[CPU] Fixed zero-point fusion transformation (#18435)
Proper hanlding for subgraphs where ZP parent node has more than one output port
This commit is contained in:
Edward Shogulin
GitHub
parent
510f578aab
commit
38dec7b8cf
@ -756,8 +756,10 @@ void GraphOptimizer::FuseConvolutionAndZeroPoints(Graph &graph) {
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return false;
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}
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auto subtractArg0 = parent0->getParentEdgesAtPort(0)[0]->getParent();
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if (subtractArg0->getOriginalOutputPrecisionAtPort(0) != Precision::U8)
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const auto& parentEdge = parent0->getParentEdgeAt(0);
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const auto& subtractArg0 = parentEdge->getParent();
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const size_t portNum = parentEdge->getInputNum();
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if (subtractArg0->getOriginalOutputPrecisionAtPort(portNum) != Precision::U8)
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return false;
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auto zeroPointsConstant = dynamic_cast<node::Input*>(subtractArg1.get());
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@ -0,0 +1,56 @@
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// Copyright (C) 2018-2023 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
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#include <tuple>
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#include <vector>
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#include <string>
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#include "test_utils/cpu_test_utils.hpp"
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#include "shared_test_classes/base/layer_test_utils.hpp"
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#include "ngraph_functions/utils/ngraph_helpers.hpp"
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#include "ngraph_functions/builders.hpp"
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using namespace CPUTestUtils;
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namespace SubgraphTestsDefinitions {
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using convConcatCPUParams = std::tuple<
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nodeType, // Ngraph convolution type
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InferenceEngine::SizeVector // Input shapes
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>;
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// Subgraph:
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/*
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* Paramter Constant
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* | | i8
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* | |
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* FakeQuantise Convert
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* / \ | f32
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* / \ |
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* MaxPool FakeQuantize Mulltiply
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* \ \ /
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* \ \ /
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* \ Convolution
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* \ /
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* \ /
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* Concat
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* |
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* |
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* Result
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*/
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class ConvWithZeroPointFuseSubgraphTest : public testing::WithParamInterface<convConcatCPUParams>,
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public CPUTestsBase,
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virtual public LayerTestsUtils::LayerTestsCommon {
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public:
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static std::string getTestCaseName(testing::TestParamInfo<convConcatCPUParams> obj);
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protected:
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void SetUp() override;
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std::string pluginTypeNode;
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};
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} // namespace SubgraphTestsDefinitions
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@ -0,0 +1,152 @@
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// Copyright (C) 2018-2023 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "ngraph/opsets/opset1.hpp"
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#include "test_utils/convolution_params.hpp"
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#include "subgraph_tests/include/conv_with_zero_point_fuse.hpp"
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using namespace InferenceEngine;
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using namespace CPUTestUtils;
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namespace SubgraphTestsDefinitions {
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std::string ConvWithZeroPointFuseSubgraphTest::getTestCaseName(testing::TestParamInfo<convConcatCPUParams> obj) {
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std::ostringstream result;
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nodeType type;
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SizeVector inputShapes;
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std::tie(type, inputShapes) = obj.param;
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result << "Type=" << nodeType2str(type) << "_";
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result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
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return result.str();
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}
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void ConvWithZeroPointFuseSubgraphTest::SetUp() {
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targetDevice = CommonTestUtils::DEVICE_CPU;
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nodeType type;
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SizeVector inputShapes;
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std::tie(type, inputShapes) = this->GetParam();
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pluginTypeNode = nodeType2PluginType(type);
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const ngraph::op::PadType paddingType { ngraph::op::PadType::EXPLICIT };
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const size_t numOutChannels = 256;
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const SizeVector dilation { 1, 1 };
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const SizeVector kernelSize { 1, 1 };
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const SizeVector strides { 1, 1 };
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const std::vector<ptrdiff_t> padBegin { 0, 0 };
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const std::vector<ptrdiff_t> padEnd { 0, 0 };
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selectedType = ".*_I8";
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auto inputParams = ngraph::builder::makeParams(ngraph::element::f32, {inputShapes});
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const auto fq = ngraph::builder::makeFakeQuantize(
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inputParams[0],
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ov::element::f32,
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256,
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{1, 1, 1, 1},
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{-12.8f},
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{12.7f},
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{-12.8f},
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{12.7f});
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auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(inputParams));
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std::vector<std::shared_ptr<ngraph::Node>> branches(2);
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{
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ngraph::Strides strides{1, 1};
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ngraph::Shape pads_begin{0, 0}, pads_end{0, 0}, kernel{1, 1};
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branches[0] = std::make_shared<ngraph::opset1::MaxPool>(fq,
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strides,
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pads_begin,
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pads_end,
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kernel);
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}
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{
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const auto fq_conv_data = ngraph::builder::makeFakeQuantize(
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fq,
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ov::element::f32,
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256,
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{1, 1, 1, 1},
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{-12.8f},
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{12.7f},
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{-12.8f},
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{12.7f});
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const InferenceEngine::SizeVector weights_const_shape = {numOutChannels, inputShapes[1], kernelSize[0], kernelSize[1]};
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const auto weights_const_values = std::vector<int>(ngraph::shape_size(weights_const_shape), 1);
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const auto weights_const = ngraph::builder::makeConstant(ov::element::i8, weights_const_shape, weights_const_values);
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const auto weights_convert = ngraph::builder::makeConversion(
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weights_const,
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ov::element::f32,
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ngraph::helpers::ConversionTypes::CONVERT);
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const auto weights_multiply = std::make_shared<ov::opset10::Multiply>(
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weights_convert,
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ngraph::builder::makeConstant(ov::element::f32,
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{numOutChannels, 1, 1, 1},
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std::vector<float>(numOutChannels, 1.0)));
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switch (type) {
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case nodeType::convolution: {
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branches[1] = ngraph::builder::makeConvolution(fq_conv_data,
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weights_multiply,
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ngraph::element::f32,
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kernelSize,
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strides,
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padBegin,
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padEnd,
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dilation,
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paddingType,
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numOutChannels);
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break;
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}
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case nodeType::groupConvolution: {
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branches[1] = ngraph::builder::makeGroupConvolution(
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fq_conv_data,
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std::make_shared<ov::opset10::Reshape>(
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weights_multiply,
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ngraph::builder::makeConstant(
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ov::element::i32,
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{5},
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std::vector<size_t>{1, numOutChannels, inputShapes[1], kernelSize[0], kernelSize[1]}),
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true),
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ngraph::element::f32,
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strides,
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padBegin,
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padEnd,
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dilation,
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paddingType);
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break;
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}
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default: {
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throw std::runtime_error("Subgraph concat test doesn't support this type of operation");
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}
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}
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}
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auto concat = ngraph::builder::makeConcat(ngraph::OutputVector{branches[0], branches[1]}, 1);
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ngraph::ResultVector results{std::make_shared<ngraph::opset4::Result>(concat)};
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function = std::make_shared<ngraph::Function>(results, inputParams, "ConvWithZeroPointFuseSubgraphTest");
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}
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TEST_P(ConvWithZeroPointFuseSubgraphTest, CompareWithRefs) {
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Run();
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CheckPluginRelatedResults(executableNetwork, pluginTypeNode);
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};
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const SizeVector inputShapes2D = {1, 32, 136, 136};
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const auto params2DConv = ::testing::Combine(::testing::ValuesIn({nodeType::convolution, nodeType::groupConvolution}),
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::testing::Values(inputShapes2D));
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INSTANTIATE_TEST_SUITE_P(smoke_ConvWithZeroPointFuse,
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ConvWithZeroPointFuseSubgraphTest,
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params2DConv,
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ConvWithZeroPointFuseSubgraphTest::getTestCaseName);
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} // namespace SubgraphTestsDefinitions
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@ -224,7 +224,7 @@ void CPUTestsBase::CheckPluginRelatedResultsImpl(const std::shared_ptr<const ov:
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}
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bool CPUTestsBase::primTypeCheck(std::string primType) const {
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return selectedType.find(CPUTestsBase::any_type) != std::string::npos || selectedType == primType;
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return selectedType.find(CPUTestsBase::any_type) != std::string::npos || std::regex_match(primType, std::regex(selectedType));
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}
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std::string CPUTestsBase::getTestCaseName(CPUSpecificParams params) {
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@ -96,6 +96,19 @@ std::shared_ptr<ngraph::Node> makeConvolution(const ngraph::Output<Node> &in,
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const std::vector<float> &filterWeights = {},
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const std::vector<float> &biasesWeights = {});
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std::shared_ptr<ngraph::Node> makeConvolution(const ngraph::Output<Node>& in_data,
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const ngraph::Output<Node>& in_weights,
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const element::Type& type,
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const std::vector<size_t>& filterSize,
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const std::vector<size_t>& strides,
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const std::vector<ptrdiff_t>& padsBegin,
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const std::vector<ptrdiff_t>& padsEnd,
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const std::vector<size_t>& dilations,
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const op::PadType& autoPad,
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size_t numOutChannels,
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bool addBiases = false,
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const std::vector<float>& biasesWeights = {});
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std::shared_ptr<ngraph::Node> makeGroupConvolution(const ngraph::Output<Node> &in,
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const element::Type &type,
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const std::vector<size_t> &filterSize,
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@ -39,5 +39,35 @@ std::shared_ptr<Node> makeConvolution(const ngraph::Output<Node> &in,
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}
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}
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std::shared_ptr<Node> makeConvolution(const ngraph::Output<Node>& in_data,
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const ngraph::Output<Node>& in_weights,
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const element::Type &type,
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const std::vector<size_t> &filterSize,
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const std::vector<size_t> &strides,
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const std::vector<ptrdiff_t> &padsBegin,
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const std::vector<ptrdiff_t> &padsEnd,
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const std::vector<size_t> &dilations,
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const op::PadType &autoPad,
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size_t numOutChannels,
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bool addBiases,
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const std::vector<float> &biasesWeights) {
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auto shape = in_data.get_partial_shape();
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auto conv = std::make_shared<opset1::Convolution>(in_data,
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in_weights,
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strides,
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padsBegin,
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padsEnd,
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dilations,
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autoPad);
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if (addBiases) {
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bool randomBiases = biasesWeights.empty();
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auto biasesWeightsNode = makeConstant(type, {1, numOutChannels , 1, 1}, biasesWeights, randomBiases);
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auto add = std::make_shared<ngraph::opset1::Add>(conv, biasesWeightsNode);
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return add;
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} else {
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return conv;
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}
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}
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} // namespace builder
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} // namespace ngraph
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