream) { - check_allow_gpu_index(device); - name = (GGML_SYCL_NAME + std::to_string(device)); - } - - - ~ggml_backend_sycl_buffer_context() { - if (dev_ptr != nullptr) { - ggml_sycl_set_device(device); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream))); - } - } -}; - -static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft); - -static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { - return buffer->buft->iface.get_name == ggml_backend_sycl_buffer_type_get_name; -} - -static void -ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - ggml_sycl_set_device(ctx->device); - - delete ctx; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - return ctx->dev_ptr; -} - -static void -ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - - if (tensor->view_src != NULL && tensor->view_offs == 0) { - assert(tensor->view_src->buffer->buft == buffer->buft); - tensor->backend = tensor->view_src->backend; - tensor->extra = tensor->view_src->extra; - return; - } - - - if (ggml_is_quantized(tensor->type)) { - // initialize padding to 0 to avoid possible NaN values - size_t original_size = ggml_nbytes(tensor); - size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); - - if (padded_size > original_size && tensor->view_src == nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset( - (char *)tensor->data + original_size, 0, - padded_size - original_size).wait())); - } - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor, - const void *data, size_t offset, - size_t size) try { - - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); - char* host_buf = (char*)malloc(size); - memcpy(host_buf, data, size); - SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) - .wait())); - free(host_buf); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *tensor, - void *data, size_t offset, - size_t size) try { - - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue(); - - SYCL_CHECK(CHECK_TRY_ERROR( - stream.memcpy(data, (const char *)tensor->data + offset, size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, - const void *ptr_src, size_t size) { - char *host_buf = (char *)malloc(size); - q_src.memcpy(host_buf, (const char *)ptr_src, size).wait(); - q_dst.memcpy((char *)ptr_dst, host_buf, size).wait(); - free(host_buf); -} - -static bool -ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *src, - ggml_tensor *dst) try { - if (ggml_backend_buffer_is_sycl(src->buffer)) { - ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; - ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context; - - ggml_sycl_set_device(src_ctx->device); - /* - DPCT1009:198: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); - ggml_sycl_set_device(dst_ctx->device); - /* - DPCT1009:199: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); - /* - DPCT1009:200: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - - queue_ptr stream_dst = dst_ctx->stream; - queue_ptr stream_src = src_ctx->stream; - size_t size = ggml_nbytes(src); - - //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. - dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); - -//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove -#if 0 - SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( - (char *)dst->data, (const char *)src->data, size).wait())); - - /* - DPCT1009:201: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); -#endif - return true; - } - return false; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - - -static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, - uint8_t value) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; - - ggml_sycl_set_device(ctx->device); - queue_ptr stream = ctx->stream; - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); - - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memset(ctx->dev_ptr, value, buffer->size) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static const ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { - /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, - /* .get_base = */ ggml_backend_sycl_buffer_get_base, - /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, - /* .clear = */ ggml_backend_sycl_buffer_clear, - /* .reset = */ NULL, -}; - -// sycl buffer type -struct ggml_backend_sycl_buffer_type_context { - int device; - std::string name; - - // each buffer type has its own stream - queue_ptr stream = nullptr; -}; - -static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - - return ctx->name.c_str(); -} - -static ggml_backend_buffer_t -ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, - size_t size) try { - ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_sycl_set_device(buft_ctx->device); - const queue_ptr stream = buft_ctx->stream; - size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 - - void * dev_ptr; - SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( - size, *stream))); - if (!dev_ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, size); - return nullptr; - } - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); - return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - GGML_UNUSED(buft); -} - -static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - return dpct::get_current_device().get_max_mem_alloc_size(); - - GGML_UNUSED(buft); -} - -static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; - - if (ggml_is_quantized(tensor->type)) { - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return size; - - GGML_UNUSED(buft); -} - -static const ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { - /* .get_name = */ ggml_backend_sycl_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, - /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, - /* .is_host = */ NULL, -}; - -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n"); - - auto dev_count = ggml_backend_sycl_get_device_count(); - - if (device>=dev_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device, dev_count-1); - GGML_ASSERT(devicedevice; - if (device>=ggml_sycl_info().device_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device, ggml_sycl_info().device_count-1); - GGML_ASSERT(devicestream(i, 0)}, - }; - } - ggml_backend_sycl_buffer_type_initialized = true; - } - return &ggml_backend_sycl_buffer_types[device]; -} - -// sycl split buffer - -static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { - int64_t min_compute_capability = INT_MAX; - int64_t max_compute_capability = INT_MIN; - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) { - if (min_compute_capability > ggml_sycl_info().devices[i].cc) { - min_compute_capability = ggml_sycl_info().devices[i].cc; - } - if (max_compute_capability < ggml_sycl_info().devices[i].cc) { - max_compute_capability = ggml_sycl_info().devices[i].cc; - } - } - } - - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ4_NL: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_IQ3_S: - return max_compute_capability >= VER_GEN9 ? 128 : 64; - case GGML_TYPE_Q6_K: - return 64; - default: - GGML_ABORT("fatal error"); - } -} - -static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { - const int64_t nrows = ggml_nrows(tensor); - const int64_t rounding = get_row_rounding(tensor->type, tensor_split); - - *row_low = id == 0 ? 0 : nrows*tensor_split[id]; - *row_low -= *row_low % rounding; - if (id == ggml_sycl_info().device_count - 1) { - *row_high = nrows; - } else { - *row_high = nrows*tensor_split[id + 1]; - *row_high -= *row_high % rounding; - } -} - -static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); -} - -struct ggml_backend_sycl_split_buffer_type_context { - std::array tensor_split; -}; - -struct ggml_backend_sycl_split_buffer_context { - ~ggml_backend_sycl_split_buffer_context() try { - for (ggml_tensor_extra_gpu * extra : tensor_extras) { - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { - if (extra->events[i][is] != nullptr) { - /* - DPCT1009:206: SYCL uses exceptions to report errors and - does not use the error codes. The original code was - commented out and a warning string was inserted. You - need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[i][is]))); - } - } - if (extra->data_device[i] != nullptr) { - /* - DPCT1009:207: SYCL uses exceptions to report errors and does - not use the error codes. The original code was commented out - and a warning string was inserted. You need to rewrite this - code. - */ - ggml_sycl_set_device(i); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( - extra->data_device[i], *(streams[i])))); - } - } - delete extra; - } - } - catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); - } - - std::vector tensor_extras; - std::vector streams; -}; - -static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - delete ctx; -} - -static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { - // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced - return (void *)0x1000; - - GGML_UNUSED(buffer); -} - -static void -ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - - ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; - - ctx->tensor_extras.push_back(extra); - ctx->streams.push_back(&(dpct::get_current_device().default_queue())); - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - // FIXME: do not crash if cudaMalloc fails - // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - char * buf; - /* - DPCT1009:208: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *stream))); - if (!buf) { - char err_buf[1024]; - snprintf(err_buf, 1023, "%s: can't malloc %lu Bytes memory on device", __func__, size); - throw std::runtime_error(err_buf); - } - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - /* - DPCT1009:209: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memset(buf + original_size, 0, size - original_size) - .wait())); - } - - extra->data_device[i] = buf; - - for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { - /* - DPCT1009:210: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ - SYCL_CHECK( - CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); - } - } - tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void -ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor, const void *data, - size_t offset, size_t size) try { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - const char * buf_host = (const char *)data + offset_split; - /* - DPCT1009:211: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(extra->data_device[i], buf_host, original_size) - .wait())); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void -ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, - const ggml_tensor *tensor, void *data, - size_t offset, size_t size) try { - // split tensors must always be set in their entirety at once - GGML_ASSERT(offset == 0); - GGML_ASSERT(size == ggml_nbytes(tensor)); - - ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; - - const int64_t ne0 = tensor->ne[0]; - const size_t nb1 = tensor->nb[1]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf_host = (char *)data + offset_split; - /* - DPCT1009:212: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - ggml_sycl_set_device(i); - const queue_ptr stream = ctx->streams[i]; - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(buf_host, extra->data_device[i], original_size) - .wait())); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - GGML_UNUSED(buffer); - GGML_UNUSED(value); -} - -static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { - /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, - /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, - /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_sycl_split_buffer_clear, - /* .reset = */ NULL, -}; - -// sycl split buffer type - -static const char * ggml_backend_sycl_split_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return GGML_SYCL_NAME "_Split"; - - GGML_UNUSED(buft); -} - -static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { - return buffer->buft->iface.get_name == ggml_backend_sycl_split_buffer_type_get_name; -} - -static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point - // instead, we allocate them for each tensor separately in init_tensor - // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, - // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. - ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); - - return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); -} - -static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return 128; - GGML_UNUSED(buft); -} - -static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; - - size_t total_size = 0; - - const int64_t ne0 = tensor->ne[0]; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - int64_t row_low, row_high; - get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); - - int64_t nrows_split = row_high - row_low; - if (nrows_split == 0) { - continue; - } - - total_size += ggml_nbytes_split(tensor, nrows_split); - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - } - - return total_size; -} - -static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { - /* .get_name = */ ggml_backend_sycl_split_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, -}; - -ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { - static std::mutex mutex; - std::lock_guard lock(mutex); - - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n"); - ggml_check_sycl(); - // FIXME: this is not thread safe - static std::map, struct ggml_backend_buffer_type> buft_map; - - std::array tensor_split_arr = {}; - - bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); - if (all_zero) { - tensor_split_arr = ggml_sycl_info().default_tensor_split; - } else { - float split_sum = 0.0f; - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - tensor_split_arr[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - tensor_split_arr[i] /= split_sum; - } - } - - auto it = buft_map.find(tensor_split_arr); - if (it != buft_map.end()) { - return &it->second; - } - - struct ggml_backend_buffer_type buft { - /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), 0), - /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, - }; - - auto result = buft_map.emplace(tensor_split_arr, buft); - return &result.first->second; -} - -// host buffer type - -static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_SYCL_NAME "_Host"; - - GGML_UNUSED(buft); -} - -static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_sycl_host_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_sycl_host_malloc(size); - - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - // FIXME: this is a hack to avoid having to implement a new buffer type - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n"); - static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), 0), - /* .context = */ nullptr, - }; - - return &ggml_backend_sycl_buffer_type_host; -} - -// buffer pool for sycl (legacy) -struct ggml_sycl_pool_leg : public ggml_sycl_pool { - static const int MAX_SYCL_BUFFERS = 256; - - int device; - queue_ptr qptr; - struct ggml_sycl_buffer { - void * ptr = nullptr; - size_t size = 0; - }; - - ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {}; - size_t pool_size = 0; - - explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : - qptr(qptr_), - device(device_) { - } - - ~ggml_sycl_pool_leg() { - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer & b = buffer_pool[i]; - if (b.ptr != nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr))); - pool_size -= b.size; - } - } - GGML_ASSERT(pool_size == 0); - } - - void * alloc(size_t size, size_t * actual_size) override { -#ifdef DEBUG_sycl_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer& b = buffer_pool[i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_sycl_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - } - } - } - } - if (ibest >= 0) { - ggml_sycl_buffer& b = buffer_pool[ibest]; - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - return ptr; - } - void * ptr; - size_t look_ahead_size = (size_t) (1.05 * size); - - SYCL_CHECK( - CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( - look_ahead_size, *qptr))); - if (!ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, look_ahead_size); - return nullptr; - } - - *actual_size = look_ahead_size; - pool_size += look_ahead_size; - - #ifdef DEBUG_SYCL_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); - #endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); - return ptr; - } - - void free(void * ptr, size_t size) override { - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - ggml_sycl_buffer& b = buffer_pool[i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; - } - } - fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); - pool_size -= size; - } -}; - -std::unique_ptr ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) { - // TBD: NO VMM support - // if (ggml_sycl_info().devices[device].vmm) { - // return std::unique_ptr(new ggml_sycl_pool_vmm(device)); - // } - return std::unique_ptr(new ggml_sycl_pool_leg(qptr, device)); -} - -// TBD pool with virtual memory management -// struct ggml_sycl_pool_vmm : public ggml_sycl_pool - -/// kernels - -typedef void (*cpy_kernel_t)(const char * cx, char * cdst); -typedef void (*ggml_sycl_func_t)(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); -typedef void (*ggml_sycl_op_mul_mat_t)( - ggml_backend_sycl_context & ctx, - const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, - const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, - float *dst_dd_i, const int64_t row_low, const int64_t row_high, - const int64_t src1_ncols, const int64_t src1_padded_row_size, - const queue_ptr &stream); - - - -template -static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded, - const sycl::nd_item<3> &item_ct1) { - const int ix = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2)) * QUANT_BLOCK_TILE; - - if (ix >= kx_padded) { - return; - } - - const int iy = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - - const int i_padded = iy*kx_padded + ix; - - block_q8_1 * y = (block_q8_1 *) vy; - - const int ib = i_padded / QK8_1; // block index - const int iqs = i_padded % QK8_1; // quant index - typedef sycl::vec TC; - typedef sycl::vec TQ; - TC zeros; - TQ qzeros; -#pragma unroll - for (int i = 0; i < QUANT_BLOCK_TILE; i++) - { - zeros[i] = 0.f; - qzeros[i] = 0; - } - const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros; - float sum = xi[0]; - float amax = sycl::fabs(xi[0]); -#pragma unroll - for (int i = 1; i < QUANT_BLOCK_TILE; i++) - { - sum += xi[i]; - amax = sycl::fmax(sycl::fabs(xi[i]), amax); - } - sum = warp_reduce_sum(sum, item_ct1); - amax = warp_reduce_max(amax, item_ct1); - - const float d = amax / 127; - TQ q = qzeros; - if (amax != 0.0f) - { -#pragma unroll - for (int i = 0; i < QUANT_BLOCK_TILE; i++) { - q[i] = sycl::round(xi[i] / d); - } - } - - *(TQ *)&y[ib].qs[iqs] = q; - - if (iqs > 0) { - return; - } - - reinterpret_cast(y[ib].ds.x()) = d; - reinterpret_cast(y[ib].ds.y()) = sum; -} - -template -static void k_get_rows( - const void * src0, const int32_t * src1, dst_t * dst, - int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ - /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ - /*size_t s0,*/ size_t s1, size_t s2, size_t s3, - /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, - size_t s10, size_t s11, size_t s12, - const sycl::nd_item<3> &item_ct1/*, size_t s13*/) { - - const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) + - item_ct1.get_local_id(2)) * - 2; - const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + - item_ct1.get_local_id(0)) / - ne12; - const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + - item_ct1.get_local_id(0)) % - ne12; - - if (i00 >= ne00) { - return; - } - - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; - - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03; - - const int ib = i00/qk; // block index - const int iqs = (i00%qk)/qr; // quant index - const int iybs = i00 - i00%qk; // dst block start index - const int y_offset = qr == 1 ? 1 : qk/2; - - // dequantize - dfloat2 v; - dequantize_kernel(src0_row, ib, iqs, v); - - dst_row[iybs + iqs + 0] = v.x(); - dst_row[iybs + iqs + y_offset] = v.y(); -} - -template -static void k_get_rows_float( - const src0_t * src0, const int32_t * src1, dst_t * dst, - int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ - /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ - /*size_t s0,*/ size_t s1, size_t s2, size_t s3, - /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, - size_t s10, size_t s11, size_t s12, - const sycl::nd_item<3> &item_ct1/*, size_t s13*/) { - - const int i00 = item_ct1.get_group(2) * item_ct1.get_local_range(2) + - item_ct1.get_local_id(2); - const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + - item_ct1.get_local_id(0)) / - ne12; - const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + - item_ct1.get_local_id(0)) % - ne12; - - if (i00 >= ne00) { - return; - } - - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; - - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03); - - dst_row[i00] = src0_row[i00]; -} - -static void mul_mat_p021_f16_f32( - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y, - const sycl::nd_item<3> &item_ct1) { - - const sycl::half *x = (const sycl::half *)vx; - - const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) + - item_ct1.get_local_id(0); - const int channel_x = channel / (nchannels_y / nchannels_x); - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; - col_x0 += item_ct1.get_local_range(2)) { - const int col_x = col_x0 + item_ct1.get_local_id(2); - - if (col_x >= ncols_x) { - break; - } - - // x is transposed and permuted - const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; - const float xi = - sycl::vec(x[ix]) - .convert()[0]; - - const int row_y = col_x; - - - // y is not transposed but permuted - const int iy = channel*nrows_y + row_y; - - tmp += xi * y[iy]; - } - - // dst is not transposed and not permuted - const int idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result -#pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { - tmp += - dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); - } - - if (item_ct1.get_local_id(2) == 0) { - dst[idst] = tmp; - } -} - -static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, - const int row_stride_x, const int channel_stride_x, const int channel_x_divisor, - const sycl::nd_item<3> &item_ct1) { - - const sycl::half *x = (const sycl::half *)vx; - - const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) + - item_ct1.get_local_id(0); - const int channel_x = channel / channel_x_divisor; - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - const int idst = channel*nrows_dst + row_dst; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; - col_x0 += item_ct1.get_local_range(2)) { - const int col_x = col_x0 + item_ct1.get_local_id(2); - - if (col_x >= ncols_x) { - break; - } - - const int row_y = col_x; - - const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; - const int iy = channel*nrows_y + row_y; - - const float xi = - sycl::vec(x[ix]) - .convert()[0]; - - tmp += xi * y[iy]; - } - - // sum up partial sums and write back result -#pragma unroll - for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) { - tmp += - dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); - } - - if (item_ct1.get_local_id(2) == 0) { - dst[idst] = tmp; - } -} - -static void cpy_1_f32_f32(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - float * dsti = (float *) cdsti; - - *dsti = *xi; -} - -static void cpy_1_f32_f16(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - sycl::half *dsti = (sycl::half *)cdsti; - - *dsti = sycl::vec(*xi) - .convert()[0]; -} - -static void cpy_1_f16_f16(const char * cxi, char * cdsti) { - const sycl::half *xi = (const sycl::half *)cxi; - sycl::half *dsti = (sycl::half *)cdsti; - - *dsti = *xi; -} - -static void cpy_1_f16_f32(const char * cxi, char * cdsti) { - const sycl::half *xi = (const sycl::half *)cxi; - float * dsti = (float *) cdsti; - - *dsti = *xi; -} - -static void cpy_1_i16_i16(const char * cxi, char * cdsti) { - const int16_t *xi = (const int16_t *)cxi; - int16_t *dsti = (int16_t *)cdsti; - - *dsti = *xi; -} - -static void cpy_1_i32_i32(const char * cxi, char * cdsti) { - const int32_t *xi = (const int32_t *)cxi; - int32_t *dsti = (int32_t *)cdsti; - - *dsti = *xi; -} - -template -static void cpy_f32_f16(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= ne) { - return; - } - - // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor - // then combine those indices with the corresponding byte offsets to get the total offsets - const int i03 = i/(ne00 * ne01 * ne02); - const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - - const int i13 = i/(ne10 * ne11 * ne12); - const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; - - cpy_1(cx + x_offset, cdst + dst_offset); -} - -static void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q8_0 * dsti = (block_q8_0 *) cdsti; - - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - const float v = xi[j]; - amax = sycl::fmax(amax, sycl::fabs((float)v)); - } - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - dsti->d = d; - - for (int j = 0; j < QK8_0; ++j) { - const float x0 = xi[j]*id; - - dsti->qs[j] = sycl::round((float)x0); - } -} - -static void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q4_0 * dsti = (block_q4_0 *) cdsti; - - float amax = 0.0f; - float vmax = 0.0f; - - for (int j = 0; j < QK4_0; ++j) { - const float v = xi[j]; - if (amax < sycl::fabs((float)v)) { - amax = sycl::fabs((float)v); - vmax = v; - } - } - - const float d = vmax / -8; - const float id = d ? 1.0f/d : 0.0f; - - dsti->d = d; - - for (int j = 0; j < QK4_0/2; ++j) { - const float x0 = xi[0 + j]*id; - const float x1 = xi[QK4_0/2 + j]*id; - - const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 8.5f)); - const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 8.5f)); - - dsti->qs[j] = xi0; - dsti->qs[j] |= xi1 << 4; - } -} - -static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { - const float * xi = (const float *) cxi; - block_q4_1 * dsti = (block_q4_1 *) cdsti; - - float vmin = FLT_MAX; - float vmax = -FLT_MAX; - - for (int j = 0; j < QK4_1; ++j) { - const float v = xi[j]; - - if (v < vmin) vmin = v; - if (v > vmax) vmax = v; - } - - const float d = (vmax - vmin) / ((1 << 4) - 1); - const float id = d ? 1.0f/d : 0.0f; - - dsti->dm.x() = d; - dsti->dm.y() = vmin; - - for (int j = 0; j < QK4_1/2; ++j) { - const float x0 = (xi[0 + j] - vmin)*id; - const float x1 = (xi[QK4_1/2 + j] - vmin)*id; - - const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 0.5f)); - const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 0.5f)); - - dsti->qs[j] = xi0; - dsti->qs[j] |= xi1 << 4; - } -} - -template -static void cpy_f32_q(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { - const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2)) * - qk; - - if (i >= ne) { - return; - } - - const int i03 = i/(ne00 * ne01 * ne02); - const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - - const int i13 = i/(ne10 * ne11 * ne12); - const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; - - cpy_blck(cx + x_offset, cdst + dst_offset); -} - -static void k_sum_rows_f32(const float * x, float * dst, const int ncols, - const sycl::nd_item<3> &item_ct1) { - const int row = item_ct1.get_group(1); - const int col = item_ct1.get_local_id(2); - - float sum = 0.0f; - for (int i = col; i < ncols; i += item_ct1.get_local_range(2)) { - sum += x[row * ncols + i]; - } - - sum = warp_reduce_sum(sum, item_ct1); - - if (col == 0) { - dst[row] = sum; - } -} - - -template -static inline void ggml_sycl_swap(T & a, T & b) { - T tmp = a; - a = b; - b = tmp; -} - -template -__dpct_inline__ static void -k_argsort_f32_i32(const float *x, int *dst, const int ncols, int ncols_pad, - const sycl::nd_item<3> &item_ct1, uint8_t *dpct_local) { - // bitonic sort - int col = item_ct1.get_local_id(2); - int row = item_ct1.get_group(1); - - if (col >= ncols_pad) { - return; - } - - const float * x_row = x + row * ncols; - auto dst_row = (int *)dpct_local; - - // initialize indices - dst_row[col] = col; - - item_ct1.barrier(sycl::access::fence_space::local_space); - - for (int k = 2; k <= ncols_pad; k *= 2) { - for (int j = k / 2; j > 0; j /= 2) { - int ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= ncols || - (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] > x_row[dst_row[ixj]] : - x_row[dst_row[col]] < x_row[dst_row[ixj]])) - ) { - ggml_sycl_swap(dst_row[col], dst_row[ixj]); - } - } else { - if (dst_row[ixj] >= ncols || - (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] < x_row[dst_row[ixj]] : - x_row[dst_row[col]] > x_row[dst_row[ixj]])) - ) { - ggml_sycl_swap(dst_row[col], dst_row[ixj]); - } - } - } - /* - DPCT1118:1: SYCL group functions and algorithms must be encountered - in converged control flow. You may need to adjust the code. - */ - item_ct1.barrier(sycl::access::fence_space::local_space); - } - } - - // copy the result to dst without the padding - if (col < ncols) { - dst[row * ncols + col] = dst_row[col]; - } -} - - -static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past, - const sycl::nd_item<3> &item_ct1) { - const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (col >= ncols) { - return; - } - - const int i = row*ncols + col; - //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i]; - //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU - dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; -} - -static void scale_f32(const float * x, float * dst, const float scale, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - - dst[i] = scale * x[i]; -} - -static void clamp_f32(const float * x, float * dst, const float min, const float max, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - - dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); -} - -template -static void pool2d_nchw_kernel( - const int ih, const int iw, const int oh, const int ow, - const int kh, const int kw, const int sh, const int sw, - const int ph, const int pw, const int parallel_elements, - const Ti* src, To* dst, const enum ggml_op_pool op, - const sycl::nd_item<3> &item_ct1) { - int idx = item_ct1.get_local_id(2) + - item_ct1.get_group(2) * item_ct1.get_local_range(2); - if (idx >= parallel_elements) { - return; - } - - const int I_HW = ih * iw; - const int O_HW = oh * ow; - const int nc = idx / O_HW; - const int cur_oh = idx % O_HW / ow; - const int cur_ow = idx % O_HW % ow; - const Ti* i_ptr = src + nc * I_HW; - To* o_ptr = dst + nc * O_HW; - const int start_h = cur_oh * sh - ph; - const int bh = sycl::max(0, start_h); - const int eh = sycl::min(ih, start_h + kh); - const int start_w = cur_ow * sw - pw; - const int bw = sycl::max(0, start_w); - const int ew = sycl::min(iw, start_w + kw); - - To res = 0; - - switch (op) { - case GGML_OP_POOL_AVG: res = 0; break; - case GGML_OP_POOL_MAX: res = -FLT_MAX; break; - } - - for (int i = bh; i < eh; i += 1) { - for (int j = bw; j < ew; j += 1) { -#if DPCT_COMPATIBILITY_TEMP >= 350 - /* - DPCT1098:106: The '*' expression is used instead of the __ldg - call. These two expressions do not provide the exact same - functionality. Check the generated code for potential precision - and/or performance issues. - */ - Ti cur = *(i_ptr + i * iw + j); -#else - Ti cur = i_ptr[i * iw + j]; -#endif - switch (op) { - case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break; - case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break; - } - } - } - o_ptr[cur_oh * ow + cur_ow] = res; -} - -template -static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const void *src0_dd, - const int32_t *src1_dd, float *dst_dd, - queue_ptr stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - - const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); - const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE); - const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); - - // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); - - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); - - GGML_ASSERT(ne00 % 2 == 0); - - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_get_rows( - src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, - s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); - }); - - (void) dst; -} - -template -static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const src0_t *src0_dd, const int32_t *src1_dd, - float *dst_dd, queue_ptr stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - - const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); - const int block_num_x = (ne00 + SYCL_GET_ROWS_BLOCK_SIZE - 1) / SYCL_GET_ROWS_BLOCK_SIZE; - const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); - - // strides in elements - //const size_t s0 = nb0 / ggml_element_size(dst); - const size_t s1 = nb1 / ggml_element_size(dst); - const size_t s2 = nb2 / ggml_element_size(dst); - const size_t s3 = nb3 / ggml_element_size(dst); - - const size_t s10 = nb10 / ggml_element_size(src1); - const size_t s11 = nb11 / ggml_element_size(src1); - const size_t s12 = nb12 / ggml_element_size(src1); - //const size_t s13 = nb13 / ggml_element_size(src1); - - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, - s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); - }); - } - - (void) dst; -} - - -static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, - const int ky, const int kx_padded, - queue_ptr stream) { - const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE; - const sycl::range<3> num_blocks(1, ky, block_num_x); - int constexpr QUANT_BLOCK_TILE = QK8_1 / WARP_SIZE; - static_assert(QK8_1 % WARP_SIZE == 0); - const sycl::range<3> block_size(1, 1, SYCL_QUANTIZE_BLOCK_SIZE / QUANT_BLOCK_TILE); - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(num_blocks * block_size, block_size), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { - quantize_q8_1(x, vy, kx, kx_padded, item_ct1); - }); - } -} - -static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y, - float *dst, const int ncols_x, - const int nrows_x, - const int nchannels_x, - const int nchannels_y, - queue_ptr stream) { - - const sycl::range<3> block_nums(nchannels_y, nrows_x, 1); - const sycl::range<3> block_dims(1, 1, WARP_SIZE); - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x, - nchannels_y, item_ct1); - }); - } -} - -static void ggml_mul_mat_vec_nc_f16_f32_sycl( - const void *vx, const float *y, float *dst, const int ncols_x, - const int nrows_x, const int row_stride_x, const int nchannels_x, - const int nchannels_y, const int channel_stride_x, queue_ptr stream) { - - const sycl::range<3> block_nums(nchannels_y, nrows_x, 1); - const sycl::range<3> block_dims(1, 1, WARP_SIZE); - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] { - mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x, - row_stride_x, channel_stride_x, - nchannels_y / nchannels_x, item_ct1); - }); - } -} - -static void -ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, - const int ne01, const int ne02, const int nb00, - const int nb01, const int nb02, const int nb03, - const int ne10, const int ne11, const int ne12, - const int nb10, const int nb11, const int nb12, - const int nb13, queue_ptr stream) { - - const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, - nb01, nb02, nb03, ne10, ne11, ne12, - nb10, nb11, nb12, nb13, item_ct1); - }); - } -} - -static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); - } -} - -static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); - } -} - -static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - GGML_ASSERT(ne % QK8_0 == 0); - const int num_blocks = ne / QK8_0; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), - sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q( - cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); -} - -static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - GGML_ASSERT(ne % QK4_0 == 0); - const int num_blocks = ne / QK4_0; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), - sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q( - cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); -} - -static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - GGML_ASSERT(ne % QK4_1 == 0); - const int num_blocks = ne / QK4_1; - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), - sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_q( - cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); -} - -static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); - } -} - -static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; - { - // dpct::has_capability_or_fail(stream->get_device(), - // {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); - } -} - -static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, - const int ne00, const int ne01, - const int ne02, const int nb00, - const int nb01, const int nb02, - const int nb03, const int ne10, - const int ne11, const int ne12, - const int nb10, const int nb11, - const int nb12, const int nb13, - queue_ptr stream) { - - const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; - { - // dpct::has_capability_or_fail(stream->get_device(), - // {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, - nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, - item_ct1); - }); - } -} - -static void scale_f32_sycl(const float *x, float *dst, const float scale, - const int k, queue_ptr stream) { - const int num_blocks = (k + SYCL_SCALE_BLOCK_SIZE - 1) / SYCL_SCALE_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - scale_f32(x, dst, scale, k, item_ct1); - }); -} - -static void clamp_f32_sycl(const float *x, float *dst, const float min, - const float max, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - clamp_f32(x, dst, min, max, k, item_ct1); - }); -} - -static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols, - const int nrows, queue_ptr stream) { - const sycl::range<3> block_dims(1, 1, WARP_SIZE); - const sycl::range<3> block_nums(1, nrows, 1); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) - [[intel::reqd_sub_group_size(WARP_SIZE)]] { - k_sum_rows_f32(x, dst, ncols, item_ct1); - }); -} - -static int next_power_of_2(int x) { - int n = 1; - while (n < x) { - n *= 2; - } - return n; -} - -static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, - const int nrows, ggml_sort_order order, - queue_ptr stream) { - // bitonic sort requires ncols to be power of 2 - const int ncols_pad = next_power_of_2(ncols); - - const sycl::range<3> block_dims(1, 1, ncols_pad); - const sycl::range<3> block_nums(1, nrows, 1); - const size_t shared_mem = ncols_pad * sizeof(int); - - if (order == GGML_SORT_ORDER_ASC) { - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor dpct_local_acc_ct1( - sycl::range<1>(shared_mem), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_argsort_f32_i32( - x, dst, ncols, ncols_pad, item_ct1, - dpct_local_acc_ct1.get_multi_ptr() - .get()); - }); - }); - } else if (order == GGML_SORT_ORDER_DESC) { - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor dpct_local_acc_ct1( - sycl::range<1>(shared_mem), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_argsort_f32_i32( - x, dst, ncols, ncols_pad, item_ct1, - dpct_local_acc_ct1.get_multi_ptr() - .get()); - }); - }); - } else { - GGML_ABORT("fatal error"); - } -} - -static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols, - const int nrows, queue_ptr stream) { - const sycl::range<3> block_dims(1, 1, SYCL_ARGMAX_BLOCK_SIZE); - const sycl::range<3> block_nums(1, nrows, 1); - const size_t shared_mem = 256 * sizeof(float); - - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor shared_data( - sycl::range<1>(shared_mem/sizeof(float)), cgh); - sycl::local_accessor shared_indices( - sycl::range<1>(shared_mem/sizeof(float)), cgh); - - cgh.parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - const int tid = item_ct1.get_local_id(2); - const int row = item_ct1.get_global_id(1); - - float max_val = -INFINITY; - int max_idx = -1; - - for (int col = tid; col < ncols; col += 256) { - float val = x[row * ncols + col]; - if (val > max_val) { - max_val = val; - max_idx = col; - } - } - - shared_data[tid] = max_val; - shared_indices[tid] = max_idx; - item_ct1.barrier(sycl::access::fence_space::local_space); - - for (int stride = 256/2; stride > 0; stride >>= 1) { - if (tid < stride) { - float val1 = shared_data[tid]; - float val2 = shared_data[tid + stride]; - if (val2 > val1) { - shared_data[tid] = val2; - shared_indices[tid] = shared_indices[tid + stride]; - } - } - item_ct1.barrier(sycl::access::fence_space::local_space); - } - - - if (tid == 0) { - dst[row] = shared_indices[0]; - } - }); - }); -} -static void diag_mask_inf_f32_sycl(const float *x, float *dst, - const int ncols_x, const int nrows_x, - const int rows_per_channel, const int n_past, - queue_ptr stream) { - const sycl::range<3> block_dims(1, SYCL_DIAG_MASK_INF_BLOCK_SIZE, 1); - const int block_num_x = (ncols_x + SYCL_DIAG_MASK_INF_BLOCK_SIZE - 1) / SYCL_DIAG_MASK_INF_BLOCK_SIZE; - const sycl::range<3> block_nums(1, block_num_x, nrows_x); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - diag_mask_inf_f32(x, dst, ncols_x, - rows_per_channel, n_past, - item_ct1); - }); -} - -static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, - const struct ggml_tensor *src, - int64_t i3, int64_t i2, - int64_t i1_low, int64_t i1_high, - queue_ptr stream) try { - - dpct::memcpy_direction kind; - char * src_ptr; - if (src->backend == GGML_BACKEND_TYPE_CPU) { - kind = dpct::host_to_device; - src_ptr = (char *) src->data; - // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr); - } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); - kind = dpct::device_to_device; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; - int id; - SYCL_CHECK(CHECK_TRY_ERROR( - id = get_current_device_id())); - // GGML_SYCL_DEBUG("current device index %d\n", id); - src_ptr = (char *) extra->data_device[id]; - } else { - // GGML_SYCL_DEBUG("GGML_ABORT("fatal error")\n"); - GGML_ABORT("fatal error"); - } - char * dst_ptr = (char *) dst; - - GGML_TENSOR_LOCALS_1(int64_t, ne, src, ne); - GGML_TENSOR_LOCALS(int64_t, nb, src, nb); - const enum ggml_type type = src->type; - const int64_t ts = ggml_type_size(type); - const int64_t bs = ggml_blck_size(type); - int64_t i1_diff = i1_high - i1_low; - - const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; - if (nb0 == ts && nb1 == ts*ne0/bs) { - // GGML_SYCL_DEBUG("stream->memcpy: dst_ptr=%p, x=%p, size=%lu\n", dst_ptr, x, i1_diff * nb1); - // return CHECK_TRY_ERROR(stream->memcpy(dst_ptr, x, i1_diff * nb1)); - return CHECK_TRY_ERROR(dpct::async_dpct_memcpy(dst_ptr, x, i1_diff * nb1, - kind, *stream)); - - } else if (nb0 == ts) { - return CHECK_TRY_ERROR( - dpct::async_dpct_memcpy(dst_ptr, ts * ne0 / bs, x, nb1, - ts * ne0 / bs, i1_diff, kind, *stream)); - } else { - for (int64_t i1 = 0; i1 < i1_diff; i1++) { - const void * rx = (const void *) ((const char *) x + i1*nb1); - void * rd = (void *) (dst_ptr + i1*ts*ne0/bs); - // pretend the row is a matrix with cols=1 - dpct::err0 r = CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - rd, ts / bs, rx, nb0, ts / bs, ne0, kind, *stream)); - /* - DPCT1001:85: The statement could not be removed. - */ - /* - DPCT1000:86: Error handling if-stmt was detected but could not be - rewritten. - */ - if (r != 0) return r; - } - return 0; - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_d, const float *src1_d, - float *dst_d, const queue_ptr &stream) { - - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); - GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); - - const int32_t * src1_i32 = (const int32_t *) src1_d; - - switch (src0->type) { - case GGML_TYPE_F16: - get_rows_sycl_float(ctx, src0, src1, dst, (const sycl::half *)src0_d, - src1_i32, dst_d, stream); - break; - case GGML_TYPE_F32: - get_rows_sycl_float(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; - case GGML_TYPE_Q4_0: - get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; - case GGML_TYPE_Q4_1: - get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; - case GGML_TYPE_Q5_0: - get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; - case GGML_TYPE_Q5_1: - get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; - case GGML_TYPE_Q8_0: - get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); - break; - default: - // TODO: k-quants - fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); - GGML_ABORT("fatal error"); - break; - } -} - - -static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_d, const float *src1_d, - float *dst_d, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, dst, src0, dst, nullptr, src0_d, dst_d, main_stream); - - (void) src1; - (void) src1_d; -} - - -inline void ggml_sycl_op_mul_mat_sycl( - ggml_backend_sycl_context & ctx, - const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, - const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, - float *dst_dd_i, const int64_t row_low, const int64_t row_high, - const int64_t src1_ncols, const int64_t src1_padded_row_size, - const queue_ptr &stream) try { - - GGML_ASSERT(src0_dd_i != nullptr); - GGML_ASSERT(src1_ddf_i != nullptr); - GGML_ASSERT(dst_dd_i != nullptr); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - - const int64_t row_diff = row_high - row_low; - - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_id())); - - // the main device has a larger memory buffer to hold the results from all GPUs - // ldc == nrows of the matrix that cuBLAS writes into - int ldc = id == ctx.device ? ne0 : row_diff; - -#ifdef GGML_SYCL_F16 - bool use_fp16 = true; // TODO(Yu) SYCL capability check -#else - bool use_fp16 = false; -#endif - if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && - dst->op_params[0] == GGML_PREC_DEFAULT) { - - // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n"); - ggml_sycl_pool_alloc src0_as_f16(ctx.pool()); - if (src0->type != GGML_TYPE_F16) { - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type); - GGML_ASSERT(to_fp16_sycl != nullptr); - size_t ne = row_diff*ne00; - src0_as_f16.alloc(ne); - to_fp16_sycl(src0_dd_i, src0_as_f16.get(), ne, stream); - } - const sycl::half *src0_ptr = src0->type == GGML_TYPE_F16 - ? (const sycl::half *)src0_dd_i - : src0_as_f16.get(); - - ggml_sycl_pool_alloc src1_as_f16(ctx.pool()); - if (src1->type != GGML_TYPE_F16) { - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - GGML_ASSERT(to_fp16_sycl != nullptr); - size_t ne = src1_ncols*ne10; - src1_as_f16.alloc(ne); - to_fp16_sycl(src1_ddf_i, src1_as_f16.get(), ne, stream); - } - const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16 - ? (const sycl::half *)src1->data + src1_padded_row_size - : src1_as_f16.get(); - ggml_sycl_pool_alloc dst_f16(ctx.pool(), row_diff * src1_ncols); - - const sycl::half alpha_f16 = 1.0f; - const sycl::half beta_f16 = 0.0f; -#if !GGML_SYCL_DNNL - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( - *stream, oneapi::mkl::transpose::trans, - oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, - &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00, - src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16, - dst_f16.get(), dpct::library_data_t::real_half, ldc, - dpct::library_data_t::real_half))); - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); -#else - auto dnnl_stream = ctx.stream_dnnl(stream); - DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ptr, DnnlGemmWrapper::to_dt(), - src0_ptr, DnnlGemmWrapper::to_dt(), dst_f16.get(), DnnlGemmWrapper::to_dt()); - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff* src1_ncols, stream); -#endif - } - else { - // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n"); - ggml_sycl_pool_alloc src0_ddq_as_f32(ctx.pool()); - ggml_sycl_pool_alloc src1_ddq_as_f32(ctx.pool()); - if (src0->type != GGML_TYPE_F32) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type); - GGML_ASSERT(to_fp32_sycl != nullptr); - src0_ddq_as_f32.alloc(row_diff*ne00); - to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); - } - if (src1->type != GGML_TYPE_F32) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type); - GGML_ASSERT(to_fp32_sycl != nullptr); - src1_ddq_as_f32.alloc(src1_ncols*ne10); - to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); - } - const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); - const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); - - const float alpha = 1.0f; - const float beta = 0.0f; -#if !GGML_SYCL_DNNL - SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm( - *stream, oneapi::mkl::transpose::trans, - oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, - dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, - src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), - dst_dd_i, ldc))); -#else - auto dnnl_stream = ctx.stream_dnnl(stream); - DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ddf1_i, DnnlGemmWrapper::to_dt(), - src0_ddf_i, DnnlGemmWrapper::to_dt(), dst_dd_i, DnnlGemmWrapper::to_dt()); -#endif - } - (void) dst; - (void) src1_ddq_i; - (void) src1_padded_row_size; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = static_cast(opts[0]); - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - - const int64_t IH = src0->ne[1]; - const int64_t IW = src0->ne[0]; - - const int64_t N = dst->ne[3]; - const int64_t OC = dst->ne[2]; - const int64_t OH = dst->ne[1]; - const int64_t OW = dst->ne[0]; - - const int parallel_elements = N * OC * OH * OW; - const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE; - sycl::range<3> block_nums(1, 1, num_blocks); - main_stream->parallel_for( - sycl::nd_range<3>(block_nums * - sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, - parallel_elements, src0_dd, dst_dd, op, - item_ct1); - }); - - (void) src1; - (void) src1_dd; -} - -inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne = ggml_nelements(src0); - - sum_rows_f32_sycl(src0_dd, dst_dd, ne, 1, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ncols = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); - - sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); - - const int64_t ncols = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - - argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); - - const int64_t ncols = src0->ne[0]; - const int64_t nrows = ggml_nrows(src0); - - argmax_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int nrows0 = ggml_nrows(src0); - - const int n_past = ((int32_t *) dst->op_params)[0]; - - diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - float scale; - memcpy(&scale, dst->op_params, sizeof(float)); - - scale_f32_sycl(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream); - /* - DPCT1010:87: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this code. - */ - SYCL_CHECK(0); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - float min; - float max; - memcpy(&min, dst->op_params, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - clamp_f32_sycl(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream); - /* - DPCT1010:88: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this code. - */ - SYCL_CHECK(0); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) { - static bool peer_access_enabled = false; - - const bool enable_peer_access = n_tokens <= GGML_SYCL_PEER_MAX_BATCH_SIZE; - - if (peer_access_enabled == enable_peer_access) { - return; - } - -#ifdef NDEBUG - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); - } - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); - - for (int id_other = 0; id_other < ggml_sycl_info().device_count; ++id_other) { - if (i == id_other) { - continue; - } - if (i != main_device && id_other != main_device) { - continue; - } - - // int can_access_peer; - // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other)); - // if (can_access_peer) { - // if (enable_peer_access) { - // SYCL_CHECK(syclDeviceEnablePeerAccess(id_other, 0)); - // } else { - // SYCL_CHECK(syclDeviceDisablePeerAccess(id_other)); - // } - // } - } - } -#endif // NDEBUG - - peer_access_enabled = enable_peer_access; -} - -static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - ggml_sycl_op_mul_mat_t op, - const bool convert_src1_to_q8_1) try { - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); - const int64_t nrows1 = ggml_nrows(src1); - - GGML_ASSERT(ne03 == ne13); - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - - GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); - - GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); - - const int64_t i02_divisor = ne12 / ne02; - - const size_t src0_ts = ggml_type_size(src0->type); - const size_t src0_bs = ggml_blck_size(src0->type); - const size_t q8_1_ts = sizeof(block_q8_1); - const size_t q8_1_bs = QK8_1; - - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - - const bool src0_is_contiguous = ggml_is_contiguous(src0); - const bool src1_is_contiguous = ggml_is_contiguous(src1); - - int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; - GGML_ASSERT(!(split && ne02 > 1)); - GGML_ASSERT(!(split && ne03 > 1)); - GGML_ASSERT(!(split && ne02 < ne12)); - - std::array tensor_split; - if (split) { - // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check - // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context; - tensor_split = buft_ctx->tensor_split; - } - - struct dev_data { - ggml_sycl_pool_alloc src0_dd_alloc; - ggml_sycl_pool_alloc src1_ddf_alloc; - ggml_sycl_pool_alloc src1_ddq_alloc; - ggml_sycl_pool_alloc dst_dd_alloc; - - char *src0_dd = nullptr; - float *src1_ddf = nullptr; // float - char *src1_ddq = nullptr; // q8_1 - float *dst_dd = nullptr; - - int64_t row_low; - int64_t row_high; - }; - - dev_data dev[GGML_SYCL_MAX_DEVICES]; - - int used_devices = 0; - queue_ptr main_stream = ctx.stream(); - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - // by default, use all rows - dev[i].row_low = 0; - dev[i].row_high = ne01; - - // for multi GPU, get the row boundaries from tensor split - // and round to mul_mat_q tile sizes - if (split) { - const int64_t rounding = get_row_rounding(src0->type, tensor_split); - - if (i != 0) { - dev[i].row_low = ne01*tensor_split[i]; - if (dev[i].row_low < ne01) { - dev[i].row_low -= dev[i].row_low % rounding; - } - } - - if (i != ggml_sycl_info().device_count - 1) { - dev[i].row_high = ne01*tensor_split[i + 1]; - if (dev[i].row_high < ne01) { - dev[i].row_high -= dev[i].row_high % rounding; - } - } - } - } - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) { - continue; - } - - used_devices++; - - const bool src1_on_device = i == ctx.device; - const bool dst_on_device = i == ctx.device; - - ggml_sycl_set_device(i); - queue_ptr stream = ctx.stream(i, 0); - - if (src0_is_contiguous) { - dev[i].src0_dd = (char *) src0->data; - } else { - dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ctx.pool(i), ggml_nbytes(src0)); - } - - if (src1_on_device && src1_is_contiguous) { - dev[i].src1_ddf = (float *) src1->data; - } else { - dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ctx.pool(i), ggml_nelements(src1)); - } - - if (convert_src1_to_q8_1) { - dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(ctx.pool(i), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); - - if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); - /* - DPCT1010:90: SYCL uses exceptions to report errors and does not - use the error codes. The call was replaced with 0. You need to - rewrite this code. - */ - SYCL_CHECK(0); - } - } - - if (dst_on_device) { - dev[i].dst_dd = (float *) dst->data; - } else { - const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst); - dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(ctx.pool(i), size_dst_ddf); - } - } - - // if multiple devices are used they need to wait for the main device - // here an event is recorded that signals that the main device has finished calculating the input data - if (split && used_devices > 1) { - ggml_sycl_set_device(ctx.device); - /* - DPCT1024:91: The original code returned the error code that was further - consumed by the program logic. This original code was replaced with 0. - You may need to rewrite the program logic consuming the error code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[ctx.device][0] = - ctx.stream()->ext_oneapi_submit_barrier())); - } - - const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; - for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { - const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_SYCL_MAX_STREAMS : 0; - const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) { - continue; - } - - const bool src1_on_device = i == ctx.device; - const bool dst_on_device = i == ctx.device; - const int64_t row_diff = dev[i].row_high - dev[i].row_low; - - ggml_sycl_set_device(i); - queue_ptr stream = ctx.stream(i, is); - - // wait for main GPU data if necessary - if (split && (i != ctx.device || is != 0)) { - /* - DPCT1009:163: SYCL uses exceptions to report errors and does not - use the error codes. The original code was commented out and a - warning string was inserted. You need to rewrite this code. - */ - SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier( - {*src0_extra->events[ctx.device][0]}))); - } - - for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { - const int64_t i03 = i0 / ne12; - const int64_t i02 = i0 % ne12; - - const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; - - // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = dev[i].src1_ddq + src1_ddq_i_offset; - float * dst_dd_i = dev[i].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); - - // the main device memory buffer can be on VRAM scratch, with space for all partial results - // in that case an offset on dst_ddf_i is needed - if (i == ctx.device) { - dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split - } - - // copy src0, src1 to device if necessary - if (src1_is_contiguous) { - if (i != ctx.device) { - if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( - src1_ddq_i, src1_ddq_i_source, - src1_ncols * src1_padded_col_size * q8_1_ts / - q8_1_bs).wait())); - } else { - - float * src1_ddf_i_source = (float *) src1_extra->data_device[ctx.device]; - src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - - SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream, - src1_ddf_i, src1_ddf_i_source, - src1_ncols * ne10 * sizeof(float)))); - } - } - } else if (src1_on_device && !src1_is_contiguous) { - SYCL_CHECK(ggml_sycl_cpy_tensor_2d( - src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); - } else { - GGML_ABORT("fatal error"); - } - - if (convert_src1_to_q8_1 && !src1_is_contiguous) { - quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); - /* - DPCT1010:92: SYCL uses exceptions to report errors and does - not use the error codes. The call was replaced with 0. You - need to rewrite this code. - */ - SYCL_CHECK(0); - } - - if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) { - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream)); - } - if (src1->type == GGML_TYPE_F16) { - src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10; - } - // do the computation - SYCL_CHECK(CHECK_TRY_ERROR(op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream))); - /* - DPCT1010:93: SYCL uses exceptions to report errors and does not - use the error codes. The call was replaced with 0. You need to - rewrite this code. - */ - SYCL_CHECK(0); - - // copy dst to host or other device if necessary - if (!dst_on_device) { - void * dst_off_device = dst->data; - if (split) { - // src0 = weight matrix is saved as a transposed matrix for better memory layout. - // dst is NOT transposed. - // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU. - // Instead they need to be copied to the correct slice in ne0 = dst row index. - // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. - float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); - GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + dev[i].row_low; - - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - dhf_dst_i, ne0 * sizeof(float), dst_dd_i, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, dpct::device_to_device, *stream))); - } else { - float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); - GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0; - SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(dhf_dst_i, dst_dd_i, - src1_ncols * ne0 * sizeof(float)).wait())); - } - } - - // add event for the main device to wait on until other device is done - if (split && (i != ctx.device || is != 0)) { - /* - DPCT1024:94: The original code returned the error code that - was further consumed by the program logic. This original - code was replaced with 0. You may need to rewrite the - program logic consuming the error code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[i][is] = - stream->ext_oneapi_submit_barrier())); - } - } - } - } - - // main device waits for all other devices to be finished - if (split && ggml_sycl_info().device_count > 1) { - int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; - is_max = is_max <= GGML_SYCL_MAX_STREAMS ? is_max : GGML_SYCL_MAX_STREAMS; - - ggml_sycl_set_device(ctx.device); - for (int i = 0; i < ggml_sycl_info().device_count; ++i) { - if (dev[i].row_low == dev[i].row_high) { - continue; - } - for (int64_t is = 0; is < is_max; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - ctx.stream()->ext_oneapi_submit_barrier( - {*src0_extra->events[i][is]}))); - } - } - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - - -static void ggml_sycl_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_repeat); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_get_rows); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_norm); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rms_norm); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { - GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation - GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t ne12 = src1->ne[2]; - - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - queue_ptr main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; - - const int64_t ne12 = src1->ne[2]; - - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - queue_ptr main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - const int64_t row_stride_x = nb01 / sizeof(sycl::half); - const int64_t channel_stride_x = nb02 / sizeof(sycl::half); - - ggml_mul_mat_vec_nc_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, - const sycl::half *src1_as_f16, char *dst, - const void **ptrs_src, void **ptrs_dst, - int64_t ne12, int64_t ne13, int64_t ne23, - size_t nb02, size_t nb03, size_t nb12, - size_t nb13, size_t nbd2, size_t nbd3, - int64_t r2, int64_t r3, - const sycl::nd_item<3> &item_ct1) { - int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) + - item_ct1.get_local_id(2); - int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) + - item_ct1.get_local_id(1); - - if (i13 >= ne13 || i12 >= ne12) { - return; - } - - int64_t i03 = i13 / r3; - int64_t i02 = i12 / r2; - - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; -} - -static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, - const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t ne_dst = ggml_nelements(dst); - - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - queue_ptr main_stream = ctx.stream();; - - void * src0_ddq = src0->data; - sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - // convert src1 to fp16 - ggml_sycl_pool_alloc src1_f16_alloc(ctx.pool()); - if (src1->type != GGML_TYPE_F16) { - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - const int64_t ne_src1 = ggml_nelements(src1); - src1_f16_alloc.alloc(ne_src1); - GGML_ASSERT(to_fp16_sycl != nullptr); - to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); - } - sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf - : src1_f16_alloc.get(); - - char * dst_t; - - dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float; - dpct::library_data_t cu_data_type = dpct::library_data_t::real_float; - - // dst strides - size_t nbd2 = dst->nb[2]; - size_t nbd3 = dst->nb[3]; - - const float alpha_f32 = 1.0f; - const float beta_f32 = 0.0f; - - const void * alpha = &alpha_f32; - const void * beta = &beta_f32; - - dst_t = (char *) dst_ddf; - - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; - - if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { - // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *main_stream, oneapi::mkl::transpose::trans, - oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, - (const char *)src0_as_f16, dpct::library_data_t::real_half, - nb01 / nb00, nb02 / nb00, - (const char *)src1_f16, dpct::library_data_t::real_half, - nb11 / nb10, nb12 / nb10, beta, - (char *)dst_t, cu_data_type, ne01, nb2 / nb0, - ne12 * ne13, cu_compute_type))); - } else { - const int ne23 = ne12*ne13; - - ggml_sycl_pool_alloc ptrs_src(ctx.pool(), 2*ne23); - ggml_sycl_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23); - - sycl::range<3> block_dims(1, ne12, ne13); - /* - DPCT1049:47: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ - { - dpct::has_capability_or_fail(main_stream->get_device(), - {sycl::aspect::fp16}); - - main_stream->submit([&](sycl::handler &cgh) { - const void **ptrs_src_get = ptrs_src.get(); - void **ptrs_dst_get = ptrs_dst.get(); - size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2; - size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2; - cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_compute_batched_ptrs( - src0_as_f16, src1_f16, - dst_t, ptrs_src_get, - ptrs_dst_get, ne12, ne13, ne23, - nb02, nb03, nb12_scaled, nb13_scaled, - nbd2, nbd3, r2, r3, item_ct1); - }); - }); - } - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *main_stream, oneapi::mkl::transpose::trans, - oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, - (const void **)(ptrs_src.get() + 0 * ne23), - dpct::library_data_t::real_half, nb01 / nb00, - (const void **)(ptrs_src.get() + 1 * ne23), - dpct::library_data_t::real_half, nb11 / nb10, beta, - (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, - cu_compute_type))); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -inline bool ggml_sycl_supports_mmq(enum ggml_type type) { - // TODO: accuracy issues in MMQ - return false; -} - -bool ggml_sycl_supports_dmmv(enum ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_F16: - return true; - default: - return false; - } -} - -static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer); - int64_t min_compute_capability = INT_MAX; - - if (split) { - ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context; - auto & tensor_split = buft_ctx->tensor_split; - for (int id = 0; id < ggml_sycl_info().device_count; ++id) { - // skip devices that are not going to do any work: - if (tensor_split[id] >= (id + 1 < ggml_sycl_info().device_count ? tensor_split[id + 1] : 1.0f)) { - continue; - } - - if (min_compute_capability > ggml_sycl_info().devices[id].cc) { - min_compute_capability = ggml_sycl_info().devices[id].cc; - } - } - } else { - min_compute_capability = ggml_sycl_info().devices[ctx.device].cc; - } - - // check data types and tensor shapes for custom matrix multiplication kernels: - bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1; - - bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; - - bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type) - && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - - // mmvq and mmq need the __dp4a instruction which is available for gen12+ - // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e - use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); -#ifdef SYCL_USE_XMX - use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); -#endif // SYCL_USE_XMX - - // mmvq path is faster in the CUDA backend. - if (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda) - use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; - - if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // KQ single-batch - ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst); - } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // KQV single-batch - ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst); - } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { - // KQ + KQV multi-batch - ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst); - } else if (use_dequantize_mul_mat_vec) { - ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); - } else if (use_mul_mat_vec_q) { - ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); - } else if (use_mul_mat_q) { - ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_q, true); - } else { - ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); - } -} - - -struct mmid_row_mapping { - int32_t i1; - int32_t i2; -}; - -__dpct_inline__ static void k_copy_src1_to_contiguous( - const char *__restrict__ src1_original, char *__restrict__ src1_contiguous, - int *__restrict__ cur_src1_row, mmid_row_mapping *__restrict__ row_mapping, - const char *__restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, - int64_t ne11, int64_t ne10, size_t nb11, size_t nb12, - const sycl::nd_item<3> &item_ct1, int &src1_row) { - int32_t iid1 = item_ct1.get_group(2); - int32_t id = item_ct1.get_group(1); - - const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); - - if (row_id_i != i02) { - return; - } - - const int64_t i11 = id % ne11; - const int64_t i12 = iid1; - - if (item_ct1.get_local_id(2) == 0) { - src1_row = - dpct::atomic_fetch_add( - cur_src1_row, 1); - row_mapping[src1_row] = {id, iid1}; - } - /* - DPCT1065:194: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better - performance if there is no access to global memory. - */ - item_ct1.barrier(); - - const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); - float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); - -#pragma unroll - for (int i = item_ct1.get_local_id(2); i < ne10; - i += item_ct1.get_local_range(2)) { - src1_row_contiguous[i] = src1_row_original[i]; - } -} - -__dpct_inline__ static void k_copy_dst_from_contiguous( - char *__restrict__ dst_original, const char *__restrict__ dst_contiguous, - const mmid_row_mapping *__restrict__ row_mapping, int64_t ne0, size_t nb1, - size_t nb2, const sycl::nd_item<3> &item_ct1) { - int32_t i = item_ct1.get_group(2); - - const int32_t i1 = row_mapping[i].i1; - const int32_t i2 = row_mapping[i].i2; - - const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); - float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); - -#pragma unroll - for (int j = item_ct1.get_local_id(2); j < ne0; - j += item_ct1.get_local_range(2)) { - dst_row_original[j] = dst_row_contiguous[j]; - } -} - -static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { - GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers"); - - const ggml_tensor *ids = dst->src[2]; - GGML_TENSOR_BINARY_OP_LOCALS - - const queue_ptr stream = ctx.stream(); - - const int64_t n_as = ne02; - const int64_t n_ids = ids->ne[0]; - - std::vector ids_host(ggml_nbytes(ids)); - const char * ids_dev = (const char *) ids->data; - - SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); - SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); - - ggml_tensor src0_row = *src0; - ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; - - char *src0_original = (char *)src0->data; - char *src1_original = (char *)src1->data; - char *dst_original = (char *)dst->data; - - src0_row.ne[2] = 1; - src0_row.ne[3] = 1; - src0_row.nb[3] = nb02; - - src1_row.ne[1] = 1; - src1_row.ne[2] = 1; - src1_row.ne[3] = 1; - src1_row.nb[2] = nb11; - src1_row.nb[3] = nb11; - - dst_row.ne[1] = 1; - dst_row.ne[2] = 1; - dst_row.ne[3] = 1; - dst_row.nb[2] = nb1; - dst_row.nb[3] = nb1; - if (ne12 == 1) { - for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { - for (int64_t id = 0; id < n_ids; id++) { - const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - GGML_ASSERT(i02 >= 0 && i02 < n_as); - - const int64_t i11 = id % ne11; - const int64_t i12 = iid1; - - const int64_t i1 = id; - const int64_t i2 = i12; - - src0_row.data = src0_original + i02*nb02; - src1_row.data = src1_original + + i11*nb11 + i12*nb12; - dst_row.data = dst_original + i1*nb1 + i2*nb2; - - ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - } - } - } else { - ggml_sycl_pool_alloc src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1)); - ggml_sycl_pool_alloc dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst)); - - src1_row.data = src1_contiguous.get(); - dst_row.data = dst_contiguous.get(); - - for (int64_t i02 = 0; i02 < n_as; i02++) { - int64_t num_src1_rows = 0; - for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { - for (int64_t id = 0; id < n_ids; id++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - - GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); - - if (row_id_i != i02) { - continue; - } - - num_src1_rows++; - } - } - - if (num_src1_rows == 0) { - continue; - } - - - ggml_sycl_pool_alloc dev_cur_src1_row(ctx.pool(), 1); - ggml_sycl_pool_alloc dev_row_mapping(ctx.pool(), num_src1_rows); - SYCL_CHECK(CHECK_TRY_ERROR( - stream->memset(dev_cur_src1_row.get(), 0, sizeof(int)))); - - { - sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u)); - sycl::range<3> grid_dims(1, n_ids, ids->ne[1]); - stream->submit([&](sycl::handler &cgh) { - sycl::local_accessor src1_row_acc(cgh); - - char *__restrict src1_contiguous_get = - src1_contiguous.get(); - int *__restrict dev_cur_src1_row_get = - dev_cur_src1_row.get(); - mmid_row_mapping *__restrict dev_row_mapping_get = - dev_row_mapping.get(); - size_t ids_nb_ct6 = ids->nb[1]; - size_t ids_nb_ct7 = ids->nb[0]; - - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_copy_src1_to_contiguous( - src1_original, src1_contiguous_get, - dev_cur_src1_row_get, - dev_row_mapping_get, ids_dev, i02, - ids_nb_ct6, ids_nb_ct7, ne11, ne10, nb11, nb12, - item_ct1, src1_row_acc); - }); - }); - } - - src0_row.data = src0_original + i02*nb02; - - GGML_ASSERT(nb11 == sizeof(float)*ne10); - GGML_ASSERT(nb1 == sizeof(float)*ne0); - src1_row.ne[1] = num_src1_rows; - - src1_row.nb[1] = nb11; - src1_row.nb[2] = num_src1_rows*nb11; - src1_row.nb[3] = num_src1_rows*nb11; - - dst_row.ne[1] = num_src1_rows; - dst_row.nb[1] = nb1; - dst_row.nb[2] = num_src1_rows*nb1; - dst_row.nb[3] = num_src1_rows*nb1; - - ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - - { - sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u)); - sycl::range<3> grid_dims(1, 1, num_src1_rows); - stream->submit([&](sycl::handler &cgh) { - const char *__restrict dst_contiguous_get = - dst_contiguous.get(); - const mmid_row_mapping *__restrict dev_row_mapping_get = - dev_row_mapping.get(); - - cgh.parallel_for( - sycl::nd_range<3>(grid_dims * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_copy_dst_from_contiguous(dst_original, - dst_contiguous_get, - dev_row_mapping_get, - ne0, nb1, nb2, item_ct1); - }); - }); - } - } - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_scale); -} - -static void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_clamp); -} - -static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst) try { - const int64_t ne = ggml_nelements(src0); - GGML_ASSERT(ne == ggml_nelements(src1)); - - GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); - GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); - - GGML_TENSOR_BINARY_OP_LOCALS01; - - SYCL_CHECK(ggml_sycl_set_device(ctx.device)); - queue_ptr main_stream = ctx.stream(); - - char * src0_ddc = (char *) src0->data; - char * src1_ddc = (char *) src1->data; - - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { - ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { - ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); - } else { - fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, - ggml_type_name(src0->type), ggml_type_name(src1->type)); - GGML_ABORT("fatal error"); - } - - (void) dst; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_dup(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - // TODO: why do we pass dst as src1 here? - ggml_sycl_cpy(ctx, src0, dst, nullptr); - (void) src1; -} - -static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_diag_mask_inf); -} - -static void ggml_sycl_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_soft_max); -} - -static void ggml_sycl_rope(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rope); -} - -static void ggml_sycl_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pool2d); -} - -static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_im2col); -} - -static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum); -} - -static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum_rows); -} - -static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argsort); -} - -static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argmax); -} - -static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - (void) src0; - (void) src1; - (void) dst; -} - -void ggml_sycl_set_main_device(const int main_device) try { - if (dpct::get_current_device_id() == main_device) return; - check_allow_gpu_index(main_device); - dpct::select_device(main_device); - - if (g_ggml_sycl_debug) { - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(main_device)))); - fprintf(stderr, "Using device %d (%s) as main device\n", - main_device, prop.get_name()); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tensor * tensor) { - if (!g_sycl_loaded) return false; - - ggml_sycl_func_t func; - - switch (tensor->op) { - case GGML_OP_ARGMAX: - func = ggml_sycl_argmax; - break; - case GGML_OP_CONV_TRANSPOSE_1D: - func = ggml_sycl_op_conv_transpose_1d; - break; - case GGML_OP_REPEAT: - func = ggml_sycl_repeat; - break; - case GGML_OP_GET_ROWS: - func = ggml_sycl_get_rows; - break; - case GGML_OP_DUP: - func = ggml_sycl_dup; - break; - case GGML_OP_ADD: - case GGML_OP_ADD1: // TODO: more efficient implementation - func = ggml_sycl_add; - break; - case GGML_OP_SUB: - func = ggml_sycl_sub; - break; - case GGML_OP_ACC: - func = ggml_sycl_acc; - break; - case GGML_OP_MUL: - func = ggml_sycl_mul; - break; - case GGML_OP_LOG: - func = ggml_sycl_log; - break; - case GGML_OP_DIV: - func = ggml_sycl_div; - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_NEG: - func = ggml_sycl_neg; - break; - case GGML_UNARY_OP_STEP: - func = ggml_sycl_step; - break; - case GGML_UNARY_OP_GELU: - func = ggml_sycl_gelu; - break; - case GGML_UNARY_OP_SILU: - func = ggml_sycl_silu; - break; - case GGML_UNARY_OP_GELU_QUICK: - func = ggml_sycl_gelu_quick; - break; - case GGML_UNARY_OP_TANH: - func = ggml_sycl_tanh; - break; - case GGML_UNARY_OP_RELU: - func = ggml_sycl_relu; - break; - case GGML_UNARY_OP_SIGMOID: - func = ggml_sycl_sigmoid; - break; - case GGML_UNARY_OP_HARDSIGMOID: - func = ggml_sycl_hardsigmoid; - break; - case GGML_UNARY_OP_HARDSWISH: - func = ggml_sycl_hardswish; - break; - case GGML_UNARY_OP_EXP: - func = ggml_sycl_exp; - break; - default: - return false; - } - break; - case GGML_OP_NORM: - func = ggml_sycl_norm; - break; - case GGML_OP_GROUP_NORM: - func = ggml_sycl_group_norm; - break; - case GGML_OP_CONCAT: - func = ggml_sycl_op_concat; - break; - case GGML_OP_UPSCALE: - func = ggml_sycl_upscale; - break; - case GGML_OP_PAD: - func = ggml_sycl_pad; - break; - case GGML_OP_LEAKY_RELU: - func = ggml_sycl_leaky_relu; - break; - case GGML_OP_RMS_NORM: - func = ggml_sycl_rms_norm; - break; - case GGML_OP_MUL_MAT: - if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { - return false; - } - func = ggml_sycl_mul_mat; - break; - case GGML_OP_MUL_MAT_ID: - if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { - return false; - } - func = ggml_sycl_mul_mat_id; - break; - case GGML_OP_OUT_PROD: - func = ggml_sycl_op_out_prod; - break; - case GGML_OP_SCALE: - func = ggml_sycl_scale; - break; - case GGML_OP_SQR: - func = ggml_sycl_sqr; - break; - case GGML_OP_SQRT: - func = ggml_sycl_sqrt; - break; - case GGML_OP_SIN: - func = ggml_sycl_sin; - break; - case GGML_OP_COS: - func = ggml_sycl_cos; - break; - case GGML_OP_CLAMP: - func = ggml_sycl_clamp; - break; - case GGML_OP_CPY: - func = ggml_sycl_cpy; - break; - case GGML_OP_CONT: - func = ggml_sycl_dup; - break; - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - func = ggml_sycl_nop; - break; - case GGML_OP_DIAG_MASK_INF: - func = ggml_sycl_diag_mask_inf; - break; - case GGML_OP_SOFT_MAX: - func = ggml_sycl_soft_max; - break; - case GGML_OP_ROPE: - func = ggml_sycl_rope; - break; - case GGML_OP_IM2COL: - func = ggml_sycl_im2col; - break; - case GGML_OP_POOL_2D: - func = ggml_sycl_pool2d; - break; - case GGML_OP_SUM: - func = ggml_sycl_sum; - break; - case GGML_OP_SUM_ROWS: - func = ggml_sycl_sum_rows; - break; - case GGML_OP_ARGSORT: - func = ggml_sycl_argsort; - break; - case GGML_OP_TIMESTEP_EMBEDDING: - func = ggml_sycl_op_timestep_embedding; - break; - case GGML_OP_RWKV_WKV6: - func = ggml_sycl_op_rwkv_wkv6; - break; - default: - return false; - } - - if (tensor->src[0] != nullptr && ggml_backend_buffer_is_sycl_split(tensor->src[0]->buffer)) { - ggml_sycl_set_peer_access(tensor->src[1]->ne[1], ctx.device); - } - - func(ctx, tensor->src[0], tensor->src[1], tensor); - return true; -} - -GGML_API void ggml_backend_sycl_get_device_description(int device, char *description, - size_t description_size) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_description\n"); - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device)))); - snprintf(description, description_size, "%s", prop.get_name()); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_backend_sycl_get_device_memory(int device, size_t *free, - size_t *total) try { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n"); - ggml_sycl_set_device(device); - - /* - DPCT1009:218: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string was - inserted. You need to rewrite this code. - */ - /* - DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for - device information which may not be supported by all compilers or runtimes. - You may need to adjust the code. - */ - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(device).get_memory_info(*free, *total))); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -//////////////////////////////////////////////////////////////////////////////// - -// backend - -static const char * ggml_backend_sycl_get_name(ggml_backend_t backend) { - - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - - return sycl_ctx->name.c_str(); -} - -static void ggml_backend_sycl_free(ggml_backend_t backend) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - - delete sycl_ctx; - delete backend; -} - -static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, - ggml_tensor *tensor, - const void *data, size_t offset, - size_t size) try { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - - GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); - const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); - SYCL_CHECK(CHECK_TRY_ERROR( - (stream)->memcpy((char *)tensor->data + offset, data, size))); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, - const ggml_tensor *tensor, - void *data, size_t offset, - size_t size) try { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - - GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); - const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); - SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( - data, (const char *)tensor->data + offset, size).wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, - const ggml_tensor *src, - ggml_tensor *dst) try { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) { - /* - DPCT1009:215: SYCL uses exceptions to report errors and does not use the - error codes. The original code was commented out and a warning string - was inserted. You need to rewrite this code. - */ - const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); - SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( - dst->data, src->data, ggml_nbytes(dst)).wait())); - return true; - } - - return false; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); - SYCL_CHECK(CHECK_TRY_ERROR((stream)->wait())); - - GGML_UNUSED(backend); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - ggml_sycl_set_main_device(sycl_ctx->device); - - - for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { - continue; - } -#ifndef NDEBUG - assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); - for (int j = 0; j < GGML_MAX_SRC; j++) { - if (node->src[j] != nullptr) { - assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); - } - } -#endif - bool ok = ggml_sycl_compute_forward(*sycl_ctx, node); - if (!ok) { - fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); - } - GGML_ASSERT(ok); - } - - return GGML_STATUS_SUCCESS; -} - -static void ggml_backend_sycl_event_record(ggml_backend_t backend, ggml_backend_event_t event) -try -{ - ggml_backend_sycl_context *sycl_ctx = - (ggml_backend_sycl_context *)backend->context; - sycl::event *sycl_event = static_cast(event->context); - - const queue_ptr &stream = sycl_ctx->stream(sycl_ctx->device, 0); - // Record the current state of the queue - SYCL_CHECK(CHECK_TRY_ERROR(*sycl_event = stream->ext_oneapi_submit_barrier())); -} -catch (sycl::exception const &exc) -{ - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_event_t event) try { - ggml_backend_sycl_context* sycl_ctx = static_cast(backend->context); - sycl::event* sycl_event = static_cast(event->context); - - if (ggml_backend_is_sycl(backend)) { - SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait())); - } else - GGML_ABORT("fatal error"); -} catch (sycl::exception const& exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static ggml_backend_i ggml_backend_sycl_interface = { - /* .get_name = */ ggml_backend_sycl_get_name, - /* .free = */ ggml_backend_sycl_free, - /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, - /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ NULL, // ggml_backend_sycl_cpy_tensor_async, - // // TODO: update for the new - // interface - /* .synchronize = */ ggml_backend_sycl_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_sycl_graph_compute, - /* .event_record = */ ggml_backend_sycl_event_record, - /* .event_wait = */ ggml_backend_sycl_event_wait, -}; - -static ggml_guid_t ggml_backend_sycl_guid() { - static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; - return &guid; -} - -bool ggml_backend_is_sycl(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); -} - -int ggml_backend_sycl_get_device_count() { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n"); - return ggml_sycl_info().device_count; -} - - -// backend device - -struct ggml_backend_sycl_device_context { - int device; - std::string name; - std::string description; -}; - -static const char * ggml_backend_sycl_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; - return ctx->name.c_str(); -} - -static const char * ggml_backend_sycl_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; - return ctx->description.c_str(); -} - -static void ggml_backend_sycl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; - ggml_sycl_set_device(ctx->device); - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(ctx->device).get_memory_info(*free, *total))); -} - -static enum ggml_backend_dev_type ggml_backend_sycl_device_get_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU; -} - -static void ggml_backend_sycl_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { - props->name = ggml_backend_sycl_device_get_name(dev); - props->description = ggml_backend_sycl_device_get_description(dev); - props->type = ggml_backend_sycl_device_get_type(dev); - ggml_backend_sycl_device_get_memory(dev, &props->memory_free, &props->memory_total); - - bool host_buffer = getenv("GGML_SYCL_NO_PINNED") == nullptr; -#ifdef GGML_SYCL_NO_PEER_COPY - bool events = false; -#else - bool events = true; -#endif - - props->caps = { - /* .async = */ true, - /* .host_buffer = */ host_buffer, - /* .buffer_from_host_ptr = */ false, - /* .events = */ events, - }; -} - -static ggml_backend_t ggml_backend_sycl_device_init(ggml_backend_dev_t dev, const char * params) { - GGML_UNUSED(params); - ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; - return ggml_backend_sycl_init(ctx->device); -} - -static ggml_backend_buffer_type_t ggml_backend_sycl_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; - return ggml_backend_sycl_buffer_type(ctx->device); -} - -static ggml_backend_buffer_type_t ggml_backend_sycl_device_get_host_buffer_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); - return ggml_backend_sycl_host_buffer_type(); -} - -static ggml_backend_buffer_t ggml_backend_sycl_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - GGML_UNUSED(dev); - GGML_UNUSED(ptr); - GGML_UNUSED(size); - GGML_UNUSED(max_tensor_size); - return nullptr; -} - -static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - switch (op->op) { - case GGML_OP_CONV_TRANSPOSE_1D: - { - ggml_type src0_type = op->src[0]->type; - ggml_type src1_type = op->src[1]->type; - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return true; - } - return false; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_EXP: - return ggml_is_contiguous(op->src[0]); - default: - return false; - } - break; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - { - struct ggml_tensor * a; - struct ggml_tensor * b; - if (op->op == GGML_OP_MUL_MAT) { - a = op->src[0]; - b = op->src[1]; - if (ggml_is_permuted(a) || ggml_is_permuted(b)) { - // TODO: fix like https://github.com/ggerganov/llama.cpp/pull/10021 - return false; - } - } else { - a = op->src[2]; - b = op->src[1]; - } - if (a->ne[3] != b->ne[3]) { - return false; - } - ggml_type a_type = a->type; - if (a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ4_XS || - a_type == GGML_TYPE_IQ3_XXS || a_type == GGML_TYPE_IQ3_S || - a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ2_S || - a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ1_M - ) { - if (b->ne[1] == 1 && ggml_nrows(b) > 1) { - return false; - } - } - ggml_type src0_type = op->src[0]->type; - if (src0_type == GGML_TYPE_BF16) { - return false; - } - return true; - } break; - case GGML_OP_OUT_PROD: - return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1; - case GGML_OP_GET_ROWS: - { - switch (op->src[0]->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return true; - default: - return false; - } - } break; - case GGML_OP_CPY: - { - ggml_type src0_type = op->src[0]->type; - ggml_type src1_type = op->src[1]->type; - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) { - return true; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return true; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { - return true; - } - return false; - } break; - case GGML_OP_CONCAT: - { - ggml_type src0_type = op->src[0]->type; - return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; - } break; - case GGML_OP_DUP: - case GGML_OP_ARGMAX: - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_REPEAT: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NORM: - case GGML_OP_ADD: - case GGML_OP_ADD1: - case GGML_OP_LOG: - case GGML_OP_SUB: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_RMS_NORM: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - return true; - case GGML_OP_CONT: - return op->src[0]->type != GGML_TYPE_BF16; - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - return true; - case GGML_OP_ROPE: - return ggml_is_contiguous(op->src[0]); - case GGML_OP_IM2COL: - // TODO: add support for the new F32 operations - return op->src[0]->type == GGML_TYPE_F16; - case GGML_OP_POOL_2D: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_ARGSORT: - case GGML_OP_ACC: - case GGML_OP_GROUP_NORM: - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_LEAKY_RELU: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_RWKV_WKV6: - return true; - default: - return false; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_sycl_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_sycl_buffer_type_get_name) { - return false; - } - ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_backend_sycl_device_context * sycl_ctx = (ggml_backend_sycl_device_context *)dev->context; - return buft_ctx->device == sycl_ctx->device; -} - -static int64_t get_op_batch_size(const ggml_tensor * op) { - switch (op->op) { - case GGML_OP_GET_ROWS: - return op->ne[1]; // this will increse the speed of prefill in test - case GGML_OP_MUL_MAT: - return op->ne[1]; - case GGML_OP_MUL_MAT_ID: - case GGML_OP_ROPE: - return op->ne[2]; - default: - return ggml_nrows(op); - } -} - -static bool ggml_backend_sycl_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - const int min_batch_size = 32; - return get_op_batch_size(op) >= min_batch_size; - GGML_UNUSED(dev); -} - -static ggml_backend_event_t -ggml_backend_sycl_device_event_new(ggml_backend_dev_t dev) { - -#ifdef GGML_SYCL_NO_PEER_COPY - return nullptr; -#else - sycl::event *event_ptr = new sycl::event(); - - return new ggml_backend_event{ - /* .device = */ dev, - /* .context = */ event_ptr, - }; -#endif -} - -static void ggml_backend_sycl_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) try { - GGML_UNUSED(dev); - if (event == nullptr) { - return; - } - - if (event->context != nullptr) { - sycl::event *sycl_event = static_cast(event->context); - delete sycl_event; - event->context = nullptr; - } - - delete event; -} catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - - -static void ggml_backend_sycl_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) try { - GGML_UNUSED(dev); - - sycl::event *sycl_event = static_cast(event->context); - SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait())); -} catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static const ggml_backend_device_i ggml_backend_sycl_device_interface = { - /* .get_name = */ ggml_backend_sycl_device_get_name, - /* .get_description = */ ggml_backend_sycl_device_get_description, - /* .get_memory = */ ggml_backend_sycl_device_get_memory, - /* .get_type = */ ggml_backend_sycl_device_get_type, - /* .get_props = */ ggml_backend_sycl_device_get_props, - /* .init_backend = */ ggml_backend_sycl_device_init, - /* .get_buffer_type = */ ggml_backend_sycl_device_get_buffer_type, - /* .get_host_buffer_type = */ ggml_backend_sycl_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ ggml_backend_sycl_device_buffer_from_host_ptr, - /* .supports_op = */ ggml_backend_sycl_device_supports_op, - /* .supports_buft = */ ggml_backend_sycl_device_supports_buft, - /* .offload_op = */ ggml_backend_sycl_device_offload_op, - /* .event_new = */ ggml_backend_sycl_device_event_new, - /* .event_free = */ ggml_backend_sycl_device_event_free, - /* .event_synchronize = */ ggml_backend_sycl_device_event_synchronize, -}; - -// backend reg - -struct ggml_backend_sycl_reg_context { - std::vector devices; -}; - -static const char * ggml_backend_sycl_reg_get_name(ggml_backend_reg_t reg) { - GGML_UNUSED(reg); - return GGML_SYCL_NAME; -} - -static size_t ggml_backend_sycl_reg_get_device_count(ggml_backend_reg_t reg) { - ggml_backend_sycl_reg_context * ctx = (ggml_backend_sycl_reg_context *)reg->context; - return ctx->devices.size(); -} - -static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t reg, size_t index) { - ggml_backend_sycl_reg_context * ctx = (ggml_backend_sycl_reg_context *)reg->context; - GGML_ASSERT(index < ctx->devices.size()); - return ctx->devices[index]; -} - -static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) { - GGML_UNUSED(reg); - - // TODO: update to the current function signature - //if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { - // return (void *)ggml_backend_sycl_split_buffer_type; - //} - - // SYCL doesn't support registering host memory, left here for reference - // "ggml_backend_register_host_buffer" - // "ggml_backend_unregister_host_buffer" - return nullptr; -} - -static const ggml_backend_reg_i ggml_backend_sycl_reg_interface = { - /* .get_name = */ ggml_backend_sycl_reg_get_name, - /* .get_device_count = */ ggml_backend_sycl_reg_get_device_count, - /* .get_device_get = */ ggml_backend_sycl_reg_get_device, - /* .get_proc_address = */ ggml_backend_sycl_reg_get_proc_address, -}; - - -// backend registry - -ggml_backend_reg_t ggml_backend_sycl_reg() { - static ggml_backend_reg reg; - static bool initialized = false; - - { - static std::mutex mutex; - std::lock_guard lock(mutex); - if (!initialized) { - ggml_backend_sycl_reg_context * ctx = new ggml_backend_sycl_reg_context; - - for (int i = 0; i < ggml_sycl_info().device_count; i++) { - ggml_backend_sycl_device_context * dev_ctx = new ggml_backend_sycl_device_context; - dev_ctx->device = i; - dev_ctx->name = GGML_SYCL_NAME + std::to_string(i); - - ggml_sycl_set_device(i); - - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(i)))); - - dev_ctx->description = prop.get_name(); - - ggml_backend_dev_t dev = new ggml_backend_device { - /* .interface = */ ggml_backend_sycl_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx - }; - ctx->devices.push_back(dev); - } - - reg = ggml_backend_reg { - /* .interface = */ ggml_backend_sycl_reg_interface, - /* .context = */ ctx - }; - } - - initialized = true; - } - - return ® -} - -ggml_backend_t ggml_backend_sycl_init(int device) { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n"); - ggml_check_sycl(); - - check_allow_gpu_index(device); - - ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context(device); - if (ctx == nullptr) { - fprintf(stderr, "%s: error: failed to allocate context\n", __func__); - return nullptr; - }; - - ggml_backend_t sycl_backend = new ggml_backend { - /* .guid = */ ggml_backend_sycl_guid(), - /* .interface = */ ggml_backend_sycl_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_sycl_reg(), device), - /* .context = */ ctx - }; - - return sycl_backend; -} - diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp deleted file mode 100644 index 169b5a3b..00000000 --- a/ggml/src/ggml-vulkan.cpp +++ /dev/null @@ -1,7648 +0,0 @@ -#include "ggml-vulkan.h" -#include -#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_PERF) -#include -#endif - -#include - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include "ggml-impl.h" -#include "ggml-backend-impl.h" - -#include "ggml-vulkan-shaders.hpp" - -#define VK_API_VERSION VK_API_VERSION_1_2 - -#define CEIL_DIV(M, N) (((M) + (N)-1) / (N)) - -#define VK_VENDOR_ID_AMD 0x1002 -#define VK_VENDOR_ID_APPLE 0x106b -#define VK_VENDOR_ID_INTEL 0x8086 -#define VK_VENDOR_ID_NVIDIA 0x10de - -#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 32 - -#define GGML_VK_MAX_NODES 8192 - -#define MAX_VK_BUFFERS 256 - -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 1 -#else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); -#endif - -#define VK_CHECK(err, msg) \ - do { \ - vk::Result err_ = (err); \ - if (err_ != vk::Result::eSuccess) { \ - fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n", \ - #err, to_string(err_).c_str(), __FILE__, __LINE__); \ - exit(1); \ - } \ - } while (0) - -#ifdef GGML_VULKAN_DEBUG -#define VK_LOG_DEBUG(msg) std::cerr << msg << std::endl -#else -#define VK_LOG_DEBUG(msg) ((void) 0) -#endif // GGML_VULKAN_DEBUG - -struct ggml_backend_vk_context; - -struct vk_queue { - uint32_t queue_family_index; - vk::Queue queue; - vk::CommandPool pool; - uint32_t cmd_buffer_idx; - std::vector cmd_buffers; - - vk::PipelineStageFlags stage_flags; - - bool transfer_only; -}; - -struct vk_pipeline_struct { - std::string name; - vk::ShaderModule shader_module; - vk::DescriptorSetLayout dsl; - std::vector descriptor_pools; - std::vector descriptor_sets; - uint32_t descriptor_set_idx; - vk::PipelineLayout layout; - vk::Pipeline pipeline; - uint32_t push_constant_size; - uint32_t parameter_count; - std::array wg_denoms; - uint32_t align; -}; - -typedef std::shared_ptr vk_pipeline; -typedef std::weak_ptr vk_pipeline_ref; - -static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline); - -struct vk_matmul_pipeline_struct { - vk_pipeline l, m, s; - vk_pipeline a_l, a_m, a_s; -}; - -typedef std::shared_ptr vk_matmul_pipeline; - -struct vk_device_struct; -typedef std::shared_ptr vk_device; -typedef std::weak_ptr vk_device_ref; - -struct vk_buffer_struct; -typedef std::shared_ptr vk_buffer; -typedef std::weak_ptr vk_buffer_ref; - -struct ggml_backend_vk_buffer_type_context { - std::string name; - vk_device device; -}; - -static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft); -static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size); -static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft); -static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft); -static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor); -static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { - /* .get_name = */ ggml_backend_vk_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_vk_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_vk_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, - /* .get_alloc_size = */ ggml_backend_vk_buffer_type_get_alloc_size, - /* .is_host = */ NULL, -}; - -#ifdef GGML_VULKAN_MEMORY_DEBUG -class vk_memory_logger; -#endif -#ifdef GGML_VULKAN_PERF -class vk_perf_logger; -#endif -static void ggml_vk_destroy_buffer(vk_buffer& buf); - -struct vk_device_struct { - std::mutex mutex; - - vk::PhysicalDevice physical_device; - vk::PhysicalDeviceProperties properties; - std::string name; - uint64_t max_memory_allocation_size; - bool fp16; - vk::Device device; - uint32_t vendor_id; - vk_queue compute_queue; - vk_queue transfer_queue; - bool single_queue; - uint32_t subgroup_size; - bool uma; - - size_t idx; - - vk_matmul_pipeline pipeline_matmul_f32; - vk_matmul_pipeline pipeline_matmul_f32_f16; - vk_matmul_pipeline pipeline_matmul_f16; - vk_matmul_pipeline pipeline_matmul_f16_f32; - vk_pipeline pipeline_matmul_split_k_reduce; - - vk_matmul_pipeline pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT]; - - vk_matmul_pipeline pipeline_matmul_id_f32; - vk_matmul_pipeline pipeline_matmul_id_f16; - vk_matmul_pipeline pipeline_matmul_id_f16_f32; - - vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT]; - - vk_pipeline pipeline_dequant[GGML_TYPE_COUNT]; - vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT]; - vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT]; - vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT]; - - vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; - vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; - vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT]; - vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT]; - vk_pipeline pipeline_acc_f32; - vk_pipeline pipeline_add_f32, pipeline_add_f16_f32_f16; - vk_pipeline pipeline_mul_f32; - vk_pipeline pipeline_div_f32; - vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32; - vk_pipeline pipeline_upscale_f32; - vk_pipeline pipeline_scale_f32; - vk_pipeline pipeline_sqr_f32; - vk_pipeline pipeline_sin_f32; - vk_pipeline pipeline_cos_f32; - vk_pipeline pipeline_clamp_f32; - vk_pipeline pipeline_pad_f32; - vk_pipeline pipeline_repeat_f32; - vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; - vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16; - vk_pipeline pipeline_norm_f32; - vk_pipeline pipeline_group_norm_f32; - vk_pipeline pipeline_rms_norm_f32; - vk_pipeline pipeline_gelu_f32; - vk_pipeline pipeline_gelu_quick_f32; - vk_pipeline pipeline_silu_f32; - vk_pipeline pipeline_relu_f32; - vk_pipeline pipeline_leaky_relu_f32; - vk_pipeline pipeline_tanh_f32; - vk_pipeline pipeline_diag_mask_inf_f32; - vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; - vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16; - vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; - vk_pipeline pipeline_argsort_f32; - vk_pipeline pipeline_sum_rows_f32; - vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16; - vk_pipeline pipeline_timestep_embedding_f32; - vk_pipeline pipeline_pool2d_f32; - - std::unordered_map pipelines; - std::unordered_map pipeline_descriptor_set_requirements; - - std::vector> pinned_memory; - - vk::Fence fence; - vk_buffer sync_staging; - - ggml_backend_buffer_type buffer_type; - -#ifdef GGML_VULKAN_MEMORY_DEBUG - std::unique_ptr memory_logger; -#endif -#ifdef GGML_VULKAN_PERF - std::unique_ptr perf_logger; -#endif - - ~vk_device_struct() { - VK_LOG_DEBUG("destroy device " << name); - - device.destroyFence(fence); - - ggml_vk_destroy_buffer(sync_staging); - - device.destroyCommandPool(compute_queue.pool); - if (!single_queue) { - device.destroyCommandPool(transfer_queue.pool); - } - - for (auto& pipeline : pipelines) { - if (pipeline.second.expired()) { - continue; - } - - vk_pipeline pl = pipeline.second.lock(); - ggml_vk_destroy_pipeline(device, pl); - } - pipelines.clear(); - - device.destroy(); - } -}; - -struct vk_buffer_struct { - vk::Buffer buffer = VK_NULL_HANDLE; - vk::DeviceMemory device_memory = VK_NULL_HANDLE; - vk::MemoryPropertyFlags memory_property_flags; - void * ptr; - size_t size = 0; - - vk_device device; - - ~vk_buffer_struct() { - if (size == 0) { - return; - } - VK_LOG_DEBUG("~vk_buffer_struct(" << buffer << ", " << size << ")"); - - device->device.freeMemory(device_memory); - device->device.destroyBuffer(buffer); - } -}; - -struct vk_subbuffer { - vk_buffer buffer; - uint64_t offset; - uint64_t size; - - operator vk::DescriptorBufferInfo() const { - return { buffer->buffer, offset, size }; - } -}; - -struct vk_semaphore { - vk::Semaphore s; - uint64_t value; -}; - -struct vk_submission { - vk::CommandBuffer buffer; - std::vector wait_semaphores; - std::vector signal_semaphores; -}; - -typedef std::vector vk_sequence; - -struct vk_mat_mat_push_constants { - uint32_t M; uint32_t N; uint32_t K; - uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; - uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; - uint32_t k_split; - uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; -}; -struct vk_mat_vec_push_constants { - uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; - uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; - uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; -}; - -struct vk_mat_mat_id_push_constants { - uint32_t M; uint32_t N; uint32_t K; - uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; - uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; - uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11; -}; -struct vk_mat_vec_id_push_constants { - uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; - uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; - uint32_t nei0; uint32_t ne11; -}; - -struct vk_op_push_constants { - uint32_t KX; - uint32_t KY; - float param1; - float param2; -}; - -struct vk_op_unary_push_constants { - uint32_t ne; - uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; - uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; - uint32_t d_offset; - float param1; float param2; -}; - -struct vk_op_binary_push_constants { - uint32_t ne; - uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; - uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; - uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23; - uint32_t d_offset; - float param1; float param2; int32_t param3; -}; - -struct vk_op_diag_mask_push_constants { - uint32_t ncols; - uint32_t rows_per_channel; - int32_t n_past; -}; - -struct vk_op_rope_push_constants { - uint32_t ncols; - uint32_t n_dims; - float freq_scale; - uint32_t p_delta_rows; - float freq_base; - float ext_factor; - float attn_factor; - float corr_dims[2]; - float theta_scale; - uint32_t has_ff; -}; - -struct vk_op_soft_max_push_constants { - uint32_t KX; - uint32_t KY; - float scale; - float max_bias; - float m0; - float m1; - uint32_t n_head_log2; -}; - -struct vk_op_argsort_push_constants { - uint32_t ncols; - uint32_t ncols_pad; - int32_t order; -}; - -struct vk_op_im2col_push_constants { - uint32_t batch_offset; uint32_t offset_delta; - uint32_t IC; - uint32_t IW; uint32_t IH; - uint32_t OW; uint32_t OH; - uint32_t KW; uint32_t KH; - uint32_t pelements; - uint32_t CHW; - int32_t s0; int32_t s1; - int32_t p0; int32_t p1; - int32_t d0; int32_t d1; -}; - -struct vk_op_timestep_embedding_push_constants { - uint32_t nb1; - uint32_t dim; - uint32_t max_period; -}; - -struct vk_op_pool2d_push_constants { - uint32_t IW; uint32_t IH; - uint32_t OW; uint32_t OH; - uint32_t OC; - uint32_t pelements; - uint32_t op; - int32_t k0; int32_t k1; - int32_t s0; int32_t s1; - int32_t p0; int32_t p1; -}; - -// Allow pre-recording command buffers -struct vk_staging_memcpy { - vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} - - void * dst; - const void * src; - size_t n; -}; - -struct vk_op_upscale_push_constants { - uint32_t ne; uint32_t d_offset; - uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; - uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; - float sf0; float sf1; float sf2; float sf3; -}; - -struct vk_context_struct { - vk_submission * s; - std::vector seqs; - - int exit_tensor_idx; - - std::vector in_memcpys; - std::vector out_memcpys; - - vk_queue * q; -}; -typedef std::shared_ptr vk_context; -typedef std::weak_ptr vk_context_ref; - -struct ggml_vk_garbage_collector { - std::vector tl_semaphores; - std::vector semaphores; - std::vector events; - std::vector temp_buffers; - std::vector contexts; -}; - -#if defined(GGML_VULKAN_MEMORY_DEBUG) || defined(GGML_VULKAN_DEBUG) -#define VK_LOG_MEMORY(msg) std::cerr << "ggml_vulkan memory: " << msg << std::endl - -static std::string format_size(size_t size) { - const size_t kib = 1024; - const size_t mib = kib * 1024; - const size_t gib = mib * 1024; - - std::ostringstream oss; - oss << std::fixed << std::setprecision(2); - - if (size >= gib) { - oss << static_cast(size) / gib << " GiB"; - } else if (size >= mib) { - oss << static_cast(size) / mib << " MiB"; - } else if (size >= kib) { - oss << static_cast(size) / kib << " KiB"; - } else { - oss << size << " B"; - } - - return oss.str(); -} - -static std::mutex log_mutex; - -class vk_memory_logger { -public: - vk_memory_logger(): total_device(0), total_host(0) {} - void log_allocation(vk_buffer_ref buf_ref, size_t size); - void log_deallocation(vk_buffer_ref buf_ref); - -private: - std::map allocations; // Track allocations - size_t total_device; - size_t total_host; -}; -#else -#define VK_LOG_MEMORY(msg) ((void) 0) -#endif // GGML_VULKAN_MEMORY_DEBUG - -#if defined(GGML_VULKAN_PERF) - -class vk_perf_logger { -public: - void print_timings() { - std::cerr << "----------------\nVulkan Timings:" << std::endl; - for (const auto& t : timings) { - uint64_t total = 0; - for (const auto& time : t.second) { - total += time; - } - std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " ms" << std::endl; - } - - timings.clear(); - } - - void log_timing(const ggml_tensor * node, uint64_t time) { - if (node->op == GGML_OP_UNARY) { - timings[ggml_unary_op_name(ggml_get_unary_op(node))].push_back(time); - return; - } - if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { - const uint64_t m = node->src[0]->ne[1]; - const uint64_t n = node->src[1]->ne[1]; - const uint64_t k = node->src[1]->ne[0]; - std::string name = ggml_op_name(node->op); - if (n == 1) { - name += "_VEC m=" + std::to_string(m) + " k=" + std::to_string(k); - } else { - name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k); - } - timings[name].push_back(time); - return; - } - timings[ggml_op_name(node->op)].push_back(time); - } -private: - std::map> timings; -}; -#endif // GGML_VULKAN_PERF - -struct ggml_backend_vk_context { - std::string name; - - vk_device device; - - size_t semaphore_idx, event_idx; - ggml_vk_garbage_collector gc; - size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k; - vk_buffer prealloc_x, prealloc_y, prealloc_split_k; - vk::Fence fence; - - vk_buffer buffer_pool[MAX_VK_BUFFERS]; - - vk_context_ref compute_ctx; - vk_context_ref transfer_ctx; - - std::vector tensor_ctxs; -}; - -static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT - -static uint64_t vk_tensor_offset(const ggml_tensor * tensor) { - if (tensor->view_src) { - return (uint8_t *) tensor->view_src->data - (uint8_t *) vk_ptr_base; - } - return (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; -} - -struct ggml_backend_vk_buffer_context { - vk_device_ref device; - vk_buffer dev_buffer; - std::string name; - - ggml_backend_vk_buffer_context(vk_device_ref device, vk_buffer&& dev_buffer, std::string& name) : - device(device), - dev_buffer(dev_buffer), - name(name) { - } - - ~ggml_backend_vk_buffer_context() { - ggml_vk_destroy_buffer(dev_buffer); - } -}; - -#ifdef GGML_VULKAN_MEMORY_DEBUG -void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) { - std::lock_guard guard(log_mutex); - vk_buffer buf = buf_ref.lock(); - const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal); - const std::string type = device ? "device" : "host"; - allocations[buf->buffer] = size; - total_device += device ? size : 0; - total_host += device ? 0 : size; - VK_LOG_MEMORY(buf->device->name << ": +" << format_size(size) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host)); -} - -void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) { - if (buf_ref.expired() || buf_ref.lock()->size == 0) { - return; - } - - std::lock_guard guard(log_mutex); - vk_buffer buf = buf_ref.lock(); - const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal); - std::string type = device ? "device" : "host"; - auto it = allocations.find(buf->buffer); - total_device -= device ? it->second : 0; - total_host -= device ? 0 : it->second; - if (it != allocations.end()) { - VK_LOG_MEMORY(buf->device->name << ": -" << format_size(it->second) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host)); - allocations.erase(it); - } else { - VK_LOG_MEMORY("ERROR " << buf->device->name << ": Attempted to deallocate unknown " << type << " memory at " << buf->buffer); - } -} -#endif // GGML_VULKAN_MEMORY_DEBUG - -struct vk_instance_t { - vk::Instance instance; - - std::vector device_indices; - vk_device devices[GGML_VK_MAX_DEVICES]; -}; - -static bool vk_instance_initialized = false; -static vk_instance_t vk_instance; - -#ifdef GGML_VULKAN_CHECK_RESULTS -static size_t vk_skip_checks; -static size_t vk_output_tensor; - -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); -static void ggml_vk_check_results_0(ggml_tensor * tensor); -static void ggml_vk_check_results_1(ggml_tensor * tensor); -#endif - -typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); - -static void ggml_backend_vk_free(ggml_backend_t backend); - -// variables to track number of compiles in progress -static uint32_t compile_count = 0; -static std::mutex compile_count_mutex; -static std::condition_variable compile_count_cond; - -static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector specialization_constants, uint32_t align) { - VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")"); - GGML_ASSERT(parameter_count > 0); - GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - - pipeline = std::make_shared(); - pipeline->name = name; - pipeline->parameter_count = parameter_count; - pipeline->push_constant_size = push_constant_size; - pipeline->wg_denoms = wg_denoms; - pipeline->align = align; - - vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); - pipeline->shader_module = device->device.createShaderModule(shader_module_create_info); - - std::vector dsl_binding; - std::vector dsl_binding_flags; - for (uint32_t i = 0; i < parameter_count; i++) { - dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute}); - dsl_binding_flags.push_back({}); - } - - vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags }; - - vk::PushConstantRange pcr( - vk::ShaderStageFlagBits::eCompute, - 0, - pipeline->push_constant_size - ); - - vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info( - {}, - dsl_binding); - descriptor_set_layout_create_info.setPNext(&dslbfci); - pipeline->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info); - - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE); - vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size); - pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); - - pipeline->descriptor_set_idx = 0; - - vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline->dsl, pcr); - pipeline->layout = device->device.createPipelineLayout(pipeline_layout_create_info); - - std::vector specialization_entries(specialization_constants.size()); - - for (size_t i = 0; i < specialization_constants.size(); i++) { - specialization_entries[i].constantID = i; - specialization_entries[i].offset = i * sizeof(uint32_t); - specialization_entries[i].size = sizeof(uint32_t); - } - - vk::SpecializationInfo specialization_info( - specialization_entries.size(), - specialization_entries.data(), - specialization_constants.size() * sizeof(uint32_t), - specialization_constants.data() - ); - - vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( - vk::PipelineShaderStageCreateFlags(), - vk::ShaderStageFlagBits::eCompute, - pipeline->shader_module, - entrypoint.c_str(), - &specialization_info); - vk::ComputePipelineCreateInfo compute_pipeline_create_info( - vk::PipelineCreateFlags(), - pipeline_shader_create_info, - pipeline->layout); - pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - - { - std::lock_guard guard(device->mutex); - device->pipelines.insert({ pipeline->name, pipeline }); - } - - { - std::lock_guard guard(compile_count_mutex); - assert(compile_count > 0); - compile_count--; - - // "Progress bar" for shader compiles - static uint32_t total_compile_count = 0; - if ((total_compile_count++ % 10) == 0) { - std::cerr << "."; - } - } - compile_count_cond.notify_all(); -} - -static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { - VK_LOG_DEBUG("ggml_pipeline_destroy_pipeline(" << pipeline->name << ")"); - for (auto& pool : pipeline->descriptor_pools) { - device.destroyDescriptorPool(pool); - } - pipeline->descriptor_pools.clear(); - pipeline->descriptor_sets.clear(); - pipeline->descriptor_set_idx = 0; - - device.destroyDescriptorSetLayout(pipeline->dsl); - - device.destroyPipelineLayout(pipeline->layout); - - device.destroyShaderModule(pipeline->shader_module); - - device.destroyPipeline(pipeline->pipeline); -} - -static void ggml_pipeline_request_descriptor_sets(vk_device& device, vk_pipeline& pipeline, uint32_t n) { - VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")"); - device->pipeline_descriptor_set_requirements[pipeline->name] += n; -} - -static void ggml_pipeline_allocate_descriptor_sets(vk_device& device) { - std::lock_guard guard(device->mutex); - - for (auto& pair : device->pipeline_descriptor_set_requirements) { - vk_pipeline pipeline = device->pipelines.at(pair.first).lock(); - const uint64_t n = pair.second; - - VK_LOG_DEBUG("ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")"); - - if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) { - // Enough descriptors are available - continue; - } - - uint32_t to_alloc = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size(); - uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - pipeline->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE; - uint32_t pool_idx = pipeline->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE; - - while (to_alloc > 0) { - const uint32_t alloc_count = std::min(pool_remaining, to_alloc); - to_alloc -= alloc_count; - pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE; - - if (pool_idx >= pipeline->descriptor_pools.size()) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE); - vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size); - pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); - } - - std::vector layouts(alloc_count); - for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline->dsl; - } - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[pool_idx], alloc_count, layouts.data()); - std::vector sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end()); - - pool_idx++; - } - } -} - -static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { - VK_LOG_DEBUG("ggml_pipeline_cleanup(" << pipeline->name << ")"); - pipeline->descriptor_set_idx = 0; -} - -static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_queue& q) { - VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()"); - std::lock_guard guard(device->mutex); - - if (q.cmd_buffers.size() > q.cmd_buffer_idx) { - // Reuse command buffer - return q.cmd_buffers[q.cmd_buffer_idx++]; - } - - vk::CommandBufferAllocateInfo command_buffer_alloc_info( - q.pool, - vk::CommandBufferLevel::ePrimary, - 1); - const std::vector cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info); - auto buf = cmd_buffers.front(); - - q.cmd_buffers.push_back(buf); - q.cmd_buffer_idx++; - - return buf; -} - -static vk_submission ggml_vk_create_submission(vk_device& device, vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { - VK_LOG_DEBUG("ggml_vk_create_submission()"); - vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(device, q); - s.wait_semaphores = std::move(wait_semaphores); - s.signal_semaphores = std::move(signal_semaphores); - return s; -} - -static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { - if (ctx->seqs.empty()) { - if (fence) { - ctx->q->queue.submit({}, fence); - } - return; - } - VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")"); - - std::vector> tl_wait_vals; - std::vector> tl_signal_vals; - std::vector> tl_wait_semaphores; - std::vector> tl_signal_semaphores; - std::vector tl_submit_infos; - std::vector submit_infos; - int idx = -1; - std::vector> stage_flags; - - size_t reserve = 0; - - for (const auto& sequence : ctx->seqs) { - reserve += sequence.size(); - } - - // Pre-reserve vectors to prevent reallocation, which invalidates pointers - tl_wait_semaphores.reserve(reserve); - tl_wait_vals.reserve(reserve); - tl_signal_semaphores.reserve(reserve); - tl_signal_vals.reserve(reserve); - tl_submit_infos.reserve(reserve); - submit_infos.reserve(reserve); - stage_flags.reserve(reserve); - - for (const auto& sequence : ctx->seqs) { - for (const auto& submission : sequence) { - stage_flags.push_back({}); - idx++; - tl_wait_vals.push_back({}); - tl_wait_semaphores.push_back({}); - tl_signal_vals.push_back({}); - tl_signal_semaphores.push_back({}); - for (size_t i = 0; i < submission.wait_semaphores.size(); i++) { - stage_flags[idx].push_back(ctx->q->stage_flags); - tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value); - tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s); - } - for (size_t i = 0; i < submission.signal_semaphores.size(); i++) { - tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value); - tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s); - } - tl_submit_infos.push_back({ - (uint32_t) submission.wait_semaphores.size(), - tl_wait_vals[idx].data(), - (uint32_t) submission.signal_semaphores.size(), - tl_signal_vals[idx].data(), - }); - tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo; - tl_submit_infos[idx].pNext = nullptr; - vk::SubmitInfo si{ - (uint32_t) submission.wait_semaphores.size(), - tl_wait_semaphores[idx].data(), - stage_flags[idx].data(), - 1, - &submission.buffer, - (uint32_t) submission.signal_semaphores.size(), - tl_signal_semaphores[idx].data(), - }; - si.setPNext(&tl_submit_infos[idx]); - submit_infos.push_back(si); - } - } - - ctx->q->queue.submit(submit_infos, fence); - - ctx->seqs.clear(); -} - -static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { - VK_LOG_DEBUG("ggml_vk_find_queue_family_index()"); - const uint32_t qfsize = queue_family_props.size(); - - // Try with avoid preferences first - for (uint32_t i = 0; i < qfsize; i++) { - if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) { - return i; - } - } - - // Fall back to only required - for (size_t i = 0; i < qfsize; i++) { - if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) { - return i; - } - } - - // Fall back to reusing compute queue - for (size_t i = 0; i < qfsize; i++) { - if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) { - return i; - } - } - - // Fall back to ignoring min_num_queries - for (size_t i = 0; i < qfsize; i++) { - if (queue_family_props[i].queueFlags & required) { - return i; - } - } - - // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations. - // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional. - if (compute_index >= 0) { - return compute_index; - } - - std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl; - - for(auto &q_family : queue_family_props) { - std::cerr << "Queue number: " + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl; - } - abort(); -} - -static void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) { - VK_LOG_DEBUG("ggml_vk_create_queue()"); - std::lock_guard guard(device->mutex); - - q.queue_family_index = queue_family_index; - q.transfer_only = transfer_only; - - vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index); - q.pool = device->device.createCommandPool(command_pool_create_info_compute); - - q.cmd_buffer_idx = 0; - - q.queue = device->device.getQueue(queue_family_index, queue_index); - - q.stage_flags = stage_flags; -} - -static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) { - vk_context result = std::make_shared(); - VK_LOG_DEBUG("ggml_vk_create_context(" << result << ")"); - ctx->gc.contexts.emplace_back(result); - result->q = &q; - return result; -} - -static vk_context ggml_vk_create_temporary_context(vk_queue& q) { - vk_context result = std::make_shared(); - VK_LOG_DEBUG("ggml_vk_create_temporary_context(" << result << ")"); - result->q = &q; - return result; -} - -static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) { - VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()"); - vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; - vk::SemaphoreCreateInfo ci{}; - ci.setPNext(&tci); - vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); - ctx->gc.semaphores.push_back({ semaphore, 0 }); - return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; -} - -static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) { - VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()"); - if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) { - vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; - vk::SemaphoreCreateInfo ci{}; - ci.setPNext(&tci); - vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); - ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); - } - return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; -} - -static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { - if (ctx->event_idx >= ctx->gc.events.size()) { - ctx->gc.events.push_back(ctx->device->device.createEvent({})); - } - return ctx->gc.events[ctx->event_idx++]; -} - -static void ggml_vk_queue_cleanup(vk_device& device, vk_queue& q) { - VK_LOG_DEBUG("ggml_vk_queue_cleanup()"); - std::lock_guard guard(device->mutex); - - // Requires command buffers to be done - device->device.resetCommandPool(q.pool); - q.cmd_buffer_idx = 0; -} - -static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) { - for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) { - vk::MemoryType memory_type = mem_props->memoryTypes[i]; - if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) && - (flags & memory_type.propertyFlags) == flags && - mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) { - return static_cast(i); - } - } - return UINT32_MAX; -} - -static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) { - VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")"); - if (size > device->max_memory_allocation_size) { - throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device memory allocation limit"); - } - - std::lock_guard guard(device->mutex); - - vk_buffer buf = std::make_shared(); - - if (size == 0) { - buf->size = 0; - return buf; - } - - vk::BufferCreateInfo buffer_create_info{ - vk::BufferCreateFlags(), - size, - vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst, - vk::SharingMode::eExclusive, - 0, - nullptr, - }; - - buf->buffer = device->device.createBuffer(buffer_create_info); - - vk::MemoryRequirements mem_req = device->device.getBufferMemoryRequirements(buf->buffer); - - vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties(); - - uint32_t memory_type_index = UINT32_MAX; - - memory_type_index = find_properties(&mem_props, &mem_req, req_flags); - buf->memory_property_flags = req_flags; - - if (memory_type_index == UINT32_MAX && fallback_flags) { - memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags); - buf->memory_property_flags = fallback_flags; - } - - if (memory_type_index == UINT32_MAX) { - device->device.destroyBuffer(buf->buffer); - throw vk::OutOfDeviceMemoryError("No suitable memory type found"); - } - - try { - buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index }); - } catch (const vk::SystemError& e) { - if (buf->memory_property_flags != fallback_flags) { - // Try again with fallback flags - memory_type_index = find_properties(&mem_props, &mem_req, fallback_flags); - buf->memory_property_flags = fallback_flags; - - try { - buf->device_memory = device->device.allocateMemory({ mem_req.size, memory_type_index }); - } - catch (const vk::SystemError& e) { - device->device.destroyBuffer(buf->buffer); - throw e; - } - } else { - // Out of Host/Device memory, clean up buffer - device->device.destroyBuffer(buf->buffer); - throw e; - } - } - buf->ptr = nullptr; - - if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - buf->ptr = device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); - } - - device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); - - buf->device = device; - buf->size = size; - -#ifdef GGML_VULKAN_MEMORY_DEBUG - device->memory_logger->log_allocation(buf, size); -#endif - - return buf; -} - -static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) { - try { - return ggml_vk_create_buffer(device, size, req_flags, fallback_flags); - } catch (const vk::SystemError& e) { - std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; - std::cerr << "ggml_vulkan: " << e.what() << std::endl; - throw e; - } -} - -static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) { - vk_buffer buf; - try { - if (device->uma) { - // Fall back to host memory type - buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); - } else { - // use rebar if available, otherwise fallback to device only visible memory - buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal); - } - } catch (const vk::SystemError& e) { - std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; - std::cerr << "ggml_vulkan: " << e.what() << std::endl; - throw e; - } - - return buf; -} - -static void ggml_vk_destroy_buffer(vk_buffer& buf) { - if (buf == nullptr) { - return; - } - -#ifdef GGML_VULKAN_MEMORY_DEBUG - if (buf->device != nullptr) { - buf->device->memory_logger->log_deallocation(buf); - } -#endif - - buf.reset(); -} - -static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { - return { buf, 0, VK_WHOLE_SIZE }; -} - -static void ggml_vk_sync_buffers(vk_context& ctx) { - VK_LOG_DEBUG("ggml_vk_sync_buffers()"); - - const bool transfer_queue = ctx->q->transfer_only; - - ctx->s->buffer.pipelineBarrier( - ctx->q->stage_flags, - ctx->q->stage_flags, - {}, - { { - { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }, - { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) } - } }, - {}, - {} - ); -} - -static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events) { - VK_LOG_DEBUG("ggml_vk_wait_events()"); - if (events.empty()) { - return; - } - - ctx->s->buffer.waitEvents( - events, - ctx->q->stage_flags, - ctx->q->stage_flags, - {}, - {}, - {} - ); -} - -static void ggml_vk_load_shaders(vk_device& device) { - VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")"); - - std::cerr << "ggml_vulkan: Compiling shaders"; - - // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_s = { std::max(device->subgroup_size, 16u), 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; - - std::initializer_list warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; - std::initializer_list warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_mmq_s = { std::max(device->subgroup_size, 16u), 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; - - std::array l_wg_denoms = {128, 128, 1 }; - std::array m_wg_denoms = { 64, 64, 1 }; - std::array s_wg_denoms = { 32, 32, 1 }; - - uint32_t l_align = 128; - uint32_t m_align = 64; - uint32_t s_align = 32; - - device->pipeline_matmul_f32 = std::make_shared(); - device->pipeline_matmul_f32_f16 = std::make_shared(); - device->pipeline_matmul_f16_f32 = std::make_shared(); - device->pipeline_matmul_f16 = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL] = std::make_shared(); - - device->pipeline_matmul_id_f32 = std::make_shared(); - device->pipeline_matmul_id_f16_f32 = std::make_shared(); - device->pipeline_matmul_id_f16 = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared(); - - std::vector> compiles; - auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { - { - // wait until fewer than N compiles are in progress - uint32_t N = std::max(1u, std::thread::hardware_concurrency()); - std::unique_lock guard(compile_count_mutex); - while (compile_count >= N) { - compile_count_cond.wait(guard); - } - compile_count++; - } - compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align)); - }; - - if (device->fp16) { - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - } else { - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - } - - // mul mat vec - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - - // dequant shaders - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_k", dequant_q2_k_len, dequant_q2_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_k", dequant_q3_k_len, dequant_q3_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL], "dequant_iq4_nl", dequant_iq4_nl_len, dequant_iq4_nl_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - - // get_rows - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32", get_rows_f32_len, get_rows_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32", get_rows_f32_f32_len, get_rows_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl_f32", get_rows_iq4_nl_f32_len, get_rows_iq4_nl_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_concat_f16, "concat_f16", concat_f16_len, concat_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_concat_i32, "concat_i32", concat_i32_len, concat_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_upscale_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_sin_f32, "sin_f32", sin_f32_len, sin_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_cos_f32, "cos_f32", cos_f32_len, cos_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_repeat_f32, "repeat_f32", repeat_f32_len, repeat_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_gelu_quick_f32, "gelu_quick_f32", gelu_quick_f32_len, gelu_quick_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_leaky_relu_f32, "leaky_relu_f32", leaky_relu_f32_len, leaky_relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_tanh_f32, "tanh_f32", tanh_f32_len, tanh_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - - ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32_len, im2col_f32_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1); - - ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1); - - for (auto &c : compiles) { - c.wait(); - } - std::cerr << "Done!" << std::endl; -} - -static vk_device ggml_vk_get_device(size_t idx) { - VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")"); - - if (vk_instance.devices[idx] == nullptr) { - VK_LOG_DEBUG("Initializing new vk_device"); - vk_device device = std::make_shared(); - vk_instance.devices[idx] = device; - -#ifdef GGML_VULKAN_MEMORY_DEBUG - device->memory_logger = std::unique_ptr(new vk_memory_logger()); -#endif -#ifdef GGML_VULKAN_PERF - device->perf_logger = std::unique_ptr(new vk_perf_logger()); -#endif - - size_t dev_num = vk_instance.device_indices[idx]; - - std::vector physical_devices = vk_instance.instance.enumeratePhysicalDevices(); - - if (dev_num >= physical_devices.size()) { - std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; - throw std::runtime_error("Device not found"); - } - - device->physical_device = physical_devices[dev_num]; - const std::vector ext_props = device->physical_device.enumerateDeviceExtensionProperties(); - - bool maintenance4_support = false; - - // Check if maintenance4 is supported - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { - maintenance4_support = true; - } - } - - vk::PhysicalDeviceProperties2 props2; - vk::PhysicalDeviceMaintenance3Properties props3; - vk::PhysicalDeviceMaintenance4Properties props4; - vk::PhysicalDeviceSubgroupProperties subgroup_props; - props2.pNext = &props3; - props3.pNext = &subgroup_props; - if (maintenance4_support) { - subgroup_props.pNext = &props4; - } - device->physical_device.getProperties2(&props2); - device->properties = props2.properties; - - const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE"); - - if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) { - device->max_memory_allocation_size = std::stoi(GGML_VK_FORCE_MAX_ALLOCATION_SIZE); - } else if (maintenance4_support) { - device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); - } else { - device->max_memory_allocation_size = props3.maxMemoryAllocationSize; - } - - device->vendor_id = device->properties.vendorID; - device->subgroup_size = subgroup_props.subgroupSize; - device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; - - bool fp16_storage = false; - bool fp16_compute = false; - - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { - fp16_storage = true; - } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { - fp16_compute = true; - } - } - - const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); - const bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; - - device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - - std::vector queue_family_props = device->physical_device.getQueueFamilyProperties(); - - // Try to find a non-graphics compute queue and transfer-focused queues - const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); - const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); - - const float priorities[] = { 1.0f, 1.0f }; - device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; - - std::vector device_queue_create_infos; - if (compute_queue_family_index != transfer_queue_family_index) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!device->single_queue) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); - } else { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - } - vk::DeviceCreateInfo device_create_info; - std::vector device_extensions; - vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures(); - - VkPhysicalDeviceFeatures2 device_features2; - device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; - device_features2.pNext = nullptr; - device_features2.features = (VkPhysicalDeviceFeatures)device_features; - - VkPhysicalDeviceVulkan11Features vk11_features; - vk11_features.pNext = nullptr; - vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; - device_features2.pNext = &vk11_features; - - VkPhysicalDeviceVulkan12Features vk12_features; - vk12_features.pNext = nullptr; - vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; - vk11_features.pNext = &vk12_features; - - vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2); - - device->fp16 = device->fp16 && vk12_features.shaderFloat16; - - if (!vk11_features.storageBuffer16BitAccess) { - std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; - throw std::runtime_error("Unsupported device"); - } - - device_extensions.push_back("VK_KHR_16bit_storage"); - -#ifdef GGML_VULKAN_VALIDATE - device_extensions.push_back("VK_KHR_shader_non_semantic_info"); -#endif - - if (device->fp16) { - device_extensions.push_back("VK_KHR_shader_float16_int8"); - } - device->name = GGML_VK_NAME + std::to_string(idx); - - device_create_info = { - vk::DeviceCreateFlags(), - device_queue_create_infos, - {}, - device_extensions - }; - device_create_info.setPNext(&device_features2); - device->device = device->physical_device.createDevice(device_create_info); - - // Queues - ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); - - // Shaders - ggml_vk_load_shaders(device); - - if (!device->single_queue) { - const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); - } else { - // TODO: Use pointer or reference to avoid copy - device->transfer_queue = device->compute_queue; - } - - device->buffer_type = { - /* .iface = */ ggml_backend_vk_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), idx), - /* .context = */ new ggml_backend_vk_buffer_type_context{ device->name, device }, - }; - - device->fence = device->device.createFence({}); - - device->idx = idx; - - return device; - } - - return vk_instance.devices[idx]; -} - - -static void ggml_vk_print_gpu_info(size_t idx) { - GGML_ASSERT(idx < vk_instance.device_indices.size()); - size_t dev_num = vk_instance.device_indices[idx]; - VK_LOG_DEBUG("ggml_vk_print_gpu_info(" << dev_num << ")"); - GGML_ASSERT(vk_instance_initialized); - - std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); - - if (dev_num >= devices.size()) { - std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; - throw std::runtime_error("Device not found"); - } - - vk::PhysicalDevice physical_device = devices[dev_num]; - std::vector ext_props = physical_device.enumerateDeviceExtensionProperties(); - - vk::PhysicalDeviceProperties2 props2; - vk::PhysicalDeviceMaintenance3Properties props3; - vk::PhysicalDeviceSubgroupProperties subgroup_props; - vk::PhysicalDeviceDriverProperties driver_props; - props2.pNext = &props3; - props3.pNext = &subgroup_props; - subgroup_props.pNext = &driver_props; - physical_device.getProperties2(&props2); - - const size_t subgroup_size = subgroup_props.subgroupSize; - const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; - - bool fp16_storage = false; - bool fp16_compute = false; - - for (auto properties : ext_props) { - if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { - fp16_storage = true; - } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { - fp16_compute = true; - } - } - - const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); - bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; - - bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - - vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures(); - - VkPhysicalDeviceFeatures2 device_features2; - device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; - device_features2.pNext = nullptr; - device_features2.features = (VkPhysicalDeviceFeatures)device_features; - - VkPhysicalDeviceVulkan11Features vk11_features; - vk11_features.pNext = nullptr; - vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; - device_features2.pNext = &vk11_features; - - VkPhysicalDeviceVulkan12Features vk12_features; - vk12_features.pNext = nullptr; - vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; - vk11_features.pNext = &vk12_features; - - vkGetPhysicalDeviceFeatures2(physical_device, &device_features2); - - fp16 = fp16 && vk12_features.shaderFloat16; - - std::string device_name = props2.properties.deviceName.data(); - GGML_LOG_DEBUG("ggml_vulkan: %d = %s (%s) | uma: %d | fp16: %d | warp size: %d\n", - idx, device_name.c_str(), driver_props.driverName, uma, fp16, subgroup_size); - - if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { - std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; - } -} - -static bool ggml_vk_instance_validation_ext_available(const std::vector& instance_extensions); -static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions); - -void ggml_vk_instance_init() { - if (vk_instance_initialized) { - return; - } - VK_LOG_DEBUG("ggml_vk_instance_init()"); - - vk_instance_initialized = true; - - vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; - - const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); - const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions); -#ifdef __APPLE__ - const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); -#endif - - std::vector layers; - - if (validation_ext) { - layers.push_back("VK_LAYER_KHRONOS_validation"); - } - std::vector extensions; - if (validation_ext) { - extensions.push_back("VK_EXT_validation_features"); - } -#ifdef __APPLE__ - if (portability_enumeration_ext) { - extensions.push_back("VK_KHR_portability_enumeration"); - } -#endif - vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); -#ifdef __APPLE__ - if (portability_enumeration_ext) { - instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; - } -#endif - - std::vector features_enable; - vk::ValidationFeaturesEXT validation_features; - - if (validation_ext) { - features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; - validation_features = { - features_enable, - {}, - }; - validation_features.setPNext(nullptr); - instance_create_info.setPNext(&validation_features); - GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n"); - } - vk_instance.instance = vk::createInstance(instance_create_info); - - size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size(); - - // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan - char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES"); - if (devices_env != nullptr) { - std::string devices(devices_env); - std::replace(devices.begin(), devices.end(), ',', ' '); - - std::stringstream ss(devices); - size_t tmp; - while (ss >> tmp) { - if(tmp >= num_available_devices) { - std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl; - throw std::runtime_error("Invalid Vulkan device index"); - } - vk_instance.device_indices.push_back(tmp); - } - } else { - std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); - - // Make sure at least one device exists - if (devices.empty()) { - std::cerr << "ggml_vulkan: Error: No devices found." << std::endl; - GGML_ABORT("fatal error"); - } - - // Default to using all dedicated GPUs - for (size_t i = 0; i < devices.size(); i++) { - vk::PhysicalDeviceProperties2 new_props; - vk::PhysicalDeviceDriverProperties new_driver; - vk::PhysicalDeviceIDProperties new_id; - new_props.pNext = &new_driver; - new_driver.pNext = &new_id; - devices[i].getProperties2(&new_props); - - if (new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) { - // Check if there are two physical devices corresponding to the same GPU - auto old_device = std::find_if( - vk_instance.device_indices.begin(), - vk_instance.device_indices.end(), - [&devices, &new_id](const size_t k){ - vk::PhysicalDeviceProperties2 old_props; - vk::PhysicalDeviceIDProperties old_id; - old_props.pNext = &old_id; - devices[k].getProperties2(&old_props); - return std::equal(std::begin(old_id.deviceUUID), std::end(old_id.deviceUUID), std::begin(new_id.deviceUUID)); - } - ); - if (old_device == vk_instance.device_indices.end()) { - vk_instance.device_indices.push_back(i); - } else { - // There can be two physical devices corresponding to the same GPU if there are 2 different drivers - // This can cause error when splitting layers aross the devices, need to keep only 1 - VK_LOG_DEBUG("Device " << i << " and device " << *old_device << " have the same deviceUUID"); - - vk::PhysicalDeviceProperties2 old_props; - vk::PhysicalDeviceDriverProperties old_driver; - old_props.pNext = &old_driver; - devices[*old_device].getProperties2(&old_props); - - std::map driver_priorities {}; - int old_priority = std::numeric_limits::max(); - int new_priority = std::numeric_limits::max(); - - // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id - // Smaller number -> higher priority - switch (old_props.properties.vendorID) { - case VK_VENDOR_ID_AMD: - driver_priorities[vk::DriverId::eMesaRadv] = 1; - driver_priorities[vk::DriverId::eAmdOpenSource] = 2; - driver_priorities[vk::DriverId::eAmdProprietary] = 3; - break; - case VK_VENDOR_ID_INTEL: - driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1; - driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2; - break; - case VK_VENDOR_ID_NVIDIA: - driver_priorities[vk::DriverId::eNvidiaProprietary] = 1; -#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235 - driver_priorities[vk::DriverId::eMesaNvk] = 2; -#endif - break; - } - - if (driver_priorities.count(old_driver.driverID)) { - old_priority = driver_priorities[old_driver.driverID]; - } - if (driver_priorities.count(new_driver.driverID)) { - new_priority = driver_priorities[new_driver.driverID]; - } - - if (new_priority < old_priority) { - auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device); - vk_instance.device_indices.erase(r, vk_instance.device_indices.end()); - vk_instance.device_indices.push_back(i); - - VK_LOG_DEBUG("Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName); - } - else { - VK_LOG_DEBUG("Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl); - } - } - } - } - - // If no dedicated GPUs found, fall back to GPU 0 - if (vk_instance.device_indices.empty()) { - vk_instance.device_indices.push_back(0); - } - } - GGML_LOG_DEBUG("ggml_vulkan: Found %d Vulkan devices:\n", vk_instance.device_indices.size()); - - - for (size_t i = 0; i < vk_instance.device_indices.size(); i++) { - ggml_vk_print_gpu_info(i); - } -} - -static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { - VK_LOG_DEBUG("ggml_vk_init(" << ctx->name << ", " << idx << ")"); - ggml_vk_instance_init(); - GGML_ASSERT(idx < vk_instance.device_indices.size()); - - ctx->name = GGML_VK_NAME + std::to_string(idx); - - ctx->device = ggml_vk_get_device(idx); - - ctx->semaphore_idx = 0; - ctx->event_idx = 0; - - ctx->prealloc_size_x = 0; - ctx->prealloc_size_y = 0; - ctx->prealloc_size_split_k = 0; - - ctx->fence = ctx->device->device.createFence({}); - -#ifdef GGML_VULKAN_CHECK_RESULTS - const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); - vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks)); - const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR"); - vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor)); -#endif -} - -static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { - VK_LOG_DEBUG("ggml_vk_get_to_fp16()"); - switch (type) { - case GGML_TYPE_F32: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ4_NL: - break; - default: - return nullptr; - } - - return ctx->device->pipeline_dequant[type]; -} - -static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { - VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline(" << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return ctx->device->pipeline_matmul_f32; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { - return ctx->device->pipeline_matmul_f32_f16; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { - return ctx->device->pipeline_matmul_f16_f32; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return ctx->device->pipeline_matmul_f16; - } - - if (src1_type != GGML_TYPE_F32) { - return nullptr; - } - - switch (src0_type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ4_NL: - break; - default: - return nullptr; - } - - return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; -} - -static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { - VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()"); - GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); - - switch (a_type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ4_NL: - break; - default: - return nullptr; - } - - return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type]; -} - -static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { - VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_id_pipeline()"); - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return ctx->device->pipeline_matmul_id_f32; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { - return ctx->device->pipeline_matmul_id_f16_f32; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return ctx->device->pipeline_matmul_id_f16; - } - - GGML_ASSERT(src1_type == GGML_TYPE_F32); - - switch (src0_type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ4_NL: - break; - default: - return nullptr; - } - - return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type]; -} - -static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { - VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()"); - GGML_ASSERT(b_type == GGML_TYPE_F32); - - switch (a_type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ4_NL: - break; - default: - return nullptr; - } - - return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[a_type]; -} - -static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { - VK_LOG_DEBUG("ggml_vk_pool_malloc(" << size << ")"); - VK_LOG_MEMORY("ggml_vk_pool_malloc"); - - int best_i = -1; - size_t best_size = std::numeric_limits::max(); //smallest unused buffer that fits our needs - int worst_i = -1; - size_t worst_size = 0; //largest unused buffer seen so far - for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer &b = ctx->buffer_pool[i]; - if (b != nullptr && b->size >= size && b->size < best_size) { - best_i = i; - best_size = b->size; - } - if (b != nullptr && b->size > worst_size) { - worst_i = i; - worst_size = b->size; - } - } - if(best_i != -1) { - //found the smallest buffer that fits our needs - vk_buffer b = ctx->buffer_pool[best_i]; - ctx->buffer_pool[best_i].reset(); - return b; - } - if(worst_i != -1) { - //no buffer that fits our needs, resize largest one to save memory - vk_buffer& b = ctx->buffer_pool[worst_i]; - ggml_vk_destroy_buffer(b); - } - - return ggml_vk_create_buffer_device(ctx->device, size); -} - -static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) { - VK_LOG_DEBUG("ggml_vk_pool_free(" << buffer->size << ")"); - for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer& b = ctx->buffer_pool[i]; - if (b == nullptr) { - b = buffer; - return; - } - } - std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl; - ggml_vk_destroy_buffer(buffer); -} - -// Returns an available temporary buffer that may only be used temporarily, it will be reused -static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) { - // Try to find existing temp buffer with enough capacity - for (auto& buffer : ctx->gc.temp_buffers) { - if (buffer->size >= size) { - return buffer; - } - } - - VK_LOG_MEMORY("ggml_vk_create_buffer_temp(" << size << ")"); - - // Otherwise create new buffer - vk_buffer buf = ggml_vk_pool_malloc(ctx, size); - ctx->gc.temp_buffers.push_back(buf); - - return buf; -} - -static void * ggml_vk_host_malloc(vk_device& device, size_t size) { - VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")"); - vk_buffer buf = ggml_vk_create_buffer(device, size, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); - - if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { - fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", - size/1024.0/1024.0); - device->device.freeMemory(buf->device_memory); - device->device.destroyBuffer(buf->buffer); - return nullptr; - } - - device->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf)); - - return buf->ptr; -} - -static void ggml_vk_host_free(vk_device& device, void* ptr) { - if (ptr == nullptr) { - return; - } - VK_LOG_MEMORY("ggml_vk_host_free(" << ptr << ")"); - vk_buffer buf; - size_t index; - for (size_t i = 0; i < device->pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]); - if (ptr >= addr && ptr < endr) { - buf = std::get<2>(device->pinned_memory[i]); - index = i; - break; - } - } - if (buf == nullptr) { - fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n"); - return; - } - - ggml_vk_destroy_buffer(buf); - - device->pinned_memory.erase(device->pinned_memory.begin() + index); -} - -static void ggml_vk_host_get(vk_device& device, const void * ptr, vk_buffer& buf, size_t& buf_offset) { - buf = nullptr; - buf_offset = 0; - for (size_t i = 0; i < device->pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]); - if (ptr >= addr && ptr < endr) { - buf = std::get<2>(device->pinned_memory[i]); - buf_offset = ((const uint8_t *)ptr) - addr; - break; - } - } -} - -static vk_submission ggml_vk_begin_submission(vk_device& device, vk_queue& q, bool one_time = true) { - vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(device, q); - if (one_time) { - s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); - } else { - s.buffer.begin({ vk::CommandBufferUsageFlags{} }); - } - - return s; -} - - - -static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& subctx, vk_pipeline& pipeline, std::initializer_list const& descriptor_buffer_infos, size_t push_constant_size, const void* push_constants, std::array elements) { - const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]); - const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); - const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); - VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {"; - for (auto& buffer : descriptor_buffer_infos) { - std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.range << "), "; - } - std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))"); - GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size()); - GGML_ASSERT(descriptor_buffer_infos.size() == pipeline->parameter_count); - - vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++]; - vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() }; - ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {}); - - subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); - subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); - subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, - pipeline->layout, - 0, - { descriptor_set }, - {}); - subctx->s->buffer.dispatch(wg0, wg1, wg2); -} - -static void ggml_vk_end_submission(vk_submission& s, std::vector wait_semaphores, std::vector signal_semaphores) { - s.buffer.end(); - - s.wait_semaphores = std::move(wait_semaphores); - s.signal_semaphores = std::move(signal_semaphores); -} - -static void ggml_vk_ctx_end(vk_context& ctx) { - VK_LOG_DEBUG("ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")"); - if (ctx->s == nullptr) { - return; - } - - ctx->s->buffer.end(); - ctx->s = nullptr; -} - -static void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) { - VK_LOG_DEBUG("ggml_vk_ctx_begin(" << device->name << ")"); - if (subctx->s != nullptr) { - ggml_vk_ctx_end(subctx); - } - - subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->q) }); - subctx->s = subctx->seqs[subctx->seqs.size() - 1].data(); -} - -static size_t ggml_vk_align_size(size_t width, size_t align) { - VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")"); - return CEIL_DIV(width, align) * align; -} - -static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector* memcpys = nullptr) { - if (memcpys == nullptr) { - memcpy(dst, src, size); - } else { - memcpys->emplace_back(dst, src, size); - } -} - -static void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) { - if (device->sync_staging == nullptr || device->sync_staging->size < size) { - VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")"); - ggml_vk_destroy_buffer(device->sync_staging); - device->sync_staging = ggml_vk_create_buffer_check(device, size, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); - } -} - -static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context& subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { - VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")"); - GGML_ASSERT(!ggml_is_contiguous(tensor)); - // Buffer is already mapped - if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl; - GGML_ABORT("fatal error"); - } - // Check if src is pinned memory - vk_buffer buf; - size_t buf_offset; - ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset); - - const uint64_t ne0 = tensor->ne[0]; - const uint64_t ne1 = tensor->ne[1]; - const uint64_t ne2 = tensor->ne[2]; - const uint64_t ne3 = tensor->ne[3]; - const uint64_t nb0 = tensor->nb[0]; - const uint64_t nb1 = tensor->nb[1]; - const uint64_t nb2 = tensor->nb[2]; - const uint64_t nb3 = tensor->nb[3]; - const ggml_type type = tensor->type; - const uint64_t ts = ggml_type_size(type); - const uint64_t bs = ggml_blck_size(type); - - const uint64_t dstnb0 = ts; - const uint64_t dstnb1 = dstnb0*(ne0/bs); - const uint64_t dstnb2 = dstnb1*ne1; - const uint64_t dstnb3 = dstnb2*ne2; - - const uint64_t ne = ggml_nelements(tensor); - - if (buf != nullptr) { - // Memory is pinned, use as staging buffer - std::vector slices; - - for (uint64_t i3 = 0; i3 < ne3; i3++) { - for (uint64_t i2 = 0; i2 < ne2; i2++) { - // Find longest contiguous slice - if (ne1*nb1 == dstnb2) { - slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 }); - } else { - for (uint64_t i1 = 0; i1 < ne1; i1++) { - if (ne0*nb0/bs == dstnb1) { - slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 }); - } else { - const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; - const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; - for (uint64_t i0 = 0; i0 < ne0; i0++) { - slices.push_back({ s_off + i1*nb0, d_off + i0*dstnb0, dstnb0 }); - } - } - } - } - } - } - - ggml_vk_sync_buffers(subctx); - subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); - return; - } - - if (!sync_staging) { - GGML_ABORT("Asynchronous write to non-pinned memory not supported"); - } - - // Staging buffer required - vk_buffer& staging = ctx->device->sync_staging; - const uint64_t copy_size = ts*ne/bs; - ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size); - VkBufferCopy buf_copy{ 0, offset, copy_size }; - - ggml_vk_sync_buffers(subctx); - vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); - - for (uint64_t i3 = 0; i3 < ne3; i3++) { - for (uint64_t i2 = 0; i2 < ne2; i2++) { - // Find longest contiguous slice - if (ne1*nb1 == dstnb2) { - deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); - } else { - for (uint64_t i1 = 0; i1 < ne1; i1++) { - if (ne0*nb0/bs == dstnb1) { - deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); - } else { - const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; - const uint64_t d_off = i3*dstnb3 + i2*dstnb2 + i1*dstnb1; - for (uint64_t i0 = 0; i0 < ne0; i0++) { - deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys); - } - } - } - } - } - } -} - -static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { - VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")"); - // Buffer is already mapped - if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; - GGML_ABORT("fatal error"); - } - // Check if src is pinned memory - vk_buffer buf = nullptr; - size_t buf_offset; - ggml_vk_host_get(dst->device, src, buf, buf_offset); - - if (buf != nullptr) { - // Memory is pinned, use as staging buffer - std::vector slices(1); - if (width == spitch) { - // Only do single write if stride is equal - slices[0].srcOffset = buf_offset; - slices[0].dstOffset = offset; - slices[0].size = width * height; - } else { - slices.resize(height); - for (size_t i = 0; i < height; i++) { - slices[i].srcOffset = buf_offset + i * spitch; - slices[i].dstOffset = offset + i * width; - slices[i].size = width; - } - } - - ggml_vk_sync_buffers(subctx); - subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); - return; - } - VK_LOG_DEBUG("STAGING"); - - if (!sync_staging) { - GGML_ABORT("Asynchronous write to non-pinned memory not supported"); - } - - // Staging buffer required - const size_t copy_size = width*height; - ggml_vk_ensure_sync_staging_buffer(dst->device, copy_size); - - vk_buffer& staging_buffer = dst->device->sync_staging; - - VkBufferCopy buf_copy = { - 0, - offset, - copy_size}; - - ggml_vk_sync_buffers(subctx); - vkCmdCopyBuffer(subctx->s->buffer, staging_buffer->buffer, dst->buffer, 1, &buf_copy); - - if (width == spitch) { - deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys); - } else { - for (size_t i = 0; i < height; i++) { - deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); - } - } -} - -static void ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { - VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")"); - return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, 1, sync_staging); -} - -static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { - VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")"); - // Buffer is already mapped - if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); - - for (size_t i = 0; i < height; i++) { - memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); - } - } else { - vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue); - ggml_vk_ctx_begin(dst->device, subctx); - ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, width, height, true); - ggml_vk_ctx_end(subctx); - - for (auto& cpy : subctx->in_memcpys) { - memcpy(cpy.dst, cpy.src, cpy.n); - } - - ggml_vk_submit(subctx, dst->device->fence); - VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); - dst->device->device.resetFences({ dst->device->fence }); - } -} - -static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size) { - VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")"); - ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); -} - -static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { - VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")"); - GGML_ASSERT(width > 0); - GGML_ASSERT(height > 0); - GGML_ASSERT(src != nullptr); - - // TODO: staging_offset is not used - - // Check if dst is pinned memory - vk_buffer buf = nullptr; - size_t buf_offset; - ggml_vk_host_get(src->device, dst, buf, buf_offset); - - std::vector slices(1); - if (width == spitch && width == dpitch) { - // Only do single write if stride is equal - slices[0].srcOffset = offset; - slices[0].dstOffset = buf_offset; - slices[0].size = width * height; - } else { - slices.resize(height); - for (size_t i = 0; i < height; i++) { - slices[i].srcOffset = offset + i * spitch; - slices[i].dstOffset = buf_offset + i * dpitch; - slices[i].size = width; - } - } - - if (buf != nullptr) { - // Memory is pinned, use as staging buffer - ggml_vk_sync_buffers(subctx); - subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); - - return; - } - VK_LOG_DEBUG("STAGING"); - - if (!sync_staging) { - GGML_ABORT("Asynchronous read from non-pinned memory not supported"); - } - - // Fall back to staging buffer - const size_t copy_size = dpitch * height; - ggml_vk_ensure_sync_staging_buffer(src->device, copy_size); - - vk_buffer& staging_buffer = src->device->sync_staging; - - ggml_vk_sync_buffers(subctx); - subctx->s->buffer.copyBuffer(src->buffer, staging_buffer->buffer, slices); - - deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys); -} - -static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) { - return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging); -} - -static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) { - VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")"); - - // If the device is not an UMA device the memory is host-accessible through rebar. While writing - // through PCIe is sufficient fast reading back data from PCIe is slower than going through - // the HW device to host copy path. - if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) { - GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); - - memcpy(dst, (uint8_t *) src->ptr + offset, size); - } else { - vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue); - ggml_vk_ctx_begin(src->device, subctx); - ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true); - ggml_vk_ctx_end(subctx); - - ggml_vk_submit(subctx, src->device->fence); - VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); - src->device->device.resetFences({ src->device->fence }); - - for (auto& cpy : subctx->out_memcpys) { - memcpy(cpy.dst, cpy.src, cpy.n); - } - } -} - -static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { - VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")"); - // Make sure both buffers are on same device - GGML_ASSERT(src->device == dst->device); - - VkBufferCopy bc{ src_offset, dst_offset, size }; - - vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); -} - -static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { - if (src->device == dst->device) { - VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")"); - // Copy within the device - vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue); - ggml_vk_ctx_begin(src->device, subctx); - ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); - ggml_vk_ctx_end(subctx); - ggml_vk_submit(subctx, src->device->fence); - VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); - src->device->device.resetFences({ src->device->fence }); - } else { - VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")"); - // Copy device to device - ggml_vk_ensure_sync_staging_buffer(src->device, size); - ggml_vk_ensure_sync_staging_buffer(dst->device, size); - - // Copy to src staging buffer - ggml_vk_buffer_copy(src->device->sync_staging, 0, src, src_offset, size); - // memcpy to dst staging buffer - memcpy(dst->device->sync_staging->ptr, src->device->sync_staging->ptr, size); - // Copy to dst buffer - ggml_vk_buffer_copy(dst, dst_offset, dst->device->sync_staging, 0, size); - } -} - -static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) { - VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")"); - - vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue); - ggml_vk_ctx_begin(dst->device, subctx); - subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); - ggml_vk_ctx_end(subctx); - - ggml_vk_submit(subctx, dst->device->fence); - VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences"); - dst->device->device.resetFences({ dst->device->fence }); -} - -static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { - VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"); - // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { - // return 4; - // } - - return 1; - - GGML_UNUSED(m); GGML_UNUSED(n); GGML_UNUSED(k); -} - -static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { - if (m <= 32 || n <= 32) { - return aligned ? mmp->a_s : mmp->s; - } - return aligned ? mmp->a_m : mmp->m; - - GGML_UNUSED(ctx); -} - -static vk_pipeline ggml_vk_guess_matmul_pipeline_apple(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) { - return aligned ? mmp->a_m : mmp->m; - - GGML_UNUSED(ctx); -} - -static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, bool aligned) { - return aligned ? mmp->a_s : mmp->s; - - GGML_UNUSED(ctx); -} - -static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { - VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")"); - switch (ctx->device->vendor_id) { - case VK_VENDOR_ID_AMD: - return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned); - case VK_VENDOR_ID_APPLE: - return ggml_vk_guess_matmul_pipeline_apple(ctx, mmp, aligned); - case VK_VENDOR_ID_INTEL: - return ggml_vk_guess_matmul_pipeline_intel(ctx, mmp, aligned); - default: - break; - } - - if (m <= 32 || n <= 32) { - return aligned ? mmp->a_s : mmp->s; - } - if (m <= 64 || n <= 64) { - return aligned ? mmp->a_m : mmp->m; - } - return aligned ? mmp->a_l : mmp->l; -} - -static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) { - VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")"); - return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align; -} - -static void ggml_vk_matmul( - ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline, - vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, - uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, - uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, - uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3) { - VK_LOG_DEBUG("ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ")"); - ggml_vk_sync_buffers(subctx); - if (split_k == 1) { - const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k, ne02, ne12, broadcast2, broadcast3 }; - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch }); - return; - } - - GGML_ASSERT(batch_stride_d == m * n); - - const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3 }; - // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); - ggml_vk_sync_buffers(subctx); - const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); -} - -static void ggml_vk_matmul_id( - ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline, - vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& ids, - uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, - uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, - uint32_t n_as, uint32_t nei0, uint32_t nei1, uint32_t nbi1, uint32_t ne11) { - VK_LOG_DEBUG("ggml_vk_matmul_id(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), ids: (" << ids.buffer->buffer << ", " << ids.offset << ", " << ids.size << "), " << - "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " << - "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " << - "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")"); - ggml_vk_sync_buffers(subctx); - const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, - nei0, nei1, nbi1, ne11 }; - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids }, sizeof(vk_mat_mat_id_push_constants), &pc, { m, nei1, n_as }); -} - -static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { - return - tensor->nb[0] == ggml_type_size(tensor->type) && - tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) && - tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; -} - -static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src, const ggml_tensor * dst, ggml_type to) { - - // Choose "contiguous copy" shader if src/dst are contiguous - bool contig = ggml_is_contiguous(src) && (!dst || ggml_is_contiguous(dst)); - - if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - if (contig) { - return ctx->device->pipeline_contig_cpy_f32_f32; - } else { - return ctx->device->pipeline_cpy_f32_f32; - } - } - if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - if (contig) { - return ctx->device->pipeline_contig_cpy_f32_f16; - } else { - return ctx->device->pipeline_cpy_f32_f16; - } - } - if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - if (contig) { - return ctx->device->pipeline_contig_cpy_f16_f16; - } else { - return ctx->device->pipeline_cpy_f16_f16; - } - } - - std::cerr << "Missing CPY op for types: " << ggml_type_name(src->type) << " " << ggml_type_name(to) << std::endl; - GGML_ABORT("fatal error"); -} - -static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) { - VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; - std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")"); - const int tensor_type_size = ggml_type_size(tensor->type); - - const uint32_t ne = ggml_nelements(tensor); - std::array elements; - - if (ne > 262144) { - elements = { 512, 512, CEIL_DIV(ne, 262144) }; - } else if (ne > 512) { - elements = { 512, CEIL_DIV(ne, 512), 1 }; - } else { - elements = { ne, 1, 1 }; - } - - const vk_op_unary_push_constants pc = { - (uint32_t)ne, - (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size, - (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], 1 , (uint32_t)tensor->ne[0] , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]), - 0, - 0.0f, 0.0f, - }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, elements); -} - -static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; - std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); - GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT - GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - const uint64_t ne03 = src0->ne[3]; - - const uint64_t ne10 = src1->ne[0]; - const uint64_t ne11 = src1->ne[1]; - const uint64_t ne12 = src1->ne[2]; - const uint64_t ne13 = src1->ne[3]; - - const uint64_t ne20 = dst->ne[0]; - const uint64_t ne21 = dst->ne[1]; - - const uint64_t r2 = ne12 / ne02; - const uint64_t r3 = ne13 / ne03; - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - - vk_buffer d_Qx; - size_t qx_buf_offset = 0; - vk_buffer d_Qy; - size_t qy_buf_offset = 0; - - bool src0_uma = false; - bool src1_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); - src0_uma = d_Qx != nullptr; - src1_uma = d_Qy != nullptr; - } - - const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); - const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); - - const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; - - vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type); - - const bool qx_needs_dequant = mmp == nullptr || x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; - - if (qx_needs_dequant) { - // Fall back to dequant + f16 mulmat - mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16); - } - - // Not implemented - GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); - const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8; - - const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned); - - const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); - const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; - const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; - const uint64_t d_sz = sizeof(float) * d_ne; - - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); - } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - - if (dryrun) { - const uint64_t x_sz_upd = x_sz * ne02 * ne03; - const uint64_t y_sz_upd = y_sz * ne12 * ne13; - const uint64_t split_k_size = split_k > 1 ? d_sz * ne12 * ne13 * 4 : 0; - if ( - (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || - (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size) || - (split_k > 1 && split_k_size > ctx->device->max_memory_allocation_size)) { - GGML_ABORT("Requested preallocation size is too large"); - } - if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { - ctx->prealloc_size_x = x_sz_upd; - } - if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { - ctx->prealloc_size_y = y_sz_upd; - } - if (split_k > 1 && ctx->prealloc_size_split_k < split_k_size) { - ctx->prealloc_size_split_k = split_k_size; - } - - // Request descriptor sets - ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); - if (qx_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); - } - if (split_k > 1) { - ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, 1); - } - return; - } - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; - GGML_ASSERT(d_D != nullptr); - GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); - vk_buffer d_X; - uint64_t x_buf_offset = 0; - vk_buffer d_Y; - uint64_t y_buf_offset = 0; - if (!src0_uma) { - d_Qx = src0_buf_ctx->dev_buffer; - qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_Qx != nullptr); - } - if (!src1_uma) { - d_Qy = src1_buf_ctx->dev_buffer; - qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Qy != nullptr); - } - if (qx_needs_dequant) { - d_X = ctx->prealloc_x; - GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); - } else { - d_X = d_Qx; - x_buf_offset = qx_buf_offset; - GGML_ASSERT(qx_sz == x_sz); - } - if (qy_needs_dequant) { - d_Y = ctx->prealloc_y; - GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); - } else { - d_Y = d_Qy; - y_buf_offset = qy_buf_offset; - GGML_ASSERT(qy_sz == y_sz); - } - - if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); - } else if (qx_needs_dequant) { - const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); - } - if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); - } - - uint32_t stride_batch_x = ne00*ne01; - uint32_t stride_batch_y = ne10*ne11; - - if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { - stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); - } - - if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { - stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); - } - - // compute - ggml_vk_matmul( - ctx, subctx, pipeline, - { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, - { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, - ne01, ne11, ne10, - ne10, ne10, ne01, stride_batch_x, stride_batch_y, ne20*ne21, - split_k, ne12*ne13, ne02, ne12, r2, r3 - ); // NOLINT -} - -static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; - std::cerr << "), " << (dryrun ? "dryrun" : "") << "),)"); - GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT - GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - const uint64_t ne03 = src0->ne[3]; - - const uint64_t ne10 = src1->ne[0]; - const uint64_t ne11 = src1->ne[1]; - const uint64_t ne12 = src1->ne[2]; - const uint64_t ne13 = src1->ne[3]; - - GGML_ASSERT(ne11 == 1); - - const uint64_t ne20 = dst->ne[0]; - const uint64_t ne21 = dst->ne[1]; - const uint64_t ne22 = dst->ne[2]; - const uint64_t ne23 = dst->ne[3]; - - const uint64_t r2 = ne12 / ne02; - const uint64_t r3 = ne13 / ne03; - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - - vk_buffer d_Qx; - size_t qx_buf_offset = 0; - vk_buffer d_Qy; - size_t qy_buf_offset = 0; - - bool src0_uma = false; - bool src1_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); - src0_uma = d_Qx != nullptr; - src1_uma = d_Qy != nullptr; - } - - const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); - const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); - - const bool f16_f32_kernel = src1->type == GGML_TYPE_F32; - - const bool qx_needs_dequant = x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; - - // Not implemented - GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - - const uint64_t x_ne = ne01 * ne00; - const uint64_t y_ne = ne11 * ne10; - const uint64_t d_ne = ne11 * ne01; - - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); - const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; - const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; - const uint64_t d_sz = sizeof(float) * d_ne; - - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - GGML_ASSERT(dmmv != nullptr); - - if (dryrun) { - const uint64_t x_sz_upd = x_sz * ne02 * ne03; - const uint64_t y_sz_upd = y_sz * ne12 * ne13; - if ( - (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || - (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) { - GGML_ABORT("Requested preallocation size is too large"); - } - if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { - ctx->prealloc_size_x = x_sz_upd; - } - if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { - ctx->prealloc_size_y = y_sz_upd; - } - - // Request descriptor sets - if (qx_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); - } - ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1); - return; - } - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; - GGML_ASSERT(d_D != nullptr); - vk_buffer d_X; - uint64_t x_buf_offset = 0; - vk_buffer d_Y; - uint64_t y_buf_offset = 0; - if(!src0_uma) { - d_Qx = src0_buf_ctx->dev_buffer; - qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_Qx != nullptr); - } - if(!src1_uma) { - d_Qy = src1_buf_ctx->dev_buffer; - qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Qy != nullptr); - } - if (qx_needs_dequant) { - d_X = ctx->prealloc_x; - } else { - d_X = d_Qx; - x_buf_offset = qx_buf_offset; - GGML_ASSERT(qx_sz == x_sz); - } - if (qy_needs_dequant) { - d_Y = ctx->prealloc_y; - } else { - d_Y = d_Qy; - y_buf_offset = qy_buf_offset; - GGML_ASSERT(qy_sz == y_sz); - } - - if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); - } - if (y_non_contig) { - GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); - } - - uint32_t stride_batch_x = ne00*ne01; - uint32_t stride_batch_y = ne10*ne11; - - if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { - stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); - } - - if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { - stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); - } - - const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0]; - - uint32_t groups_x = ne01; - uint32_t groups_z = 1; - - if (ne01 > max_groups_x) { - groups_z = 64; - groups_x /= groups_z; - } - - // compute - const vk_mat_vec_push_constants pc = { - (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, - stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21), - (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, - }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, - { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} }, - sizeof(vk_mat_vec_push_constants), &pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z }); -} - -static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32(" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; - std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); - GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT - GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - // const uint64_t ne03 = src0->ne[3]; - - const uint64_t ne10 = src1->ne[0]; - const uint64_t ne11 = src1->ne[1]; - const uint64_t ne12 = src1->ne[2]; - // const uint64_t ne13 = src1->ne[3]; - - GGML_ASSERT(ne11 == 1); - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - - vk_buffer d_Qy; - size_t qy_buf_offset = 0; - - bool src1_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); - src1_uma = d_Qy != nullptr; - } - - const uint64_t x_ne = ne00 * ne01 * ne02; - const uint64_t y_ne = ne10 * ne11 * ne12; - const uint64_t d_ne = ne01 * ne11 * ne12; - - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); - const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t d_sz = sizeof(float) * d_ne; - - if (dryrun) { - // Request descriptor sets - ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1); - return; - } - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; - GGML_ASSERT(d_D != nullptr); - vk_buffer d_Qx = src0_buf_ctx->dev_buffer; - const uint64_t qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_Qx != nullptr); - if (!src1_uma) { - d_Qy = src1_buf_ctx->dev_buffer; - qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Qx != nullptr); - } - - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - - const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; - - // compute - const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); -} - -static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; - std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - // const uint64_t ne03 = src0->ne[3]; - - const uint64_t nb01 = src0->nb[1]; - const uint64_t nb02 = src0->nb[2]; - - // const uint64_t ne10 = src1->ne[0]; - const uint64_t ne11 = src1->ne[1]; - const uint64_t ne12 = src1->ne[2]; - // const uint64_t ne13 = src1->ne[3]; - - GGML_ASSERT(ne11 == 1); - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - - vk_buffer d_Qy = nullptr; - size_t qy_buf_offset = 0; - - bool src1_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); - src1_uma = d_Qy != nullptr; - } - - const uint64_t d_ne = ne01 * ne11 * ne12; - - const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t); - const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t); - - const uint64_t qx_sz = ggml_nbytes(src0); - const uint64_t qy_sz = ggml_nbytes(src1); - const uint64_t d_sz = sizeof(float) * d_ne; - - if (dryrun) { - // Request descriptor sets - ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1); - return; - } - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; - GGML_ASSERT(d_D != nullptr); - vk_buffer d_Qx = src0_buf_ctx->dev_buffer; - const uint64_t qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_Qx != nullptr); - if (!src1_uma) { - d_Qy = src1_buf_ctx->dev_buffer; - qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Qx != nullptr); - } - - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - - const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; - - // compute - const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, - { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); -} - -static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")"); - if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1 && - // detect 0213 permutation, and batch size of 1 - src0->nb[0] <= src0->nb[2] && - src0->nb[2] <= src0->nb[1] && - src0->nb[1] <= src0->nb[3] && - src1->nb[0] <= src1->nb[2] && - src1->nb[2] <= src1->nb[1] && - src1->nb[1] <= src1->nb[3] && - src0->ne[3] == 1 && - src1->ne[3] == 1) { - ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst, dryrun); - } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 && - !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) { - ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun); - } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun); - } else { - ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, dryrun); - } -} - -static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); - GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT - GGML_ASSERT(ids->type == GGML_TYPE_I32); - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - const uint64_t ne03 = src0->ne[3]; - - const uint64_t ne10 = src1->ne[0]; - const uint64_t ne11 = src1->ne[1]; - const uint64_t ne12 = src1->ne[2]; - const uint64_t ne13 = src1->ne[3]; - - const uint64_t nei0 = ids->ne[0]; - const uint64_t nei1 = ids->ne[1]; - GGML_ASSERT(nei0 * nei1 <= 3072); - - const uint32_t nbi1 = ids->nb[1]; - const uint32_t nbi2 = ids->nb[2]; - - const uint64_t ne20 = dst->ne[0]; - const uint64_t ne21 = dst->ne[1]; - const uint64_t ne22 = dst->ne[2]; - const uint64_t ne23 = dst->ne[3]; - - const uint64_t n_as = ne02; - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context; - - vk_buffer d_Qx; - size_t qx_buf_offset = 0; - vk_buffer d_Qy; - size_t qy_buf_offset = 0; - vk_buffer d_ids; - size_t ids_buf_offset = 0; - - bool src0_uma = false; - bool src1_uma = false; - bool ids_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); - ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset); - src0_uma = d_Qx != nullptr; - src1_uma = d_Qy != nullptr; - ids_uma = d_ids != nullptr; - } - - const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); - const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); - - const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; - - vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type); - - const bool qx_needs_dequant = mmp == nullptr || x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; - - if (qx_needs_dequant) { - GGML_ABORT("fatal error"); - } - - // Not implemented - GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - - const uint64_t x_ne = ne01 * ne00; - const uint64_t y_ne = ne11 * ne10; - const uint64_t d_ne = ne21 * ne20; - - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, nei1)); - const bool aligned = ne10 == kpad && ne01 > 8 && nei1 > 8; - - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, nei1, aligned); - - const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); - const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; - const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; - const uint64_t ids_sz = nbi2; - const uint64_t d_sz = sizeof(float) * d_ne; - - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); - } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - - if (dryrun) { - const uint64_t x_sz_upd = x_sz * ne02 * ne03; - const uint64_t y_sz_upd = y_sz * ne12 * ne13; - if ( - (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || - (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) { - GGML_ABORT("Requested preallocation size is too large"); - } - if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { - ctx->prealloc_size_x = x_sz_upd; - } - if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { - ctx->prealloc_size_y = y_sz_upd; - } - - // Request descriptor sets - ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); - if (qx_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); - } - return; - } - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; - GGML_ASSERT(d_D != nullptr); - vk_buffer d_X; - uint64_t x_buf_offset = 0; - vk_buffer d_Y; - uint64_t y_buf_offset = 0; - if (!src0_uma) { - d_Qx = src0_buf_ctx->dev_buffer; - qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_Qx != nullptr); - } - if (!src1_uma) { - d_Qy = src1_buf_ctx->dev_buffer; - qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Qy != nullptr); - } - if (!ids_uma) { - d_ids = ids_buf_ctx->dev_buffer; - ids_buf_offset = vk_tensor_offset(ids) + ids->view_offs; - GGML_ASSERT(d_ids != nullptr); - } - if (qx_needs_dequant) { - d_X = ctx->prealloc_x; - GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); - } else { - d_X = d_Qx; - x_buf_offset = qx_buf_offset; - GGML_ASSERT(qx_sz == x_sz); - } - if (qy_needs_dequant) { - d_Y = ctx->prealloc_y; - GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); - } else { - d_Y = d_Qy; - y_buf_offset = qy_buf_offset; - GGML_ASSERT(qy_sz == y_sz); - } - - if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); - } else if (qx_needs_dequant) { - const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, - { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); - } - if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); - } - - uint32_t stride_batch_x = ne00*ne01; - uint32_t stride_batch_y = ne10*ne11; - - if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { - stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); - } - - if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { - stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); - } - - // compute - ggml_vk_matmul_id( - ctx, subctx, pipeline, - { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, - { d_D, d_buf_offset, d_sz * ne22 * ne23 }, { d_ids, ids_buf_offset, ids_sz }, - ne01, ne21, ne10, ne10, ne10, ne01, - stride_batch_x, stride_batch_y, ne20*ne21, - n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11 - ); // NOLINT -} - -static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; - std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); - GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT - GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT - GGML_ASSERT(ids->type == GGML_TYPE_I32); - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - const uint64_t ne03 = src0->ne[3]; - - const uint64_t ne10 = src1->ne[0]; - const uint64_t ne11 = src1->ne[1]; - const uint64_t ne12 = src1->ne[2]; - const uint64_t ne13 = src1->ne[3]; - - const uint64_t nei0 = ids->ne[0]; - const uint64_t nei1 = ids->ne[1]; - - const uint64_t nbi2 = ids->nb[2]; - - GGML_ASSERT(nei1 == 1); - - const uint64_t ne20 = dst->ne[0]; - const uint64_t ne21 = dst->ne[1]; - const uint64_t ne22 = dst->ne[2]; - const uint64_t ne23 = dst->ne[3]; - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context; - - vk_buffer d_Qx; - size_t qx_buf_offset = 0; - vk_buffer d_Qy; - size_t qy_buf_offset = 0; - vk_buffer d_ids; - size_t ids_buf_offset = 0; - - bool src0_uma = false; - bool src1_uma = false; - bool ids_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); - ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset); - src0_uma = d_Qx != nullptr; - src1_uma = d_Qy != nullptr; - ids_uma = d_ids != nullptr; - } - - const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); - const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); - - const bool f16_f32_kernel = src1->type == GGML_TYPE_F32; - - const bool qx_needs_dequant = x_non_contig; - const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; - - // Not implemented - GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - - const uint64_t x_ne = ne01 * ne00; - const uint64_t y_ne = ne11 * ne10; - const uint64_t d_ne = ne21 * ne20; - - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); - const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; - const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; - const uint64_t ids_sz = nbi2; - const uint64_t d_sz = sizeof(float) * d_ne; - - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type); - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - GGML_ASSERT(dmmv != nullptr); - - if (dryrun) { - const uint64_t x_sz_upd = x_sz * ne02 * ne03; - const uint64_t y_sz_upd = y_sz * ne12 * ne13; - if ( - (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || - (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) { - GGML_ABORT("Requested preallocation size is too large"); - } - if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { - ctx->prealloc_size_x = x_sz_upd; - } - if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { - ctx->prealloc_size_y = y_sz_upd; - } - - // Request descriptor sets - if (qx_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); - } - ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1); - return; - } - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; - GGML_ASSERT(d_D != nullptr); - vk_buffer d_X; - uint64_t x_buf_offset = 0; - vk_buffer d_Y; - uint64_t y_buf_offset = 0; - if(!src0_uma) { - d_Qx = src0_buf_ctx->dev_buffer; - qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_Qx != nullptr); - } - if(!src1_uma) { - d_Qy = src1_buf_ctx->dev_buffer; - qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Qy != nullptr); - } - if(!ids_uma) { - d_ids = ids_buf_ctx->dev_buffer; - ids_buf_offset = vk_tensor_offset(ids) + ids->view_offs; - GGML_ASSERT(d_ids != nullptr); - } - if (qx_needs_dequant) { - d_X = ctx->prealloc_x; - } else { - d_X = d_Qx; - x_buf_offset = qx_buf_offset; - GGML_ASSERT(qx_sz == x_sz); - } - if (qy_needs_dequant) { - d_Y = ctx->prealloc_y; - } else { - d_Y = d_Qy; - y_buf_offset = qy_buf_offset; - GGML_ASSERT(qy_sz == y_sz); - } - - if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); - } - if (y_non_contig) { - GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); - } - - uint32_t stride_batch_y = ne10*ne11; - - if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { - stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); - } - - const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0]; - - uint32_t groups_x = ne01; - uint32_t groups_z = 1; - - if (ne01 > max_groups_x) { - groups_z = 64; - groups_x /= groups_z; - } - - // compute - const vk_mat_vec_id_push_constants pc = { - (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, - (uint32_t)x_ne, stride_batch_y, (uint32_t)(ne20*ne21), - (uint32_t)nei0, (uint32_t)ne11, - }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, - { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, - vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23}, vk_subbuffer{ d_ids, ids_buf_offset, ids_sz } }, - sizeof(vk_mat_vec_id_push_constants), &pc, { groups_x, (uint32_t)nei0, groups_z }); -} - -static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")"); - if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun); - } else { - ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun); - } -} - -static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { - switch (op) { - case GGML_OP_GET_ROWS: - GGML_ASSERT(src1->type == GGML_TYPE_I32); - if (dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_get_rows[src0->type]; - } - if (dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_get_rows_f32[src0->type]; - } - return nullptr; - case GGML_OP_ACC: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_acc_f32; - } - return nullptr; - case GGML_OP_ADD: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_add_f32; - } - if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_add_f16_f32_f16; - } - return nullptr; - case GGML_OP_MUL: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_mul_f32; - } - return nullptr; - case GGML_OP_DIV: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_div_f32; - } - return nullptr; - case GGML_OP_CONCAT: - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_concat_f32; - } - if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_concat_f16; - } - if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { - return ctx->device->pipeline_concat_i32; - } - return nullptr; - case GGML_OP_UPSCALE: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_upscale_f32; - } - return nullptr; - case GGML_OP_SCALE: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_scale_f32; - } - return nullptr; - case GGML_OP_SQR: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_sqr_f32; - } - return nullptr; - case GGML_OP_SIN: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_sin_f32; - } - return nullptr; - case GGML_OP_COS: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_cos_f32; - } - return nullptr; - case GGML_OP_CLAMP: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_clamp_f32; - } - return nullptr; - case GGML_OP_PAD: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_pad_f32; - } - return nullptr; - case GGML_OP_REPEAT: - if (ggml_type_size(src0->type) == sizeof(float) && ggml_type_size(dst->type) == sizeof(float)) { - return ctx->device->pipeline_repeat_f32; - } - return nullptr; - case GGML_OP_CPY: - case GGML_OP_CONT: - case GGML_OP_DUP: - return ggml_vk_get_cpy_pipeline(ctx, src0, dst, dst->type); - case GGML_OP_NORM: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_norm_f32; - } - return nullptr; - case GGML_OP_GROUP_NORM: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_group_norm_f32; - } - return nullptr; - case GGML_OP_RMS_NORM: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_rms_norm_f32; - } - return nullptr; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(dst)) { - case GGML_UNARY_OP_SILU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_silu_f32; - } - break; - case GGML_UNARY_OP_GELU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_gelu_f32; - } - break; - case GGML_UNARY_OP_GELU_QUICK: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_gelu_quick_f32; - } - break; - case GGML_UNARY_OP_RELU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_relu_f32; - } - break; - case GGML_UNARY_OP_TANH: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_tanh_f32; - } - break; - default: - break; - } - return nullptr; - case GGML_OP_DIAG_MASK_INF: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_diag_mask_inf_f32; - } - return nullptr; - case GGML_OP_SOFT_MAX: - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); - - if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_soft_max_f32; - } - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_soft_max_f32_f16; - } - return nullptr; - case GGML_OP_ROPE: - { - const int mode = ((const int32_t *) dst->op_params)[2]; - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - if (is_neox) { - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_rope_neox_f32; - } - if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_rope_neox_f16; - } - } else { - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_rope_norm_f32; - } - if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_rope_norm_f16; - } - } - return nullptr; - } - case GGML_OP_ARGSORT: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) { - return ctx->device->pipeline_argsort_f32; - } - return nullptr; - case GGML_OP_SUM_ROWS: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_sum_rows_f32; - } - return nullptr; - case GGML_OP_IM2COL: - if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_im2col_f32; - } - if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_im2col_f32_f16; - } - return nullptr; - case GGML_OP_TIMESTEP_EMBEDDING: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_timestep_embedding_f32; - } - return nullptr; - case GGML_OP_POOL_2D: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_pool2d_f32; - } - return nullptr; - case GGML_OP_LEAKY_RELU: - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_leaky_relu_f32; - } - return nullptr; - default: - return nullptr; - } - - GGML_UNUSED(src2); -} - -static bool ggml_vk_op_supports_incontiguous(ggml_op op) { - switch (op) { - case GGML_OP_CPY: - case GGML_OP_GET_ROWS: - case GGML_OP_ADD: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_SQR: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_REPEAT: - return true; - default: - return false; - } -} - -template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc, bool dryrun = false) { - VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - if (src1 != nullptr) { - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - } - if (src2 != nullptr) { - std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; - } - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; - std::cerr << "), " << ggml_op_name(op) << ", " << (dryrun ? "dryrun" : "") << ")"); - GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT - GGML_ASSERT(ggml_vk_op_supports_incontiguous(op) || ggml_vk_dim01_contiguous(src0)); // NOLINT - GGML_ASSERT(dst->buffer != nullptr); - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - const uint64_t ne03 = src0->ne[3]; - const uint64_t ne0 = ne00 * ne01; - - const bool use_src1 = src1 != nullptr; - const uint64_t ne10 = use_src1 ? src1->ne[0] : 0; - const uint64_t ne11 = use_src1 ? src1->ne[1] : 0; - const uint64_t ne12 = use_src1 ? src1->ne[2] : 0; - const uint64_t ne13 = use_src1 ? src1->ne[3] : 0; - const uint64_t ne1 = ne10 * ne11; - // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0; - - const bool use_src2 = src2 != nullptr; - const uint64_t ne20 = use_src2 ? src2->ne[0] : 0; - const uint64_t ne21 = use_src2 ? src2->ne[1] : 0; - const uint64_t ne22 = use_src2 ? src2->ne[2] : 0; - const uint64_t ne23 = use_src2 ? src2->ne[3] : 0; - const uint64_t ne2 = ne20 * ne21; - - const uint64_t ned0 = dst->ne[0]; - const uint64_t ned1 = dst->ne[1]; - const uint64_t ned2 = dst->ne[2]; - const uint64_t ned3 = dst->ne[3]; - const uint64_t ned = ned0 * ned1; - - vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); - - if (pipeline == nullptr) { - std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type); - if (src1 != nullptr) { - std::cerr << " and " << ggml_type_name(src1->type); - } - std::cerr << " to " << ggml_type_name(dst->type) << std::endl; - GGML_ABORT("fatal error"); - } - - if (dryrun) { - ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); - return; - } - - const bool op_supports_incontiguous = ggml_vk_op_supports_incontiguous(op); - - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - ggml_backend_vk_buffer_context * src1_buf_ctx = use_src1 ? (ggml_backend_vk_buffer_context *)src1->buffer->context : nullptr; - ggml_backend_vk_buffer_context * src2_buf_ctx = use_src2 ? (ggml_backend_vk_buffer_context *)src2->buffer->context : nullptr; - - vk_buffer d_X = nullptr; - size_t x_buf_offset = 0; - vk_buffer d_Y = nullptr; - size_t y_buf_offset = 0; - vk_buffer d_Z = nullptr; - size_t z_buf_offset = 0; - - bool src0_uma = false; - bool src1_uma = false; - bool src2_uma = false; - - if (ctx->device->uma) { - ggml_vk_host_get(ctx->device, src0->data, d_X, x_buf_offset); - src0_uma = d_X != nullptr; - if (use_src1) { - ggml_vk_host_get(ctx->device, src1->data, d_Y, y_buf_offset); - src1_uma = d_Y != nullptr; - } - if (use_src2) { - ggml_vk_host_get(ctx->device, src2->data, d_Z, z_buf_offset); - src2_uma = d_Z != nullptr; - } - } - - uint64_t x_sz = ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0; - uint64_t y_sz = use_src1 ? ggml_type_size(src1->type) * ne1 : 0; - uint64_t z_sz = use_src2 ? ggml_type_size(src2->type) * ne2 : 0; - uint64_t d_sz = ggml_type_size(dst->type) * ned; - - vk_buffer d_D = dst_buf_ctx->dev_buffer; - - // Workaround for tiny tensor inputs on ROPE - if (op == GGML_OP_ROPE && use_src1 && y_sz > d_D->size) { - y_sz = VK_WHOLE_SIZE; - } - - GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = ((vk_tensor_offset(dst) + dst->view_offs) / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - GGML_ASSERT(d_buf_offset == vk_tensor_offset(dst) || op == GGML_OP_CPY); // NOLINT - if(!src0_uma) { - d_X = src0_buf_ctx->dev_buffer; - x_buf_offset = vk_tensor_offset(src0) + src0->view_offs; - GGML_ASSERT(d_X != nullptr); - } - if (use_src1 && !src1_uma) { - d_Y = src1_buf_ctx->dev_buffer; - y_buf_offset = vk_tensor_offset(src1) + src1->view_offs; - GGML_ASSERT(d_Y != nullptr); - } - if (use_src2 && !src2_uma) { - d_Z = src2_buf_ctx->dev_buffer; - z_buf_offset = vk_tensor_offset(src2) + src2->view_offs; - GGML_ASSERT(d_Z != nullptr); - } - - if (op_supports_incontiguous) { - x_sz = ggml_nbytes(src0); - y_sz = use_src1 ? ggml_nbytes(src1) : 0; - z_sz = use_src2 ? ggml_nbytes(src2) : 0; - d_sz = ggml_nbytes(dst); - - if (x_buf_offset + x_sz >= d_X->size) { - x_sz = VK_WHOLE_SIZE; - } - if (use_src1 && y_buf_offset + y_sz >= d_Y->size) { - y_sz = VK_WHOLE_SIZE; - } - if (use_src2 && z_buf_offset + z_sz >= d_Z->size) { - z_sz = VK_WHOLE_SIZE; - } - if (d_buf_offset + d_sz >= d_D->size) { - d_sz = VK_WHOLE_SIZE; - } - } - - std::array elements; - - // Single call if dimension 2 is contiguous - GGML_ASSERT(op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))); - - switch (op) { - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_SOFT_MAX: - case GGML_OP_SUM_ROWS: - { - const uint32_t nr = ggml_nrows(src0); - if (nr > 262144) { - elements = { 512, 512, CEIL_DIV(nr, 262144) }; - } else if (nr > 512) { - elements = { 512, CEIL_DIV(nr, 512), 1 }; - } else { - elements = { nr, 1, 1 }; - } - } break; - case GGML_OP_GROUP_NORM: - { - const uint32_t num_groups = dst->op_params[0]; - elements = { num_groups * (uint32_t)src0->ne[3], 1, 1 }; - } break; - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_ROPE: - elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; - break; - case GGML_OP_GET_ROWS: - elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; - break; - case GGML_OP_ARGSORT: - elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 }; - break; - case GGML_OP_IM2COL: - { - const bool is_2D = dst->op_params[6] == 1; - - const uint32_t IC = src1->ne[is_2D ? 2 : 1]; - - const uint32_t KH = is_2D ? src0->ne[1] : 1; - const uint32_t KW = src0->ne[0]; - - const uint32_t OH = is_2D ? dst->ne[2] : 1; - const uint32_t OW = dst->ne[1]; - - const uint32_t batch = src1->ne[is_2D ? 3 : 2]; - - elements = { OW * KW * KH, OH, batch * IC }; - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - const uint32_t dim = dst->op_params[0]; - uint32_t half_ceil = (dim + 1) / 2; - elements = { half_ceil, (uint32_t)src0->ne[0], 1 }; - } break; - case GGML_OP_POOL_2D: - { - const uint32_t N = dst->ne[3]; - const uint32_t OC = dst->ne[2]; - const uint32_t OH = dst->ne[1]; - const uint32_t OW = dst->ne[0]; - elements = { N * OC * OH * OW, 1, 1}; - } break; - case GGML_OP_ADD: - case GGML_OP_DIV: - case GGML_OP_MUL: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_REPEAT: - case GGML_OP_CPY: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_UNARY: - { - const uint32_t ne = ggml_nelements(dst); - if (ne > 262144) { - elements = { 512, 512, CEIL_DIV(ne, 262144) }; - } else if (ne > 512) { - elements = { 512, CEIL_DIV(ne, 512), 1 }; - } else { - elements = { ne, 1, 1 }; - } - } break; - default: - elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; - break; - } - - if (!op_supports_incontiguous) { - if (x_sz != VK_WHOLE_SIZE) { - x_sz *= ne02 * ne03; - } - if (use_src1 && y_sz != VK_WHOLE_SIZE) { - y_sz *= ne12 * ne13; - } - if (use_src2 && z_sz != VK_WHOLE_SIZE) { - z_sz *= ne22 * ne23; - } - if (d_sz != VK_WHOLE_SIZE) { - d_sz *= ned2 * ned3; - } - } - - if (op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible in soft_max, but the shader needs a buffer - vk_subbuffer subbuf_y; - if (use_src1) { - subbuf_y = { d_Y, y_buf_offset, y_sz }; - } else { - subbuf_y = { d_X, 0, x_sz }; - } - - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (op == GGML_OP_ROPE) { - // Empty src2 is possible in rope, but the shader needs a buffer - vk_subbuffer subbuf_z; - if (use_src2) { - subbuf_z = { d_Z, z_buf_offset, z_sz }; - } else { - subbuf_z = { d_X, 0, x_sz }; - } - - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (op == GGML_OP_IM2COL) { - // im2col uses only src1 and dst buffers - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (use_src2) { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (use_src1) { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } -} - -static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, 0, - }, dryrun); -} - -static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = ((vk_tensor_offset(dst) + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - - int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 - int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 - // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused - int offset = dst->op_params[3] / 4; // offset in bytes - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ACC, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t) dst->nb[3] / dst_type_size, - d_offset, - 0.0f, 0.0f, offset, - }, dryrun); -} - -static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, 0, - }, dryrun); -} - -static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, 0, - }, dryrun); -} - -static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_DIV, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, 0, - }, dryrun); -} - -static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - int * op_params = (int *)dst->op_params; - - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONCAT, { - (uint32_t)ggml_nelements(dst), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, op_params[0], - }, dryrun); -} - -static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - - const float sf0 = (float)dst->ne[0] / src0->ne[0]; - const float sf1 = (float)dst->ne[1] / src0->ne[1]; - const float sf2 = (float)dst->ne[2] / src0->ne[2]; - const float sf3 = (float)dst->ne[3] / src0->ne[3]; - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UPSCALE, { - (uint32_t)ggml_nelements(dst), 0, - (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], (uint32_t)dst->ne[2],(uint32_t)dst->ne[3], - sf0, sf1, sf2, sf3, - }, dryrun); -} - -static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - float * op_params = (float *)dst->op_params; - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - op_params[0], 0.0f - }, dryrun); -} - -static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, - }, dryrun); -} - -static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SIN, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, - }, dryrun); -} - -static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_COS, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, - }, dryrun); -} - -static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - float * op_params = (float *)dst->op_params; - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - op_params[0], op_params[1], - }, dryrun); -} - -static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_PAD, { - (uint32_t)ggml_nelements(dst), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, - }, dryrun); -} - -static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_REPEAT, { - (uint32_t)ggml_nelements(dst), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - 0, - 0.0f, 0.0f, - }, dryrun); -} - -static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = ((vk_tensor_offset(dst) + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { - (uint32_t)ggml_nelements(src0), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, - d_offset, - 0.0f, 0.0f, - }, dryrun); -} - -static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - float * op_params = (float *)dst->op_params; - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun); -} - -static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const int * int_op_params = (const int *)dst->op_params; - const float * float_op_params = (const float *)dst->op_params; - - const uint32_t num_groups = int_op_params[0]; - const float eps = float_op_params[1]; - const uint32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f }, dryrun); -} - -static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun); -} - -static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }, dryrun); -} - -static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }, dryrun); -} - -static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - float * op_params = (float *)dst->op_params; - - float scale = op_params[0]; - float max_bias = op_params[1]; - - const uint32_t ncols = (uint32_t)src0->ne[0]; - const uint32_t nrows_x = (uint32_t)ggml_nrows(src0); - const uint32_t nrows_y = (uint32_t)src0->ne[1]; - - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, { - ncols, - src1 != nullptr ? nrows_y : (uint32_t)0, - scale, max_bias, - m0, m1, - n_head_log2, - }, dryrun); -} - -static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) { - const int n_dims = ((int32_t *) dst->op_params)[1]; - // const int mode = ((int32_t *) dst->op_params)[2]; - // const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - const float freq_base = ((float *) dst->op_params)[5]; - const float freq_scale = ((float *) dst->op_params)[6]; - const float ext_factor = ((float *) dst->op_params)[7]; - const float attn_factor = ((float *) dst->op_params)[8]; - const float beta_fast = ((float *) dst->op_params)[9]; - const float beta_slow = ((float *) dst->op_params)[10]; - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { - (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], - freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale, - src2 != nullptr, - }, dryrun); -} - -static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - int32_t * op_params = (int32_t *)dst->op_params; - - uint32_t ncols = src0->ne[0]; - - uint32_t ncols_pad = 1; - while (ncols_pad < ncols) { - ncols_pad *= 2; - } - - GGML_ASSERT(ncols_pad <= 1024); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { - ncols, - ncols_pad, - op_params[0], - }, dryrun); -} - -static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun); -} - -static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { - const int32_t s0 = dst->op_params[0]; - const int32_t s1 = dst->op_params[1]; - const int32_t p0 = dst->op_params[2]; - const int32_t p1 = dst->op_params[3]; - const int32_t d0 = dst->op_params[4]; - const int32_t d1 = dst->op_params[5]; - - const bool is_2D = dst->op_params[6] == 1; - - const uint32_t IC = src1->ne[is_2D ? 2 : 1]; - const uint32_t IH = is_2D ? src1->ne[1] : 1; - const uint32_t IW = src1->ne[0]; - - const uint32_t KH = is_2D ? src0->ne[1] : 1; - const uint32_t KW = src0->ne[0]; - - const uint32_t OH = is_2D ? dst->ne[2] : 1; - const uint32_t OW = dst->ne[1]; - - const uint32_t offset_delta = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - const uint32_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 - - const uint32_t pelements = OW * KW * KH; - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_IM2COL, { - batch_offset, offset_delta, - IC, IW, IH, OW, OH, KW, KH, - pelements, - IC * KH * KW, - s0, s1, p0, p1, d0, d1, - }, dryrun); -} - -static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const uint32_t dim = dst->op_params[0]; - const uint32_t max_period = dst->op_params[1]; - const uint32_t nb1 = dst->nb[1] / ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_TIMESTEP_EMBEDDING, { - nb1, dim, max_period, - }, dryrun); -} - -static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - uint32_t op = static_cast(dst->op_params[0]); - const int32_t k1 = dst->op_params[1]; - const int32_t k0 = dst->op_params[2]; - const int32_t s1 = dst->op_params[3]; - const int32_t s0 = dst->op_params[4]; - const int32_t p1 = dst->op_params[5]; - const int32_t p0 = dst->op_params[6]; - - const uint32_t IH = src0->ne[1]; - const uint32_t IW = src0->ne[0]; - - const uint32_t N = dst->ne[3]; - - const uint32_t OC = dst->ne[2]; - const uint32_t OH = dst->ne[1]; - const uint32_t OW = dst->ne[0]; - - const uint32_t parallel_elements = N * OC * OH * OW; - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_POOL_2D, { - IW, IH, OW, OH, OC, - parallel_elements, - op, - k0, k1, s0, s1, p0, p1, - }, dryrun); -} - -static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { - const float * op_params = (const float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun); -} - -#ifdef GGML_VULKAN_RUN_TESTS -static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { - if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { - return; - } - i0 = std::max(i0, 5); - i1 = std::max(i1, 5); - i2 = std::max(i2, 0); - fprintf(stderr, " "); - for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { - fprintf(stderr, "%7d ", idx1); - } - fprintf(stderr, "\n"); - for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { - fprintf(stderr, "%7d: ", idx0); - for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { - if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) { - float val; - if (type == GGML_TYPE_F32) { - val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0); - } else if (type == GGML_TYPE_F16) { - val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0)); - } else { - GGML_ABORT("fatal error"); - } - fprintf(stderr, "% 7.2f ", val); - } else { - fprintf(stderr, " "); - } - } - fprintf(stderr, "\n"); - } -} - -template -static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { - VK_LOG_DEBUG("ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")"); - const size_t x_ne = m * k * batch; - const size_t y_ne = k * n * batch; - const size_t d_ne = m * n * batch; - - vk_pipeline p; - std::string shname; - if (shader_size == 0) { - if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32->a_s; - shname = "F32_ALIGNED_S"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32_f16->a_s; - shname = "F32_F16_ALIGNED_S"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_s; - shname = "F16_F32_ALIGNED_S"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_s; - shname = "F16_ALIGNED_S"; - } else { - GGML_ABORT("fatal error"); - } - } else if (shader_size == 1) { - if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32->a_m; - shname = "F32_ALIGNED_M"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32_f16->a_m; - shname = "F32_F16_ALIGNED_M"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_m; - shname = "F16_F32_ALIGNED_M"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_m; - shname = "F16_ALIGNED_M"; - } else { - GGML_ABORT("fatal error"); - } - } else if (shader_size == 2) { - if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32->a_l; - shname = "F32_ALIGNED_L"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32_f16->a_l; - shname = "F32_F16_ALIGNED_L"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_l; - shname = "F16_F32_ALIGNED_L"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_l; - shname = "F16_ALIGNED_L"; - } else { - GGML_ABORT("fatal error"); - } - } else { - GGML_ASSERT(0); - } - - const size_t kpad = ggml_vk_align_size(k, p->align); - - if (k != kpad) { - if (shader_size == 0) { - if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32->s; - shname = "F32_S"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32_f16->s; - shname = "F32_F16_S"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->s; - shname = "F16_F32_S"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->s; - shname = "F16_S"; - } - } else if (shader_size == 1) { - if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32->m; - shname = "F32_M"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32_f16->m; - shname = "F32_F16_M"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->m; - shname = "F16_F32_M"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->m; - shname = "F16_M"; - } - } else if (shader_size == 2) { - if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32->l; - shname = "F32_L"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f32_f16->l; - shname = "F32_F16_L"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->l; - shname = "F16_F32_L"; - } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->l; - shname = "F16_L"; - } - } - } - - ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it); - if (split_k > 1) { - ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it); - - if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { - // Resize buffer - if (ctx->prealloc_split_k != nullptr) { - ggml_vk_destroy_buffer(ctx->prealloc_split_k); - } - ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); - } - } - - ggml_pipeline_allocate_descriptor_sets(ctx->device); - - vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - - X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); - Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); - float* d = (float *) malloc(sizeof(float) * d_ne); - - for (size_t i = 0; i < x_ne; i++) { - if (std::is_same()) { - x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; - } else if (std::is_same()) { - x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); - } else { - GGML_ABORT("fatal error"); - } - } - for (size_t i = 0; i < y_ne; i++) { - if (std::is_same()) { - // y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; - y[i] = (i % k == i / k) ? 1.0f : 0.0f; - } else if (std::is_same()) { - // y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); - y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f); - } else { - GGML_ABORT("fatal error"); - } - } - - ggml_vk_buffer_write(d_X, 0, x, sizeof(X_TYPE) * k * m * batch); - ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); - - vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); - for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx->device, subctx); - ggml_vk_matmul( - ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), - m, n, k, - k, k, m, k*m, k*n, m*n, - split_k, batch, batch, batch, 1, 1 - ); - ggml_vk_ctx_end(subctx); - } - - auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); - ctx->device->device.resetFences({ ctx->fence }); - - auto end = std::chrono::high_resolution_clock::now(); - double time = std::chrono::duration_cast(end-begin).count() / 1000.0; - - // copy dst to host - ggml_vk_buffer_read(d_D, 0, d, sizeof(float) * d_ne); - - float * d_chk = (float *) malloc(sizeof(float) * d_ne); - - ggml_init_params iparams = { - /*.mem_size =*/ 1024*1024*1024, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - - ggml_context * ggml_ctx = ggml_init(iparams); - - ggml_type src0_type; - ggml_type src1_type; - - if (std::is_same()) { - src0_type = GGML_TYPE_F32; - } else if (std::is_same()) { - src0_type = GGML_TYPE_F16; - } else { - GGML_ABORT("fatal error"); - } - if (std::is_same()) { - src1_type = GGML_TYPE_F32; - } else if (std::is_same()) { - src1_type = GGML_TYPE_F16; - } else { - GGML_ABORT("fatal error"); - } - - ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch); - ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch); - ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); - - src0_ggml->data = x; - src1_ggml->data = y; - tensor_ggml->data = d_chk; - - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); - ggml_build_forward_expand(cgraph, tensor_ggml); - - ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - - ggml_free(ggml_ctx); - - double avg_err = 0.0; - int first_err_n = -1; - int first_err_m = -1; - int first_err_b = -1; - - for (size_t i = 0; i < m*n*batch; i++) { - double err = std::fabs(d[i] - d_chk[i]); - avg_err += err; - - if (err > 0.05f && first_err_n == -1) { - first_err_b = i / (m * n); - first_err_n = (i % (m * n)) / m; - first_err_m = (i % (m * n)) % m; - } - } - - avg_err /= m * n; - - double tflops = 2.0*m*n*k*batch*num_it / (time / 1000.0) / (1000.0*1000.0*1000.0*1000.0); - - std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl; - - if (avg_err > 0.1) { - std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; - std::cerr << "Actual result: " << std::endl << std::endl; - ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - std::cerr << std::endl; - ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n + 15, first_err_b); - std::cerr << "Expected result: " << std::endl << std::endl; - ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - if (split_k > 1) { - float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); - ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); - - std::cerr << "d_buf0: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - std::cerr << "d_buf1: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - std::cerr << "d_buf2: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - std::cerr << "d_buf3: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - free(split_k_buf); - } - } - - free(d_chk); - - ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue); - ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue); - - ggml_vk_destroy_buffer(d_X); - ggml_vk_destroy_buffer(d_Y); - ggml_vk_destroy_buffer(d_D); - - ggml_pipeline_cleanup(p); - ggml_pipeline_cleanup(ctx->device->pipeline_matmul_split_k_reduce); - - free(x); - free(y); - free(d); -} - -static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { - return; - } - i0 = std::max(i0, 5); - i1 = std::max(i1, 5); - i2 = std::max(i2, 0); - i3 = std::max(i3, 0); - fprintf(stderr, " "); - for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { - fprintf(stderr, "%7d ", idx1); - } - fprintf(stderr, "\n"); - for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { - fprintf(stderr, "%7d: ", idx0); - for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { - if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { - float val; - if (tensor->type == GGML_TYPE_F32) { - val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); - } else if (tensor->type == GGML_TYPE_F16) { - val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); - } else { - GGML_ABORT("fatal error"); - } - fprintf(stderr, "% 7.2f ", val); - } else { - fprintf(stderr, " "); - } - } - fprintf(stderr, "\n"); - } -} - -static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) { - ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr); -} - -static void ggml_vk_dequantize_data(const void * from, float * to, size_t ne, ggml_type quant) { - if (quant == GGML_TYPE_F32) { - memcpy(to, from, sizeof(float) * ne); - return; - } - - const auto * tt = ggml_get_type_traits(quant); - - ggml_to_float_t dequant_fn = tt->to_float; - - dequant_fn(from, to, ne); -} - -static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { - VK_LOG_DEBUG("ggml_vk_test_dequant(" << ne << ")"); - const size_t x_sz = sizeof(float) * ne; - const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; - const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); - float * x = (float *) malloc(x_sz); - void * qx = malloc(qx_sz); - vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); - float * x_ref = (float *) malloc(x_sz); - ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); - - for (size_t i = 0; i < ne; i++) { - x[i] = rand() / (float)RAND_MAX; - } - - vk_pipeline p = ggml_vk_get_to_fp16(ctx, quant); - - ggml_vk_quantize_data(x, qx, ne, quant); - ggml_vk_dequantize_data(qx, x_ref, ne, quant); - - ggml_pipeline_request_descriptor_sets(ctx->device, p, 1); - - ggml_pipeline_allocate_descriptor_sets(ctx->device); - - ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); - - vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); - ggml_vk_ctx_begin(ctx->device, subctx); - const std::vector pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne }; - ggml_vk_dispatch_pipeline(ctx, subctx, p, { vk_subbuffer{ qx_buf, 0, qx_sz }, vk_subbuffer{ x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); - ggml_vk_ctx_end(subctx); - - auto begin = std::chrono::high_resolution_clock::now(); - - ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); - ctx->device->device.resetFences({ ctx->fence }); - - auto end = std::chrono::high_resolution_clock::now(); - - double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_buffer_read(x_buf, 0, x_chk, x_sz_f16); - - int first_err = -1; - - double avg_err = 0.0; - for (size_t i = 0; i < ne; i++) { - double error = std::fabs(x_ref[i] - ggml_fp16_to_fp32(x_chk[i])); - avg_err += error; - - if (first_err < 0 && error > 0.05) { - first_err = i; - } - } - - avg_err /= ne; - - std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl; - - if (avg_err > 0.1) { - std::cerr << "first_error = " << first_err << std::endl; - std::cerr << "Actual result: " << std::endl << std::endl; - for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { - std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", "; - } - std::cerr << std::endl << "Expected result: " << std::endl << std::endl; - for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { - std::cerr << x_ref[i] << ", "; - } - std::cerr << std::endl; - } - - ggml_vk_destroy_buffer(x_buf); - ggml_vk_destroy_buffer(qx_buf); - - free(x); - free(qx); - free(x_ref); - free(x_chk); -} - -static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) { - VK_LOG_DEBUG("ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")"); - const size_t x_ne = m * k * batch; - const size_t y_ne = k * n * batch; - const size_t d_ne = m * n * batch; - - vk_pipeline p; - std::string shname; - if (shader_size == 0) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s; - shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S"; - } else if (shader_size == 1) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m; - shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M"; - } else if (shader_size == 2) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l; - shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L"; - } else { - GGML_ASSERT(0); - } - - const size_t kpad = ggml_vk_align_size(k, p->align); - - if (k != kpad) { - if (shader_size == 0) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s; - shname = std::string(ggml_type_name(quant)) + "_S"; - } else if (shader_size == 1) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m; - shname = std::string(ggml_type_name(quant)) + "_M"; - } else if (shader_size == 2) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l; - shname = std::string(ggml_type_name(quant)) + "_L"; - } else { - GGML_ASSERT(0); - } - } - - const size_t x_sz = sizeof(float) * x_ne; - const size_t y_sz = sizeof(float) * y_ne; - const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant); - const size_t d_sz = sizeof(float) * d_ne; - float * x = (float *) malloc(x_sz); - float * y = (float *) malloc(y_sz); - void * qx = malloc(qx_sz); - vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - float * d = (float *) malloc(d_sz); - float * d_chk = (float *) malloc(d_sz); - - for (size_t i = 0; i < x_ne; i++) { - x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; - } - - ggml_vk_quantize_data(x, qx, x_ne, quant); - - for (size_t i = 0; i < y_ne; i++) { - // y[i] = rand() / (float)RAND_MAX; - y[i] = (i % k == i / k) ? 1.0f : 0.0f; - } - - ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it); - if (split_k > 1) { - ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it); - - if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { - // Resize buffer - if (ctx->prealloc_split_k != nullptr) { - ggml_vk_destroy_buffer(ctx->prealloc_split_k); - } - ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); - } - } - - ggml_pipeline_allocate_descriptor_sets(ctx->device); - - ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); - ggml_vk_buffer_write(y_buf, 0, y, y_sz); - - vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); - for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx->device, subctx); - ggml_vk_matmul( - ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), - m, n, k, - k, k, m, k*m, k*n, m*n, - split_k, batch, batch, batch, 1, 1 - ); - ggml_vk_ctx_end(subctx); - } - - auto begin = std::chrono::high_resolution_clock::now(); - - ggml_vk_submit(subctx, ctx->fence); - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); - ctx->device->device.resetFences({ ctx->fence }); - - auto end = std::chrono::high_resolution_clock::now(); - - double time_ms = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_buffer_read(d_buf, 0, d, d_sz); - - ggml_init_params iparams = { - /*.mem_size =*/ 1024*1024*1024, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ true, - }; - - ggml_context * ggml_ctx = ggml_init(iparams); - - ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch); - ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch); - ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); - - src0_ggml->data = qx; - src1_ggml->data = y; - tensor_ggml->data = d_chk; - - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); - ggml_build_forward_expand(cgraph, tensor_ggml); - - ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - - ggml_free(ggml_ctx); - - double avg_err = 0.0; - int first_err_n = -1; - int first_err_m = -1; - int first_err_b = -1; - - for (size_t i = 0; i < m*n*batch; i++) { - double err = std::fabs(d[i] - d_chk[i]); - avg_err += err; - - if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) { - first_err_b = i / (m * n); - first_err_n = (i % (m * n)) / m; - first_err_m = (i % (m * n)) % m; - } - } - - avg_err /= m * n; - - double tflops = 2.0*m*n*k*batch*num_it / (time_ms / 1000.0) / (1000.0*1000.0*1000.0*1000.0); - - std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl; - - if (avg_err > 0.01 || std::isnan(avg_err)) { - std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; - std::cerr << "Actual result: " << std::endl << std::endl; - ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - std::cerr << std::endl; - std::cerr << "Expected result: " << std::endl << std::endl; - ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - if (split_k > 1) { - float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); - ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); - - std::cerr << "d_buf0: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - std::cerr << "d_buf1: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - std::cerr << "d_buf2: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - std::cerr << "d_buf3: " << std::endl << std::endl; - ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); - - free(split_k_buf); - } - } - - ggml_vk_destroy_buffer(qx_buf); - ggml_vk_destroy_buffer(y_buf); - ggml_vk_destroy_buffer(d_buf); - - free(x); - free(qx); - free(y); - free(d); - free(d_chk); -} -#endif - -static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { -#if defined(GGML_VULKAN_RUN_TESTS) - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_0); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_1); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q8_0); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q2_K); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q3_K); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K); - ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_IQ4_NL); - - ggml_vk_test_matmul(ctx, 512, 512, 100, 32, 100, 1, 2); - - ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 0); - ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 1); - ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 1, 2); - // ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 0); - // ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 1); - // ggml_vk_test_matmul(ctx, 128, 512, 512, 2, 100, 4, 2); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q2_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q2_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q2_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q3_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q3_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q3_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q6_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q6_K); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q6_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K); - // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K); - - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_IQ4_NL); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_IQ4_NL); - ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_IQ4_NL); - - std::cerr << std::endl; - - const std::vector vals { - 8, 8, 8, - 100, 46, 576, - 623, 111, 128, - 100, 46, 558, - 512, 1, 256, - 128, 110, 622, - 511, 511, 127, - 511, 511, 7, - 511, 511, 17, - 49, 49, 128, - 128, 49, 49, - 4096, 49, 4096, - 11008, 49, 4096, - 4096, 49, 11008, - 32000, 49, 4096, - 512, 512, 128, - 128, 512, 512, - 4096, 512, 4096, - 11008, 512, 4096, - 4096, 512, 11008, - 32000, 512, 4096, - }; - const size_t num_it = 1; - for (size_t i = 0; i < vals.size(); i += 3) { - ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); - ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); - ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); - // ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); - // ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); - // ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); - std::cerr << std::endl; - } - - GGML_ABORT("fatal error"); -#endif - - if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) { - VK_LOG_MEMORY("ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << ")"); - // Resize buffer - if (ctx->prealloc_x != nullptr) { - ggml_vk_destroy_buffer(ctx->prealloc_x); - } - ctx->prealloc_x = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_x); - } - if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) { - VK_LOG_MEMORY("ggml_vk_preallocate_buffers(y_size: " << ctx->prealloc_size_y << ")"); - // Resize buffer - if (ctx->prealloc_y != nullptr) { - ggml_vk_destroy_buffer(ctx->prealloc_y); - } - ctx->prealloc_y = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_y); - } - if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) { - VK_LOG_MEMORY("ggml_vk_preallocate_buffers(split_k_size: " << ctx->prealloc_size_split_k << ")"); - // Resize buffer - if (ctx->prealloc_split_k != nullptr) { - ggml_vk_destroy_buffer(ctx->prealloc_split_k); - } - ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_split_k); - } -} - -static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_tensor* tensor, int tensor_idx, bool use_fence); - -// Returns true if node has enqueued work into the queue, false otherwise -// If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution. -static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool dryrun, bool last_node, bool submit){ - if (ggml_is_empty(node) || !node->buffer) { - return false; - } - - VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")"); - ctx->semaphore_idx = 0; - - const ggml_tensor * src0 = node->src[0]; - const ggml_tensor * src1 = node->src[1]; - const ggml_tensor * src2 = node->src[2]; - - switch (node->op) { - // Return on empty ops to avoid generating a compute_ctx and setting exit_tensor - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NONE: - return false; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_TANH: - break; - default: - return false; - } - break; - case GGML_OP_REPEAT: - case GGML_OP_GET_ROWS: - case GGML_OP_ADD: - case GGML_OP_ACC: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_CPY: - case GGML_OP_CONT: - case GGML_OP_DUP: - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - case GGML_OP_ARGSORT: - case GGML_OP_SUM_ROWS: - case GGML_OP_IM2COL: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_POOL_2D: - case GGML_OP_LEAKY_RELU: - break; - default: - std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl; - GGML_ABORT("fatal error"); - return false; - } - - vk_context compute_ctx; - - if (!dryrun) { - if (ctx->compute_ctx.expired()) { - compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); - } - } - - switch (node->op) { - case GGML_OP_REPEAT: - ggml_vk_repeat(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_ACC: - ggml_vk_acc(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_GET_ROWS: - ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_ADD: - ggml_vk_add(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_MUL: - ggml_vk_mul(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_DIV: - ggml_vk_div(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_CONCAT: - ggml_vk_concat(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_UPSCALE: - ggml_vk_upscale(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_SCALE: - ggml_vk_scale(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_SQR: - ggml_vk_sqr(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_SIN: - ggml_vk_sin(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_COS: - ggml_vk_cos(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_CLAMP: - ggml_vk_clamp(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_PAD: - ggml_vk_pad(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_CPY: - case GGML_OP_CONT: - case GGML_OP_DUP: - ggml_vk_cpy(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_NORM: - ggml_vk_norm(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_GROUP_NORM: - ggml_vk_group_norm(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_RMS_NORM: - ggml_vk_rms_norm(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_TANH: - ggml_vk_unary(ctx, compute_ctx, src0, node, dryrun); - break; - default: - return false; - } - break; - case GGML_OP_DIAG_MASK_INF: - ggml_vk_diag_mask_inf(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_ROPE: - ggml_vk_rope(ctx, compute_ctx, src0, src1, src2, node, dryrun); - - break; - case GGML_OP_ARGSORT: - ggml_vk_argsort(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_SUM_ROWS: - ggml_vk_sum_rows(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_IM2COL: - ggml_vk_im2col(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_TIMESTEP_EMBEDDING: - ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_POOL_2D: - ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_LEAKY_RELU: - ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun); - - break; - case GGML_OP_MUL_MAT: - ggml_vk_mul_mat(ctx, compute_ctx, src0, src1, node, dryrun); - - break; - case GGML_OP_MUL_MAT_ID: - ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node, dryrun); - - break; - default: - return false; - } - - if (dryrun) { - return false; - } - - ctx->tensor_ctxs[node_idx] = compute_ctx; - -#if defined(GGML_VULKAN_CHECK_RESULTS) || defined(GGML_VULKAN_PERF) - // Force context reset on each node so that each tensor ends up in its own context - // and can be run and compared to its CPU equivalent separately - last_node = true; -#endif - - if (submit || last_node) { - ggml_vk_ctx_end(compute_ctx); - - // TODO probably it'd be better to pass a exit_node flag to ggml_vk_compute_forward - if (last_node) { - compute_ctx->exit_tensor_idx = node_idx_begin; - } - else { - compute_ctx->exit_tensor_idx = -1; - } - - ctx->compute_ctx.reset(); - - bool ok = ggml_vk_compute_forward(ctx, node_begin, node_idx_begin, false); - if (!ok) { - if (node->op == GGML_OP_UNARY) { - std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast(node->op_params[0])) << ")" << std::endl; - } - else { - std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl; - } - } - - } - return true; -} - -static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx, bool use_fence = true){ - ggml_backend_buffer * buf = nullptr; - - switch (tensor->op) { - case GGML_OP_ADD: - case GGML_OP_ACC: - case GGML_OP_GET_ROWS: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_CPY: - case GGML_OP_CONT: - case GGML_OP_DUP: - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NONE: - case GGML_OP_ARGSORT: - case GGML_OP_SUM_ROWS: - case GGML_OP_IM2COL: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_POOL_2D: - case GGML_OP_LEAKY_RELU: - case GGML_OP_REPEAT: - buf = tensor->buffer; - - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_TANH: - buf = tensor->buffer; - break; - default: - return false; - } - break; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - buf = tensor->buffer; - - break; - default: - return false; - } - - if (buf == nullptr) { - return false; - } - - VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")"); - - vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock(); - - // always wait for the GPU work to be done for the last submit - if (tensor_idx == subctx->exit_tensor_idx) { - use_fence = true; - } - - // Only run if ctx hasn't been submitted yet - if (!subctx->seqs.empty()) { -#ifdef GGML_VULKAN_CHECK_RESULTS - ggml_vk_check_results_0(tensor); - use_fence = true; -#endif - - // Do staging buffer copies - for (auto& cpy : subctx->in_memcpys) { - memcpy(cpy.dst, cpy.src, cpy.n); - } - - ggml_vk_submit(subctx, use_fence ? ctx->fence : vk::Fence{}); - - if (use_fence) { - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - - ctx->device->device.resetFences({ ctx->fence }); - } -#ifdef GGML_VULKAN_CHECK_RESULTS - ggml_vk_check_results_1(tensor); -#endif - } - - if (tensor_idx == subctx->exit_tensor_idx) { - // Do staging buffer copies - for (auto& cpy : subctx->out_memcpys) { - memcpy(cpy.dst, cpy.src, cpy.n); - } - subctx->in_memcpys.clear(); - subctx->out_memcpys.clear(); - } - - return true; -} - -// Clean up after graph processing is done -static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { - VK_LOG_DEBUG("ggml_vk_graph_cleanup()"); - for (auto& buffer : ctx->gc.temp_buffers) { - ggml_vk_pool_free(ctx, buffer); - } - ctx->gc.temp_buffers.clear(); - - for (auto& dsr : ctx->device->pipeline_descriptor_set_requirements) { - vk_pipeline_ref plr = ctx->device->pipelines[dsr.first]; - - if (plr.expired()) { - continue; - } - - vk_pipeline pl = plr.lock(); - ggml_pipeline_cleanup(pl); - } - - ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue); - ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue); - - for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { - ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s }); - } - ctx->gc.semaphores.clear(); - - for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { - ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); - } - ctx->gc.tl_semaphores.clear(); - ctx->semaphore_idx = 0; - - ctx->event_idx = 0; - - for (auto& event : ctx->gc.events) { - ctx->device->device.resetEvent(event); - } - - ctx->tensor_ctxs.clear(); - ctx->gc.contexts.clear(); - ctx->device->pipeline_descriptor_set_requirements.clear(); -} - -// Clean up on backend free -static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { - VK_LOG_DEBUG("ggml_vk_cleanup(" << ctx->name << ")"); - ggml_vk_graph_cleanup(ctx); - - ggml_vk_destroy_buffer(ctx->prealloc_x); - ggml_vk_destroy_buffer(ctx->prealloc_y); - ggml_vk_destroy_buffer(ctx->prealloc_split_k); - - for (auto& buffer : ctx->buffer_pool) { - ggml_vk_destroy_buffer(buffer); - } - - ctx->prealloc_size_x = 0; - ctx->prealloc_size_y = 0; - ctx->prealloc_size_split_k = 0; - - for (auto& event : ctx->gc.events) { - ctx->device->device.destroyEvent(event); - } - ctx->gc.events.clear(); - - ctx->device->device.destroyFence(ctx->fence); -} - -static int ggml_vk_get_device_count() { - ggml_vk_instance_init(); - - return vk_instance.device_indices.size(); -} - -static void ggml_vk_get_device_description(int device, char * description, size_t description_size) { - ggml_vk_instance_init(); - - std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); - - vk::PhysicalDeviceProperties props; - devices[device].getProperties(&props); - - snprintf(description, description_size, "%s", props.deviceName.data()); -} - -// backend interface - -#define UNUSED GGML_UNUSED - -// device backend - -static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { - return buffer->buft->iface.get_name == ggml_backend_vk_buffer_type_name; -} - -static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { - VK_LOG_MEMORY("ggml_backend_vk_buffer_free_buffer()"); - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - ggml_vk_destroy_buffer(ctx->dev_buffer); - delete ctx; -} - -static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) { - return vk_ptr_base; - - UNUSED(buffer); -} - -static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - VK_LOG_DEBUG("ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")"); - if (tensor->view_src != nullptr) { - GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); - } -} - -static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; - vk_buffer buf = buf_ctx->dev_buffer; - - ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); -} - -static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; - - vk_buffer buf = buf_ctx->dev_buffer; - - ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); -} - -static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { - if (ggml_backend_buffer_is_vk(src->buffer)) { - ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - - vk_buffer src_buf = src_buf_ctx->dev_buffer; - vk_buffer dst_buf = dst_buf_ctx->dev_buffer; - - ggml_vk_buffer_copy(dst_buf, vk_tensor_offset(dst) + dst->view_offs, src_buf, vk_tensor_offset(src) + src->view_offs, ggml_nbytes(src)); - - return true; - } - return false; - - UNUSED(buffer); -} - -static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - - ggml_vk_buffer_memset(ctx->dev_buffer, 0, value, buffer->size); -} - -static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { - /* .free_buffer = */ ggml_backend_vk_buffer_free_buffer, - /* .get_base = */ ggml_backend_vk_buffer_get_base, - /* .init_tensor = */ ggml_backend_vk_buffer_init_tensor, - /* .memset_tensor = */ NULL, - /* .set_tensor = */ ggml_backend_vk_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_vk_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_vk_buffer_cpy_tensor, - /* .clear = */ ggml_backend_vk_buffer_clear, - /* .reset = */ NULL, -}; - -// vk buffer type -static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) { - ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context; - - return ctx->name.c_str(); -} - -static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - VK_LOG_MEMORY("ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")"); - ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - - vk_buffer dev_buffer = nullptr; - try { - dev_buffer = ggml_vk_create_buffer_device(ctx->device, size); - } catch (const vk::SystemError& e) { - return nullptr; - } - - ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->device, std::move(dev_buffer), ctx->name); - - return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size); -} - -static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - return ctx->device->properties.limits.minStorageBufferOffsetAlignment; -} - -static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - return ctx->device->max_memory_allocation_size; -} - -static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - return ggml_nbytes(tensor); - - UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) { - ggml_vk_instance_init(); - - VK_LOG_DEBUG("ggml_backend_vk_buffer_type(" << dev_num << ")"); - - vk_device dev = ggml_vk_get_device(dev_num); - - return &dev->buffer_type; -} - -// host buffer type - -static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return GGML_VK_NAME "_Host"; - - UNUSED(buft); -} - -static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffer) { - return GGML_VK_NAME "_Host"; - - UNUSED(buffer); -} - -static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()"); - ggml_vk_host_free(vk_instance.devices[0], buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - VK_LOG_MEMORY("ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")"); - - size += 32; // Behave like the CPU buffer type - void * ptr = nullptr; - try { - ptr = ggml_vk_host_malloc(vk_instance.devices[0], size); - } catch (vk::SystemError& e) { - std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl; - std::cerr << "ggml_vulkan: " << e.what() << std::endl; - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer; - - return buffer; - - UNUSED(buft); -} - -static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_instance.devices[0]->properties.limits.minMemoryMapAlignment; - - UNUSED(buft); -} - -// Should be changed to return device-specific host buffer type -// but that probably requires changes in llama.cpp -ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_vk_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_vk_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), 0), - /* .context = */ nullptr, - }; - - // Make sure device 0 is initialized - ggml_vk_instance_init(); - ggml_vk_get_device(0); - - return &ggml_backend_vk_buffer_type_host; -} - - -// backend - -static const char * ggml_backend_vk_name(ggml_backend_t backend) { - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - - return ctx->name.c_str(); -} - -static void ggml_backend_vk_free(ggml_backend_t backend) { - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - VK_LOG_DEBUG("ggml_backend_vk_free(" << ctx->name << ")"); - - ggml_vk_cleanup(ctx); - - delete ctx; - delete backend; -} - -static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - - return &ctx->device->buffer_type; -} - -static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")"); - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; - - vk_context transfer_ctx; - - if (ctx->transfer_ctx.expired()) { - // Initialize new transfer context - transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); - ctx->transfer_ctx = transfer_ctx; - ggml_vk_ctx_begin(ctx->device, transfer_ctx); - } else { - transfer_ctx = ctx->transfer_ctx.lock(); - } - - vk_buffer buf = buf_ctx->dev_buffer; - - ggml_vk_buffer_write_async(transfer_ctx, buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); -} - -static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")"); - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; - - vk_context transfer_ctx; - - if (ctx->transfer_ctx.expired()) { - // Initialize new transfer context - transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); - ctx->transfer_ctx = transfer_ctx; - ggml_vk_ctx_begin(ctx->device, transfer_ctx); - } else { - transfer_ctx = ctx->transfer_ctx.lock(); - } - - vk_buffer buf = buf_ctx->dev_buffer; - - ggml_vk_buffer_read_async(transfer_ctx, buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); -} - -static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { - VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async()"); - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - if ((dst->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { - ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; - ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; - - vk_context transfer_ctx; - - if (ctx->transfer_ctx.expired()) { - // Initialize new transfer context - transfer_ctx = ggml_vk_create_context(ctx, ctx->device->transfer_queue); - ctx->transfer_ctx = transfer_ctx; - ggml_vk_ctx_begin(ctx->device, transfer_ctx); - } else { - transfer_ctx = ctx->transfer_ctx.lock(); - } - - vk_buffer src_buf = src_buf_ctx->dev_buffer; - vk_buffer dst_buf = dst_buf_ctx->dev_buffer; - - ggml_vk_buffer_copy_async(transfer_ctx, dst_buf, vk_tensor_offset(dst) + dst->view_offs, src_buf, vk_tensor_offset(src) + src->view_offs, ggml_nbytes(src)); - return true; - } - - return false; -} - -static void ggml_backend_vk_synchronize(ggml_backend_t backend) { - VK_LOG_DEBUG("ggml_backend_vk_synchronize()"); - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - if(ctx->transfer_ctx.expired()) { - return; - } - - vk_context transfer_ctx = ctx->transfer_ctx.lock(); - - ggml_vk_ctx_end(transfer_ctx); - - for (auto& cpy : transfer_ctx->in_memcpys) { - memcpy(cpy.dst, cpy.src, cpy.n); - } - - ggml_vk_submit(transfer_ctx, ctx->fence); - VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - ctx->device->device.resetFences({ ctx->fence }); - - for (auto& cpy : transfer_ctx->out_memcpys) { - memcpy(cpy.dst, cpy.src, cpy.n); - } - - ctx->transfer_ctx.reset(); -} - -static bool ggml_vk_is_empty(ggml_tensor * node) { - return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE; -} - -static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)"); - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - - for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false); - } - ggml_vk_preallocate_buffers(ctx); - ggml_pipeline_allocate_descriptor_sets(ctx->device); - - int last_node = cgraph->n_nodes - 1; - - // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly - while (last_node > 0 && ggml_vk_is_empty(cgraph->nodes[last_node])) { - last_node -= 1; - } - - // Reserve tensor context space for all nodes - ctx->tensor_ctxs.resize(cgraph->n_nodes); - - bool first_node_in_batch = true; // true if next node will be first node in a batch - int submit_node_idx = 0; // index to first node in a batch - - // submit work every submit_count node to overlap CPU cmdbuffer generation with GPU execution - constexpr int submit_count = 100; - int submitted_nodes = 0; - for (int i = 0; i < cgraph->n_nodes; i++) { - if (first_node_in_batch) { - submit_node_idx = i; - } - - bool submit = (submitted_nodes >= submit_count) || (i == last_node); - - - bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, submit); - - if (enqueued) { - ++submitted_nodes; - -#ifndef GGML_VULKAN_CHECK_RESULTS - if (first_node_in_batch) { - first_node_in_batch = false; - } -#endif - } - - if (submit) { - first_node_in_batch = true; - submitted_nodes = 0; - } - } - -#ifdef GGML_VULKAN_PERF - ctx->device->perf_logger->print_timings(); -#endif - - ggml_vk_graph_cleanup(ctx); - - return GGML_STATUS_SUCCESS; - - UNUSED(backend); -} - -// TODO: enable async and synchronize -static ggml_backend_i ggml_backend_vk_interface = { - /* .get_name = */ ggml_backend_vk_name, - /* .free = */ ggml_backend_vk_free, - /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, - /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, - /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, - /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_vk_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_vk_guid() { - static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; - return &guid; -} - -ggml_backend_t ggml_backend_vk_init(size_t dev_num) { - VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); - - ggml_backend_vk_context * ctx = new ggml_backend_vk_context; - ggml_vk_init(ctx, dev_num); - - ggml_backend_t vk_backend = new ggml_backend { - /* .guid = */ ggml_backend_vk_guid(), - /* .interface = */ ggml_backend_vk_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num), - /* .context = */ ctx, - }; - - return vk_backend; -} - -bool ggml_backend_is_vk(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); -} - -int ggml_backend_vk_get_device_count() { - return ggml_vk_get_device_count(); -} - -void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { - GGML_ASSERT(device < (int) vk_instance.device_indices.size()); - int dev_idx = vk_instance.device_indices[device]; - ggml_vk_get_device_description(dev_idx, description, description_size); -} - -void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { - GGML_ASSERT(device < (int) vk_instance.device_indices.size()); - - vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; - - vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); - - for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { - if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { - *total = heap.size; - *free = heap.size; - break; - } - } -} - -////////////////////////// - -struct ggml_backend_vk_device_context { - size_t device; - std::string name; - std::string description; -}; - -static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - return ctx->name.c_str(); -} - -static const char * ggml_backend_vk_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - return ctx->description.c_str(); -} - -static void ggml_backend_vk_device_get_memory(ggml_backend_dev_t device, size_t * free, size_t * total) { - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)device->context; - ggml_backend_vk_get_device_memory(ctx->device, free, total); -} - -static ggml_backend_buffer_type_t ggml_backend_vk_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - return ggml_backend_vk_buffer_type(ctx->device); -} - -static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(ggml_backend_dev_t dev) { - UNUSED(dev); - return ggml_backend_vk_host_buffer_type(); -} - -static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) { - UNUSED(dev); - return GGML_BACKEND_DEVICE_TYPE_GPU; -} - -static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_vk_device_get_name(dev); - props->description = ggml_backend_vk_device_get_description(dev); - props->type = ggml_backend_vk_device_get_type(dev); - ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* .async = */ false, - /* .host_buffer = */ true, - /* .buffer_from_host_ptr = */ false, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_vk_device_init(ggml_backend_dev_t dev, const char * params) { - UNUSED(params); - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - return ggml_backend_vk_init(ctx->device); -} - -static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - switch (op->op) { - case GGML_OP_UNARY: - switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_TANH: - return ggml_is_contiguous(op->src[0]); - default: - return false; - } - break; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - { - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ4_NL: - break; - default: - return false; - } - struct ggml_tensor * a; - struct ggml_tensor * b; - if (op->op == GGML_OP_MUL_MAT) { - a = op->src[0]; - b = op->src[1]; - } else { - a = op->src[2]; - b = op->src[1]; - } - if (a->ne[3] != b->ne[3]) { - return false; - } - if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) || - !(ggml_vk_dim01_contiguous(op->src[1]) || op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)) { - return false; - } - - return true; - } break; - case GGML_OP_GET_ROWS: - { - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_IQ4_NL: - return true; - default: - return false; - } - } break; - case GGML_OP_CONT: - case GGML_OP_CPY: - case GGML_OP_DUP: - { - ggml_type src0_type = op->src[0]->type; - ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type; - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { - return true; - } - if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { - return true; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return true; - } - return false; - } break; - case GGML_OP_REPEAT: - return ggml_type_size(op->type) == sizeof(float) && ggml_type_size(op->src[0]->type) == sizeof(float); - case GGML_OP_ROPE: - return ggml_is_contiguous(op->src[0]); - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_ADD: - case GGML_OP_ACC: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_ARGSORT: - case GGML_OP_SUM_ROWS: - case GGML_OP_IM2COL: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_POOL_2D: - case GGML_OP_LEAKY_RELU: - return true; - default: - return false; - } - - UNUSED(dev); -} - -static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { - return false; - } - - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; - - return buft_ctx->device->idx == ctx->device; -} - -static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - const int min_batch_size = 32; - - return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || - (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); - - UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_vk_device_i = { - /* .get_name = */ ggml_backend_vk_device_get_name, - /* .get_description = */ ggml_backend_vk_device_get_description, - /* .get_memory = */ ggml_backend_vk_device_get_memory, - /* .get_type = */ ggml_backend_vk_device_get_type, - /* .get_props = */ ggml_backend_vk_device_get_props, - /* .init_backend = */ ggml_backend_vk_device_init, - /* .get_buffer_type = */ ggml_backend_vk_device_get_buffer_type, - /* .get_host_buffer_type = */ ggml_backend_vk_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_vk_device_supports_op, - /* .supports_buft = */ ggml_backend_vk_device_supports_buft, - /* .offload_op = */ ggml_backend_vk_device_offload_op, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -static const char * ggml_backend_vk_reg_get_name(ggml_backend_reg_t reg) { - UNUSED(reg); - return GGML_VK_NAME; -} - -static size_t ggml_backend_vk_reg_get_device_count(ggml_backend_reg_t reg) { - UNUSED(reg); - return ggml_backend_vk_get_device_count(); -} - -static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, size_t device) { - static std::vector devices; - - static bool initialized = false; - - { - static std::mutex mutex; - std::lock_guard lock(mutex); - if (!initialized) { - for (int i = 0; i < ggml_backend_vk_get_device_count(); i++) { - ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context; - char desc[256]; - ggml_backend_vk_get_device_description(i, desc, sizeof(desc)); - ctx->device = i; - ctx->name = GGML_VK_NAME + std::to_string(i); - ctx->description = desc; - devices.push_back(new ggml_backend_device { - /* .iface = */ ggml_backend_vk_device_i, - /* .reg = */ reg, - /* .context = */ ctx, - }); - } - initialized = true; - } - } - - GGML_ASSERT(device < devices.size()); - return devices[device]; -} - -static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = { - /* .get_name = */ ggml_backend_vk_reg_get_name, - /* .get_device_count = */ ggml_backend_vk_reg_get_device_count, - /* .get_device = */ ggml_backend_vk_reg_get_device, - /* .get_proc_address = */ NULL, -}; - -ggml_backend_reg_t ggml_backend_vk_reg() { - static ggml_backend_reg reg = { - /* .iface = */ ggml_backend_vk_reg_i, - /* .context = */ nullptr, - }; - - return ® -} - -// Extension availability -static bool ggml_vk_instance_validation_ext_available(const std::vector& instance_extensions) { -#ifdef GGML_VULKAN_VALIDATE - bool portability_enumeration_ext = false; - // Check for portability enumeration extension for MoltenVK support - for (const auto& properties : instance_extensions) { - if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { - return true; - } - } - if (!portability_enumeration_ext) { - std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; - } -#endif - return false; - - UNUSED(instance_extensions); -} -static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions) { -#ifdef __APPLE__ - bool portability_enumeration_ext = false; - // Check for portability enumeration extension for MoltenVK support - for (const auto& properties : instance_extensions) { - if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { - return true; - } - } - if (!portability_enumeration_ext) { - std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; - } -#endif - return false; - - UNUSED(instance_extensions); -} - -// checks - -#ifdef GGML_VULKAN_CHECK_RESULTS -static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector& done, int level = 0) { - if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) { - return; - } - for (int j = 0; j < level; j++) { - std::cerr << " "; - } - std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl; - - done.push_back(tensor); - - for (int i = 0; i < GGML_MAX_SRC; i++) { - if (tensor->src[i] != nullptr) { - ggml_vk_print_graph_origin(tensor->src[i], done, level + 1); - } - } -} - -static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) { - if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) { - return; - } - i0 = std::max(i0, 5); - i1 = std::max(i1, 5); - i2 = std::max(i2, 0); - i3 = std::max(i3, 0); - fprintf(stderr, " "); - for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { - fprintf(stderr, "%7d ", idx1); - } - fprintf(stderr, "\n"); - for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { - fprintf(stderr, "%7d: ", idx0); - for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { - if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { - float val; - if (tensor->type == GGML_TYPE_F32) { - val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); - } else if (tensor->type == GGML_TYPE_F16) { - val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); - } else if (tensor->type == GGML_TYPE_I32) { - val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); - } else { - GGML_ABORT("fatal error"); - } - fprintf(stderr, "% 7.2f ", val); - } else { - fprintf(stderr, " "); - } - } - fprintf(stderr, "\n"); - } -} - -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { - void * tensor_data = tensor->data; - - const bool is_gpu = tensor->buffer != nullptr && ggml_backend_buffer_is_vk(tensor->buffer); - - if (is_gpu) { - const size_t tensor_size = ggml_nbytes(tensor); - tensor_data = malloc(tensor_size); - - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; - - vk_buffer buffer_gpu = buf_ctx->dev_buffer; - ggml_vk_buffer_read(buffer_gpu, vk_tensor_offset(tensor) + tensor->view_offs, tensor_data, tensor_size); - } - - std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; - std::cerr << "tensor=" << tensor << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; - if (tensor->src[0] != nullptr) { - std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; - } - if (tensor->src[1] != nullptr) { - std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; - } - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(tensor, done); - - if (is_gpu) { - free(tensor_data); - } -} - -void * comp_result; -size_t comp_size; -size_t comp_nb[GGML_MAX_DIMS]; -size_t check_counter = 0; -static void ggml_vk_check_results_0(ggml_tensor * tensor) { - if (tensor->op == GGML_OP_TRANSPOSE) { - return; - } - - check_counter++; - if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { - return; - } - - VK_LOG_DEBUG("ggml_vk_check_results_0(" << tensor->name << ")"); - - ggml_tensor * src0 = tensor->src[0]; - ggml_tensor * src1 = tensor->src[1]; - ggml_tensor * src2 = tensor->src[2]; - - struct ggml_init_params iparams = { - /*.mem_size =*/ 2ul*1024ul*1024ul*1024ul, - /*.mem_buffer =*/ NULL, - /*.no_alloc =*/ false, - }; - - struct ggml_context * ggml_ctx = ggml_init(iparams); - - struct ggml_tensor * src0_clone = nullptr; - struct ggml_tensor * src1_clone = nullptr; - struct ggml_tensor * src2_clone = nullptr; - struct ggml_tensor * tensor_clone = nullptr; - - size_t src0_size; - size_t src1_size; - size_t src2_size; - - void * src0_buffer = nullptr; - void * src1_buffer = nullptr; - void * src2_buffer = nullptr; - - if (src0 != nullptr) { - src0_clone = ggml_dup_tensor(ggml_ctx, src0); - - src0_size = ggml_nbytes(src0); - - src0_buffer = malloc(src0_size); - src0_clone->data = src0_buffer; - if (ggml_backend_buffer_is_host(src0->buffer)) { - memcpy(src0_clone->data, src0->data, src0_size); - memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (ggml_backend_buffer_is_vk(src0->buffer)) { - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; - vk_buffer& buffer_gpu = buf_ctx->dev_buffer; - uint64_t offset = vk_tensor_offset(src0) + src0->view_offs; - if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) { - for (int i3 = 0; i3 < src0->ne[3]; i3++) { - for (int i2 = 0; i2 < src0->ne[2]; i2++) { - const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); - } - } - - src0_clone->nb[0] = src0->nb[0]; - src0_clone->nb[1] = src0->nb[1]; - for (int i = 2; i < GGML_MAX_DIMS; i++) { - src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; - } - } else { - if (offset + src0_size >= buffer_gpu->size) { - src0_size = buffer_gpu->size - offset; - } - ggml_vk_buffer_read(buffer_gpu, offset, src0_clone->data, src0_size); - memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); - } - } else { - GGML_ABORT("fatal error"); - } - - if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src0, "src0"); - } - } - if (src1 != nullptr) { - src1_clone = ggml_dup_tensor(ggml_ctx, src1); - - src1_size = ggml_nbytes(src1); - - src1_buffer = malloc(src1_size); - src1_clone->data = src1_buffer; - if (ggml_backend_buffer_is_host(src1->buffer)) { - memcpy(src1_clone->data, src1->data, src1_size); - memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (ggml_backend_buffer_is_vk(src1->buffer)) { - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; - vk_buffer& buffer_gpu = buf_ctx->dev_buffer; - uint64_t offset = vk_tensor_offset(src1) + src1->view_offs; - if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) { - for (int i3 = 0; i3 < src1->ne[3]; i3++) { - for (int i2 = 0; i2 < src1->ne[2]; i2++) { - const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); - } - } - - src1_clone->nb[0] = src1->nb[0]; - src1_clone->nb[1] = src1->nb[1]; - for (int i = 2; i < GGML_MAX_DIMS; i++) { - src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; - } - } else { - if (offset + src1_size >= buffer_gpu->size) { - src1_size = buffer_gpu->size - offset; - } - ggml_vk_buffer_read(buffer_gpu, offset, src1_clone->data, src1_size); - memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); - } - } else { - GGML_ABORT("fatal error"); - } - - if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src1, "src1"); - } - } - if (src2 != nullptr) { - src2_clone = ggml_dup_tensor(ggml_ctx, src2); - - src2_size = ggml_nbytes(src2); - - src2_buffer = malloc(src2_size); - src2_clone->data = src2_buffer; - if (ggml_backend_buffer_is_host(src2->buffer)) { - memcpy(src2_clone->data, src2->data, src2_size); - memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (ggml_backend_buffer_is_vk(src2->buffer)) { - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)src2->buffer->context; - vk_buffer& buffer_gpu = buf_ctx->dev_buffer; - uint64_t offset = vk_tensor_offset(src2) + src2->view_offs; - if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { - for (int i3 = 0; i3 < src2->ne[3]; i3++) { - for (int i2 = 0; i2 < src2->ne[2]; i2++) { - const int idx = i3*src2->ne[2] + i2; - ggml_vk_buffer_read(buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); - } - } - - src2_clone->nb[0] = src2->nb[0]; - src2_clone->nb[1] = src2->nb[1]; - for (int i = 2; i < GGML_MAX_DIMS; i++) { - src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; - } - } else { - if (offset + src2_size >= buffer_gpu->size) { - src2_size = buffer_gpu->size - offset; - } - ggml_vk_buffer_read(buffer_gpu, offset, src2_clone->data, src2_size); - memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); - } - } else { - GGML_ABORT("fatal error"); - } - - if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src2, "src2"); - } - } - - if (tensor->op == GGML_OP_MUL_MAT) { - tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); - } else if (tensor->op == GGML_OP_MUL_MAT_ID) { - tensor_clone = ggml_mul_mat_id(ggml_ctx, src0_clone, src1_clone, src2_clone); - } else if (tensor->op == GGML_OP_MUL) { - tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone); - } else if (tensor->op == GGML_OP_DIV) { - tensor_clone = ggml_div(ggml_ctx, src0_clone, src1_clone); - } else if (tensor->op == GGML_OP_CONCAT) { - tensor_clone = ggml_concat(ggml_ctx, src0_clone, src1_clone, *(int *)tensor->op_params); - } else if (tensor->op == GGML_OP_UPSCALE) { - tensor_clone = ggml_upscale_ext(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); - } else if (tensor->op == GGML_OP_SCALE) { - tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]); - } else if (tensor->op == GGML_OP_SQR) { - tensor_clone = ggml_sqr(ggml_ctx, src0_clone); - } else if (tensor->op == GGML_OP_SIN) { - tensor_clone = ggml_sin(ggml_ctx, src0_clone); - } else if (tensor->op == GGML_OP_COS) { - tensor_clone = ggml_cos(ggml_ctx, src0_clone); - } else if (tensor->op == GGML_OP_CLAMP) { - tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); - } else if (tensor->op == GGML_OP_PAD) { - tensor_clone = ggml_pad(ggml_ctx, src0_clone, tensor->ne[0] - src0_clone->ne[0], tensor->ne[1] - src0_clone->ne[1], tensor->ne[2] - src0_clone->ne[2], tensor->ne[3] - src0_clone->ne[3]); - } else if (tensor->op == GGML_OP_REPEAT) { - tensor_clone = ggml_repeat(ggml_ctx, src0_clone, tensor); - } else if (tensor->op == GGML_OP_ADD) { - tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone); - } else if (tensor->op == GGML_OP_ACC) { - tensor_clone = ggml_acc(ggml_ctx, src0_clone, src1_clone, tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3]); - } else if (tensor->op == GGML_OP_NORM) { - tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); - } else if (tensor->op == GGML_OP_GROUP_NORM) { - tensor_clone = ggml_group_norm(ggml_ctx, src0_clone, *(int *)tensor->op_params, ((float *)tensor->op_params)[1]); - } else if (tensor->op == GGML_OP_RMS_NORM) { - tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); - } else if (tensor->op == GGML_OP_SOFT_MAX) { - if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); - } else { - tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); - } - } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { - tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(int *)tensor->op_params); - } else if (tensor->op == GGML_OP_ROPE) { - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - //const int n_ctx_ggml = ((int32_t *) tensor->op_params)[3]; - const int n_ctx_orig_ggml = ((int32_t *) tensor->op_params)[4]; - const float freq_base = ((float *) tensor->op_params)[5]; - const float freq_scale = ((float *) tensor->op_params)[6]; - const float ext_factor = ((float *) tensor->op_params)[7]; - const float attn_factor = ((float *) tensor->op_params)[8]; - const float beta_fast = ((float *) tensor->op_params)[9]; - const float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); - } else if (tensor->op == GGML_OP_UNARY) { - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_SILU: - tensor_clone = ggml_silu(ggml_ctx, src0_clone); - break; - case GGML_UNARY_OP_GELU: - tensor_clone = ggml_gelu(ggml_ctx, src0_clone); - break; - case GGML_UNARY_OP_GELU_QUICK: - tensor_clone = ggml_gelu_quick(ggml_ctx, src0_clone); - break; - case GGML_UNARY_OP_RELU: - tensor_clone = ggml_relu(ggml_ctx, src0_clone); - break; - case GGML_UNARY_OP_TANH: - tensor_clone = ggml_tanh(ggml_ctx, src0_clone); - break; - default: - std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; - GGML_ABORT("fatal error"); - } - } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { - if (src1 == nullptr) { - tensor_clone = ggml_dup(ggml_ctx, src0_clone); - tensor_clone->type = tensor->type; - } else { - tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone); - } - } else if (tensor->op == GGML_OP_CONT) { - tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); - } else if (tensor->op == GGML_OP_RESHAPE) { - tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); - } else if (tensor->op == GGML_OP_VIEW) { - tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); - } else if (tensor->op == GGML_OP_PERMUTE) { - int32_t * params = (int32_t *)tensor->op_params; - tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]); - } else if (tensor->op == GGML_OP_TRANSPOSE) { - tensor_clone = ggml_transpose(ggml_ctx, src0_clone); - } else if (tensor->op == GGML_OP_GET_ROWS) { - tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone); - } else if (tensor->op == GGML_OP_ARGSORT) { - tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params); - } else if (tensor->op == GGML_OP_SUM_ROWS) { - tensor_clone = ggml_sum_rows(ggml_ctx, src0_clone); - } else if (tensor->op == GGML_OP_IM2COL) { - const int32_t s0 = tensor->op_params[0]; - const int32_t s1 = tensor->op_params[1]; - const int32_t p0 = tensor->op_params[2]; - const int32_t p1 = tensor->op_params[3]; - const int32_t d0 = tensor->op_params[4]; - const int32_t d1 = tensor->op_params[5]; - - const bool is_2D = tensor->op_params[6] == 1; - tensor_clone = ggml_im2col(ggml_ctx, src0_clone, src1_clone, s0, s1, p0, p1, d0, d1, is_2D, tensor->type); - } else if (tensor->op == GGML_OP_TIMESTEP_EMBEDDING) { - const int32_t dim = tensor->op_params[0]; - const int32_t max_period = tensor->op_params[1]; - tensor_clone = ggml_timestep_embedding(ggml_ctx, src0_clone, dim, max_period); - } else if (tensor->op == GGML_OP_POOL_2D) { - enum ggml_op_pool op = static_cast(dst->op_params[0]); - const int32_t k0 = tensor->op_params[1]; - const int32_t k1 = tensor->op_params[2]; - const int32_t s0 = tensor->op_params[3]; - const int32_t s1 = tensor->op_params[4]; - const int32_t p0 = tensor->op_params[5]; - const int32_t p1 = tensor->op_params[6]; - - tensor_clone = ggml_pool_2d(ggml_ctx, src0_clone, op, k0, k1, s0, s1, p0, p1); - } else if (tensor->op == GGML_OP_LEAKY_RELU) { - const float * op_params = (const float *)tensor->op_params; - tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false); - } else { - std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; - GGML_ABORT("fatal error"); - } - - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); - ggml_build_forward_expand(cgraph, tensor_clone); - - ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - - if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(tensor_clone, "tensor_clone"); - } - - comp_size = ggml_nbytes(tensor_clone); - - comp_result = malloc(comp_size); - memcpy(comp_result, tensor_clone->data, comp_size); - memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS); - - if (src0 != nullptr) { - free(src0_buffer); - } - if (src1 != nullptr) { - free(src1_buffer); - } - - ggml_free(ggml_ctx); - - VK_LOG_DEBUG("END ggml_vk_check_results_0(" << tensor->name << ")"); -} - -static void ggml_vk_check_results_1(ggml_tensor * tensor) { - if (tensor->op == GGML_OP_TRANSPOSE) { - return; - } - if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { - return; - } - - VK_LOG_DEBUG("ggml_vk_check_results_1(" << tensor->name << ")"); - - ggml_tensor * src0 = tensor->src[0]; - ggml_tensor * src1 = tensor->src[1]; - ggml_tensor * src2 = tensor->src[2]; - - void * tensor_data = tensor->data; - - if (ggml_backend_buffer_is_vk(tensor->buffer)) { - size_t tensor_size = ggml_nbytes(tensor); - tensor_data = malloc(tensor_size); - - ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; - - vk_buffer& buffer_gpu = buf_ctx->dev_buffer; - uint64_t offset = vk_tensor_offset(tensor) + tensor->view_offs; - if (offset + tensor_size >= buffer_gpu->size) { - tensor_size = buffer_gpu->size - offset; - } - - ggml_vk_buffer_read(buffer_gpu, offset, tensor_data, tensor_size); - } - - float first_error_result = -1.0f; - float first_error_correct = -1.0f; - std::array first_error = { -1, -1, -1, -1 }; - double avg_err = 0.0; - size_t counter = 0; - - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size; - float correct = 0.0f; - float result = 0.0f; - - if (buffer_size_fit) { - if (tensor->type == GGML_TYPE_F32) { - correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); - result = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); - } else if (tensor->type == GGML_TYPE_F16) { - correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0])); - result = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); - } else if (tensor->type == GGML_TYPE_I32) { - correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); - result = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); - } else { - std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl; - } - } else { - std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl; - GGML_ABORT("fatal error"); - } - - if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) { - std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; - if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; - } - if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; - } - if (src2 != nullptr) { - std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; - } - std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3); - std::cerr << std::endl << "Correct:" << std::endl; - ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(tensor, done); - GGML_ABORT("fatal error"); - } - if (first_error[0] == -1 && std::fabs(correct - result) > 0.1f) { - first_error[0] = i0; - first_error[1] = i1; - first_error[2] = i2; - first_error[3] = i3; - first_error_result = result; - first_error_correct = correct; - } - - // Special case, value is infinite, avoid NaN result in avg_err - // NaN also appears in results, if both are nan error is 0 - if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) { - avg_err += std::fabs(correct - result); - } - counter++; - } - } - } - } - - avg_err /= counter; - - if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; - if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; - } - if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; - } - if (src2 != nullptr) { - std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; - } - std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); - std::cerr << std::endl << "Correct:" << std::endl; - ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(tensor, done); - } - - if (avg_err > 0.05 || std::isnan(avg_err)) { - std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; - if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; - } - if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; - } - if (src2 != nullptr) { - std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; - } - std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]); - std::cerr << std::endl << "Correct:" << std::endl; - ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(tensor, done); - GGML_ABORT("fatal error"); - } else { - std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl; - } - - free(comp_result); - comp_result = nullptr; - comp_size = 0; - - if (ggml_backend_buffer_is_vk(tensor->buffer)) { - free(tensor_data); - } - - VK_LOG_DEBUG("END ggml_vk_check_results_1(" << tensor->name << ")"); -} -#endif diff --git a/ggml/src/kompute-shaders/common.comp b/ggml/src/kompute-shaders/common.comp deleted file mode 100644 index 62d62b02..00000000 --- a/ggml/src/kompute-shaders/common.comp +++ /dev/null @@ -1,102 +0,0 @@ -#extension GL_EXT_shader_16bit_storage: require -#extension GL_EXT_shader_8bit_storage: require -#extension GL_EXT_shader_explicit_arithmetic_types_float16: require -#extension GL_EXT_shader_explicit_arithmetic_types_int8: require -#extension GL_EXT_shader_explicit_arithmetic_types_int16: require -#extension GL_EXT_control_flow_attributes: enable -#extension GL_KHR_shader_subgroup_arithmetic : require -#extension GL_EXT_debug_printf : enable - -#define QK4_0 32 -#define QK4_1 32 - -#define GELU_COEF_A 0.044715 -#define SQRT_2_OVER_PI 0.79788456080286535587989211986876 -#define TWOPI_F 6.283185307179586f - -#define QK_K 256 - -#define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx]) -#define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx) -#define u8BufToU32(buf, idx) (((uint32_t u8BufToU16(buf, idx + 2) << 8 | buf[idx + 1]) << 8) | buf[idx]) -#define u8BufToFloat(buf, idx) uintBitsToFloat u8BufToU32(buf, idx) - -#define sizeof_block_q4_0 0x12 -struct block_q4_0 { - float16_t d; - uint8_t qs[QK4_0 / 2]; -}; -mat4 dequantize_q4_0(const block_q4_0 xb, uint il) { - const float d1 = il != 0 ? (xb.d / 16.f) : xb.d; - const float d2 = d1 / 256.f; - const float md = -8.f * xb.d; - const uint16_t mask0 = il != 0 ? uint16_t(0x00F0) : uint16_t(0x000F); - const uint16_t mask1 = mask0 << 8; - - mat4 reg; - for (int i=0;i<8;i++) { - uint16_t b = (uint16_t(xb.qs[2 * i + 1]) << 8) | uint16_t(xb.qs[2 * i]); - reg[i/2][2*(i%2)+0] = d1 * (b & mask0) + md; - reg[i/2][2*(i%2)+1] = d2 * (b & mask1) + md; - } - return reg; -} - -#define sizeof_block_q4_1 0x14 -struct block_q4_1 { - float16_t d; - float16_t m; - uint8_t qs[QK4_1 / 2]; -}; -mat4 dequantize_q4_1(const block_q4_1 xb, uint il) { - const float d1 = il != 0 ? (xb.d / 16.f) : xb.d; - const float d2 = d1 / 256.f; - const float m = xb.m; - const uint16_t mask0 = il != 0 ? uint16_t(0x00F0) : uint16_t(0x000F); - const uint16_t mask1 = mask0 << 8; - - mat4 reg; - for (int i=0;i<8;i++) { - uint16_t b = (uint16_t(xb.qs[2 * i + 1]) << 8) | uint16_t(xb.qs[2 * i]); - reg[i/2][2*(i%2)+0] = ((b & mask0) * d1) + m; - reg[i/2][2*(i%2)+1] = ((b & mask1) * d2) + m; - } - return reg; -} - -#define sizeof_block_q6_k 210 -struct block_q6_k { - uint8_t ql[QK_K/2]; // quants, lower 4 bits - uint8_t qh[QK_K/4]; // quants, upper 2 bits - int8_t scales[QK_K/16]; // scales, quantized with 8 bits - float16_t d; // super-block scale -}; -mat4 dequantize_q6_k(const block_q6_k xb, uint il) { - const float16_t d_all = xb.d; - - const uint qlIndex = 64*(il/8) + 32*((il/2)&1) + 16*(il&1); - const uint qhIndex = 32*(il/8) + 16*(il&1); - float16_t sc = xb.scales[(il%2) + 2 * ((il/2))]; - il = (il/2) & 3; - - const uint16_t kmask1 = il>1 ? uint16_t(il>2 ? 192 : 48) : uint16_t(il>0 ? 12 : 3); - const uint16_t kmask2 = il>1 ? uint8_t(0xF0) : uint8_t(0x0F); - const float16_t coef = il>1 ? float16_t(1.f/16.f) : float16_t(1.f); - const float16_t ml = float16_t(d_all * sc * 32.f); - const float16_t dl = float16_t(d_all * sc * coef); - mat4 reg; - for (int i = 0; i < 16; ++i) { - const float16_t q = (il&1) != 0 ? ((xb.ql[qlIndex + i] & kmask2) | ((xb.qh[qhIndex + i] & kmask1) << 2)) - : ((xb.ql[qlIndex + i] & kmask2) | ((xb.qh[qhIndex + i] & kmask1) << 4)); - reg[i/4][i%4] = dl * q - ml; - } - return reg; -} - - -#define QK8_0 32 -// struct block_q8_0 { -// float16_t d; // delta -// int8_t qs[QK8_0]; // quants -// }; -#define sizeof_block_q8_0 34 diff --git a/ggml/src/kompute-shaders/op_add.comp b/ggml/src/kompute-shaders/op_add.comp deleted file mode 100644 index b7b76a79..00000000 --- a/ggml/src/kompute-shaders/op_add.comp +++ /dev/null @@ -1,58 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1024) in; - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb00; - int nb01; - int nb02; - int nb03; - int ne10; - int ne11; - int ne12; - int ne13; - int nb10; - int nb11; - int nb12; - int nb13; - int ne0; - int nb0; - int nb1; - int nb2; - int nb3; - //int offs; // TODO: needed for GGML_OP_ACC, see metal code -} pcs; - -// general-purpose kernel for addition of two tensors -// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3 -// cons: not very efficient -void main() { - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const uint i13 = i03 % pcs.ne13; - const uint i12 = i02 % pcs.ne12; - const uint i11 = i01 % pcs.ne11; - - int offs = 0; // TMP (see above) - - uint src0_off = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + offs) / 4); - uint src1_off = uint((i13*pcs.nb13 + i12*pcs.nb12 + i11*pcs.nb11 ) / 4); - uint dst_off = uint((i03*pcs.nb3 + i02*pcs.nb2 + i01*pcs.nb1 + offs) / 4); - - for (uint i0 = gl_LocalInvocationID.x; i0 < pcs.ne0; i0 += gl_WorkGroupSize.x) { - const uint i10 = i0 % pcs.ne10; - out_[pcs.outOff + dst_off + i0] = inA[pcs.inAOff + src0_off + i0] + inB[pcs.inBOff + src1_off + i10]; - } -} diff --git a/ggml/src/kompute-shaders/op_addrow.comp b/ggml/src/kompute-shaders/op_addrow.comp deleted file mode 100644 index 2376a6b8..00000000 --- a/ggml/src/kompute-shaders/op_addrow.comp +++ /dev/null @@ -1,25 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inAOff; - uint inBOff; - uint outOff; - uint row; -} pcs; - -void main() { - const uint baseIndex = gl_WorkGroupID.x * 4; - - for (uint x = 0; x < 4; x++) { - const uint i = baseIndex + x; - out_[i + pcs.outOff] = inA[i + pcs.inAOff] + inB[(i % pcs.row) + pcs.inBOff]; - } -} diff --git a/ggml/src/kompute-shaders/op_cpy_f16_f16.comp b/ggml/src/kompute-shaders/op_cpy_f16_f16.comp deleted file mode 100644 index d57247d2..00000000 --- a/ggml/src/kompute-shaders/op_cpy_f16_f16.comp +++ /dev/null @@ -1,52 +0,0 @@ -#version 450 - -#include "common.comp" - -#define IN_TYPE float16_t -#define IN_TYPE_SIZE 2 -#define OUT_TYPE float16_t -#define OUT_TYPE_SIZE 2 - -layout(local_size_x = 1024) in; - -layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; -layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; - -layout (push_constant) uniform parameter { - uint inOff; - uint outOff; - int ne00; - int ne01; - int ne02; - uint nb00; - uint nb01; - uint nb02; - uint nb03; - int ne0; - int ne1; - int ne2; - uint nb0; - uint nb1; - uint nb2; - uint nb3; -} pcs; - -void main() { - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; - - const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); - const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); - const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; - const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); - - const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ - - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ - out_[dst_data+i00] = OUT_TYPE(in_[src]); - } -} diff --git a/ggml/src/kompute-shaders/op_cpy_f16_f32.comp b/ggml/src/kompute-shaders/op_cpy_f16_f32.comp deleted file mode 100644 index b568bcd7..00000000 --- a/ggml/src/kompute-shaders/op_cpy_f16_f32.comp +++ /dev/null @@ -1,52 +0,0 @@ -#version 450 - -#include "common.comp" - -#define IN_TYPE float16_t -#define IN_TYPE_SIZE 2 -#define OUT_TYPE float -#define OUT_TYPE_SIZE 4 - -layout(local_size_x = 1024) in; - -layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; -layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; - -layout (push_constant) uniform parameter { - uint inOff; - uint outOff; - int ne00; - int ne01; - int ne02; - uint nb00; - uint nb01; - uint nb02; - uint nb03; - int ne0; - int ne1; - int ne2; - uint nb0; - uint nb1; - uint nb2; - uint nb3; -} pcs; - -void main() { - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; - - const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); - const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); - const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; - const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); - - const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ - - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ - out_[dst_data+i00] = OUT_TYPE(in_[src]); - } -} diff --git a/ggml/src/kompute-shaders/op_cpy_f32_f16.comp b/ggml/src/kompute-shaders/op_cpy_f32_f16.comp deleted file mode 100644 index 99b22834..00000000 --- a/ggml/src/kompute-shaders/op_cpy_f32_f16.comp +++ /dev/null @@ -1,52 +0,0 @@ -#version 450 - -#include "common.comp" - -#define IN_TYPE float -#define IN_TYPE_SIZE 4 -#define OUT_TYPE float16_t -#define OUT_TYPE_SIZE 2 - -layout(local_size_x = 1024) in; - -layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; -layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; - -layout (push_constant) uniform parameter { - uint inOff; - uint outOff; - int ne00; - int ne01; - int ne02; - uint nb00; - uint nb01; - uint nb02; - uint nb03; - int ne0; - int ne1; - int ne2; - uint nb0; - uint nb1; - uint nb2; - uint nb3; -} pcs; - -void main() { - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; - - const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); - const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); - const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; - const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); - - const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ - - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ - out_[dst_data+i00] = OUT_TYPE(in_[src]); - } -} diff --git a/ggml/src/kompute-shaders/op_cpy_f32_f32.comp b/ggml/src/kompute-shaders/op_cpy_f32_f32.comp deleted file mode 100644 index 2fc99849..00000000 --- a/ggml/src/kompute-shaders/op_cpy_f32_f32.comp +++ /dev/null @@ -1,52 +0,0 @@ -#version 450 - -#include "common.comp" - -#define IN_TYPE float -#define IN_TYPE_SIZE 4 -#define OUT_TYPE float -#define OUT_TYPE_SIZE 4 - -layout(local_size_x = 1024) in; - -layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; -layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; - -layout (push_constant) uniform parameter { - uint inOff; - uint outOff; - int ne00; - int ne01; - int ne02; - uint nb00; - uint nb01; - uint nb02; - uint nb03; - int ne0; - int ne1; - int ne2; - uint nb0; - uint nb1; - uint nb2; - uint nb3; -} pcs; - -void main() { - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; - - const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); - const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); - const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; - const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); - - const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ - - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ - out_[dst_data+i00] = OUT_TYPE(in_[src]); - } -} diff --git a/ggml/src/kompute-shaders/op_diagmask.comp b/ggml/src/kompute-shaders/op_diagmask.comp deleted file mode 100644 index 291c3fc1..00000000 --- a/ggml/src/kompute-shaders/op_diagmask.comp +++ /dev/null @@ -1,30 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; - uint n_past; - int ne00; - int ne01; -} pcs; - -void main() { - const uint i02 = gl_WorkGroupID.z; - const uint i01 = gl_WorkGroupID.y; - const uint i00 = gl_WorkGroupID.x; - - const uint index = i02*pcs.ne01*pcs.ne00 + i01*pcs.ne00 + i00; - - if (i00 > pcs.n_past + i01) { - out_[index + pcs.outOff] = uintBitsToFloat(0xFF800000); - } else { - out_[index + pcs.outOff] = in_[index + pcs.inOff]; - } -} diff --git a/ggml/src/kompute-shaders/op_gelu.comp b/ggml/src/kompute-shaders/op_gelu.comp deleted file mode 100644 index 9d8c5371..00000000 --- a/ggml/src/kompute-shaders/op_gelu.comp +++ /dev/null @@ -1,22 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; -} pcs; - -void main() { - const uint baseIndex = gl_WorkGroupID.x * 8; - - for (uint x = 0; x < 8; x++) { - const uint i = baseIndex + x; - const float y = in_[i + pcs.inOff]; - out_[i + pcs.outOff] = 0.5*y*(1.0 + tanh(clamp(SQRT_2_OVER_PI*y*(1.0 + GELU_COEF_A*y*y), -15.0, 15.0))); - } -} diff --git a/ggml/src/kompute-shaders/op_getrows.comp b/ggml/src/kompute-shaders/op_getrows.comp deleted file mode 100644 index 1a5581b2..00000000 --- a/ggml/src/kompute-shaders/op_getrows.comp +++ /dev/null @@ -1,17 +0,0 @@ -void main() { - const uint i = gl_WorkGroupID.x; - const int r = inB[i + pcs.inBOff]; - - int z = 0; - for (uint ind = gl_LocalInvocationID.x; ind < pcs.ne00/16; ind += gl_WorkGroupSize.x) { - const uint inIndex = (r * pcs.nb01 + pcs.inAOff) + ind/NL * SIZE_OF_BLOCK; - const mat4 result = dequantize_block(inIndex, ind%NL); - for (uint j = 0; j < 4; ++j) { - for (uint k = 0; k < 4; ++k) { - const uint outIndex = i * pcs.nb1/BYTES_FOR_TYPE + pcs.outOff + z; - out_[outIndex] = result[j][k]; - ++z; - } - } - } -} diff --git a/ggml/src/kompute-shaders/op_getrows_f16.comp b/ggml/src/kompute-shaders/op_getrows_f16.comp deleted file mode 100644 index 48c93610..00000000 --- a/ggml/src/kompute-shaders/op_getrows_f16.comp +++ /dev/null @@ -1,31 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout (binding = 0) readonly buffer tensorInA { float16_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { int inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb01; - int nb1; -} pcs; - -void dequantize_row_f16(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) { - for (int j = 0; j < k; j++) { - out_[y + j] = inA[x + j]; - } -} - -void main() { - const uint i = gl_WorkGroupID.x; - const int r = inB[i + pcs.inBOff]; - - dequantize_row_f16(r*pcs.nb01/2/*bytes for float16*/ + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00); -} diff --git a/ggml/src/kompute-shaders/op_getrows_f32.comp b/ggml/src/kompute-shaders/op_getrows_f32.comp deleted file mode 100644 index 9d7acdaf..00000000 --- a/ggml/src/kompute-shaders/op_getrows_f32.comp +++ /dev/null @@ -1,31 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout (binding = 0) readonly buffer tensorInA { float inA[]; }; -layout (binding = 1) readonly buffer tensorInB { int inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb01; - int nb1; -} pcs; - -void dequantize_row_f32(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) { - for (int j = 0; j < k; j++) { - out_[y + j] = inA[x + j]; - } -} - -void main() { - const uint i = gl_WorkGroupID.x; - const int r = inB[i + pcs.inBOff]; - - dequantize_row_f32(r*pcs.nb01/4 + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00); -} diff --git a/ggml/src/kompute-shaders/op_getrows_q4_0.comp b/ggml/src/kompute-shaders/op_getrows_q4_0.comp deleted file mode 100644 index 32b2e891..00000000 --- a/ggml/src/kompute-shaders/op_getrows_q4_0.comp +++ /dev/null @@ -1,38 +0,0 @@ -#version 450 - -#include "common.comp" - -#define NL 2 -#define BYTES_FOR_TYPE 4 /*bytes for float*/ -#define SIZE_OF_BLOCK sizeof_block_q4_0 - -layout(local_size_x = 1) in; - -layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { int inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb01; - int nb1; -} pcs; - -block_q4_0 get_unaligned_block_q4_0(uint index) { - block_q4_0 fres; - fres.d = u8BufToFloat16(inA, index); - [[unroll]] for (uint it = 0; it != QK4_0 / 2; it++) { - fres.qs[it] = inA[index+2+it]; - } - return fres; -} - -mat4 dequantize_block(uint index, uint il) { - const block_q4_0 block = get_unaligned_block_q4_0(index); - return dequantize_q4_0(block, il); -} - -#include "op_getrows.comp" diff --git a/ggml/src/kompute-shaders/op_getrows_q4_1.comp b/ggml/src/kompute-shaders/op_getrows_q4_1.comp deleted file mode 100644 index 87f2fbe1..00000000 --- a/ggml/src/kompute-shaders/op_getrows_q4_1.comp +++ /dev/null @@ -1,39 +0,0 @@ -#version 450 - -#include "common.comp" - -#define NL 2 -#define BYTES_FOR_TYPE 4 /*bytes for float*/ -#define SIZE_OF_BLOCK sizeof_block_q4_1 - -layout(local_size_x = 1) in; - -layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { int inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb01; - int nb1; -} pcs; - -block_q4_1 get_unaligned_block_q4_1(uint index) { - block_q4_1 fres; - fres.d = u8BufToFloat16(inA, index); - fres.m = u8BufToFloat16(inA, index+2); - [[unroll]] for (uint it = 0; it != QK4_1 / 2; it++) { - fres.qs[it] = inA[index+4+it]; - } - return fres; -} - -mat4 dequantize_block(uint index, uint il) { - const block_q4_1 block = get_unaligned_block_q4_1(index); - return dequantize_q4_1(block, il); -} - -#include "op_getrows.comp" diff --git a/ggml/src/kompute-shaders/op_getrows_q6_k.comp b/ggml/src/kompute-shaders/op_getrows_q6_k.comp deleted file mode 100644 index 9ce3545d..00000000 --- a/ggml/src/kompute-shaders/op_getrows_q6_k.comp +++ /dev/null @@ -1,44 +0,0 @@ -#version 450 - -#include "common.comp" - -#define NL 16 -#define BYTES_FOR_TYPE 4 /*bytes for float*/ -#define SIZE_OF_BLOCK sizeof_block_q6_k - -layout(local_size_x = 1) in; - -layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { int inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb01; - int nb1; -} pcs; - -block_q6_k get_unaligned_block_q6_k(uint index) { - block_q6_k fres; - [[unroll]] for (uint it = 0; it != QK_K / 2; it++) { - fres.ql[it] = inA[index + it]; - } - [[unroll]] for (uint it = 0; it != QK_K / 4; it++) { - fres.qh[it] = inA[index + QK_K/2 + it]; - } - [[unroll]] for (uint it = 0; it != QK_K / 16; it++) { - fres.scales[it] = int8_t(inA[index + QK_K/2 + QK_K/4 + it]); - } - fres.d = u8BufToFloat16(inA, index + QK_K/2 + QK_K/4 + QK_K/16); - return fres; -} - -mat4 dequantize_block(uint index, uint il) { - const block_q6_k block = get_unaligned_block_q6_k(index); - return dequantize_q6_k(block, il); -} - -#include "op_getrows.comp" diff --git a/ggml/src/kompute-shaders/op_mul.comp b/ggml/src/kompute-shaders/op_mul.comp deleted file mode 100644 index c92647c4..00000000 --- a/ggml/src/kompute-shaders/op_mul.comp +++ /dev/null @@ -1,52 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1024) in; - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int nb00; - int nb01; - int nb02; - int nb03; - int ne10; - int ne11; - int ne12; - int ne13; - int nb10; - int nb11; - int nb12; - int nb13; - int ne0; - int nb0; - int nb1; - int nb2; - int nb3; -} pcs; - -void main() { - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const uint i13 = i03 % pcs.ne13; - const uint i12 = i02 % pcs.ne12; - const uint i11 = i01 % pcs.ne11; - - uint src0_off = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01) / 4); - uint src1_off = uint((i13*pcs.nb13 + i12*pcs.nb12 + i11*pcs.nb11) / 4); - uint dst_off = uint((i03*pcs.nb3 + i02*pcs.nb2 + i01*pcs.nb1) / 4); - - for (uint i0 = gl_LocalInvocationID.x; i0 < pcs.ne0; i0 += gl_WorkGroupSize.x) { - const uint i10 = i0 % pcs.ne10; - out_[pcs.outOff + dst_off + i0] = inA[pcs.inAOff + src0_off + i0] * inB[pcs.inBOff + src1_off + i10]; - } -} diff --git a/ggml/src/kompute-shaders/op_mul_mat_f16.comp b/ggml/src/kompute-shaders/op_mul_mat_f16.comp deleted file mode 100644 index 8f0a9031..00000000 --- a/ggml/src/kompute-shaders/op_mul_mat_f16.comp +++ /dev/null @@ -1,67 +0,0 @@ -#version 450 - -#include "common.comp" - -#extension GL_KHR_shader_subgroup_arithmetic : require - -layout(local_size_x_id = 0) in; - -layout (binding = 0) readonly buffer tensorInA { float16_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { float inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int ne01; - int ne02; - uint nb00; - uint nb01; - uint nb02; - int ne10; - int ne11; - int ne12; - uint nb10; - uint nb11; - uint nb12; - int ne0; - int ne1; - uint r2; - uint r3; -} pcs; - -#define N_F16_F32 4 - -void main() { - const uint r0 = gl_WorkGroupID.x; - const uint rb = gl_WorkGroupID.y*N_F16_F32; - const uint im = gl_WorkGroupID.z; - - const uint i12 = im%pcs.ne12; - const uint i13 = im/pcs.ne12; - - const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb02*pcs.ne02; - - const uint x = offset0 / 2 + pcs.inAOff; // Based from inA - - for (uint row = 0; row < N_F16_F32; ++row) { - uint r1 = rb + row; - if (r1 >= pcs.ne11) { - break; - } - - const uint y = (r1*pcs.nb11 + im*pcs.nb12) / 4 + pcs.inBOff; // Based from inB - - float sumf = 0; - for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) { - sumf += float(inA[x+i]) * float(inB[y+i]); - } - - const float all_sum = subgroupAdd(sumf); - if (subgroupElect()) { - out_[im*pcs.ne1*pcs.ne0 + r1*pcs.ne0 + r0 + pcs.outOff] = all_sum; - } - } -} diff --git a/ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp b/ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp deleted file mode 100644 index d1ca4ad6..00000000 --- a/ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp +++ /dev/null @@ -1,51 +0,0 @@ -#version 450 - -#include "common.comp" - -#extension GL_KHR_shader_subgroup_arithmetic : require -#extension GL_EXT_debug_printf : enable - -// device subgroup size -layout (local_size_x_id = 0) in; - -layout(binding = 0) readonly buffer tensorInA { float inA[]; }; -layout(binding = 1) readonly buffer tensorInB { float inB[]; }; -layout(binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout(push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int ne01; - int ne02; - int ne11; - int ne12; - uint nb01; - uint nb02; - uint nb11; - uint nb12; - uint nb1; - uint nb2; -} -pcs; - - -void main() { - uvec3 gid = gl_WorkGroupID; - - uint bc_ab = pcs.ne12 > pcs.ne02 ? gid.z / (pcs.ne12 / pcs.ne02) : gid.z; - uint bc_ba = pcs.ne02 > pcs.ne12 ? gid.z / (pcs.ne02 / pcs.ne12) : gid.z; - - const uint x = (gid.x*pcs.nb01 + bc_ab*pcs.nb02) / 4 + pcs.inAOff; // Based from inA - const uint y = (gid.y*pcs.nb11 + bc_ba*pcs.nb12) / 4 + pcs.inBOff; // based from inB - float sum = 0.0f; - for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) { - sum += float(inA[x+i]) * float(inB[y+i]); - } - - const float all_sum = subgroupAdd(sum); - if (subgroupElect()) { - out_[gid.z*(pcs.nb2/4) + gid.y*(pcs.nb1/4) + gid.x + pcs.outOff] = all_sum; - } -} diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_0.comp b/ggml/src/kompute-shaders/op_mul_mat_q4_0.comp deleted file mode 100644 index b0cea8bb..00000000 --- a/ggml/src/kompute-shaders/op_mul_mat_q4_0.comp +++ /dev/null @@ -1,33 +0,0 @@ -#version 450 - -#include "common.comp" - -#define BLOCKS_IN_QUANT QK4_0 -#define SIZE_OF_BLOCK sizeof_block_q4_0 -#define N_ROWS 4 - -#include "op_mul_mv_q_n_pre.comp" - -// The q4_0 version of this function -float block_q_n_dot_y(uint block_index, uint yb, uint il) { - vec2 acc = vec2(0.0, 0.0); - const uint index = (block_index) * SIZE_OF_BLOCK + pcs.inAOff; - float d = float(u8BufToFloat16(inA, index)); - float sumy = 0.0f; - for (int i = 0; i < BLOCKS_IN_QUANT/4; i+=2) { - const uint16_t b = u8BufToU16(inA, index + 2 + il + i); - - const float yl0 = inB[yb + i]; - const float yl1 = inB[yb + i + 1]; - const float yl8 = inB[yb + i + BLOCKS_IN_QUANT/2]; - const float yl9 = inB[yb + i + BLOCKS_IN_QUANT/2 + 1]; - - sumy += yl0 + yl1 + yl8 + yl9; - - acc[0] += yl0 * (b & 0x000F) + yl1 / 256.f * (b & 0x0F00); - acc[1] += yl8 / 16.f * (b & 0x00F0) + yl9 / 4096.f * (b & 0xF000); - } - return d * (sumy * -8.f + acc[0] + acc[1]); -} - -#include "op_mul_mv_q_n.comp" diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_1.comp b/ggml/src/kompute-shaders/op_mul_mat_q4_1.comp deleted file mode 100644 index 8582c61a..00000000 --- a/ggml/src/kompute-shaders/op_mul_mat_q4_1.comp +++ /dev/null @@ -1,35 +0,0 @@ -#version 450 - -#include "common.comp" - -#define BLOCKS_IN_QUANT QK4_1 -#define SIZE_OF_BLOCK sizeof_block_q4_1 -#define N_ROWS 4 - -#include "op_mul_mv_q_n_pre.comp" - -// The q4_1 version of this function -float block_q_n_dot_y(uint block_index, uint yb, uint il) { - vec2 acc = vec2(0.0, 0.0); - const uint index = (block_index) * SIZE_OF_BLOCK + pcs.inAOff; - float d = float(u8BufToFloat16(inA, index)); - float m = float(u8BufToFloat16(inA, index+2)); - - float sumy = 0.0f; - for (int i = 0; i < BLOCKS_IN_QUANT/4; i+=2) { - const uint16_t b = u8BufToU16(inA, index + 4 + il + i); - - const float yl0 = inB[yb + i]; - const float yl1 = inB[yb + i + 1]; - const float yl8 = inB[yb + i + BLOCKS_IN_QUANT/2]; - const float yl9 = inB[yb + i + BLOCKS_IN_QUANT/2 + 1]; - - sumy += yl0 + yl1 + yl8 + yl9; - - acc[0] += yl0 * (b & 0x000F) + yl1 / 256.f * (b & 0x0F00); - acc[1] += yl8 / 16.f * (b & 0x00F0) + yl9 / 4096.f * (b & 0xF000); - } - return d * (acc[0] + acc[1]) + sumy * m; -} - -#include "op_mul_mv_q_n.comp" diff --git a/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp b/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp deleted file mode 100644 index c9baebdf..00000000 --- a/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp +++ /dev/null @@ -1,94 +0,0 @@ -#version 450 - -#include "common.comp" - -#define SIZE_OF_BLOCK sizeof_block_q6_k - -layout(local_size_x_id = 0) in; -layout(local_size_y_id = 1) in; -layout(local_size_z = 1) in; - -layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { float inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int ne10; - int ne0; - int ne1; - int ne01; - int gqa; -} pcs; - -void main() { - const uint8_t kmask1 = uint8_t(0x03); - const uint8_t kmask2 = uint8_t(0x0C); - const uint8_t kmask3 = uint8_t(0x30); - const uint8_t kmask4 = uint8_t(0xC0); - - const uint nb = pcs.ne00/QK_K; - - const uint r0 = gl_WorkGroupID.x; - const uint r1 = gl_WorkGroupID.y; - const uint r2 = gl_WorkGroupID.z; - - const uint row = (r0 * gl_NumSubgroups + gl_SubgroupID); - const uint offset0 = r2/pcs.gqa*(nb*pcs.ne0); - const uint x = row * nb + offset0; // Based from inA without base offset - const uint yy = r1*pcs.ne10 + r2*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB - - float sumf = 0; - - // bits of invocation ID for gl_SubgroupSize=32: - // x x x x x - // 4 3 2 1 0 - // ( tid ) ix - // ip ( il ) - - const uint block_stride = gl_SubgroupSize / 16; // number of blocks each subgroup processes - const uint tid = gl_SubgroupInvocationID/block_stride; // first block_stride groups have tid=0 - const uint ix = gl_SubgroupInvocationID%block_stride; // first block is 0..block_stride-1 - const uint ip = tid/8; // first or second half of block (0 or 1) - const uint il = tid%8; // each half has 8 parts, one per scale - const uint n = 4; // 4 scales at a time (and 4 sums) - const uint l0 = n*il; // offset into half-block, 0..28 - const uint is = 8*ip + l0/16; // 0, 1, 8, 9 - - const uint y_offset = 128*ip + l0; - const uint q_offset_l = 64*ip + l0; - const uint q_offset_h = 32*ip + l0; - - for (uint i = ix; i < nb; i += block_stride) { - - const uint baseIndex = (x + i) * SIZE_OF_BLOCK + pcs.inAOff; - - const uint qlIndex = q_offset_l; - const uint q2Index = qlIndex + QK_K/8; - const uint qhIndex = q_offset_h; - const uint y = yy + i * QK_K + y_offset; - - float sums[4] = {0.0f, 0.0f, 0.0f, 0.0f}; - for (uint l = 0; l < n; ++l) { - const uint8_t currentQ1 = inA[baseIndex + qlIndex + l]; - const uint8_t currentQ2 = inA[baseIndex + q2Index + l]; - const uint8_t currentQh = inA[baseIndex + QK_K/2 + qhIndex + l]; - - sums[0] += inB[y+l+ 0] * (int8_t((currentQ1 & 0xF) | ((currentQh & kmask1) << 4)) - 32); - sums[1] += inB[y+l+32] * (int8_t((currentQ2 & 0xF) | ((currentQh & kmask2) << 2)) - 32); - sums[2] += inB[y+l+64] * (int8_t((currentQ1 >> 4) | ((currentQh & kmask3) << 0)) - 32); - sums[3] += inB[y+l+96] * (int8_t((currentQ2 >> 4) | ((currentQh & kmask4) >> 2)) - 32); - } - - float d = u8BufToFloat16(inA, baseIndex + QK_K/2 + QK_K/4 + QK_K/16); - sumf += d * (sums[0] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + is]) + sums[1] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 2 + is]) + sums[2] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 4 + is]) + sums[3] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 6 + is])); - } - - const float tot = subgroupAdd(sumf); - if (subgroupElect()) { - out_[r1*pcs.ne0 + r2*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot; - } -} diff --git a/ggml/src/kompute-shaders/op_mul_mat_q8_0.comp b/ggml/src/kompute-shaders/op_mul_mat_q8_0.comp deleted file mode 100644 index 34d015e9..00000000 --- a/ggml/src/kompute-shaders/op_mul_mat_q8_0.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "common.comp" - -#include "op_mul_mv_q_n_pre.comp" - -#define SIZE_OF_D 2 - -#define N_DST 4 // each SIMD group works on 4 rows -#define N_SIMDGROUP 2 // number of SIMD groups in a thread group -#define N_SIMDWIDTH 32 // assuming SIMD group size is 32 - -#define NB_Q8_0 8 - -void main() { - // NB: hack to make compatible with AMD GPUs that have a subgroup size of 64 - if (gl_SubgroupInvocationID > 31) - return; - - const int nr = N_DST; - const int nsg = N_SIMDGROUP; - const int nw = N_SIMDWIDTH; - - const int nb = pcs.ne00/QK8_0; - const uint r0 = gl_WorkGroupID.x; - const uint r1 = gl_WorkGroupID.y; - const uint im = gl_WorkGroupID.z; - - const uint first_row = (r0 * nsg + gl_SubgroupID) * nr; - - const uint i12 = im%pcs.ne12; - const uint i13 = im/pcs.ne12; - - const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); - - const uint x = offset0*sizeof_block_q8_0 + pcs.inAOff; // Based from inA - const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff; // based from inB - - float yl[NB_Q8_0]; - float sumf[N_DST]={0.f, 0.f, 0.f, 0.f}; - - const uint ix = gl_SubgroupInvocationID.x/4; - const uint il = gl_SubgroupInvocationID.x%4; - - uint yb = y + ix * QK8_0 + NB_Q8_0*il; - - // each thread in a SIMD group deals with NB_Q8_0 quants at a time - for (uint ib = ix; ib < nb; ib += nw/4) { - for (int i = 0; i < NB_Q8_0; ++i) { - yl[i] = inB[yb + i]; - } - - for (int row = 0; row < nr; row++) { - const uint block_offset = (ib+row*nb) * sizeof_block_q8_0; - float sumq = 0.f; - for (int iq = 0; iq < NB_Q8_0; ++iq) { - const int8_t qs_iq = int8_t(inA[x + block_offset + SIZE_OF_D + NB_Q8_0*il + iq]); - sumq += qs_iq * yl[iq]; - } - const float16_t d = u8BufToFloat16(inA, x + block_offset); - sumf[row] += sumq*d; - } - - yb += NB_Q8_0 * nw; - } - - for (int row = 0; row < nr; ++row) { - const float tot = subgroupAdd(sumf[row]); - if (subgroupElect() && first_row + row < pcs.ne01) { - out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row] = tot; - } - } -} diff --git a/ggml/src/kompute-shaders/op_mul_mv_q_n.comp b/ggml/src/kompute-shaders/op_mul_mv_q_n.comp deleted file mode 100644 index 440b5ab2..00000000 --- a/ggml/src/kompute-shaders/op_mul_mv_q_n.comp +++ /dev/null @@ -1,48 +0,0 @@ -void main() { - // NB: hack to make compatible with AMD GPUs that have a subgroup size of 64 - if (gl_SubgroupInvocationID > 31) - return; - - const uint nb = uint(pcs.ne00/BLOCKS_IN_QUANT); - - const uint r0 = gl_WorkGroupID.x; - const uint r1 = gl_WorkGroupID.y; - const uint im = gl_WorkGroupID.z; - - const uint first_row = (r0 * gl_NumSubgroups + gl_SubgroupID) * N_ROWS; - - const uint i12 = im%pcs.ne12; - const uint i13 = im/pcs.ne12; - - const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); - - const uint x = offset0; // Based from inA without base offset - const uint y = r1*uint(pcs.ne10)+im*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB - - float sumf[N_ROWS] = {0.0f, 0.0f, 0.0f, 0.0f}; - - const uint ix = gl_SubgroupInvocationID/2; - const uint il = (BLOCKS_IN_QUANT/4)*(gl_SubgroupInvocationID%2); - - uint yb = y + ix * BLOCKS_IN_QUANT + il; - - //debugPrintfEXT("gl_NumSubgroups=%d, gl_SubgroupID=%d, gl_SubgroupInvocationID=%d, glSubgroupSize=%d, gl_WorkGroupSize.x=%d, gl_WorkGroupSize.y=%d, gl_WorkGroupSize.z=%d\n", - // gl_NumSubgroups, gl_SubgroupID, gl_SubgroupInvocationID, gl_SubgroupSize, - // gl_WorkGroupSize.x, gl_WorkGroupSize.y, gl_WorkGroupSize.z); - - for (uint ib = ix; ib < nb; ib += 16) { - for (int row = 0; row < N_ROWS; row++) { - const uint block_index = x + ib + row * nb; - sumf[row] += block_q_n_dot_y(block_index, yb, il); - } - - yb += BLOCKS_IN_QUANT * 16; - } - - for (int row = 0; row < N_ROWS; ++row) { - const float tot = subgroupAdd(sumf[row]); - if (first_row + row < pcs.ne01 && subgroupElect()) { - out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = tot; - } - } -} diff --git a/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp b/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp deleted file mode 100644 index 7912b09a..00000000 --- a/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp +++ /dev/null @@ -1,22 +0,0 @@ -layout(local_size_x_id = 0) in; -layout(local_size_y = 1) in; -layout(local_size_z = 1) in; - -layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; -layout (binding = 1) readonly buffer tensorInB { float inB[]; }; -layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int ne01; - int ne02; - int ne10; - int ne12; - int ne0; - int ne1; - uint r2; - uint r3; -} pcs; diff --git a/ggml/src/kompute-shaders/op_norm.comp b/ggml/src/kompute-shaders/op_norm.comp deleted file mode 100644 index ad0c3c01..00000000 --- a/ggml/src/kompute-shaders/op_norm.comp +++ /dev/null @@ -1,84 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 256) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; - uint ne00; - uint nb01; - float eps; -} pcs; - -shared float sum[gl_WorkGroupSize.x]; - -void main() { - const uint x = (gl_WorkGroupID.x*pcs.nb01/4) + pcs.inOff; // Based from in_ - // MEAN - // parallel sum - sum[gl_LocalInvocationID.x] = 0.0; - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - sum[gl_LocalInvocationID.x] += in_[x+i00]; - } - - // reduce - barrier(); - memoryBarrierShared(); - [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) { - if (gl_LocalInvocationID.x < i) { - sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i]; - } - barrier(); - memoryBarrierShared(); - } - - // broadcast - if (gl_LocalInvocationID.x == 0) { - sum[0] /= float(pcs.ne00); - } - barrier(); - memoryBarrierShared(); - const float mean = sum[0]; - - // recenter - const uint y = (gl_WorkGroupID.x*pcs.ne00) + pcs.outOff; // Based from out_ - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - out_[y+i00] = in_[x+i00] - mean; - } - - // VARIANCE - // parallel sum - sum[gl_LocalInvocationID.x] = 0.0; - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - sum[gl_LocalInvocationID.x] += out_[y+i00] * out_[y+i00]; - } - - // reduce - barrier(); - memoryBarrierShared(); - [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) { - if (gl_LocalInvocationID.x < i) { - sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i]; - } - barrier(); - memoryBarrierShared(); - } - - // broadcast - if (gl_LocalInvocationID.x == 0) { - sum[0] /= float(pcs.ne00); - } - barrier(); - memoryBarrierShared(); - const float variance = sum[0]; - - const float scale = 1.0f/sqrt(variance + pcs.eps); - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - out_[y+i00] *= scale; - } -} diff --git a/ggml/src/kompute-shaders/op_relu.comp b/ggml/src/kompute-shaders/op_relu.comp deleted file mode 100644 index 52a601fe..00000000 --- a/ggml/src/kompute-shaders/op_relu.comp +++ /dev/null @@ -1,21 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; -} pcs; - -void main() { - const uint baseIndex = gl_WorkGroupID.x * 4; - - for (uint x = 0; x < 4; x++) { - const uint i = baseIndex + x; - out_[i + pcs.outOff] = max(0.0, in_[i + pcs.inOff]); - } -} diff --git a/ggml/src/kompute-shaders/op_rmsnorm.comp b/ggml/src/kompute-shaders/op_rmsnorm.comp deleted file mode 100644 index da658c16..00000000 --- a/ggml/src/kompute-shaders/op_rmsnorm.comp +++ /dev/null @@ -1,53 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 512) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; - uint ne00; - uint nb01; - float eps; -} pcs; - -shared float sum[gl_WorkGroupSize.x]; - -void main() { - const uint x = (gl_WorkGroupID.x*pcs.nb01/4) + pcs.inOff; // Based from in_ - - // parallel sum - sum[gl_LocalInvocationID.x] = 0.0; - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - sum[gl_LocalInvocationID.x] += in_[x+i00] * in_[x+i00]; - } - - // reduce - barrier(); - memoryBarrierShared(); - [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) { - if (gl_LocalInvocationID.x < i) { - sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i]; - } - barrier(); - memoryBarrierShared(); - } - - // broadcast - if (gl_LocalInvocationID.x == 0) { - sum[0] /= float(pcs.ne00); - } - barrier(); - memoryBarrierShared(); - - const float scale = 1.0f/sqrt(sum[0] + pcs.eps); - - const uint y = (gl_WorkGroupID.x*pcs.ne00) + pcs.outOff; // Based from out_ - for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { - out_[y+i00] = in_[x+i00] * scale; - } -} diff --git a/ggml/src/kompute-shaders/op_rope_f16.comp b/ggml/src/kompute-shaders/op_rope_f16.comp deleted file mode 100644 index 1a4058b3..00000000 --- a/ggml/src/kompute-shaders/op_rope_f16.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "rope_common.comp" - -layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float16_t out_[]; }; - -void main() { - const uint i3 = gl_WorkGroupID.z; - const uint i2 = gl_WorkGroupID.y; - const uint i1 = gl_WorkGroupID.x; - - const bool is_neox = (pcs.mode & 2) != 0; - - float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); - - const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); - - const int p = inB[pcs.inBOff + i2]; - - float theta = float(p); - - if (!is_neox) { - for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - const float x0 = float(inA[src]); - const float x1 = float(inA[src+1]); - - out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); - out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta); - } - } else { - const float inv_ndims = -1.f/pcs.n_dims; - for (uint ic = 0; ic < pcs.n_dims; ic += 2) { - const uint cur_rot = ic; - - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint i0 = ic/2; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - const float x0 = float(inA[src]); - const float x1 = float(inA[src+pcs.n_dims/2]); - - out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); - out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta); - } - - for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { - const uint i0 = ic; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - out_[dst_data + 0] = inA[src + 0]; - out_[dst_data + 1] = inA[src + 1]; - } - } -} diff --git a/ggml/src/kompute-shaders/op_rope_f32.comp b/ggml/src/kompute-shaders/op_rope_f32.comp deleted file mode 100644 index 65e03827..00000000 --- a/ggml/src/kompute-shaders/op_rope_f32.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "rope_common.comp" - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -void main() { - const uint i3 = gl_WorkGroupID.z; - const uint i2 = gl_WorkGroupID.y; - const uint i1 = gl_WorkGroupID.x; - - const bool is_neox = (pcs.mode & 2) != 0; - - float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); - - const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); - - const int p = inB[pcs.inBOff + i2]; - - float theta = float(p); - - if (!is_neox) { - for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - const float x0 = inA[src]; - const float x1 = inA[src+1]; - - out_[dst_data] = x0*cos_theta - x1*sin_theta; - out_[dst_data+1] = x0*sin_theta + x1*cos_theta; - } - } else { - const float inv_ndims = -1.f/pcs.n_dims; - for (uint ic = 0; ic < pcs.n_dims; ic += 2) { - const uint cur_rot = ic; - - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint i0 = ic/2; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - const float x0 = inA[src]; - const float x1 = inA[src+pcs.n_dims/2]; - - out_[dst_data] = x0*cos_theta - x1*sin_theta; - out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta; - } - - for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { - const uint i0 = ic; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - out_[dst_data + 0] = inA[src + 0]; - out_[dst_data + 1] = inA[src + 1]; - } - } -} diff --git a/ggml/src/kompute-shaders/op_scale.comp b/ggml/src/kompute-shaders/op_scale.comp deleted file mode 100644 index bdae2673..00000000 --- a/ggml/src/kompute-shaders/op_scale.comp +++ /dev/null @@ -1,19 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; - float scale; -} pcs; - -void main() { - const uint i = gl_WorkGroupID.x; - out_[i + pcs.outOff] = in_[i + pcs.inOff] * pcs.scale; -} diff --git a/ggml/src/kompute-shaders/op_scale_8.comp b/ggml/src/kompute-shaders/op_scale_8.comp deleted file mode 100644 index ada69754..00000000 --- a/ggml/src/kompute-shaders/op_scale_8.comp +++ /dev/null @@ -1,23 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; - float scale; -} pcs; - -void main() { - const uint baseIndex = gl_WorkGroupID.x * 8; - - for (uint x = 0; x < 8; x++) { - const uint i = baseIndex + x; - out_[i + pcs.outOff] = in_[i + pcs.inOff] * pcs.scale; - } -} diff --git a/ggml/src/kompute-shaders/op_silu.comp b/ggml/src/kompute-shaders/op_silu.comp deleted file mode 100644 index 0fb8e4b7..00000000 --- a/ggml/src/kompute-shaders/op_silu.comp +++ /dev/null @@ -1,22 +0,0 @@ -#version 450 - -#include "common.comp" - -layout(local_size_x = 1) in; - -layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; -layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; -layout(push_constant) uniform PushConstants { - uint inOff; - uint outOff; -} pcs; - -void main() { - const uint baseIndex = gl_WorkGroupID.x * 4; - - for (uint x = 0; x < 4; x++) { - const uint i = baseIndex + x; - const float y = in_[i + pcs.inOff]; - out_[i + pcs.outOff] = y / (1.0 + exp(-y)); - } -} diff --git a/ggml/src/kompute-shaders/op_softmax.comp b/ggml/src/kompute-shaders/op_softmax.comp deleted file mode 100644 index 7bc9176c..00000000 --- a/ggml/src/kompute-shaders/op_softmax.comp +++ /dev/null @@ -1,56 +0,0 @@ -// TODO: implement multi-simd softmax (llama.cpp commit e16b9fa4) - -#version 450 - -#include "common.comp" - -layout(local_size_x_id = 0) in; - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -layout(push_constant) uniform PushConstants { - uint inAOff; - uint inBOff; - uint outOff; - int ne00; - int ne01; - int ne02; - float scale; - int mask; -} pcs; - -void main() { - if (gl_SubgroupInvocationID > 31) - return; - - const uint i03 = gl_WorkGroupID.z; - const uint i02 = gl_WorkGroupID.y; - const uint i01 = gl_WorkGroupID.x; - - const uint extra_off = i03*pcs.ne02*pcs.ne01*pcs.ne00 + i02*pcs.ne01*pcs.ne00 + i01*pcs.ne00; - const uint psrc0 = extra_off + pcs.inAOff; // Based from inA - const uint pmask = i01*pcs.ne00 + pcs.inBOff; // Based from inB - const uint pdst = extra_off + pcs.outOff; // Based from out_ - - // parallel max - float localMax = uintBitsToFloat(0xFF800000); - for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f)); - } - float max_ = subgroupMax(localMax); - - // parallel sum - float localSum = 0.0f; - for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f) - max_); - localSum += exp_psrc0; - out_[pdst + i00] = exp_psrc0; - } - - const float sum = subgroupAdd(localSum); - for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - out_[pdst + i00] /= sum; - } -} diff --git a/ggml/src/kompute-shaders/rope_common.comp b/ggml/src/kompute-shaders/rope_common.comp deleted file mode 100644 index 7b9394cb..00000000 --- a/ggml/src/kompute-shaders/rope_common.comp +++ /dev/null @@ -1,67 +0,0 @@ -#include "common.comp" - -// TODO: use a local size of 32 or more (Metal uses 1024) -layout(local_size_x = 1) in; - -layout (push_constant) uniform parameter { - uint inAOff; - uint inBOff; - uint outOff; - int n_dims; - int mode; - int n_ctx_orig; - float freq_base; - float freq_scale; - float ext_factor; - float attn_factor; - float beta_fast; - float beta_slow; - uint nb00; - uint nb01; - uint nb02; - uint nb03; - int ne0; - uint nb0; - uint nb1; - uint nb2; - uint nb3; -} pcs; - -float rope_yarn_ramp(const float low, const float high, const float i0) { - const float y = (i0 / 2 - low) / max(0.001f, high - low); - return 1.0f - min(1.0f, max(0.0f, y)); -} - -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], float i0, float ext_factor, float mscale, - out float cos_theta, out float sin_theta -) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * log(1.0f / freq_scale); - } - cos_theta = cos(theta) * mscale; - sin_theta = sin(theta) * mscale; -} - -// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get -// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) { - return n_dims * log(n_ctx_orig / (n_rot * TWOPI_F)) / (2 * log(base)); -} - -void rope_yarn_corr_dims( - int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, out float dims[2] -) { - // start and end correction dims - dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base))); - dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base))); -} diff --git a/ggml/src/sgemm.cpp b/ggml/src/sgemm.cpp deleted file mode 100644 index 6626ceb2..00000000 --- a/ggml/src/sgemm.cpp +++ /dev/null @@ -1,1027 +0,0 @@ -// Copyright 2024 Mozilla Foundation -// -// Permission is hereby granted, free of charge, to any person obtaining -// a copy of this software and associated documentation files (the -// "Software"), to deal in the Software without restriction, including -// without limitation the rights to use, copy, modify, merge, publish, -// distribute, sublicense, and/or sell copies of the Software, and to -// permit persons to whom the Software is furnished to do so, subject to -// the following conditions: -// -// The above copyright notice and this permission notice shall be -// included in all copies or substantial portions of the Software. -// -// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND -// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS -// BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN -// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN -// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -// SOFTWARE. - -// -// _ _ ___ _ _ ___ -// | |_(_)_ _ _ _| _ ) | /_\ / __| -// | _| | ' \ || | _ \ |__ / _ \\__ \. -// \__|_|_||_\_, |___/____/_/ \_\___/ -// |__/ -// -// BASIC LINEAR ALGEBRA SUBPROGRAMS -// -// -// This file implements multithreaded CPU matrix multiplication for the -// common contiguous use case C = Aᵀ * B. These kernels are designed to -// have excellent performance[1] for matrices that fit in the CPU cache -// without imposing any overhead such as cache filling or malloc calls. -// -// This implementation does not guarantee any upper bound with rounding -// errors, which grow along with k. Our goal's to maximally exploit the -// hardware for performance, and then use whatever resources remain for -// improving numerical accuracy. -// -// [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online]. -// Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024]. - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Wpedantic" -#pragma GCC diagnostic ignored "-Wignored-attributes" -#endif - -#include "sgemm.h" -#include "ggml-impl.h" -#include "ggml-quants.h" - -#ifdef _MSC_VER -#define NOINLINE __declspec(noinline) -#else -#define NOINLINE __attribute__((__noinline__)) -#endif - -#if defined(__ARM_NEON) || defined(__AVX512F__) -#define VECTOR_REGISTERS 32 -#else -#define VECTOR_REGISTERS 16 -#endif - -#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) - -namespace { - -inline float unhalf(ggml_fp16_t d) { - return GGML_FP16_TO_FP32(d); -} - -//////////////////////////////////////////////////////////////////////////////////////////////////// -// VECTORIZED ARITHMETIC OPERATIONS - -#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -inline __m128 add(__m128 x, __m128 y) { return _mm_add_ps(x, y); } -inline __m128 sub(__m128 x, __m128 y) { return _mm_sub_ps(x, y); } -inline __m128 mul(__m128 x, __m128 y) { return _mm_mul_ps(x, y); } -#endif // __SSE__ - -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -inline __m256 add(__m256 x, __m256 y) { return _mm256_add_ps(x, y); } -inline __m256 sub(__m256 x, __m256 y) { return _mm256_sub_ps(x, y); } -inline __m256 mul(__m256 x, __m256 y) { return _mm256_mul_ps(x, y); } -#endif // __AVX__ - -#if defined(__AVX512F__) -inline __m512 add(__m512 x, __m512 y) { return _mm512_add_ps(x, y); } -inline __m512 sub(__m512 x, __m512 y) { return _mm512_sub_ps(x, y); } -inline __m512 mul(__m512 x, __m512 y) { return _mm512_mul_ps(x, y); } -#endif // __AVX512F__ - -#if defined(__ARM_NEON) -inline float32x4_t add(float32x4_t x, float32x4_t y) { return vaddq_f32(x, y); } -inline float32x4_t sub(float32x4_t x, float32x4_t y) { return vsubq_f32(x, y); } -inline float32x4_t mul(float32x4_t x, float32x4_t y) { return vmulq_f32(x, y); } -#endif // __ARM_NEON - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) -inline float16x8_t add(float16x8_t x, float16x8_t y) { return vaddq_f16(x, y); } -inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); } -inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); } -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - -//////////////////////////////////////////////////////////////////////////////////////////////////// -// VECTORIZED FUSED MULTIPLY ADD - -/** - * Computes a * b + c. - */ -template -inline U madd(T a, T b, U c) { - return add(mul(a, b), c); -} - -#if defined(__FMA__) -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -template <> -inline __m256 madd(__m256 a, __m256 b, __m256 c) { - return _mm256_fmadd_ps(a, b, c); -} -#endif -#if defined(__AVX512F__) -template <> -inline __m512 madd(__m512 a, __m512 b, __m512 c) { - return _mm512_fmadd_ps(a, b, c); -} -#endif -#endif - -#if defined(__ARM_FEATURE_FMA) -template <> -inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) { - return vfmaq_f32(c, b, a); -} -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) -template <> -inline float16x8_t madd(float16x8_t a, float16x8_t b, float16x8_t c) { - return vfmaq_f16(c, b, a); -} -#endif -#endif - -//////////////////////////////////////////////////////////////////////////////////////////////////// -// VECTORIZED HORIZONTAL SUM - -#if defined(__ARM_NEON) -inline float hsum(float32x4_t x) { - return vaddvq_f32(x); -} -#endif // __ARM_NEON - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) -inline float hsum(float16x8_t x) { - return vaddvq_f32(vaddq_f32(vcvt_f32_f16(vget_low_f16(x)), - vcvt_f32_f16(vget_high_f16(x)))); -} -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - -#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -inline float hsum(__m128 x) { -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) - x = _mm_add_ps(x, _mm_movehl_ps(x, x)); - x = _mm_add_ss(x, _mm_movehdup_ps(x)); -#else - __m128 t; - t = _mm_shuffle_ps(x, x, _MM_SHUFFLE(2, 3, 0, 1)); - x = _mm_add_ps(x, t); - t = _mm_movehl_ps(t, x); - x = _mm_add_ss(x, t); -#endif - return _mm_cvtss_f32(x); -} -#endif - -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -inline float hsum(__m256 x) { - return hsum(_mm_add_ps(_mm256_extractf128_ps(x, 1), - _mm256_castps256_ps128(x))); -} -#endif // __AVX__ - -#if defined(__AVX512F__) -inline float hsum(__m512 x) { - return _mm512_reduce_add_ps(x); -} -#endif // __AVX512F__ - -//////////////////////////////////////////////////////////////////////////////////////////////////// -// VECTORIZED MEMORY LOADING - -template T load(const U *); - -#if defined(__ARM_NEON) -template <> inline float32x4_t load(const float *p) { - return vld1q_f32(p); -} -#if !defined(_MSC_VER) -template <> inline float16x8_t load(const ggml_fp16_t *p) { - return vld1q_f16((const float16_t *)p); -} -template <> inline float32x4_t load(const ggml_fp16_t *p) { - return vcvt_f32_f16(vld1_f16((const float16_t *)p)); -} -#endif // _MSC_VER -#endif // __ARM_NEON - -#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -template <> inline __m128 load(const float *p) { - return _mm_loadu_ps(p); -} -#endif // __SSE__ - -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) -template <> inline __m256 load(const float *p) { - return _mm256_loadu_ps(p); -} -#endif // __AVX__ - -#if defined(__F16C__) -template <> inline __m256 load(const ggml_fp16_t *p) { - return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p)); -} -#endif // __F16C__ - -#if defined(__AVX512F__) -template <> inline __m512 load(const float *p) { - return _mm512_loadu_ps(p); -} -template <> inline __m512 load(const ggml_fp16_t *p) { - return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p)); -} -#endif // __AVX512F__ - -//////////////////////////////////////////////////////////////////////////////////////////////////// -// FLOATING POINT MATRIX MULTIPLICATION - -template -class tinyBLAS { - public: - tinyBLAS(int64_t k, - const TA *A, int64_t lda, - const TB *B, int64_t ldb, - TC *C, int64_t ldc, - int ith, int nth) - : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { - } - - void matmul(int64_t m, int64_t n) { - mnpack(0, m, 0, n); - } - - private: - NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t mc, nc, mp, np; - switch ((MIN(m - m0, 5) << 4) | MIN(n - n0, 5)) { -#if VECTOR_REGISTERS == 32 - case 0x55: - mc = 5; - nc = 5; - gemm<5, 5>(m0, m, n0, n); - break; - case 0x45: - mc = 4; - nc = 5; - gemm<4, 5>(m0, m, n0, n); - break; - case 0x54: - mc = 5; - nc = 4; - gemm<5, 4>(m0, m, n0, n); - break; - case 0x44: - mc = 4; - nc = 4; - gemm<4, 4>(m0, m, n0, n); - break; - case 0x53: - mc = 5; - nc = 3; - gemm<5, 3>(m0, m, n0, n); - break; - case 0x35: - mc = 3; - nc = 5; - gemm<3, 5>(m0, m, n0, n); - break; - case 0x43: - mc = 4; - nc = 3; - gemm<4, 3>(m0, m, n0, n); - break; -#else - case 0x55: - case 0x54: - case 0x53: - case 0x45: - case 0x44: - case 0x43: - mc = 4; - nc = 3; - gemm<4, 3>(m0, m, n0, n); - break; - case 0x35: -#endif - case 0x34: - mc = 3; - nc = 4; - gemm<3, 4>(m0, m, n0, n); - break; - case 0x52: - mc = 5; - nc = 2; - gemm<5, 2>(m0, m, n0, n); - break; - case 0x33: - mc = 3; - nc = 3; - gemm<3, 3>(m0, m, n0, n); - break; - case 0x25: - mc = 2; - nc = 5; - gemm<2, 5>(m0, m, n0, n); - break; - case 0x42: - mc = 4; - nc = 2; - gemm<4, 2>(m0, m, n0, n); - break; - case 0x24: - mc = 2; - nc = 4; - gemm<2, 4>(m0, m, n0, n); - break; - case 0x32: - mc = 3; - nc = 2; - gemm<3, 2>(m0, m, n0, n); - break; - case 0x23: - mc = 2; - nc = 3; - gemm<2, 3>(m0, m, n0, n); - break; - case 0x51: - mc = 5; - nc = 1; - gemm<5, 1>(m0, m, n0, n); - break; - case 0x41: - mc = 4; - nc = 1; - gemm<4, 1>(m0, m, n0, n); - break; - case 0x22: - mc = 2; - nc = 2; - gemm<2, 2>(m0, m, n0, n); - break; - case 0x15: - mc = 1; - nc = 5; - gemm<1, 5>(m0, m, n0, n); - break; - case 0x14: - mc = 1; - nc = 4; - gemm<1, 4>(m0, m, n0, n); - break; - case 0x31: - mc = 3; - nc = 1; - gemm<3, 1>(m0, m, n0, n); - break; - case 0x13: - mc = 1; - nc = 3; - gemm<1, 3>(m0, m, n0, n); - break; - case 0x21: - mc = 2; - nc = 1; - gemm<2, 1>(m0, m, n0, n); - break; - case 0x12: - mc = 1; - nc = 2; - gemm<1, 2>(m0, m, n0, n); - break; - case 0x11: - mc = 1; - nc = 1; - gemm<1, 1>(m0, m, n0, n); - break; - default: - return; - } - mp = m0 + (m - m0) / mc * mc; - np = n0 + (n - n0) / nc * nc; - mnpack(mp, m, n0, np); - mnpack(m0, m, np, n); - } - - template - NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t ytiles = (m - m0) / RM; - int64_t xtiles = (n - n0) / RN; - int64_t tiles = xtiles * ytiles; - int64_t duty = (tiles + nth - 1) / nth; - int64_t start = duty * ith; - int64_t end = start + duty; - if (end > tiles) - end = tiles; - for (int64_t job = start; job < end; ++job) { - int64_t ii = m0 + job / xtiles * RM; - int64_t jj = n0 + job % xtiles * RN; - D Cv[RN][RM] = {}; - for (int64_t l = 0; l < k; l += KN) - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - Cv[j][i] = madd(load(A + lda * (ii + i) + l), - load(B + ldb * (jj + j) + l), - Cv[j][i]); - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); - } - } - - const TA *const A; - const TB *const B; - TC *const C; - const int64_t k; - const int64_t lda; - const int64_t ldb; - const int64_t ldc; - const int ith; - const int nth; -}; - -////////////////////////////////////////////////////////////////////////////////////////// -// QUANT ZERO MATRIX MULTIPLICATION - -#if defined(__ARM_FEATURE_DOTPROD) -template -class tinyBLAS_Q0_ARM { - public: - tinyBLAS_Q0_ARM(int64_t k, - const TA *A, int64_t lda, - const block_q8_0 *B, int64_t ldb, - float *C, int64_t ldc, - int ith, int nth) - : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { - } - - void matmul(int64_t m, int64_t n) { - mnpack(0, m, 0, n); - } - - private: - NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t mc, nc, mp, np; - switch ((MIN(m - m0, 3) << 4) | MIN(n - n0, 3ll)) { - case 0x33: - mc = 3; - nc = 3; - gemm<3, 3>(m0, m, n0, n); - break; - case 0x32: - mc = 3; - nc = 2; - gemm<3, 2>(m0, m, n0, n); - break; - case 0x23: - mc = 2; - nc = 3; - gemm<2, 3>(m0, m, n0, n); - break; - case 0x22: - mc = 2; - nc = 2; - gemm<2, 2>(m0, m, n0, n); - break; - case 0x31: - mc = 3; - nc = 1; - gemm<3, 1>(m0, m, n0, n); - break; - case 0x13: - mc = 1; - nc = 3; - gemm<1, 3>(m0, m, n0, n); - break; - case 0x21: - mc = 2; - nc = 1; - gemm<2, 1>(m0, m, n0, n); - break; - case 0x12: - mc = 1; - nc = 2; - gemm<1, 2>(m0, m, n0, n); - break; - case 0x11: - mc = 1; - nc = 1; - gemm<1, 1>(m0, m, n0, n); - break; - default: - return; - } - mp = m0 + (m - m0) / mc * mc; - np = n0 + (n - n0) / nc * nc; - mnpack(mp, m, n0, np); - mnpack(m0, m, np, n); - } - - template - NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t ytiles = (m - m0) / RM; - int64_t xtiles = (n - n0) / RN; - int64_t tiles = xtiles * ytiles; - int64_t duty = (tiles + nth - 1) / nth; - int64_t start = duty * ith; - int64_t end = start + duty; - if (end > tiles) - end = tiles; - for (int64_t job = start; job < end; ++job) { - int64_t ii = m0 + job / xtiles * RM; - int64_t jj = n0 + job % xtiles * RN; - float32x4_t Cv[RN][RM] = {}; - for (int64_t l = 0; l < k; ++l) - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - Cv[j][i] = vmlaq_n_f32(Cv[j][i], - vcvtq_f32_s32(vdotq_s32( - vdotq_s32(vdupq_n_s32(0), - load_lo(A + lda * (ii + i) + l), - load_lo(B + ldb * (jj + j) + l)), - load_hi(A + lda * (ii + i) + l), - load_hi(B + ldb * (jj + j) + l))), - unhalf(A[lda * (ii + i) + l].d) * - unhalf(B[ldb * (jj + j) + l].d)); - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); - } - } - - inline int8x16_t load_lo(const block_q8_0 *b) { - return vld1q_s8(b->qs); - } - - inline int8x16_t load_hi(const block_q8_0 *b) { - return vld1q_s8(b->qs + 16); - } - - inline int8x16_t load_lo(const block_q4_0 *b) { - return vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vld1q_u8(b->qs), - vdupq_n_u8(0x0f))), - vdupq_n_s8(0x8)); - } - - inline int8x16_t load_hi(const block_q4_0 *b) { - return vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(vld1q_u8(b->qs), 4)), - vdupq_n_s8(0x8)); - } - - const TA *const A; - const block_q8_0 *const B; - float *const C; - const int64_t k; - const int64_t lda; - const int64_t ldb; - const int64_t ldc; - const int ith; - const int nth; -}; -#endif // __ARM_FEATURE_DOTPROD - -#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) -template -class tinyBLAS_Q0_AVX { - public: - tinyBLAS_Q0_AVX(int64_t k, - const TA *A, int64_t lda, - const TB *B, int64_t ldb, - TC *C, int64_t ldc, - int ith, int nth) - : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { - } - - void matmul(int64_t m, int64_t n) { - mnpack(0, m, 0, n); - } - - private: - void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t mc, nc, mp, np; - switch ((MIN(m - m0, 4) << 4) | MIN(n - n0, 4)) { -#if VECTOR_REGISTERS == 32 - case 0x44: - mc = 4; - nc = 4; - gemm<4, 4>(m0, m, n0, n); - break; - case 0x43: - mc = 4; - nc = 3; - gemm<4, 3>(m0, m, n0, n); - break; - case 0x34: - mc = 3; - nc = 4; - gemm<3, 4>(m0, m, n0, n); - break; - case 0x33: - mc = 3; - nc = 3; - gemm<3, 3>(m0, m, n0, n); - break; - case 0x42: - mc = 4; - nc = 2; - gemm<4, 2>(m0, m, n0, n); - break; - case 0x24: - mc = 2; - nc = 4; - gemm<2, 4>(m0, m, n0, n); - break; -#else - case 0x44: - case 0x43: - case 0x42: - mc = 4; - nc = 2; - gemm<4, 2>(m0, m, n0, n); - break; - case 0x34: - case 0x24: - mc = 2; - nc = 4; - gemm<2, 4>(m0, m, n0, n); - break; - case 0x33: -#endif - case 0x32: - mc = 3; - nc = 2; - gemm<3, 2>(m0, m, n0, n); - break; - case 0x23: - mc = 2; - nc = 3; - gemm<2, 3>(m0, m, n0, n); - break; - case 0x41: - mc = 4; - nc = 1; - gemm<4, 1>(m0, m, n0, n); - break; - case 0x22: - mc = 2; - nc = 2; - gemm<2, 2>(m0, m, n0, n); - break; - case 0x14: - mc = 1; - nc = 4; - gemm<1, 4>(m0, m, n0, n); - break; - case 0x31: - mc = 3; - nc = 1; - gemm<3, 1>(m0, m, n0, n); - break; - case 0x13: - mc = 1; - nc = 3; - gemm<1, 3>(m0, m, n0, n); - break; - case 0x21: - mc = 2; - nc = 1; - gemm<2, 1>(m0, m, n0, n); - break; - case 0x12: - mc = 1; - nc = 2; - gemm<1, 2>(m0, m, n0, n); - break; - case 0x11: - mc = 1; - nc = 1; - gemm<1, 1>(m0, m, n0, n); - break; - default: - return; - } - mp = m0 + (m - m0) / mc * mc; - np = n0 + (n - n0) / nc * nc; - mnpack(mp, m, n0, np); - mnpack(m0, m, np, n); - } - - template - NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { - int64_t ytiles = (m - m0) / RM; - int64_t xtiles = (n - n0) / RN; - int64_t tiles = xtiles * ytiles; - int64_t duty = (tiles + nth - 1) / nth; - int64_t start = duty * ith; - int64_t end = start + duty; - if (end > tiles) - end = tiles; - for (int64_t job = start; job < end; ++job) { - int64_t ii = m0 + job / xtiles * RM; - int64_t jj = n0 + job % xtiles * RN; - __m256 Cv[RN][RM] = {}; - for (int64_t l = 0; l < k; ++l) - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) { -#if defined(__AVX2__) - __m256 udTmp = updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l), - load(A + lda * (ii + i) + l)), - _mm256_sign_epi8(load(B + ldb * (jj + j) + l), - load(A + lda * (ii + i) + l))); -#else - __m128i ali0 = load0(A + lda * (ii + i) + l); - __m128i ali1 = load1(A + lda * (ii + i) + l); - __m128i blj0 = load0(B + ldb * (jj + j) + l); - __m128i blj1 = load1(B + ldb * (jj + j) + l); - - __m128i sepAA0 = _mm_sign_epi8(ali0, ali0); - __m128i sepAA1 = _mm_sign_epi8(ali1, ali1); - __m128i sepBA0 = _mm_sign_epi8(blj0, ali0); - __m128i sepBA1 = _mm_sign_epi8(blj1, ali1); - - // updot - const __m128i oneFill = _mm_set1_epi16(1); - __m128i mad0 = _mm_maddubs_epi16(sepAA0, sepBA0); - __m128i mad1 = _mm_maddubs_epi16(sepAA1, sepBA1); - __m256 udTmp = _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_madd_epi16(oneFill, mad1), _mm_madd_epi16(oneFill, mad0))); -#endif - Cv[j][i] = madd(_mm256_set1_ps(unhalf(A[lda * (ii + i) + l].d) * - unhalf(B[ldb * (jj + j) + l].d)), - udTmp, - Cv[j][i]); - } - for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) - C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); - } - } - - inline __m256i load(const block_q8_0 *b) { - return _mm256_loadu_si256((const __m256i *)b->qs); - } - - inline __m128i load0(const block_q8_0 *b) { - return _mm_loadu_si128((const __m128i *)b->qs); - } - - inline __m128i load1(const block_q8_0 *b) { - return _mm_loadu_si128(((const __m128i *)b->qs) + 1); - } - - inline __m256i load(const block_q4_0 *b) { - return _mm256_sub_epi8(denibble(b->qs), _mm256_set1_epi8(8)); - } - - inline __m128i load0(const block_q4_0 *b) { - const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); - return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), x), _mm_set1_epi8(8)); - } - - inline __m128i load1(const block_q4_0 *b) { - const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); - return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8)); - } - - inline __m256 updot(__m256i u, __m256i s) { - __m256i res; -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - res = _mm256_dpbusd_epi32(_mm256_setzero_si256(), u, s); -#else - res = _mm256_madd_epi16(_mm256_set1_epi16(1), _mm256_maddubs_epi16(u, s)); -#endif - return _mm256_cvtepi32_ps(res); - } - - static inline __m256i denibble(const uint8_t *p) { - __m128i x = _mm_loadu_si128((const __m128i *)p); - return _mm256_and_si256(_mm256_set1_epi8(15), - _mm256_insertf128_si256(_mm256_castsi128_si256(x), - _mm_srli_epi16(x, 4), 1)); - } - - const TA *const A; - const TB *const B; - TC *const C; - const int64_t k; - const int64_t lda; - const int64_t ldb; - const int64_t ldc; - const int ith; - const int nth; -}; -#endif // __AVX__ - -} // namespace - -/** - * Performs optimized matrix multiplication on CPU. - * - * This subroutine may compute C = Aᵀ * B with column major ordering. - * Despite its name, this isn't a generalized implementation. Work is - * only performed when a handwritten kernel is written and available. - * Otherwise the caller should fall back to a general matmul routine. - * - * For example, for single-threaded single-precision GEMM you can say - * - * llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc, - * 0, 1, - * GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32); - * - * @param m is rows in `A` and `C` - * @param n is cols in `B` and `C` - * @param k is cols in `A` and rows in `B` - * @param A is first input matrix (always transposed) - * @param lda is row stride of `A` - * @param B is second input matrix (never transposed) - * @param ldb is row stride of `B` - * @param C is input/output array of output matrices - * @param ldc is row stride of `C` - * @param ith is thread id (must be less than `nth`) - * @param nth is number of threads (must be greater than zero) - * @param Atype is GGML data type of `A` - * @param Btype is GGML data type of `B` - * @param Ctype is GGML data type of `C` - * @return true if this function was able to service the matmul request - */ -bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C, - int64_t ldc, int ith, int nth, int Atype, int Btype, int Ctype) { - - assert(m >= 0); - assert(n >= 0); - assert(k >= 0); - assert(lda >= k); - assert(ldb >= k); - assert(ldc >= m); - assert(nth > 0); - assert(ith < nth); - - if (Ctype != GGML_TYPE_F32) - return false; - - switch (Atype) { - - case GGML_TYPE_F32: { - if (Btype != GGML_TYPE_F32) - return false; -#if defined(__AVX512F__) - if (k % 16) - return false; - tinyBLAS<16, __m512, __m512, float, float, float> tb{ - k, (const float *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif defined(__AVX__) || defined(__AVX2__) - if (k % 8) - return false; - tinyBLAS<8, __m256, __m256, float, float, float> tb{ - k, (const float *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif defined(__ARM_NEON) - if (n < 4) - return false; - if (k % 4) - return false; - tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ - k, (const float *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#else - return false; -#endif - } - - case GGML_TYPE_F16: { -#if defined(__AVX512F__) - if (k % 16) - return false; - if (Btype != GGML_TYPE_F32) - return false; - tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__) - if (k % 8) - return false; - if (Btype != GGML_TYPE_F32) - return false; - tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) - if (n < 8) - return false; - if (k % 8) - return false; - if (Btype != GGML_TYPE_F16) - return false; - tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const ggml_fp16_t *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif defined(__ARM_NEON) && !defined(_MSC_VER) - if (k % 4) - return false; - if (Btype != GGML_TYPE_F32) - return false; - tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{ - k, (const ggml_fp16_t *)A, lda, - (const float *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#else - return false; -#endif - } - - case GGML_TYPE_Q8_0: { - if (Btype != GGML_TYPE_Q8_0) - return false; -#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) - tinyBLAS_Q0_AVX tb{ - k, (const block_q8_0 *)A, lda, - (const block_q8_0 *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif defined(__ARM_FEATURE_DOTPROD) - tinyBLAS_Q0_ARM tb{ - k, (const block_q8_0 *)A, lda, - (const block_q8_0 *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#else - return false; -#endif - } - - case GGML_TYPE_Q4_0: { - if (Btype != GGML_TYPE_Q8_0) - return false; -#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) - tinyBLAS_Q0_AVX tb{ - k, (const block_q4_0 *)A, lda, - (const block_q8_0 *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#elif defined(__ARM_FEATURE_DOTPROD) - tinyBLAS_Q0_ARM tb{ - k, (const block_q4_0 *)A, lda, - (const block_q8_0 *)B, ldb, - (float *)C, ldc, - ith, nth}; - tb.matmul(m, n); - return true; -#else - return false; -#endif - } - - default: - return false; - } - - (void)m; - (void)n; - (void)k; - (void)A; - (void)lda; - (void)B; - (void)ldb; - (void)C; - (void)ldc; - (void)ith; - (void)nth; - (void)Atype; - (void)Btype; - (void)Ctype; -} diff --git a/ggml/src/sgemm.h b/ggml/src/sgemm.h deleted file mode 100644 index caf6dd55..00000000 --- a/ggml/src/sgemm.h +++ /dev/null @@ -1,14 +0,0 @@ -#pragma once -#include -#include -#ifdef __cplusplus -extern "C" { -#endif - -bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t, - const void *, int64_t, void *, int64_t, int, int, - int, int, int); - -#ifdef __cplusplus -} -#endif diff --git a/ggml/src/vulkan-shaders/CMakeLists.txt b/ggml/src/vulkan-shaders/CMakeLists.txt deleted file mode 100644 index 10075db3..00000000 --- a/ggml/src/vulkan-shaders/CMakeLists.txt +++ /dev/null @@ -1,7 +0,0 @@ -find_package (Threads REQUIRED) - -set(TARGET vulkan-shaders-gen) -add_executable(${TARGET} vulkan-shaders-gen.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_compile_features(${TARGET} PRIVATE cxx_std_11) -target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads) diff --git a/ggml/src/vulkan-shaders/acc.comp b/ggml/src/vulkan-shaders/acc.comp deleted file mode 100644 index 4c8739ef..00000000 --- a/ggml/src/vulkan-shaders/acc.comp +++ /dev/null @@ -1,24 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = gl_GlobalInvocationID.x; - if (idx >= p.ne) { - return; - } - - const uint offset = p.param3; - const uint src1_i = idx - offset; - const uint oz = src1_i / p.nb02; - const uint oy = (src1_i - (oz * p.nb02)) / p.nb01; - const uint ox = src1_i % p.nb01; - - if (ox < p.ne10 && oy < p.ne11 && oz < p.ne12) { - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); - } else { - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)])); - } -} - diff --git a/ggml/src/vulkan-shaders/add.comp b/ggml/src/vulkan-shaders/add.comp deleted file mode 100644 index 3974845d..00000000 --- a/ggml/src/vulkan-shaders/add.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/argsort.comp b/ggml/src/vulkan-shaders/argsort.comp deleted file mode 100644 index d4fa45b1..00000000 --- a/ggml/src/vulkan-shaders/argsort.comp +++ /dev/null @@ -1,69 +0,0 @@ -#version 450 - -#include "types.comp" - -#define BLOCK_SIZE 1024 -#define ASC 0 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) buffer D {int data_d[];}; - -layout (push_constant) uniform parameter { - uint ncols; - uint ncols_pad; - uint order; -} p; - -shared int dst_row[BLOCK_SIZE]; - -void swap(uint idx0, uint idx1) { - int tmp = dst_row[idx0]; - dst_row[idx0] = dst_row[idx1]; - dst_row[idx1] = tmp; -} - -void main() { - // bitonic sort - const int col = int(gl_LocalInvocationID.x); - const uint row = gl_WorkGroupID.y; - - const uint row_offset = row * p.ncols; - - // initialize indices - if (col < p.ncols_pad) { - dst_row[col] = col; - } - barrier(); - - for (uint k = 2; k <= p.ncols_pad; k *= 2) { - for (uint j = k / 2; j > 0; j /= 2) { - const uint ixj = col ^ j; - if (col < p.ncols_pad && ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= p.ncols || - (dst_row[ixj] < p.ncols && (p.order == ASC ? - data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] : - data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]])) - ) { - swap(col, ixj); - } - } else { - if (dst_row[ixj] >= p.ncols || - (dst_row[col] < p.ncols && (p.order == ASC ? - data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] : - data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]])) - ) { - swap(col, ixj); - } - } - } - barrier(); - } - } - - if (col < p.ncols) { - data_d[row_offset + col] = dst_row[col]; - } -} diff --git a/ggml/src/vulkan-shaders/clamp.comp b/ggml/src/vulkan-shaders/clamp.comp deleted file mode 100644 index ae8fa875..00000000 --- a/ggml/src/vulkan-shaders/clamp.comp +++ /dev/null @@ -1,17 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); -} diff --git a/ggml/src/vulkan-shaders/concat.comp b/ggml/src/vulkan-shaders/concat.comp deleted file mode 100644 index c23b6eb1..00000000 --- a/ggml/src/vulkan-shaders/concat.comp +++ /dev/null @@ -1,39 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - const int dim = p.param3; - - if (idx >= p.ne) { - return; - } - - const uint i3 = idx / (p.ne22*p.ne21*p.ne20); - const uint i3_offset = i3 * p.ne22*p.ne21*p.ne20; - const uint i2 = (idx - i3_offset) / (p.ne21*p.ne20); - const uint i2_offset = i2*p.ne21*p.ne20; - const uint i1 = (idx - i3_offset - i2_offset) / p.ne20; - const uint i0 = idx - i3_offset - i2_offset - i1*p.ne20; - - uint o[4] = {0, 0, 0, 0}; - o[dim] = dim == 0 ? p.ne00 : (dim == 1 ? p.ne01 : (dim == 2 ? p.ne02 : p.ne03)); - - const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00; - const uint src1_idx = (i3 - o[3])*p.nb13 + (i2 - o[2])*p.nb12 + (i1 - o[1])*p.nb11 + (i0 - o[0])*p.nb10; - const uint dst_idx = i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20; - - const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03; - -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : data_b[src1_idx]); -#else - if (is_src0) { - data_d[p.d_offset + dst_idx] = data_a[src0_idx]; - } else { - data_d[p.d_offset + dst_idx] = data_b[src1_idx]; - } -#endif -} diff --git a/ggml/src/vulkan-shaders/contig_copy.comp b/ggml/src/vulkan-shaders/contig_copy.comp deleted file mode 100644 index 9acbdd3d..00000000 --- a/ggml/src/vulkan-shaders/contig_copy.comp +++ /dev/null @@ -1,42 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -#extension GL_EXT_control_flow_attributes : require - -const uint num_threads = 128; - -layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; - -void main() { - uint idx = get_idx(); - - // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation - const uint num_iter = 4; - - // fast path for when all four iterations are in-bounds - if (idx + (num_iter-1)*num_threads < p.ne) { - [[unroll]] for (uint i = 0; i < num_iter; ++i) { -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); -#else - data_d[p.d_offset + idx] = data_a[idx]; -#endif - idx += num_threads; - } - } else { - [[unroll]] for (uint i = 0; i < num_iter; ++i) { - if (idx >= p.ne) { - continue; - } - -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); -#else - data_d[p.d_offset + idx] = data_a[idx]; -#endif - idx += num_threads; - } - } -} diff --git a/ggml/src/vulkan-shaders/copy.comp b/ggml/src/vulkan-shaders/copy.comp deleted file mode 100644 index 2775068f..00000000 --- a/ggml/src/vulkan-shaders/copy.comp +++ /dev/null @@ -1,20 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx(idx)]); -#else - data_d[p.d_offset + dst_idx(idx)] = data_a[src0_idx(idx)]; -#endif -} diff --git a/ggml/src/vulkan-shaders/cos.comp b/ggml/src/vulkan-shaders/cos.comp deleted file mode 100644 index fbd9d272..00000000 --- a/ggml/src/vulkan-shaders/cos.comp +++ /dev/null @@ -1,17 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(cos(val)); -} diff --git a/ggml/src/vulkan-shaders/dequant_f32.comp b/ggml/src/vulkan-shaders/dequant_f32.comp deleted file mode 100644 index a4d3fca5..00000000 --- a/ggml/src/vulkan-shaders/dequant_f32.comp +++ /dev/null @@ -1,20 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_GlobalInvocationID.x * 16; - - if (i >= p.nel) { - return; - } - - [[unroll]] for (uint l = 0; l < 16; l++) { - data_b[i + l] = D_TYPE(data_a[i + l]); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_funcs.comp b/ggml/src/vulkan-shaders/dequant_funcs.comp deleted file mode 100644 index d5b98973..00000000 --- a/ggml/src/vulkan-shaders/dequant_funcs.comp +++ /dev/null @@ -1,68 +0,0 @@ -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -#endif - -#if defined(DATA_A_F32) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -#endif - -#if defined(DATA_A_F16) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -#endif - -#if defined(DATA_A_Q4_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; -} -#endif - -#if defined(DATA_A_Q4_1) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(vui & 0xF, vui >> 4) * d + m; -} -#endif - -#if defined(DATA_A_Q5_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; -} -#endif - -#if defined(DATA_A_Q5_1) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint uint_qh = data_a[a_offset + ib].qh; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; -} -#endif - -#if defined(DATA_A_Q8_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; -} -#endif - -#if defined(DATA_A_IQ4_NL) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d; -} -#endif diff --git a/ggml/src/vulkan-shaders/dequant_head.comp b/ggml/src/vulkan-shaders/dequant_head.comp deleted file mode 100644 index 8d806435..00000000 --- a/ggml/src/vulkan-shaders/dequant_head.comp +++ /dev/null @@ -1,13 +0,0 @@ -#extension GL_EXT_control_flow_attributes : require -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint M; - uint K; - uint stride_a; - uint stride_b; - uint nel; -} p; - -#include "types.comp" diff --git a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp b/ggml/src/vulkan-shaders/dequant_iq4_nl.comp deleted file mode 100644 index 34ef3da3..00000000 --- a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp +++ /dev/null @@ -1,30 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_iq4_nl data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint q_idx = 8*il; - const uint b_idx = 1024*i + 32*ir + q_idx; - - const float d = float(data_a[ib].d); - - [[unroll]] for (uint l = 0; l < 8; ++l) { - data_b[b_idx + l + 0] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] & 0xF]); - data_b[b_idx + l + 16] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] >> 4]); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q2_k.comp b/ggml/src/vulkan-shaders/dequant_q2_k.comp deleted file mode 100644 index 157154af..00000000 --- a/ggml/src/vulkan-shaders/dequant_q2_k.comp +++ /dev/null @@ -1,34 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint tid = gl_LocalInvocationID.x; - const uint ip = tid / 32; - const uint il = tid - 32 * ip; - const uint is = 8 * ip + il / 16; - - const uint y_idx = i * QUANT_K + 128 * ip + il; - - const uint ql_idx = 32 * ip + il; - const uint8_t qs = data_a[i].qs[32 * ip + il]; - - FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); - FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - data_b[y_idx + 0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4)); - data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4)); - data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4)); - data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4)); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q3_k.comp b/ggml/src/vulkan-shaders/dequant_q3_k.comp deleted file mode 100644 index c17dd0d9..00000000 --- a/ggml/src/vulkan-shaders/dequant_q3_k.comp +++ /dev/null @@ -1,42 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = uint(gl_WorkGroupID.x * 256 + wgy); - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint r = gl_LocalInvocationID.x / 4; - const uint tid = r / 2; - const uint is0 = r % 2; - const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4); - const uint n = tid / 4; - const uint j = tid - 4*n; - - const uint8_t m = uint8_t(1 << (4*n + j)); - const uint is = 8*n + 2*j + is0; - const uint shift = 2*j; - - const int8_t us = int8_t(is < 4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) : - (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4)); - const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d); - const FLOAT_TYPE dl = d_all * FLOAT_TYPE(us - 32); - - const uint y_idx = i * QUANT_K + 128 * n + 32 * j; - const uint qs_idx = 32*n; - - for (uint l = l0; l < l0 + 4; ++l) { - data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4))); - } - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q4_0.comp b/ggml/src/vulkan-shaders/dequant_q4_0.comp deleted file mode 100644 index 40818532..00000000 --- a/ggml/src/vulkan-shaders/dequant_q4_0.comp +++ /dev/null @@ -1,30 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q4_0 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint q_idx = 8*il; - const uint b_idx = 1024*i + 32*ir + q_idx; - - const float d = float(data_a[ib].d); - - [[unroll]] for (uint l = 0; l < 8; ++l) { - data_b[b_idx + l + 0] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] & 0xF) - 8.0f)); - data_b[b_idx + l + 16] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] >> 4) - 8.0f)); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q4_1.comp b/ggml/src/vulkan-shaders/dequant_q4_1.comp deleted file mode 100644 index 2f27eee6..00000000 --- a/ggml/src/vulkan-shaders/dequant_q4_1.comp +++ /dev/null @@ -1,32 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q4_1 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m); - data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + m); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q4_k.comp b/ggml/src/vulkan-shaders/dequant_q4_k.comp deleted file mode 100644 index 92acb754..00000000 --- a/ggml/src/vulkan-shaders/dequant_q4_k.comp +++ /dev/null @@ -1,56 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint tid = gl_LocalInvocationID.x; - const uint il = tid / 8; - const uint ir = tid % 8; - const uint is = 2 * il; - const uint n = 4; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - - const uint y_idx = i * QUANT_K + 64 * il + n * ir; - const uint qs_idx = 32*il + n * ir; - - uint8_t sc; - uint8_t m; - if (is < 4) { - sc = uint8_t(data_a[i].scales[is] & 63); - m = uint8_t(data_a[i].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 4] >> 4) | ((data_a[i].scales[is ] >> 6) << 4)); - } - const FLOAT_TYPE d1 = dall * sc; - const FLOAT_TYPE m1 = dmin * m; - - if (is < 4) { - sc = uint8_t(data_a[i].scales[is + 1] & 63); - m = uint8_t(data_a[i].scales[is + 5] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 5] >> 4) | ((data_a[i].scales[is + 1] >> 6) << 4)); - } - const FLOAT_TYPE d2 = dall * sc; - const FLOAT_TYPE m2 = dmin * m; - - [[unroll]] for (uint l = 0; l < n; ++l) { - data_b[y_idx + l ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1); - data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >> 4) - m2); - } - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q5_0.comp b/ggml/src/vulkan-shaders/dequant_q5_0.comp deleted file mode 100644 index b20b8052..00000000 --- a/ggml/src/vulkan-shaders/dequant_q5_0.comp +++ /dev/null @@ -1,34 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q5_0 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - const uint iqs = q_idx + l; - const uint vui = uint(data_a[ib].qs[iqs]); - data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f)); - data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f)); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q5_1.comp b/ggml/src/vulkan-shaders/dequant_q5_1.comp deleted file mode 100644 index dc59fe3b..00000000 --- a/ggml/src/vulkan-shaders/dequant_q5_1.comp +++ /dev/null @@ -1,35 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q5_1 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - const uint qh = data_a[ib].qh; - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - const uint iqs = q_idx + l; - const uint vui = uint(data_a[ib].qs[iqs]); - data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m); - data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10))) + m); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q5_k.comp b/ggml/src/vulkan-shaders/dequant_q5_k.comp deleted file mode 100644 index f314a76d..00000000 --- a/ggml/src/vulkan-shaders/dequant_q5_k.comp +++ /dev/null @@ -1,58 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint tid = gl_LocalInvocationID.x; - const uint il = tid / 16; - const uint ir = tid % 16; - const uint is = 2 * il; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - - const uint y_idx = i * QUANT_K + 64 * il + 2 * ir; - const uint qs_idx = 32*il + 2 * ir; - const uint qh_idx = 2 * ir; - - uint8_t sc; - uint8_t m; - if (is < 4) { - sc = uint8_t(data_a[i].scales[is] & 63); - m = uint8_t(data_a[i].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 4] >> 4) | ((data_a[i].scales[is ] >> 6) << 4)); - } - const FLOAT_TYPE d1 = dall * sc; - const FLOAT_TYPE m1 = dmin * m; - - if (is < 4) { - sc = uint8_t(data_a[i].scales[is + 1] & 63); - m = uint8_t(data_a[i].scales[is + 5] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 5] >> 4) | ((data_a[i].scales[is + 1] >> 6) << 4)); - } - const FLOAT_TYPE d2 = dall * sc; - const FLOAT_TYPE m2 = dmin * m; - - const uint8_t hm1 = uint8_t(1 << (2 * il )); - const uint8_t hm2 = uint8_t(1 << (2 * il + 1)); - data_b[y_idx ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx ] & 0xF) + (((data_a[i].qh[qh_idx ] & hm1) != 0) ? 16 : 0)) - m1); - data_b[y_idx + 1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1); - data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx ] >> 4) + (((data_a[i].qh[qh_idx ] & hm2) != 0) ? 16 : 0)) - m2); - data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q6_k.comp b/ggml/src/vulkan-shaders/dequant_q6_k.comp deleted file mode 100644 index 0b913175..00000000 --- a/ggml/src/vulkan-shaders/dequant_q6_k.comp +++ /dev/null @@ -1,33 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - const uint tid = gl_LocalInvocationID.x; - const uint ip = tid / 32; - const uint il = tid - 32 * ip; - const uint is = 8 * ip + il / 16; - - const uint y_idx = i * QUANT_K + 128 * ip + il; - - const uint ql_idx = 64 * ip + il; - const uint8_t qh = data_a[i].qh[32 * ip + il]; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d); - - data_b[y_idx + 0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx + 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32))); - data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32))); - data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx + 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32))); - data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >> 4) | (((qh >> 6) & 3) << 4)) - 32))); - } -} diff --git a/ggml/src/vulkan-shaders/dequant_q8_0.comp b/ggml/src/vulkan-shaders/dequant_q8_0.comp deleted file mode 100644 index bd1344a8..00000000 --- a/ggml/src/vulkan-shaders/dequant_q8_0.comp +++ /dev/null @@ -1,31 +0,0 @@ -#version 450 - -#include "dequant_head.comp" - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q8_0 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 16*il; - - const float d = float(data_a[ib].d); - - const uint q_idx = 16*il; - - [[unroll]] for (uint l = 0; l < 16; l += 2) { - data_b[b_idx + l ] = D_TYPE(d * data_a[ib].qs[q_idx + l ]); - data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]); - } -} diff --git a/ggml/src/vulkan-shaders/diag_mask_inf.comp b/ggml/src/vulkan-shaders/diag_mask_inf.comp deleted file mode 100644 index 4e68742b..00000000 --- a/ggml/src/vulkan-shaders/diag_mask_inf.comp +++ /dev/null @@ -1,34 +0,0 @@ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_control_flow_attributes : enable - -layout (push_constant) uniform parameter -{ - uint ncols; - uint rows_per_channel; - uint n_past; -} p; - -#include "types.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint col = gl_GlobalInvocationID.y; - const uint row = gl_GlobalInvocationID.x; - - if (col >= p.ncols) { - return; - } - - const uint i = row*p.ncols + col; - if (col > p.n_past + row % p.rows_per_channel) { - data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000)); - } else { - data_d[i] = D_TYPE(data_a[i]); - } -} diff --git a/ggml/src/vulkan-shaders/div.comp b/ggml/src/vulkan-shaders/div.comp deleted file mode 100644 index 8cfce58b..00000000 --- a/ggml/src/vulkan-shaders/div.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) / FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/gelu.comp b/ggml/src/vulkan-shaders/gelu.comp deleted file mode 100644 index 4cc7a68c..00000000 --- a/ggml/src/vulkan-shaders/gelu.comp +++ /dev/null @@ -1,25 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const float GELU_COEF_A = 0.044715f; - const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - const float xi = float(data_a[i]); - const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi); - data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1))); -} diff --git a/ggml/src/vulkan-shaders/gelu_quick.comp b/ggml/src/vulkan-shaders/gelu_quick.comp deleted file mode 100644 index e6e6fcfd..00000000 --- a/ggml/src/vulkan-shaders/gelu_quick.comp +++ /dev/null @@ -1,23 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const float GELU_QUICK_COEF = -1.702f; - const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - const float x = float(data_a[i]); - data_d[i] = D_TYPE(x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x)))); -} diff --git a/ggml/src/vulkan-shaders/generic_binary_head.comp b/ggml/src/vulkan-shaders/generic_binary_head.comp deleted file mode 100644 index b6beaff1..00000000 --- a/ggml/src/vulkan-shaders/generic_binary_head.comp +++ /dev/null @@ -1,52 +0,0 @@ -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint ne; - uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; - uint d_offset; - float param1; float param2; int param3; -} p; - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -uint get_idx() { - return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; -} - -uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; -} - -uint src1_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - - return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10; -} - -uint dst_idx(uint idx) { - const uint i23 = idx / (p.ne22*p.ne21*p.ne20); - const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; - const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20); - const uint i22_offset = i22*p.ne21*p.ne20; - const uint i21 = (idx - i23_offset - i22_offset) / p.ne20; - const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20; - return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20; -} diff --git a/ggml/src/vulkan-shaders/generic_head.comp b/ggml/src/vulkan-shaders/generic_head.comp deleted file mode 100644 index 66e46ae6..00000000 --- a/ggml/src/vulkan-shaders/generic_head.comp +++ /dev/null @@ -1,9 +0,0 @@ -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint KX; - uint KY; - float param1; - float param2; -} p; diff --git a/ggml/src/vulkan-shaders/generic_unary_head.comp b/ggml/src/vulkan-shaders/generic_unary_head.comp deleted file mode 100644 index 4e1fa3af..00000000 --- a/ggml/src/vulkan-shaders/generic_unary_head.comp +++ /dev/null @@ -1,38 +0,0 @@ -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_control_flow_attributes : require - -layout (push_constant) uniform parameter -{ - uint ne; - uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint d_offset; - float param1; float param2; -} p; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -uint get_idx() { - return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; -} - -uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; -} - -uint dst_idx(uint idx) { - const uint i13 = idx / (p.ne12*p.ne11*p.ne10); - const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; - const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); - const uint i12_offset = i12*p.ne11*p.ne10; - const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; - const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; - return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; -} diff --git a/ggml/src/vulkan-shaders/get_rows.comp b/ggml/src/vulkan-shaders/get_rows.comp deleted file mode 100644 index e9ff22ef..00000000 --- a/ggml/src/vulkan-shaders/get_rows.comp +++ /dev/null @@ -1,26 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint i00 = gl_GlobalInvocationID.x; - const uint i10 = gl_GlobalInvocationID.y; - const uint i11 = (gl_GlobalInvocationID.z)/p.ne12; - const uint i12 = (gl_GlobalInvocationID.z)%p.ne12; - - if (i00 >= p.ne00) { - return; - } - - const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; - - const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03; - const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23; - -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]); -#else - data_d[d_offset + i00] = data_a[a_offset + i00]; -#endif -} diff --git a/ggml/src/vulkan-shaders/get_rows_quant.comp b/ggml/src/vulkan-shaders/get_rows_quant.comp deleted file mode 100644 index 53a9a96f..00000000 --- a/ggml/src/vulkan-shaders/get_rows_quant.comp +++ /dev/null @@ -1,31 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" -#include "dequant_funcs.comp" - -void main() { - const uint i00 = (gl_GlobalInvocationID.x)*2; - const uint i10 = gl_GlobalInvocationID.y; - const uint i11 = (gl_GlobalInvocationID.z)/p.ne12; - const uint i12 = (gl_GlobalInvocationID.z)%p.ne12; - - if (i00 >= p.ne00) { - return; - } - - const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; - - const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03; - const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23; - - const uint ib = a_offset + i00/QUANT_K; // block index - const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index - const uint iybs = i00 - i00%QUANT_K; // dst block start index - const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - - vec2 v = dequantize(ib, iqs, 0); - - data_d[d_offset + iybs + iqs ] = D_TYPE(v.x); - data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y); -} diff --git a/ggml/src/vulkan-shaders/group_norm.comp b/ggml/src/vulkan-shaders/group_norm.comp deleted file mode 100644 index 5ad9b28d..00000000 --- a/ggml/src/vulkan-shaders/group_norm.comp +++ /dev/null @@ -1,66 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -shared float tmp[BLOCK_SIZE]; - -void main() { - const uint group_size = p.KX; - const float eps = p.param1; - - const uint tid = gl_LocalInvocationID.x; - const uint start = gl_WorkGroupID.x * group_size + tid; - const uint end = start + group_size; - - tmp[tid] = 0.0f; - - // Calculate mean - [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) { - tmp[tid] += float(data_a[col]); - } - - // tmp up partial tmps and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - - const float mean = tmp[0] / group_size; - barrier(); - tmp[tid] = 0.0f; - - // Calculate variance - [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) { - const float xi = float(data_a[col]) - mean; - data_d[col] = D_TYPE(xi); - tmp[tid] += xi * xi; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - - const float variance = tmp[0] / group_size; - const float scale = inversesqrt(variance + eps); - - [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) { - data_d[col] *= D_TYPE(scale); - } -} diff --git a/ggml/src/vulkan-shaders/im2col.comp b/ggml/src/vulkan-shaders/im2col.comp deleted file mode 100644 index 4d48610a..00000000 --- a/ggml/src/vulkan-shaders/im2col.comp +++ /dev/null @@ -1,57 +0,0 @@ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint batch_offset; uint offset_delta; - uint IC; - uint IW; uint IH; - uint OW; uint OH; - uint KW; uint KH; - uint pelements; - uint CHW; - int s0; int s1; - int p0; int p1; - int d0; int d1; -} p; - -#include "types.comp" - -#define BLOCK_SIZE 256 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.x; - if (i >= p.pelements) { - return; - } - - const uint ksize = p.OW * (p.KH > 1 ? p.KW : 1); - const uint kx = i / ksize; - const uint kd = kx * ksize; - const uint ky = (i - kd) / p.OW; - const uint ix = i % p.OW; - - const uint oh = gl_GlobalInvocationID.y; - const uint batch = gl_GlobalInvocationID.z / p.IC; - const uint ic = gl_GlobalInvocationID.z % p.IC; - - const uint iiw = ix * p.s0 + kx * p.d0 - p.p0; - const uint iih = oh * p.s1 + ky * p.d1 - p.p1; - - const uint offset_dst = - ((batch * p.OH + oh) * p.OW + ix) * p.CHW + - (ic * (p.KW * p.KH) + ky * p.KW + kx); - - if (iih < 0 || iih >= p.IH || iiw < 0 || iiw >= p.IW) { - data_d[offset_dst] = D_TYPE(0.0f); - } else { - const uint offset_src = ic * p.offset_delta + batch * p.batch_offset; - data_d[offset_dst] = D_TYPE(data_a[offset_src + iih * p.IW + iiw]); - } -} diff --git a/ggml/src/vulkan-shaders/leaky_relu.comp b/ggml/src/vulkan-shaders/leaky_relu.comp deleted file mode 100644 index d90a99ae..00000000 --- a/ggml/src/vulkan-shaders/leaky_relu.comp +++ /dev/null @@ -1,22 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - const float val = float(data_a[i]); - data_d[i] = D_TYPE(max(val, 0.0f) + min(val, 0.0f) * p.param1); -} diff --git a/ggml/src/vulkan-shaders/mul.comp b/ggml/src/vulkan-shaders/mul.comp deleted file mode 100644 index bfb61c92..00000000 --- a/ggml/src/vulkan-shaders/mul.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp b/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp deleted file mode 100644 index 825b9103..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp +++ /dev/null @@ -1,29 +0,0 @@ -#version 450 - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {float data_d[];}; - -layout (push_constant) uniform parameter { - uint ne; - uint k_num; -} p; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.ne) { - return; - } - - float result = 0.0f; - - [[unroll]] for (uint i = 0; i < p.k_num; i++) { - result += data_a[i * p.ne + idx]; - } - - data_d[idx] = result; -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec.comp b/ggml/src/vulkan-shaders/mul_mat_vec.comp deleted file mode 100644 index d3ccba7f..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec.comp +++ /dev/null @@ -1,56 +0,0 @@ -#version 450 - -#ifdef FLOAT16 -#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require -#endif - -#include "mul_mat_vec_base.comp" - -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (constant_id = 0) const uint BLOCK_SIZE = 32; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - const uint tid = gl_LocalInvocationID.x; - - // There are not enough cols to use all threads - if (tid >= p.ncols) { - return; - } - - const uint block_size = min(p.ncols, BLOCK_SIZE); - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - - tmp[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint i = 0; i < p.ncols/block_size; i += 2) { - const uint col = i*block_size + 2*tid; - const uint ib = (row*p.ncols + col)/QUANT_K; // block index - const uint iqs = (col%QUANT_K)/QUANT_R; // quant index - const uint iybs = col - col%QUANT_K; // y block start index - - vec2 v = dequantize(ib, iqs, a_offset / QUANT_K); - - // matrix multiplication - tmp[tid] = fma(FLOAT_TYPE(v.x), FLOAT_TYPE(data_b[b_offset + iybs + iqs]), fma(FLOAT_TYPE(v.y), FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]), tmp[tid])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = block_size/2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/vulkan-shaders/mul_mat_vec_base.comp deleted file mode 100644 index 5920bc93..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp +++ /dev/null @@ -1,81 +0,0 @@ -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_8bit_storage : require - -#define K_QUANTS_PER_ITERATION 2 - -#ifdef MUL_MAT_ID -#define EXPERT_COUNT 8 -#endif - -#include "types.comp" - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; -#ifdef MUL_MAT_ID -layout (binding = 3) readonly buffer IDS {int data_ids[];}; -#endif - -#include "dequant_funcs.comp" - -layout (push_constant) uniform parameter -{ - uint ncols; - uint stride_a; - uint stride_b; - uint stride_d; - - uint batch_stride_a; - uint batch_stride_b; - uint batch_stride_d; - -#ifdef MUL_MAT_ID - uint nei0; - uint ne11; -#else - uint ne02; - uint ne12; - uint broadcast2; - uint broadcast3; -#endif -} p; - -void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) { -#ifdef MUL_MAT_ID - const uint expert_idx = gl_GlobalInvocationID.y; -#else - const uint batch_idx = gl_GlobalInvocationID.y; -#endif - -#ifndef MUL_MAT_ID - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; -#else - const uint expert_id = data_ids[expert_idx]; -#endif - - a_offset = -#ifdef MUL_MAT_ID - expert_id * p.batch_stride_a; -#else - batch_idx_a * p.batch_stride_a; -#endif - b_offset = -#ifdef MUL_MAT_ID - (expert_idx % p.ne11) * p.stride_b; -#else - batch_idx * p.batch_stride_b; -#endif - d_offset = -#ifdef MUL_MAT_ID - expert_idx * p.stride_d; -#else - batch_idx * p.batch_stride_d; -#endif -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp deleted file mode 100644 index 1cc4996d..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp +++ /dev/null @@ -1,71 +0,0 @@ -#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require - -#define BLOCK_SIZE 32 -#define FLOAT_TYPE float - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - -layout (push_constant) uniform parameter -{ - uint ncols_x; - uint nrows_x; - uint row_stride_x; - uint channel_stride_x; - uint channel_x_divisor; - uint b_offset; - uint d_offset; -} p; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint row_x = gl_GlobalInvocationID.y; - const uint channel = gl_GlobalInvocationID.z; - const uint channel_x = channel / p.channel_x_divisor; - - const uint nrows_y = p.ncols_x; - const uint nrows_dst = p.nrows_x; - const uint row_dst = row_x; - - const uint idst = channel*nrows_dst + row_dst; - - tmp[tid] = 0.0f; - - for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { - const uint col_x = col_x0 + tid; - - if (col_x >= p.ncols_x) { - break; - } - - const uint row_y = col_x; - - const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; - const uint iy = channel*nrows_y + row_y; - - const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); - - tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - - if (tid == 0) { - dst[idst] = tmp[0]; - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp b/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp deleted file mode 100644 index 9b443807..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require - -#define BLOCK_SIZE 32 -#define FLOAT_TYPE float - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - -layout (push_constant) uniform parameter -{ - uint ncols_x; - uint nrows_x; - uint nchannels_x; - uint nchannels_y; - uint b_offset; - uint d_offset; -} p; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint row_x = gl_GlobalInvocationID.y; - const uint channel = gl_GlobalInvocationID.z; - const uint channel_x = channel / (p.nchannels_y / p.nchannels_x); - - const uint nrows_y = p.ncols_x; - const uint nrows_dst = p.nrows_x; - const uint row_dst = row_x; - - tmp[tid] = FLOAT_TYPE(0.0f); - - for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { - const uint col_x = col_x0 + tid; - - if (col_x >= p.ncols_x) { - break; - } - - // x is transposed and permuted - const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x; - const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); - - const uint row_y = col_x; - - // y is not transposed but permuted - const uint iy = channel*nrows_y + row_y; - - tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]); - } - - // dst is not transposed and not permuted - const uint idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - - if (tid == 0) { - dst[idst] = tmp[0]; - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp deleted file mode 100644 index ec8eadcd..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp +++ /dev/null @@ -1,74 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - - const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint s_offset = 8*v_im; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); - FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3), sum1)))))))); - sum2 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF), sum2)))))))); - } - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, sum1, fma(-dmin, sum2, tmp[tmp_idx])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp deleted file mode 100644 index 3ca4ad85..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp +++ /dev/null @@ -1,67 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - - const uint8_t m = uint8_t(1 << (4 * v_im)); - - const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - const uint s_shift = 4 * v_im; - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); - } - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(d, sum, tmp[tmp_idx]); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp deleted file mode 100644 index d91e00e1..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp +++ /dev/null @@ -1,118 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - - const uint il = tid/step; // 0...3 - const uint ir = tid - step*il; // 0...7 or 0...3 - const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = n * (2 * ir + v_in); // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - -#if K_QUANTS_PER_ITERATION == 2 - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); - - const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx]), q4_0, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), q4_1, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3))); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_4, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), q4_5, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), q4_6, FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7))); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx]), q4_8, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), q4_9, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), q4_10, FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11))); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_12, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), q4_13, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), q4_14, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 3]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 35]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 3]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7))))))))))))))); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); -#else - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - - const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), q4_0, FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), q4_4, FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, - + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7))))))); - - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + - sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, (fma(sx, FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f), fma(sy, FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f), - fma(sz, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)), fma(sw, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))))))), fma(-dmin, smin, tmp[tmp_idx])); -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp deleted file mode 100644 index 2306785a..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp +++ /dev/null @@ -1,109 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 - - const uint il = tid/4; // 0...3 - const uint ir = tid - 4*il; // 0...7 or 0...3 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = 4*ir + 2*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - const uint8_t hm1 = uint8_t(1 << (2*v_im)); - const uint8_t hm2 = uint8_t(hm1 << 4); - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); - - const FLOAT_TYPE sx = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 16]), (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))))); - const FLOAT_TYPE sy = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 48]), (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))))); - const FLOAT_TYPE sz = - fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 16]), (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))))); - const FLOAT_TYPE sw = - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 48]), (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]), sc2, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]), sc3, - fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]), sc6, - (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7))); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp deleted file mode 100644 index 95c286ee..00000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp +++ /dev/null @@ -1,79 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const uint l0 = v_in; // 0...15 - const uint is = 0; -#else - const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 - const uint is = v_in / 4; -#endif - - const uint ql_offset = 64*v_im + l0; - const uint qh_offset = 32*v_im + l0; - const uint s_offset = 8*v_im + is; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - -#if K_QUANTS_PER_ITERATION == 1 - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32), tmp[tmp_idx])))))))); -#else - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 4; ++l) { - sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32), sum)))); - } - tmp[16 * ix + tid] += sum; -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mm.comp b/ggml/src/vulkan-shaders/mul_mm.comp deleted file mode 100644 index fffdd181..00000000 --- a/ggml/src/vulkan-shaders/mul_mm.comp +++ /dev/null @@ -1,508 +0,0 @@ -#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require - -#ifdef FLOAT16 -#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require -#endif - -#ifdef MUL_MAT_ID -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#endif - -#include "types.comp" - -#ifndef LOAD_VEC_A -#define LOAD_VEC_A 1 -#endif -#ifndef LOAD_VEC_B -#define LOAD_VEC_B 1 -#endif - -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -#ifdef MUL_MAT_ID -layout (binding = 3) readonly buffer IDS {int data_ids[];}; -#endif - -layout (push_constant) uniform parameter -{ - uint M; - uint N; - uint K; - uint stride_a; - uint stride_b; - uint stride_d; - - uint batch_stride_a; - uint batch_stride_b; - uint batch_stride_d; - -#ifdef MUL_MAT_ID - uint nei0; - uint nei1; - uint nbi1; - uint ne11; -#else - uint k_split; - uint ne02; - uint ne12; - uint broadcast2; - uint broadcast3; -#endif -} p; - -layout (constant_id = 1) const uint BM = 64; -layout (constant_id = 2) const uint BN = 64; -layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant -layout (constant_id = 4) const uint WM = 32; -layout (constant_id = 5) const uint WN = 32; -layout (constant_id = 6) const uint WMITER = 2; -layout (constant_id = 7) const uint TM = 4; -layout (constant_id = 8) const uint TN = 2; -layout (constant_id = 9) const uint WARP = 32; - -shared FLOAT_TYPE buf_a[BM * (BK+1)]; -shared FLOAT_TYPE buf_b[BN * (BK+1)]; - -#ifdef MUL_MAT_ID -shared u16vec2 row_ids[3072]; -#endif - -void main() { -#ifdef MUL_MAT_ID - const uint expert_idx = gl_GlobalInvocationID.z; -#else - const uint batch_idx = gl_GlobalInvocationID.z; - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; -#endif - - const uint blocks_m = (p.M + BM - 1) / BM; - const uint ir = gl_WorkGroupID.x % blocks_m; - const uint ik = gl_WorkGroupID.x / blocks_m; - const uint ic = gl_WorkGroupID.y; - - const uint warp_i = gl_LocalInvocationID.x / WARP; - const uint warp_r = warp_i % (BM / WM); - const uint warp_c = warp_i / (BM / WM); - - const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER); - const uint WSUBM = WM / WMITER; - const uint WSUBN = WN / WNITER; - - const uint tiw = gl_LocalInvocationID.x % WARP; - const uint tiwr = tiw % (WSUBM / TM); - const uint tiwc = tiw / (WSUBM / TM); - - const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A); - const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A); - const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B); - const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B); - - const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK; - const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK; - -#ifdef MUL_MAT_ID - uint _ne1 = 0; - for (uint ii1 = 0; ii1 < p.nei1; ii1++) { - for (uint ii0 = 0; ii0 < p.nei0; ii0++) { - if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) { - row_ids[_ne1] = u16vec2(ii0, ii1); - _ne1++; - } - } - } - - barrier(); - - // Workgroup has no work - if (ic * BN >= _ne1) return; -#endif - -#ifdef MUL_MAT_ID - const uint start_k = 0; - const uint end_k = p.K; -#else - const uint start_k = ik * p.k_split; - const uint end_k = min(p.K, (ik + 1) * p.k_split); -#endif - - uint pos_a = ( -#ifdef MUL_MAT_ID - expert_idx * p.batch_stride_a + -#else - batch_idx_a * p.batch_stride_a + -#endif - ir * BM * p.stride_a + start_k) / LOAD_VEC_A; -#ifdef MUL_MAT_ID - uint pos_b = 0; -#else - uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B; -#endif - - float sums[WMITER * TM * WNITER * TN]; - FLOAT_TYPE cache_a[WMITER * TM]; - FLOAT_TYPE cache_b[WNITER * TN]; - - [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) { - sums[i] = 0.0f; - } - - [[unroll]] for (uint block = start_k; block < end_k; block += BK) { - [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) { - -#if defined(DATA_A_F32) || defined(DATA_A_F16) -#if LOAD_VEC_A == 8 - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx][0].x); - buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y); - buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z); - buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w); - buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x); - buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y); - buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z); - buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w); -#elif LOAD_VEC_A == 4 - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx].x); - buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y); - buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z); - buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w); -#else - if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) { - buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]); - } else { - buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f); - } -#endif -#elif defined(DATA_A_Q4_0) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y); -#elif defined(DATA_A_Q4_1) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y); -#elif defined(DATA_A_Q5_0) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y); -#elif defined(DATA_A_Q5_1) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - const uint uint_qh = data_a[ib].qh; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y); -#elif defined(DATA_A_Q8_0) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 16; - const uint iqs = (idx & 0xF) * 2; - - const float d = float(data_a[ib].d); - const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 1] = FLOAT_TYPE(v.y); -#elif defined(DATA_A_Q2_K) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30 - const uint scalesi = iqs / 8; // 0..15 - const uint qsshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 - - const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]); - const uint scales = data_a[ib].scales[scalesi]; - const vec2 d = vec2(data_a[ib].d); - - const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4); - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 1] = FLOAT_TYPE(v.y); -#elif defined(DATA_A_Q3_K) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 64; // 0,1 - const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 - const uint hmi = (iqs % 16) * 2; // 0,2,4..30 - const uint j = (iqs % 64) / 4; // 0..3 - const uint is = iqs / 8; // 0..15 - const uint halfsplit = ((iqs % 64) / 16); // 0,1,2,3 - const uint qsshift = halfsplit * 2; // 0,2,4,6 - const uint m = 1 << (4 * n + halfsplit); // 1,2,4,8,16,32,64,128 - - const int8_t us = int8_t(is < 4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (data_a[ib].scales[is-8] >> 4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) : - (data_a[ib].scales[is-8] >> 4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4)); - const float dl = float(data_a[ib].d) * float(us - 32); - - buf_a[buf_idx ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi ] & m) != 0) ? 0 : 4))); - buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4))); -#elif defined(DATA_A_Q4_K) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 32; // 0,1,2,3 - const uint b = (iqs % 32) / 16; // 0,1 - const uint is = 2 * n + b; // 0..7 - const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 - - const vec2 loadd = vec2(data_a[ib].d); - - uint8_t sc; - uint8_t mbyte; - if (is < 4) { - sc = uint8_t(data_a[ib].scales[is ] & 63); - mbyte = uint8_t(data_a[ib].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4)); - mbyte = uint8_t((data_a[ib].scales[is + 4] >> 4) | ((data_a[ib].scales[is ] >> 6) << 4)); - } - const float d = loadd.x * sc; - const float m = -loadd.y * mbyte; - - buf_a[buf_idx ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF), m)); - buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF), m)); -#elif defined(DATA_A_Q5_K) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 32; // 0,1,2,3 - const uint b = (iqs % 32) / 16; // 0,1 - const uint is = 2 * n + b; // 0..7 - const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 - const uint qhi = (iqs % 16) * 2; // 0,2,4..30 - - const uint8_t hm = uint8_t(1 << (iqs / 16)); - - const vec2 loadd = vec2(data_a[ib].d); - - uint8_t sc; - uint8_t mbyte; - if (is < 4) { - sc = uint8_t(data_a[ib].scales[is ] & 63); - mbyte = uint8_t(data_a[ib].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4)); - mbyte = uint8_t((data_a[ib].scales[is + 4] >> 4) | ((data_a[ib].scales[is ] >> 6) << 4)); - } - const float d = loadd.x * sc; - const float m = -loadd.y * mbyte; - - buf_a[buf_idx ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi ] & hm) != 0 ? 16 : 0), m)); - buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0), m)); -#elif defined(DATA_A_Q6_K) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 64; // 0,1 - const uint b = (iqs % 64) / 32; // 0,1 - const uint is_b = (iqs % 16) / 8; // 0,1 - const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 - const uint is = 8 * n + qhshift + is_b; // 0..15 - const uint qsi = n * 64 + (iqs % 32) * 2; // 0,2,4..126 - const uint qhi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 - - const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]); - - buf_a[buf_idx ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi ] >> qhshift) & 3) << 4)) - 32)); - buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32)); -#elif defined(DATA_A_IQ4_NL) - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y); -#endif - } - [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) { -#if LOAD_VEC_B == 8 -#ifdef MUL_MAT_ID - const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l]; - const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b; -#else - const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; -#endif - const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; - buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x); - buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y); - buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z); - buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w); - buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x); - buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y); - buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z); - buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w); -#elif LOAD_VEC_B == 4 -#ifdef MUL_MAT_ID - const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l]; - const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b; -#else - const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; -#endif - const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; - buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x); - buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y); - buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z); - buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w); -#elif !MUL_MAT_ID - if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) { - buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); - } else { - buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); - } -#else - const uint row_i = ic * BN + loadc_b + l; - if (row_i < _ne1) { - const u16vec2 row_idx = row_ids[row_i]; - buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]); - } else { - buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); - } -#endif - } - - barrier(); - - pos_a += BK / LOAD_VEC_A; - pos_b += BK / LOAD_VEC_B; - - for (uint i = 0; i < BK; i++) { - // Load from shared into cache - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - [[unroll]] for (uint j = 0; j < TM; j++) { - cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i]; - } - } - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint j = 0; j < TN; j++) { - cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i]; - } - } - - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - [[unroll]] for (uint cc = 0; cc < TN; cc++) { - [[unroll]] for (uint cr = 0; cr < TM; cr++) { - const uint sums_idx = (wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr; - sums[sums_idx] = fma(float(cache_a[wsir * TM + cr]), float(cache_b[wsic * TN + cc]), sums[sums_idx]); - } - } - } - } - } - - barrier(); - } - - const uint dr = ir * BM + warp_r * WM; - const uint dc = ic * BN + warp_c * WN; - -#ifndef MUL_MAT_ID - const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; -#endif - - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - - const uint dr_warp = dr + wsir * WSUBM + tiwr * TM; - const uint dc_warp = dc + wsic * WSUBN + tiwc * TN; - [[unroll]] for (uint cc = 0; cc < TN; cc++) { -#ifdef MUL_MAT_ID - const uint row_i = dc_warp + cc; - if (row_i >= _ne1) break; - - const u16vec2 row_idx = row_ids[row_i]; -#endif - [[unroll]] for (uint cr = 0; cr < TM; cr++) { -#ifdef MUL_MAT_ID - data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); -#else - if (dr_warp + cr < p.M && dc_warp + cc < p.N) { - data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); - } -#endif - } - } - } - } -} diff --git a/ggml/src/vulkan-shaders/norm.comp b/ggml/src/vulkan-shaders/norm.comp deleted file mode 100644 index 6627a50b..00000000 --- a/ggml/src/vulkan-shaders/norm.comp +++ /dev/null @@ -1,44 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -shared vec2 sum[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint tid = gl_LocalInvocationID.x; - - sum[tid] = vec2(0.0f, 0.0f); - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - const float xi = float(data_a[row*p.KX + col]); - sum[tid].x += xi; - sum[tid].y += xi * xi; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - sum[tid] += sum[tid + s]; - } - barrier(); - } - - const float mean = sum[0].x / p.KX; - const float var = sum[0].y / p.KX - mean * mean; - const float inv_std = inversesqrt(var + p.param1); - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std); - } -} diff --git a/ggml/src/vulkan-shaders/pad.comp b/ggml/src/vulkan-shaders/pad.comp deleted file mode 100644 index e87d8b18..00000000 --- a/ggml/src/vulkan-shaders/pad.comp +++ /dev/null @@ -1,28 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -void main() { - const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (idx >= p.ne) { - return; - } - - const uint i3 = idx / (p.ne12*p.ne11*p.ne10); - const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10; - const uint i2 = (idx - i3_offset) / (p.ne11*p.ne10); - const uint i2_offset = i2*p.ne11*p.ne10; - const uint i1 = (idx - i3_offset - i2_offset) / p.ne10; - const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10; - - const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00; - const uint dst_idx = i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0*p.nb10; - - const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03; - - data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : 0.0f); -} diff --git a/ggml/src/vulkan-shaders/pool2d.comp b/ggml/src/vulkan-shaders/pool2d.comp deleted file mode 100644 index b6124411..00000000 --- a/ggml/src/vulkan-shaders/pool2d.comp +++ /dev/null @@ -1,74 +0,0 @@ -#version 450 - -#include "types.comp" - -#extension GL_EXT_shader_16bit_storage : require - -layout(push_constant) uniform parameter { - uint IW; uint IH; - uint OW; uint OH; - uint OC; - uint pelements; - uint op; - int k0; int k1; - int s0; int s1; - int p0; int p1; -} p; - -#define BLOCK_SIZE 512 -#define FLT_MAX 3.402823466e+38F -#define OP_POOL_MAX 0u -#define OP_POOL_AVG 1u - -layout (local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout(binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout(binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - if (idx >= p.pelements) { - return; - } - - const uint O_HW = p.OW * p.OH; - - const uint nc = idx / O_HW; - const uint cur_oh = (idx % O_HW) / p.OW; - const uint cur_ow = (idx % O_HW) % p.OW; - - const int start_h = int(cur_oh) * p.s0 - p.p0; - const uint bh = max(start_h, 0); - const uint eh = min(start_h + p.k0, p.IH); - - const int start_w = int(cur_ow) * p.s1 - p.p1; - const uint bw = max(start_w, 0); - const uint ew = min(start_w + p.k1, p.IW); - - const float scale = 1.0 / float(p.k0 * p.k1); - float res; - - if (p.op == OP_POOL_AVG) { - res = 0.0; - } else if (p.op == OP_POOL_MAX) { - res = -FLT_MAX; - } else { - return; - } - - #pragma unroll - for (uint i = bh; i < eh; i++) { - #pragma unroll - for (uint j = bw; j < ew; j++) { - const float cur = D_TYPE(data_a[nc * p.IH * p.IW + i * p.IW + j]); - - if (p.op == OP_POOL_AVG) { - res += cur * scale; - } else if (p.op == OP_POOL_MAX) { - res = max(res, cur); - } - } - } - - data_d[nc * O_HW + cur_oh * p.OW + cur_ow] = res; -} diff --git a/ggml/src/vulkan-shaders/relu.comp b/ggml/src/vulkan-shaders/relu.comp deleted file mode 100644 index 52a19b62..00000000 --- a/ggml/src/vulkan-shaders/relu.comp +++ /dev/null @@ -1,21 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - data_d[i] = max(float(data_a[i]), 0); -} diff --git a/ggml/src/vulkan-shaders/repeat.comp b/ggml/src/vulkan-shaders/repeat.comp deleted file mode 100644 index c03f737c..00000000 --- a/ggml/src/vulkan-shaders/repeat.comp +++ /dev/null @@ -1,26 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -uint src0_idx_mod(uint idx) { - const uint i13 = idx / (p.ne12*p.ne11*p.ne10); - const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; - const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); - const uint i12_offset = i12*p.ne11*p.ne10; - const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; - const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; - return (i13 % p.ne03)*p.nb03 + (i12 % p.ne02)*p.nb02 + (i11 % p.ne01)*p.nb01 + (i10 % p.ne00)*p.nb00; -} - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx_mod(idx)]); -} diff --git a/ggml/src/vulkan-shaders/rms_norm.comp b/ggml/src/vulkan-shaders/rms_norm.comp deleted file mode 100644 index b554400b..00000000 --- a/ggml/src/vulkan-shaders/rms_norm.comp +++ /dev/null @@ -1,42 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -shared FLOAT_TYPE sum[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint tid = gl_LocalInvocationID.x; - - sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]); - sum[tid] += xi * xi; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - sum[tid] += sum[tid + s]; - } - barrier(); - } - - const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX); - const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1)); - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col])); - } -} diff --git a/ggml/src/vulkan-shaders/rope_head.comp b/ggml/src/vulkan-shaders/rope_head.comp deleted file mode 100644 index ea895422..00000000 --- a/ggml/src/vulkan-shaders/rope_head.comp +++ /dev/null @@ -1,44 +0,0 @@ -#include "types.comp" - -#extension GL_EXT_shader_16bit_storage : require - -layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {int data_pos[];}; -layout (binding = 2) readonly buffer Z {float data_ff[];}; -layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; - -layout (push_constant) uniform parameter { - uint ncols; - uint n_dims; - float freq_scale; - uint p_delta_rows; - float freq_base; - float ext_factor; - float attn_factor; - float corr_dims[2]; - float theta_scale; - uint has_ff; -} p; - -float rope_yarn_ramp(const float low, const float high, const uint i0) { - const float y = (i0 / 2 - low) / max(0.001f, high - low); - return 1.0f - min(1.0f, max(0.0f, y)); -} - -void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) { - float mscale = p.attn_factor; - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = p.freq_scale * theta_extrap; - float theta = theta_interp; - if (p.ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale); - } - cos_theta = cos(theta) * mscale; - sin_theta = sin(theta) * mscale; -} diff --git a/ggml/src/vulkan-shaders/rope_neox.comp b/ggml/src/vulkan-shaders/rope_neox.comp deleted file mode 100644 index 83b46b69..00000000 --- a/ggml/src/vulkan-shaders/rope_neox.comp +++ /dev/null @@ -1,37 +0,0 @@ -#version 450 - -#include "rope_head.comp" - -void main() { - const uint col = gl_GlobalInvocationID.y * 2; - const uint row = gl_GlobalInvocationID.x; - - if (col >= p.ncols) { - return; - } - - if (col >= p.n_dims) { - const uint i = row*p.ncols + col; - - data_d[i + 0] = data_a[i + 0]; - data_d[i + 1] = data_a[i + 1]; - - return; - } - - const uint i = row*p.ncols + col/2; - const uint i2 = row/p.p_delta_rows; - - const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f); - - const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f; - - float cos_theta, sin_theta; - rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta); - - const float x0 = float(data_a[i + 0]); - const float x1 = float(data_a[i + p.n_dims/2]); - - data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta); - data_d[i + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta); -} diff --git a/ggml/src/vulkan-shaders/rope_norm.comp b/ggml/src/vulkan-shaders/rope_norm.comp deleted file mode 100644 index e416ad93..00000000 --- a/ggml/src/vulkan-shaders/rope_norm.comp +++ /dev/null @@ -1,37 +0,0 @@ -#version 450 - -#include "rope_head.comp" - -void main() { - const uint col = gl_GlobalInvocationID.y * 2; - const uint row = gl_GlobalInvocationID.x; - - if (col >= p.ncols) { - return; - } - - if (col >= p.n_dims) { - const uint i = row*p.ncols + col; - - data_d[i + 0] = data_a[i + 0]; - data_d[i + 1] = data_a[i + 1]; - - return; - } - - const uint i = row*p.ncols + col; - const uint i2 = row/p.p_delta_rows; - - const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f); - - const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f; - - float cos_theta, sin_theta; - rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta); - - const float x0 = float(data_a[i + 0]); - const float x1 = float(data_a[i + 1]); - - data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta); - data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta); -} diff --git a/ggml/src/vulkan-shaders/scale.comp b/ggml/src/vulkan-shaders/scale.comp deleted file mode 100644 index 5cfee8c3..00000000 --- a/ggml/src/vulkan-shaders/scale.comp +++ /dev/null @@ -1,24 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -const uint num_threads = 128; - -layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; - -void main() { - uint idx = get_idx(); - - // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation - const uint num_iter = 4; - - [[unroll]] for (uint i = 0; i < num_iter; ++i) { - if (idx >= p.ne) { - continue; - } - - data_d[p.d_offset + idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); - idx += num_threads; - } -} diff --git a/ggml/src/vulkan-shaders/silu.comp b/ggml/src/vulkan-shaders/silu.comp deleted file mode 100644 index 4d36f88e..00000000 --- a/ggml/src/vulkan-shaders/silu.comp +++ /dev/null @@ -1,22 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - const float xi = float(data_a[i]); - data_d[i] = D_TYPE(xi / (1.0f + exp(-xi))); -} diff --git a/ggml/src/vulkan-shaders/sin.comp b/ggml/src/vulkan-shaders/sin.comp deleted file mode 100644 index 67c48fb9..00000000 --- a/ggml/src/vulkan-shaders/sin.comp +++ /dev/null @@ -1,17 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(sin(val)); -} diff --git a/ggml/src/vulkan-shaders/soft_max.comp b/ggml/src/vulkan-shaders/soft_max.comp deleted file mode 100644 index 0bd51eca..00000000 --- a/ggml/src/vulkan-shaders/soft_max.comp +++ /dev/null @@ -1,106 +0,0 @@ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint KX; - uint KY; - float scale; - float max_bias; - float m0; - float m1; - uint n_head_log2; -} p; - -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) buffer D {D_TYPE data_d[];}; - -shared FLOAT_TYPE vals[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint rowy = rowx % p.KY; - - float slope = 1.0f; - - // ALiBi - if (p.max_bias > 0.0f) { - const uint h = rowx/p.KY; // head index - - const float base = h < p.n_head_log2 ? p.m0 : p.m1; - const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; - - slope = pow(base, exp); - } - - // Find max - FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); - } - vals[tid] = max_val; - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] = max(vals[tid], vals[tid + s]); - } - barrier(); - } - - max_val = vals[0]; - barrier(); - - // Sum up values - vals[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); - vals[tid] += val; - data_d[i] = D_TYPE(val); - } - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] += vals[tid + s]; - } - barrier(); - } - - const D_TYPE divisor = D_TYPE(vals[0]); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - data_d[rowx*p.KX + col] /= divisor; - } -} diff --git a/ggml/src/vulkan-shaders/square.comp b/ggml/src/vulkan-shaders/square.comp deleted file mode 100644 index 2ff48ddc..00000000 --- a/ggml/src/vulkan-shaders/square.comp +++ /dev/null @@ -1,17 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(idx)]); - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(val * val); -} diff --git a/ggml/src/vulkan-shaders/sum_rows.comp b/ggml/src/vulkan-shaders/sum_rows.comp deleted file mode 100644 index 961e5ffa..00000000 --- a/ggml/src/vulkan-shaders/sum_rows.comp +++ /dev/null @@ -1,37 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -layout (constant_id = 0) const uint BLOCK_SIZE = 32; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint col = gl_LocalInvocationID.x; - - tmp[col] = FLOAT_TYPE(0.0f); - - for (uint i = col; i < p.KX; i += BLOCK_SIZE) { - tmp[col] += FLOAT_TYPE(data_a[row*p.KX + i]); - } - - barrier(); - [[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) { - if (col < s) { - tmp[col] += tmp[col + s]; - } - barrier(); - } - - if (col == 0) { - data_d[row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/tanh.comp b/ggml/src/vulkan-shaders/tanh.comp deleted file mode 100644 index 74630dc7..00000000 --- a/ggml/src/vulkan-shaders/tanh.comp +++ /dev/null @@ -1,21 +0,0 @@ -#version 450 - -#include "generic_head.comp" -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - data_d[i] = D_TYPE(tanh(data_a[i])); -} diff --git a/ggml/src/vulkan-shaders/timestep_embedding.comp b/ggml/src/vulkan-shaders/timestep_embedding.comp deleted file mode 100644 index 79e065a9..00000000 --- a/ggml/src/vulkan-shaders/timestep_embedding.comp +++ /dev/null @@ -1,41 +0,0 @@ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint nb1; - uint dim; - uint max_period; -} p; - -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 256 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_WorkGroupID.y; - const uint j = gl_GlobalInvocationID.x; - const uint d_offset = i * p.nb1; - - if (p.dim % 2 != 0 && j == ((p.dim + 1) / 2)) { - data_d[d_offset + p.dim] = 0.f; - } - - const uint half_dim = p.dim / 2; - if (j >= half_dim) { - return; - } - - const float timestep = float(data_a[i]); - const float freq = float(exp(-log(p.max_period) * j / half_dim)); - const float arg = timestep * freq; - data_d[d_offset + j] = D_TYPE(cos(arg)); - data_d[d_offset + j + half_dim] = D_TYPE(sin(arg)); -} diff --git a/ggml/src/vulkan-shaders/types.comp b/ggml/src/vulkan-shaders/types.comp deleted file mode 100644 index 21dce72f..00000000 --- a/ggml/src/vulkan-shaders/types.comp +++ /dev/null @@ -1,200 +0,0 @@ -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -#endif - -#if defined(DATA_A_F32) -#define QUANT_K 1 -#define QUANT_R 1 - -#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 -#define A_TYPE float -#elif LOAD_VEC_A == 4 -#define A_TYPE vec4 -#elif LOAD_VEC_A == 8 -#define A_TYPE mat2x4 -#endif -#endif - -#if defined(DATA_A_F16) -#define QUANT_K 1 -#define QUANT_R 1 - -#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 -#define A_TYPE float16_t -#elif LOAD_VEC_A == 4 -#define A_TYPE f16vec4 -#elif LOAD_VEC_A == 8 -#define A_TYPE f16mat2x4 -#endif -#endif - -#if defined(DATA_A_Q4_0) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q4_0 -{ - float16_t d; - uint8_t qs[16]; -}; - -#define A_TYPE block_q4_0 -#endif - -#if defined(DATA_A_Q4_1) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q4_1 -{ - float16_t d; - float16_t m; - uint8_t qs[16]; -}; - -#define A_TYPE block_q4_1 -#endif - -#if defined(DATA_A_Q5_0) -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q5_0 -{ - float16_t d; - uint16_t qh[2]; - uint8_t qs[16]; -}; - -#define A_TYPE block_q5_0 -#endif - -#if defined(DATA_A_Q5_1) -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q5_1 -{ - float16_t d; - float16_t m; - uint qh; - uint8_t qs[16]; -}; - -#define A_TYPE block_q5_1 -#endif - -#if defined(DATA_A_Q8_0) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 1 - -struct block_q8_0 -{ - float16_t d; - int8_t qs[32]; -}; - -#define A_TYPE block_q8_0 -#endif - -// K-quants -#if defined(DATA_A_Q2_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q2_K -{ - uint8_t scales[QUANT_K/16]; - uint8_t qs[QUANT_K/4]; - f16vec2 d; -}; - -#define A_TYPE block_q2_K -#endif - -#if defined(DATA_A_Q3_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q3_K -{ - uint8_t hmask[QUANT_K/8]; - uint8_t qs[QUANT_K/4]; - uint8_t scales[12]; - float16_t d; -}; - -#define A_TYPE block_q3_K -#endif - -#if defined(DATA_A_Q4_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q4_K -{ - f16vec2 d; - uint8_t scales[3*QUANT_K/64]; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_q4_K -#endif - -#if defined(DATA_A_Q5_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q5_K -{ - f16vec2 d; - uint8_t scales[12]; - uint8_t qh[QUANT_K/8]; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_q5_K -#endif - -#if defined(DATA_A_Q6_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q6_K -{ - uint8_t ql[QUANT_K/2]; - uint8_t qh[QUANT_K/4]; - int8_t scales[QUANT_K/16]; - float16_t d; -}; - -#define A_TYPE block_q6_K -#endif - -// IQuants - -#if defined(DATA_A_IQ4_NL) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_iq4_nl -{ - float16_t d; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_iq4_nl - -const int8_t kvalues_iq4nl[16] = { - int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10), - int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113) -}; -#endif diff --git a/ggml/src/vulkan-shaders/upscale.comp b/ggml/src/vulkan-shaders/upscale.comp deleted file mode 100644 index 511a086e..00000000 --- a/ggml/src/vulkan-shaders/upscale.comp +++ /dev/null @@ -1,36 +0,0 @@ -#version 450 - -layout (push_constant) uniform parameter -{ - uint ne; uint d_offset; - uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; - float sf0; float sf1; float sf2; float sf3; -} p; - -#include "types.comp" - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; - - if (idx >= p.ne) { - return; - } - - const uint i10 = idx % p.ne10; - const uint i11 = (idx / p.ne10) % p.ne11; - const uint i12 = (idx / (p.ne10 * p.ne11)) % p.ne12; - const uint i13 = (idx / (p.ne10 * p.ne11 * p.ne12)) % p.ne13; - - const uint i00 = uint(i10 / p.sf0); - const uint i01 = uint(i11 / p.sf1); - const uint i02 = uint(i12 / p.sf2); - const uint i03 = uint(i13 / p.sf3); - - data_d[p.d_offset + idx] = D_TYPE(data_a[i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]); -} diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp deleted file mode 100644 index 5c84f473..00000000 --- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +++ /dev/null @@ -1,519 +0,0 @@ - - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#ifdef _WIN32 - #include - #include // For _mkdir on Windows - #include // For std::replace on w64devkit -#else - #include - #include - #include -#endif - -#define ASYNCIO_CONCURRENCY 64 - -std::mutex lock; -std::vector> shader_fnames; - -std::string GLSLC = "glslc"; -std::string input_dir = "vulkan-shaders"; -std::string output_dir = "/tmp"; -std::string target_hpp = "ggml-vulkan-shaders.hpp"; -std::string target_cpp = "ggml-vulkan-shaders.cpp"; -bool no_clean = false; - -const std::vector type_names = { - "f32", - "f16", - "q4_0", - "q4_1", - "q5_0", - "q5_1", - "q8_0", - "q2_k", - "q3_k", - "q4_k", - "q5_k", - "q6_k", - "iq4_nl" -}; - -void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) { -#ifdef _WIN32 - HANDLE stdout_read, stdout_write; - HANDLE stderr_read, stderr_write; - SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE }; - - if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) || - !SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) { - throw std::runtime_error("Failed to create stdout pipe"); - } - - if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) || - !SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) { - throw std::runtime_error("Failed to create stderr pipe"); - } - - PROCESS_INFORMATION pi; - STARTUPINFOA si = { sizeof(STARTUPINFOA) }; - si.dwFlags = STARTF_USESTDHANDLES; - si.hStdOutput = stdout_write; - si.hStdError = stderr_write; - - std::vector cmd(command.begin(), command.end()); - cmd.push_back('\0'); - - if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) { - throw std::runtime_error("Failed to create process"); - } - - CloseHandle(stdout_write); - CloseHandle(stderr_write); - - std::array buffer; - DWORD bytes_read; - - while (ReadFile(stdout_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { - stdout_str.append(buffer.data(), bytes_read); - } - - while (ReadFile(stderr_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { - stderr_str.append(buffer.data(), bytes_read); - } - - CloseHandle(stdout_read); - CloseHandle(stderr_read); - WaitForSingleObject(pi.hProcess, INFINITE); - CloseHandle(pi.hProcess); - CloseHandle(pi.hThread); -#else -int stdout_pipe[2]; - int stderr_pipe[2]; - - if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) { - throw std::runtime_error("Failed to create pipes"); - } - - pid_t pid = fork(); - if (pid < 0) { - throw std::runtime_error("Failed to fork process"); - } - - if (pid == 0) { - close(stdout_pipe[0]); - close(stderr_pipe[0]); - dup2(stdout_pipe[1], STDOUT_FILENO); - dup2(stderr_pipe[1], STDERR_FILENO); - close(stdout_pipe[1]); - close(stderr_pipe[1]); - execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr); - _exit(EXIT_FAILURE); - } else { - close(stdout_pipe[1]); - close(stderr_pipe[1]); - - std::array buffer; - ssize_t bytes_read; - - while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) { - stdout_str.append(buffer.data(), bytes_read); - } - - while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) { - stderr_str.append(buffer.data(), bytes_read); - } - - close(stdout_pipe[0]); - close(stderr_pipe[0]); - waitpid(pid, nullptr, 0); - } -#endif -} - -bool directory_exists(const std::string& path) { - struct stat info; - if (stat(path.c_str(), &info) != 0) { - return false; // Path doesn't exist or can't be accessed - } - return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory -} - -bool create_directory(const std::string& path) { -#ifdef _WIN32 - return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists -#else - return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions -#endif -} - -std::string to_uppercase(const std::string& input) { - std::string result = input; - for (char& c : result) { - c = std::toupper(c); - } - return result; -} - -bool string_ends_with(const std::string& str, const std::string& suffix) { - if (suffix.size() > str.size()) { - return false; - } - return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin()); -} - -static const char path_separator = '/'; - -std::string join_paths(const std::string& path1, const std::string& path2) { - return path1 + path_separator + path2; -} - -std::string basename(const std::string &path) { - return path.substr(path.find_last_of("/\\") + 1); -} - -// variables to track number of compiles in progress -static uint32_t compile_count = 0; -static std::mutex compile_count_mutex; -static std::condition_variable compile_count_cond; - -void string_to_spv_func(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true) { - std::string name = _name + (fp16 ? "" : "_fp32"); - std::string out_fname = join_paths(output_dir, name + ".spv"); - std::string in_path = join_paths(input_dir, in_fname); - - #ifdef _WIN32 - std::vector cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""}; - #else - std::vector cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname}; - #endif - - #ifdef GGML_VULKAN_SHADER_DEBUG_INFO - cmd.push_back("-g"); - #endif - - for (const auto& define : defines) { - cmd.push_back("-D" + define.first + "=" + define.second); - } - - std::string command; - for (const auto& part : cmd) { - command += part + " "; - } - - std::string stdout_str, stderr_str; - try { - // std::cout << "Executing command: "; - // for (const auto& part : cmd) { - // std::cout << part << " "; - // } - // std::cout << std::endl; - - execute_command(command, stdout_str, stderr_str); - if (!stderr_str.empty()) { - std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl; - return; - } - - std::lock_guard guard(lock); - shader_fnames.push_back(std::make_pair(name, out_fname)); - } catch (const std::exception& e) { - std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl; - } - { - std::lock_guard guard(compile_count_mutex); - assert(compile_count > 0); - compile_count--; - } - compile_count_cond.notify_all(); -} - -std::map merge_maps(const std::map& a, const std::map& b) { - std::map result = a; - result.insert(b.begin(), b.end()); - return result; -} - -static std::vector> compiles; -void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true) { - { - // wait until fewer than N compiles are in progress. - // 16 is an arbitrary limit, the goal is to avoid "failed to create pipe" errors. - uint32_t N = 16; - std::unique_lock guard(compile_count_mutex); - while (compile_count >= N) { - compile_count_cond.wait(guard); - } - compile_count++; - } - compiles.push_back(std::async(string_to_spv_func, _name, in_fname, defines, fp16)); -} - -void matmul_shaders(bool fp16, bool matmul_id) { - std::string load_vec = fp16 ? "8" : "4"; - std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4"; - std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4"; - - std::map base_dict = {{"FLOAT_TYPE", fp16 ? "float16_t" : "float"}}; - std::string shader_name = "matmul"; - - if (matmul_id) { - base_dict["MUL_MAT_ID"] = "1"; - shader_name = "matmul_id"; - } - - if (fp16) { - base_dict["FLOAT16"] = "1"; - } - - // Shaders with f16 B_TYPE - string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); - string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - - string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); - string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - - for (const auto& tname : type_names) { - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - // For unaligned, load one at a time for f32/f16, or two at a time for quants - std::string load_vec_a_unaligned = (tname == "f32" || tname == "f16") ? "1" : "2"; - // For aligned matmul loads - std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2"; - string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16); - string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16); - } -} - -void process_shaders() { - std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl; - std::map base_dict = {{"FLOAT_TYPE", "float"}}; - - for (const auto& fp16 : {false, true}) { - matmul_shaders(fp16, false); - matmul_shaders(fp16, true); - } - - for (const auto& tname : type_names) { - // mul mat vec - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp"; - - string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - - string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - - // Dequant shaders - if (tname != "f16") { - string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); - } - - if (!string_ends_with(tname, "_k")) { - shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp"; - - if (tname == "f16") { - string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - } else { - string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}); - } - string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}); - } - } - - string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - - // Norms - string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - - string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); - string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - string_to_spv("contig_cpy_f32_f32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); - string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - - string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("acc_f32", "acc.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}); - - string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - - string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - - string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - - string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}}); - - string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - - string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - - string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - - string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - - string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - - string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - - string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}}); - - string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - - string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}})); - - string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - - string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - - for (auto &c : compiles) { - c.wait(); - } -} - -void write_output_files() { - FILE* hdr = fopen(target_hpp.c_str(), "w"); - FILE* src = fopen(target_cpp.c_str(), "w"); - - fprintf(hdr, "#include \n\n"); - fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str()); - - for (const auto& pair : shader_fnames) { - const std::string& name = pair.first; - #ifdef _WIN32 - std::string path = pair.second; - std::replace(path.begin(), path.end(), '/', '\\' ); - #else - const std::string& path = pair.second; - #endif - - FILE* spv = fopen(path.c_str(), "rb"); - if (!spv) { - std::cerr << "Error opening SPIR-V file: " << path << " (" << strerror(errno) << ")\n"; - continue; - } - - fseek(spv, 0, SEEK_END); - size_t size = ftell(spv); - fseek(spv, 0, SEEK_SET); - - std::vector data(size); - size_t read_size = fread(data.data(), 1, size, spv); - fclose(spv); - if (read_size != size) { - std::cerr << "Error reading SPIR-V file: " << path << " (" << strerror(errno) << ")\n"; - continue; - } - - fprintf(hdr, "extern unsigned char %s_data[%zu];\n", name.c_str(), size); - fprintf(hdr, "const uint64_t %s_len = %zu;\n\n", name.c_str(), size); - - fprintf(src, "unsigned char %s_data[%zu] = {\n", name.c_str(), size); - for (size_t i = 0; i < size; ++i) { - fprintf(src, "0x%02x,", data[i]); - if ((i + 1) % 12 == 0) fprintf(src, "\n"); - } - fprintf(src, "\n};\n\n"); - - if (!no_clean) { - std::remove(path.c_str()); - } - } - - fclose(hdr); - fclose(src); -} - -int main(int argc, char** argv) { - std::map args; - for (int i = 1; i < argc; i += 2) { - if (i + 1 < argc) { - args[argv[i]] = argv[i + 1]; - } - } - - if (args.find("--glslc") != args.end()) { - GLSLC = args["--glslc"]; // Path to glslc - } - if (args.find("--input-dir") != args.end()) { - input_dir = args["--input-dir"]; // Directory containing shader sources - } - if (args.find("--output-dir") != args.end()) { - output_dir = args["--output-dir"]; // Directory for containing SPIR-V output - } - if (args.find("--target-hpp") != args.end()) { - target_hpp = args["--target-hpp"]; // Path to generated header file - } - if (args.find("--target-cpp") != args.end()) { - target_cpp = args["--target-cpp"]; // Path to generated cpp file - } - if (args.find("--no-clean") != args.end()) { - no_clean = true; // Keep temporary SPIR-V files in output-dir after build - } - - if (!directory_exists(input_dir)) { - std::cerr << "\"" << input_dir << "\" must be a valid directory containing shader sources" << std::endl; - return EXIT_FAILURE; - } - - if (!directory_exists(output_dir)) { - if (!create_directory(output_dir)) { - std::cerr << "Error creating output directory: " << output_dir << "\n"; - return EXIT_FAILURE; - } - } - - process_shaders(); - - write_output_files(); - - return EXIT_SUCCESS; -}