int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = 200; // new tokens to predict
int32_t n_parallel = 1; // number of parallel streams
- int32_t n_batch = 8; // batch size for prompt processing
+ int32_t n_batch = 32; // batch size for prompt processing
int32_t n_ctx = 2048; // context size (this is the KV cache max size)
int32_t n_gpu_layers = 0; // number of layers to offlload to the GPU
--- /dev/null
+#include "ggml-blas.h"
+#include "ggml-backend-impl.h"
+
+#include <future>
+#include <vector>
+
+#if defined(GGML_USE_ACCELERATE)
+# include <Accelerate/Accelerate.h>
+#elif defined(GGML_BLAS_USE_MKL)
+# include <mkl.h>
+#else
+# include <cblas.h>
+# ifdef BLIS_ENABLE_CBLAS
+# include <blis.h>
+# endif
+#endif
+
+struct ggml_backend_blas_context {
+ int n_threads = GGML_DEFAULT_N_THREADS;
+ std::unique_ptr<char[]> work_data;
+ 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;
+}
--- /dev/null
+#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
--- /dev/null
+#include "ggml-blas.h"
+#include "ggml-backend-impl.h"
+
+#include <future>
+#include <vector>
+
+#if defined(GGML_USE_ACCELERATE)
+# include <Accelerate/Accelerate.h>
+#elif defined(GGML_BLAS_USE_MKL)
+# include <mkl.h>
+#else
+# include <cblas.h>
+# ifdef BLIS_ENABLE_CBLAS
+# include <blis.h>
+# endif
+#endif
+
+struct ggml_backend_blas_context {
+ int n_threads = GGML_DEFAULT_N_THREADS;
+ std::unique_ptr<char[]> work_data;
+ 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;
+}
--- /dev/null
+#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
overview / pkg / ggml / sources / whisper.cpp / commitdiff