--- /dev/null
+#include "ggml-zendnn.h"
+
+#include "ggml-backend-impl.h"
+#include "ggml-impl.h"
+#include "ggml-cpu.h"
+#include "zendnnl.hpp"
+
+#include <cstring>
+
+
+struct ggml_backend_zendnn_context {
+ int n_threads = GGML_DEFAULT_N_THREADS;
+ std::unique_ptr<char[]> work_data;
+ size_t work_size = 0;
+};
+
+template<typename T>
+zendnnl::common::data_type_t ggml_to_zendnn_type() {
+ if constexpr (std::is_same_v<T, float>) {
+ return zendnnl::common::data_type_t::f32;
+ } else if constexpr (std::is_same_v<T, ggml_bf16_t>) {
+ return zendnnl::common::data_type_t::bf16;
+ } else {
+ return zendnnl::common::data_type_t::none;
+ }
+}
+
+/**
+ * ZenDNN matmul: computes C = B * A.
+ *
+ * - A: weights, shape (k, m), column-major (each column is a weight vector for one output).
+ * - B: input, shape (n, k), row-major (each row is an input sample).
+ * - C: output, shape (n, m), row-major.
+ *
+ * Dimensions:
+ * m = output features (columns of C, columns of A)
+ * n = batch size (rows of C, rows of B)
+ * k = inner dimension (columns of B, rows of A)
+ */
+template <typename TA, typename TB, typename TC>
+static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k,
+ const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C,
+ int64_t ldc) {
+
+ zendnnl::lowoha::lowoha_params params;
+ params.dtypes.src = ggml_to_zendnn_type<TB>();
+ params.dtypes.wei = ggml_to_zendnn_type<TA>();
+ params.dtypes.dst = ggml_to_zendnn_type<TC>();
+ params.num_threads = ctx->n_threads;
+
+ zendnnl::lowoha::status_t status = zendnnl::lowoha::matmul_direct(
+ 'r', false, true, // row-major, don't transpose B, transpose A (because it's column-major)
+ n, // M: rows of B and C
+ m, // N: cols of A^T and C
+ k, // K: cols of B, rows of A
+ 1.0f, // alpha
+ B, ldb, // src: B[n,k]
+ A, lda, // weight: A[k,m] column-major (transposed)
+ nullptr, // bias
+ 0.0f, // beta
+ C, ldc, // output C[n,m]
+ true, // is_weights_const
+ {}, // batch_params
+ params // params
+ );
+
+ if (status != zendnnl::lowoha::status_t::success) {
+ GGML_LOG_ERROR("%s, ZenDNN matmul failed: status=%d\n", __func__, static_cast<int>(status));
+ return false;
+ }
+ return true;
+}
+
+static bool ggml_zendnn_sgemm(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k,
+ const void * A, int64_t lda, const void * B, int64_t ldb, void * C,
+ int64_t ldc, int Atype, int Btype, int Ctype) {
+
+ assert(m >= 0);
+ assert(n >= 0);
+ assert(k >= 0);
+ assert(lda >= k);
+ assert(ldb >= k);
+ assert(ldc >= m);
+
+ // categorize types
+ switch (Atype) {
+ case GGML_TYPE_F32:
+ if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32)
+ return false;
+ return ggml_zendnn_matmul<float, float, float>(
+ ctx, m, n, k,
+ (const float *)A, lda,
+ (const float *)B, ldb,
+ (float *)C, ldc);
+ case GGML_TYPE_BF16:
+ if (Btype != GGML_TYPE_BF16)
+ return false;
+ if (Ctype == GGML_TYPE_BF16)
+ return ggml_zendnn_matmul<ggml_bf16_t, ggml_bf16_t, ggml_bf16_t>(
+ ctx, m, n, k,
+ (const ggml_bf16_t *)A, lda,
+ (const ggml_bf16_t *)B, ldb,
+ (ggml_bf16_t *)C, ldc);
+ if (Ctype == GGML_TYPE_F32)
+ return ggml_zendnn_matmul<ggml_bf16_t, ggml_bf16_t, float>(
+ ctx, m, n, k,
+ (const ggml_bf16_t *)A, lda,
+ (const ggml_bf16_t *)B, ldb,
+ (float *)C, ldc);
+ return false;
+ default:
+ return false; // unsupported type
+ }
+}
+
+static void ggml_zendnn_compute_forward_mul_mat(
+ ggml_backend_zendnn_context * ctx,
+ ggml_tensor * dst) {
+
+ const ggml_tensor * src0 = dst->src[0]; // weights
+ const ggml_tensor * src1 = dst->src[1]; // inputs
+
+ GGML_TENSOR_BINARY_OP_LOCALS
+
+ ggml_type const vec_dot_type = ggml_get_type_traits_cpu(src0->type)->vec_dot_type;
+ ggml_from_float_t const from_float = ggml_get_type_traits_cpu(vec_dot_type)->from_float;
+
+ 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(src0->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;
+
+ void * work_data = ctx->work_data.get();
+ if (src1->type != vec_dot_type) {
+ const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
+ const size_t nbw2 = nbw1 * ne11;
+ const size_t nbw3 = nbw2 * ne12;
+ const size_t desired_wsize = ne13 * nbw3;
+ if (ctx->work_size < desired_wsize) {
+ ctx->work_data.reset(new char[desired_wsize]);
+ ctx->work_size = desired_wsize;
+ }
+ work_data = ctx->work_data.get();
+
+ // #pragma omp parallel for num_threads(ctx->n_threads)
+ #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static)
+ for (int64_t i13 = 0; i13 < ne13; ++i13) {
+ for (int64_t i12 = 0; i12 < ne12; ++i12) {
+ for (int64_t i11 = 0; i11 < ne11; ++i11) {
+ const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+ void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3;
+ from_float(src1_f32, src1_conv, ne10);
+ }
+ }
+ }
+ }
+
+ for (int64_t i13 = 0; i13 < ne13; i13++) {
+ for (int64_t i12 = 0; i12 < ne12; i12++) {
+ const void* wdata = src1->type == vec_dot_type ? src1->data : work_data;
+ const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+ if (!ggml_zendnn_sgemm(ctx,
+ ne01, // m
+ ne11, // n
+ ne10, // k
+ static_cast<const char *>(src0->data) + (i12/r2)*nb02 + (i13/r3)*nb03,
+ ne00, // lda
+ static_cast<const char *>(wdata) + (i12*ne11 + i13*ne12*ne11)*row_size,
+ ne10, // ldb
+ static_cast<char *>(dst->data) + i12*nb2 + i13*nb3,
+ ne01, // ldc
+ src0->type,
+ vec_dot_type,
+ dst->type))
+ GGML_ABORT("%s: ZenDNN sgemm failed\n", __func__);
+ }
+ }
+}
+
+// backend interface
+
+static const char * ggml_backend_zendnn_get_name(ggml_backend_t backend) {
+ return "ZenDNN";
+
+ GGML_UNUSED(backend);
+}
+
+static void ggml_backend_zendnn_free(ggml_backend_t backend) {
+ ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context;
+ delete ctx;
+ delete backend;
+}
+
+static ggml_status ggml_backend_zendnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+ ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_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_zendnn_compute_forward_mul_mat(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:
+ GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node));
+ }
+ }
+
+ return GGML_STATUS_SUCCESS;
+
+ GGML_UNUSED(backend);
+}
+
+static struct ggml_backend_i ggml_backend_zendnn_i = {
+ /* .get_name = */ ggml_backend_zendnn_get_name,
+ /* .free = */ ggml_backend_zendnn_free,
+ /* .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_zendnn_graph_compute,
+ /* .event_record = */ NULL,
+ /* .event_wait = */ NULL,
+ /* .graph_optimize = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_zendnn_guid(void) {
+ static const char * guid_str = "AMD-ZENDNN-ACCEL";
+ return reinterpret_cast<ggml_guid_t>(const_cast<char*>(guid_str));
+}
+
+ggml_backend_t ggml_backend_zendnn_init(void) {
+ ggml_backend_zendnn_context * ctx = new ggml_backend_zendnn_context;
+
+ ggml_backend_t backend = new ggml_backend {
+ /* .guid = */ ggml_backend_zendnn_guid(),
+ /* .iface = */ ggml_backend_zendnn_i,
+ /* .device = */ ggml_backend_reg_dev_get(ggml_backend_zendnn_reg(), 0),
+ /* .context = */ ctx,
+ };
+
+ return backend;
+}
+
+bool ggml_backend_is_zendnn(ggml_backend_t backend) {
+ return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_zendnn_guid());
+}
+
+void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads) {
+ GGML_ASSERT(ggml_backend_is_zendnn(backend_zendnn));
+
+ ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend_zendnn->context;
+ ctx->n_threads = n_threads;
+}
+
+// device interface
+static const char * ggml_backend_zendnn_device_get_name(ggml_backend_dev_t dev) {
+ return "ZenDNN";
+
+ GGML_UNUSED(dev);
+}
+/**
+ * ZenDNN is AMD's performance library providing optimized primitives and implementations
+ * for deep learning workloads on AMD CPUs. It targets improved performance for common
+ * neural network operations on AMD architectures. For more information, see:
+ * https://www.amd.com/en/developer/zendnn.html
+ */
+static const char * ggml_backend_zendnn_device_get_description(ggml_backend_dev_t dev) {
+ return "ZenDNN: AMD optimized primitives backend for GGML (optimized for AMD CPUs)";
+
+ GGML_UNUSED(dev);
+}
+
+static void ggml_backend_zendnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+ *free = 0;
+ *total = 0;
+
+ GGML_UNUSED(dev);
+}
+
+static enum ggml_backend_dev_type ggml_backend_zendnn_device_get_type(ggml_backend_dev_t dev) {
+ return GGML_BACKEND_DEVICE_TYPE_ACCEL;
+
+ GGML_UNUSED(dev);
+}
+
+static void ggml_backend_zendnn_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
+ props->name = ggml_backend_zendnn_device_get_name(dev);
+ props->description = ggml_backend_zendnn_device_get_description(dev);
+ props->type = ggml_backend_zendnn_device_get_type(dev);
+ ggml_backend_zendnn_device_get_memory(dev, &props->memory_free, &props->memory_total);
+ props->caps = {
+ /* .async = */ false,
+ /* .host_buffer = */ false,
+ /* .buffer_from_host_ptr = */ true,
+ /* .events = */ false
+ };
+}
+
+static ggml_backend_t ggml_backend_zendnn_device_init_backend(ggml_backend_dev_t dev, const char * params) {
+ ggml_backend_t backend = ggml_backend_zendnn_init();
+ if (backend == NULL) {
+ GGML_LOG_ERROR("%s: error: failed to initialize ZenDNN backend\n", __func__);
+ return NULL;
+ }
+
+ return backend;
+
+ GGML_UNUSED(dev);
+ GGML_UNUSED(params);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zendnn_device_get_buffer_type(ggml_backend_dev_t dev) {
+ return ggml_backend_cpu_buffer_type();
+
+ GGML_UNUSED(dev);
+}
+
+static ggml_backend_buffer_t ggml_backend_zendnn_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+ return ggml_backend_cpu_buffer_from_ptr(ptr, size);
+
+ GGML_UNUSED(dev);
+ GGML_UNUSED(max_tensor_size);
+}
+
+static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
+ switch (op->op) {
+ case GGML_OP_NONE:
+ case GGML_OP_RESHAPE:
+ case GGML_OP_VIEW:
+ case GGML_OP_PERMUTE:
+ case GGML_OP_TRANSPOSE:
+ return true;
+
+ case GGML_OP_MUL_MAT:
+ {
+ const ggml_tensor * weights = op->src[0];
+ const ggml_tensor * inputs = op->src[1];
+
+ const int64_t ne10 = inputs->ne[0];
+ const int64_t ne0 = op->ne[0];
+ const int64_t ne1 = op->ne[1];
+
+ const int64_t min_batch = 1;
+ if (!ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs) ||
+ ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
+ return false;
+ }
+ switch (weights->type) {
+ case GGML_TYPE_F32:
+ case GGML_TYPE_BF16:
+ return true;
+ default:
+ return false;
+ }
+ } break;
+
+ default:
+ return false;
+ }
+
+ GGML_UNUSED(dev);
+}
+
+static bool ggml_backend_zendnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+ return ggml_backend_buft_is_host(buft);
+
+ GGML_UNUSED(dev);
+}
+
+static const struct ggml_backend_device_i ggml_backend_zendnn_device_i = {
+ /* .get_name = */ ggml_backend_zendnn_device_get_name,
+ /* .get_description = */ ggml_backend_zendnn_device_get_description,
+ /* .get_memory = */ ggml_backend_zendnn_device_get_memory,
+ /* .get_type = */ ggml_backend_zendnn_device_get_type,
+ /* .get_props = */ ggml_backend_zendnn_device_get_props,
+ /* .init_backend = */ ggml_backend_zendnn_device_init_backend,
+ /* .get_buffer_type = */ ggml_backend_zendnn_device_get_buffer_type,
+ /* .get_host_buffer_type = */ NULL,
+ /* .buffer_from_host_ptr = */ ggml_backend_zendnn_device_buffer_from_host_ptr,
+ /* .supports_op = */ ggml_backend_zendnn_device_supports_op,
+ /* .supports_buft = */ ggml_backend_zendnn_device_supports_buft,
+ /* .offload_op = */ NULL,
+ /* .event_new = */ NULL,
+ /* .event_free = */ NULL,
+ /* .event_synchronize = */ NULL,
+};
+
+// backend reg interface
+static const char * ggml_backend_zendnn_reg_get_name(ggml_backend_reg_t reg) {
+ return "ZenDNN";
+
+ GGML_UNUSED(reg);
+}
+
+static size_t ggml_backend_zendnn_reg_get_device_count(ggml_backend_reg_t reg) {
+ return 1;
+
+ GGML_UNUSED(reg);
+}
+
+static ggml_backend_dev_t ggml_backend_zendnn_reg_get_device(ggml_backend_reg_t reg, size_t index) {
+ GGML_ASSERT(index == 0);
+
+ static ggml_backend_device ggml_backend_zendnn_device = {
+ /* .iface = */ ggml_backend_zendnn_device_i,
+ /* .reg = */ reg,
+ /* .context = */ nullptr,
+ };
+
+ return &ggml_backend_zendnn_device;
+}
+
+static void * ggml_backend_zendnn_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+ if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
+ return (void *) ggml_backend_zendnn_set_n_threads;
+ }
+ return NULL;
+
+ GGML_UNUSED(reg);
+ GGML_UNUSED(name);
+}
+
+static const struct ggml_backend_reg_i ggml_backend_zendnn_reg_i = {
+ /* .get_name = */ ggml_backend_zendnn_reg_get_name,
+ /* .get_device_count = */ ggml_backend_zendnn_reg_get_device_count,
+ /* .get_device = */ ggml_backend_zendnn_reg_get_device,
+ /* .get_proc_address = */ ggml_backend_zendnn_get_proc_address,
+};
+
+ggml_backend_reg_t ggml_backend_zendnn_reg(void) {
+ static struct ggml_backend_reg ggml_backend_zendnn_reg = {
+ /* .api_version = */ GGML_BACKEND_API_VERSION,
+ /* .iface = */ ggml_backend_zendnn_reg_i,
+ /* .context = */ NULL,
+ };
+
+ return &ggml_backend_zendnn_reg;
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_zendnn_reg)
overview / pkg / ggml / sources / ggml / commitdiff