mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 13:28:50 +01:00
CUDA: use mul_mat_q kernels by default (#2683)
This commit is contained in:
parent
3b6cfe7c92
commit
c63bb1d16a
@ -387,11 +387,11 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
} else if (arg == "--mul-mat-q" || arg == "-mmq") {
|
||||
} else if (arg == "--no-mul-mat-q" || arg == "-nommq") {
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.mul_mat_q = true;
|
||||
params.mul_mat_q = false;
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
} else if (arg == "--low-vram" || arg == "-lv") {
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
@ -599,11 +599,11 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
||||
fprintf(stdout, " number of layers to store in VRAM\n");
|
||||
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
|
||||
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
|
||||
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
|
||||
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
|
||||
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
|
||||
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
|
||||
#endif
|
||||
fprintf(stdout, " --mtest compute maximum memory usage\n");
|
||||
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
|
||||
|
@ -68,7 +68,7 @@ struct gpt_params {
|
||||
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
|
||||
|
||||
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
|
||||
bool mul_mat_q = false; // if true, use experimental mul_mat_q kernels
|
||||
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
|
||||
bool memory_f16 = true; // use f16 instead of f32 for memory kv
|
||||
bool random_prompt = false; // do not randomize prompt if none provided
|
||||
bool use_color = false; // use color to distinguish generations and inputs
|
||||
|
@ -671,12 +671,11 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms,
|
||||
fprintf(stdout, " number of layers to store in VRAM\n");
|
||||
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
|
||||
fprintf(stdout, " -mmq, --mul-mat-q use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
|
||||
fprintf(stdout, " Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
|
||||
fprintf(stdout, " is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
|
||||
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
|
||||
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
|
||||
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
|
||||
#endif
|
||||
fprintf(stdout, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
|
||||
@ -867,12 +866,12 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--mul-mat-q" || arg == "-mmq")
|
||||
else if (arg == "--no-mul-mat-q" || arg == "-nommq")
|
||||
{
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.mul_mat_q = true;
|
||||
params.mul_mat_q = false;
|
||||
#else
|
||||
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
|
||||
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--main-gpu" || arg == "-mg")
|
||||
|
@ -287,7 +287,7 @@ static int g_device_count = -1;
|
||||
static int g_main_device = 0;
|
||||
static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
|
||||
static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
|
||||
static bool g_mul_mat_q = false;
|
||||
static bool g_mul_mat_q = true;
|
||||
|
||||
static void * g_scratch_buffer = nullptr;
|
||||
static size_t g_scratch_size = 1024*1024*1024; // 1 GB by default
|
||||
|
Loading…
Reference in New Issue
Block a user