fix reduce perferred format selection and layout for partial shape (#19319)
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Wilson Seok
GitHub
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f6dca869b2
@ -781,7 +781,7 @@ void reorder_inputs::run(program& p, layout_optimizer& lo, reorder_factory& rf)
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auto new_format = lo.get_preferred_format(deconv_node);
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if (new_format == format::b_fs_zyx_fsv16 || new_format == format::bs_fs_zyx_bsv16_fsv16) {
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auto reorder = rf.get_reorder(input.id(), input_layout,
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layout{ input_layout.data_type, new_format, input_layout.get_tensor() });
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layout{ input_layout.get_partial_shape(), input_layout.data_type, new_format });
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if (reorder.first) {
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p.add_intermediate(reorder.first, deconv_node, 0, !reorder.second);
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}
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@ -1735,11 +1735,11 @@ format layout_optimizer::get_preferred_format(program_node& node) {
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}
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} else if (node.is_type<reduce>()) {
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auto& reduce_node = node.as<reduce>();
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auto input_layout = reduce_node.input().get_output_layout();
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if (!use_onednn_impls && input_layout.is_dynamic()) {
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if (input_layout.format.dimension() > 4) {
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expected = format::get_default_format(input_layout.format.dimension());
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} else if (input_layout.format.dimension() == 4) {
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auto output_layout = reduce_node.get_output_layout();
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if (!use_onednn_impls && output_layout.is_dynamic()) {
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if (output_layout.format.dimension() > 4) {
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expected = format::get_default_format(output_layout.format.dimension());
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} else if (output_layout.format.dimension() == 4) {
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expected = format::any;
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}
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}
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@ -0,0 +1,137 @@
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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 <tuple>
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#include <string>
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#include <vector>
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#include <memory>
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#include "ngraph_functions/utils/ngraph_helpers.hpp"
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#include "ngraph_functions/builders.hpp"
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#include "shared_test_classes/base/ov_subgraph.hpp"
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#include "shared_test_classes/single_layer/reduce_ops.hpp"
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#include "shared_test_classes/single_layer/convolution_backprop_data.hpp"
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#include <shared_test_classes/single_layer/concat.hpp>
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#include <common_test_utils/ov_tensor_utils.hpp>
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using namespace ngraph;
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using namespace InferenceEngine;
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using namespace ov::test;
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namespace GPULayerTestsDefinitions {
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typedef std::tuple<
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std::vector<InputShape>, // input shapes
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ElementType, // Network precision
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TargetDevice, // Device name
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std::map<std::string, std::string> // Additional network configuration
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> reduceDeconvConcatDynamicGPUTestParamsSet;
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const std::vector<ElementType> netPrecisions = {
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ElementType::f16,
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};
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// Reduce should have preferred format for ouput layout
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class ReduceDeconvConcatDynamicGPUTest : public testing::WithParamInterface<reduceDeconvConcatDynamicGPUTestParamsSet>,
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virtual public SubgraphBaseTest {
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public:
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static std::string getTestCaseName(const testing::TestParamInfo<reduceDeconvConcatDynamicGPUTestParamsSet>& obj) {
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reduceDeconvConcatDynamicGPUTestParamsSet basicParamsSet = obj.param;
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std::ostringstream result;
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std::vector<InputShape> inputShapes;
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ElementType netType;
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TargetDevice targetDevice;
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std::map<std::string, std::string> additionalConfig;
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std::tie(inputShapes, netType, targetDevice, additionalConfig) = basicParamsSet;
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result << "IS=";
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for (const auto& shape : inputShapes) {
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result << ov::test::utils::partialShape2str({shape.first}) << "_";
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for (const auto& actual_shape : shape.second) {
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result << ov::test::utils::partialShape2str({actual_shape}) << "_";
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}
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}
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result << "NetType=" << netType << "_";
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result << "targetDevice=" << targetDevice;
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return result.str();
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}
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protected:
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void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
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inputs.clear();
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const auto& funcInputs = function->inputs();
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for (size_t i = 0; i < funcInputs.size(); ++i) {
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const auto& funcInput = funcInputs[i];
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ov::Tensor tensor;
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tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
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targetInputStaticShapes[i],
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80,
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0,
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8);
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inputs.insert({funcInput.get_node_shared_ptr(), tensor});
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}
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}
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void SetUp() override {
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reduceDeconvConcatDynamicGPUTestParamsSet basicParamsSet = this->GetParam();
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std::vector<InputShape> inputShapes;
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ElementType netType;
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std::map<std::string, std::string> additionalConfig;
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std::tie(inputShapes, netType, targetDevice, additionalConfig) = basicParamsSet;
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init_input_shapes(inputShapes);
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const auto inShapeDeconv = inputDynamicShapes[0];
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const auto inShapeEReduce = inputDynamicShapes[1];
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auto params = builder::makeDynamicParams(netType, {inShapeDeconv, inShapeEReduce});
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auto paramOuts = helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::opset3::Parameter>(params));
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auto deconvOp = ngraph::builder::makeConvolutionBackpropData(paramOuts[0], netType, {2, 2, 2}, {2, 2, 2}, {0, 0, 0},
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{0, 0, 0}, {1, 1, 1}, ov::op::PadType::EXPLICIT, 16);
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deconvOp->set_friendly_name("deconv");
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std::vector<int> reduce_axes = {5};
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auto reduceAxesNode = std::dynamic_pointer_cast<ngraph::Node>(
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std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::i64, ngraph::Shape({1}), reduce_axes));
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auto reduceOp = ngraph::builder::makeReduce(paramOuts[1], reduceAxesNode, false, ngraph::helpers::ReductionType::Max);
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reduceOp->set_friendly_name("reduce");
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auto concatOp = ngraph::builder::makeConcat({deconvOp, reduceOp}, 1);
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concatOp->set_friendly_name("concat");
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std::vector<int> transpose_order = {0, 1, 2, 4, 3};
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auto transposeOrderNode = std::dynamic_pointer_cast<ngraph::Node>(
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std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::i64, ngraph::Shape({5}), transpose_order));
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auto transposeOp = std::make_shared<ngraph::opset3::Transpose>(concatOp, transposeOrderNode);
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transposeOp->set_friendly_name("transpose");
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ngraph::ResultVector results = {std::make_shared<ngraph::opset1::Result>(transposeOp)};
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function = std::make_shared<ngraph::Function>(results, params, "transpose_out");
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}
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};
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TEST_P(ReduceDeconvConcatDynamicGPUTest, CompareWithRefs) {
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SKIP_IF_CURRENT_TEST_IS_DISABLED()
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run();
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}
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namespace {
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std::map<std::string, std::string> emptyAdditionalConfig;
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const std::vector<std::vector<ov::test::InputShape>> dynInputShapes = {
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{
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// Input for Deconv
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{{1, 32, 64, ov::Dimension::dynamic(), ov::Dimension::dynamic()}, {{1, 32, 64, 64, 64}}},
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// Input for Reduce
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{{1, 8, 128, ov::Dimension::dynamic(), ov::Dimension::dynamic(), 4}, {{1, 8, 128, 128, 128, 4}}}
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}
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};
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const auto testParams_smoke = ::testing::Combine(::testing::ValuesIn(dynInputShapes),
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::testing::ValuesIn(netPrecisions), // netprec
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::testing::Values(ov::test::utils::DEVICE_GPU),
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::testing::Values(emptyAdditionalConfig));
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INSTANTIATE_TEST_SUITE_P(smoke_dynamic_reduce_deconv_concat, ReduceDeconvConcatDynamicGPUTest,
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testParams_smoke, ReduceDeconvConcatDynamicGPUTest::getTestCaseName);
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} // namespace
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} // namespace GPULayerTestsDefinitions
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