mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 15:29:43 +00:00
cuBLAS: use host pinned memory and dequantize while copying (#1207)
* cuBLAS: dequantize simultaneously while copying memory * cuBLAS: use host pinned memory * cuBLAS: improve ggml_compute_forward_mul_mat_f16_f32 with pinned memory * cuBLAS: also pin kv cache * fix rebase
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7fc50c051a
6 changed files with 110 additions and 54 deletions
5
Makefile
5
Makefile
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@ -106,6 +106,7 @@ ifdef LLAMA_OPENBLAS
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endif
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ifdef LLAMA_CUBLAS
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CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
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CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
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LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
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OBJS += ggml-cuda.o
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NVCC = nvcc
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@ -164,10 +165,10 @@ $(info )
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# Build library
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#
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ggml.o: ggml.c ggml.h
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ggml.o: ggml.c ggml.h ggml-cuda.h
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$(CC) $(CFLAGS) -c $< -o $@
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llama.o: llama.cpp ggml.h llama.h llama_util.h
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llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama_util.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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common.o: examples/common.cpp examples/common.h
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45
ggml-cuda.cu
45
ggml-cuda.cu
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@ -227,6 +227,25 @@ void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t st
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dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
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}
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dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
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switch (type) {
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case GGML_TYPE_Q4_0:
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return dequantize_row_q4_0_cuda;
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case GGML_TYPE_Q4_1:
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return dequantize_row_q4_1_cuda;
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case GGML_TYPE_Q4_2:
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return dequantize_row_q4_2_cuda;
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case GGML_TYPE_Q5_0:
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return dequantize_row_q5_0_cuda;
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case GGML_TYPE_Q5_1:
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return dequantize_row_q5_1_cuda;
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case GGML_TYPE_Q8_0:
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return dequantize_row_q8_0_cuda;
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default:
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return nullptr;
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}
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}
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// buffer pool for cuda
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#define MAX_CUDA_BUFFERS 16
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@ -286,18 +305,22 @@ void ggml_cuda_pool_free(void * ptr, size_t size) {
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CUDA_CHECK(cudaFree(ptr));
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}
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cublasHandle_t g_cublasH = NULL;
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cudaStream_t g_cudaStream = NULL;
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cublasHandle_t g_cublasH = nullptr;
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cudaStream_t g_cudaStream = nullptr;
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cudaStream_t g_cudaStream2 = nullptr;
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cudaEvent_t g_cudaEvent = nullptr;
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void ggml_init_cublas(void) {
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if (g_cublasH == NULL) {
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void ggml_init_cublas() {
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if (g_cublasH == nullptr) {
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// create cublas handle, bind a stream
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CUBLAS_CHECK(cublasCreate(&g_cublasH));
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CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
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CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
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// create additional stream and event for synchronization
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CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream2, cudaStreamNonBlocking));
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CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvent, cudaEventDisableTiming));
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// configure logging to stdout
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// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
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}
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@ -330,3 +353,13 @@ cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src,
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return cudaSuccess;
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}
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}
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void * ggml_cuda_host_malloc(size_t size) {
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void * ptr;
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CUDA_CHECK(cudaMallocHost((void **) &ptr, size));
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return ptr;
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}
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void ggml_cuda_host_free(void * ptr) {
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CUDA_CHECK(cudaFreeHost(ptr));
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}
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@ -27,8 +27,13 @@ extern "C" {
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extern cublasHandle_t g_cublasH;
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extern cudaStream_t g_cudaStream;
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extern cudaStream_t g_cudaStream2;
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extern cudaEvent_t g_cudaEvent;
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void ggml_init_cublas(void);
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void * ggml_cuda_host_malloc(size_t size);
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void ggml_cuda_host_free(void * ptr);
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void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size);
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void ggml_cuda_pool_free(void * ptr, size_t size);
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@ -41,6 +46,9 @@ void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t st
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cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream);
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typedef void (*dequantize_row_q_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
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dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(enum ggml_type type);
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#ifdef __cplusplus
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}
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#endif
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70
ggml.c
70
ggml.c
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@ -8033,7 +8033,7 @@ static void ggml_compute_forward_mul_mat_f32(
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#if defined(GGML_USE_CUBLAS)
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const float alpha = 1.0f;
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const float beta = 0.0f;
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const int x_ne = ne01 * ne10;
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const int x_ne = ne01 * ne00;
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const int y_ne = ne11 * ne10;
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const int d_ne = ne11 * ne01;
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@ -8235,25 +8235,27 @@ static void ggml_compute_forward_mul_mat_f16_f32(
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}
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#if defined(GGML_USE_CUBLAS)
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ggml_fp16_t * const wdata = params->wdata;
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const float alpha = 1.0f;
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const float beta = 0.0f;
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const int x_ne = ne01 * ne10;
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const int x_ne = ne01 * ne00;
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const int y_ne = ne11 * ne10;
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const int d_ne = ne11 * ne01;
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size_t x_size, y_size, d_size;
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float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
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float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
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float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
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ggml_fp16_t * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
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ggml_fp16_t * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
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float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
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#else
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float * const wdata = params->wdata;
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#endif
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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#if defined(GGML_USE_CUBLAS)
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// copy src0 while converting src1
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CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream));
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// with cuBlAS, instead of converting src0 to fp32, we convert src1 to fp16
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ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + (ne11 * ne10) * (i03 * ne02 + i02);
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{
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size_t id = 0;
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for (int64_t i01 = 0; i01 < ne11; ++i01) {
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@ -8275,11 +8277,9 @@ static void ggml_compute_forward_mul_mat_f16_f32(
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#if defined(GGML_USE_CUBLAS)
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const ggml_fp16_t * y = (ggml_fp16_t *) wdata;
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float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
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// copy data to device
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CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream));
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CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream));
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// compute
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@ -8498,39 +8498,19 @@ static void ggml_compute_forward_mul_mat_q_f32(
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#if defined(GGML_USE_CUBLAS)
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const float alpha = 1.0f;
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const float beta = 0.0f;
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const int x_ne = ne01 * ne10;
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const int x_ne = ne01 * ne00;
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const int y_ne = ne11 * ne10;
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const int d_ne = ne11 * ne01;
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size_t x_size, y_size, d_size, q_size;
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float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
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float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
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float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
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float *d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size);
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float * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
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float * d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
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float * d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
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void * d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size);
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void (*dequantize_row_q_cuda)(const void * x, float * y, int k, cudaStream_t stream) = NULL;
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if (type == GGML_TYPE_Q4_0) {
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dequantize_row_q_cuda = dequantize_row_q4_0_cuda;
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}
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else if (type == GGML_TYPE_Q4_1) {
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dequantize_row_q_cuda = dequantize_row_q4_1_cuda;
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}
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else if (type == GGML_TYPE_Q4_2) {
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dequantize_row_q_cuda = dequantize_row_q4_2_cuda;
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}
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else if (type == GGML_TYPE_Q5_0) {
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dequantize_row_q_cuda = dequantize_row_q5_0_cuda;
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}
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else if (type == GGML_TYPE_Q5_1) {
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dequantize_row_q_cuda = dequantize_row_q5_1_cuda;
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}
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else if (type == GGML_TYPE_Q8_0) {
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dequantize_row_q_cuda = dequantize_row_q8_0_cuda;
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}
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else {
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GGML_ASSERT(false);
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}
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#elif !defined(GGML_USE_CLBLAST)
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const dequantize_row_q_cuda_t dequantize_row_q_cuda = ggml_get_dequantize_row_q_cuda(type);
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GGML_ASSERT(dequantize_row_q_cuda != NULL);
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#else
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float * const wdata = params->wdata;
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dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
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#endif
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@ -8543,10 +8523,11 @@ static void ggml_compute_forward_mul_mat_q_f32(
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#if defined(GGML_USE_CUBLAS)
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// copy and dequantize on device
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CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, src0, i03, i02, g_cudaStream));
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CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, src0, i03, i02, g_cudaStream2));
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dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, g_cudaStream);
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dequantize_row_q_cuda(d_Q, d_X, x_ne, g_cudaStream2);
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CUDA_CHECK(cudaGetLastError());
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CUDA_CHECK(cudaEventRecord(g_cudaEvent, g_cudaStream2));
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#elif defined(GGML_USE_CLBLAST)
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const void* x = (char *) src0->data + i03*nb03 + i02*nb02;
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#else
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@ -8560,11 +8541,13 @@ static void ggml_compute_forward_mul_mat_q_f32(
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const float * x = wdata;
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#endif
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#if defined(GGML_USE_CUBLAS)
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// copy data to device
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CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream));
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// wait for dequantization
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CUDA_CHECK(cudaStreamWaitEvent(g_cudaStream, g_cudaEvent, 0));
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// compute
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CUBLAS_CHECK(
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cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
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@ -11588,7 +11571,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
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node->n_tasks = 1; // TODO: this actually is doing nothing
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// the threads are still spinning
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cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
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cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*MAX(ggml_nelements(node->src1), ggml_nelements(node->src0));
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//printf("src0: ne0 = %d, ne1 = %d, ne = %d\n", node->src0->ne[0], node->src0->ne[1], node->src0->ne[0]*node->src0->ne[1]);
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//printf("src1: ne0 = %d, ne1 = %d, ne = %d\n", node->src1->ne[0], node->src1->ne[1], node->src1->ne[0]*node->src1->ne[1]);
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//printf("cur = %zu\n", cur);
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@ -11600,6 +11583,11 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
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#endif
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} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
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cur = 0;
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS)
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if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
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node->n_tasks = 1;
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}
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#endif
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} else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
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if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
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@ -136,7 +136,7 @@ struct llama_kv_cache {
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struct ggml_context * ctx = NULL;
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llama_buffer buf;
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llama_ctx_buffer buf;
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int n; // number of tokens currently in the cache
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@ -167,7 +167,7 @@ struct llama_model {
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struct llama_kv_cache kv_self;
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// the model memory buffer
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llama_buffer buf;
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llama_ctx_buffer buf;
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// model memory mapped file
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std::unique_ptr<llama_mmap> mapping;
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@ -228,8 +228,8 @@ struct llama_context {
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// memory buffers used to evaluate the model
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// TODO: move in llama_state
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llama_buffer buf_compute;
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llama_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
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llama_ctx_buffer buf_compute;
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llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
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int buf_last = 0;
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size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
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26
llama_util.h
26
llama_util.h
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@ -405,4 +405,30 @@ struct llama_buffer {
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delete[] addr;
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}
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};
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#ifdef GGML_USE_CUBLAS
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#include "ggml-cuda.h"
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struct llama_ctx_buffer {
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uint8_t * addr = NULL;
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size_t size = 0;
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void resize(size_t size) {
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if (addr) {
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ggml_cuda_host_free(addr);
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}
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addr = (uint8_t *) ggml_cuda_host_malloc(size);
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this->size = size;
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}
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~llama_ctx_buffer() {
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if (addr) {
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ggml_cuda_host_free(addr);
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}
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}
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};
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#else
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typedef llama_buffer llama_ctx_buffer;
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#endif
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#endif
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