Developer Guide and Reference

  • 2023.0
  • 12/16/2022
  • Public Content
Contents

Primitive Example

This C++ API example demonstrates how to create and execute an PReLU primitive in forward training propagation mode.
/******************************************************************************* * 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 <algorithm> #include <cmath> #include <string> #include <vector> #include "dnnl.hpp" #include "example_utils.hpp" using namespace dnnl; using tag = memory::format_tag; using dt = memory::data_type; void prelu_example(dnnl::engine::kind engine_kind) { // Create execution dnnl::engine. dnnl::engine engine(engine_kind, 0); // Create dnnl::stream. dnnl::stream engine_stream(engine); // Tensor dimensions. const 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 memory::dims src_dims = {N, IC, IH, IW}; const memory::dims weights_dims = {N, IC, IH, IW}; const 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<float> src_data(product(src_dims)); std::vector<float> weights_data(product(weights_dims)); std::vector<float> 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 = memory({src_dims, dt::f32, tag::nchw}, engine); auto user_weights_mem = memory({weights_dims, dt::f32, tag::nchw}, engine); auto user_dst_mem = memory({dst_dims, dt::f32, tag::nchw}, 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 src_md = memory::desc(src_dims, dt::f32, tag::nchw); auto weights_md = memory::desc(weights_dims, dt::f32, tag::any); auto dst_md = memory::desc(src_dims, dt::f32, 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 = prelu_forward::primitive_desc( engine, 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.get_desc()) { prelu_weights_mem = memory(prelu_pd.weights_desc(), engine); reorder(user_weights_mem, prelu_weights_mem) .execute(engine_stream, user_weights_mem, prelu_weights_mem); } // Create the primitive. auto prelu_prim = prelu_forward(prelu_pd); // Primitive arguments. std::unordered_map<int, memory> prelu_args; prelu_args.insert({DNNL_ARG_SRC, user_src_mem}); prelu_args.insert({DNNL_ARG_WEIGHTS, prelu_weights_mem}); prelu_args.insert({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)); }

Product and Performance Information

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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.