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CUDA: mmq CLI option, fixed mmq build issues (#2453)

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Johannes Gäßler 2023-07-31 15:44:35 +02:00 committed by GitHub
parent 1215ed7d5c
commit 0728c5a8b9
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10 changed files with 67 additions and 27 deletions
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16
CMakeLists.txt
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@ -68,7 +68,7 @@ option(LLAMA_ACCELERATE "llama: enable Accelerate framework
option(LLAMA_BLAS "llama: use BLAS" OFF)
set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
option(LLAMA_CUBLAS "llama: use CUDA" OFF)
option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF)
#option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF)
set(LLAMA_CUDA_MMQ_Y "64" CACHE STRING "llama: y tile size for mmq CUDA kernels")
option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF)
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
@ -253,9 +253,9 @@ if (LLAMA_CUBLAS)
set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
add_compile_definitions(GGML_USE_CUBLAS)
if (LLAMA_CUDA_CUBLAS)
add_compile_definitions(GGML_CUDA_CUBLAS)
endif()
# if (LLAMA_CUDA_CUBLAS)
# add_compile_definitions(GGML_CUDA_CUBLAS)
# endif()
add_compile_definitions(GGML_CUDA_MMQ_Y=${LLAMA_CUDA_MMQ_Y})
if (LLAMA_CUDA_FORCE_DMMV)
add_compile_definitions(GGML_CUDA_FORCE_DMMV)
@ -277,10 +277,14 @@ if (LLAMA_CUBLAS)
endif()
if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
# 52 == lowest CUDA 12 standard
# 60 == f16 CUDA intrinsics
# 61 == integer CUDA intrinsics
# 70 == (assumed) compute capability at which unrolling a loop in mul_mat_q kernels is faster
if (LLAMA_CUDA_DMMV_F16)
set(CMAKE_CUDA_ARCHITECTURES "60;61") # needed for f16 CUDA intrinsics
set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
else()
set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
endif()
endif()
message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")

6
Makefile
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@ -236,9 +236,9 @@ ifdef LLAMA_CUDA_MMQ_Y
else
NVCCFLAGS += -DGGML_CUDA_MMQ_Y=64
endif # LLAMA_CUDA_MMQ_Y
ifdef LLAMA_CUDA_CUBLAS
NVCCFLAGS += -DGGML_CUDA_CUBLAS
endif # LLAMA_CUDA_CUBLAS
#ifdef LLAMA_CUDA_CUBLAS
# NVCCFLAGS += -DGGML_CUDA_CUBLAS
#endif # LLAMA_CUDA_CUBLAS
ifdef LLAMA_CUDA_CCBIN
NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif

4
README.md
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@ -400,9 +400,11 @@ Building the program with BLAS support may lead to some performance improvements
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
<!---
| LLAMA_CUDA_CUBLAS | Boolean | false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
--->
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_CUBLAS | Boolean | false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
| LLAMA_CUDA_MMQ_Y | Positive integer >= 32 | 64 | Tile size in y direction when using the custom CUDA kernels for prompt processing. Higher values can be faster depending on the amount of shared memory available. Power of 2 heavily recommended. |
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |

16
examples/common.cpp
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@ -352,7 +352,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
#ifdef GGML_USE_CUBLAS
params.main_gpu = std::stoi(argv[i]);
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
#endif
} else if (arg == "--tensor-split" || arg == "-ts") {
if (++i >= argc) {
@ -376,13 +376,19 @@ 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");
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") {
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--low-vram" || arg == "-lv") {
#ifdef GGML_USE_CUBLAS
params.low_vram = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--no-mmap") {
params.use_mmap = false;
@ -585,6 +591,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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" );
#endif
fprintf(stdout, " --mtest compute maximum memory usage\n");
fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n");
@ -637,6 +646,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
lparams.main_gpu = params.main_gpu;
lparams.tensor_split = params.tensor_split;
lparams.low_vram = params.low_vram;
lparams.mul_mat_q = params.mul_mat_q;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;

1
examples/common.h
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@ -74,6 +74,7 @@ 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 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

15
examples/server/server.cpp
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@ -631,6 +631,9 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
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" );
#endif
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
@ -827,7 +830,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
}
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.", {});
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--low-vram" || arg == "-lv")
@ -835,7 +838,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
#ifdef GGML_USE_CUBLAS
params.low_vram = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
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")
{
#ifdef GGML_USE_CUBLAS
params.mul_mat_q = true;
#else
LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
#endif // GGML_USE_CUBLAS
}
else if (arg == "--main-gpu" || arg == "-mg")

24
ggml-cuda.cu
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@ -3898,10 +3898,9 @@ static size_t g_scratch_offset = 0;
static int g_device_count = -1;
static int g_main_device = 0;
#ifndef GGML_CUDA_FORCE_DMMV
static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
#endif
static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
static bool g_mul_mat_q = false;
static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
@ -3923,9 +3922,7 @@ void ggml_init_cublas() {
g_tensor_split[id] = total_vram;
total_vram += prop.totalGlobalMem;
#ifndef GGML_CUDA_FORCE_DMMV
g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
#endif
}
for (int id = 0; id < g_device_count; ++id) {
g_tensor_split[id] /= total_vram;
@ -4278,6 +4275,7 @@ inline void ggml_cuda_op_mul_mat_vec(
#ifdef GGML_CUDA_FORCE_DMMV
const bool use_mul_mat_vec_q = false;
(void) g_compute_capabilities[0];
#else
int id;
CUDA_CHECK(cudaGetDevice(&id));
@ -5021,12 +5019,14 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_
if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false);
} else {
#ifdef GGML_CUDA_CUBLAS
const bool use_mul_mat_q = false;
#else
const bool use_mul_mat_q = ggml_is_quantized(src0->type);
#endif // GGML_CUDA_CUBLAS
if (use_mul_mat_q) {
int min_compute_capability = INT_MAX;
for (int id = 0; id < g_device_count; ++id) {
if (min_compute_capability > g_compute_capabilities[id]) {
min_compute_capability = g_compute_capabilities[id];
}
}
if (g_mul_mat_q && ggml_is_quantized(src0->type) && min_compute_capability >= MIN_CC_DP4A) {
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_q, false, false);
} else {
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
@ -5320,6 +5320,10 @@ void ggml_cuda_set_main_device(int main_device) {
}
}
void ggml_cuda_set_mul_mat_q(bool mul_mat_q) {
g_mul_mat_q = mul_mat_q;
}
void ggml_cuda_set_scratch_size(size_t scratch_size) {
g_scratch_size = scratch_size;
}

1
ggml-cuda.h
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@ -27,6 +27,7 @@ void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);

10
llama.cpp
View File

@ -901,6 +901,7 @@ struct llama_context_params llama_context_default_params() {
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.low_vram =*/ false,
/*.mul_mat_q =*/ false,
/*.f16_kv =*/ true,
/*.logits_all =*/ false,
/*.vocab_only =*/ false,
@ -1028,6 +1029,7 @@ static void llama_model_load_internal(
int n_gpu_layers,
int main_gpu,
const float * tensor_split,
const bool mul_mat_q,
float rope_freq_base,
float rope_freq_scale,
bool low_vram,
@ -1156,9 +1158,11 @@ static void llama_model_load_internal(
}
(void) main_gpu;
(void) mul_mat_q;
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
ggml_cuda_set_mul_mat_q(mul_mat_q);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
#elif defined(GGML_USE_CLBLAST)
@ -1367,6 +1371,7 @@ static bool llama_model_load(
int n_gpu_layers,
int main_gpu,
const float * tensor_split,
const bool mul_mat_q,
float rope_freq_base,
float rope_freq_scale,
bool low_vram,
@ -1377,7 +1382,8 @@ static bool llama_model_load(
llama_progress_callback progress_callback,
void *progress_callback_user_data) {
try {
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers,
main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
return true;
} catch (const std::exception & err) {
@ -3192,7 +3198,7 @@ struct llama_model * llama_load_model_from_file(
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa, params.rms_norm_eps, params.n_gpu_layers,
params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
params.progress_callback_user_data)) {
delete model;

1
llama.h
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@ -108,6 +108,7 @@ extern "C" {
// Keep the booleans together to avoid misalignment during copy-by-value.
bool low_vram; // if true, reduce VRAM usage at the cost of performance
bool mul_mat_q; // if true, use experimental mul_mat_q kernels
bool f16_kv; // use fp16 for KV cache
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool vocab_only; // only load the vocabulary, no weights