RuntimeError: Tensor: invalid storage offset
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- Dominant language
- Python
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Description
This error raised when the weights of RNN are not part of single contiguous chunk of memory. In pure pytorch calculation it is just a warning, but with apex it will fail: https://github.com/NVIDIA/apex/blob/4212b3e943ea8d1b4c0c2749f4b7753de39a6d3a/apex/amp/utils.py#L177-L188
(the offsets may become negative, which are invalid)
While I am not sure why this (weights are not part of single contiguous chunk of memory) happens in PyTorch, but a simple workaround is to call rnn.flatten_parameters() before each forward call.
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Research direction
Start by reading apex/amp/utils.py at lines 177-188 and reproducing the RuntimeError with RNN weights that are not in one contiguous memory chunk. Compare the failing Apex path with the reported rnn.flatten_parameters() workaround; the issue does not specify a final fix or test, so the expected completion criteria need clarification.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100