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https://github.com/ggerganov/llama.cpp.git
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Basic Vulkan Multi-GPU implementation (#5321)
* 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>
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@ -46,6 +46,10 @@
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#define GGML_USE_CUBLAS_SYCL
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#endif
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#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
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#define GGML_USE_CUBLAS_SYCL_VULKAN
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#endif
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int32_t get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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@ -660,8 +664,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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params.tensor_split[i] = 0.0f;
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}
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}
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#ifndef GGML_USE_CUBLAS_SYCL
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n");
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#ifndef GGML_USE_CUBLAS_SYCL_VULKAN
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fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n");
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#endif // GGML_USE_CUBLAS_SYCL
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} else if (arg == "--no-mmap") {
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params.use_mmap = false;
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2637
ggml-vulkan.cpp
2637
ggml-vulkan.cpp
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@ -8,24 +8,29 @@ extern "C" {
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#endif
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#define GGML_VK_NAME "Vulkan"
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#define GGML_VK_MAX_DEVICES 16
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GGML_API void ggml_vk_init(void);
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GGML_API void ggml_vk_init_cpu_assist(void);
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GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node);
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GGML_API void ggml_vk_preallocate_buffers(void);
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GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node);
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GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
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GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);
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GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void);
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GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node);
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GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
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#ifdef GGML_VULKAN_CHECK_RESULTS
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void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor);
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void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
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#endif
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GGML_API void ggml_vk_graph_cleanup(void);
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GGML_API void ggml_vk_graph_cleanup_cpu_assist(void);
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GGML_API void ggml_vk_free_cpu_assist(void);
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// backend API
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GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void);
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GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
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GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend);
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GGML_API GGML_CALL int ggml_backend_vk_get_device_count(void);
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GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
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GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
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GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void);
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GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
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// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
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GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
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14
ggml.c
14
ggml.c
@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
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#elif defined(GGML_USE_CLBLAST)
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ggml_cl_init();
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#elif defined(GGML_USE_VULKAN)
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ggml_vk_init();
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ggml_vk_init_cpu_assist();
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#elif defined(GGML_USE_SYCL)
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ggml_init_sycl();
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#endif
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@ -14850,10 +14850,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU);
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GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU);
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#elif defined(GGML_USE_VULKAN)
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const bool skip_cpu = ggml_vk_compute_forward(params, tensor);
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const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor);
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#ifdef GGML_VULKAN_CHECK_RESULTS
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if (skip_cpu) {
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ggml_vk_check_results_1(params, tensor);
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ggml_vk_check_results_1_cpu_assist(params, tensor);
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}
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#endif
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if (skip_cpu) {
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@ -17269,12 +17269,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
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#ifdef GGML_USE_VULKAN
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]);
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ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]);
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}
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ggml_vk_preallocate_buffers();
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ggml_vk_preallocate_buffers_cpu_assist();
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1);
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ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1);
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}
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#endif
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@ -17330,7 +17330,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
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}
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#ifdef GGML_USE_VULKAN
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ggml_vk_graph_cleanup();
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ggml_vk_graph_cleanup_cpu_assist();
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#endif
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// performance stats (graph)
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61
llama.cpp
61
llama.cpp
@ -1355,7 +1355,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
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#elif defined(GGML_USE_CUBLAS)
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buft = ggml_backend_cuda_buffer_type(gpu);
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#elif defined(GGML_USE_VULKAN)
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buft = ggml_backend_vk_buffer_type();
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buft = ggml_backend_vk_buffer_type(gpu);
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#elif defined(GGML_USE_SYCL)
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buft = ggml_backend_sycl_buffer_type(gpu);
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#elif defined(GGML_USE_CLBLAST)
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@ -1392,6 +1392,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g
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GGML_UNUSED(tensor_split);
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}
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static size_t llama_get_device_count() {
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#if defined(GGML_USE_CUBLAS)
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return ggml_backend_cuda_get_device_count();
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#elif defined(GGML_USE_VULKAN)
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return ggml_backend_vk_get_device_count();
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#else
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return 1;
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#endif
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}
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static size_t llama_get_device_memory(int device) {
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#if defined(GGML_USE_CUBLAS)
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size_t total;
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size_t free;
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ggml_backend_cuda_get_device_memory(device, &total, &free);
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return free;
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#elif defined(GGML_USE_VULKAN)
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size_t total;
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size_t free;
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ggml_backend_vk_get_device_memory(device, &total, &free);
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return free;
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#else
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return 1;
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GGML_UNUSED(device);
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#endif
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}
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//
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// globals
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//
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@ -1763,6 +1790,10 @@ struct llama_context {
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ggml_backend_free(backend);
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}
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#ifdef GGML_USE_VULKAN
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ggml_vk_free_cpu_assist();
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#endif
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ggml_backend_buffer_free(buf_input);
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ggml_free(ctx_input);
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}
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@ -3436,22 +3467,18 @@ static bool llm_load_tensors(
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model.buft_layer[i] = llama_default_buffer_type_cpu(true);
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}
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#ifdef GGML_USE_CUBLAS
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if (split_mode == LLAMA_SPLIT_LAYER) {
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// calculate the split points
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int device_count = ggml_backend_cuda_get_device_count();
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int device_count = llama_get_device_count();
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bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
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float splits[GGML_CUDA_MAX_DEVICES];
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std::vector<float> splits(device_count);
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if (all_zero) {
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// default split, by free memory
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for (int i = 0; i < device_count; ++i) {
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size_t total;
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size_t free;
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ggml_backend_cuda_get_device_memory(i, &total, &free);
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splits[i] = free;
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splits[i] = llama_get_device_memory(i);
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}
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} else {
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std::copy(tensor_split, tensor_split + device_count, splits);
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std::copy(tensor_split, tensor_split + device_count, splits.begin());
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}
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// sum and normalize the splits to get the split points
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@ -3467,19 +3494,17 @@ static bool llm_load_tensors(
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// assign the repeating layers to the devices according to the splits
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int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
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for (int64_t i = i_gpu_start; i < n_layer; ++i) {
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int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits;
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int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
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model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
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}
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// assign the output layer
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if (n_gpu_layers > n_layer) {
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int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits;
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int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
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model.buft_output = llama_default_buffer_type_offload(layer_gpu);
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} else {
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model.buft_output = llama_default_buffer_type_cpu(true);
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}
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} else
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#endif
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{
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} else {
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ggml_backend_buffer_type_t split_buft;
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if (split_mode == LLAMA_SPLIT_ROW) {
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split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
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@ -10483,6 +10508,8 @@ size_t llama_max_devices(void) {
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return GGML_CUDA_MAX_DEVICES;
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#elif defined(GGML_USE_SYCL)
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return GGML_SYCL_MAX_DEVICES;
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#elif defined(GGML_USE_VULKAN)
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return GGML_VK_MAX_DEVICES;
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#else
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return 1;
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#endif
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@ -10690,14 +10717,16 @@ struct llama_context * llama_new_context_with_model(
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}
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#elif defined(GGML_USE_VULKAN)
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if (model->n_gpu_layers > 0) {
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ggml_backend_t backend = ggml_backend_vk_init();
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for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
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ggml_backend_t backend = ggml_backend_vk_init(device);
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if (backend == nullptr) {
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LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
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LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
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llama_free(ctx);
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return nullptr;
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}
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ctx->backends.push_back(backend);
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}
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}
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#elif defined(GGML_USE_SYCL)
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if (model->n_gpu_layers > 0) {
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ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
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