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…nces between x86 vs aarch64 (pytorch#176085) In the test: ``` python test/cpp_extensions/test_libtorch_agnostic.py TestLibtorchAgnosticCUDA.test_std_cuda_check_error_show_cpp_stacktraces_True_cuda ``` it raises an exception when calling `STD_CUDA_CHECK(cudaSetDevice(99999));` which got the expected `CUDA error: invalid device` message. However, the expected string for the C++ stack trace is different between `x86` vs `aarch64` due perhaps in these issues: - pytorch#119905 - pytorch#134387 In the current setup when getting a stack trace string: - x86 contains `C++ CapturedTraceback:` - aarch64 contains `Exception raised from` + `frame #` An example of the full string from an aarch64 system when : ``` AssertionError: 'C++ CapturedTraceback:' not found in 'CUDA error: invalid device ordinal\nGPU device may be out of range, do you have enough GPUs?\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n\nException raised from test_std_cuda_check_error at /opt/pytorch/pytorch/test/cpp_extensions/libtorch_agn_2_10_extension/csrc/test_std_cuda_check.cu:23 (most recent call first):\nframe #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0xd4 (0xe471ebcd39f4 in /usr/local/lib/python3.12/dist-packages/torch/lib/libc10.so)\nframe #1: <unknown function> + 0x43f998 (0xe471ebdcf998 in /usr/local/lib/python3.12/dist-packages/torch/lib/libc10_cuda.so)\nframe #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, unsigned int, bool) + 0x1bc (0xe471ebdcfc0c in /usr/local/lib/python3.12/dist-packages/torch/lib/libc10_cuda.so)\nframe #3: torch_c10_cuda_check_msg + 0x1c (0xe471ef335c4c in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_cuda.so)\nframe #4: test_std_cuda_check_error() + 0x58 (0xe470cd396678 in /opt/pytorch/pytorch/test/cpp_extensions/libtorch_agn_2_10_extension/install/usr/local/lib/python3.12/dist-packages/libtorch_agn_2_10/_C.so)\nframe pytorch#5: c10::BoxedKernel::makeFromFunctor<StableIValueBoxedKernel>(std::unique_ptr<StableIValueBoxedKernel, std::default_delete<StableIValueBoxedKernel> >)::{lambda(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)#1}::_FUN(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) + 0x16c (0xe47211cd419c in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_cpu.so)\nframe pytorch#6: <unknown function> + 0x61d34bc (0xe47211cf34bc in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_cpu.so)\nframe pytorch#7: <unknown function> + 0xe6c324 (0xe4721532c324 in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_python.so)\nframe pytorch#8: <unknown function> + 0xe6c7e0 (0xe4721532c7e0 in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_python.so)\nframe pytorch#9: <unknown function> + 0xd3907c (0xe472151f907c in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_python.so)\nframe pytorch#10: <unknown function> + 0x5ccbf8 (0xe47214a8cbf8 in /usr/local/lib/python3.12/dist-packages/torch/lib/libtorch_python.so)\nframe pytorch#11: /usr/bin/python() [0x504a34]\nframe pytorch#12: PyObject_Call + 0x6c (0x4c633c in /usr/bin/python)\nframe pytorch#13: _PyEval_EvalFrameDefault + 0x3ea0 (0x568564 in /usr/bin/python)\nframe pytorch#14: _PyObject_Call_Prepend + 0xc4 (0x4c5934 in /usr/bin/python)\nframe pytorch#15: /usr/bin/python() [0x52a070]\nframe pytorch#16: _PyObject_MakeTpCall + 0x78 (0x4c3e58 in /usr/bin/python)\nframe pytorch#17: _PyEval_EvalFrameDefault + 0x8a0 (0x564f64 in /usr/bin/python)\nframe pytorch#18: PyEval_EvalCode + 0x130 (0x5632b4 in /usr/bin/python)\nframe pytorch#19: PyRun_StringFlags + 0xe0 (0x59c330 in /usr/bin/python)\nframe pytorch#20: PyRun_SimpleStringFlags + 0x44 (0x67ebc4 in /usr/bin/python)\nframe pytorch#21: Py_RunMain + 0x390 (0x68b380 in /usr/bin/python)\nframe pytorch#22: Py_BytesMain + 0x28 (0x68ae88 in /usr/bin/python)\nframe pytorch#23: <unknown function> + 0x284c4 (0xe47216b084c4 in /lib/aarch64-linux-gnu/libc.so.6)\nframe pytorch#24: __libc_start_main + 0x98 (0xe47216b08598 in /lib/aarch64-linux-gnu/libc.so.6)\nframe pytorch#25: _start + 0x30 (0x5f6770 in /usr/bin/python)\n\n' To execute this test, run the following from the base repo dir: python test/cpp_extensions/test_libtorch_agnostic.py TestLibtorchAgnosticCUDA.test_std_cuda_check_error_show_cpp_stacktraces_True_cuda ``` Pull Request resolved: pytorch#176085 Approved by: https://github.com/eqy
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Mar 28, 2026
## Summary Add `module_error_inputs_torch_nn_Embedding` function to test error messages for invalid inputs to `nn.Embedding` module. ## Motivation Currently, `torch.nn.Embedding` does not have `module_error_inputs_func` defined in `common_modules.py`. This PR adds error input tests to enable regression testing for error messages and follow the pattern already established for other modules (BatchNorm, GroupNorm, RNN cells, etc.). ## Test Cases Added 1. **Out of range indices**: Tests IndexError when index exceeds `num_embeddings` - Input: `Embedding(10, 3)` with indices `[0, 5, 15]` (15 is out of range) - Expected: `IndexError: index out of range in self` - **Note**: Only tested on CPU - CUDA triggers kernel assertion instead of Python exception 2. **Float indices**: Tests RuntimeError when float tensor used instead of Long/Int - Input: `Embedding(10, 3)` with float tensor `[1.5, 2.5]` - Expected: `RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int` 3. **Negative num_embeddings**: Tests RuntimeError when constructor has negative dimension - Input: `Embedding(-1, 3)` - Expected: `RuntimeError: Trying to create tensor with negative dimension` ## Test Environment - Tested on H200 GPU with CUDA 12.8 - Verified error messages match on CPU - CUDA tests pass for dtype and construction errors Fixes pytorch#174179 Pull Request resolved: pytorch#174180 Approved by: https://github.com/albanD
bobrenjc93
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Apr 9, 2026
## Summary Add `module_error_inputs_torch_nn_Embedding` function to test error messages for invalid inputs to `nn.Embedding` module. ## Motivation Currently, `torch.nn.Embedding` does not have `module_error_inputs_func` defined in `common_modules.py`. This PR adds error input tests to enable regression testing for error messages and follow the pattern already established for other modules (BatchNorm, GroupNorm, RNN cells, etc.). ## Test Cases Added 1. **Out of range indices**: Tests IndexError when index exceeds `num_embeddings` - Input: `Embedding(10, 3)` with indices `[0, 5, 15]` (15 is out of range) - Expected: `IndexError: index out of range in self` - **Note**: Only tested on CPU - CUDA triggers kernel assertion instead of Python exception 2. **Float indices**: Tests RuntimeError when float tensor used instead of Long/Int - Input: `Embedding(10, 3)` with float tensor `[1.5, 2.5]` - Expected: `RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int` 3. **Negative num_embeddings**: Tests RuntimeError when constructor has negative dimension - Input: `Embedding(-1, 3)` - Expected: `RuntimeError: Trying to create tensor with negative dimension` ## Test Environment - Tested on H200 GPU with CUDA 12.8 - Verified error messages match on CPU - CUDA tests pass for dtype and construction errors Fixes pytorch#174179 Pull Request resolved: pytorch#174180 Approved by: https://github.com/albanD
bobrenjc93
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Apr 10, 2026
## Summary Add `module_error_inputs_torch_nn_Embedding` function to test error messages for invalid inputs to `nn.Embedding` module. ## Motivation Currently, `torch.nn.Embedding` does not have `module_error_inputs_func` defined in `common_modules.py`. This PR adds error input tests to enable regression testing for error messages and follow the pattern already established for other modules (BatchNorm, GroupNorm, RNN cells, etc.). ## Test Cases Added 1. **Out of range indices**: Tests IndexError when index exceeds `num_embeddings` - Input: `Embedding(10, 3)` with indices `[0, 5, 15]` (15 is out of range) - Expected: `IndexError: index out of range in self` - **Note**: Only tested on CPU - CUDA triggers kernel assertion instead of Python exception 2. **Float indices**: Tests RuntimeError when float tensor used instead of Long/Int - Input: `Embedding(10, 3)` with float tensor `[1.5, 2.5]` - Expected: `RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int` 3. **Negative num_embeddings**: Tests RuntimeError when constructor has negative dimension - Input: `Embedding(-1, 3)` - Expected: `RuntimeError: Trying to create tensor with negative dimension` ## Test Environment - Tested on H200 GPU with CUDA 12.8 - Verified error messages match on CPU - CUDA tests pass for dtype and construction errors Fixes pytorch#174179 Pull Request resolved: pytorch#174180 Approved by: https://github.com/albanD
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