[PyTorch] Fix backward compatibility with checkpoint API

transformer_engine/pytorch/distributed.py

@@ -516,13 +516,40 @@ def checkpoint(
     kwargs : dict
             dictionary of string keys for keyword arguments to :attr:`function`.
     """
+    only_tensor_args = True
+    for arg in args:
+        if not isinstance(arg, torch.Tensor):
+            only_tensor_args = False
+            break
+
     # Pop out te.distributed.checkpoint() arguments
     global _USE_REENTRANT_ACTIVATION_RECOMPUTE
     _USE_REENTRANT_ACTIVATION_RECOMPUTE = kwargs.pop("use_reentrant", True)
     distribute_saved_activations = kwargs.pop("distribute_saved_activations", False)
     tp_group = kwargs.pop("tp_group", None)
     get_rng_state_tracker = kwargs.pop("get_rng_state_tracker", None)
 
+    # Ensure backward compatibility.
+    if not only_tensor_args:
+        warnings.warn(
+            "Passing non-tensor non-keyword arguments in deprecated and support will be removed in "
+            "future releases of TransformerEngine. `distribute_saved_activations`, `tp_group`, and "
+            "`get_rng_state_tracker` must be passed as keyword arguments to `checkpoint`.",
+            DeprecationWarning, stacklevel=2,
+        )
+        assert len(args) > 3, "Incorrect number of arguments for deprecated `checkpoint` API."
+        assert (
+            isinstance(args[0], bool) and callable(args[1])
+            and isinstance(args[2], None | dist_group_type)
+        ), "Incorrect arguments for deprecated `checkpoint` API."
+        for arg in args[3:]:
+            assert (
+                isinstance(arg, None | torch.Tensor)
+            ), f"Expected tensor argument, found {type(arg)}."
+
+        distribute_saved_activations, get_rng_state_tracker, tp_group = args[:3]
+        args = args[3:]
+
     # Trigger the native PyTorch checkpoint if:
     # 1. `function` is a `torch.nn.Module`
     #    AND
@@ -555,16 +582,6 @@ def checkpoint(
             assert torch.distributed.is_initialized(), "torch.distributed is not initialized."
             tp_group = torch.distributed.GroupMember.WORLD if tp_group is None else tp_group
 
-        # Make sure at least one tensor input has `requires_grad=True`
-        input_requires_grad = False
-        for arg in args:
-            if isinstance(arg, torch.Tensor) and arg.requires_grad:
-                input_requires_grad = True
-                break
-        assert input_requires_grad, (
-            "`use_reentrant=True` requires at least one input tensor with `requires_grad=True`."
-        )
-
         return _CheckpointFunction.apply(
             function,
             distribute_saved_activations,