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Deprecate torch._utils.is_compiling() and torch._dynamo.external_utils.is_compiling() (#127690)

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This PR is split from PR #126898.

- #126898

------

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127690
Approved by: https://github.com/Skylion007, https://github.com/malfet
This commit is contained in:
Xuehai Pan
2024-08-03 14:30:03 +08:00
committed by PyTorch MergeBot
parent 159d508f03
commit 0e7e61f7ce
29 changed files with 87 additions and 83 deletions
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6
torch/optim/radam.py
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@ -276,7 +276,7 @@ def _single_tensor_radam(
step_t = state_steps[i]
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
if not torch.compiler.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices()
assert (
param.device.type == step_t.device.type
@ -374,7 +374,7 @@ def _multi_tensor_radam(
assert not differentiable, "_foreach ops don't support autograd"
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch._utils.is_compiling() and capturable:
if not torch.compiler.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices(
supports_xla=False
)
@ -398,7 +398,7 @@ def _multi_tensor_radam(
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
# wrapped it once now. The alpha is required to assure we go to the right overload.
if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu:
if not torch.compiler.is_compiling() and grouped_state_steps[0].is_cpu:
torch._foreach_add_(
grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
)