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https://github.com/ggerganov/whisper.cpp.git
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vulkan: use smaller combined allocations to avoid fragmentation (llama/11551)
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@ -989,19 +989,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
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this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
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this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
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}
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}
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if (this_size > max_size) {
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if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) {
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GGML_LOG_ERROR("%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
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__func__, t->name,
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ggml_backend_buft_name(buft),
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this_size, max_size);
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for (size_t i = 0; i < n_buffers; i++) {
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ggml_backend_buffer_free(buffers[i]);
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}
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free(buffers);
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return NULL;
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}
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if ((cur_buf_size + this_size) > max_size) {
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// allocate tensors in the current buffer
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// allocate tensors in the current buffer
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if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
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if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
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return NULL;
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return NULL;
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@ -156,6 +156,7 @@ struct vk_device_struct {
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vk::PhysicalDeviceProperties properties;
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vk::PhysicalDeviceProperties properties;
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std::string name;
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std::string name;
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uint64_t max_memory_allocation_size;
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uint64_t max_memory_allocation_size;
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uint64_t suballocation_block_size;
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bool fp16;
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bool fp16;
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bool pipeline_robustness;
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bool pipeline_robustness;
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vk::Device device;
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vk::Device device;
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@ -2269,6 +2270,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
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device->physical_device.getProperties2(&props2);
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device->physical_device.getProperties2(&props2);
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device->properties = props2.properties;
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device->properties = props2.properties;
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device->vendor_id = device->properties.vendorID;
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const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
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const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
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@ -2280,7 +2282,20 @@ static vk_device ggml_vk_get_device(size_t idx) {
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device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
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device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
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}
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}
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device->vendor_id = device->properties.vendorID;
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const char* GGML_VK_SUBALLOCATION_BLOCK_SIZE = getenv("GGML_VK_SUBALLOCATION_BLOCK_SIZE");
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if (GGML_VK_SUBALLOCATION_BLOCK_SIZE != nullptr) {
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device->suballocation_block_size = std::stoul(GGML_VK_SUBALLOCATION_BLOCK_SIZE);
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#if defined(_WIN32)
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} else if (device->vendor_id == VK_VENDOR_ID_NVIDIA) {
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// Limit batching of allocations to 1GB by default to avoid fragmentation issues
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device->suballocation_block_size = 1024*1024*1024;
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#endif
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} else {
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device->suballocation_block_size = device->max_memory_allocation_size;
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}
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device->suballocation_block_size = std::min(device->suballocation_block_size, device->max_memory_allocation_size);
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device->subgroup_size = subgroup_props.subgroupSize;
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device->subgroup_size = subgroup_props.subgroupSize;
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device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
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device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
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if (sm_builtins) {
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if (sm_builtins) {
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@ -7561,7 +7576,7 @@ static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type
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static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
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static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
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ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
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ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
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return ctx->device->max_memory_allocation_size;
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return ctx->device->suballocation_block_size;
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}
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}
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static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
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static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
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