Avoid nan by using zeros instead of empty dummy tensor #2648
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This pull request was exported from Phabricator. Differential Revision: D67535635 |
yunjiangster
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Summary:
This appears to solve the nan issue when we enable
`torch.autograd.set_detect_anomaly(True)`
The error below appears non-deterministically, and after a few training steps, presumably because `self._dummy_tensor` can be very large but not nan at the beginning, and after a few iterations reaches `nan`.
```
RuntimeError: Function 'All2All_Seq_Req_WaitBackward' returned nan values in its 0th output; num_outputs = 1; num_inputs = 0; outputs[0].shape = [1, ]; outputs[i] = nan
[ torch.cuda.FloatTensor{1} ]
```
Differential Revision: D67535635
yunjiangster
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This pull request was exported from Phabricator. Differential Revision: D67535635 |
yunjiangster
added a commit
to yunjiangster/torchrec
that referenced
this pull request
Dec 20, 2024
Summary:
This appears to solve the nan issue when we enable
`torch.autograd.set_detect_anomaly(True)`
The error below appears non-deterministically, and after a few training steps, presumably because `self._dummy_tensor` can be very large but not nan at the beginning, and after a few iterations reaches `nan`.
```
RuntimeError: Function 'All2All_Seq_Req_WaitBackward' returned nan values in its 0th output; num_outputs = 1; num_inputs = 0; outputs[0].shape = [1, ]; outputs[i] = nan
[ torch.cuda.FloatTensor{1} ]
```
Differential Revision: D67535635
yunjiangster
force-pushed
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c406374
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This pull request was exported from Phabricator. Differential Revision: D67535635 |
Summary:
This appears to solve the nan issue when we enable
`torch.autograd.set_detect_anomaly(True)`
The error below appears non-deterministically, and after a few training steps, presumably because `self._dummy_tensor` can be very large but not nan at the beginning, and after a few iterations reaches `nan`.
```
RuntimeError: Function 'All2All_Seq_Req_WaitBackward' returned nan values in its 0th output; num_outputs = 1; num_inputs = 0; outputs[0].shape = [1, ]; outputs[i] = nan
[ torch.cuda.FloatTensor{1} ]
```
Reviewed By: iamzainhuda
Differential Revision: D67535635
yunjiangster
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This pull request was exported from Phabricator. Differential Revision: D67535635 |
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Summary:
This appears to solve the nan issue when we enable
torch.autograd.set_detect_anomaly(True)The error below appears non-deterministically, and after a few training steps, presumably because
self._dummy_tensorcan be very large but not nan at the beginning, and after a few iterations reachesnan.Differential Revision: D67535635