Expose API to specify custom context manager for checkpoint

test/test_autograd.py

@@ -5608,6 +5608,39 @@ def foo(x, y, z):
         out = checkpoint(foo, x, y, z, use_reentrant=False)
         out.sum().backward()
 
+    def test_checkpointing_without_reentrant_with_context_fn(self):
+        class VerboseTorchDispatchMode(TorchDispatchMode):
+            def __init__(self):
+                self.operators = []
+
+            def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+                if kwargs is None:
+                    kwargs = {}
+                self.operators.append(func.__name__)
+                return func(*args, **kwargs)
+
+        x = torch.tensor(1., requires_grad=True)
+        verbose_mode = VerboseTorchDispatchMode()
+
+        def context_fn():
+            return verbose_mode, contextlib.nullcontext()
+        out = checkpoint(lambda x: x.sin(), x, use_reentrant=False, context_fn=context_fn)
+        self.assertEqual(verbose_mode.operators, ['sin.default'])
+
+        verbose_mode.operators = []
+
+        def context_fn():
+            return contextlib.nullcontext(), verbose_mode
+        out = checkpoint(lambda x: x.sin(), x, use_reentrant=False, context_fn=context_fn)
+        out.backward()
+        self.assertEqual(
+            verbose_mode.operators,
+            ['detach.default', 'detach.default', 'detach.default', 'detach.default', 'sin.default']
+        )
+
+        with self.assertRaisesRegex(Exception, "only supported when use_reentrant=False"):
+            out = checkpoint(lambda x: x.sin(), x, use_reentrant=True, context_fn=context_fn)
+
     def test_access_saved_tensor_twice_without_recomputation_works(self):
         count = [0]
 

torch/utils/checkpoint.py

@@ -2,15 +2,15 @@
 import warnings
 import weakref
 from weakref import ReferenceType
-from typing import Any, Iterable, List, Tuple, Dict, Optional, DefaultDict
+from typing import Any, Callable, ContextManager, Iterable, List, Tuple, Dict, Optional, DefaultDict
 from collections import defaultdict
 import uuid
 import contextlib
 
 __all__ = [
     "checkpoint", "checkpoint_sequential", "CheckpointFunction",
     "check_backward_validity", "detach_variable", "get_device_states",
-    "set_device_states",
+    "set_device_states", "noop_context_fn"
 ]
 
 def detach_variable(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]:
@@ -165,7 +165,17 @@ def backward(ctx, *args):
         return (None, None) + grads
 
 
-def checkpoint(function, *args, use_reentrant: bool = True, **kwargs):
+def noop_context_fn():
+    return contextlib.nullcontext(), contextlib.nullcontext()
+
+
+def checkpoint(
+    function,
+    *args,
+    use_reentrant: bool = True,
+    context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn,
+    **kwargs
+):
     r"""Checkpoint a model or part of the model
 
     Checkpointing works by trading compute for memory. Rather than storing all
@@ -239,6 +249,10 @@ def checkpoint(function, *args, use_reentrant: bool = True, **kwargs):
             keyword arguments input into the checkpointed function. Note that future
             versions of PyTorch will default to ``use_reentrant=False``.
             Default: ``True``
+        context_fn(Callable, optional): A callable returning a tuple of two
+            context managers. The function and its recomputation will be run
+            under the first and second context managers respectively.
+            This argument is only supported if ``use_reentrant=False``.
         args: tuple containing inputs to the :attr:`function`
 
     Returns:
@@ -250,11 +264,14 @@ def checkpoint(function, *args, use_reentrant: bool = True, **kwargs):
         raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
 
     if use_reentrant:
+        if context_fn is not noop_context_fn:
+            raise ValueError("Passing context_fn is only supported when use_reentrant=False.")
         return CheckpointFunction.apply(function, preserve, *args)
     else:
         return _checkpoint_without_reentrant(
             function,
             preserve,
+            context_fn,
             *args,
             **kwargs,
         )
@@ -626,7 +643,13 @@ def unpack_hook(holder):
 
 # NB: this helper wraps fn before calling checkpoint_impl. kwargs and
 #     saving/restoring of global state is handled here.
-def _checkpoint_without_reentrant(fn, preserve_rng_state=True, *args, **kwargs):
+def _checkpoint_without_reentrant(
+    fn,
+    preserve_rng_state=True,
+    context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = noop_context_fn,
+    *args,
+    **kwargs
+):
     """Checkpointining without re-entrant autograd
     Args:
         function: describes what to run in the forward pass of the model or
@@ -637,9 +660,13 @@ def _checkpoint_without_reentrant(fn, preserve_rng_state=True, *args, **kwargs):
         preserve_rng_state(bool, optional):  Omit stashing and restoring
             the RNG state during each checkpoint.
             Default: ``True``
+        context_fn(Callable, optional): A callable returning a tuple of two
+            context managers. The function and its recomputation will be run
+            under the first and second context managers respectively.
         *args: Arguments to pass in to the given ``function``.
         **kwargs: Keyword arguments to pass into the given ``function``.
     """
+    forward_context, recompute_context = context_fn()
     # Accommodates the (remote) possibility that autocast is enabled for cpu AND gpu.
     gpu_autocast_kwargs, cpu_autocast_kwargs = _get_autocast_kwargs()
 
@@ -669,7 +696,8 @@ def recompute_fn(*inputs):
                     set_device_states(fwd_gpu_devices, fwd_gpu_states)
 
             with torch.cuda.amp.autocast(**gpu_autocast_kwargs), \
-                 torch.cpu.amp.autocast(**cpu_autocast_kwargs):
+                 torch.cpu.amp.autocast(**cpu_autocast_kwargs), \
+                 recompute_context:
                 fn(*args, **kwargs)
 
     new_frame = _CheckpointFrame(recompute_fn)
@@ -680,7 +708,8 @@ def recompute_fn(*inputs):
     if new_frame.input_saver.grad_fn is None:
         return fn(*args, **kwargs)
 
-    with _checkpoint_hook(new_frame):
+    with _checkpoint_hook(new_frame), \
+         forward_context:
         ret = fn(*args, **kwargs)
 
     if torch.cuda._initialized and preserve_rng_state and not had_cuda_in_fwd: