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vulkan: use smaller combined allocations to avoid fragmentation (llama/11551)

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Jeff Bolz 2025-02-06 00:02:18 -06:00 committed by Georgi Gerganov
parent 2f0cf44915
commit 7c165d7fa8
2 changed files with 18 additions and 15 deletions
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14
ggml/src/ggml-alloc.c
View File

@ -989,19 +989,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
}
if (this_size > max_size) {
GGML_LOG_ERROR("%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
__func__, t->name,
ggml_backend_buft_name(buft),
this_size, max_size);
for (size_t i = 0; i < n_buffers; i++) {
ggml_backend_buffer_free(buffers[i]);
}
free(buffers);
return NULL;
}
if ((cur_buf_size + this_size) > max_size) {
if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) {
// allocate tensors in the current buffer
if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
return NULL;

19
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@ -156,6 +156,7 @@ struct vk_device_struct {
vk::PhysicalDeviceProperties properties;
std::string name;
uint64_t max_memory_allocation_size;
uint64_t suballocation_block_size;
bool fp16;
bool pipeline_robustness;
vk::Device device;
@ -2269,6 +2270,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
device->physical_device.getProperties2(&props2);
device->properties = props2.properties;
device->vendor_id = device->properties.vendorID;
const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");
@ -2280,7 +2282,20 @@ static vk_device ggml_vk_get_device(size_t idx) {
device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
}
device->vendor_id = device->properties.vendorID;
const char* GGML_VK_SUBALLOCATION_BLOCK_SIZE = getenv("GGML_VK_SUBALLOCATION_BLOCK_SIZE");
if (GGML_VK_SUBALLOCATION_BLOCK_SIZE != nullptr) {
device->suballocation_block_size = std::stoul(GGML_VK_SUBALLOCATION_BLOCK_SIZE);
#if defined(_WIN32)
} else if (device->vendor_id == VK_VENDOR_ID_NVIDIA) {
// Limit batching of allocations to 1GB by default to avoid fragmentation issues
device->suballocation_block_size = 1024*1024*1024;
#endif
} else {
device->suballocation_block_size = device->max_memory_allocation_size;
}
device->suballocation_block_size = std::min(device->suballocation_block_size, device->max_memory_allocation_size);
device->subgroup_size = subgroup_props.subgroupSize;
device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
if (sm_builtins) {
@ -7561,7 +7576,7 @@ static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type
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;
return ctx->device->suballocation_block_size;
}
static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {