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Basic Vulkan Multi-GPU implementation (#5321)

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* Initial Vulkan multi-gpu implementation

Move most global variables into backend context

* Add names to backend device functions

* Add further missing cleanup code

* Reduce code duplication in tensor split layer assignment

* generalize LLAMA_SPLIT_LAYER for all backends, do not expose device count and memory in llama.h

* Only do device info print in the beginning and initialize one backend for cpu assist

Add missing cleanup code

* Rework backend memory management to make sure devices and buffers get properly allocated and freed

* Rename cpu assist free function

---------

Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
0cc4m
2024-02-07 07:54:50 +01:00
committed by GitHub
parent ed0bf32290
commit ee1628bdfe
5 changed files with 1587 additions and 1166 deletions
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69
llama.cpp
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@@ -1355,7 +1355,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
#elif defined(GGML_USE_CUBLAS)
buft = ggml_backend_cuda_buffer_type(gpu);
#elif defined(GGML_USE_VULKAN)
buft = ggml_backend_vk_buffer_type();
buft = ggml_backend_vk_buffer_type(gpu);
#elif defined(GGML_USE_SYCL)
buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
@@ -1392,6 +1392,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g
GGML_UNUSED(tensor_split);
}
static size_t llama_get_device_count() {
#if defined(GGML_USE_CUBLAS)
return ggml_backend_cuda_get_device_count();
#elif defined(GGML_USE_VULKAN)
return ggml_backend_vk_get_device_count();
#else
return 1;
#endif
}
static size_t llama_get_device_memory(int device) {
#if defined(GGML_USE_CUBLAS)
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(device, &total, &free);
return free;
#elif defined(GGML_USE_VULKAN)
size_t total;
size_t free;
ggml_backend_vk_get_device_memory(device, &total, &free);
return free;
#else
return 1;
GGML_UNUSED(device);
#endif
}
//
// globals
//
@@ -1763,6 +1790,10 @@ struct llama_context {
ggml_backend_free(backend);
}
#ifdef GGML_USE_VULKAN
ggml_vk_free_cpu_assist();
#endif
ggml_backend_buffer_free(buf_input);
ggml_free(ctx_input);
}
@@ -3436,22 +3467,18 @@ static bool llm_load_tensors(
model.buft_layer[i] = llama_default_buffer_type_cpu(true);
}
#ifdef GGML_USE_CUBLAS
if (split_mode == LLAMA_SPLIT_LAYER) {
// calculate the split points
int device_count = ggml_backend_cuda_get_device_count();
int device_count = llama_get_device_count();
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
float splits[GGML_CUDA_MAX_DEVICES];
std::vector<float> splits(device_count);
if (all_zero) {
// default split, by free memory
for (int i = 0; i < device_count; ++i) {
size_t total;
size_t free;
ggml_backend_cuda_get_device_memory(i, &total, &free);
splits[i] = free;
splits[i] = llama_get_device_memory(i);
}
} else {
std::copy(tensor_split, tensor_split + device_count, splits);
std::copy(tensor_split, tensor_split + device_count, splits.begin());
}
// sum and normalize the splits to get the split points
@@ -3467,19 +3494,17 @@ static bool llm_load_tensors(
// assign the repeating layers to the devices according to the splits
int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits;
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
}
// assign the output layer
if (n_gpu_layers > n_layer) {
int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits;
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
model.buft_output = llama_default_buffer_type_offload(layer_gpu);
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
}
} else
#endif
{
} else {
ggml_backend_buffer_type_t split_buft;
if (split_mode == LLAMA_SPLIT_ROW) {
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
@@ -10483,6 +10508,8 @@ size_t llama_max_devices(void) {
return GGML_CUDA_MAX_DEVICES;
#elif defined(GGML_USE_SYCL)
return GGML_SYCL_MAX_DEVICES;
#elif defined(GGML_USE_VULKAN)
return GGML_VK_MAX_DEVICES;
#else
return 1;
#endif
@@ -10690,13 +10717,15 @@ struct llama_context * llama_new_context_with_model(
}
#elif defined(GGML_USE_VULKAN)
if (model->n_gpu_layers > 0) {
ggml_backend_t backend = ggml_backend_vk_init();
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
llama_free(ctx);
return nullptr;
for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_vk_init(device);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
ctx->backends.push_back(backend);
}
#elif defined(GGML_USE_SYCL)
if (model->n_gpu_layers > 0) {