From 50cb666b8a2e35a49b08c0f6bc81138c8f6f2ac1 Mon Sep 17 00:00:00 2001 From: slaren <2141330+slaren@users.noreply.github.com> Date: Fri, 21 Apr 2023 21:59:17 +0200 Subject: Improve cuBLAS performance by using a memory pool (#1094) * Improve cuBLAS performance by using a memory pool * Move cuda specific definitions to ggml-cuda.h/cu * Add CXX flags to nvcc * Change memory pool synchronization mechanism to a spin lock General code cleanup --- ggml.c | 124 +++++++++++++++++++++-------------------------------------------- 1 file changed, 40 insertions(+), 84 deletions(-) (limited to 'ggml.c') diff --git a/ggml.c b/ggml.c index 6cea937..2ea4e68 100644 --- a/ggml.c +++ b/ggml.c @@ -148,44 +148,7 @@ inline static void* ggml_aligned_malloc(size_t size) { #elif defined(GGML_USE_OPENBLAS) #include #elif defined(GGML_USE_CUBLAS) -#include -#include #include "ggml-cuda.h" - -#define CUDA_CHECK(err) \ - do { \ - cudaError_t err_ = (err); \ - if (err_ != cudaSuccess) { \ - printf("CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \ - cudaGetErrorString(err_)); \ - exit(1); \ - } \ - } while (0) - -#define CUBLAS_CHECK(err) \ - do { \ - cublasStatus_t err_ = (err); \ - if (err_ != CUBLAS_STATUS_SUCCESS) { \ - printf("cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \ - exit(1); \ - } \ - } while (0) - -static cublasHandle_t cublasH = NULL; -static cudaStream_t cudaStream = NULL; -static void init_cublas(void) { - if (cublasH == NULL) { - // create cublas handle, bind a stream - CUBLAS_CHECK(cublasCreate(&cublasH)); - - CUDA_CHECK(cudaStreamCreateWithFlags(&cudaStream, cudaStreamNonBlocking)); - - CUBLAS_CHECK(cublasSetStream(cublasH, cudaStream)); - - // configure logging to stdout - // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL)); - } -} #endif #undef MIN @@ -3748,7 +3711,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { // initialize cuBLAS #if defined(GGML_USE_CUBLAS) - init_cublas(); + ggml_init_cublas(); #endif is_first_call = false; @@ -7594,18 +7557,16 @@ static void ggml_compute_forward_mul_mat_f32( } #if defined(GGML_USE_CUBLAS) - float *d_X = NULL; - float *d_Y = NULL; - float *d_D = NULL; const float alpha = 1.0f; const float beta = 0.0f; const int x_ne = ne01 * ne10; const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(float) * x_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne)); + size_t x_size, y_size, d_size; + float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); + float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); + float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); #endif for (int64_t i03 = 0; i03 < ne03; i03++) { @@ -7617,19 +7578,19 @@ static void ggml_compute_forward_mul_mat_f32( #if defined(GGML_USE_CUBLAS) // copy data to device - CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(float) * x_ne, cudaMemcpyHostToDevice, cudaStream)); - CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(float) * x_ne, cudaMemcpyHostToDevice, g_cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); // compute CUBLAS_CHECK( - cublasSgemm(cublasH, CUBLAS_OP_T, CUBLAS_OP_N, + cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha, d_X, ne00, d_Y, ne10, &beta, d_D, ne01)); // copy data to host - CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); #else // zT = y * xT cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, @@ -7641,10 +7602,10 @@ static void ggml_compute_forward_mul_mat_f32( } } #if defined(GGML_USE_CUBLAS) - CUDA_CHECK(cudaStreamSynchronize(cudaStream)); - CUDA_CHECK(cudaFree(d_X)); - CUDA_CHECK(cudaFree(d_Y)); - CUDA_CHECK(cudaFree(d_D)); + CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_Y, y_size); + ggml_cuda_pool_free(d_D, d_size); #endif //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); @@ -7794,18 +7755,16 @@ static void ggml_compute_forward_mul_mat_f16_f32( #if defined(GGML_USE_CUBLAS) ggml_fp16_t * const wdata = params->wdata; - float *d_X = NULL; - float *d_Y = NULL; - float *d_D = NULL; const float alpha = 1.0f; const float beta = 0.0f; const int x_ne = ne01 * ne10; const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(ggml_fp16_t) * x_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne)); + size_t x_size, y_size, d_size; + float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); + float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); + float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); #else float * const wdata = params->wdata; #endif @@ -7839,12 +7798,12 @@ static void ggml_compute_forward_mul_mat_f16_f32( float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); // copy data to device - CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(ggml_fp16_t) * x_ne, cudaMemcpyHostToDevice, cudaStream)); - CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(ggml_fp16_t) * x_ne, cudaMemcpyHostToDevice, g_cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); // compute CUBLAS_CHECK( - cublasGemmEx(cublasH, CUBLAS_OP_T, CUBLAS_OP_N, + cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha, d_X, CUDA_R_16F, ne00, d_Y, CUDA_R_16F, ne10, @@ -7853,7 +7812,7 @@ static void ggml_compute_forward_mul_mat_f16_f32( CUBLAS_GEMM_DEFAULT)); // copy data to host - CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); #else const float * x = wdata; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); @@ -7871,10 +7830,10 @@ static void ggml_compute_forward_mul_mat_f16_f32( } #if defined(GGML_USE_CUBLAS) - CUDA_CHECK(cudaStreamSynchronize(cudaStream)); - CUDA_CHECK(cudaFree(d_X)); - CUDA_CHECK(cudaFree(d_Y)); - CUDA_CHECK(cudaFree(d_D)); + CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_Y, y_size); + ggml_cuda_pool_free(d_D, d_size); #endif /*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/ @@ -8042,20 +8001,17 @@ static void ggml_compute_forward_mul_mat_q_f32( } #if defined(GGML_USE_CUBLAS) - float *d_X = NULL; - float *d_Y = NULL; - float *d_D = NULL; - float *d_Q = NULL; const float alpha = 1.0f; const float beta = 0.0f; const int x_ne = ne01 * ne10; const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(float) * x_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne)); - CUDA_CHECK(cudaMalloc((void **)(&d_Q), GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type])); + size_t x_size, y_size, d_size, q_size; + float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size); + float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size); + float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size); + float *d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size); void (*dequantize_row_q_cuda)(const void * x, float * y, int k, cudaStream_t stream) = NULL; if (type == GGML_TYPE_Q4_0) { @@ -8085,9 +8041,9 @@ static void ggml_compute_forward_mul_mat_q_f32( // copy and dequantize on device CUDA_CHECK( cudaMemcpyAsync(d_Q, (char *) src0->data + i03*nb03 + i02*nb02, - GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], cudaMemcpyHostToDevice, cudaStream)); + GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], cudaMemcpyHostToDevice, g_cudaStream)); - dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, cudaStream); + dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, g_cudaStream); CUDA_CHECK(cudaGetLastError()); #else { @@ -8103,18 +8059,18 @@ static void ggml_compute_forward_mul_mat_q_f32( #if defined(GGML_USE_CUBLAS) // copy data to device - CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, g_cudaStream)); // compute CUBLAS_CHECK( - cublasSgemm(cublasH, CUBLAS_OP_T, CUBLAS_OP_N, + cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha, d_X, ne00, d_Y, ne10, &beta, d_D, ne01)); // copy data to host - CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream)); + CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream)); #else // zT = y * xT cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, @@ -8127,11 +8083,11 @@ static void ggml_compute_forward_mul_mat_q_f32( } #if defined(GGML_USE_CUBLAS) - CUDA_CHECK(cudaStreamSynchronize(cudaStream)); - CUDA_CHECK(cudaFree(d_X)); - CUDA_CHECK(cudaFree(d_Y)); - CUDA_CHECK(cudaFree(d_D)); - CUDA_CHECK(cudaFree(d_Q)); + CUDA_CHECK(cudaStreamSynchronize(g_cudaStream)); + ggml_cuda_pool_free(d_X, x_size); + ggml_cuda_pool_free(d_Y, y_size); + ggml_cuda_pool_free(d_D, d_size); + ggml_cuda_pool_free(d_Q, q_size); #endif //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); -- cgit v1.2.3