Add context manager to use next batch context for postprocs (#2939)

Summary:

Small refactor to reduce code repetition of setting and reverting pipelined postprocs context to the next batch's context

Differential Revision: D73824600

Author: che-sh

torchrec/distributed/train_pipeline/train_pipelines.py

@@ -58,6 +58,7 @@
     StageOut,
     StageOutputWithEvent,
     TrainPipelineContext,
+    use_context_for_postprocs,
 )
 from torchrec.distributed.types import Awaitable
 from torchrec.pt2.checks import is_torchdynamo_compiling
@@ -792,19 +793,9 @@ def start_sparse_data_dist(
             with self._stream_context(self._data_dist_stream):
                 _wait_for_batch(batch, self._memcpy_stream)
 
-                original_contexts = [p.get_context() for p in self._pipelined_postprocs]
-
                 # Temporarily set context for next iter to populate cache
-                for postproc_mod in self._pipelined_postprocs:
-                    postproc_mod.set_context(context)
-
-                _start_data_dist(self._pipelined_modules, batch, context)
-
-                # Restore context for model fwd
-                for module, context in zip(
-                    self._pipelined_postprocs, original_contexts
-                ):
-                    module.set_context(context)
+                with use_context_for_postprocs(self._pipelined_postprocs, context):
+                    _start_data_dist(self._pipelined_modules, batch, context)
 
     def wait_sparse_data_dist(self, context: TrainPipelineContext) -> None:
         """
@@ -1325,22 +1316,15 @@ def start_sparse_data_dist(
             return
 
         # Temporarily set context for next iter to populate cache
-        original_contexts = [p.get_context() for p in self._pipelined_postprocs]
-        for postproc_mod in self._pipelined_postprocs:
-            postproc_mod.set_context(context)
-
-        with record_function(f"## start_sparse_data_dist {context.index} ##"):
-            with self._stream_context(self._data_dist_stream):
-                _wait_for_events(batch, context, self._data_dist_stream)
-                model_input = self.extract_model_input_from_batch(batch)
-                _start_data_dist(self._pipelined_modules, model_input, context)
-                event = torch.get_device_module(self._device).Event()
-                event.record()
-                context.events.append(event)
-
-        # Restore context for model forward
-        for module, context in zip(self._pipelined_postprocs, original_contexts):
-            module.set_context(context)
+        with use_context_for_postprocs(self._pipelined_postprocs, context):
+            with record_function(f"## start_sparse_data_dist {context.index} ##"):
+                with self._stream_context(self._data_dist_stream):
+                    _wait_for_events(batch, context, self._data_dist_stream)
+                    model_input = self.extract_model_input_from_batch(batch)
+                    _start_data_dist(self._pipelined_modules, model_input, context)
+                    event = torch.get_device_module(self._device).Event()
+                    event.record()
+                    context.events.append(event)
 
     def start_embedding_lookup(
         self,

torchrec/distributed/train_pipeline/utils.py

@@ -7,6 +7,7 @@
 
 # pyre-strict
 
+import contextlib
 import copy
 import itertools
 import logging
@@ -21,6 +22,7 @@
     Callable,
     cast,
     Dict,
+    Generator,
     Generic,
     Iterable,
     Iterator,
@@ -1797,6 +1799,28 @@ def _prefetch_embeddings(
     return data_per_sharded_module
 
 
+@contextlib.contextmanager
+def use_context_for_postprocs(
+    pipelined_postprocs: List[PipelinedPostproc],
+    next_batch_context: TrainPipelineContext,
+) -> Generator[None, None, None]:
+    """
+    Temporarily set pipelined postproc context for next iter to populate cache.
+    """
+    # Save original context for model fwd
+    original_contexts = [p.get_context() for p in pipelined_postprocs]
+
+    # Temporarily set context for next iter to populate cache
+    for postproc_mod in pipelined_postprocs:
+        postproc_mod.set_context(next_batch_context)
+
+    yield
+
+    # Restore context for model fwd
+    for module, context in zip(pipelined_postprocs, original_contexts):
+        module.set_context(context)
+
+
 class SparseDataDistUtil(Generic[In]):
     """
     Helper class exposing methods for sparse data dist and prefetch pipelining.
@@ -1808,6 +1832,7 @@ class SparseDataDistUtil(Generic[In]):
         apply_jit (bool): apply torch.jit.script to non-pipelined (unsharded) modules.
         prefetch_stream (Optional[torch.cuda.Stream]): Stream on which model prefetch runs
             Defaults to `None`. This needs to be passed in to enable prefetch pipelining.
+        pipeline_postproc (bool): whether to pipeline postproc modules. Defaults to `False`.
 
     Example::
         sdd = SparseDataDistUtil(