mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-18 20:27:53 +00:00
move BLAS to a separate backend (cont) (llama/6210)
ggml-ci
This commit is contained in:
slaren
Georgi Gerganov
parent
922971041b
commit
de29b193f6
@ -21,7 +21,7 @@ struct gpt_params {
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_predict = 200; // new tokens to predict
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int32_t n_parallel = 1; // number of parallel streams
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int32_t n_batch = 8; // batch size for prompt processing
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int32_t n_batch = 32; // batch size for prompt processing
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int32_t n_ctx = 2048; // context size (this is the KV cache max size)
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int32_t n_gpu_layers = 0; // number of layers to offlload to the GPU
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363
ggml-blas.cpp
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363
ggml-blas.cpp
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@ -0,0 +1,363 @@
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#include "ggml-blas.h"
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#include "ggml-backend-impl.h"
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#include <future>
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#include <vector>
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#if defined(GGML_USE_ACCELERATE)
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# include <Accelerate/Accelerate.h>
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#elif defined(GGML_BLAS_USE_MKL)
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# include <mkl.h>
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#else
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# include <cblas.h>
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# ifdef BLIS_ENABLE_CBLAS
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# include <blis.h>
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# endif
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#endif
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struct ggml_backend_blas_context {
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int n_threads = GGML_DEFAULT_N_THREADS;
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std::unique_ptr<char[]> work_data;
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size_t work_size = 0;
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#ifndef GGML_USE_OPENMP
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std::vector<std::future<void>> tasks;
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#endif
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};
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// helper function to determine if it is better to use BLAS or not
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// for large matrices, BLAS is faster
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static bool ggml_backend_blas_use_blas(const struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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const struct ggml_tensor * src1 = dst->src[1];
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const int64_t ne10 = src1->ne[0];
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const int64_t ne0 = dst->ne[0];
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const int64_t ne1 = dst->ne[1];
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// TODO: find the optimal values for these
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if (ggml_is_contiguous(src0) &&
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ggml_is_contiguous(src1) &&
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src1->type == GGML_TYPE_F32 &&
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(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
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/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
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return true;
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}
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return false;
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}
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static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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const struct ggml_tensor * src1 = dst->src[1];
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GGML_TENSOR_BINARY_OP_LOCALS
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const enum ggml_type type = src0->type;
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GGML_ASSERT(ne0 == ne01);
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GGML_ASSERT(ne1 == ne11);
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GGML_ASSERT(ne2 == ne12);
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GGML_ASSERT(ne3 == ne13);
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// we don't support permuted src0 or src1
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GGML_ASSERT(nb00 == ggml_type_size(type));
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GGML_ASSERT(nb10 == ggml_type_size(src1->type));
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// dst cannot be transposed or permuted
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GGML_ASSERT(nb0 == sizeof(float));
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GGML_ASSERT(nb0 <= nb1);
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GGML_ASSERT(nb1 <= nb2);
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GGML_ASSERT(nb2 <= nb3);
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// broadcast factors
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const int64_t r2 = ne12/ne02;
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const int64_t r3 = ne13/ne03;
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const int64_t ne_plane = ne01*ne00;
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const size_t desired_wsize = type == GGML_TYPE_F32 ? 0 : ne03*ne02*ne_plane*sizeof(float);
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if (ctx->work_size < desired_wsize) {
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ctx->work_data.reset(new char[desired_wsize]);
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ctx->work_size = desired_wsize;
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}
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void * wdata = ctx->work_data.get();
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// convert src0 to float
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if (type != GGML_TYPE_F32) {
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ggml_type_traits_t type_traits = ggml_internal_get_type_traits(type);
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ggml_to_float_t const to_float = type_traits.to_float;
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
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float * const wplane = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
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const int min_cols_per_thread = 4096;
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const int min_rows_per_thread = std::max((int)(min_cols_per_thread/ne00), 1);
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const int n_threads = std::max(std::min(ctx->n_threads, (int)(ne01/min_rows_per_thread)), 1);
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#ifdef GGML_USE_OPENMP
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#pragma omp parallel for num_threads(n_threads)
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for (int64_t i01 = 0; i01 < ne01; i01++) {
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to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
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}
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#else
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for (int i = 1; i < n_threads; i++) {
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const int64_t start = i*ne01/n_threads;
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const int64_t end = (i + 1)*ne01/n_threads;
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if (start < end) {
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ctx->tasks.push_back(std::async(std::launch::async, [=]() {
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for (int64_t i01 = start; i01 < end; i01++) {
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to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
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}
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}));
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}
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}
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{
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// reuse the current thread for the first task
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const int64_t start = 0;
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const int64_t end = ne01/n_threads;
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for (int64_t i01 = start; i01 < end; i01++) {
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to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
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}
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}
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#endif
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}
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}
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#ifndef GGML_USE_OPENMP
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// wait for all tasks to finish
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for (auto & task : ctx->tasks) {
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task.get();
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}
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ctx->tasks.clear();
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#endif
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}
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#if defined(OPENBLAS_VERSION)
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openblas_set_num_threads(ctx->n_threads);
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#endif
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#if defined(BLIS_ENABLE_CBLAS)
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bli_thread_set_num_threads(ctx->n_threads);
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#endif
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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const int64_t i03 = i13/r3;
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const int64_t i02 = i12/r2;
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const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
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const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
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float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
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if (type != GGML_TYPE_F32) {
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x = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
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}
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cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
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ne1, ne01, ne10,
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1.0f, y, ne10,
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x, ne00,
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0.0f, d, ne01);
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}
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}
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}
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static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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const struct ggml_tensor * src1 = dst->src[1];
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GGML_TENSOR_BINARY_OP_LOCALS
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GGML_ASSERT(ne0 == ne00);
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GGML_ASSERT(ne1 == ne10);
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GGML_ASSERT(ne2 == ne02);
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GGML_ASSERT(ne02 == ne12);
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GGML_ASSERT(ne3 == ne13);
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GGML_ASSERT(ne03 == ne13);
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// we don't support permuted src0 or src1
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GGML_ASSERT(nb00 == sizeof(float));
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// dst cannot be transposed or permuted
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GGML_ASSERT(nb0 == sizeof(float));
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// GGML_ASSERT(nb0 <= nb1);
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// GGML_ASSERT(nb1 <= nb2);
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// GGML_ASSERT(nb2 <= nb3);
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// Arguments to ggml_compute_forward_out_prod (expressed as major,minor)
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// src0: (k,n)
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// src1: (k,m)
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// dst: (m,n)
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//
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// Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f)
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// Also expressed as (major,minor)
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// a: (m,k): so src1 transposed
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// b: (k,n): so src0
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// c: (m,n)
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//
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// However, if ggml_is_transposed(src1) is true, then
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// src1->data already contains a transposed version, so sgemm mustn't
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// transpose it further.
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int n = src0->ne[0];
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int k = src0->ne[1];
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int m = src1->ne[0];
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CBLAS_TRANSPOSE transposeA;
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int lda;
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if (!ggml_is_transposed(src1)) {
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transposeA = CblasTrans;
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lda = m;
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} else {
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transposeA = CblasNoTrans;
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lda = k;
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}
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float * a = (float *) ((char *) src1->data);
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float * b = (float *) ((char *) src0->data);
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float * c = (float *) ((char *) dst->data);
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cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n);
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GGML_UNUSED(ctx);
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}
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// backend interface
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GGML_CALL static const char * ggml_backend_blas_name(ggml_backend_t backend) {
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return "BLAS";
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GGML_UNUSED(backend);
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}
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GGML_CALL static void ggml_backend_blas_free(ggml_backend_t backend) {
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ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
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delete ctx;
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delete backend;
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}
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GGML_CALL static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) {
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return ggml_backend_cpu_buffer_type();
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GGML_UNUSED(backend);
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}
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GGML_CALL static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
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ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
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for (int i = 0; i < cgraph->n_nodes; i++) {
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struct ggml_tensor * node = cgraph->nodes[i];
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switch (node->op) {
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case GGML_OP_MUL_MAT:
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ggml_backend_blas_mul_mat(ctx, node);
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break;
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case GGML_OP_OUT_PROD:
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ggml_backend_blas_out_prod(ctx, node);
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break;
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case GGML_OP_NONE:
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case GGML_OP_RESHAPE:
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case GGML_OP_VIEW:
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case GGML_OP_PERMUTE:
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case GGML_OP_TRANSPOSE:
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break;
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default:
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fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node));
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GGML_ASSERT(false);
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}
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}
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return GGML_STATUS_SUCCESS;
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GGML_UNUSED(backend);
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}
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GGML_CALL static bool ggml_backend_blas_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
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const struct ggml_tensor * src0 = op->src[0];
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const struct ggml_tensor * src1 = op->src[1];
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return (op->op == GGML_OP_MUL_MAT && ggml_backend_blas_use_blas(op)) ||
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(op->op == GGML_OP_OUT_PROD && op->src[0]->type == GGML_TYPE_F32 &&
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op->src[1]->type == GGML_TYPE_F32 &&
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ggml_is_matrix(src0) &&
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ggml_is_matrix(src1) &&
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ggml_is_contiguous(src0) &&
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(ggml_is_contiguous(src1) || ggml_is_transposed(src1)));
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GGML_UNUSED(backend);
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}
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GGML_CALL static bool ggml_backend_blas_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
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return ggml_backend_buft_is_host(buft);
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GGML_UNUSED(backend);
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}
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static struct ggml_backend_i blas_backend_i = {
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/* .get_name = */ ggml_backend_blas_name,
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/* .free = */ ggml_backend_blas_free,
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/* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type,
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/* .set_tensor_async = */ NULL,
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/* .get_tensor_async = */ NULL,
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/* .cpy_tensor_async = */ NULL,
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/* .synchronize = */ NULL,
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/* .graph_plan_create = */ NULL,
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/* .graph_plan_free = */ NULL,
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/* .graph_plan_update = */ NULL,
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/* .graph_plan_compute = */ NULL,
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/* .graph_compute = */ ggml_backend_blas_graph_compute,
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/* .supports_op = */ ggml_backend_blas_supports_op,
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/* .supports_buft = */ ggml_backend_blas_supports_buft,
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/* .offload_op = */ NULL,
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/* .event_new = */ NULL,
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/* .event_free = */ NULL,
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/* .event_record = */ NULL,
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/* .event_wait = */ NULL,
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/* .event_synchronize = */ NULL,
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};
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static ggml_guid_t ggml_backend_blas_guid(void) {
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static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d };
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return &guid;
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}
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ggml_backend_t ggml_backend_blas_init(void) {
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ggml_backend_blas_context * ctx = new ggml_backend_blas_context;
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ggml_backend_t backend = new ggml_backend {
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/* .guid = */ ggml_backend_blas_guid(),
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/* .interface = */ blas_backend_i,
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/* .context = */ ctx,
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};
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#if !defined(NDEBUG) && defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
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if (openblas_get_parallel() != OPENBLAS_OPENMP) {
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fprintf(stderr, "%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
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}
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#endif
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#if !defined(NDEBUG) && defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
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fprintf(stderr, "%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
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#endif
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return backend;
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}
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GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend) {
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return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid());
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}
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void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads) {
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GGML_ASSERT(ggml_backend_is_blas(backend_blas));
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ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
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ctx->n_threads = n_threads;
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}
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23
ggml-blas.h
Normal file
23
ggml-blas.h
Normal file
@ -0,0 +1,23 @@
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#pragma once
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#include "ggml.h"
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#include "ggml-backend.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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// backend API
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GGML_API GGML_CALL ggml_backend_t ggml_backend_blas_init(void);
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GGML_API GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend);
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// number of threads used for conversion to float
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// for openblas and blis, this will also set the number of threads used for blas operations
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GGML_API GGML_CALL void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
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#ifdef __cplusplus
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}
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#endif
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363
src/ggml-blas.cpp
Normal file
363
src/ggml-blas.cpp
Normal file
@ -0,0 +1,363 @@
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#include "ggml-blas.h"
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#include "ggml-backend-impl.h"
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#include <future>
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#include <vector>
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#if defined(GGML_USE_ACCELERATE)
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# include <Accelerate/Accelerate.h>
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#elif defined(GGML_BLAS_USE_MKL)
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# include <mkl.h>
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#else
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# include <cblas.h>
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# ifdef BLIS_ENABLE_CBLAS
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# include <blis.h>
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# endif
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#endif
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struct ggml_backend_blas_context {
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int n_threads = GGML_DEFAULT_N_THREADS;
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std::unique_ptr<char[]> work_data;
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||||
size_t work_size = 0;
|
||||
#ifndef GGML_USE_OPENMP
|
||||
std::vector<std::future<void>> tasks;
|
||||
#endif
|
||||
};
|
||||
|
||||
// helper function to determine if it is better to use BLAS or not
|
||||
// for large matrices, BLAS is faster
|
||||
static bool ggml_backend_blas_use_blas(const struct ggml_tensor * dst) {
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
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 (ggml_is_contiguous(src0) &&
|
||||
ggml_is_contiguous(src1) &&
|
||||
src1->type == GGML_TYPE_F32 &&
|
||||
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
|
||||
|
||||
/*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/
|
||||
return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
|
||||
GGML_ASSERT(ne0 == ne01);
|
||||
GGML_ASSERT(ne1 == ne11);
|
||||
GGML_ASSERT(ne2 == ne12);
|
||||
GGML_ASSERT(ne3 == ne13);
|
||||
|
||||
// we don't support permuted src0 or src1
|
||||
GGML_ASSERT(nb00 == ggml_type_size(type));
|
||||
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
|
||||
|
||||
// dst cannot be transposed or permuted
|
||||
GGML_ASSERT(nb0 == sizeof(float));
|
||||
GGML_ASSERT(nb0 <= nb1);
|
||||
GGML_ASSERT(nb1 <= nb2);
|
||||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// broadcast factors
|
||||
const int64_t r2 = ne12/ne02;
|
||||
const int64_t r3 = ne13/ne03;
|
||||
|
||||
const int64_t ne_plane = ne01*ne00;
|
||||
const size_t desired_wsize = type == GGML_TYPE_F32 ? 0 : ne03*ne02*ne_plane*sizeof(float);
|
||||
|
||||
if (ctx->work_size < desired_wsize) {
|
||||
ctx->work_data.reset(new char[desired_wsize]);
|
||||
ctx->work_size = desired_wsize;
|
||||
}
|
||||
void * wdata = ctx->work_data.get();
|
||||
|
||||
// convert src0 to float
|
||||
if (type != GGML_TYPE_F32) {
|
||||
ggml_type_traits_t type_traits = ggml_internal_get_type_traits(type);
|
||||
ggml_to_float_t const to_float = type_traits.to_float;
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
|
||||
float * const wplane = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
|
||||
|
||||
const int min_cols_per_thread = 4096;
|
||||
const int min_rows_per_thread = std::max((int)(min_cols_per_thread/ne00), 1);
|
||||
const int n_threads = std::max(std::min(ctx->n_threads, (int)(ne01/min_rows_per_thread)), 1);
|
||||
|
||||
#ifdef GGML_USE_OPENMP
|
||||
#pragma omp parallel for num_threads(n_threads)
|
||||
for (int64_t i01 = 0; i01 < ne01; i01++) {
|
||||
to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
|
||||
}
|
||||
#else
|
||||
for (int i = 1; i < n_threads; i++) {
|
||||
const int64_t start = i*ne01/n_threads;
|
||||
const int64_t end = (i + 1)*ne01/n_threads;
|
||||
if (start < end) {
|
||||
ctx->tasks.push_back(std::async(std::launch::async, [=]() {
|
||||
for (int64_t i01 = start; i01 < end; i01++) {
|
||||
to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
|
||||
}
|
||||
}));
|
||||
}
|
||||
}
|
||||
{
|
||||
// reuse the current thread for the first task
|
||||
const int64_t start = 0;
|
||||
const int64_t end = ne01/n_threads;
|
||||
for (int64_t i01 = start; i01 < end; i01++) {
|
||||
to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
#ifndef GGML_USE_OPENMP
|
||||
// wait for all tasks to finish
|
||||
for (auto & task : ctx->tasks) {
|
||||
task.get();
|
||||
}
|
||||
ctx->tasks.clear();
|
||||
#endif
|
||||
}
|
||||
|
||||
#if defined(OPENBLAS_VERSION)
|
||||
openblas_set_num_threads(ctx->n_threads);
|
||||
#endif
|
||||
|
||||
#if defined(BLIS_ENABLE_CBLAS)
|
||||
bli_thread_set_num_threads(ctx->n_threads);
|
||||
#endif
|
||||
|
||||
for (int64_t i13 = 0; i13 < ne13; i13++) {
|
||||
for (int64_t i12 = 0; i12 < ne12; i12++) {
|
||||
const int64_t i03 = i13/r3;
|
||||
const int64_t i02 = i12/r2;
|
||||
|
||||
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
|
||||
const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13);
|
||||
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
|
||||
|
||||
if (type != GGML_TYPE_F32) {
|
||||
x = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane;
|
||||
}
|
||||
|
||||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||||
ne1, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) {
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS
|
||||
|
||||
GGML_ASSERT(ne0 == ne00);
|
||||
GGML_ASSERT(ne1 == ne10);
|
||||
GGML_ASSERT(ne2 == ne02);
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
GGML_ASSERT(ne3 == ne13);
|
||||
GGML_ASSERT(ne03 == ne13);
|
||||
|
||||
// we don't support permuted src0 or src1
|
||||
GGML_ASSERT(nb00 == sizeof(float));
|
||||
|
||||
// dst cannot be transposed or permuted
|
||||
GGML_ASSERT(nb0 == sizeof(float));
|
||||
// GGML_ASSERT(nb0 <= nb1);
|
||||
// GGML_ASSERT(nb1 <= nb2);
|
||||
// GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// Arguments to ggml_compute_forward_out_prod (expressed as major,minor)
|
||||
// src0: (k,n)
|
||||
// src1: (k,m)
|
||||
// dst: (m,n)
|
||||
//
|
||||
// Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f)
|
||||
// Also expressed as (major,minor)
|
||||
// a: (m,k): so src1 transposed
|
||||
// b: (k,n): so src0
|
||||
// c: (m,n)
|
||||
//
|
||||
// However, if ggml_is_transposed(src1) is true, then
|
||||
// src1->data already contains a transposed version, so sgemm mustn't
|
||||
// transpose it further.
|
||||
|
||||
int n = src0->ne[0];
|
||||
int k = src0->ne[1];
|
||||
int m = src1->ne[0];
|
||||
|
||||
CBLAS_TRANSPOSE transposeA;
|
||||
int lda;
|
||||
|
||||
if (!ggml_is_transposed(src1)) {
|
||||
transposeA = CblasTrans;
|
||||
lda = m;
|
||||
} else {
|
||||
transposeA = CblasNoTrans;
|
||||
lda = k;
|
||||
}
|
||||
|
||||
float * a = (float *) ((char *) src1->data);
|
||||
float * b = (float *) ((char *) src0->data);
|
||||
float * c = (float *) ((char *) dst->data);
|
||||
|
||||
cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n);
|
||||
|
||||
GGML_UNUSED(ctx);
|
||||
}
|
||||
|
||||
// backend interface
|
||||
|
||||
GGML_CALL static const char * ggml_backend_blas_name(ggml_backend_t backend) {
|
||||
return "BLAS";
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
GGML_CALL static void ggml_backend_blas_free(ggml_backend_t backend) {
|
||||
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
|
||||
delete ctx;
|
||||
delete backend;
|
||||
}
|
||||
|
||||
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) {
|
||||
return ggml_backend_cpu_buffer_type();
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
GGML_CALL static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context;
|
||||
|
||||
for (int i = 0; i < cgraph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = cgraph->nodes[i];
|
||||
|
||||
switch (node->op) {
|
||||
case GGML_OP_MUL_MAT:
|
||||
ggml_backend_blas_mul_mat(ctx, node);
|
||||
break;
|
||||
|
||||
case GGML_OP_OUT_PROD:
|
||||
ggml_backend_blas_out_prod(ctx, node);
|
||||
break;
|
||||
|
||||
case GGML_OP_NONE:
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_VIEW:
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
break;
|
||||
|
||||
default:
|
||||
fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node));
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
return GGML_STATUS_SUCCESS;
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
GGML_CALL static bool ggml_backend_blas_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
const struct ggml_tensor * src0 = op->src[0];
|
||||
const struct ggml_tensor * src1 = op->src[1];
|
||||
|
||||
return (op->op == GGML_OP_MUL_MAT && ggml_backend_blas_use_blas(op)) ||
|
||||
(op->op == GGML_OP_OUT_PROD && op->src[0]->type == GGML_TYPE_F32 &&
|
||||
op->src[1]->type == GGML_TYPE_F32 &&
|
||||
ggml_is_matrix(src0) &&
|
||||
ggml_is_matrix(src1) &&
|
||||
ggml_is_contiguous(src0) &&
|
||||
(ggml_is_contiguous(src1) || ggml_is_transposed(src1)));
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
GGML_CALL static bool ggml_backend_blas_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
|
||||
return ggml_backend_buft_is_host(buft);
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static struct ggml_backend_i blas_backend_i = {
|
||||
/* .get_name = */ ggml_backend_blas_name,
|
||||
/* .free = */ ggml_backend_blas_free,
|
||||
/* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type,
|
||||
/* .set_tensor_async = */ NULL,
|
||||
/* .get_tensor_async = */ NULL,
|
||||
/* .cpy_tensor_async = */ NULL,
|
||||
/* .synchronize = */ NULL,
|
||||
/* .graph_plan_create = */ NULL,
|
||||
/* .graph_plan_free = */ NULL,
|
||||
/* .graph_plan_update = */ NULL,
|
||||
/* .graph_plan_compute = */ NULL,
|
||||
/* .graph_compute = */ ggml_backend_blas_graph_compute,
|
||||
/* .supports_op = */ ggml_backend_blas_supports_op,
|
||||
/* .supports_buft = */ ggml_backend_blas_supports_buft,
|
||||
/* .offload_op = */ NULL,
|
||||
/* .event_new = */ NULL,
|
||||
/* .event_free = */ NULL,
|
||||
/* .event_record = */ NULL,
|
||||
/* .event_wait = */ NULL,
|
||||
/* .event_synchronize = */ NULL,
|
||||
};
|
||||
|
||||
static ggml_guid_t ggml_backend_blas_guid(void) {
|
||||
static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d };
|
||||
return &guid;
|
||||
}
|
||||
|
||||
ggml_backend_t ggml_backend_blas_init(void) {
|
||||
ggml_backend_blas_context * ctx = new ggml_backend_blas_context;
|
||||
|
||||
ggml_backend_t backend = new ggml_backend {
|
||||
/* .guid = */ ggml_backend_blas_guid(),
|
||||
/* .interface = */ blas_backend_i,
|
||||
/* .context = */ ctx,
|
||||
};
|
||||
|
||||
#if !defined(NDEBUG) && defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
|
||||
if (openblas_get_parallel() != OPENBLAS_OPENMP) {
|
||||
fprintf(stderr, "%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
|
||||
}
|
||||
#endif
|
||||
|
||||
#if !defined(NDEBUG) && defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
|
||||
fprintf(stderr, "%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
|
||||
#endif
|
||||
|
||||
return backend;
|
||||
}
|
||||
|
||||
GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend) {
|
||||
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid());
|
||||
}
|
||||
|
||||
void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads) {
|
||||
GGML_ASSERT(ggml_backend_is_blas(backend_blas));
|
||||
|
||||
ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context;
|
||||
ctx->n_threads = n_threads;
|
||||
}
|
||||
23
src/ggml-blas.h
Normal file
23
src/ggml-blas.h
Normal file
@ -0,0 +1,23 @@
|
||||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-backend.h"
|
||||
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
// backend API
|
||||
GGML_API GGML_CALL ggml_backend_t ggml_backend_blas_init(void);
|
||||
|
||||
GGML_API GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend);
|
||||
|
||||
// number of threads used for conversion to float
|
||||
// for openblas and blis, this will also set the number of threads used for blas operations
|
||||
GGML_API GGML_CALL void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
|
||||
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
Loading…
Reference in New Issue
Block a user