add postproc module name in trace (#3384)

Summary:
Pull Request resolved: #3384

# context
* simple BE work to add postproc module name to the trace

Reviewed By: spmex

Differential Revision: D82700705

fbshipit-source-id: 572b120d008bfe85eb25b7488c6f46830e5e382f

Author: TroyGarden

torchrec/distributed/train_pipeline/postproc.py

@@ -79,11 +79,13 @@ def __init__(
         self._dist_stream = dist_stream
         if not default_stream:
             logger.warning(
-                f"Postproc module {fqn} has no default stream. This may cause race conditions and NaNs during training!"
+                f"Postproc module {fqn} has no default stream. "
+                "This may cause race conditions and NaNs during training!"
             )
         if not dist_stream:
             logger.warning(
-                f"Postproc module {fqn} has no dist stream. This may cause race conditions and NaNs during training!"
+                f"Postproc module {fqn} has no dist stream. "
+                "This may cause race conditions and NaNs during training!"
             )
 
         if self._dist_stream:
@@ -139,7 +141,9 @@ def forward(self, *input, **kwargs) -> Any:
         # Use input[0] as _start_data_dist only passes 1 arg
         args, kwargs = self._args.build_args_kwargs(input[0])
 
-        with record_function(f"## sdd_input_postproc {self._context.index} ##"):
+        with record_function(
+            f"## input_postproc {type(self.postproc_module)} {self._context.index} ##"
+        ):
             # should be no-op as we call this in dist stream
             with self._stream_context(self._dist_stream):
                 res = self._postproc_module(*args, **kwargs)
@@ -160,7 +164,11 @@ def forward(self, *input, **kwargs) -> Any:
                     PipelinedPostproc.recursive_record_stream(res, self._default_stream)
                 elif self._context.index == 0:
                     logger.warning(
-                        f"Result of postproc module {self._fqn} is of type {type(res)}. We currently expect it to be a Tensor, Pipelineable, Iterable, or Dict to handle memory safety. If your output is not of this type, please add support for it above. Otherwise you might run into NaNs or CUDA Illegal Memory issues during training!"
+                        f"Result of postproc module {self._fqn} is of type {type(res)}. "
+                        "We currently expect it to be a Tensor, Pipelineable, Iterable, "
+                        "or Dict to handle memory safety. If your output is not of this "
+                        "type, please add support for it above. Otherwise you might run "
+                        "into NaNs or CUDA Illegal Memory issues during training!"
                     )
 
             with self._stream_context(self._default_stream):