Revert D66521351 (#2701)

Summary:
Pull Request resolved: #2701

This diff reverts D66521351
Need to revert this to fix lowering import error breaking aps tests

Reviewed By: PoojaAg18

Differential Revision: D68528333

fbshipit-source-id: d20da94f0c1b37944f1e985f3398e0684092388b

Author: kausv

torchrec/distributed/embedding.py

@@ -26,7 +26,6 @@
 )
 
 import torch
-from tensordict import TensorDict
 from torch import distributed as dist, nn
 from torch.autograd.profiler import record_function
 from torch.distributed._shard.sharding_spec import EnumerableShardingSpec
@@ -91,7 +90,6 @@
 from torchrec.optim.fused import EmptyFusedOptimizer, FusedOptimizerModule
 from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizer
 from torchrec.sparse.jagged_tensor import _to_offsets, JaggedTensor, KeyedJaggedTensor
-from torchrec.sparse.tensor_dict import maybe_td_to_kjt
 
 try:
     torch.ops.load_library("//deeplearning/fbgemm/fbgemm_gpu:sparse_ops")
@@ -1200,15 +1198,8 @@ def _compute_sequence_vbe_context(
     def input_dist(
         self,
         ctx: EmbeddingCollectionContext,
-        features: TypeUnion[KeyedJaggedTensor, TensorDict],
+        features: KeyedJaggedTensor,
     ) -> Awaitable[Awaitable[KJTList]]:
-        need_permute: bool = True
-        if isinstance(features, TensorDict):
-            feature_keys = list(features.keys())  # pyre-ignore[6]
-            if self._features_order:
-                feature_keys = [feature_keys[i] for i in self._features_order]
-                need_permute = False
-            features = maybe_td_to_kjt(features, feature_keys)  # pyre-ignore[6]
         if self._has_uninitialized_input_dist:
             self._create_input_dist(input_feature_names=features.keys())
             self._has_uninitialized_input_dist = False
@@ -1218,7 +1209,7 @@ def input_dist(
                 unpadded_features = features
                 features = pad_vbe_kjt_lengths(unpadded_features)
 
-            if need_permute and self._features_order:
+            if self._features_order:
                 features = features.permute(
                     self._features_order,
                     # pyre-fixme[6]: For 2nd argument expected `Optional[Tensor]`

torchrec/distributed/test_utils/test_sharding.py

@@ -147,7 +147,6 @@ def gen_model_and_input(
     long_indices: bool = True,
     global_constant_batch: bool = False,
     num_inputs: int = 1,
-    input_type: str = "kjt",  # "kjt" or "td"
 ) -> Tuple[nn.Module, List[Tuple[ModelInput, List[ModelInput]]]]:
     torch.manual_seed(0)
     if dedup_feature_names:
@@ -178,9 +177,9 @@ def gen_model_and_input(
             feature_processor_modules=feature_processor_modules,
         )
     inputs = []
-    if input_type == "kjt" and generate == ModelInput.generate_variable_batch_input:
-        for _ in range(num_inputs):
-            inputs.append(
+    for _ in range(num_inputs):
+        inputs.append(
+            (
                 cast(VariableBatchModelInputCallable, generate)(
                     average_batch_size=batch_size,
                     world_size=world_size,
@@ -189,26 +188,8 @@ def gen_model_and_input(
                     weighted_tables=weighted_tables or [],
                     global_constant_batch=global_constant_batch,
                 )
-            )
-    elif generate == ModelInput.generate:
-        for _ in range(num_inputs):
-            inputs.append(
-                ModelInput.generate(
-                    world_size=world_size,
-                    tables=tables,
-                    dedup_tables=dedup_tables,
-                    weighted_tables=weighted_tables or [],
-                    num_float_features=num_float_features,
-                    variable_batch_size=variable_batch_size,
-                    batch_size=batch_size,
-                    long_indices=long_indices,
-                    input_type=input_type,
-                )
-            )
-    else:
-        for _ in range(num_inputs):
-            inputs.append(
-                cast(ModelInputCallable, generate)(
+                if generate == ModelInput.generate_variable_batch_input
+                else cast(ModelInputCallable, generate)(
                     world_size=world_size,
                     tables=tables,
                     dedup_tables=dedup_tables,
@@ -219,6 +200,7 @@ def gen_model_and_input(
                     long_indices=long_indices,
                 )
             )
+        )
     return (model, inputs)
 
 
@@ -315,7 +297,6 @@ def sharding_single_rank_test(
     global_constant_batch: bool = False,
     world_size_2D: Optional[int] = None,
     node_group_size: Optional[int] = None,
-    input_type: str = "kjt",  # "kjt" or "td"
 ) -> None:
     with MultiProcessContext(rank, world_size, backend, local_size) as ctx:
         # Generate model & inputs.
@@ -338,7 +319,6 @@ def sharding_single_rank_test(
             batch_size=batch_size,
             feature_processor_modules=feature_processor_modules,
             global_constant_batch=global_constant_batch,
-            input_type=input_type,
         )
         global_model = global_model.to(ctx.device)
         global_input = inputs[0][0].to(ctx.device)

torchrec/distributed/tests/test_sequence_model_parallel.py

@@ -376,44 +376,3 @@ def _test_sharding(
             variable_batch_per_feature=variable_batch_per_feature,
             global_constant_batch=True,
         )
-
-
-@skip_if_asan_class
-class TDSequenceModelParallelTest(SequenceModelParallelTest):
-
-    def test_sharding_variable_batch(self) -> None:
-        pass
-
-    def _test_sharding(
-        self,
-        sharders: List[TestEmbeddingCollectionSharder],
-        backend: str = "gloo",
-        world_size: int = 2,
-        local_size: Optional[int] = None,
-        constraints: Optional[Dict[str, ParameterConstraints]] = None,
-        model_class: Type[TestSparseNNBase] = TestSequenceSparseNN,
-        qcomms_config: Optional[QCommsConfig] = None,
-        apply_optimizer_in_backward_config: Optional[
-            Dict[str, Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]]
-        ] = None,
-        variable_batch_size: bool = False,
-        variable_batch_per_feature: bool = False,
-    ) -> None:
-        self._run_multi_process_test(
-            callable=sharding_single_rank_test,
-            world_size=world_size,
-            local_size=local_size,
-            model_class=model_class,
-            tables=self.tables,
-            embedding_groups=self.embedding_groups,
-            sharders=sharders,
-            optim=EmbOptimType.EXACT_SGD,
-            backend=backend,
-            constraints=constraints,
-            qcomms_config=qcomms_config,
-            apply_optimizer_in_backward_config=apply_optimizer_in_backward_config,
-            variable_batch_size=variable_batch_size,
-            variable_batch_per_feature=variable_batch_per_feature,
-            global_constant_batch=True,
-            input_type="td",
-        )

torchrec/modules/embedding_modules.py

@@ -219,10 +219,7 @@ def __init__(
         self._feature_names: List[List[str]] = [table.feature_names for table in tables]
         self.reset_parameters()
 
-    def forward(
-        self,
-        features: KeyedJaggedTensor,  # can also take TensorDict as input
-    ) -> KeyedTensor:
+    def forward(self, features: KeyedJaggedTensor) -> KeyedTensor:
         """
         Run the EmbeddingBagCollection forward pass. This method takes in a `KeyedJaggedTensor`
         and returns a `KeyedTensor`, which is the result of pooling the embeddings for each feature.
@@ -453,7 +450,7 @@ def __init__(  # noqa C901
 
     def forward(
         self,
-        features: KeyedJaggedTensor,  # can also take TensorDict as input
+        features: KeyedJaggedTensor,
     ) -> Dict[str, JaggedTensor]:
         """
         Run the EmbeddingBagCollection forward pass. This method takes in a `KeyedJaggedTensor`
@@ -466,7 +463,6 @@ def forward(
             Dict[str, JaggedTensor]
         """
 
-        features = maybe_td_to_kjt(features, None)
         feature_embeddings: Dict[str, JaggedTensor] = {}
         jt_dict: Dict[str, JaggedTensor] = features.to_dict()
         for i, emb_module in enumerate(self.embeddings.values()):