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[QNN EP] Revert workarounds for QNN <= 2.35 #25479

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Merged
HectorSVC merged 1 commit into from
Jul 22, 2025
Merged

[QNN EP] Revert workarounds for QNN <= 2.35 #25479

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7 changes: 0 additions & 7 deletions onnxruntime/test/onnx/TestCase.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1396,11 +1396,7 @@ std::unique_ptr<std::set<BrokenTest>> 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
Expand All @@ -1410,7 +1406,6 @@ std::unique_ptr<std::set<BrokenTest>> 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
Expand All @@ -1424,8 +1419,6 @@ std::unique_ptr<std::set<BrokenTest>> 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
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2 changes: 1 addition & 1 deletion onnxruntime/test/providers/qnn/layer_norm_test.cc
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Expand Up @@ -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
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8 changes: 2 additions & 6 deletions onnxruntime/test/providers/qnn/lrn_op_test.cc
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Expand Up @@ -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) {
Expand All @@ -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) {
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10 changes: 2 additions & 8 deletions onnxruntime/test/providers/qnn/resize_test.cc
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Expand Up @@ -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 <F0-00 00-00 00-00 00-00 40-01 57-DD 45-01 00-00> are an almost-equal pair
// Actual: 16-byte object <F0-00 00-00 00-00 00-00 40-01 39-DD 45-01 00-00>, 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<float> input_data = GetFloatDataInRange(-10.0f, 10.0f, 60);
RunCPUResizeOpTest(TestInputDef<float>({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 <F0-00 00-00 00-00 00-00 40-01 57-4B C9-02 00-00> are an almost-equal pair
// Actual: 16-byte object <F0-00 00-00 00-00 00-00 40-01 39-4B C9-02 00-00>, 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<float> input_data = GetFloatDataInRange(-10.0f, 10.0f, 60);
RunCPUResizeOpTestWithScales(TestInputDef<float>({1, 3, 4, 5}, false, input_data),
{1.0f, 1.0f, 2.0f, 2.0f}, "linear", "half_pixel", "",
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