.. index:: pair: page; Primitive Example .. _doxid-prelu_example_cpp: Primitive Example ================= This C++ API example demonstrates how to create and execute an :ref:`PReLU ` primitive in forward training propagation mode. .. 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 "dnnl.hpp" #include "example_utils.hpp" using namespace :ref:`dnnl `; using :ref:`tag ` = :ref:`memory::format_tag `; using :ref:`dt ` = :ref:`memory::data_type `; void prelu_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, // channels IH = 227, // tensor height IW = 227; // tensor width // Source (src), weights and destination (dst) tensors dimensions. const :ref:`memory::dims ` src_dims = {N, IC, IH, IW}; const :ref:`memory::dims ` weights_dims = {N, IC, IH, IW}; const :ref:`memory::dims ` dst_dims = {N, IC, IH, IW}; // Allocate buffers. In this example, out-of-place primitive execution is // demonstrated since both src and dst are required for later backward // propagation. std::vector src_data(product(src_dims)); std::vector weights_data(product(weights_dims)); std::vector dst_data(product(dst_dims)); // Initialize src tensor. std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Initialize weights tensor. std::fill(weights_data.begin(), weights_data.end(), 0.3f); // Create memory objects for tensor data (src, weights, dst). In this // example, NCHW layout is assumed for src, weights and dst. auto user_src_mem = :ref:`memory `({src_dims, :ref:`dt::f32 `, tag::nchw}, :ref:`engine `); auto user_weights_mem = :ref:`memory `({weights_dims, :ref:`dt::f32 `, tag::nchw}, :ref:`engine `); auto user_dst_mem = :ref:`memory `({dst_dims, :ref:`dt::f32 `, tag::nchw}, :ref:`engine `); // Create memory descriptors for the primitive. Src tag is set // to match src memory object. Setting weights tag to format_tag::any // enables the PReLU primitive to choose memory layout for an optimized // primitive implementation, and that layout may differ from the one // provided by the user. auto :ref:`src_md ` = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, tag::nchw); auto :ref:`weights_md ` = :ref:`memory::desc `(weights_dims, :ref:`dt::f32 `, :ref:`tag::any `); auto :ref:`dst_md ` = :ref:`memory::desc `(src_dims, :ref:`dt::f32 `, :ref:`tag::any `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), user_src_mem); write_to_dnnl_memory(weights_data.data(), user_weights_mem); // Create primitive descriptor. auto prelu_pd = :ref:`prelu_forward::primitive_desc `( :ref:`engine `, :ref:`prop_kind::forward_training `, src_md, weights_md, dst_md); // For now, assume that the weights memory layout generated // by the primitive and the one provided by the user are identical. auto prelu_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 object with internal buffers that will // contain the reordered data. if (prelu_pd.weights_desc() != user_weights_mem.:ref:`get_desc `()) { prelu_weights_mem = :ref:`memory `(prelu_pd.weights_desc(), :ref:`engine `); :ref:`reorder `(user_weights_mem, prelu_weights_mem) .:ref:`execute `(engine_stream, user_weights_mem, prelu_weights_mem); } // Create the primitive. auto prelu_prim = :ref:`prelu_forward `(prelu_pd); // Primitive arguments. std::unordered_map prelu_args; prelu_args.insert({:ref:`DNNL_ARG_SRC `, user_src_mem}); prelu_args.insert({:ref:`DNNL_ARG_WEIGHTS `, prelu_weights_mem}); prelu_args.insert({:ref:`DNNL_ARG_DST `, user_dst_mem}); // Primitive execution: PReLU. prelu_prim.execute(engine_stream, prelu_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(dst_data.data(), user_dst_mem); } int main(int argc, char **argv) { return handle_example_errors(prelu_example, parse_engine_kind(argc, argv)); }