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118 lines
3.7 KiB
C++
118 lines
3.7 KiB
C++
/*******************************************************************************
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* Copyright 2020-2025 Intel Corporation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*******************************************************************************/
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/// @example concat.cpp
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/// > Annotated version: @ref concat_example_cpp
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/// @page concat_example_cpp_brief
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/// @brief This C++ API example demonstrates how to create and execute a
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/// [Concat](@ref dev_guide_concat) primitive.
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/// @page concat_example_cpp Concat Primitive Example
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/// \copybrief concat_example_cpp_brief
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///
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/// Key optimizations included in this example:
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/// - Identical source (src) memory formats.
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/// - Creation of optimized memory format for destination (dst) from the
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/// primitive descriptor
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///
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/// @include concat.cpp
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <string>
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#include <vector>
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#include "example_utils.hpp"
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#include "oneapi/dnnl/dnnl.hpp"
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using namespace dnnl;
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void concat_example(dnnl::engine::kind engine_kind) {
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// Create execution dnnl::engine.
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dnnl::engine engine(engine_kind, 0);
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// Create dnnl::stream.
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dnnl::stream engine_stream(engine);
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// Tensor dimensions.
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const memory::dim N = 3, // batch size
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IC = 3, // channels
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IH = 120, // tensor height
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IW = 120; // tensor width
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// Number of source (src) tensors.
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const int num_src = 10;
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// Concatenation axis.
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const int axis = 1;
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// src tensors dimensions
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memory::dims src_dims = {N, IC, IH, IW};
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// Allocate buffers.
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std::vector<float> src_data(product(src_dims));
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// Initialize src.
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// NOTE: In this example, the same src memory buffer is used to demonstrate
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// concatenation for simplicity
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std::generate(src_data.begin(), src_data.end(), []() {
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static int i = 0;
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return std::cos(i++ / 10.f);
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});
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// Create a memory descriptor and memory object for each src tensor.
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std::vector<memory::desc> src_mds;
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std::vector<memory> src_mems;
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for (int n = 0; n < num_src; ++n) {
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src_mds.emplace_back(
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src_dims, memory::data_type::f32, memory::format_tag::nchw);
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src_mems.emplace_back(src_mds.back(), engine);
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// Write data to memory object's handle.
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write_to_dnnl_memory(src_data.data(), src_mems.back());
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}
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// Create primitive descriptor.
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auto concat_pd = concat::primitive_desc(engine, axis, src_mds);
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// Create destination (dst) memory object using the memory descriptor
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// created by the primitive.
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auto dst_mem = memory(concat_pd.dst_desc(), engine);
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// Create the primitive.
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auto concat_prim = concat(concat_pd);
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// Primitive arguments.
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std::unordered_map<int, memory> concat_args;
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for (int n = 0; n < num_src; ++n)
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concat_args.insert({DNNL_ARG_MULTIPLE_SRC + n, src_mems[n]});
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concat_args.insert({DNNL_ARG_DST, dst_mem});
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// Primitive execution: concatenation.
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concat_prim.execute(engine_stream, concat_args);
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// Wait for the computation to finalize.
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engine_stream.wait();
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}
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int main(int argc, char **argv) {
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return handle_example_errors(concat_example, parse_engine_kind(argc, argv));
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}
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