Developer Guide and Reference

  • 2022.1
  • 04/11/2022
  • Public Content


Annotated version: Binary Primitive Example
Annotated version: Binary Primitive Example
/******************************************************************************* * Copyright 2020 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 * * * * 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 binary_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 = 150, // tensor height IW = 150; // tensor width // Source (src_0 and src_1) and destination (dst) tensors dimensions. memory::dims src_0_dims = {N, IC, IH, IW}; memory::dims src_1_dims = {N, IC, IH, 1}; // Allocate buffers. std::vector<float> src_0_data(product(src_0_dims)); std::vector<float> src_1_data(product(src_1_dims)); // Initialize src_0 and src_1 (src). std::generate(src_0_data.begin(), src_0_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); std::generate(src_1_data.begin(), src_1_data.end(), []() { static int i = 0; return std::sin(i++ * 2.f); }); // Create src and dst memory descriptors. auto src_0_md = memory::desc(src_0_dims, dt::f32, tag::nchw); auto src_1_md = memory::desc(src_1_dims, dt::f32, tag::nchw); auto dst_md = memory::desc(src_0_dims, dt::f32, tag::nchw); // Create src memory objects. auto src_0_mem = memory(src_0_md, engine); auto src_1_mem = memory(src_1_md, engine); // Write data to memory object's handle. write_to_dnnl_memory(, src_0_mem); write_to_dnnl_memory(, src_1_mem); // Create operation descriptor. auto binary_d = binary::desc(algorithm::binary_mul, src_0_md, src_1_md, dst_md); // Create primitive post-ops (ReLU). const float scale = 1.0f; const float alpha = 0.f; const float beta = 0.f; post_ops binary_ops; binary_ops.append_eltwise(scale, algorithm::eltwise_relu, alpha, beta); primitive_attr binary_attr; binary_attr.set_post_ops(binary_ops); // Create primitive descriptor. auto binary_pd = binary::primitive_desc(binary_d, binary_attr, engine); // Create the primitive. auto binary_prim = binary(binary_pd); // Primitive arguments. Set up in-place execution by assigning src_0 as DST. std::unordered_map<int, memory> binary_args; binary_args.insert({DNNL_ARG_SRC_0, src_0_mem}); binary_args.insert({DNNL_ARG_SRC_1, src_1_mem}); binary_args.insert({DNNL_ARG_DST, src_0_mem}); // Primitive execution: binary with ReLU. binary_prim.execute(engine_stream, binary_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(, src_0_mem); } int main(int argc, char **argv) { return handle_example_errors(binary_example, parse_engine_kind(argc, argv)); }

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