.. index:: pair: example; inner_product.cpp .. _doxid-inner_product_8cpp-example: inner_product.cpp ================= Annotated version: :ref:`Inner Product Primitive Example ` Annotated version: :ref:`Inner Product Primitive Example ` .. ref-code-block:: cpp /******************************************************************************* * Copyright 2020-2022 Intel Corporation * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *******************************************************************************/ #include #include #include #include #include #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace :ref:`dnnl `; using :ref:`tag ` = :ref:`memory::format_tag `; using :ref:`dt ` = :ref:`memory::data_type `; void inner_product_example(:ref:`dnnl::engine::kind ` engine_kind) { // Create execution dnnl::engine. :ref:`dnnl::engine ` :ref:`engine `(engine_kind, 0); // Create dnnl::stream. :ref:`dnnl::stream ` engine_stream(:ref:`engine `); // Tensor dimensions. const :ref:`memory::dim ` N = 3, // batch size IC = 3, // input channels IH = 227, // tensor height IW = 227, // tensor width OC = 96; // output channels // Source (src), weights, bias, and destination (dst) tensors // dimensions. :ref:`memory::dims ` src_dims = {N, IC, IH, IW}; :ref:`memory::dims ` weights_dims = {OC, IC, IH, IW}; :ref:`memory::dims ` bias_dims = {OC}; :ref:`memory::dims ` dst_dims = {N, OC}; // Allocate buffers. std::vector src_data(product(src_dims)); std::vector weights_data(product(weights_dims)); std::vector bias_data(OC); std::vector dst_data(product(dst_dims)); // Initialize src, weights, and bias tensors. std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); std::generate(weights_data.begin(), weights_data.end(), []() { static int i = 0; return std::sin(i++ * 2.f); }); std::generate(bias_data.begin(), bias_data.end(), []() { static int i = 0; return std::tanh(float(i++)); }); // Create memory descriptors and memory objects for src and dst. In this // example, NCHW layout is assumed. auto :ref:`src_md ` = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, tag::nchw); auto bias_md = :ref:`memory::desc `(bias_dims, :ref:`dt::f32 `, tag::a); auto :ref:`dst_md ` = :ref:`memory::desc `(dst_dims, :ref:`dt::f32 `, tag::nc); auto src_mem = :ref:`memory `(src_md, :ref:`engine `); auto bias_mem = :ref:`memory `(bias_md, :ref:`engine `); auto dst_mem = :ref:`memory `(dst_md, :ref:`engine `); // Create memory object for user's layout for weights. In this example, OIHW // is assumed. auto user_weights_mem = :ref:`memory `({weights_dims, :ref:`dt::f32 `, tag::oihw}, :ref:`engine `); // Write data to memory object's handles. write_to_dnnl_memory(src_data.data(), src_mem); write_to_dnnl_memory(bias_data.data(), bias_mem); write_to_dnnl_memory(weights_data.data(), user_weights_mem); // Create memory descriptor for weights with format_tag::any. This enables // the inner product primitive to choose the memory layout for an optimized // primitive implementation, and this format may differ from the one // provided by the user. auto inner_product_weights_md = :ref:`memory::desc `(weights_dims, :ref:`dt::f32 `, :ref:`tag::any `); // Create primitive post-ops (ReLU). const float alpha = 0.f; const float beta = 0.f; :ref:`post_ops ` inner_product_ops; inner_product_ops.:ref:`append_eltwise `(:ref:`algorithm::eltwise_relu `, alpha, beta); :ref:`primitive_attr ` inner_product_attr; inner_product_attr.:ref:`set_post_ops `(inner_product_ops); // Create inner product primitive descriptor. auto inner_product_pd = :ref:`inner_product_forward::primitive_desc `(:ref:`engine `, :ref:`prop_kind::forward_training `, src_md, inner_product_weights_md, bias_md, dst_md, inner_product_attr); // For now, assume that the weights memory layout generated by the primitive // and the one provided by the user are identical. auto inner_product_weights_mem = user_weights_mem; // Reorder the data in case the weights memory layout generated by the // primitive and the one provided by the user are different. In this case, // we create additional memory objects with internal buffers that will // contain the reordered data. if (inner_product_pd.weights_desc() != user_weights_mem.:ref:`get_desc `()) { inner_product_weights_mem = :ref:`memory `(inner_product_pd.weights_desc(), :ref:`engine `); :ref:`reorder `(user_weights_mem, inner_product_weights_mem) .:ref:`execute `(engine_stream, user_weights_mem, inner_product_weights_mem); } // Create the primitive. auto inner_product_prim = :ref:`inner_product_forward `(inner_product_pd); // Primitive arguments. std::unordered_map inner_product_args; inner_product_args.insert({:ref:`DNNL_ARG_SRC `, src_mem}); inner_product_args.insert({:ref:`DNNL_ARG_WEIGHTS `, inner_product_weights_mem}); inner_product_args.insert({:ref:`DNNL_ARG_BIAS `, bias_mem}); inner_product_args.insert({:ref:`DNNL_ARG_DST `, dst_mem}); // Primitive execution: inner-product with ReLU. inner_product_prim.execute(engine_stream, inner_product_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(dst_data.data(), dst_mem); } int main(int argc, char **argv) { return handle_example_errors( inner_product_example, parse_engine_kind(argc, argv)); }