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ggml : sync ggml (clBLAST + tensor names)

fix-vzip
Georgi Gerganov 2023-05-02 21:23:54 +03:00
parent 0bf680fea2
commit 0bcb64b184
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GPG Key ID: 449E073F9DC10735
7 changed files with 960 additions and 271 deletions
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4
extra/sync-ggml.sh
View File

@ -1,8 +1,10 @@
#!/bin/bash
cp -rpv ../ggml/src/ggml.c ./ggml.c
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
cp -rpv ../ggml/src/ggml-opencl.c ./ggml-opencl.c
cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/examples/common.h ./examples/common.h
cp -rpv ../ggml/examples/common.cpp ./examples/common.cpp

427
ggml-cuda.cu
View File

@ -1,11 +1,38 @@
#include <cstddef>
#include <cstdint>
#include <stdint.h>
#include <stdio.h>
#include <cuda_fp16.h>
#include <atomic>
#include "ggml-cuda.h"
typedef uint16_t ggml_fp16_t;
static_assert(sizeof(__half) == sizeof(ggml_fp16_t), "wrong fp16 size");
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
#include "ggml-cuda.h"
#include "ggml.h"
static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
#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)
typedef void (*to_fp32_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
#define QK4_0 32
typedef struct {
@ -24,14 +51,14 @@ static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 b
#define QK4_2 16
typedef struct {
__half d; // delta
half d; // delta
uint8_t qs[QK4_2 / 2]; // nibbles / quants
} block_q4_2;
static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
#define QK5_0 32
typedef struct {
__half d; // delta
half d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
@ -39,9 +66,9 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5
#define QK5_1 32
typedef struct {
__half d; // delta
__half m; // min
uint32_t qh; // 5-th bit of quants
half d; // delta
half m; // min
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
@ -162,7 +189,8 @@ static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
const uint8_t * pp = x[i].qs;
const uint32_t qh = x[i].qh;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vi = pp[l/2];
@ -197,37 +225,50 @@ static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
}
}
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static 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);
}
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static 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);
}
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static 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);
}
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_0;
dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_1;
dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
static void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK8_0;
dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
}
dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
// TODO: optimize
static __global__ void convert_fp16_to_fp32(const void * vx, float * y) {
const half * x = (const half *) vx;
const int i = blockIdx.x;
y[i] = __half2float(x[i]);
}
static void convert_fp16_to_fp32_cuda(const void * x, float * y, int k, cudaStream_t stream) {
convert_fp16_to_fp32<<<k, 1, 0, stream>>>(x, y);
}
static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
return dequantize_row_q4_0_cuda;
@ -241,6 +282,8 @@ dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
return dequantize_row_q5_1_cuda;
case GGML_TYPE_Q8_0:
return dequantize_row_q8_0_cuda;
case GGML_TYPE_F16:
return convert_fp16_to_fp32_cuda;
default:
return nullptr;
}
@ -271,7 +314,7 @@ struct cuda_buffer {
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) {
static 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) {
@ -290,7 +333,7 @@ void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
return ptr;
}
void ggml_cuda_pool_free(void * ptr, size_t size) {
static 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) {
@ -305,28 +348,55 @@ void ggml_cuda_pool_free(void * ptr, size_t size) {
CUDA_CHECK(cudaFree(ptr));
}
cublasHandle_t g_cublasH = nullptr;
cudaStream_t g_cudaStream = nullptr;
cudaStream_t g_cudaStream2 = nullptr;
cudaEvent_t g_cudaEvent = nullptr;
#define GGML_CUDA_MAX_STREAMS 8
#define GGML_CUDA_MAX_EVENTS 64
static cublasHandle_t g_cublasH = nullptr;
static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaStream_t g_cudaStreams2[GGML_CUDA_MAX_STREAMS] = { nullptr };
static cudaEvent_t g_cudaEvents[GGML_CUDA_MAX_EVENTS] = { nullptr };
void ggml_init_cublas() {
if (g_cublasH == nullptr) {
// create cublas handle, bind a stream
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
// create streams
for (int i = 0; i < GGML_CUDA_MAX_STREAMS; ++i) {
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[i], cudaStreamNonBlocking));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams2[i], cudaStreamNonBlocking));
}
// create events
for (int i = 0; i < GGML_CUDA_MAX_EVENTS; ++i) {
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvents[i], cudaEventDisableTiming));
}
// create additional stream and event for synchronization
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream2, cudaStreamNonBlocking));
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvent, cudaEventDisableTiming));
// create cublas handle
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUBLAS_CHECK(cublasSetMathMode(g_cublasH, CUBLAS_TF32_TENSOR_OP_MATH));
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
}
}
cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
void * ggml_cuda_host_malloc(size_t size) {
if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
return nullptr;
}
void * ptr = nullptr;
cudaError_t err = cudaMallocHost((void **) &ptr, size);
if (err != cudaSuccess) {
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
size/1024.0/1024.0, cudaGetErrorString(err));
return nullptr;
}
return ptr;
}
void ggml_cuda_host_free(void * ptr) {
CUDA_CHECK(cudaFreeHost(ptr));
}
static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
const uint64_t ne0 = src->ne[0];
const uint64_t ne1 = src->ne[1];
const uint64_t nb0 = src->nb[0];
@ -354,12 +424,293 @@ cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src,
}
}
void * ggml_cuda_host_malloc(size_t size) {
void * ptr;
CUDA_CHECK(cudaMallocHost((void **) &ptr, size));
return ptr;
static void ggml_cuda_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
size_t x_size, y_size, d_size;
float * d_X = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
float * d_Y = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
float * c_X = d_X + i * x_ne;
float * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
// copy data to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, ne00,
c_Y, ne10,
&beta, c_D, ne01));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
}
void ggml_cuda_host_free(void * ptr) {
CUDA_CHECK(cudaFreeHost(ptr));
static void ggml_cuda_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t /* wsize */) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
size_t x_size, y_size, d_size;
half * d_X = (half *) ggml_cuda_pool_malloc(n_mm * sizeof(half) * x_ne, &x_size);
half * d_Y = (half *) ggml_cuda_pool_malloc(n_mm * sizeof(half) * y_ne, &y_size);
float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
bool src1_cont_rows = nb10 == sizeof(float);
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
half * c_X = d_X + i * x_ne;
half * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
// copy src0 to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
// convert src1 to fp16
// TODO: use multiple threads
ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i03 * ne02 + i02);
char * src1i = (char *) src1->data + i03*nb13 + i02*nb12;
if (src1_cont_rows) {
if (src1_cont_cols) {
ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
ggml_fp32_to_fp16_row((float *) (src1i + i01*nb11), tmp + i01*ne10, ne10);
}
}
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
for (int64_t i00 = 0; i00 < ne10; i00++) {
// very slow due to no inlining
tmp[i01*ne10 + i00] = ggml_fp32_to_fp16(*(float *) (src1i + i01*nb11 + i00*nb10));
}
}
}
// copy src1 to device
CUDA_CHECK(cudaMemcpyAsync(c_Y, tmp, sizeof(half) * y_ne, cudaMemcpyHostToDevice, cudaStream));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, CUDA_R_16F, ne00,
c_Y, CUDA_R_16F, ne10,
&beta, c_D, CUDA_R_32F, ne01,
CUBLAS_COMPUTE_32F_FAST_16F,
CUBLAS_GEMM_DEFAULT));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
}
static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const ggml_type type = src0->type;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
const size_t q_sz = ggml_type_size(type) * x_ne / ggml_blck_size(type);
size_t x_size, y_size, d_size, q_size;
float * d_X = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
float * d_Y = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
float * d_D = (float *) ggml_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
char * d_Q = (char *) ggml_cuda_pool_malloc(n_mm * q_sz, &q_size);
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(type);
GGML_ASSERT(to_fp32_cuda != nullptr);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_CUDA_MAX_STREAMS];
cudaStream_t cudaStream2 = g_cudaStreams2[i % GGML_CUDA_MAX_STREAMS];
cudaEvent_t cudaEvent = g_cudaEvents[i % GGML_CUDA_MAX_EVENTS];
float * c_X = d_X + i * x_ne;
float * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
char * c_Q = d_Q + i * q_sz;
// copy src0 and convert to fp32 on device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Q, src0, i03, i02, cudaStream2));
to_fp32_cuda(c_Q, c_X, x_ne, cudaStream2);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));
// copy src1 to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));
// wait for conversion
CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, ne00,
c_Y, ne10,
&beta, c_D, ne01));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
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);
}
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
// TODO: find the optimal values for these
if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
src1->type == GGML_TYPE_F32 &&
dst->type == GGML_TYPE_F32 &&
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
return true;
}
return false;
}
bool ggml_cuda_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) {
size_t src0_sz = ggml_nbytes(src0);
size_t src1_sz = ggml_nbytes(src1);
// mul_mat_q: src0 is converted to fp32 on device
size_t mul_mat_q_transfer = src0_sz + src1_sz;
// mul_mat_f16: src1 is converted to fp16 on cpu
size_t mul_mat_f16_transfer = src0_sz + sizeof(half) * ggml_nelements(src1);
// choose the smaller one to transfer to the device
// TODO: this is not always the best choice due to the overhead of converting to fp16
return mul_mat_f16_transfer < mul_mat_q_transfer;
}
void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
GGML_ASSERT(ggml_cuda_can_mul_mat(src0, src1, dst));
if (src0->type == GGML_TYPE_F32) {
ggml_cuda_mul_mat_f32(src0, src1, dst);
}
else if (src0->type == GGML_TYPE_F16) {
if (ggml_cuda_mul_mat_use_f16(src0, src1, dst)) {
ggml_cuda_mul_mat_f16(src0, src1, dst, wdata, wsize);
}
else {
ggml_cuda_mul_mat_q_f32(src0, src1, dst);
}
}
else if (ggml_is_quantized(src0->type)) {
ggml_cuda_mul_mat_q_f32(src0, src1, dst);
}
else {
GGML_ASSERT(false);
}
}
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
if (ggml_cuda_mul_mat_use_f16(src0, src1, dst)) {
return ggml_nelements(src1) * sizeof(ggml_fp16_t);
}
else {
return 0;
}
}

47
ggml-cuda.h
View File

@ -1,54 +1,19 @@
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include "ggml.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;
extern cudaStream_t g_cudaStream2;
extern cudaEvent_t g_cudaEvent;
void ggml_init_cublas(void);
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
// TODO: export these with GGML_API
void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
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);
void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream);
typedef void (*dequantize_row_q_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(enum ggml_type type);
#ifdef __cplusplus
}
#endif

398
ggml-opencl.c 100644
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@ -0,0 +1,398 @@
#include "ggml-opencl.h"
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast_c.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "ggml.h"
#define MULTILINE_QUOTE(...) #__VA_ARGS__
const char * clblast_dequant = MULTILINE_QUOTE(
struct block_q4_0
{
float d;
uchar qs[16];
};
__kernel void dequantize_row_q4_0(__global struct block_q4_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q4_1
{
float d;
float m;
uchar qs[16];
};
__kernel void dequantize_row_q4_1(__global struct block_q4_1* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const float m = blocks[i].m;
const uchar vi = blocks[i].qs[l];
const uint index = i*32 + l*2;
result[index + 0] = (vi & 0xf) * d + m;
result[index + 1] = (vi >> 4) * d + m;
}
struct block_q4_2
{
ushort d;
uchar qs[8];
};
__kernel void dequantize_row_q4_2(__global struct block_q4_2* blocks, __global float* result) {
const uint i = get_global_id(0) / 16;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &blocks[i].d);
const uchar vi = blocks[i].qs[l];
const uint index = i*16 + l*2;
result[index + 0] = ((vi & 0xf) - 8)*d;
result[index + 1] = ((vi >> 4) - 8)*d;
}
struct block_q5_0
{
float d;
uint qh;
uchar qs[16];
};
__kernel void dequantize_row_q5_0(__global struct block_q5_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = blocks[i].d;
const uchar vi = blocks[i].qs[l];
const uint l2 = l * 2;
const uchar vh0 = ((blocks[i].qh & (1 << (l2 + 0))) >> (l2 + 0)) << 4;
const uchar vh1 = ((blocks[i].qh & (1 << (l2 + 1))) >> (l2 + 1)) << 4;
const uint index = i*32 + l2;
result[index + 0] = (((vi & 0xf) | vh0) - 16)*d;
result[index + 1] = (((vi >> 4) | vh1) - 16)*d;
}
struct block_q5_1
{
ushort d;
ushort m;
uint qh;
uchar qs[16];
};
__kernel void dequantize_row_q5_1(__global struct block_q5_1* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
const float d = vload_half(0, (__global half*) &blocks[i].d);
const float m = vload_half(0, (__global half*) &blocks[i].m);
const uchar vi = blocks[i].qs[l];
const uint l2 = l * 2;
const uchar vh0 = ((blocks[i].qh & (1 << (l2 + 0))) >> (l2 + 0)) << 4;
const uchar vh1 = ((blocks[i].qh & (1 << (l2 + 1))) >> (l2 + 1)) << 4;
const uint index = i*32 + l2;
result[index + 0] = ((vi & 0xf) | vh0)*d + m;
result[index + 1] = ((vi >> 4) | vh1)*d + m;
}
struct block_q8_0
{
float d;
char qs[32];
};
__kernel void dequantize_row_q8_0(__global struct block_q8_0* blocks, __global float* result) {
const uint i = get_global_id(0) / 32;
const uint l = get_local_id(0);
result[i*32 + l] = blocks[i].qs[l] * blocks[i].d;
}
);
#define CL_CHECK(err, name) \
do { \
cl_int err_ = (err); \
if (err_ != CL_SUCCESS) { \
fprintf(stderr, "OpenCL %s error %d at %s:%d\n", name, err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
#define QK5_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
typedef struct {
float d; // delta
uint32_t qh; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} cl_block_q5_0;
static cl_platform_id platform;
static cl_device_id device;
static cl_context context;
static cl_command_queue queue;
static cl_program program;
static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q4_2, kernel_q5_0, kernel_q5_1, kernel_q8_0;
static cl_mem cl_buffer_a, cl_buffer_qb, cl_buffer_b, cl_buffer_c;
static size_t cl_size_a = 0, cl_size_qb = 0, cl_size_b = 0, cl_size_c = 0;
static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
cl_program p;
char *program_log;
size_t program_size, log_size;
int err;
program_size = strlen(program_buffer);
p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
if(err < 0) {
fprintf(stderr, "OpenCL error creating program");
exit(1);
}
err = clBuildProgram(p, 0, NULL, NULL, NULL, NULL);
if(err < 0) {
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
program_log = (char*) malloc(log_size + 1);
program_log[log_size] = '\0';
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
printf("%s\n", program_log);
free(program_log);
exit(1);
}
return p;
}
void ggml_cl_init(void) {
cl_int err = 0;
char * GGML_CLBLAST_PLATFORM = getenv("GGML_CLBLAST_PLATFORM");
char * GGML_CLBLAST_DEVICE = getenv("GGML_CLBLAST_DEVICE");
int plat_num = (GGML_CLBLAST_PLATFORM == NULL ? 0 : atoi(GGML_CLBLAST_PLATFORM));
int dev_num = (GGML_CLBLAST_DEVICE == NULL ? 0 : atoi(GGML_CLBLAST_DEVICE));
printf("\nInitializing CLBlast (First Run)...");
printf("\nAttempting to use: Platform=%d, Device=%d (If invalid, program will crash)\n",plat_num,dev_num);
cl_uint num_platforms;
clGetPlatformIDs(0, NULL, &num_platforms);
cl_platform_id* platforms = (cl_platform_id*)malloc(num_platforms*sizeof(cl_platform_id));
clGetPlatformIDs(num_platforms, platforms, NULL);
platform = platforms[plat_num];
char platform_buffer[1024];
clGetPlatformInfo(platform, CL_PLATFORM_NAME, sizeof(platform_buffer), &platform_buffer, NULL);
cl_uint num_devices;
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, 0, NULL, &num_devices);
cl_device_id* devices = (cl_device_id*)malloc(num_devices*sizeof(cl_device_id));
clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, num_devices, devices, NULL);
device = devices[dev_num];
char device_buffer[1024];
clGetDeviceInfo(device, CL_DEVICE_NAME, sizeof(device_buffer), &device_buffer, NULL);
printf("Using Platform: %s Device: %s\n", platform_buffer, device_buffer);
context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
CL_CHECK(err, "clCreateContext");
queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
CL_CHECK(err, "clCreateCommandQueue");
free(platforms);
free(devices);
program = build_program_from_source(context, device, clblast_dequant);
// Prepare dequantize kernels
kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q4_2 = clCreateKernel(program, "dequantize_row_q4_2", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err);
CL_CHECK(err, "clCreateKernel");
kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err);
CL_CHECK(err, "clCreateKernel");
}
static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
if (req_size <= *cur_size) {
return;
}
// Reallocate buffer with enough space
if (*cur_size > 0) {
clReleaseMemObject(*buf);
}
cl_int err;
*buf = clCreateBuffer(context, flags, req_size, NULL, &err);
*cur_size = req_size;
CL_CHECK(err, "clCreateBuffer");
}
void ggml_cl_sgemm_wrapper(
const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b,
const int m, const int n, const int k,
const float alpha, const void *host_a, const int lda,
const float *host_b, const int ldb, const float beta,
float *host_c, const int ldc, const int btype) {
cl_int err = 0;
cl_kernel kernel;
size_t global = n * k, local, size_qb;
bool dequant;
cl_block_q5_0* cl_host_b;
switch (btype) {
case GGML_TYPE_F32:
dequant = false;
break;
case GGML_TYPE_Q4_0:
dequant = true;
kernel = kernel_q4_0;
local = 16;
size_qb = global * (sizeof(float) + local) / 32;
break;
case GGML_TYPE_Q4_1:
dequant = true;
kernel = kernel_q4_1;
local = 16;
size_qb = global * (sizeof(float) * 2 + local) / 32;
break;
case GGML_TYPE_Q4_2:
dequant = true;
kernel = kernel_q4_2;
local = 8;
size_qb = global * (sizeof(ggml_fp16_t) + local) / 16;
break;
case GGML_TYPE_Q5_0:
dequant = true;
kernel = kernel_q5_0;
local = 16;
// For some reason OpenCL seems to be incapable of working with structs of size 22.
// 20 and 24 bytes are fine. Workaround to do the fp16 to fp32 step on CPU...
// TODO Find the reason, fix and remove workaround.
const block_q5_0* b = (const block_q5_0*) host_b;
cl_host_b = (cl_block_q5_0*) malloc(sizeof(cl_block_q5_0) * global / 32);
for (size_t i = 0; i < global / 32; i++) {
cl_host_b[i].d = ggml_fp16_to_fp32(b[i].d);
memcpy(&cl_host_b[i].qh, b[i].qh, sizeof(uint32_t));
memcpy(&cl_host_b[i].qs, b[i].qs, QK5_0 / 2);
}
host_b = (const float*) cl_host_b;
size_qb = global * (sizeof(float) + sizeof(uint32_t) + local) / 32;
break;
case GGML_TYPE_Q5_1:
dequant = true;
kernel = kernel_q5_1;
local = 16;
size_qb = global * (sizeof(ggml_fp16_t) * 2 + sizeof(uint32_t) + local) / 32;
break;
case GGML_TYPE_Q8_0:
dequant = true;
kernel = kernel_q8_0;
local = 32;
size_qb = global * (sizeof(float) + local) / 32;
break;
default:
fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
abort();
}
const size_t size_a = m * k * sizeof(float);
const size_t size_b = n * k * sizeof(float);
const size_t size_c = m * n * sizeof(float);
// Prepare buffers
ggml_cl_malloc(size_a, &cl_size_a, CL_MEM_READ_ONLY, &cl_buffer_a);
if (dequant) {
ggml_cl_malloc(size_qb, &cl_size_qb, CL_MEM_READ_ONLY, &cl_buffer_qb);
}
ggml_cl_malloc(size_b, &cl_size_b, CL_MEM_READ_WRITE, &cl_buffer_b);
ggml_cl_malloc(size_c, &cl_size_c, CL_MEM_WRITE_ONLY, &cl_buffer_c);
cl_event ev_a, ev_qb, ev_b;
if (dequant) {
err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb);
err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b);
CL_CHECK(err, "clSetKernelArg");
err = clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb);
CL_CHECK(err, "clEnqueueWriteBuffer qb");
} else {
err = clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b);
CL_CHECK(err, "clEnqueueWriteBuffer b");
}
err = clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a);
CL_CHECK(err, "clEnqueueWriteBuffer a");
if (dequant) {
err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b);
CL_CHECK(err, "clEnqueueNDRangeKernel");
clReleaseEvent(ev_qb);
}
clWaitForEvents(1, &ev_a);
clWaitForEvents(1, &ev_b);
clReleaseEvent(ev_a);
clReleaseEvent(ev_b);
cl_event ev_sgemm;
CLBlastStatusCode status = CLBlastSgemm((CLBlastLayout)order,
(CLBlastTranspose)trans_a, (CLBlastTranspose)trans_b,
m, n, k,
alpha,
cl_buffer_a, 0, lda,
cl_buffer_b, 0, ldb,
beta,
cl_buffer_c, 0, ldc,
&queue, &ev_sgemm);
if (status != CLBlastSuccess) {
fprintf(stderr, "Error: CLBlast SGEMM %d\n", status);
abort();
}
cl_event ev_c;
clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c);
// Wait for completion
clWaitForEvents(1, &ev_c);
clReleaseEvent(ev_sgemm);
clReleaseEvent(ev_c);
if (btype == GGML_TYPE_Q5_0) {
free((void*) cl_host_b);
}
}

24
ggml-opencl.h 100644
View File

@ -0,0 +1,24 @@
#pragma once
#ifdef __cplusplus
extern "C" {
#endif
void ggml_cl_init(void);
enum ggml_blas_order {
GGML_BLAS_ORDER_ROW_MAJOR = 101,
GGML_BLAS_ORDER_COLUMN_MAJOR = 102,
};
enum ggml_blas_op {
GGML_BLAS_OP_N = 111,
GGML_BLAS_OP_T = 112,
GGML_BLAS_OP_C = 113,
};
void ggml_cl_sgemm_wrapper(const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b, const int m, const int n, const int k, const float alpha, const void *host_a, const int lda, const float *host_b, const int ldb, const float beta, float *host_c, const int ldc, const int btype);
#ifdef __cplusplus
}
#endif

311
ggml.c
View File

@ -135,14 +135,6 @@ inline static void* ggml_aligned_malloc(size_t size) {
#define UNUSED(x) (void)(x)
#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#if defined(GGML_USE_ACCELERATE)
#include <Accelerate/Accelerate.h>
#elif defined(GGML_USE_OPENBLAS)
@ -370,6 +362,32 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) {
return GGML_FP32_TO_FP16(x);
}
void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) {
for (size_t i = 0; i < n; i++) {
y[i] = GGML_FP16_TO_FP32(x[i]);
}
}
void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
size_t i = 0;
#if defined(__F16C__)
for (; i + 7 < n; i += 8) {
__m256 x_vec = _mm256_loadu_ps(x + i);
__m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm_storeu_si128((__m128i *)(y + i), y_vec);
}
for(; i + 3 < n; i += 4) {
__m128 x_vec = _mm_loadu_ps(x + i);
__m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
_mm_storel_epi64((__m128i *)(y + i), y_vec);
}
#endif
for (; i < n; i++) {
y[i] = GGML_FP32_TO_FP16(x[i]);
}
}
//
// timing
//
@ -808,6 +826,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
float max = 0.0f;
float min = 0.0f;
vector float asrcv [8];
vector float srcv [8];
vector float maxv[8];
vector float minv[8];
@ -4325,12 +4344,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
}
// initialize cuBLAS
#if defined(GGML_USE_CUBLAS)
#if defined(GGML_USE_CUBLAS)
ggml_init_cublas();
#elif defined(GGML_USE_CLBLAST)
#elif defined(GGML_USE_CLBLAST)
ggml_cl_init();
#endif
#endif
is_first_call = false;
}
@ -4411,7 +4429,7 @@ void ggml_free(struct ggml_context * ctx) {
}
size_t ggml_used_mem(const struct ggml_context * ctx) {
return ctx->objects_end->offs + ctx->objects_end->size;
return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size;
}
size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) {
@ -4524,6 +4542,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
/*.perf_cycles =*/ 0,
/*.perf_time_us =*/ 0,
/*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data,
/*.name =*/ { 0 },
/*.pad =*/ { 0 },
};
@ -4878,6 +4897,15 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
return (float *)(tensor->data);
}
const char * ggml_get_name(const struct ggml_tensor * tensor) {
return tensor->name;
}
void ggml_set_name(struct ggml_tensor * tensor, const char * name) {
strncpy(tensor->name, name, sizeof(tensor->name));
tensor->name[sizeof(tensor->name) - 1] = '\0';
}
struct ggml_tensor * ggml_view_tensor(
struct ggml_context * ctx,
const struct ggml_tensor * src) {
@ -5977,6 +6005,7 @@ struct ggml_tensor * ggml_diag_mask_inf(
//struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
struct ggml_tensor * b = ggml_new_i32(ctx, n_past);
ggml_set_name(b, "n_past");
result->op = GGML_OP_DIAG_MASK_INF;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -6034,6 +6063,7 @@ struct ggml_tensor * ggml_rope(
((int32_t *) b->data)[0] = n_past;
((int32_t *) b->data)[1] = n_dims;
((int32_t *) b->data)[2] = mode;
ggml_set_name(b, "n_past, n_dims, mode");
result->op = GGML_OP_ROPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@ -8101,7 +8131,7 @@ static void ggml_compute_forward_rms_norm(
// ggml_compute_forward_mul_mat
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
// helper function to determine if it is better to use BLAS or not
// for large matrices, BLAS is faster
static bool ggml_compute_forward_mul_mat_use_blas(
@ -8117,12 +8147,9 @@ static bool ggml_compute_forward_mul_mat_use_blas(
const int64_t ne1 = dst->ne[1];
// TODO: find the optimal values for these
if (
#if !defined(GGML_USE_CUBLAS)
ggml_is_contiguous(src0) &&
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
#endif
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32))) {
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
return true;
@ -8130,7 +8157,6 @@ static bool ggml_compute_forward_mul_mat_use_blas(
return false;
}
#endif
static void ggml_compute_forward_mul_mat_f32(
@ -8146,7 +8172,7 @@ static void ggml_compute_forward_mul_mat_f32(
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
const int64_t ne10 = src1->ne[0];
#endif
const int64_t ne11 = src1->ne[1];
@ -8203,7 +8229,16 @@ static void ggml_compute_forward_mul_mat_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}
return;
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
@ -8217,43 +8252,13 @@ static void ggml_compute_forward_mul_mat_f32(
return;
}
#if defined(GGML_USE_CUBLAS)
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
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++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
#if !defined(GGML_USE_CUBLAS)
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
#endif
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
#if defined(GGML_USE_CUBLAS)
// copy data to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream));
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream));
// compute
CUBLAS_CHECK(
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, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
@ -8270,12 +8275,6 @@ static void ggml_compute_forward_mul_mat_f32(
#endif
}
}
#if defined(GGML_USE_CUBLAS)
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);
return;
@ -8405,7 +8404,16 @@ static void ggml_compute_forward_mul_mat_f16_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}
return;
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
GGML_ASSERT(nb10 == sizeof(float));
@ -8421,37 +8429,8 @@ static void ggml_compute_forward_mul_mat_f16_f32(
return;
}
#if defined(GGML_USE_CUBLAS)
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
size_t x_size, y_size, d_size;
ggml_fp16_t * d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
ggml_fp16_t * 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++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
#if defined(GGML_USE_CUBLAS)
// copy src0 while converting src1
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_X, src0, i03, i02, g_cudaStream));
// with cuBlAS, instead of converting src0 to fp32, we convert src1 to fp16
ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + (ne11 * ne10) * (i03 * ne02 + i02);
{
size_t id = 0;
for (int64_t i01 = 0; i01 < ne11; ++i01) {
for (int64_t i00 = 0; i00 < ne10; ++i00) {
wdata[id++] = GGML_FP32_TO_FP16(*(float *) ((char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11 + i00*nb10));
}
}
assert(id*sizeof(ggml_fp16_t) <= params->wsize);
}
#else
float * const wdata = params->wdata;
{
size_t id = 0;
@ -8463,28 +8442,8 @@ static void ggml_compute_forward_mul_mat_f16_f32(
assert(id*sizeof(float) <= params->wsize);
}
#endif
#if defined(GGML_USE_CUBLAS)
const ggml_fp16_t * y = (ggml_fp16_t *) wdata;
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// copy data to device
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream));
// compute
CUBLAS_CHECK(
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,
&beta, d_D, CUDA_R_32F, ne01,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
@ -8513,12 +8472,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
}
}
#if defined(GGML_USE_CUBLAS)
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);*/
return;
@ -8671,7 +8624,16 @@ static void ggml_compute_forward_mul_mat_q_f32(
// nb01 >= nb00 - src0 is not transposed
// compute by src0 rows
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}
return;
}
#endif
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
if (params->ith != 0) {
return;
@ -8685,25 +8647,8 @@ static void ggml_compute_forward_mul_mat_q_f32(
return;
}
#if defined(GGML_USE_CUBLAS)
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
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);
void * d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size);
const dequantize_row_q_cuda_t dequantize_row_q_cuda = ggml_get_dequantize_row_q_cuda(type);
GGML_ASSERT(dequantize_row_q_cuda != NULL);
#else
float * const wdata = params->wdata;
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
@ -8711,14 +8656,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
#if defined(GGML_USE_CUBLAS)
// copy and dequantize on device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, src0, i03, i02, g_cudaStream2));
dequantize_row_q_cuda(d_Q, d_X, x_ne, g_cudaStream2);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaEventRecord(g_cudaEvent, g_cudaStream2));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
const void* x = (char *) src0->data + i03*nb03 + i02*nb02;
#else
{
@ -8734,24 +8672,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
const float * x = wdata;
#endif
#if defined(GGML_USE_CUBLAS)
// copy data to device
CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Y, src1, i03, i02, g_cudaStream));
// wait for dequantization
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStream, g_cudaEvent, 0));
// compute
CUBLAS_CHECK(
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, g_cudaStream));
#elif defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST)
// zT = y * xT
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
ne11, ne01, ne10,
@ -8769,13 +8690,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
}
}
#if defined(GGML_USE_CUBLAS)
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);
return;
@ -11759,18 +11673,21 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
size_t cur = 0;
#if defined(GGML_USE_CUBLAS)
if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
cur = ggml_cuda_mul_mat_get_wsize(node->src0, node->src1, node);
}
else
#endif
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1; // TODO: this actually is doing nothing
// the threads are still spinning
#if defined(GGML_USE_CUBLAS)
// with cuBLAS, we need memory for the full 3D / 4D data of src1
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
#else
// here we need memory just for single 2D matrix from src0
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
#endif
} else {
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
}
@ -11779,13 +11696,13 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
#endif
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
cur = 0;
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
}
#endif
} else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
node->n_tasks = 1;
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
@ -12214,10 +12131,16 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
snprintf(color, sizeof(color), "white");
}
fprintf(fp, " \"%p\" [ \
style = filled; fillcolor = %s; shape = record; \
label=\"%d [%" PRId64 ", %" PRId64 "] | <x>%s",
(void *) node, color,
fprintf(fp, " \"%p\" [ "
"style = filled; fillcolor = %s; shape = record; "
"label=\"",
(void *) node, color);
if (strlen(node->name) > 0) {
fprintf(fp, "%s |", node->name);
}
fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | <x>%s",
i, node->ne[0], node->ne[1],
GGML_OP_SYMBOL[node->op]);
@ -12233,18 +12156,26 @@ label=\"%d [%" PRId64 ", %" PRId64 "] | <x>%s",
snprintf(color, sizeof(color), "pink");
if (ggml_nelements(node) == 1) {
fprintf(fp, " \"%p\" [ \
style = filled; fillcolor = %s; shape = record; \
label=\"<x>%.1e\"; ]\n",
(void *) node, color, (double)ggml_get_f32_1d(node, 0));
} else {
fprintf(fp, " \"%p\" [ \
style = filled; fillcolor = %s; shape = record; \
label=\"<x>CONST %d [%" PRId64 ", %" PRId64 "]\"; ]\n",
(void *) node, color,
i, node->ne[0], node->ne[1]);
fprintf(fp, " \"%p\" [ "
"style = filled; fillcolor = %s; shape = record; "
"label=\"<x>",
(void *) node, color);
if (strlen(node->name) > 0) {
fprintf(fp, "%s | ", node->name);
}
if (ggml_nelements(node) == 1) {
if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
fprintf(fp, "%d", ggml_get_i32_1d(node, 0));
}
else {
fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, 0));
}
}
else {
fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]);
}
fprintf(fp, "\"; ]\n");
}
for (int i = 0; i < gb->n_nodes; i++) {

20
ggml.h
View File

@ -197,6 +197,14 @@
#define GGML_MAX_OPT 4
#define GGML_DEFAULT_N_THREADS 4
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#ifdef __cplusplus
extern "C" {
#endif
@ -212,6 +220,9 @@ extern "C" {
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
struct ggml_object;
struct ggml_context;
@ -339,7 +350,10 @@ extern "C" {
int64_t perf_time_us;
void * data;
char padding[8];
char name[32];
char padding[8]; // TODO: remove and add padding to name?
};
// computation graph
@ -399,6 +413,7 @@ extern "C" {
GGML_API bool ggml_is_quantized(enum ggml_type type);
// TODO: temporary until model loading of ggml examples is refactored
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
// main
@ -461,6 +476,9 @@ extern "C" {
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
GGML_API void ggml_set_name(struct ggml_tensor * tensor, const char * name);
//
// operations on tensors with backpropagation
//