Developer Guide and Reference

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

bnorm_u8_via_binary_postops.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 "dnnl.hpp" #include "example_utils.hpp" using namespace dnnl; using tag = memory::format_tag; using dt = memory::data_type; void bnorm_u8_via_binary_postops(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 // Tensors dimensions. memory::dims src_dims = {N, IC, IH, IW}; memory::dims params_dims = {1, IC, 1, 1}; // Allocate buffers. std::vector<float> src_data(product(src_dims)); std::vector<float> mean_data(product(params_dims)); std::vector<float> variance_data(product(params_dims)); std::vector<float> scale_data(product(params_dims)); std::vector<float> shift_data(product(params_dims)); std::vector<float> oscale_data(product(params_dims)); // Initialize std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); std::generate(mean_data.begin(), mean_data.end(), []() { static int i = 0; return std::sin(i++ * 2.f); }); std::generate(variance_data.begin(), variance_data.end(), []() { static int i = 0; float value = std::abs(std::sin(i++ * 4.f)); // Avoid division by zero. Variance should be positive. return value == 0.f ? 1.f : value; }); std::generate(scale_data.begin(), scale_data.end(), []() { static int i = 0; return std::sin(i++ * 6.f); }); std::generate(shift_data.begin(), shift_data.end(), []() { static int i = 0; return std::sin(i++ * 8.f); }); std::generate( oscale_data.begin(), oscale_data.end(), []() { return 0.5f; }); // Create descriptors. auto src_md = memory::desc(src_dims, dt::u8, tag::nhwc); auto mean_md = memory::desc(params_dims, dt::f32, tag::nhwc); auto variance_md = memory::desc(params_dims, dt::f32, tag::nhwc); auto scale_md = memory::desc(params_dims, dt::f32, tag::nhwc); auto shift_md = memory::desc(params_dims, dt::f32, tag::nhwc); auto oscale_md = memory::desc(params_dims, dt::f32, tag::nhwc); // Create src memory objects. auto src_mem = memory(src_md, engine); auto mean_mem = memory(mean_md, engine); auto variance_mem = memory(variance_md, engine); auto scale_mem = memory(scale_md, engine); auto shift_mem = memory(shift_md, engine); auto oscale_mem = memory(oscale_md, engine); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); write_to_dnnl_memory(mean_data.data(), mean_mem); write_to_dnnl_memory(variance_data.data(), variance_mem); write_to_dnnl_memory(scale_data.data(), scale_mem); write_to_dnnl_memory(shift_data.data(), shift_mem); write_to_dnnl_memory(oscale_data.data(), oscale_mem); // Create operation descriptor. // dst_tmp = src - mean auto binary_d = binary::desc(algorithm::binary_sub, src_md, mean_md, src_md); // Bnorm operation with scale and shift post_ops binary_ops; // dst_tmp = dst_tmp / variance binary_ops.append_binary(algorithm::binary_div, variance_md); // dst_tmp = dst_tmp * scale binary_ops.append_binary(algorithm::binary_mul, scale_md); // dst_tmp = dst_tmp + shift binary_ops.append_binary(algorithm::binary_add, shift_md); // dst = dst_tmp * output_scale (only for re-quantization) binary_ops.append_binary(algorithm::binary_mul, oscale_md); 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. std::unordered_map<int, memory> binary_args; binary_args.insert({DNNL_ARG_SRC_0, src_mem}); binary_args.insert({DNNL_ARG_SRC_1, mean_mem}); // In-place mode (dst is src) binary_args.insert({DNNL_ARG_DST, src_mem}); binary_args.insert( {DNNL_ARG_ATTR_MULTIPLE_POST_OP(0) | DNNL_ARG_SRC_1, variance_mem}); binary_args.insert( {DNNL_ARG_ATTR_MULTIPLE_POST_OP(1) | DNNL_ARG_SRC_1, scale_mem}); binary_args.insert( {DNNL_ARG_ATTR_MULTIPLE_POST_OP(2) | DNNL_ARG_SRC_1, shift_mem}); binary_args.insert( {DNNL_ARG_ATTR_MULTIPLE_POST_OP(3) | DNNL_ARG_SRC_1, oscale_mem}); // Primitive execution 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_data.data(), src_mem); } int main(int argc, char **argv) { return handle_example_errors( bnorm_u8_via_binary_postops, parse_engine_kind(argc, argv)); }

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