Implement _compute_intra_grad_corr_mean for gradient computation #1095
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Add ``is_scaled_loss`` flag to support both scaled / unscaled loss Fix ``test_grad_accum`` and``test_set_num_gradients_to_accumulate``
- Revert the changes in `step` method - Add a method `scale_grad_by_num_grads_to_accum`to handle gradient accumulation using unscaled loss more explicitly - Add gradient tests
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Use ubuntu-20.04 to fix the `arch x64 not found` issue [Version 3.10 with arch x64 not found聽actions/setup-python#401](actions/setup-python#401)
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min-xu-ai
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fairscale/optim/adascale.py
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| @@ -449,6 +487,9 @@ def _final_callback(self) -> None: | |||
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| # Since self._local_grad_sqr is FP32, sum shouldn't overflow. | |||
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| # TODO: Hongbo says param.grad might be FP16 should do this before converting to FP32. | |||
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this is a bit too sparse. Maybe you can expand this and provide more context?
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What does this PR do?
Implement
_compute_intra_grad_corr_meanand testsThis utility function can be helpful for analyzing learning behavior.
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