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Improve cuBLAS performance by using a memory pool (#1094)

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* 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
This commit is contained in:
slaren 2023-04-21 21:59:17 +02:00 committed by GitHub
parent 25d7abbd1f
commit 50cb666b8a
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 170 additions and 109 deletions
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4
Makefile
View File

@ -104,8 +104,10 @@ ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64
OBJS += ggml-cuda.o
NVCC = nvcc
NVCCFLAGS = --forward-unknown-to-host-linker -arch=native
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
nvcc -arch=native -c -o $@ $<
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -c $< -o $@
endif
ifdef LLAMA_GPROF
CFLAGS += -pg

94
ggml-cuda.cu
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@ -1,5 +1,7 @@
#include <stdint.h>
#include <stdio.h>
#include <cuda_fp16.h>
#include <atomic>
#include "ggml-cuda.h"
typedef uint16_t ggml_fp16_t;
@ -35,8 +37,6 @@ typedef struct {
} block_q4_3;
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
static __global__ void dequantize_block_q4_0(const void * vx, float * y) {
const block_q4_0 * x = (const block_q4_0 *) vx;
@ -131,24 +131,98 @@ static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
}
}
extern "C" {
__host__ void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_0;
dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
}
}
__host__ void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_1;
dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
}
}
__host__ void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_2;
dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
}
}
__host__ void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_3;
dequantize_block_q4_3<<<nb, 1, 0, stream>>>(vx, y);
}
// buffer pool for cuda
#define MAX_CUDA_BUFFERS 16
struct scoped_spin_lock {
std::atomic_flag& lock;
scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
while (lock.test_and_set(std::memory_order_acquire)) {
; // spin
}
}
~scoped_spin_lock() {
lock.clear(std::memory_order_release);
}
scoped_spin_lock(const scoped_spin_lock&) = delete;
scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
};
struct cuda_buffer {
void * ptr = nullptr;
size_t size = 0;
};
static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS];
static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[i];
if (b.size >= size && b.ptr != nullptr) {
void * ptr = b.ptr;
*actual_size = b.size;
b.ptr = nullptr;
b.size = 0;
return ptr;
}
}
void * ptr;
CUDA_CHECK(cudaMalloc((void **) &ptr, size));
*actual_size = size;
return ptr;
}
void ggml_cuda_pool_free(void * ptr, size_t size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[i];
if (b.ptr == nullptr) {
b.ptr = ptr;
b.size = size;
return;
}
}
fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
CUDA_CHECK(cudaFree(ptr));
}
cublasHandle_t g_cublasH = NULL;
cudaStream_t g_cudaStream = NULL;
void ggml_init_cublas(void) {
if (g_cublasH == NULL) {
// create cublas handle, bind a stream
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
}
}

29
ggml-cuda.h
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@ -1,7 +1,36 @@
#include <cublas_v2.h>
#include <cuda_runtime.h>
#ifdef __cplusplus
extern "C" {
#endif
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
fprintf(stderr, "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) { \
fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
extern cublasHandle_t g_cublasH;
extern cudaStream_t g_cudaStream;
void ggml_init_cublas(void);
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size);
void ggml_cuda_pool_free(void * ptr, size_t size);
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream);

124
ggml.c
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@ -148,44 +148,7 @@ inline static void* ggml_aligned_malloc(size_t size) {
#elif defined(GGML_USE_OPENBLAS)
#include <cblas.h>
#elif defined(GGML_USE_CUBLAS)
#include <cublas_v2.h>
#include <cuda_runtime.h>
#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);