[test] Add error_inputs for nn.Embedding module by subinz1 · Pull Request #174180 · pytorch/pytorch

subinz1 · 2026-02-03T08:41:29Z

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

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
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
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 #174179

cc @mruberry @albanD @jbschlosser

pytorch-bot · 2026-02-03T08:41:33Z

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pytorch-bot · 2026-02-03T08:41:36Z

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subinz1 · 2026-02-03T08:56:43Z

@pytorchbot label "topic: not user facing"

albanD

Thanks!

albanD · 2026-02-04T15:08:53Z

torch/testing/_internal/common_modules.py

+    samples = []
+
+    # Out of range indices: index exceeds num_embeddings
+    # Only test on CPU - CUDA triggers kernel assertion instead of Python exception


cc @eqy I assume there is nothing we can do to make that behavior better on device?

albanD · 2026-02-04T15:09:15Z

@pytorchbot merge

pytorchmergebot · 2026-02-04T15:11:24Z

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pytorchmergebot · 2026-02-04T15:11:40Z

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subinz1 · 2026-02-10T05:57:06Z

@pytorchbot merge

pytorchmergebot · 2026-02-10T05:59:15Z

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pytorchmergebot · 2026-02-10T05:59:33Z

Merge failed

Reason: 2 jobs have failed, first few of them are: Claude Code, trunk / linux-jammy-cuda13.0-py3.10-gcc11 / test (default, 4, 5, linux.g6.4xlarge.experimental.nvidia.gpu)

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albanD · 2026-03-27T18:16:36Z

@pytorchbot merge -r

pytorchmergebot · 2026-03-27T18:19:20Z

@pytorchbot started a rebase job onto refs/remotes/origin/viable/strict. Check the current status here

Add `module_error_inputs_torch_nn_Embedding` function to test error messages for invalid inputs to Embedding module: 1. Out of range indices: tests IndexError when index exceeds num_embeddings (CPU only - CUDA triggers kernel assertion instead of Python exception) 2. Float indices: tests RuntimeError when float tensor used instead of Long/Int 3. Negative num_embeddings: tests RuntimeError when constructor has negative dim This enables regression testing for error messages and follows the existing pattern used for other modules (BatchNorm, GroupNorm, RNN cells, etc.). Fixes pytorch#174179

pytorchmergebot · 2026-03-27T18:19:25Z

Successfully rebased test-embedding-error-inputs onto refs/remotes/origin/viable/strict, please pull locally before adding more changes (for example, via git checkout test-embedding-error-inputs && git pull --rebase)

pytorchmergebot · 2026-03-27T18:20:47Z

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pytorchmergebot · 2026-03-27T21:15:16Z

Merge failed

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subinz1 · 2026-03-28T04:04:07Z

@pytorchbot merge

pytorchmergebot · 2026-03-28T04:06:13Z

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## 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 pytorch#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

## 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 #174179 Pull Request resolved: #174180 Approved by: https://github.com/albanD

# Motivation #174180 introduces `nn.Embedding` module, which causes CI failures on XPU. # Additional Context fix #178854 fix #178762 Pull Request resolved: #178987 Approved by: https://github.com/mikaylagawarecki

## 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

# Motivation #174180 introduces `nn.Embedding` module, which causes CI failures on XPU. # Additional Context fix #178854 fix #178762 Pull Request resolved: #178987 Approved by: https://github.com/mikaylagawarecki

## 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

# Motivation pytorch#174180 introduces `nn.Embedding` module, which causes CI failures on XPU. # Additional Context fix pytorch#178854 fix pytorch#178762 Pull Request resolved: pytorch#178987 Approved by: https://github.com/mikaylagawarecki

## 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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guangyey mentioned this pull request Apr 1, 2026

[xpu][fix] Fix nn.Embedding module failures on XPU #178987

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subinz1 commented Feb 3, 2026

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