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8c10545d3c
Fast gemm for einsum #24509 ## This PR adds performance tests for Einsum Layer with FastGemm. See below results of performance test on different inputs ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
110 lines
3.4 KiB
C++
110 lines
3.4 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "perf_precomp.hpp"
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namespace opencv_test {
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struct EinsumParams {
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int inputSize;
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int outputSize;
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std::string equation;
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std::vector<MatShape> einsumInpShapes;
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EinsumParams(std::string equation_, std::vector<MatShape> einsumInpShapes_ = std::vector<MatShape>())
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{
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inputSize = einsumInpShapes_.size();
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equation = equation_;
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einsumInpShapes = einsumInpShapes_;
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}
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};
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static inline void PrintTo(const EinsumParams& params, ::std::ostream* os) {
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(*os) << "Equation=" << params.equation << " ";
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(*os) << "InputShape={";
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for(int i = 0; i < params.einsumInpShapes.size(); i++)
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{
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(*os) << "{";
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for(int j = 0; j < params.einsumInpShapes[i].size(); j++)
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{
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(*os) << params.einsumInpShapes[i][j] << ((j < params.einsumInpShapes[i].size() - 1) ? ", " : "");
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}
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(*os) << ((i < params.einsumInpShapes.size() - 1) ? "}, " : "}");
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}
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(*os) << "}";
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}
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// test cases
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static const EinsumParams testEinsumConfigs[] = {
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// TODO: Add tests with one input after ellips merge
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{"ij, jk -> ik", {{2, 3}, {3, 2}}},
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{"ij, jk -> ik", {{20, 30}, {30, 20}}},
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{"ij, jk -> ik", {{113, 127}, {127, 113}}},
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{"imkj, injs -> imnks", {{1, 4, 7, 9}, {1, 5, 9, 8}}},
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{"imkj, injs -> imnks", {{1, 4, 70, 90}, {1, 5, 90, 80}}},
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{"imkj, injs -> imnks", {{1, 4, 73, 91}, {1, 5, 91, 57}}},
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{"ij -> i", {{30, 40}}},
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{"ij -> i", {{113, 374}}},
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{"...ij -> ...i", {{30, 40}}},
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{"...ij -> ...i", {{113, 374}}},
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{"...ij, ...jk -> ...ik", {{40, 50}, {50, 80}}},
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{"...ij, ...jk -> ...ik", {{47, 51}, {51, 83}}},
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};
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class Layer_Einsum: public TestBaseWithParam<EinsumParams> {};
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PERF_TEST_P_(Layer_Einsum, einsum) {
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const EinsumParams& params = GetParam();
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LayerParams lp;
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lp.type = "Einsum";
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lp.name = "testEinsum";
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lp.set("equation", params.equation);
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lp.set("inputSize", params.inputSize);
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lp.set("outputSize", 1);
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CV_CheckFalse(params.einsumInpShapes.empty(), "ERROR no inputs shapes provided");
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for (int i = 0; i < params.einsumInpShapes.size(); i++) {
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lp.set("inputShapes" + cv::format("%d", i), DictValue::arrayInt(params.einsumInpShapes[i].begin(), params.einsumInpShapes[i].size()));
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}
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Net net;
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std::vector<Mat> inputs;
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std::vector<std::string> input_names;
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int id = net.addLayer(lp.name, lp.type, lp);
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for (int i = 0; i < params.inputSize; ++i) {
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// create inputs
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inputs.emplace_back(Mat(params.einsumInpShapes[i].size(), params.einsumInpShapes[i].data(), CV_32FC1));
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// connect each input to the layer
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net.connect(0, i, id, i);
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// create input names dynamically, assuming input naming follows a consistent pattern
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input_names.emplace_back("input" + std::to_string(i + 1));
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}
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//warm up
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std::vector<Mat> outputs;
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net.setInputsNames(input_names);
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for (int i = 0; i < input_names.size(); i++){
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net.setInput(inputs[i], input_names[i]);
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}
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net.forward(outputs, "testEinsum");
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TEST_CYCLE()
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{
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net.forward(outputs, "testEinsum");
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}
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SANITY_CHECK_NOTHING();
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}
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Einsum, testing::ValuesIn(testEinsumConfigs));
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}; //namespace
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