+#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 {
#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;
#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");
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];
}
}
-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;
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;
}
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) {
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) {
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));
+ }
+}
+
+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));
}
-cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
+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];
}
}
-void * ggml_cuda_host_malloc(size_t size) {
- if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
- return nullptr;
+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));
+ }
}
- 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;
+ 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_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));
+ }
}
- return ptr;
+ 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_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;
+ }
}
#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)
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
//
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;
}
// 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(
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;
return false;
}
-
#endif
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];
// 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;
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,
#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;
// 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));
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;
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);
}
}
-#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;
// 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;
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++) {
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
{
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,
}
}
-#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;
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);
}
#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]);
overview / pkg / ggml / sources / llama.cpp / commitdiff