diff --git a/onnxruntime/test/onnx/TestCase.cc b/onnxruntime/test/onnx/TestCase.cc index a74ecacc1f26e..779616d511849 100644 --- a/onnxruntime/test/onnx/TestCase.cc +++ b/onnxruntime/test/onnx/TestCase.cc @@ -1396,11 +1396,7 @@ std::unique_ptr> GetBrokenTests(const std::string& provider broken_tests->insert({"resize_downsample_scales_linear", "result differs"}); broken_tests->insert({"gridsample_volumetric_nearest_align_corners_0", "unknown version"}); broken_tests->insert({"gridsample_volumetric_nearest_align_corners_1", "unknown version"}); - broken_tests->insert({"rotary_embedding", "unknown version"}); - broken_tests->insert({"rotary_embedding_no_position_ids", "unknown version"}); - broken_tests->insert({"rotary_embedding_interleaved", "unknown version"}); broken_tests->insert({"rotary_embedding_no_position_ids_expanded", "unknown version"}); - broken_tests->insert({"rotary_embedding_no_position_ids_interleaved", "unknown version"}); broken_tests->insert({"rotary_embedding_no_position_ids_interleaved_expanded", "unknown version"}); // Fails since QNN SDK 2.17.0: // expected 7.70947 (40f6b3f3), got 7.84096 (40fae920), diff: 0.131491, tol=0.00870947 idx=419. 100 of 1715 differ @@ -1410,7 +1406,6 @@ std::unique_ptr> GetBrokenTests(const std::string& provider broken_tests->insert({"gemm_default_vector_bias", "result differs with 2.34"}); // expected 0.0505495 (3d4f0d00), got 0.0506369 (3d4f68ae), diff: 8.74326e-05, tol=6.05495e-05 idx=448 broken_tests->insert({"mobilenetv2-1.0", "result differs with 2.34"}); - broken_tests->insert({"facedetection_op8", "segfault with CPU backend, will be fixed by QNN 2.36"}); // These next 3 Resize tests fail on CPU backend with QNN SDK 2.22.0 due to inaccuracy. // output=Y:expected 1 (3f800000), got 3 (40400000), diff: 2, tol=0.002 idx=24. 8 of 56 differ @@ -1424,8 +1419,6 @@ std::unique_ptr> GetBrokenTests(const std::string& provider // Fails with QNN 2.31 on Windows x64 for CPU broken_tests->insert({"gelu_tanh_2", "y:expected -0.0131778 (bc57e7d5), got -0.0136333 (bc5f5e38), diff: 0.000455472, tol=2.31778e-05."}); broken_tests->insert({"averagepool_2d_ceil", "result differs. expected 13.5 (41580000), got 0 (0)"}); - // Fails with QNN 2.32 - broken_tests->insert({"resize_upsample_scales_linear", "expected 1 (3f800000), got 0.25 (3e800000)"}); } #ifdef DISABLE_CONTRIB_OPS diff --git a/onnxruntime/test/providers/qnn/layer_norm_test.cc b/onnxruntime/test/providers/qnn/layer_norm_test.cc index 7aa3f030d9f43..7d92d1e10c39e 100644 --- a/onnxruntime/test/providers/qnn/layer_norm_test.cc +++ b/onnxruntime/test/providers/qnn/layer_norm_test.cc @@ -204,7 +204,7 @@ TEST_F(QnnHTPBackendTests, LayerNorm1D_LastAxis_StaticScale_AU16_WU8) { // Test accuracy of 8-bit QDQ LayerNorm with a dynamic scale input. // -// TODO(adrianlizarraga): Fails to finalize with QNN SDK 2.22. Still fails on QNN SDK 2.35.0. +// TODO(adrianlizarraga): Fails to finalize with QNN SDK 2.22. Still fails on QNN SDK 2.36.1. // Verbose logs: // Starting stage: Graph Transformations and Optimizations // C:\...\QNN\HTP\HTP\src\hexagon\prepare\graph_prepare.cc:203:ERROR:could not create op: q::flat_to_vtcm diff --git a/onnxruntime/test/providers/qnn/lrn_op_test.cc b/onnxruntime/test/providers/qnn/lrn_op_test.cc index 35ec2cb450691..08a7d663bddf8 100644 --- a/onnxruntime/test/providers/qnn/lrn_op_test.cc +++ b/onnxruntime/test/providers/qnn/lrn_op_test.cc @@ -131,9 +131,7 @@ TEST_F(QnnHTPBackendTests, LRNSize3) { 0.0001f, // alpha 0.75f, // beta 1.0f, // bias - 13, // opset - // Need to use tolerance of 0.8% of output range after QNN SDK 2.22 - QDQTolerance(0.008f)); + 13); // opset } TEST_F(QnnHTPBackendTests, LRNSize5) { @@ -143,9 +141,7 @@ TEST_F(QnnHTPBackendTests, LRNSize5) { 0.0001f, // alpha 0.75f, // beta 1.0f, // bias - 13, // opset - // Need to use tolerance of 0.8% of output range after QNN SDK 2.22 - QDQTolerance(0.008f)); + 13); // opset } TEST_F(QnnHTPBackendTests, LRN_size_larger_than_channel) { diff --git a/onnxruntime/test/providers/qnn/resize_test.cc b/onnxruntime/test/providers/qnn/resize_test.cc index 415e36b9cb93b..9875a52e1d2b4 100644 --- a/onnxruntime/test/providers/qnn/resize_test.cc +++ b/onnxruntime/test/providers/qnn/resize_test.cc @@ -259,20 +259,14 @@ TEST_F(QnnCPUBackendTests, ResizeDownsampleNearestAlignCorners_rpf) { // Cpu tests that use the "linear" mode. // -// accuracy issue since QNN 2.31 -// Expected: contains 240 values, where each value and its corresponding value in 16-byte object are an almost-equal pair -// Actual: 16-byte object , where the value pair (-10, -10.5084743) at index #0 don't match, which is -0.508474 from -10 -TEST_F(QnnCPUBackendTests, DISABLED_Resize2xLinearHalfPixel) { +TEST_F(QnnCPUBackendTests, Resize2xLinearHalfPixel) { std::vector input_data = GetFloatDataInRange(-10.0f, 10.0f, 60); RunCPUResizeOpTest(TestInputDef({1, 3, 4, 5}, false, input_data), {1, 3, 8, 10}, "linear", "half_pixel", "", ExpectedEPNodeAssignment::All); } -// accuracy issue since QNN 2.31 -// Expected: contains 240 values, where each value and its corresponding value in 16-byte object are an almost-equal pair -// Actual: 16-byte object , where the value pair (-10, -10.5084743) at index #0 don't match, which is -0.508474 from -10 -TEST_F(QnnCPUBackendTests, DISABLED_Resize2xLinearHalfPixel_scales) { +TEST_F(QnnCPUBackendTests, Resize2xLinearHalfPixel_scales) { std::vector input_data = GetFloatDataInRange(-10.0f, 10.0f, 60); RunCPUResizeOpTestWithScales(TestInputDef({1, 3, 4, 5}, false, input_data), {1.0f, 1.0f, 2.0f, 2.0f}, "linear", "half_pixel", "",