[optim] abstract out _default_to_foreach_util (#92305)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/92305
Approved by: https://github.com/albanD

torch/optim/adadelta.py

@@ -1,7 +1,7 @@
 import torch
 from torch import Tensor
 
-from .optimizer import Optimizer, _use_grad_for_differentiable
+from .optimizer import Optimizer, _use_grad_for_differentiable, _default_to_foreach
 from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
 from typing import List, Optional
 
@@ -192,19 +192,9 @@ def adadelta(
     See :class:`~torch.optim.Adadelta` for details.
     """
 
-    # We try to use the foreach implementation on CUDA whenever possible since
-    # it is faster than the for-loop implementation. However, the foreach
-    # implementation is not differentiable, so we must check differentiable=False.
     # We still respect when the user inputs False for foreach.
     if foreach is None:
-        all_tensors = []
+        foreach = _default_to_foreach([params, grads, square_avgs, acc_deltas], differentiable=differentiable)
-        all_tensors.extend(params)
-        all_tensors.extend(grads)
-        all_tensors.extend(square_avgs)
-        all_tensors.extend(acc_deltas)
-        foreach = not torch.jit.is_scripting() and not differentiable and all(
-            p.is_cuda for p in all_tensors
-        )
 
     if foreach and torch.jit.is_scripting():
         raise RuntimeError("torch.jit.script not supported with foreach optimizers")

torch/optim/optimizer.py

@@ -6,7 +6,7 @@ import warnings
 import functools
 import math
 
-from typing import Callable, Dict
+from typing import Callable, Dict, List
 
 import torch.utils.hooks as hooks
 from torch.utils.hooks import RemovableHandle
@@ -54,6 +54,19 @@ def _dispatch_sqrt(x: float):  # float annotation is needed because of torchscri
     else:
         return math.sqrt(x)
 
+
+# We try to use the foreach implementation on CUDA whenever possible since
+# it is faster than the for-loop implementation. However, the foreach
+# implementation is not differentiable, so we must check differentiable=False.
+def _default_to_foreach(tensorlists: List[List[torch.Tensor]], differentiable: bool = False) -> bool:
+    all_tensors = []
+    for tensorlist in tensorlists:
+        all_tensors.extend(tensorlist)
+    return not torch.jit.is_scripting() and not differentiable and all(
+        p.is_cuda for p in all_tensors
+    )
+
+
 def register_optimizer_step_pre_hook(hook: Callable[..., None]) -> RemovableHandle:
     r"""Register a pre hook common to all optimizers. The hook should have the following
     signature::