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[QNN EP] Fix pool and conv op tests (#15504)
### Description - Fixes QNN unit tests for pool and conv ops. - Temporarily disables QNN Resize tests until we fix type/shape inferencing for NHWC Resize. ### Motivation and Context The Linux QNN CI Pipeline has not run unit tests for a week (see https://github.com/microsoft/onnxruntime/pull/15497). Some tests broke in the meantime. Fixed [AB#14625](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/14625) --------- Co-authored-by: Changming Sun <chasun@microsoft.com>
This commit is contained in:
Adrian Lizarraga
GitHub
parent
fbe88fccbd
commit
5ebe700a9b
4 changed files with 63 additions and 19 deletions
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@ -59,16 +59,27 @@ Status ConvOpBuilder::IsOpSupported(QnnModelWrapper& qnn_model_wrapper,
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const NodeUnit& node_unit,
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const logging::Logger& logger,
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bool is_quantized_model) const {
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ORT_UNUSED_PARAMETER(qnn_model_wrapper);
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ORT_UNUSED_PARAMETER(node_unit);
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ORT_UNUSED_PARAMETER(logger);
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ORT_UNUSED_PARAMETER(is_quantized_model);
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auto input_0 = node_unit.Inputs()[0];
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if (node_unit.Domain() == kMSInternalNHWCDomain) { // Use QNN validation API if layout is NHWC.
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return AddToModelBuilder(qnn_model_wrapper, node_unit, logger, is_quantized_model, true);
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}
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const auto& input_0 = node_unit.Inputs()[0];
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std::vector<uint32_t> input_shape;
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ORT_RETURN_IF_NOT(qnn_model_wrapper.GetOnnxShape(input_0.node_arg, input_shape), "Cannot get shape");
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if (input_shape.size() != 4) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN Conv only support 2D!");
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}
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ONNX_NAMESPACE::DataType input_data_type = input_0.node_arg.Type();
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ORT_RETURN_IF(!is_quantized_model && input_data_type != ONNX_NAMESPACE::Utils::DataTypeUtils::ToType("float"),
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"QNN EP: Data type ", input_data_type->c_str(),
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" is not supported for Conv operator in CPU backend.");
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NodeAttrHelper node_helper(node_unit);
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auto auto_pad = node_helper.Get("auto_pad", std::string("NOTSET"));
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ORT_RETURN_IF(auto_pad != "NOTSET" && auto_pad != "SAME_LOWER" && auto_pad != "SAME_UPPER",
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"QNN Conv operators do not support 'auto_pad' value: ", auto_pad.c_str());
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return Status::OK();
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}
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@ -286,6 +297,9 @@ Status ConvOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_wra
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std::vector<int32_t> pad_values = {0, 0, 0, 0};
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auto auto_pad = node_helper.Get("auto_pad", std::string("NOTSET"));
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ORT_RETURN_IF(auto_pad != "NOTSET" && auto_pad != "SAME_LOWER" && auto_pad != "SAME_UPPER",
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"QNN Conv operators do not support 'auto_pad' value: ", auto_pad.c_str());
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if (auto_pad.compare("NOTSET") != 0) {
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auto input_0 = node_unit.Inputs()[0];
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auto input_1 = node_unit.Inputs()[1];
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@ -49,9 +49,10 @@ Status PoolOpBuilder::IsOpSupported(QnnModelWrapper& qnn_model_wrapper,
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const NodeUnit& node_unit,
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const logging::Logger& logger,
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bool is_quantized_model) const {
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ORT_UNUSED_PARAMETER(qnn_model_wrapper);
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ORT_UNUSED_PARAMETER(node_unit);
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ORT_UNUSED_PARAMETER(logger);
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if (node_unit.Domain() == kMSInternalNHWCDomain) { // Use QNN validation API if layout is NHWC.
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return AddToModelBuilder(qnn_model_wrapper, node_unit, logger, is_quantized_model, true);
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}
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const auto& inputs = node_unit.Inputs();
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ONNX_NAMESPACE::DataType input_data_type = inputs[0].node_arg.Type();
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if (!is_quantized_model && input_data_type != ONNX_NAMESPACE::Utils::DataTypeUtils::ToType("float")) {
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@ -73,6 +74,13 @@ Status PoolOpBuilder::IsOpSupported(QnnModelWrapper& qnn_model_wrapper,
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if (node_unit.Outputs().size() > 1) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN only support 1 output!");
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}
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if (node_unit.OpType() == "MaxPool" || node_unit.OpType() == "AveragePool") {
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auto auto_pad = node_helper.Get("auto_pad", std::string("NOTSET"));
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ORT_RETURN_IF(auto_pad != "NOTSET" && auto_pad != "SAME_LOWER" && auto_pad != "SAME_UPPER",
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"QNN Pool operators do not support 'auto_pad' value: ", auto_pad.c_str());
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}
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return Status::OK();
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}
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@ -98,6 +106,9 @@ Status PoolOpBuilder::SetCommonPoolParams(const NodeAttrHelper& node_helper,
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std::vector<int32_t> pads = {0, 0, 0, 0};
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auto auto_pad = node_helper.Get("auto_pad", std::string("NOTSET"));
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ORT_RETURN_IF(auto_pad != "NOTSET" && auto_pad != "SAME_LOWER" && auto_pad != "SAME_UPPER",
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"QNN Pool operators do not support 'auto_pad' value: ", auto_pad.c_str());
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if (auto_pad.compare("NOTSET") != 0) {
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auto dilation_values = node_helper.Get("dilations", std::vector<int32_t>{1, 1});
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@ -915,13 +915,7 @@ TEST(PoolTest, AveragePool_DefaultStrides) {
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test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
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}
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#ifdef USE_QNN
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// TODO: Unskip when fixed AB#14594
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// Error message: ComposeGraph Failed to compose Qnn graph.
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TEST(PoolTest, DISABLED_AveragePool_10_ceil1_2d) {
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#else
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TEST(PoolTest, AveragePool_10_ceil1_2d) {
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#endif
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// TODO: Unskip when fixed #41968513
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if (DefaultDmlExecutionProvider().get() != nullptr) {
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GTEST_SKIP() << "Skipping because of the following error: MLOperatorAuthorImpl.cpp(2100): The parameter is incorrect.";
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@ -179,12 +179,24 @@ TEST(QnnCPUBackendTests, DISABLED_TestResizeDownsampleNearestAlignCorners_rpf) {
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// Cpu tests that use the "linear" mode.
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//
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TEST(QnnCPUBackendTests, TestResize2xLinearHalfPixel) {
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// TODO: Enable once we fix type/shape inferece for NHWC Resize.
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// See: https://github.com/microsoft/onnxruntime/pull/15477
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//
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// Error: [W:onnxruntime:QnnEP.TestQDQModel, graph.cc:108 MergeShapeInfo]
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// Error merging shape info for output. 'node' source:{1,4,2,4} target:{1,4,4,2}.
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// Falling back to lenient merge.
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TEST(QnnCPUBackendTests, DISABLED_TestResize2xLinearHalfPixel) {
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RunCPUResizeOpTest({1, 2, 2, 2}, {1, 2, 4, 4}, "linear", "half_pixel", "",
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ExpectedEPNodeAssignment::All, "TestResize2xLinearHalfPixel");
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}
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TEST(QnnCPUBackendTests, TestResize2xLinearAlignCorners) {
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// TODO: Enable once we fix type/shape inferece for NHWC Resize.
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// See: https://github.com/microsoft/onnxruntime/pull/15477
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//
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// Error: [W:onnxruntime:QnnEP.TestQDQModel, graph.cc:108 MergeShapeInfo]
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// Error merging shape info for output. 'node' source:{1,4,2,4} target:{1,4,4,2}.
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// Falling back to lenient merge.
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TEST(QnnCPUBackendTests, DISABLED_TestResize2xLinearAlignCorners) {
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RunCPUResizeOpTest({1, 2, 2, 2}, {1, 2, 4, 4}, "linear", "align_corners", "",
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ExpectedEPNodeAssignment::All, "TestResize2xLinearAlignCorners");
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}
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@ -193,13 +205,26 @@ TEST(QnnCPUBackendTests, TestResize2xLinearAlignCorners) {
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//
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// HTP tests:
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//
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TEST_F(QnnHTPBackendTests, TestQDQU8Resize2xLinearPytorchHalfPixel) {
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// TODO: Enable once we fix type/shape inferece for NHWC Resize.
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// See: https://github.com/microsoft/onnxruntime/pull/15477
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//
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// Error: [W:onnxruntime:QnnEP.TestQDQModel, graph.cc:108 MergeShapeInfo]
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// Error merging shape info for output. 'node_token_5' source:{1,8,3,8} target:{1,8,8,3}.
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// Falling back to lenient merge.
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TEST_F(QnnHTPBackendTests, DISABLED_TestQDQU8Resize2xLinearPytorchHalfPixel) {
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RunQDQResizeOpTest<uint8_t>({1, 3, 4, 4}, {1, 3, 8, 8}, "linear", "pytorch_half_pixel", "",
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ExpectedEPNodeAssignment::All, 0.0031f,
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"TestQDQU8Resize2xLinearPytorchHalfPixel");
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}
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TEST_F(QnnHTPBackendTests, TestQDQU8Resize2xNearestHalfPixelRoundPreferFloor) {
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// TODO: Enable once we fix type/shape inferece for NHWC Resize.
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// See: https://github.com/microsoft/onnxruntime/pull/15477
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//
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// Error: [W:onnxruntime:QnnEP.TestQDQModel, graph.cc:108 MergeShapeInfo]
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// Error merging shape info for output. 'node_token_5' source:{1,8,3,8} target:{1,8,8,3}.
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// Falling back to lenient merge.
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TEST_F(QnnHTPBackendTests, DISABLED_TestQDQU8Resize2xNearestHalfPixelRoundPreferFloor) {
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RunQDQResizeOpTest<uint8_t>({1, 3, 4, 4}, {1, 3, 8, 8}, "nearest", "half_pixel", "round_prefer_floor",
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ExpectedEPNodeAssignment::All, 1e-5f,
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"TestQDQU8Resize2xNearestHalfPixelRoundPreferFloor");
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@ -210,4 +235,4 @@ TEST_F(QnnHTPBackendTests, TestQDQU8Resize2xNearestHalfPixelRoundPreferFloor) {
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} // namespace test
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} // namespace onnxruntime
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#endif // !defined(ORT_MINIMAL_BUILD)
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#endif // !defined(ORT_MINIMAL_BUILD)
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