Add custom model fwd in train pipelines (#2324)

Summary:
Pull Request resolved: #2324

Add missing pipelline_preproc and custom_moel_fwd args.

Reviewed By: chrisxcai

Differential Revision: D61564467

Author: sarckk

torchrec/distributed/train_pipeline/tests/test_train_pipelines.py

@@ -827,6 +827,54 @@ def test_multi_dataloader_pipelining(self) -> None:
             )
         )
 
+    # pyre-ignore
+    @unittest.skipIf(
+        not torch.cuda.is_available(),
+        "Not enough GPUs, this test requires at least one GPU",
+    )
+    def test_custom_fwd(
+        self,
+    ) -> None:
+        data = self._generate_data(
+            num_batches=4,
+            batch_size=32,
+        )
+        dataloader = iter(data)
+
+        fused_params_pipelined = {}
+        sharding_type = ShardingType.ROW_WISE.value
+        kernel_type = EmbeddingComputeKernel.FUSED.value
+        sharded_model_pipelined: torch.nn.Module
+
+        model = self._setup_model()
+
+        (
+            sharded_model_pipelined,
+            optim_pipelined,
+        ) = self._generate_sharded_model_and_optimizer(
+            model, sharding_type, kernel_type, fused_params_pipelined
+        )
+
+        def custom_model_fwd(
+            input: Optional[ModelInput],
+        ) -> Tuple[torch.Tensor, torch.Tensor]:
+            loss, pred = sharded_model_pipelined(input)
+            batch_size = pred.size(0)
+            return loss, pred.expand(batch_size * 2, -1)
+
+        pipeline = TrainPipelineSparseDist(
+            model=sharded_model_pipelined,
+            optimizer=optim_pipelined,
+            device=self.device,
+            execute_all_batches=True,
+            custom_model_fwd=custom_model_fwd,
+        )
+
+        for _ in data:
+            # Forward + backward w/ pipelining
+            pred_pipeline = pipeline.progress(dataloader)
+            self.assertEqual(pred_pipeline.size(0), 64)
+
 
 class TrainPipelinePreprocTest(TrainPipelineSparseDistTestBase):
     def setUp(self) -> None:

torchrec/distributed/train_pipeline/train_pipelines.py

@@ -312,7 +312,7 @@ def __init__(
         context_type: Type[TrainPipelineContext] = TrainPipelineContext,
         pipeline_preproc: bool = False,
         custom_model_fwd: Optional[
-            Callable[[In], Tuple[torch.Tensor, List[torch.Tensor]]]
+            Callable[[Optional[In]], Tuple[torch.Tensor, Out]]
         ] = None,
     ) -> None:
         self._model = model
@@ -366,6 +366,10 @@ def __init__(
         self._dataloader_exhausted: bool = False
         self._context_type: Type[TrainPipelineContext] = context_type
 
+        self._model_fwd: Callable[[Optional[In]], Tuple[torch.Tensor, Out]] = (
+            custom_model_fwd if custom_model_fwd else model
+        )
+
         # DEPRECATED FIELDS
         self._batch_i: Optional[In] = None
         self._batch_ip1: Optional[In] = None
@@ -483,9 +487,7 @@ def progress(self, dataloader_iter: Iterator[In]) -> Out:
 
         # forward
         with record_function("## forward ##"):
-            losses, output = cast(
-                Tuple[torch.Tensor, Out], self._model(self.batches[0])
-            )
+            losses, output = self._model_fwd(self.batches[0])
 
         if len(self.batches) >= 2:
             self.wait_sparse_data_dist(self.contexts[1])
@@ -718,7 +720,7 @@ def __init__(
         stash_gradients: bool = False,
         pipeline_preproc: bool = False,
         custom_model_fwd: Optional[
-            Callable[[In], Tuple[torch.Tensor, List[torch.Tensor]]]
+            Callable[[Optional[In]], Tuple[torch.Tensor, Out]]
         ] = None,
     ) -> None:
         super().__init__(
@@ -729,6 +731,7 @@ def __init__(
             apply_jit=apply_jit,
             context_type=EmbeddingTrainPipelineContext,
             pipeline_preproc=pipeline_preproc,
+            custom_model_fwd=custom_model_fwd,
         )
         self._start_batch = start_batch
         self._stash_gradients = stash_gradients
@@ -893,7 +896,7 @@ def _mlp_forward(
             _wait_for_events(
                 batch, context, torch.get_device_module(self._device).current_stream()
             )
-            return cast(Tuple[torch.Tensor, Out], self._model_fwd(batch))
+            return self._model_fwd(batch)
 
     def embedding_backward(self, context: EmbeddingTrainPipelineContext) -> None:
         default_stream = torch.get_device_module(self._device).current_stream()
@@ -1020,6 +1023,10 @@ def __init__(
         device: torch.device,
         execute_all_batches: bool = True,
         apply_jit: bool = False,
+        pipeline_preproc: bool = False,
+        custom_model_fwd: Optional[
+            Callable[[Optional[In]], Tuple[torch.Tensor, Out]]
+        ] = None,
     ) -> None:
         super().__init__(
             model=model,
@@ -1028,6 +1035,8 @@ def __init__(
             execute_all_batches=execute_all_batches,
             apply_jit=apply_jit,
             context_type=PrefetchTrainPipelineContext,
+            pipeline_preproc=pipeline_preproc,
+            custom_model_fwd=custom_model_fwd,
         )
         self._context = PrefetchTrainPipelineContext(version=0)
         self._prefetch_stream: Optional[torch.Stream] = (
@@ -1084,7 +1093,7 @@ def progress(self, dataloader_iter: Iterator[In]) -> Out:
         self._wait_sparse_data_dist()
         # forward
         with record_function("## forward ##"):
-            losses, output = cast(Tuple[torch.Tensor, Out], self._model(self._batch_i))
+            losses, output = self._model_fwd(self._batch_i)
 
         self._prefetch(self._batch_ip1)