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Fixing tensor.numpy on wrapped tensors #627
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Fixes pytorch#626 Description: - Fixing tensor.numpy on wrapped tensors
zou3519
reviewed
Mar 31, 2022
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| level = _C.maybe_get_level(tensor) | ||
| if level == -1: | ||
| return _old_numpy(tensor) | ||
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| if _C.is_functionaltensor(tensor): | ||
| # Since we're unwrapping the FunctionalTensorWrapper, we need to make sure | ||
| # that it's up to date first | ||
| torch._sync(tensor) | ||
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| value = _C.get_unwrapped(tensor) | ||
| dl_enabled = _C.tls_set_is_included() | ||
| try: | ||
| # Disable temporarily kDynamicLayerFrontModeKey/kDynamicLayerBackModeKey as included dispatch keys | ||
| if (dl_enabled): | ||
| _C._set_dynamic_layer_keys_included(False) | ||
| return value.numpy() | ||
| finally: | ||
| # Reenable kDynamicLayerFrontModeKey/kDynamicLayerBackModeKey as included dispatch keys | ||
| if (dl_enabled): | ||
| _C._set_dynamic_layer_keys_included(True) |
Contributor
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Hmm, so this is a little more complicated than this I think.
When someone calls .numpy() under vmap, we probably want to error out. Otherwise some weird things might happen:
def f(x):
return torch.tensor(x.numpy())
x = torch.randn(B)
vmap(f)(x) # returns a Tensor of size B, B -- is that what we want?
Contributor
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When someone calls .numpy() under the grad transform then we should support this (as long as there are no vmaps involved). I'm not sure what the best way to support this is... one thing we can do is keep unwrapping the Tensor and seeing that no BatchedTensors are involved.
In the long-term we want a better fix for this that perhaps involves making the pytorch dispatcher recognize .numpy() as an operation
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Fixes #626
Description: