Fix ADOPT on older PyTorch (tested back to 1.13)

timm/optim/adopt.py

@@ -38,9 +38,13 @@ def _get_scalar_dtype(is_fused=None):
     )
 
 
+def _is_compiling():
+    return torch.compiler.is_compiling() if hasattr(torch, 'compiler') else False
+
+
 def _get_value(x):
     # item is significantly faster than a cpu tensor in eager mode
-    if not torch.jit.is_scripting() and torch.compiler.is_compiling():
+    if not torch.jit.is_scripting() and _is_compiling():
         return x
     else:
         return x.item() if isinstance(x, torch.Tensor) else x
@@ -271,7 +275,7 @@ def _single_tensor_adopt(
         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 capturable and not _is_compiling():
             from torch.optim.optimizer import _get_capturable_supported_devices
             capturable_supported_devices = _get_capturable_supported_devices()
             assert (
@@ -340,7 +344,7 @@ def _multi_tensor_adopt(
         )
 
     # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
-    if not torch._utils.is_compiling() and capturable:
+    if capturable and not _is_compiling():
         from torch.optim.optimizer import _get_capturable_supported_devices
         capturable_supported_devices = _get_capturable_supported_devices(
             supports_xla=False
@@ -384,7 +388,7 @@ def _multi_tensor_adopt(
         # 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 device_state_steps[0].is_cpu:
+        if not _is_compiling() and device_state_steps[0].is_cpu:
             torch._foreach_add_(
                 device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0
             )
@@ -457,9 +461,7 @@ def adopt(
 
     # this check is slow during compilation, so we skip it
     # if it's strictly needed we can add this check back in dynamo
-    if not torch._utils.is_compiling() and not all(
-            isinstance(t, torch.Tensor) for t in state_steps
-    ):
+    if not _is_compiling() and not all(isinstance(t, torch.Tensor) for t in state_steps):
         raise RuntimeError(
             "API has changed, `state_steps` argument must contain a list of singleton tensors"
         )