.. index:: pair: page; Group Normalization Primitive Example .. _doxid-group_normalization_example_cpp: Group Normalization Primitive Example ===================================== This C++ API example demonstrates how to create and execute a :ref:`Group Normalization ` primitive in forward training propagation mode. Key optimizations included in this example: * In-place primitive execution; * Source memory format for an optimized primitive implementation; .. ref-code-block:: cpp /******************************************************************************* * Copyright 2023-2025 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 #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace :ref:`dnnl `; void group_normalization_example(:ref:`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 = 6, // batch size IC = 256, // channels ID = 20, // tensor depth IH = 28, // tensor height IW = 28; // tensor width // Normalization groups :ref:`memory::dim ` groups = IC; // Instance normalization // Source (src) and destination (dst) tensors dimensions. :ref:`memory::dims ` src_dims = {N, IC, ID, IH, IW}; // Scale/shift tensor dimensions. :ref:`memory::dims ` scaleshift_dims = {IC}; // Allocate buffers. std::vector src_data(product(src_dims)); std::vector scale_data(product(scaleshift_dims)); std::vector shift_data(product(scaleshift_dims)); // Initialize src. std::generate(src_data.begin(), src_data.end(), []() { static int i = 0; return std::cos(i++ / 10.f); }); // Initialize scale. std::generate(scale_data.begin(), scale_data.end(), []() { static int i = 0; return std::sin(i++ * 2.f); }); // Initialize shift. std::generate(shift_data.begin(), shift_data.end(), []() { static int i = 0; return std::tan(float(i++)); }); // Create src and scale/shift memory descriptors and memory objects. auto :ref:`src_md ` = :ref:`memory::desc `( src_dims, :ref:`memory::data_type::f32 `, :ref:`memory::format_tag::ncdhw `); auto :ref:`dst_md ` = :ref:`memory::desc `( src_dims, :ref:`memory::data_type::f32 `, :ref:`memory::format_tag::ncdhw `); auto scaleshift_md = :ref:`memory::desc `( scaleshift_dims, :ref:`memory::data_type::f32 `, :ref:`memory::format_tag::x `); auto src_mem = :ref:`memory `(src_md, :ref:`engine `); auto scale_mem = :ref:`memory `(scaleshift_md, :ref:`engine `); auto shift_mem = :ref:`memory `(scaleshift_md, :ref:`engine `); // Write data to memory object's handle. write_to_dnnl_memory(src_data.data(), src_mem); write_to_dnnl_memory(scale_data.data(), scale_mem); write_to_dnnl_memory(shift_data.data(), shift_mem); // Create primitive descriptor. auto gnorm_pd = :ref:`group_normalization_forward::primitive_desc `(:ref:`engine `, :ref:`prop_kind::forward_training `, src_md, dst_md, groups, 1.e-10f, :ref:`normalization_flags::use_scale ` | :ref:`normalization_flags::use_shift `); // Create memory objects using memory descriptors created by the primitive // descriptor: mean, variance. auto mean_mem = :ref:`memory `(gnorm_pd.mean_desc(), :ref:`engine `); auto variance_mem = :ref:`memory `(gnorm_pd.variance_desc(), :ref:`engine `); // Create the primitive. auto gnorm_prim = :ref:`group_normalization_forward `(gnorm_pd); // Primitive arguments. Set up in-place execution by assigning src as DST. std::unordered_map gnorm_args; gnorm_args.insert({:ref:`DNNL_ARG_SRC `, src_mem}); gnorm_args.insert({:ref:`DNNL_ARG_MEAN `, mean_mem}); gnorm_args.insert({:ref:`DNNL_ARG_VARIANCE `, variance_mem}); gnorm_args.insert({:ref:`DNNL_ARG_SCALE `, scale_mem}); gnorm_args.insert({:ref:`DNNL_ARG_SHIFT `, shift_mem}); gnorm_args.insert({:ref:`DNNL_ARG_DST `, src_mem}); // Primitive execution: group normalization. gnorm_prim.execute(engine_stream, gnorm_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) { auto engine_kind = parse_engine_kind(argc, argv); return handle_example_errors(group_normalization_example, engine_kind); }