mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-18 20:27:53 +00:00
SYCL: Reduce most of the compiler warnings (llama/10748)
* Try to reduce some unused and typecast warnings * Reduce compiler warnings step 2 * add a newline at the end of the file * Initialize nreduce as size_t * [SYCL] Remove pragma directives from mmq.cpp * SYCL: mmq add condition to prevent blocks_per_tile_x_row variable from becoming 0 * SYCL softmax: Initialize nreduce as size_t * ggml-sycl.cpp: fix some trailing whitespaces * SYCL: remove the unused variables instead of commenting it out * SYCL poo2d kernel: set NAN for invalid pooling op * SYCL gemm.hpp: remove pragma directives * SYCL gemm.hpp: use const cast to properly support dnnl::memory * SYCL: wkv6 remove a comment * SYCL: clean comments step 2 * SYCL: clean comments and variables step 3 * SYCL: Use GGML_UNUSED for unused variables * SYCL: remove extra empty lines and a comment * Remove TODO * cleanup spaces * add a stdout for unsupported op * use sycl printf over fprintf * remove prints for CI * SYCL ggml-sycl: pool2D use sycl::nan and remove if-else block --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
This commit is contained in:
Akarshan Biswas
Georgi Gerganov
parent
e6eed605cf
commit
26c9fd0cdc
@ -11,6 +11,7 @@
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//
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#include "common.hpp"
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#include "ggml-impl.h"
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int get_current_device_id() {
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return dpct::dev_mgr::instance().current_device_id();
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@ -28,11 +29,7 @@ void* ggml_sycl_host_malloc(size_t size) try {
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if (err != 0) {
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// clear the error
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fprintf(
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stderr,
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"WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
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size / 1024.0 / 1024.0,
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"syclGetErrorString is not supported");
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GGML_LOG_ERROR("WARNING: failed to allocate %.2f MB of pinned memory: %s\n", size / 1024.0 / 1024.0, "syclGetErrorString is not supported");
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return nullptr;
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}
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@ -66,18 +63,12 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block
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void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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const ggml_tensor *src1, ggml_tensor *dst,
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const ggml_sycl_op_flatten_t op) try {
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const int64_t nrows0 = ggml_nrows(src0);
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const bool use_src1 = src1 != nullptr;
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const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
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GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
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GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
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ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
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ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
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ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
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// dd = data device
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float * src0_ddf = (float *) src0->data;
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float * src1_ddf = use_src1 ? (float *) src1->data : nullptr;
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@ -626,6 +626,7 @@ struct bin_bcast_sycl {
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});
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}
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}
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GGML_UNUSED(ctx);
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}
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};
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@ -47,7 +47,7 @@ static void concat_f32_dim1(const float *x, const float *y, float *dst,
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// operation
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int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
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item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
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if (item_ct1.get_group(1) < ne01) { // src0
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if (item_ct1.get_group(1) < (size_t) ne01) { // src0
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int offset_src =
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nidx + item_ct1.get_group(1) * ne0 + item_ct1.get_group(0) * ne0 * ne01;
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dst[offset_dst] = x[offset_src];
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@ -70,7 +70,7 @@ static void concat_f32_dim2(const float *x, const float *y, float *dst,
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// operation
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int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
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item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
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if (item_ct1.get_group(0) < ne02) { // src0
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if (item_ct1.get_group(0) < (size_t) ne02) { // src0
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int offset_src = nidx + item_ct1.get_group(1) * ne0 +
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item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
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dst[offset_dst] = x[offset_src];
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@ -424,7 +424,7 @@ static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y,
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const int64_t global_id = item_ct1.get_local_id(2) + work_group_size * item_ct1.get_group(2);
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// make each work-item deal with more elements since sycl global range can not exceed max int
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const src_t * x = (src_t *) vx;
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const src_t * x = (const src_t *) vx;
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for (int64_t i = global_id; i < k; i += work_group_size * item_ct1.get_group_range(2)) {
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y[i] = x[i];
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}
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@ -1015,9 +1015,9 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
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break;
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}
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(void) src1;
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(void) dst;
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(void) src1_ddq_i;
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(void) src1_ncols;
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(void) src1_padded_row_size;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_ddq_i);
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GGML_UNUSED(src1_ncols);
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GGML_UNUSED(src1_padded_row_size);
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}
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@ -1237,7 +1237,7 @@ namespace dpct
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std::map<byte_t *, allocation>::iterator get_map_iterator(const void *ptr)
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{
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auto it = m_map.upper_bound((byte_t *)ptr);
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auto it = m_map.upper_bound(const_cast<byte_t *>(reinterpret_cast<const byte_t *>(ptr)));
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if (it == m_map.end())
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{
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// Not a virtual pointer.
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@ -237,7 +237,7 @@ void upscale_f32(const float *x, float *dst, const int nb00, const int nb01,
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int i02 = i12 / sf2;
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int i03 = i13 / sf3;
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dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
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dst[index] = *(const float *)((const char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
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}
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void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02,
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@ -251,8 +251,7 @@ void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const i
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// operation
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int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
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item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
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if (nidx < ne00 && item_ct1.get_group(1) < ne01 &&
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item_ct1.get_group(0) < ne02) {
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if (nidx < ne00 && item_ct1.get_group(1) < (size_t) ne01 && item_ct1.get_group(0) < (size_t) ne02) {
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int offset_src = nidx + item_ct1.get_group(1) * ne00 +
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item_ct1.get_group(0) * ne00 * ne01;
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dst[offset_dst] = x[offset_src];
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@ -520,9 +519,10 @@ inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor
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silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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@ -535,9 +535,10 @@ inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor
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gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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const ggml_tensor *src1, ggml_tensor *dst,
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@ -550,9 +551,10 @@ inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_
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gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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@ -564,9 +566,10 @@ inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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@ -579,9 +582,10 @@ inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor
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relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -595,9 +599,10 @@ inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml
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hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -610,9 +615,10 @@ inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_t
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hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -625,9 +631,10 @@ inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, const ggml_tensor
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exp_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -640,9 +647,10 @@ inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, const ggml_tensor
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log_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -655,9 +663,10 @@ inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, const ggml_ten
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sigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -670,9 +679,10 @@ inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor
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sqrt_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -685,9 +695,10 @@ inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, const ggml_tensor
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sin_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -700,9 +711,10 @@ inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, const ggml_tensor
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cos_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -715,9 +727,10 @@ inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, const ggml_tensor
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step_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -730,9 +743,10 @@ inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, const ggml_tensor
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neg_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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@ -749,9 +763,10 @@ inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_
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leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
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@ -764,9 +779,10 @@ inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor
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sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
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(void) src1;
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(void) dst;
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(void) src1_dd;
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GGML_UNUSED(src1);
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GGML_UNUSED(dst);
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GGML_UNUSED(src1_dd);
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GGML_UNUSED(ctx);
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}
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|
||||
inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
|
||||
@ -787,9 +803,10 @@ inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_ten
|
||||
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3,
|
||||
main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
|
||||
@ -805,9 +822,10 @@ inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor
|
||||
src0->ne[0], src0->ne[1], src0->ne[2],
|
||||
dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
|
||||
@ -827,7 +845,8 @@ inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor
|
||||
|
||||
acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
|
||||
|
||||
(void) dst;
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
|
||||
|
||||
@ -51,8 +51,8 @@ public:
|
||||
const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
|
||||
const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
|
||||
const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
|
||||
auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
|
||||
auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
|
||||
auto a_mem = dnnl::memory(a_in_md, eng, const_cast<void*>(a));
|
||||
auto b_mem = dnnl::memory(b_in_md, eng, const_cast<void*>(b));
|
||||
auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
|
||||
auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
|
||||
|
||||
@ -79,8 +79,8 @@ public:
|
||||
const auto a_in_md = dnnl::memory::desc(a_dims, at, a_trans ? tag::ba : tag::ab);
|
||||
const auto b_in_md = dnnl::memory::desc(b_dims, bt, b_trans ? tag::ba : tag::ab);
|
||||
const auto c_md = dnnl::memory::desc(c_dims, ct, tag::ab);
|
||||
auto a_mem = dnnl::memory(a_in_md, eng, (void*)a);
|
||||
auto b_mem = dnnl::memory(b_in_md, eng, (void*)b);
|
||||
auto a_mem = dnnl::memory(a_in_md, eng, const_cast<void*>(a));
|
||||
auto b_mem = dnnl::memory(b_in_md, eng, const_cast<void*>(b));
|
||||
auto matmul_pd = dnnl::matmul::primitive_desc(eng, a_in_md, b_in_md, c_md);
|
||||
auto c_mem = dnnl::memory(matmul_pd.dst_desc(), eng, c);
|
||||
|
||||
|
||||
@ -47,7 +47,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
|
||||
|
||||
info.device_count = dpct::dev_mgr::instance().device_count();
|
||||
if (info.device_count == 0) {
|
||||
GGML_LOG_ERROR("%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__);
|
||||
GGML_LOG_ERROR("%s: failed to initialize: %s\n", GGML_SYCL_NAME, __func__);
|
||||
return info;
|
||||
}
|
||||
|
||||
@ -64,7 +64,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
|
||||
#else
|
||||
GGML_LOG_INFO("%s: SYCL_USE_XMX: no\n", __func__);
|
||||
#endif
|
||||
GGML_LOG_INFO("%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count);
|
||||
GGML_LOG_INFO("%s: found %d %s devices:\n", __func__, info.device_count, GGML_SYCL_NAME);
|
||||
|
||||
for (int i = 0; i < info.device_count; ++i) {
|
||||
info.devices[i].vmm = 0;
|
||||
@ -137,7 +137,6 @@ void ggml_backend_sycl_print_sycl_devices() {
|
||||
|
||||
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;
|
||||
@ -420,13 +419,11 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
GGML_UNUSED(buffer);
|
||||
} catch (const sycl::exception & exc) {
|
||||
std::cerr << exc.what() << "Exception caught at file:" << __FILE__ << ", line:" << __LINE__ << std::endl;
|
||||
std::exit(1);
|
||||
}
|
||||
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 {
|
||||
@ -1092,10 +1089,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
|
||||
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_) {
|
||||
}
|
||||
explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : device(device_), qptr(qptr_) {}
|
||||
|
||||
~ggml_sycl_pool_leg() {
|
||||
for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
|
||||
@ -1238,7 +1232,7 @@ static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy,
|
||||
zeros[i] = 0.f;
|
||||
qzeros[i] = 0;
|
||||
}
|
||||
const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros;
|
||||
const TC xi = ix < kx ? *(const TC *)&x[iy * kx + ix] : zeros;
|
||||
float sum = xi[0];
|
||||
float amax = sycl::fabs(xi[0]);
|
||||
#pragma unroll
|
||||
@ -1799,6 +1793,9 @@ static void pool2d_nchw_kernel(
|
||||
switch (op) {
|
||||
case GGML_OP_POOL_AVG: res = 0; break;
|
||||
case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
|
||||
default:
|
||||
res = (To) sycl::nan(uint32_t(0));
|
||||
break;
|
||||
}
|
||||
|
||||
for (int i = bh; i < eh; i += 1) {
|
||||
@ -1817,6 +1814,9 @@ static void pool2d_nchw_kernel(
|
||||
switch (op) {
|
||||
case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
|
||||
case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
|
||||
default:
|
||||
res = (To) sycl::nan(uint32_t(0));
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1855,7 +1855,8 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
|
||||
s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
|
||||
});
|
||||
|
||||
(void) dst;
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
template <typename src0_t>
|
||||
@ -1893,10 +1894,10 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens
|
||||
});
|
||||
}
|
||||
|
||||
(void) dst;
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
|
||||
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) {
|
||||
@ -2464,8 +2465,8 @@ static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tens
|
||||
|
||||
ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_repeat>>(ctx, dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) src1_d;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src1_d);
|
||||
}
|
||||
|
||||
|
||||
@ -2484,17 +2485,18 @@ inline void ggml_sycl_op_mul_mat_sycl(
|
||||
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()));
|
||||
|
||||
#if !GGML_SYCL_DNNL
|
||||
const int64_t ne0 = dst->ne[0];
|
||||
// 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;
|
||||
#endif
|
||||
|
||||
#ifdef GGML_SYCL_F16
|
||||
bool use_fp16 = true; // TODO(Yu) SYCL capability check
|
||||
@ -2531,9 +2533,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
|
||||
: src1_as_f16.get();
|
||||
ggml_sycl_pool_alloc<sycl::half> 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
|
||||
const sycl::half alpha_f16 = 1.0f;
|
||||
const sycl::half beta_f16 = 0.0f;
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
|
||||
*stream, oneapi::mkl::transpose::trans,
|
||||
oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
|
||||
@ -2570,9 +2572,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
|
||||
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
|
||||
const float alpha = 1.0f;
|
||||
const float beta = 0.0f;
|
||||
# ifdef GGML_SYCL_NVIDIA
|
||||
SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
|
||||
oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ *stream }, oneapi::mkl::transpose::trans,
|
||||
@ -2590,9 +2592,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
|
||||
src0_ddf_i, DnnlGemmWrapper::to_dt<float>(), dst_dd_i, DnnlGemmWrapper::to_dt<float>());
|
||||
#endif
|
||||
}
|
||||
(void) dst;
|
||||
(void) src1_ddq_i;
|
||||
(void) src1_padded_row_size;
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_ddq_i);
|
||||
GGML_UNUSED(src1_padded_row_size);
|
||||
}
|
||||
catch (sycl::exception const &exc) {
|
||||
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
|
||||
@ -2638,8 +2640,9 @@ static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tens
|
||||
item_ct1);
|
||||
});
|
||||
|
||||
(void) src1;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
|
||||
@ -2654,9 +2657,10 @@ inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor
|
||||
|
||||
sum_rows_f32_sycl(src0_dd, dst_dd, ne, 1, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
|
||||
@ -2673,9 +2677,10 @@ inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_te
|
||||
|
||||
sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
|
||||
@ -2694,9 +2699,10 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_ten
|
||||
|
||||
argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
|
||||
@ -2713,9 +2719,10 @@ inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tens
|
||||
|
||||
argmax_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
|
||||
@ -2735,9 +2742,10 @@ inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const gg
|
||||
|
||||
diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
|
||||
@ -2758,9 +2766,10 @@ inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tenso
|
||||
*/
|
||||
SYCL_CHECK(0);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
|
||||
@ -2783,9 +2792,10 @@ inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tenso
|
||||
*/
|
||||
SYCL_CHECK(0);
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) {
|
||||
@ -2862,7 +2872,6 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
|
||||
|
||||
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);
|
||||
@ -3289,7 +3298,6 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
|
||||
|
||||
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();;
|
||||
@ -3397,6 +3405,7 @@ catch (sycl::exception const &exc) {
|
||||
|
||||
inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
|
||||
// TODO: accuracy issues in MMQ
|
||||
GGML_UNUSED(type);
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -3772,7 +3781,7 @@ static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
|
||||
(void) dst;
|
||||
GGML_UNUSED(dst);
|
||||
}
|
||||
catch (sycl::exception const &exc) {
|
||||
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
|
||||
@ -3783,7 +3792,7 @@ catch (sycl::exception const &exc) {
|
||||
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;
|
||||
GGML_UNUSED(src1);
|
||||
}
|
||||
|
||||
static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
@ -3828,13 +3837,16 @@ static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor
|
||||
}
|
||||
|
||||
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;
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
void ggml_sycl_set_main_device(const int main_device) try {
|
||||
if (dpct::get_current_device_id() == main_device) return;
|
||||
if (dpct::get_current_device_id() == static_cast<unsigned int> (main_device)) {
|
||||
return;
|
||||
}
|
||||
check_allow_gpu_index(main_device);
|
||||
dpct::select_device(main_device);
|
||||
|
||||
@ -4202,6 +4214,7 @@ try
|
||||
{
|
||||
ggml_backend_sycl_context *sycl_ctx =
|
||||
(ggml_backend_sycl_context *)backend->context;
|
||||
|
||||
sycl::event *sycl_event = static_cast<sycl::event *>(event->context);
|
||||
|
||||
const queue_ptr &stream = sycl_ctx->stream(sycl_ctx->device, 0);
|
||||
@ -4216,7 +4229,7 @@ catch (sycl::exception const &exc)
|
||||
}
|
||||
|
||||
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<ggml_backend_sycl_context*>(backend->context);
|
||||
|
||||
sycl::event* sycl_event = static_cast<sycl::event*>(event->context);
|
||||
|
||||
if (ggml_backend_is_sycl(backend)) {
|
||||
@ -4624,6 +4637,7 @@ static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, cons
|
||||
// SYCL doesn't support registering host memory, left here for reference
|
||||
// "ggml_backend_register_host_buffer"
|
||||
// "ggml_backend_unregister_host_buffer"
|
||||
GGML_UNUSED(name);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
||||
@ -120,6 +120,7 @@ void ggml_sycl_op_im2col(
|
||||
im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
|
||||
}
|
||||
|
||||
(void) src0;
|
||||
(void) src0_dd;
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src0_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
@ -813,7 +813,7 @@ load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql,
|
||||
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
||||
}
|
||||
|
||||
const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
|
||||
constexpr int blocks_per_tile_x_row = QI4_K > WARP_SIZE ? 1 : WARP_SIZE / QI4_K; // == 1 if QK_K == 256
|
||||
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
||||
|
||||
#pragma unroll
|
||||
@ -961,7 +961,7 @@ load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql,
|
||||
x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
|
||||
}
|
||||
|
||||
const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
|
||||
constexpr int blocks_per_tile_x_row = QI5_K > WARP_SIZE ? 1 : WARP_SIZE / QI5_K; // == 1 if QK_K == 256
|
||||
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
||||
|
||||
#pragma unroll
|
||||
@ -1109,7 +1109,7 @@ load_tiles_q6_K(const void *__restrict__ vx, int *__restrict__ x_ql,
|
||||
dpct::sub_sat());
|
||||
}
|
||||
|
||||
const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
|
||||
constexpr int blocks_per_tile_x_row = QI6_K > WARP_SIZE ? 1 : WARP_SIZE / QI6_K; // == 1 if QK_K == 256
|
||||
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
||||
float * x_dmf = (float *) x_dm;
|
||||
|
||||
@ -3020,9 +3020,9 @@ void ggml_sycl_op_mul_mat_q(
|
||||
break;
|
||||
}
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_ddf_i;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_ddf_i);
|
||||
}
|
||||
catch (sycl::exception const &exc) {
|
||||
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
|
||||
|
||||
@ -753,11 +753,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
|
||||
const sycl::range<3> block_nums(1, 1, block_num_y);
|
||||
const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
|
||||
{
|
||||
|
||||
stream->submit([&](sycl::handler &cgh) {
|
||||
auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
|
||||
auto ksigns64_ptr_ct1 = &ksigns64[0];
|
||||
|
||||
stream->submit([&](sycl::handler & cgh) {
|
||||
cgh.parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1)
|
||||
@ -780,9 +776,6 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
|
||||
{
|
||||
|
||||
stream->submit([&](sycl::handler &cgh) {
|
||||
auto iq2xs_grid_ptr_ct1 = &iq2xs_grid[0];
|
||||
auto ksigns64_ptr_ct1 = &ksigns64[0];
|
||||
|
||||
cgh.parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1)
|
||||
@ -805,9 +798,6 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
|
||||
{
|
||||
|
||||
stream->submit([&](sycl::handler &cgh) {
|
||||
auto iq3xxs_grid_ptr_ct1 = &iq3xxs_grid[0];
|
||||
auto ksigns64_ptr_ct1 = &ksigns64[0];
|
||||
|
||||
cgh.parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1)
|
||||
@ -830,8 +820,6 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
|
||||
{
|
||||
|
||||
stream->submit([&](sycl::handler &cgh) {
|
||||
auto iq3s_grid_ptr_ct1 = &iq3s_grid[0];
|
||||
|
||||
cgh.parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1)
|
||||
@ -854,9 +842,6 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
|
||||
{
|
||||
|
||||
stream->submit([&](sycl::handler &cgh) {
|
||||
auto iq1s_grid_ptr_ct1 = &iq1s_grid_gpu[0];
|
||||
auto ksigns64_ptr_ct1 = &ksigns64[0];
|
||||
|
||||
cgh.parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1)
|
||||
@ -954,7 +939,7 @@ void ggml_sycl_op_mul_mat_vec_q(
|
||||
const size_t q8_1_bs = QK8_1;
|
||||
// the main device has a larger memory buffer to hold the results from all GPUs
|
||||
// nrows_dst == nrows of the matrix that the kernel writes into
|
||||
const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff;
|
||||
|
||||
for (int i = 0; i < src1_ncols; i++)
|
||||
{
|
||||
const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs;
|
||||
@ -1023,7 +1008,8 @@ void ggml_sycl_op_mul_mat_vec_q(
|
||||
break;
|
||||
}
|
||||
}
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_ddf_i;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_ddf_i);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
@ -31,7 +31,7 @@ static void norm_f32(const float* x, float* dst, const int ncols, const float ep
|
||||
*/
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
mean_var = 0.f;
|
||||
int nreduce = nwarps / WARP_SIZE;
|
||||
size_t nreduce = nwarps / WARP_SIZE;
|
||||
for (size_t i = 0; i < nreduce; i += 1)
|
||||
{
|
||||
mean_var += s_sum[lane_id + i * WARP_SIZE];
|
||||
@ -55,7 +55,7 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
|
||||
const int nthreads = item_ct1.get_local_range(2);
|
||||
const int nwarps = nthreads / WARP_SIZE;
|
||||
start += item_ct1.get_local_id(2);
|
||||
int nreduce = nwarps / WARP_SIZE;
|
||||
size_t nreduce = nwarps / WARP_SIZE;
|
||||
|
||||
if (end >= ne_elements) {
|
||||
end = ne_elements;
|
||||
@ -163,7 +163,7 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const floa
|
||||
converged control flow. You may need to adjust the code.
|
||||
*/
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
int nreduce = nwarps / WARP_SIZE;
|
||||
size_t nreduce = nwarps / WARP_SIZE;
|
||||
tmp = 0.f;
|
||||
for (size_t i = 0; i < nreduce; i += 1)
|
||||
{
|
||||
@ -352,6 +352,7 @@ void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor*
|
||||
(void)src1;
|
||||
(void)dst;
|
||||
(void)src1_dd;
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
|
||||
|
||||
@ -269,7 +269,8 @@ void ggml_sycl_op_rope(
|
||||
}
|
||||
}
|
||||
|
||||
(void) src1;
|
||||
(void) dst;
|
||||
(void) src1_dd;
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(dst);
|
||||
GGML_UNUSED(src1_dd);
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
@ -16,7 +16,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
|
||||
const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
|
||||
const int nthreads = block_size;
|
||||
const int nwarps = nthreads / WARP_SIZE;
|
||||
int nreduce = nwarps / WARP_SIZE;
|
||||
size_t nreduce = nwarps / WARP_SIZE;
|
||||
float slope = 1.0f;
|
||||
|
||||
// ALiBi
|
||||
@ -53,8 +53,9 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
|
||||
if (block_size > WARP_SIZE) {
|
||||
if (warp_id == 0) {
|
||||
buf[lane_id] = -INFINITY;
|
||||
for (size_t i = 1; i < nreduce; i += 1)
|
||||
for (size_t i = 1; i < nreduce; i += 1) {
|
||||
buf[lane_id + i * WARP_SIZE] = -INFINITY;
|
||||
}
|
||||
}
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
|
||||
@ -63,8 +64,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
|
||||
}
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
max_val = buf[lane_id];
|
||||
for (size_t i = 1; i < nreduce; i += 1)
|
||||
{
|
||||
for (size_t i = 1; i < nreduce; i += 1) {
|
||||
max_val = std::max(max_val, buf[lane_id + i * WARP_SIZE]);
|
||||
}
|
||||
max_val = warp_reduce_max(max_val, item_ct1);
|
||||
@ -89,8 +89,9 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
if (warp_id == 0) {
|
||||
buf[lane_id] = 0.f;
|
||||
for (size_t i = 1; i < nreduce; i += 1)
|
||||
for (size_t i = 1; i < nreduce; i += 1) {
|
||||
buf[lane_id + i * WARP_SIZE] = 0.f;
|
||||
}
|
||||
}
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
|
||||
@ -100,8 +101,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
|
||||
item_ct1.barrier(sycl::access::fence_space::local_space);
|
||||
|
||||
tmp = buf[lane_id];
|
||||
for (size_t i = 1; i < nreduce; i += 1)
|
||||
{
|
||||
for (size_t i = 1; i < nreduce; i += 1) {
|
||||
tmp += buf[lane_id + i * WARP_SIZE];
|
||||
}
|
||||
tmp = warp_reduce_sum(tmp, item_ct1);
|
||||
|
||||
@ -68,4 +68,5 @@ void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml
|
||||
const int max_period = dst->op_params[1];
|
||||
|
||||
timestep_embedding_f32_sycl(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
|
||||
GGML_UNUSED(src1);
|
||||
}
|
||||
|
||||
@ -59,7 +59,7 @@ static void rwkv_wkv_f32_kernel(
|
||||
float y = 0;
|
||||
|
||||
// Process in chunks of 4 for better vectorization
|
||||
sycl::float4 k4, r4, tf4, td4, s4, kv4;
|
||||
sycl::float4 k4, r4, tf4, td4, s4;
|
||||
#pragma unroll
|
||||
for (int j = 0; j < head_size; j += 4) {
|
||||
// Load data in vec4 chunks
|
||||
@ -135,4 +135,7 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* s
|
||||
);
|
||||
});
|
||||
});
|
||||
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src1);
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user