vbatts/llama.cpp
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Initial Vulkan multi-gpu implementation

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Move most global variables into backend context
This commit is contained in:
0cc4m 2024-02-04 12:35:01 +01:00
parent 277fad30c6
commit ca8110cfc9
5 changed files with 1286 additions and 1037 deletions
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8
common/common.cpp
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@ -46,6 +46,10 @@
#define GGML_USE_CUBLAS_SYCL
#endif
#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
#define GGML_USE_CUBLAS_SYCL_VULKAN
#endif
int32_t get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
@ -648,8 +652,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
params.tensor_split[i] = 0.0f;
}
}
#ifndef GGML_USE_CUBLAS_SYCL
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n");
#ifndef GGML_USE_CUBLAS_SYCL_VULKAN
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n");
#endif // GGML_USE_CUBLAS_SYCL
} else if (arg == "--no-mmap") {
params.use_mmap = false;

2219
ggml-vulkan.cpp
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File diff suppressed because it is too large Load diff

22
ggml-vulkan.h
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@ -8,24 +8,28 @@ extern "C" {
#endif
#define GGML_VK_NAME "Vulkan"
#define GGML_VK_MAX_DEVICES 16
GGML_API void ggml_vk_init(void);
GGML_API void ggml_vk_init_cpu_assist(void);
GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node);
GGML_API void ggml_vk_preallocate_buffers(void);
GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node);
GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);
GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void);
GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node);
GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#ifdef GGML_VULKAN_CHECK_RESULTS
void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor);
void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#endif
GGML_API void ggml_vk_graph_cleanup(void);
GGML_API void ggml_vk_graph_cleanup_cpu_assist(void);
// backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void);
GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend);
GGML_API GGML_CALL int ggml_backend_vk_get_device_count(void);
GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void);
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);

14
ggml.c
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@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
#elif defined(GGML_USE_CLBLAST)
ggml_cl_init();
#elif defined(GGML_USE_VULKAN)
ggml_vk_init();
ggml_vk_init_cpu_assist();
#elif defined(GGML_USE_SYCL)
ggml_init_sycl();
#endif
@ -14847,10 +14847,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
#elif defined(GGML_USE_VULKAN)
const bool skip_cpu = ggml_vk_compute_forward(params, tensor);
const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
#ifdef GGML_VULKAN_CHECK_RESULTS
if (skip_cpu) {
ggml_vk_check_results_1(params, tensor);
ggml_vk_check_results_1_cpu_assist(params, tensor);
}
#endif
if (skip_cpu) {
@ -17266,12 +17266,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
#ifdef GGML_USE_VULKAN
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]);
ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]);
}
ggml_vk_preallocate_buffers();
ggml_vk_preallocate_buffers_cpu_assist();
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1);
ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1);
}
#endif
@ -17327,7 +17327,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
}
#ifdef GGML_USE_VULKAN
ggml_vk_graph_cleanup();
ggml_vk_graph_cleanup_cpu_assist();
#endif
// performance stats (graph)

60
llama.cpp
View file

@ -1330,7 +1330,7 @@ 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)
@ -3430,6 +3430,48 @@ static bool llm_load_tensors(
splits[i] /= split_sum;
}
// 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;
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;
model.buft_output = llama_default_buffer_type_offload(layer_gpu);
} else {
model.buft_output = llama_default_buffer_type_cpu(true);
}
} else
#elif defined(GGML_USE_VULKAN)
if (split_mode == LLAMA_SPLIT_LAYER) {
// calculate the split points
int device_count = ggml_backend_vk_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_VK_MAX_DEVICES];
if (all_zero) {
// default split, by free memory
for (int i = 0; i < device_count; ++i) {
size_t total;
size_t free;
ggml_backend_vk_get_device_memory(i, &total, &free);
splits[i] = free;
}
} else {
std::copy(tensor_split, tensor_split + device_count, splits);
}
// sum and normalize the splits to get the split points
float split_sum = 0.0f;
for (int i = 0; i < device_count; ++i) {
split_sum += splits[i];
splits[i] = split_sum;
}
for (int i = 0; i < device_count; ++i) {
splits[i] /= split_sum;
}
// 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) {
@ -10295,6 +10337,8 @@ 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
@ -10502,13 +10546,15 @@ 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) {