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Merge pull request #24509 from Abdurrahheem:ash/dev_einsum_fast_gemm
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
This commit is contained in:
Abduragim Shtanchaev
GitHub
parent
83d70b0f36
commit
8c10545d3c
@ -11,19 +11,16 @@ struct EinsumParams {
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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_, int inputSize_, int outputSize_, std::vector<MatShape> einsumInpShapes_ = std::vector<MatShape>())
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EinsumParams(std::string equation_, std::vector<MatShape> einsumInpShapes_ = std::vector<MatShape>())
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{
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inputSize = inputSize_;
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outputSize = outputSize_;
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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) << "Eqiation=" << params.equation << ", "
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<< "InputSize=" << params.inputSize << ", "
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<< "OutputSize=" << params.outputSize << ", ";
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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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@ -41,22 +38,22 @@ static inline void PrintTo(const EinsumParams& params, ::std::ostream* os) {
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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, 1, {{2, 3}, {3, 2}}},
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{"ij, jk -> ik", 2, 1, {{20, 30}, {30, 20}}},
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{"ij, jk -> ik", 2, 1, {{113, 127}, {127, 113}}},
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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", 2, 1, {{1, 4, 7, 9}, {1, 5, 9, 8}}},
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{"imkj, injs -> imnks", 2, 1, {{1, 4, 70, 90}, {1, 5, 90, 80}}},
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{"imkj, injs -> imnks", 2, 1, {{1, 4, 73, 91}, {1, 5, 91, 57}}},
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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", 1, 1, {{30, 40}}},
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{"ij -> i", 1, 1, {{113, 374}}},
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{"ij -> i", {{30, 40}}},
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{"ij -> i", {{113, 374}}},
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{"...ij -> ...i", 1, 1, {{30, 40}}},
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{"...ij -> ...i", 1, 1, {{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", 2, 1, {{40, 50}, {50, 80}}},
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{"...ij, ...jk -> ...ik", 2, 1, {{47, 51}, {51, 83}}},
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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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@ -68,7 +65,7 @@ PERF_TEST_P_(Layer_Einsum, 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", params.outputSize);
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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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@ -79,38 +76,27 @@ PERF_TEST_P_(Layer_Einsum, einsum) {
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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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if (params.inputSize == 1){
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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[0].size(), params.einsumInpShapes[0].data(), CV_32FC1));
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inputs.emplace_back(Mat(params.einsumInpShapes[i].size(), params.einsumInpShapes[i].data(), CV_32FC1));
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 0, id, 0);
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// connect each input to the layer
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net.connect(0, i, id, i);
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input_names.emplace_back("input1");
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} else {
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// create inputs
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inputs.emplace_back(Mat(params.einsumInpShapes[0].size(), params.einsumInpShapes[0].data(), CV_32FC1));
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inputs.emplace_back(Mat(params.einsumInpShapes[1].size(), params.einsumInpShapes[1].data(), CV_32FC1));
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 0, id, 0);
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net.connect(0, 1, id, 1);
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input_names.emplace_back("input1");
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input_names.emplace_back("input2");
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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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Mat out = net.forward();
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net.forward(outputs, "testEinsum");
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std::vector<Mat> outputs;
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TEST_CYCLE()
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{
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net.forward(outputs, "testEinsum");
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@ -6,6 +6,7 @@
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#include <opencv2/dnn/shape_utils.hpp>
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#include "../precomp.hpp"
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#include "layers_common.hpp"
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#include "cpu_kernels/fast_gemm.hpp"
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namespace cv
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{
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@ -32,111 +33,6 @@ static bool IsTransposeReshapeForEinsum(const std::vector<size_t>& perm,
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return true;
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}
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static Mat batchwiseMatMul(
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const Mat& input1,
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const MatShape& input1ShapeOverride,
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const Mat& input2,
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const MatShape& input2ShapeOverride)
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{
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// Sanity checks before the actual MatMul
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CV_CheckType(input1.type(), input2.type(), "Data types of the inputs must match for MatMul");
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CV_CheckEQ(input1ShapeOverride.size(), (size_t) 3, "Only 1 batch dimension is allowed for MatMul");
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CV_CheckEQ(input2ShapeOverride.size(), (size_t) 3, "Only 1 batch dimension is allowed for MatMul");
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CV_CheckEQ((size_t) input1ShapeOverride[0], (size_t) input2ShapeOverride[0], "Batch dimension should match for MatMul;");
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CV_CheckEQ((size_t) input1ShapeOverride[2], (size_t) input2ShapeOverride[1], "Incompatible matrix dimensions for matMul");
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size_t batches = input1ShapeOverride[0];
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size_t M = input1ShapeOverride[1];
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size_t K = input1ShapeOverride[2];
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size_t N = input2ShapeOverride[2];
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std::vector<Mat> output;
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if (batches > 1)
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{
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Mat reshapedInput1 = input1;
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Mat reshapedInput2 = input2;
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// input1 should of size MxK
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// check if input1 needs reshape, if need reshape
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if (input1.size[0] != M || input1.size[1] != K)
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{
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int shape[] = {static_cast<int>(batches), static_cast<int>(M), static_cast<int>(K)};
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reshapedInput1 = input1.reshape(1, 3, shape);
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}
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// input2 should be of size KxN
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// check if input2 needs reshape, if needs reshape
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if (input2.size[0] != K || input2.size[1] != N)
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{
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int shape[] = {static_cast<int>(batches), static_cast<int>(K), static_cast<int>(N)};
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reshapedInput2 = input2.reshape(1, 3, shape);
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}
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for (size_t i=0; i < batches; i++)
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{
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std::vector<Range> ranges1 = {cv::Range(i, i+1)};
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for (int j = 1; j < reshapedInput1.dims; j++)
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ranges1.emplace_back(cv::Range::all());
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Mat part1 = reshapedInput1(ranges1);
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int shape[] = {static_cast<int>(M), static_cast<int>(K)};
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part1 = part1.reshape(1, sizeof(shape)/sizeof(shape[0]), shape);
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std::vector<Range> ranges2 = {cv::Range(i, i+1)};
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for (int j = 1; j < reshapedInput2.dims; j++)
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ranges2.emplace_back(cv::Range::all());
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Mat part2 = reshapedInput2(ranges2);
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int shape2[] = {static_cast<int>(K), static_cast<int>(N)};
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part2 = part2.reshape(1, sizeof(shape2)/sizeof(shape2[0]), shape2);
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Mat tmp_output;
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cv::gemm(part1, part2, 1.0, cv::Mat(), 1.0, tmp_output);
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int newShape[] = {1, static_cast<int>(M), static_cast<int>(N)};
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tmp_output = tmp_output.reshape(1, sizeof(newShape)/sizeof(newShape[0]), newShape);
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output.emplace_back(tmp_output);
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}
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} else {
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Mat reshapedInput1 = input1;
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Mat reshapedInput2 = input2;
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// input1 should of size MxK
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// check if input1 needs reshape, if need reshape
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if (input1.dims > 2 || input1.size[0] != M || input1.size[1] != K)
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{
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int shape[] = {static_cast<int>(M), static_cast<int>(K)};
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reshapedInput1 = input1.reshape(1, 2, shape);
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}
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// input2 should be of size KxN
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// check if input2 needs reshape, if needs reshape
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if (input2.dims > 2 || input2.size[0] != K || input2.size[1] != N)
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{
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int shape2[] = {static_cast<int>(K), static_cast<int>(N)};
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reshapedInput2 = input2.reshape(1, 2, shape2);
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}
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Mat tmp_output;
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cv::gemm(reshapedInput1, reshapedInput2, 1.0, cv::Mat(), 1.0, tmp_output);
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int newShape[] = {1, static_cast<int>(M), static_cast<int>(N)};
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tmp_output = tmp_output.reshape(1, sizeof(newShape)/sizeof(newShape[0]), newShape);
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output.emplace_back(tmp_output);
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}
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int outputDim[] = {static_cast<int>(output.size()), static_cast<int>(M), static_cast<int>(N)};
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Mat output_buffer = Mat::zeros(3, outputDim, CV_32F);
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for (size_t i = 0; i < output.size(); i++) {
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Mat output_slice = output_buffer.row(i);
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output[i].copyTo(output_slice);
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}
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return output_buffer;
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};
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static Mat Transpose(
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const Mat& input,
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@ -452,6 +348,8 @@ public:
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// The number of dimensions that are encompassed by an "ellipsis" - "...".
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size_t numOfEllipsisDims = 0;
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// Backend for fastgemm
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FastGemmOpt opt;
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void parseEquation(String equation);
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void processEquation(const std::vector<MatShape>& inputs);
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@ -469,7 +367,12 @@ public:
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const MatShape& reduceDims,
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bool isFinalPair
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);
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Mat batchwiseMatMul(
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const Mat& input1,
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const MatShape& input1ShapeOverride,
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const Mat& input2,
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const MatShape& input2ShapeOverride
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);
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// constructor
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LayerEinsumImpl(const LayerParams& params)
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@ -491,6 +394,7 @@ public:
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einsumInpShapes.emplace_back(shape);
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}
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opt.init();
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// Maintains a mapping between input indices and their corresponding subscript labels for each input
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inputSubscriptIndices.reserve(numInputs);
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@ -1389,6 +1293,112 @@ Mat LayerEinsumImpl::pairwiseOperandProcess(
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return output;
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};
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Mat LayerEinsumImpl::batchwiseMatMul(
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const Mat& input1,
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const MatShape& input1ShapeOverride,
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const Mat& input2,
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const MatShape& input2ShapeOverride)
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{
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// Sanity checks before the actual MatMul
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CV_CheckType(input1.type(), input2.type(), "Data types of the inputs must match for MatMul");
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CV_CheckEQ(input1ShapeOverride.size(), (size_t) 3, "Only 1 batch dimension is allowed for MatMul");
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CV_CheckEQ(input2ShapeOverride.size(), (size_t) 3, "Only 1 batch dimension is allowed for MatMul");
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CV_CheckEQ((size_t) input1ShapeOverride[0], (size_t) input2ShapeOverride[0], "Batch dimension should match for MatMul;");
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CV_CheckEQ((size_t) input1ShapeOverride[2], (size_t) input2ShapeOverride[1], "Incompatible matrix dimensions for matMul");
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int batches = input1ShapeOverride[0];
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int M = input1ShapeOverride[1];
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int K = input1ShapeOverride[2];
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int N = input2ShapeOverride[2];
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std::vector<Mat> output;
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if (batches > 1)
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{
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Mat reshapedInput1 = input1;
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Mat reshapedInput2 = input2;
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// input1 should of size MxK
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// check if input1 needs reshape, if need reshape
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if (input1.size[0] != M || input1.size[1] != K)
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{
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int shape[] = {batches, M, K};
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reshapedInput1 = input1.reshape(1, 3, shape);
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}
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// input2 should be of size KxN
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// check if input2 needs reshape, if needs reshape
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if (input2.size[0] != K || input2.size[1] != N)
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{
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int shape[] = {batches, K, N};
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reshapedInput2 = input2.reshape(1, 3, shape);
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}
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for (size_t i=0; i < batches; i++)
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{
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std::vector<Range> ranges1 = {cv::Range(i, i+1)};
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for (int j = 1; j < reshapedInput1.dims; j++)
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ranges1.emplace_back(cv::Range::all());
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Mat part1 = reshapedInput1(ranges1);
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int shape[] = {M, K};
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part1 = part1.reshape(1, sizeof(shape)/sizeof(shape[0]), shape);
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std::vector<Range> ranges2 = {cv::Range(i, i+1)};
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for (int j = 1; j < reshapedInput2.dims; j++)
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ranges2.emplace_back(cv::Range::all());
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Mat part2 = reshapedInput2(ranges2);
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int shape2[] = {K, N};
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part2 = part2.reshape(1, sizeof(shape2)/sizeof(shape2[0]), shape2);
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Mat tmp_output(M, N, part1.type());
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fastGemm(false, false, 1.0, part1, part2, 0.0, tmp_output, opt);
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int newShape[] = {1, M, N};
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tmp_output = tmp_output.reshape(1, sizeof(newShape)/sizeof(newShape[0]), newShape);
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output.emplace_back(tmp_output);
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}
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} else {
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Mat reshapedInput1 = input1;
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Mat reshapedInput2 = input2;
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// input1 should of size MxK
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// check if input1 needs reshape, if need reshape
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if (input1.dims > 2 || input1.size[0] != M || input1.size[1] != K)
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{
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int shape[] = {M, K};
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reshapedInput1 = input1.reshape(1, 2, shape);
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}
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// input2 should be of size KxN
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// check if input2 needs reshape, if needs reshape
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if (input2.dims > 2 || input2.size[0] != K || input2.size[1] != N)
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{
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int shape2[] = {K, N};
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reshapedInput2 = input2.reshape(1, 2, shape2);
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}
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Mat tmp_output(M, N, reshapedInput1.type());
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fastGemm(false, false, 1.0, reshapedInput1, reshapedInput2, 0.0, tmp_output, opt);
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int newShape[] = {1, M, N};
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tmp_output = tmp_output.reshape(1, sizeof(newShape)/sizeof(newShape[0]), newShape);
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output.emplace_back(tmp_output);
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}
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int outputDim[] = {static_cast<int>(output.size()), M, N};
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Mat output_buffer = Mat::zeros(3, outputDim, CV_32F);
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for (size_t i = 0; i < output.size(); i++) {
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Mat output_slice = output_buffer.row(i);
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output[i].copyTo(output_slice);
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}
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return output_buffer;
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};
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Ptr<EinsumLayer> EinsumLayer::create(const LayerParams& params)
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{
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return makePtr<LayerEinsumImpl>(params);
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