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add LLAMA_API llama_profile_device

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Lizonghang 2024-11-07 09:30:39 +04:00
parent b922418cca
commit ef7fdf70cc
6 changed files with 131 additions and 80 deletions
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57
common/common.cpp
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@ -9,7 +9,6 @@
#include "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include "profiler.h"
#include <algorithm>
#include <cinttypes>
@ -824,7 +823,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams;
auto mparams = llama_model_params_from_gpt_params(params);
llama_model * model = nullptr;
struct llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
@ -835,58 +834,8 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
}
// profile devices and determine the best setup
const char * dev_name = profiler::device_name();
uint32_t n_cpu_cores = profiler::device_cpu_cores();
uint64_t total_memory = profiler::device_physical_memory(false);
uint64_t available_memory = profiler::device_physical_memory(true);
uint64_t total_swap = profiler::device_swap_memory(false);
uint64_t available_swap = profiler::device_swap_memory(true);
uint64_t disk_read_bw = profiler::device_disk_read_bw(params.model.c_str(), 500);
uint64_t memory_bw = profiler::device_memory_bw(500);
int has_metal = profiler::device_has_metal();
int has_cuda = profiler::device_has_cuda();
int has_vulkan = profiler::device_has_vulkan();
int has_kompute = profiler::device_has_kompute();
int has_gpublas = profiler::device_has_gpublas();
int has_blas = profiler::device_has_blas();
int has_sycl = profiler::device_has_sycl();
ggml_backend_dev_props cpu_props;
ggml_backend_dev_props gpu_props;
profiler::device_get_props(model, -1, &cpu_props); // -1 for cpu
profiler::device_get_props(model, 0, &gpu_props); // 0 for gpu0
LOG_INF("\n");
LOG_INF("Device Info:\n");
LOG_INF(" Device Name : %s\n", dev_name);
LOG_INF(" CPU Name : %s\n", cpu_props.name);
LOG_INF(" CPU Description : %s\n", cpu_props.description);
LOG_INF(" Number of CPU cores : %u\n", n_cpu_cores);
LOG_INF(" Disk Read Bandwidth : %.2f GB/s\n", disk_read_bw / (double)(1 << 30));
LOG_INF("\n");
LOG_INF("Memory Information:\n");
LOG_INF(" Physical Mem Total : %.2f GB\n", total_memory / (double)(1 << 30));
LOG_INF(" Physical Mem Available : %.2f GB\n", available_memory / (double)(1 << 30));
LOG_INF(" Swap Memory Total : %.2f GB\n", total_swap / (double)(1 << 30));
LOG_INF(" Swap Memory Available : %.2f GB\n", available_swap / (double)(1 << 30));
LOG_INF(" Mem Bandwidth : %.2f GB/s\n", memory_bw / (double)(1 << 30));
LOG_INF("\n");
LOG_INF("GPU Support:\n");
LOG_INF(" Metal : %i\n", has_metal);
LOG_INF(" CUDA : %i\n", has_cuda);
LOG_INF(" Vulkan : %i\n", has_vulkan);
LOG_INF(" Kompute : %i\n", has_kompute);
LOG_INF(" GPU BLAS : %i\n", has_gpublas);
LOG_INF(" BLAS : %i\n", has_blas);
LOG_INF(" SYCL : %i\n", has_sycl);
LOG_INF("\n");
LOG_INF("GPU Properties:\n");
LOG_INF(" GPU Name : %s\n", gpu_props.name);
LOG_INF(" Description : %s\n", gpu_props.description);
LOG_INF(" Memory Free : %.2f GB\n", gpu_props.memory_free / (double)(1 << 30));
LOG_INF(" Memory Total : %.2f GB\n", gpu_props.memory_total / (double)(1 << 30));
device_info dev_info;
llama_profile_device(&dev_info, model, params.model.c_str());
if (model == NULL) {
LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.c_str());