Developer Guide and Reference

  • 2022.1
  • 04/11/2022
  • Public Content
Contents

batch_normalization.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 <algorithm> #include <cmath> #include <iostream> #include <string> #include <vector> #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace dnnl; using tag = memory::format_tag; using dt = memory::data_type; void batch_normalization_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) and destination (dst) tensors dimensions. memory::dims src_dims = {N, IC, IH, IW}; // Scale/shift tensor dimensions. memory::dims scale_shift_dims = {2, IC}; // Allocate buffers. std::vector<float> src_data(product(src_dims)); std::vector<float> scale_shift_data(product(scale_shift_dims)); // Initialize src. std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); auto mid = scale_shift_data.begin() + IC; // Initialize scale. std::generate(scale_shift_data.begin(), mid, []() { static int i = 0; return std::sin(i++ * 2.f); }); // Initialize shift. std::generate(mid, scale_shift_data.end(), []() { static int i = 0; return std::tan(float(i++)); }); // Create src and scale/shift memory descriptors and memory objects. auto src_md = memory::desc(src_dims, dt::f32, tag::nchw); auto scale_shift_md = memory::desc(scale_shift_dims, dt::f32, tag::nc); auto src_mem = memory(src_md, engine); auto scale_shift_mem = memory(scale_shift_md, engine); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); write_to_dnnl_memory(scale_shift_data.data(), scale_shift_mem); // Create operation descriptor. auto bnorm_d = batch_normalization_forward::desc( prop_kind::forward_training, src_md, 1.e-10f, normalization_flags::use_scale_shift | normalization_flags::fuse_norm_relu); // Create primitive descriptor. auto bnorm_pd = batch_normalization_forward::primitive_desc(bnorm_d, engine); // Create memory objects using memory descriptors created by the primitive // descriptor: mean, variance, workspace. // NOTE: Here, the ReLU post-ops require a workspace for later usage in // backward propagation mode. auto mean_mem = memory(bnorm_pd.mean_desc(), engine); auto variance_mem = memory(bnorm_pd.variance_desc(), engine); auto workspace_mem = memory(bnorm_pd.workspace_desc(), engine); // Create the primitive. auto bnorm_prim = batch_normalization_forward(bnorm_pd); // Primitive arguments. Set up in-place execution by assigning src as DST. std::unordered_map<int, memory> bnorm_args; bnorm_args.insert({DNNL_ARG_SRC, src_mem}); bnorm_args.insert({DNNL_ARG_MEAN, mean_mem}); bnorm_args.insert({DNNL_ARG_VARIANCE, variance_mem}); bnorm_args.insert({DNNL_ARG_SCALE_SHIFT, scale_shift_mem}); bnorm_args.insert({DNNL_ARG_WORKSPACE, workspace_mem}); bnorm_args.insert({DNNL_ARG_DST, src_mem}); // Primitive execution: batch normalization with ReLU. bnorm_prim.execute(engine_stream, bnorm_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(src_data.data(), src_mem); } int main(int argc, char **argv) { return handle_example_errors( batch_normalization_example, parse_engine_kind(argc, argv)); }

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