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add docstring for adam differentiable parameter (#91881)

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Fixes #90467

Pull Request resolved: https://github.com/pytorch/pytorch/pull/91881
Approved by: https://github.com/janeyx99
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Nouran Ali 2023-01-13 17:08:27 +00:00 committed by PyTorch MergeBot
parent 8f1c3c68d3
commit a60125e298
1 changed files with 4 additions and 0 deletions
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torch/optim/adam.py
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@ -105,6 +105,10 @@ class Adam(Optimizer):
capturable (bool, optional): whether this instance is safe to capture in a CUDA graph.
Passing True can impair ungraphed performance, so if you don't intend to
graph capture this instance, leave it False (default: False)
differentiable (bool, optional): whether autograd should occur through the optimizer step
in training otherwise, the step() function runs in a torch.no_grad() context.
Setting to True can impair performance, so leave it False if you don't intend to run
autograd through this instance (default: False)
fused (bool, optional): whether the fused implementation (CUDA only) is used.
Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16`
are supported. Since the fused implementation is usually significantly faster than