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[CPU][ARM] Weights compression f32->f16 is moved to CPU Plug-in side
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194 changes: 194 additions & 0 deletions
194
src/plugins/intel_cpu/tests/functional/subgraph_tests/src/arm/matmul_compress_convert.cpp
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| // Copyright (C) 2023 Intel Corporation | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
| // | ||
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| #include <tuple> | ||
| #include <string> | ||
| #include <vector> | ||
| #include <memory> | ||
| #include <debug.h> | ||
| #include <shared_test_classes/base/ov_subgraph.hpp> | ||
| #include <ov_models/builders.hpp> | ||
| #include "common_test_utils/common_utils.hpp" | ||
| #include <common_test_utils/ov_tensor_utils.hpp> | ||
| #include "functional_test_utils/skip_tests_config.hpp" | ||
| #include "test_utils/cpu_test_utils.hpp" | ||
| #include "cpp_interfaces/interface/ie_internal_plugin_config.hpp" | ||
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| #include "test_utils/cpu_test_utils.hpp" | ||
| #include "test_utils/convolution_params.hpp" | ||
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| using namespace CPUTestUtils; | ||
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| namespace ov { | ||
| namespace test { | ||
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| using MatMulCompressConvertParams = std::tuple< | ||
| std::vector<InputShape>, // input shapes | ||
| std::pair<bool, bool>, // transposeA, transposeB | ||
| element::Type, // inference precision | ||
| CPUSpecificParams | ||
| >; | ||
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| class MatMulCompressConvertCPUTest: public testing::WithParamInterface<MatMulCompressConvertParams>, | ||
| virtual public SubgraphBaseTest, public CPUTestsBase { | ||
| public: | ||
| static std::string getTestCaseName(testing::TestParamInfo<MatMulCompressConvertParams> obj) { | ||
| std::vector<InputShape> inputShapes; | ||
| std::pair<bool, bool> transpose; | ||
| element::Type inferPrecision; | ||
| CPUSpecificParams cpuParams; | ||
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| std::tie(inputShapes, transpose, inferPrecision, cpuParams) = obj.param; | ||
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| std::ostringstream result; | ||
| for (const auto& shape : inputShapes) { | ||
| result << ov::test::utils::partialShape2str({shape.first}) << "_"; | ||
| } | ||
| result << "TS="; | ||
| for (const auto& shape : inputShapes) { | ||
| result << "("; | ||
| if (!shape.second.empty()) { | ||
| auto itr = shape.second.begin(); | ||
| do { | ||
| result << ov::test::utils::vec2str(*itr); | ||
| } while (++itr != shape.second.end() && result << "_"); | ||
| } | ||
| result << ")_"; | ||
| } | ||
| result << "transpose_a=" << transpose.first << "_"; | ||
| result << "transpose_b=" << transpose.second << "_"; | ||
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| result << "infer_precision=" << inferPrecision << "_"; | ||
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| result << CPUTestsBase::getTestCaseName(cpuParams); | ||
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| return result.str(); | ||
| } | ||
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| protected: | ||
| template<typename T> | ||
| void transposeShape(T& shape) { | ||
| IE_ASSERT(shape.size() > 1); | ||
| std::swap(*(shape.end() - 1), *(shape.end() - 2)); | ||
| } | ||
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| void CheckFCWeightsPrecision(element::Type expectedWeiElemType) const { | ||
| auto getExecValue = [](const ov::Node::RTMap& rtInfo, const std::string ¶mName) -> std::string { | ||
| auto it = rtInfo.find(paramName); | ||
| IE_ASSERT(rtInfo.end() != it); | ||
| return it->second.as<std::string>(); | ||
| }; | ||
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| const auto execFunction = compiledModel.get_runtime_model(); | ||
| ASSERT_NE(nullptr, execFunction); | ||
| for (const auto &fcNode : execFunction->get_ops()) { | ||
| if (getExecValue(fcNode->get_rt_info(), ExecGraphInfoSerialization::LAYER_TYPE) == "FullyConnected") { | ||
| const auto &constNode = fcNode->get_input_node_shared_ptr(1); | ||
| element::Type expectedType(getExecValue(constNode->get_rt_info(), ExecGraphInfoSerialization::OUTPUT_PRECISIONS)); | ||
| ASSERT_EQ(expectedType, expectedWeiElemType); | ||
| } | ||
| } | ||
| } | ||
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| void SetUp() override { | ||
| targetDevice = ov::test::utils::DEVICE_CPU; | ||
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| std::vector<InputShape> inputShapes; | ||
| std::pair<bool, bool> transpose; | ||
| element::Type inferPrecision; | ||
| CPUSpecificParams cpuParams; | ||
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| std::tie(inputShapes, transpose, inferPrecision, cpuParams) = this->GetParam(); | ||
| std::tie(inFmts, outFmts, priority, selectedType) = cpuParams; | ||
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| init_input_shapes(inputShapes); | ||
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| bool transpA = transpose.first; | ||
| bool transpB = transpose.second; | ||
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| if (transpA) { | ||
| transposeShape(inputDynamicShapes[0]); | ||
| for (auto& shapes : targetStaticShapes) { | ||
| transposeShape(shapes[0]); | ||
| } | ||
| } | ||
| if (transpB) { | ||
| transposeShape(inputDynamicShapes[1]); | ||
| for (auto& shapes : targetStaticShapes) { | ||
| transposeShape(shapes[1]); | ||
| } | ||
| } | ||
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| if (inferPrecision == element::f16) { | ||
| convertCount = 2; // convert f32->f16 on the activation input and convert f16->f32 on the output | ||
| } | ||
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| const auto& inShapeA = inputDynamicShapes[0]; | ||
| const auto& inShapeB = inputDynamicShapes[1]; | ||
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| configuration.emplace(ov::hint::inference_precision(inferPrecision)); | ||
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| element::Type netType = element::f32; | ||
| inType = outType = netType; | ||
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| std::string cpuNodeType = "FullyConnected"; | ||
| selectedType = makeSelectedTypeStr(selectedType, outType); | ||
| ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(inType, inShapeA)}; | ||
| auto tensor = ov::test::utils::create_and_fill_tensor(element::f32, inShapeB.get_shape()); | ||
| std::shared_ptr<Node> inputB = std::make_shared<ov::op::v0::Constant>(tensor); | ||
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| auto matMul = std::make_shared<ov::op::v0::MatMul>(params[0], inputB, transpA, transpB); | ||
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| function = CPUTestsBase::makeNgraphFunction(netType, params, matMul, cpuNodeType); | ||
| } | ||
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| void CheckExecutionGraph() { | ||
| CheckNumberOfNodesWithType(compiledModel, "FullyConnected", 1); | ||
| CheckNumberOfNodesWithType(compiledModel, "Convert", convertCount); | ||
| CheckFCWeightsPrecision(element::f32); | ||
| } | ||
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| size_t convertCount = 0; | ||
| }; | ||
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| TEST_P(MatMulCompressConvertCPUTest, CompareWithRefs) { | ||
| SKIP_IF_CURRENT_TEST_IS_DISABLED(); | ||
| run(); | ||
| CheckExecutionGraph(); | ||
| } | ||
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| namespace { | ||
|
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| const std::vector<std::pair<bool, bool>> transposeParams = { | ||
| {false, true}, | ||
| }; | ||
|
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| const std::vector<std::vector<InputShape>> inputShapes2D = { | ||
| static_shapes_to_test_representation({{2, 3}, {3, 4}}), | ||
| { | ||
| {{-1, -1}, {{2, 3}, {5, 3}}}, | ||
| {{3, 4}, {{3, 4}, {3, 4}}} | ||
| }, | ||
| }; | ||
|
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| const std::vector<element::Type> inferPrecisions = { | ||
| element::f32, | ||
| #if defined(OV_CPU_ARM_ENABLE_FP16) | ||
| element::f16, | ||
| #endif | ||
| }; | ||
|
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| const auto testParams2D_ARM_smoke = ::testing::Combine( | ||
| ::testing::ValuesIn(inputShapes2D), | ||
| ::testing::ValuesIn(transposeParams), | ||
| ::testing::ValuesIn(inferPrecisions), | ||
| ::testing::Values(CPUSpecificParams{})); | ||
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| INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_ARM, MatMulCompressConvertCPUTest, testParams2D_ARM_smoke, | ||
| MatMulCompressConvertCPUTest::getTestCaseName); | ||
|
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| } // namespace | ||
|
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| } // namespace test | ||
| } // namespace ov |
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