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chore(deps): bump ruff from 0.1.14 to 0.2.1 in /requirements/lintrunner #1273

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merged 4 commits into from
Feb 9, 2024

Merge branch 'main' into dependabot/pip/requirements/lintrunner/ruff-…

5c544b3
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Merged

chore(deps): bump ruff from 0.1.14 to 0.2.1 in /requirements/lintrunner #1273

Merge branch 'main' into dependabot/pip/requirements/lintrunner/ruff-…
5c544b3
Select commit
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Failed to load commit list.
GitHub Actions / Test Results failed Feb 9, 2024 in 0s

1 errors, 13 fail, 2 958 skipped, 8 455 pass in 1h 42m 35s

     24 files  ±0      24 suites  ±0   1h 42m 35s ⏱️ - 21m 14s
 11 427 tests ±0   8 455 ✅  - 2    2 958 💤 ±0   13 ❌ +1  1 🔥 +1 
257 887 runs  +1  58 799 ✅  - 1  198 882 💤 ±0  205 ❌ +1  1 🔥 +1 

Results for commit 5c544b3. ± Comparison against earlier commit 2ddb700.

Annotations

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 15 runs failed: test_output_match_opinfo__ops_aten__scaled_dot_product_flash_attention_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
Meta: registered at /dev/null:241 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
Meta: registered at /dev/null:241 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
Meta: registered at /dev/null:241 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
onnxscript/tests/function_libs/torch_lib/ops_test.py:209: in run_test_output_match
    torch_output = op(*inputs, **cpu_sample.kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/testing/_internal/opinfo/core.py:1114: in __call__
    return self.op(*args, **kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/_ops.py:825: in __call__
    return self_._op(*args, **(kwargs or {}))
E   NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
E   
E   MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
E   Meta: registered at /dev/null:241 [kernel]
E   BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E   Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
E   FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
E   Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
E   Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E   Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
E   Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
E   ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
E   ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
E   AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
E   AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
E   AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
E   FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
E   BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
E   FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
E   Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
E   VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
E   FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
E   PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
E   FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
E   PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
E   PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
onnxscript/tests/function_libs/torch_lib/ops_test.py:209: in run_test_output_match
    torch_output = op(*inputs, **cpu_sample.kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/testing/_internal/opinfo/core.py:1114: in __call__
    return self.op(*args, **kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/_ops.py:825: in __call__
    return self_._op(*args, **(kwargs or {}))
E   NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
E   
E   MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
E   Meta: registered at /dev/null:241 [kernel]
E   BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E   Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
E   FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
E   Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
E   Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E   Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
E   Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
E   ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
E   ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
E   AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
E   AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
E   AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
E   FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
E   BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
E   FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
E   Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
E   VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
E   FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
E   PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
E   FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
E   PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
E   PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
onnxscript/tests/function_libs/torch_lib/ops_test.py:209: in run_test_output_match
    torch_output = op(*inputs, **cpu_sample.kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/testing/_internal/opinfo/core.py:1114: in __call__
    return self.op(*args, **kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/_ops.py:825: in __call__
    return self_._op(*args, **(kwargs or {}))
E   NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
E   
E   MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
E   Meta: registered at /dev/null:241 [kernel]
E   BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E   Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
E   FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
E   Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
E   Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E   Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
E   Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
E   ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
E   ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
E   AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
E   AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
E   AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
E   FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
E   BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
E   FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
E   Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
E   VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
E   FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
E   PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
E   FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
E   PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
E   PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 15 runs failed: test_output_match_opinfo__addmm_decomposed_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[-1.8750734 ,  7.464527  , -5.334317  , -5.367582  , -5.367906  ,
         8.094986  ,  2.99926   ,  8.660255  , -7.4274864 , -8.926886  ],
       [-7.041274  , -6.0542016 ,  3.645361  ,  3.222683  ,  7.478319  ,
        -4.647828  , -6.135406  ,  4.7752037 , -3.6378403 ,  5.4623146 ],
       [-2.135706  ,  5.1484137 , -6.992712  , -4.5418477 ,  2.743888  ,
         1.902668  , -2.2946286 ,  5.364625  ,  6.1182833 , -6.5265603 ],
       [-4.804814  ,  8.240957  , -3.0368924 , -3.1906476 , -8.708351  ,
        -5.1540318 ,  2.2482328 , -1.1879387 , -6.532974  ,  0.21111012],
       [-6.1477337 , -7.63557   , -4.9559636 , -7.876909  , -5.7306423 ,
         8.996479  ,  1.6998749 ,  2.7734375 , -8.394158  , -5.910964  ]],
      dtype=float32),
 'input_1': array([], shape=(5, 0), dtype=float32),
 'input_2': array([], shape=(0, 10), dtype=float32)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float[5,10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
   <float[5,10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([-2.9957023 ,  1.40734   , -7.919292  , -3.8778572 , -5.388017  ,
        0.02494144, -3.348929  , -0.623662  , -6.098667  , -6.1775565 ],
      dtype=float32),
 'input_1': array([], shape=(5, 0), dtype=float32),
 'input_2': array([], shape=(0, 10), dtype=float32)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float[10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
   <float[10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([[-1.8750734 ,  7.464527  , -5.334317  , -5.367582  , -5.367906  ,
E            8.094986  ,  2.99926   ,  8.660255  , -7.4274864 , -8.926886  ],
E          [-7.041274  , -6.0542016 ,  3.645361  ,  3.222683  ,  7.478319  ,
E           -4.647828  , -6.135406  ,  4.7752037 , -3.6378403 ,  5.4623146 ],
E          [-2.135706  ,  5.1484137 , -6.992712  , -4.5418477 ,  2.743888  ,
E            1.902668  , -2.2946286 ,  5.364625  ,  6.1182833 , -6.5265603 ],
E          [-4.804814  ,  8.240957  , -3.0368924 , -3.1906476 , -8.708351  ,
E           -5.1540318 ,  2.2482328 , -1.1879387 , -6.532974  ,  0.21111012],
E          [-6.1477337 , -7.63557   , -4.9559636 , -7.876909  , -5.7306423 ,
E            8.996479  ,  1.6998749 ,  2.7734375 , -8.394158  , -5.910964  ]],
E         dtype=float32),
E    'input_1': array([], shape=(5, 0), dtype=float32),
E    'input_2': array([], shape=(0, 10), dtype=float32)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float[5,10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
E      <float[5,10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([-2.9957023 ,  1.40734   , -7.919292  , -3.8778572 , -5.388017  ,
E           0.02494144, -3.348929  , -0.623662  , -6.098667  , -6.1775565 ],
E         dtype=float32),
E    'input_1': array([], shape=(5, 0), dtype=float32),
E    'input_2': array([], shape=(0, 10), dtype=float32)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float[10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
E      <float[10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check failure on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

1 out of 15 runs with error: test_output_match_opinfo__clamp_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
Raw output 
failed on setup with "worker 'gw1' crashed while running 'onnxscript/tests/function_libs/torch_lib/ops_test.py::TestOutputConsistencyEagerCPU::test_output_match_opinfo__clamp_cpu_float16'"
worker 'gw1' crashed while running 'onnxscript/tests/function_libs/torch_lib/ops_test.py::TestOutputConsistencyEagerCPU::test_output_match_opinfo__clamp_cpu_float16'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 15 runs failed: test_output_match_opinfo__addmm_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[-1.8750734 ,  7.464527  , -5.334317  , -5.367582  , -5.367906  ,
         8.094986  ,  2.99926   ,  8.660255  , -7.4274864 , -8.926886  ],
       [-7.041274  , -6.0542016 ,  3.645361  ,  3.222683  ,  7.478319  ,
        -4.647828  , -6.135406  ,  4.7752037 , -3.6378403 ,  5.4623146 ],
       [-2.135706  ,  5.1484137 , -6.992712  , -4.5418477 ,  2.743888  ,
         1.902668  , -2.2946286 ,  5.364625  ,  6.1182833 , -6.5265603 ],
       [-4.804814  ,  8.240957  , -3.0368924 , -3.1906476 , -8.708351  ,
        -5.1540318 ,  2.2482328 , -1.1879387 , -6.532974  ,  0.21111012],
       [-6.1477337 , -7.63557   , -4.9559636 , -7.876909  , -5.7306423 ,
         8.996479  ,  1.6998749 ,  2.7734375 , -8.394158  , -5.910964  ]],
      dtype=float32),
 'input_1': array([], shape=(5, 0), dtype=float32),
 'input_2': array([], shape=(0, 10), dtype=float32)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float[5,10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
   <float[5,10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([-2.9957023 ,  1.40734   , -7.919292  , -3.8778572 , -5.388017  ,
        0.02494144, -3.348929  , -0.623662  , -6.098667  , -6.1775565 ],
      dtype=float32),
 'input_1': array([], shape=(5, 0), dtype=float32),
 'input_2': array([], shape=(0, 10), dtype=float32)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float[10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
   <float[10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([[-1.8750734 ,  7.464527  , -5.334317  , -5.367582  , -5.367906  ,
E            8.094986  ,  2.99926   ,  8.660255  , -7.4274864 , -8.926886  ],
E          [-7.041274  , -6.0542016 ,  3.645361  ,  3.222683  ,  7.478319  ,
E           -4.647828  , -6.135406  ,  4.7752037 , -3.6378403 ,  5.4623146 ],
E          [-2.135706  ,  5.1484137 , -6.992712  , -4.5418477 ,  2.743888  ,
E            1.902668  , -2.2946286 ,  5.364625  ,  6.1182833 , -6.5265603 ],
E          [-4.804814  ,  8.240957  , -3.0368924 , -3.1906476 , -8.708351  ,
E           -5.1540318 ,  2.2482328 , -1.1879387 , -6.532974  ,  0.21111012],
E          [-6.1477337 , -7.63557   , -4.9559636 , -7.876909  , -5.7306423 ,
E            8.996479  ,  1.6998749 ,  2.7734375 , -8.394158  , -5.910964  ]],
E         dtype=float32),
E    'input_1': array([], shape=(5, 0), dtype=float32),
E    'input_2': array([], shape=(0, 10), dtype=float32)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float[5,10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
E      <float[5,10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([-2.9957023 ,  1.40734   , -7.919292  , -3.8778572 , -5.388017  ,
E           0.02494144, -3.348929  , -0.623662  , -6.098667  , -6.1775565 ],
E         dtype=float32),
E    'input_1': array([], shape=(5, 0), dtype=float32),
E    'input_2': array([], shape=(0, 10), dtype=float32)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float[10] input_0, float[5,0] input_1, float[0,10] input_2) => (float[5,10] _val_3) 
E      <float[10] input_0, float[5,0] input_1, float[0,10] input_2, float[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 15 runs failed: test_output_match_opinfo__ops_aten__scaled_dot_product_flash_attention_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
Meta: registered at /dev/null:241 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
Meta: registered at /dev/null:241 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
Meta: registered at /dev/null:241 [kernel]
BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
onnxscript/tests/function_libs/torch_lib/ops_test.py:209: in run_test_output_match
    torch_output = op(*inputs, **cpu_sample.kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/testing/_internal/opinfo/core.py:1114: in __call__
    return self.op(*args, **kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/_ops.py:825: in __call__
    return self_._op(*args, **(kwargs or {}))
E   NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
E   
E   MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
E   Meta: registered at /dev/null:241 [kernel]
E   BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E   Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
E   FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
E   Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
E   Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E   Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
E   Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
E   ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
E   ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
E   AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
E   AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
E   AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
E   FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
E   BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
E   FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
E   Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
E   VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
E   FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
E   PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
E   FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
E   PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
E   PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
onnxscript/tests/function_libs/torch_lib/ops_test.py:209: in run_test_output_match
    torch_output = op(*inputs, **cpu_sample.kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/testing/_internal/opinfo/core.py:1114: in __call__
    return self.op(*args, **kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/_ops.py:825: in __call__
    return self_._op(*args, **(kwargs or {}))
E   NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
E   
E   MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
E   Meta: registered at /dev/null:241 [kernel]
E   BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E   Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
E   FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
E   Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
E   Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E   Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
E   Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
E   ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
E   ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
E   AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
E   AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
E   AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
E   FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
E   BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
E   FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
E   Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
E   VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
E   FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
E   PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
E   FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
E   PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
E   PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]
onnxscript/tests/function_libs/torch_lib/ops_test.py:209: in run_test_output_match
    torch_output = op(*inputs, **cpu_sample.kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/testing/_internal/opinfo/core.py:1114: in __call__
    return self.op(*args, **kwargs)
.nox/test_torch_nightly/lib/python3.10/site-packages/torch/_ops.py:825: in __call__
    return self_._op(*args, **(kwargs or {}))
E   NotImplementedError: Could not run 'aten::_scaled_dot_product_flash_attention' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::_scaled_dot_product_flash_attention' is only available for these backends: [MPS, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradMeta, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].
E   
E   MPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/mps/MPSFallback.mm:75 [backend fallback]
E   Meta: registered at /dev/null:241 [kernel]
E   BackendSelect: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
E   Python: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:154 [backend fallback]
E   FuncTorchDynamicLayerBackMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:498 [backend fallback]
E   Functionalize: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:324 [backend fallback]
E   Named: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
E   Conjugate: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
E   Negative: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
E   ZeroTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
E   ADInplaceOrView: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:86 [backend fallback]
E   AutogradOther: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHIP: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXLA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMPS: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradIPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradXPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradHPU: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradVE: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradLazy: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMTIA: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse1: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse2: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradPrivateUse3: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradMeta: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   AutogradNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/VariableType_1.cpp:16340 [autograd kernel]
E   Tracer: registered at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/generated/TraceType_1.cpp:16033 [kernel]
E   AutocastCPU: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:378 [backend fallback]
E   AutocastCUDA: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/autocast_mode.cpp:248 [kernel]
E   FuncTorchBatched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:732 [backend fallback]
E   BatchedNestedTensor: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:759 [backend fallback]
E   FuncTorchVmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
E   Batched: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
E   VmapMode: fallthrough registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
E   FuncTorchGradWrapper: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:203 [backend fallback]
E   PythonTLSSnapshot: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:162 [backend fallback]
E   FuncTorchDynamicLayerFrontMode: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:494 [backend fallback]
E   PreDispatch: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:166 [backend fallback]
E   PythonDispatcher: registered at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:158 [backend fallback]

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

1 out of 15 runs failed: test_output_match_opinfo__mm_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: Tensor-likes are not close!

Mismatched elements: 14 / 50 (28.0%)
Greatest absolute difference: nan at index (2, 6) (up to 1e-05 allowed)
Greatest relative difference: nan at index (2, 6) (up to 1.3e-06 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 14 / 50 (28.0%)
E   Greatest absolute difference: nan at index (2, 6) (up to 1e-05 allowed)
E   Greatest relative difference: nan at index (2, 6) (up to 1.3e-06 allowed)

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 15 runs failed: test_output_match_opinfo__addmm_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:590: in executor
    return function(*args, **kwargs)
onnxscript/values.py:529: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:244: in aten_addmm
    return op.Gemm(mat1, mat2, self, alpha=alpha, beta=beta)
onnxscript/onnx_opset/_impl/opset13.py:1230: in Gemm
    return op(
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:514: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:491: in _call_ort
    result = session.run(None, session_run_input)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:590: in executor
    return function(*args, **kwargs)
onnxscript/values.py:529: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:244: in aten_addmm
    return op.Gemm(mat1, mat2, self, alpha=alpha, beta=beta)
onnxscript/onnx_opset/_impl/opset13.py:1230: in Gemm
    return op(
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:514: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:491: in _call_ort
    result = session.run(None, session_run_input)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__addmm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.
onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:590: in executor
    return function(*args, **kwargs)
onnxscript/values.py:529: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:244: in aten_addmm
    return op.Gemm(mat1, mat2, self, alpha=alpha, beta=beta)
onnxscript/onnx_opset/_impl/opset13.py:1230: in Gemm
    return op(
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:514: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:491: in _call_ort
    result = session.run(None, session_run_input)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:590: in executor
    return function(*args, **kwargs)
onnxscript/values.py:529: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:244: in aten_addmm
    return op.Gemm(mat1, mat2, self, alpha=alpha, beta=beta)
onnxscript/onnx_opset/_impl/opset13.py:1230: in Gemm
    return op(
onnxscript/values.py:304: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:514: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:491: in _call_ort
    result = session.run(None, session_run_input)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__native_batch_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 10 / 125 (8.0%)
E   Greatest absolute difference: 0.002197265625 at index (2, 1, 2) (up to 1e-05 allowed)
E   Greatest relative difference: 0.01470947265625 at index (1, 0, 0) (up to 0.001 allowed)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 1 / 3 (33.3%)
E   Greatest absolute difference: 0.000732421875 at index (1, 0) (up to 1e-05 allowed)
E   Greatest relative difference: 0.0014848709106445312 at index (1, 0) (up to 0.001 allowed)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 2 / 72 (2.8%)
E   Greatest absolute difference: 0.000732421875 at index (0, 0, 0, 2) (up to 1e-05 allowed)
E   Greatest relative difference: 0.0090484619140625 at index (1, 0, 0, 0) (up to 0.001 allowed)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__native_layer_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 1 mismatch

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__addmm_decomposed_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[ 2.398e+00, -5.598e+00, -3.727e+00, -6.230e+00, -8.883e+00,
        -6.883e+00,  8.938e+00, -2.180e+00,  3.031e+00, -4.043e+00],
       [-1.116e+00,  2.980e+00, -8.203e+00,  3.217e+00, -6.064e-01,
         3.990e+00,  3.754e+00, -4.535e+00, -2.188e+00,  8.281e+00],
       [-8.703e+00, -7.199e+00,  7.031e-01,  8.180e+00,  4.930e+00,
         7.656e+00,  3.402e+00,  8.789e-03, -2.637e-02,  3.418e+00],
       [-4.035e+00,  9.229e-01,  6.777e+00,  7.215e+00,  4.184e+00,
        -2.830e+00, -5.477e+00, -2.594e+00,  4.879e+00, -7.586e+00],
       [-7.234e+00,  8.414e+00, -2.549e-01, -6.637e+00,  7.578e+00,
        -1.837e+00,  2.373e+00, -5.000e+00,  4.051e+00,  6.383e+00]],
      dtype=float16),
 'input_1': array([], shape=(5, 0), dtype=float16),
 'input_2': array([], shape=(0, 10), dtype=float16)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
   <float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([ 2.207 , -0.0703,  6.16  , -6.406 ,  6.363 , -2.68  ,  6.574 ,
       -6.04  , -1.283 ,  0.457 ], dtype=float16),
 'input_1': array([], shape=(5, 0), dtype=float16),
 'input_2': array([], shape=(0, 10), dtype=float16)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
   <float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([[ 2.398e+00, -5.598e+00, -3.727e+00, -6.230e+00, -8.883e+00,
E           -6.883e+00,  8.938e+00, -2.180e+00,  3.031e+00, -4.043e+00],
E          [-1.116e+00,  2.980e+00, -8.203e+00,  3.217e+00, -6.064e-01,
E            3.990e+00,  3.754e+00, -4.535e+00, -2.188e+00,  8.281e+00],
E          [-8.703e+00, -7.199e+00,  7.031e-01,  8.180e+00,  4.930e+00,
E            7.656e+00,  3.402e+00,  8.789e-03, -2.637e-02,  3.418e+00],
E          [-4.035e+00,  9.229e-01,  6.777e+00,  7.215e+00,  4.184e+00,
E           -2.830e+00, -5.477e+00, -2.594e+00,  4.879e+00, -7.586e+00],
E          [-7.234e+00,  8.414e+00, -2.549e-01, -6.637e+00,  7.578e+00,
E           -1.837e+00,  2.373e+00, -5.000e+00,  4.051e+00,  6.383e+00]],
E         dtype=float16),
E    'input_1': array([], shape=(5, 0), dtype=float16),
E    'input_2': array([], shape=(0, 10), dtype=float16)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
E      <float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([ 2.207 , -0.0703,  6.16  , -6.406 ,  6.363 , -2.68  ,  6.574 ,
E          -6.04  , -1.283 ,  0.457 ], dtype=float16),
E    'input_1': array([], shape=(5, 0), dtype=float16),
E    'input_2': array([], shape=(0, 10), dtype=float16)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
E      <float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 1, beta: float = 1> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__native_layer_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2, float16[1,2,3] input_3) => (float16[1,2,3] _val_4, float16[1,1,1] _val_5, float16[1,1,1] _val_6) 
   <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2, float16[1,2,3] input_3, float16[1,2,3] _val_4, float16[1,1,1] _val_5, float16[1,1,1] _val_6>
{
   _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, input_2, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_3) => (float16[1,2,3] _val_7, float16[1,1,1] _val_8, float16[1,1,1] _val_9) 
   <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_3, float16[1,2,3] _val_7, float16[1,1,1] _val_8, float16[1,1,1] _val_9, float[1] _val_3, int64[3] _val_4, float[1,2,3] _val_5, float16[1,2,3] _val_6>
{
   _val_3 = Constant <value_floats: floats = [1]> ()
   _val_4 = Shape <start: int = -3> (input_0)
   _val_5 = Expand (_val_3, _val_4)
   _val_6 = CastLike (_val_5, input_0)
   _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, _val_6, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2) => (float16[1,2,3] _val_3, float16[1,1,1] _val_4, float16[1,1,1] _val_5) 
   <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2, float16[1,2,3] _val_3, float16[1,1,1] _val_4, float16[1,1,1] _val_5>
{
   _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2,3] input_0, int64[3] input_1) => (float16[1,2,3] _val_6, float16[1,1,1] _val_7, float16[1,1,1] _val_8) 
   <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] _val_6, float16[1,1,1] _val_7, float16[1,1,1] _val_8, float[1] _val_2, int64[3] _val_3, float[1,2,3] _val_4, float16[1,2,3] _val_5>
{
   _val_2 = Constant <value_floats: floats = [1]> ()
   _val_3 = Shape <start: int = -3> (input_0)
   _val_4 = Expand (_val_2, _val_3)
   _val_5 = CastLike (_val_4, input_0)
   _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2, float16[2,3] input_3) => (float16[2,2,3] _val_4, float16[2,1,1] _val_5, float16[2,1,1] _val_6) 
   <float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2, float16[2,3] input_3, float16[2,2,3] _val_4, float16[2,1,1] _val_5, float16[2,1,1] _val_6>
{
   _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, input_2, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_3) => (float16[2,2,3] _val_7, float16[2,1,1] _val_8, float16[2,1,1] _val_9) 
   <float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_3, float16[2,2,3] _val_7, float16[2,1,1] _val_8, float16[2,1,1] _val_9, float[1] _val_3, int64[2] _val_4, float[2,3] _val_5, float16[2,3] _val_6>
{
   _val_3 = Constant <value_floats: floats = [1]> ()
   _val_4 = Shape <start: int = -2> (input_0)
   _val_5 = Expand (_val_3, _val_4)
   _val_6 = CastLike (_val_5, input_0)
   _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, _val_6, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2) => (float16[2,2,3] _val_3, float16[2,1,1] _val_4, float16[2,1,1] _val_5) 
   <float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2, float16[2,2,3] _val_3, float16[2,1,1] _val_4, float16[2,1,1] _val_5>
{
   _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[2,2,3] input_0, int64[2] input_1) => (float16[2,2,3] _val_6, float16[2,1,1] _val_7, float16[2,1,1] _val_8) 
   <float16[2,2,3] input_0, int64[2] input_1, float16[2,2,3] _val_6, float16[2,1,1] _val_7, float16[2,1,1] _val_8, float[1] _val_2, int64[2] _val_3, float[2,3] _val_4, float16[2,3] _val_5>
{
   _val_2 = Constant <value_floats: floats = [1]> ()
   _val_3 = Shape <start: int = -2> (input_0)
   _val_4 = Expand (_val_2, _val_3)
   _val_5 = CastLike (_val_4, input_0)
   _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3) => (float16[1] _val_4, float16[1] _val_5, float16[1] _val_6) 
   <float16[1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] _val_4, float16[1] _val_5, float16[1] _val_6>
{
   _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1] input_0, int64[1] input_1, float16[1] input_3) => (float16[1] _val_7, float16[1] _val_8, float16[1] _val_9) 
   <float16[1] input_0, int64[1] input_1, float16[1] input_3, float16[1] _val_7, float16[1] _val_8, float16[1] _val_9, float[1] _val_3, int64[1] _val_4, float[1] _val_5, float16[1] _val_6>
{
   _val_3 = Constant <value_floats: floats = [1]> ()
   _val_4 = Shape <start: int = -1> (input_0)
   _val_5 = Expand (_val_3, _val_4)
   _val_6 = CastLike (_val_5, input_0)
   _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_6, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1] input_0, int64[1] input_1, float16[1] input_2) => (float16[1] _val_3, float16[1] _val_4, float16[1] _val_5) 
   <float16[1] input_0, int64[1] input_1, float16[1] input_2, float16[1] _val_3, float16[1] _val_4, float16[1] _val_5>
{
   _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1] input_0, int64[1] input_1) => (float16[1] _val_6, float16[1] _val_7, float16[1] _val_8) 
   <float16[1] input_0, int64[1] input_1, float16[1] _val_6, float16[1] _val_7, float16[1] _val_8, float[1] _val_2, int64[1] _val_3, float[1] _val_4, float16[1] _val_5>
{
   _val_2 = Constant <value_floats: floats = [1]> ()
   _val_3 = Shape <start: int = -1> (input_0)
   _val_4 = Expand (_val_2, _val_3)
   _val_5 = CastLike (_val_4, input_0)
   _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2] input_0, int64[1] input_1, float16[2] input_2, float16[2] input_3) => (float16[1,2] _val_4, float16[1,1] _val_5, float16[1,1] _val_6) 
   <float16[1,2] input_0, int64[1] input_1, float16[2] input_2, float16[2] input_3, float16[1,2] _val_4, float16[1,1] _val_5, float16[1,1] _val_6>
{
   _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2] input_0, int64[1] input_1, float16[2] input_3) => (float16[1,2] _val_7, float16[1,1] _val_8, float16[1,1] _val_9) 
   <float16[1,2] input_0, int64[1] input_1, float16[2] input_3, float16[1,2] _val_7, float16[1,1] _val_8, float16[1,1] _val_9, float[1] _val_3, int64[1] _val_4, float[2] _val_5, float16[2] _val_6>
{
   _val_3 = Constant <value_floats: floats = [1]> ()
   _val_4 = Shape <start: int = -1> (input_0)
   _val_5 = Expand (_val_3, _val_4)
   _val_6 = CastLike (_val_5, input_0)
   _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_6, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2] input_0, int64[1] input_1, float16[2] input_2) => (float16[1,2] _val_3, float16[1,1] _val_4, float16[1,1] _val_5) 
   <float16[1,2] input_0, int64[1] input_1, float16[2] input_2, float16[1,2] _val_3, float16[1,1] _val_4, float16[1,1] _val_5>
{
   _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2] input_0, int64[1] input_1) => (float16[1,2] _val_6, float16[1,1] _val_7, float16[1,1] _val_8) 
   <float16[1,2] input_0, int64[1] input_1, float16[1,2] _val_6, float16[1,1] _val_7, float16[1,1] _val_8, float[1] _val_2, int64[1] _val_3, float[2] _val_4, float16[2] _val_5>
{
   _val_2 = Constant <value_floats: floats = [1]> ()
   _val_3 = Shape <start: int = -1> (input_0)
   _val_4 = Expand (_val_2, _val_3)
   _val_5 = CastLike (_val_4, input_0)
   _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[0,1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3) => (float16[0,1] _val_4, float16[0,1] _val_5, float16[0,1] _val_6) 
   <float16[0,1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3, float16[0,1] _val_4, float16[0,1] _val_5, float16[0,1] _val_6>
{
   _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[0,1] input_0, int64[1] input_1, float16[1] input_3) => (float16[0,1] _val_7, float16[0,1] _val_8, float16[0,1] _val_9) 
   <float16[0,1] input_0, int64[1] input_1, float16[1] input_3, float16[0,1] _val_7, float16[0,1] _val_8, float16[0,1] _val_9, float[1] _val_3, int64[1] _val_4, float[1] _val_5, float16[1] _val_6>
{
   _val_3 = Constant <value_floats: floats = [1]> ()
   _val_4 = Shape <start: int = -1> (input_0)
   _val_5 = Expand (_val_3, _val_4)
   _val_6 = CastLike (_val_5, input_0)
   _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_6, input_3)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[0,1] input_0, int64[1] input_1, float16[1] input_2) => (float16[0,1] _val_3, float16[0,1] _val_4, float16[0,1] _val_5) 
   <float16[0,1] input_0, int64[1] input_1, float16[1] input_2, float16[0,1] _val_3, float16[0,1] _val_4, float16[0,1] _val_5>
{
   _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[0,1] input_0, int64[1] input_1) => (float16[0,1] _val_6, float16[0,1] _val_7, float16[0,1] _val_8) 
   <float16[0,1] input_0, int64[1] input_1, float16[0,1] _val_6, float16[0,1] _val_7, float16[0,1] _val_8, float[1] _val_2, int64[1] _val_3, float[1] _val_4, float16[1] _val_5>
{
   _val_2 = Constant <value_floats: floats = [1]> ()
   _val_3 = Shape <start: int = -1> (input_0)
   _val_4 = Expand (_val_2, _val_3)
   _val_5 = CastLike (_val_4, input_0)
   _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2, float16[1,2,3] input_3) => (float16[1,2,3] _val_4, float16[1,1,1] _val_5, float16[1,1,1] _val_6) 
E      <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2, float16[1,2,3] input_3, float16[1,2,3] _val_4, float16[1,1,1] _val_5, float16[1,1,1] _val_6>
E   {
E      _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, input_2, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_3) => (float16[1,2,3] _val_7, float16[1,1,1] _val_8, float16[1,1,1] _val_9) 
E      <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_3, float16[1,2,3] _val_7, float16[1,1,1] _val_8, float16[1,1,1] _val_9, float[1] _val_3, int64[3] _val_4, float[1,2,3] _val_5, float16[1,2,3] _val_6>
E   {
E      _val_3 = Constant <value_floats: floats = [1]> ()
E      _val_4 = Shape <start: int = -3> (input_0)
E      _val_5 = Expand (_val_3, _val_4)
E      _val_6 = CastLike (_val_5, input_0)
E      _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, _val_6, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2) => (float16[1,2,3] _val_3, float16[1,1,1] _val_4, float16[1,1,1] _val_5) 
E      <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] input_2, float16[1,2,3] _val_3, float16[1,1,1] _val_4, float16[1,1,1] _val_5>
E   {
E      _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2,3] input_0, int64[3] input_1) => (float16[1,2,3] _val_6, float16[1,1,1] _val_7, float16[1,1,1] _val_8) 
E      <float16[1,2,3] input_0, int64[3] input_1, float16[1,2,3] _val_6, float16[1,1,1] _val_7, float16[1,1,1] _val_8, float[1] _val_2, int64[3] _val_3, float[1,2,3] _val_4, float16[1,2,3] _val_5>
E   {
E      _val_2 = Constant <value_floats: floats = [1]> ()
E      _val_3 = Shape <start: int = -3> (input_0)
E      _val_4 = Expand (_val_2, _val_3)
E      _val_5 = CastLike (_val_4, input_0)
E      _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -3, epsilon: float = 0.5, stash_type: int = 1> (input_0, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2, float16[2,3] input_3) => (float16[2,2,3] _val_4, float16[2,1,1] _val_5, float16[2,1,1] _val_6) 
E      <float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2, float16[2,3] input_3, float16[2,2,3] _val_4, float16[2,1,1] _val_5, float16[2,1,1] _val_6>
E   {
E      _val_4, _val_5, _val_6 = LayerNormalization …, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_3) => (float16[2,2,3] _val_7, float16[2,1,1] _val_8, float16[2,1,1] _val_9) 
E      <float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_3, float16[2,2,3] _val_7, float16[2,1,1] _val_8, float16[2,1,1] _val_9, float[1] _val_3, int64[2] _val_4, float[2,3] _val_5, float16[2,3] _val_6>
E   {
E      _val_3 = Constant <value_floats: floats = [1]> ()
E      _val_4 = Shape <start: int = -2> (input_0)
E      _val_5 = Expand (_val_3, _val_4)
E      _val_6 = CastLike (_val_5, input_0)
E      _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, _val_6, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2) => (float16[2,2,3] _val_3, float16[2,1,1] _val_4, float16[2,1,1] _val_5) 
E      <float16[2,2,3] input_0, int64[2] input_1, float16[2,3] input_2, float16[2,2,3] _val_3, float16[2,1,1] _val_4, float16[2,1,1] _val_5>
E   {
E      _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[2,2,3] input_0, int64[2] input_1) => (float16[2,2,3] _val_6, float16[2,1,1] _val_7, float16[2,1,1] _val_8) 
E      <float16[2,2,3] input_0, int64[2] input_1, float16[2,2,3] _val_6, float16[2,1,1] _val_7, float16[2,1,1] _val_8, float[1] _val_2, int64[2] _val_3, float[2,3] _val_4, float16[2,3] _val_5>
E   {
E      _val_2 = Constant <value_floats: floats = [1]> ()
E      _val_3 = Shape <start: int = -2> (input_0)
E      _val_4 = Expand (_val_2, _val_3)
E      _val_5 = CastLike (_val_4, input_0)
E      _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -2, epsilon: float = -0.5, stash_type: int = 1> (input_0, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3) => (float16[1] _val_4, float16[1] _val_5, float16[1] _val_6) 
E      <float16[1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] _val_4, float16[1] _val_5, float16[1] _val_6>
E   {
E      _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1] input_0, int64[1] input_1, float16[1] input_3) => (float16[1] _val_7, float16[1] _val_8, float16[1] _val_9) 
E      <float16[1] input_0, int64[1] input_1, float16[1] input_3, float16[1] _val_7, float16[1] _val_8, float16[1] _val_9, float[1] _val_3, int64[1] _val_4, float[1] _val_5, float16[1] _val_6>
E   {
E      _val_3 = Constant <value_floats: floats = [1]> ()
E      _val_4 = Shape <start: int = -1> (input_0)
E      _val_5 = Expand (_val_3, _val_4)
E      _val_6 = CastLike (_val_5, input_0)
E      _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_6, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1] input_0, int64[1] input_1, float16[1] input_2) => (float16[1] _val_3, float16[1] _val_4, float16[1] _val_5) 
E      <float16[1] input_0, int64[1] input_1, float16[1] input_2, float16[1] _val_3, float16[1] _val_4, float16[1] _val_5>
E   {
E      _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1] input_0, int64[1] input_1) => (float16[1] _val_6, float16[1] _val_7, float16[1] _val_8) 
E      <float16[1] input_0, int64[1] input_1, float16[1] _val_6, float16[1] _val_7, float16[1] _val_8, float[1] _val_2, int64[1] _val_3, float[1] _val_4, float16[1] _val_5>
E   {
E      _val_2 = Constant <value_floats: floats = [1]> ()
E      _val_3 = Shape <start: int = -1> (input_0)
E      _val_4 = Expand (_val_2, _val_3)
E      _val_5 = CastLike (_val_4, input_0)
E      _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2] input_0, int64[1] input_1, float16[2] input_2, float16[2] input_3) => (float16[1,2] _val_4, float16[1,1] _val_5, float16[1,1] _val_6) 
E      <float16[1,2] input_0, int64[1] input_1, float16[2] input_2, float16[2] input_3, float16[1,2] _val_4, float16[1,1] _val_5, float16[1,1] _val_6>
E   {
E      _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2] input_0, int64[1] input_1, float16[2] input_3) => (float16[1,2] _val_7, float16[1,1] _val_8, float16[1,1] _val_9) 
E      <float16[1,2] input_0, int64[1] input_1, float16[2] input_3, float16[1,2] _val_7, float16[1,1] _val_8, float16[1,1] _val_9, float[1] _val_3, int64[1] _val_4, float[2] _val_5, float16[2] _val_6>
E   {
E      _val_3 = Constant <value_floats: floats = [1]> ()
E      _val_4 = Shape <start: int = -1> (input_0)
E      _val_5 = Expand (_val_3, _val_4)
E      _val_6 = CastLike (_val_5, input_0)
E      _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_6, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2] input_0, int64[1] input_1, float16[2] input_2) => (float16[1,2] _val_3, float16[1,1] _val_4, float16[1,1] _val_5) 
E      <float16[1,2] input_0, int64[1] input_1, float16[2] input_2, float16[1,2] _val_3, float16[1,1] _val_4, float16[1,1] _val_5>
E   {
E      _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2] input_0, int64[1] input_1) => (float16[1,2] _val_6, float16[1,1] _val_7, float16[1,1] _val_8) 
E      <float16[1,2] input_0, int64[1] input_1, float16[1,2] _val_6, float16[1,1] _val_7, float16[1,1] _val_8, float[1] _val_2, int64[1] _val_3, float[2] _val_4, float16[2] _val_5>
E   {
E      _val_2 = Constant <value_floats: floats = [1]> ()
E      _val_3 = Shape <start: int = -1> (input_0)
E      _val_4 = Expand (_val_2, _val_3)
E      _val_5 = CastLike (_val_4, input_0)
E      _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[0,1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3) => (float16[0,1] _val_4, float16[0,1] _val_5, float16[0,1] _val_6) 
E      <float16[0,1] input_0, int64[1] input_1, float16[1] input_2, float16[1] input_3, float16[0,1] _val_4, float16[0,1] _val_5, float16[0,1] _val_6>
E   {
E      _val_4, _val_5, _val_6 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[0,1] input_0, int64[1] input_1, float16[1] input_3) => (float16[0,1] _val_7, float16[0,1] _val_8, float16[0,1] _val_9) 
E      <float16[0,1] input_0, int64[1] input_1, float16[1] input_3, float16[0,1] _val_7, float16[0,1] _val_8, float16[0,1] _val_9, float[1] _val_3, int64[1] _val_4, float[1] _val_5, float16[1] _val_6>
E   {
E      _val_3 = Constant <value_floats: floats = [1]> ()
E      _val_4 = Shape <start: int = -1> (input_0)
E      _val_5 = Expand (_val_3, _val_4)
E      _val_6 = CastLike (_val_5, input_0)
E      _val_7, _val_8, _val_9 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_6, input_3)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[0,1] input_0, int64[1] input_1, float16[1] input_2) => (float16[0,1] _val_3, float16[0,1] _val_4, float16[0,1] _val_5) 
E      <float16[0,1] input_0, int64[1] input_1, float16[1] input_2, float16[0,1] _val_3, float16[0,1] _val_4, float16[0,1] _val_5>
E   {
E      _val_3, _val_4, _val_5 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:LayerNormalization, node name: LayerNormalization_4): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[0,1] input_0, int64[1] input_1) => (float16[0,1] _val_6, float16[0,1] _val_7, float16[0,1] _val_8) 
E      <float16[0,1] input_0, int64[1] input_1, float16[0,1] _val_6, float16[0,1] _val_7, float16[0,1] _val_8, float[1] _val_2, int64[1] _val_3, float[1] _val_4, float16[1] _val_5>
E   {
E      _val_2 = Constant <value_floats: floats = [1]> ()
E      _val_3 = Shape <start: int = -1> (input_0)
E      _val_4 = Expand (_val_2, _val_3)
E      _val_5 = CastLike (_val_4, input_0)
E      _val_6, _val_7, _val_8 = LayerNormalization <axis: int = -1, epsilon: float = 1e-05, stash_type: int = 1> (input_0, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__native_batch_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[5,5,5] input_0, float16[5] input_1, float16[5] input_2, float16[5] input_3, float16[5] input_4) => (float16[5,5,5] _val_6, float16[5] _val_7, float16[5] _val_8) 
   <float16[5,5,5] input_0, float16[5] input_1, float16[5] input_2, float16[5] input_3, float16[5] input_4, float16[5,5,5] _val_6, float16[5] _val_7, float16[5] _val_8, int64[2] _val_5>
{
   _val_5 = Constant <value_ints: ints = [0, 2]> ()
   _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 0.6, momentum: float = 0.5, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
_aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
{
   norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
   upcast_input = Cast <to: int = 1> (input)
   mean = ReduceMean (upcast_input, axes)
   input_sub_mean = Sub (upcast_input, mean)
   sqr = Mul (input_sub_mean, input_sub_mean)
   var = ReduceMean <keepdims: int = 0> (sqr, axes)
   const = Constant <value: tensor = float const {1}> ()
   eps = Constant <value_float: float = @eps> ()
   eps_cast = CastLike (eps, var)
   tmp = Add (var, eps_cast)
   tmp_2 = Sqrt (tmp)
   const_cast = CastLike (const, tmp_2)
   rstd = Div (const_cast, tmp_2)
   mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[3,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[3,1] _val_6, float16[1] _val_7, float16[1] _val_8) 
   <float16[3,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4, float16[3,1] _val_6, float16[1] _val_7, float16[1] _val_8, int64[1] _val_5>
{
   _val_5 = Constant <value_ints: ints = [0]> ()
   _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
_aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
{
   norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
   upcast_input = Cast <to: int = 1> (input)
   mean = ReduceMean (upcast_input, axes)
   input_sub_mean = Sub (upcast_input, mean)
   sqr = Mul (input_sub_mean, input_sub_mean)
   var = ReduceMean <keepdims: int = 0> (sqr, axes)
   const = Constant <value: tensor = float const {1}> ()
   eps = Constant <value_float: float = @eps> ()
   eps_cast = CastLike (eps, var)
   tmp = Add (var, eps_cast)
   tmp_2 = Sqrt (tmp)
   const_cast = CastLike (const, tmp_2)
   rstd = Div (const_cast, tmp_2)
   mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[3,2,3,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4) => (float16[3,2,3,4] _val_6, float16[2] _val_7, float16[2] _val_8) 
   <float16[3,2,3,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4, float16[3,2,3,4] _val_6, float16[2] _val_7, float16[2] _val_8, int64[3] _val_5>
{
   _val_5 = Constant <value_ints: ints = [0, 2, 3]> ()
   _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 0.5, momentum: float = -1, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
_aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
{
   norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
   upcast_input = Cast <to: int = 1> (input)
   mean = ReduceMean (upcast_input, axes)
   input_sub_mean = Sub (upcast_input, mean)
   sqr = Mul (input_sub_mean, input_sub_mean)
   var = ReduceMean <keepdims: int = 0> (sqr, axes)
   const = Constant <value: tensor = float const {1}> ()
   eps = Constant <value_float: float = @eps> ()
   eps_cast = CastLike (eps, var)
   tmp = Add (var, eps_cast)
   tmp_2 = Sqrt (tmp)
   const_cast = CastLike (const, tmp_2)
   rstd = Div (const_cast, tmp_2)
   mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8) 
   <float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4, float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8, int64[1] _val_5>
{
   _val_5 = Constant <value_ints: ints = [0]> ()
   _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
_aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
{
   norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
   upcast_input = Cast <to: int = 1> (input)
   mean = ReduceMean (upcast_input, axes)
   input_sub_mean = Sub (upcast_input, mean)
   sqr = Mul (input_sub_mean, input_sub_mean)
   var = ReduceMean <keepdims: int = 0> (sqr, axes)
   const = Constant <value: tensor = float const {1}> ()
   eps = Constant <value_float: float = @eps> ()
   eps_cast = CastLike (eps, var)
   tmp = Add (var, eps_cast)
   tmp_2 = Sqrt (tmp)
   const_cast = CastLike (const, tmp_2)
   rstd = Div (const_cast, tmp_2)
   mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8) 
   <float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4, float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8, int64[1] _val_5>
{
   _val_5 = Constant <value_ints: ints = [0]> ()
   _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
_aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
{
   norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
   upcast_input = Cast <to: int = 1> (input)
   mean = ReduceMean (upcast_input, axes)
   input_sub_mean = Sub (upcast_input, mean)
   sqr = Mul (input_sub_mean, input_sub_mean)
   var = ReduceMean <keepdims: int = 0> (sqr, axes)
   const = Constant <value: tensor = float const {1}> ()
   eps = Constant <value_float: float = @eps> ()
   eps_cast = CastLike (eps, var)
   tmp = Add (var, eps_cast)
   tmp_2 = Sqrt (tmp)
   const_cast = CastLike (const, tmp_2)
   rstd = Div (const_cast, tmp_2)
   mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[1,2,3] input_0) => (float16[1,2,3] _val_17, float16[2] _val_18, float16[2] _val_19) 
   <float16[1,2,3] input_0, float16[1,2,3] _val_17, float16[2] _val_18, float16[2] _val_19, float[1] _val_1, float16[1] _val_2, int64[1] _val_3, float16[2] _val_4, float[1] _val_5, float16[1] _val_6, int64[1] _val_7, float16[2] _val_8, int64[2] _val_9, float16[1,2,1] _val_10, float16[2] _val_11, float16[1,2,1] _val_12, float16[1,2,3] _val_13, float16[1,2,3] _val_14, float16[1,2,1] _val_15, float16[2] _val_16>
{
   _val_1 = Constant <value_floats: floats = [1]> ()
   _val_2 = CastLike (_val_1, input_0)
   _val_3 = Shape <end: int = 2, start: int = 1> (input_0)
   _val_4 = Expand (_val_2, _val_3)
   _val_5 = Constant <value_floats: floats = [0]> ()
   _val_6 = CastLike (_val_5, input_0)
   _val_7 = Shape <end: int = 2, start: int = 1> (input_0)
   _val_8 = Expand (_val_6, _val_7)
   _val_9 = Constant <value_ints: ints = [0, 2]> ()
   _val_10 = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (input_0, _val_9)
   _val_11 = Squeeze (_val_10)
   _val_12 = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (input_0, _val_9)
   _val_13 = Sub (input_0, _val_12)
   _val_14 = Mul (_val_13, _val_13)
   _val_15 = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (_val_14, _val_9)
   _val_16 = Squeeze (_val_15)
   _val_17, _val_18, _val_19 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 1> (input_0, _val_4, _val_8, _val_11, _val_16, _val_9)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
_aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
{
   norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
   upcast_input = Cast <to: int = 1> (input)
   mean = ReduceMean (upcast_input, axes)
   input_sub_mean = Sub (upcast_input, mean)
   sqr = Mul (input_sub_mean, input_sub_mean)
   var = ReduceMean <keepdims: int = 0> (sqr, axes)
   const = Constant <value: tensor = float const {1}> ()
   eps = Constant <value_float: float = @eps> ()
   eps_cast = CastLike (eps, var)
   tmp = Add (var, eps_cast)
   tmp_2 = Sqrt (tmp)
   const_cast = CastLike (const, tmp_2)
   rstd = Div (const_cast, tmp_2)
   mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_training_onnx, node name: _aten_native_batch_norm_training_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[5,5,5] input_0, float16[5] input_1, float16[5] input_2, float16[5] input_3, float16[5] input_4) => (float16[5,5,5] _val_6, float16[5] _val_7, float16[5] _val_8) 
E      <float16[5,5,5] input_0, float16[5] input_1, float16[5] input_2, float16[5] input_3, float16[5] input_4, float16[5,5,5] _val_6, float16[5] _val_7, float16[5] _val_8, int64[2] _val_5>
E   {
E      _val_5 = Constant <value_ints: ints = [0, 2]> ()
E      _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 0.6, momentum: float = 0.5, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   _aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
E   {
E      norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E      upcast_input = Cast <to: int = 1> (input)
E      mean = ReduceMean (upcast_input, axes)
E      input_sub_mean = Sub (upcast_input, mean)
E      sqr = Mul (input_sub_mean, input_sub_mean)
E      var = ReduceMean <keepdims: int = 0> (sqr, axes)
E      const = Constant <value: tensor = float const {1}> ()
E      eps = Constant <value_float: float = @eps> ()
E      eps_cast = CastLike (eps, var)
E      tmp = Add (var, eps_cast)
E      tmp_2 = Sqrt (tmp)
E      const_cast = CastLike (const, tmp_2)
E      rstd = Div (const_cast, tmp_2)
E      mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_training_onnx, node name: _aten_native_batch_norm_training_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[3,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[3,1] _val_6, float16[1] _val_7, float16[1] _val_8) 
E      <float16[3,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4, float16[3,1] _val_6, float16[1] _val_7, float16[1] _val_8, int64[1] _val_5>
E   {
E      _val_5 = Constant <value_ints: ints = [0]> ()
E      _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   _aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
E   {
E      norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E      upcast_input = Cast <to: int = 1> (input)
E      mean = ReduceMean (upcast_input, axes)
E      input_sub_mean = Sub (upcast_input, mean)
E      sqr = Mul (input_sub_mean, input_sub_mean)
E      var = ReduceMean <keepdims: int = 0> (sqr, axes)
E      const = Constant <value: tensor = float const {1}> ()
E      eps = Constant <value_float: float = @eps> ()
E      eps_cast = CastLike (eps, var)
E      tmp = Add (var, eps_cast)
E      tmp_2 = Sqrt (tmp)
E      const_cast = CastLike (const, tmp_2)
E      rstd = Div (const_cast, tmp_2)
E      mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_training_onnx, node name: _aten_native_batch_norm_training_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[3,2,3,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4) => (float16[3,2,3,4] _val_6, float16[2] _val_7, float16[2] _val_8) 
E      <float16[3,2,3,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4, float16[3,2,3,4] _val_6, float16[2] _val_7, float16[2] _val_8, int64[3] _val_5>
E   {
E      _val_5 = Constant <value_ints: ints = [0, 2, 3]> ()
E      _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 0.5, momentum: float = -1, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   _aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
E   {
E      norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E      upcast_input = Cast <to: int = 1> (input)
E      mean = ReduceMean (upcast_input, axes)
E      input_sub_mean = Sub (upcast_input, mean)
E      sqr = Mul (input_sub_mean, input_sub_mean)
E      var = ReduceMean <keepdims: int = 0> (sqr, axes)
E      const = Constant <value: tensor = float const {1}> ()
E      eps = Constant <value_float: float = @eps> ()
E      eps_cast = CastLike (eps, var)
E      tmp = Add (var, eps_cast)
E      tmp_2 = Sqrt (tmp)
E      const_cast = CastLike (const, tmp_2)
E      rstd = Div (const_cast, tmp_2)
E      mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_training_onnx, node name: _aten_native_batch_norm_training_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8) 
E      <float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4, float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8, int64[1] _val_5>
E   {
E      _val_5 = Constant <value_ints: ints = [0]> ()
E      _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   _aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
E   {
E      norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E      upcast_input = Cast <to: int = 1> (input)
E      mean = ReduceMean (upcast_input, axes)
E      input_sub_mean = Sub (upcast_input, mean)
E      sqr = Mul (input_sub_mean, input_sub_mean)
E      var = ReduceMean <keepdims: int = 0> (sqr, axes)
E      const = Constant <value: tensor = float const {1}> ()
E      eps = Constant <value_float: float = @eps> ()
E      eps_cast = CastLike (eps, var)
E      tmp = Add (var, eps_cast)
E      tmp_2 = Sqrt (tmp)
E      const_cast = CastLike (const, tmp_2)
E      rstd = Div (const_cast, tmp_2)
E      mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_training_onnx, node name: _aten_native_batch_norm_training_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8) 
E      <float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4, float16[2,1] _val_6, float16[1] _val_7, float16[1] _val_8, int64[1] _val_5>
E   {
E      _val_5 = Constant <value_ints: ints = [0]> ()
E      _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 1> (input_0, input_1, input_2, input_3, input_4, _val_5)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   _aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
E   {
E      norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E      upcast_input = Cast <to: int = 1> (input)
E      mean = ReduceMean (upcast_input, axes)
E      input_sub_mean = Sub (upcast_input, mean)
E      sqr = Mul (input_sub_mean, input_sub_mean)
E      var = ReduceMean <keepdims: int = 0> (sqr, axes)
E      const = Constant <value: tensor = float const {1}> ()
E      eps = Constant <value_float: float = @eps> ()
E      eps_cast = CastLike (eps, var)
E      tmp = Add (var, eps_cast)
E      tmp_2 = Sqrt (tmp)
E      const_cast = CastLike (const, tmp_2)
E      rstd = Div (const_cast, tmp_2)
E      mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:528: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:171: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_native_batch_norm_training_onnx, node name: _aten_native_batch_norm_training_onnx_16): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:530: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[1,2,3] input_0) => (float16[1,2,3] _val_17, float16[2] _val_18, float16[2] _val_19) 
E      <float16[1,2,3] input_0, float16[1,2,3] _val_17, float16[2] _val_18, float16[2] _val_19, float[1] _val_1, float16[1] _val_2, int64[1] _val_3, float16[2] _val_4, float[1] _val_5, float16[1] _val_6, int64[1] _val_7, float16[2] _val_8, int64[2] _val_9, float16[1,2,1] _val_10, float16[2] _val_11, float16[1,2,1] _val_12, float16[1,2,3] _val_13, float16[1,2,3] _val_14, float16[1,2,1] _val_15, float16[2] _val_16>
E   {
E      _val_1 = Constant <value_floats: floats = [1]> ()
E      _val_2 = CastLike (_val_1, input_0)
E      _val_3 = Shape <end: int = 2, start: int = 1> (input_0)
E      _val_4 = Expand (_val_2, _val_3)
E      _val_5 = Constant <value_floats: floats = [0]> ()
E      _val_6 = CastLike (_val_5, input_0)
E      _val_7 = Shape <end: int = 2, start: int = 1> (input_0)
E      _val_8 = Expand (_val_6, _val_7)
E      _val_9 = Constant <value_ints: ints = [0, 2]> ()
E      _val_10 = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (input_0, _val_9)
E      _val_11 = Squeeze (_val_10)
E      _val_12 = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (input_0, _val_9)
E      _val_13 = Sub (input_0, _val_12)
E      _val_14 = Mul (_val_13, _val_13)
E      _val_15 = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (_val_14, _val_9)
E      _val_16 = Squeeze (_val_15)
E      _val_17, _val_18, _val_19 = pkg.onnxscript.torch_lib._aten_native_batch_norm_training_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 1> (input_0, _val_4, _val_8, _val_11, _val_16, _val_9)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   _aten_native_batch_norm_training_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var, axes) => (norm, mean_3, rstd)
E   {
E      norm, running_mean_0, running_var_1 = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E      upcast_input = Cast <to: int = 1> (input)
E      mean = ReduceMean (upcast_input, axes)
E      input_sub_mean = Sub (upcast_input, mean)
E      sqr = Mul (input_sub_mean, input_sub_mean)
E      var = ReduceMean <keepdims: int = 0> (sqr, axes)
E      const = Constant <value: tensor = float const {1}> ()
E      eps = Constant <value_float: float = @eps> ()
E      eps_cast = CastLike (eps, var)
E      tmp = Add (var, eps_cast)
E      tmp_2 = Sqrt (tmp)
E      const_cast = CastLike (const, tmp_2)
E      rstd = Div (const_cast, tmp_2)
E      mean_3 = ReduceMean <keepdims: int = 0> (upcast_input, axes)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__addmm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[ 2.398e+00, -5.598e+00, -3.727e+00, -6.230e+00, -8.883e+00,
        -6.883e+00,  8.938e+00, -2.180e+00,  3.031e+00, -4.043e+00],
       [-1.116e+00,  2.980e+00, -8.203e+00,  3.217e+00, -6.064e-01,
         3.990e+00,  3.754e+00, -4.535e+00, -2.188e+00,  8.281e+00],
       [-8.703e+00, -7.199e+00,  7.031e-01,  8.180e+00,  4.930e+00,
         7.656e+00,  3.402e+00,  8.789e-03, -2.637e-02,  3.418e+00],
       [-4.035e+00,  9.229e-01,  6.777e+00,  7.215e+00,  4.184e+00,
        -2.830e+00, -5.477e+00, -2.594e+00,  4.879e+00, -7.586e+00],
       [-7.234e+00,  8.414e+00, -2.549e-01, -6.637e+00,  7.578e+00,
        -1.837e+00,  2.373e+00, -5.000e+00,  4.051e+00,  6.383e+00]],
      dtype=float16),
 'input_1': array([], shape=(5, 0), dtype=float16),
 'input_2': array([], shape=(0, 10), dtype=float16)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
   <float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([ 2.207 , -0.0703,  6.16  , -6.406 ,  6.363 , -2.68  ,  6.574 ,
       -6.04  , -1.283 ,  0.457 ], dtype=float16),
 'input_1': array([], shape=(5, 0), dtype=float16),
 'input_2': array([], shape=(0, 10), dtype=float16)}
Model:
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
   producer_name: "pytorch",
   producer_version: "2.3.0"
>
main_graph (float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
   <float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
{
   _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["" : 18]
>
aten_addmm (self, mat1, mat2) => (return_val)
{
   return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([[ 2.398e+00, -5.598e+00, -3.727e+00, -6.230e+00, -8.883e+00,
E           -6.883e+00,  8.938e+00, -2.180e+00,  3.031e+00, -4.043e+00],
E          [-1.116e+00,  2.980e+00, -8.203e+00,  3.217e+00, -6.064e-01,
E            3.990e+00,  3.754e+00, -4.535e+00, -2.188e+00,  8.281e+00],
E          [-8.703e+00, -7.199e+00,  7.031e-01,  8.180e+00,  4.930e+00,
E            7.656e+00,  3.402e+00,  8.789e-03, -2.637e-02,  3.418e+00],
E          [-4.035e+00,  9.229e-01,  6.777e+00,  7.215e+00,  4.184e+00,
E           -2.830e+00, -5.477e+00, -2.594e+00,  4.879e+00, -7.586e+00],
E          [-7.234e+00,  8.414e+00, -2.549e-01, -6.637e+00,  7.578e+00,
E           -1.837e+00,  2.373e+00, -5.000e+00,  4.051e+00,  6.383e+00]],
E         dtype=float16),
E    'input_1': array([], shape=(5, 0), dtype=float16),
E    'input_2': array([], shape=(0, 10), dtype=float16)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
E      <float16[5,10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:542: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:315: in _ort_session_run
    return session.run(None, ort_inputs)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:220: in run
    return self._sess.run(output_names, input_feed, run_options)
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Gemm node. Name:'_inline_aten_addmmn0' Status Message: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.h:59 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &) M_ >= 0 && K_ > 0 && N_ >= 0 was false.

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:556: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array([ 2.207 , -0.0703,  6.16  , -6.406 ,  6.363 , -2.68  ,  6.574 ,
E          -6.04  , -1.283 ,  0.457 ], dtype=float16),
E    'input_1': array([], shape=(5, 0), dtype=float16),
E    'input_2': array([], shape=(0, 10), dtype=float16)}
E   Model:
E   <
E      ir_version: 8,
E      opset_import: ["" : 18, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.3.0"
E   >
E   main_graph (float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2) => (float16[5,10] _val_3) 
E      <float16[10] input_0, float16[5,0] input_1, float16[0,10] input_2, float16[5,10] _val_3>
E   {
E      _val_3 = pkg.onnxscript.torch_lib.aten_addmm <alpha: float = 0.6, beta: float = 0.2> (input_0, input_1, input_2)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["" : 18]
E   >
E   aten_addmm (self, mat1, mat2) => (return_val)
E   {
E      return_val = Gemm <alpha: float = @alpha, beta: float = @beta> (mat1, mat2, self)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
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