remove wait in all_to_all_single custom op (pytorch#2646)

Summary:

# context
* remove the `torch.ops._c10d_functional.wait_tensor` call in all_to_all_single.
* use `autograd.function` implementation to create a `AllToAllSingle` function
* There is a wait_tensor after the all_to_all_single call: pytorch/pytorch#143533

Differential Revision: D64666999

torchrec/distributed/comm_ops.py

@@ -443,7 +443,7 @@ def all2all_pooled_sync(
         qcomm_ctx = None
 
     with record_function("## alltoall_fwd_single ##"):
-        sharded_output_embeddings = torch.ops.torchrec.all_to_all_single(
+        sharded_output_embeddings = AllToAllSingle.apply(
             sharded_input_embeddings,
             output_split_sizes,
             input_split_sizes,
@@ -572,7 +572,7 @@ def variable_batch_all2all_pooled_sync(
             torch._check(s0 <= sharded_input_embeddings.size(0))
             sharded_output_embeddings.copy_(sharded_input_embeddings[:s0])
         else:
-            sharded_output_embeddings = torch.ops.torchrec.all_to_all_single(
+            sharded_output_embeddings = AllToAllSingle.apply(
                 sharded_input_embeddings,
                 output_split_sizes,
                 input_split_sizes,
@@ -722,7 +722,7 @@ def all2all_sequence_sync(
         qcomm_ctx = None
 
     with record_function("## alltoall_seq_embedding_fwd_single ##"):
-        sharded_output_embeddings = torch.ops.torchrec.all_to_all_single(
+        sharded_output_embeddings = AllToAllSingle.apply(
             sharded_input_embeddings,
             output_splits,
             input_splits,
@@ -1004,7 +1004,7 @@ def reduce_scatter_v_sync(
             input_splits = rsi.input_splits
             output_splits = [rsi.input_splits[rank]] * world_size
             # TODO(ivankobzarev): Replace with _functional_collectives.reduce_scatter_v when it is added
-            a2a_output = torch.ops.torchrec.all_to_all_single(
+            a2a_output = AllToAllSingle.apply(
                 input,
                 output_splits,
                 input_splits,
@@ -2348,71 +2348,42 @@ def backward(ctx, grad_output: Tensor) -> Tuple[None, None, Tensor]:
 
 if not torch._running_with_deploy():  # noqa C901
     # Torch Library op def can not be used in Deploy
+    class AllToAllSingle(torch.autograd.Function):
+        @staticmethod
+        # pyre-fixme[14]: `forward` overrides method defined in `Function` inconsistently.
+        def forward(
+            # pyre-fixme[2]: Parameter must be annotated.
+            ctx,
+            input: Tensor,
+            output_split_sizes: List[int],
+            input_split_sizes: List[int],
+            group_name: str,
+            group_size: int,
+            gradient_division: bool,
+        ) -> Tensor:
+            ctx.output_split_sizes = input_split_sizes
+            ctx.input_split_sizes = output_split_sizes
+            ctx.group_name = group_name
+            ctx.group_size = group_size
+            ctx.gradient_division = gradient_division
+            return torch.distributed._functional_collectives.all_to_all_single(
+                input, output_split_sizes, input_split_sizes, group_name
+            )
 
-    # torchrec::all_to_all_single
-    @torch.library.custom_op("torchrec::all_to_all_single", mutates_args=())
-    def all_to_all_single(
-        input: Tensor,
-        output_split_sizes: List[int],
-        input_split_sizes: List[int],
-        group_name: str,
-        group_size: int,
-        gradient_division: bool,
-    ) -> Tensor:
-        out = torch.ops._c10d_functional.all_to_all_single(
-            input, output_split_sizes, input_split_sizes, group_name
-        )
-        return torch.ops._c10d_functional.wait_tensor(out)
-
-    @torch.library.register_fake("torchrec::all_to_all_single")
-    def all_to_all_single_fake(
-        input: Tensor,
-        output_split_sizes: List[int],
-        input_split_sizes: List[int],
-        group_name: str,
-        group_size: int,
-        gradient_division: bool,
-    ) -> Tensor:
-        return torch.ops._c10d_functional.all_to_all_single(
-            input, output_split_sizes, input_split_sizes, group_name
-        )
-
-    # pyre-ignore
-    def all_to_all_single_setup_context(ctx, inputs, output) -> None:
-        (
-            _,
-            output_split_sizes,
-            input_split_sizes,
-            group_name,
-            group_size,
-            gradient_division,
-        ) = inputs
-        ctx.output_split_sizes = input_split_sizes
-        ctx.input_split_sizes = output_split_sizes
-        ctx.group_name = group_name
-        ctx.group_size = group_size
-        ctx.gradient_division = gradient_division
-
-    # pyre-ignore
-    def all_to_all_single_backward(ctx, grad):
-        # TODO(ivankobzarev): Support codecs(quantization) on backward
-        a2a_out = torch.ops._c10d_functional.all_to_all_single(
-            grad,
-            ctx.output_split_sizes,
-            ctx.input_split_sizes,
-            ctx.group_name,
-        )
-        grad = torch.ops._c10d_functional.wait_tensor(a2a_out)
-        if ctx.gradient_division:
-            grad.div_(ctx.group_size)
-
-        return grad, None, None, None, None, None
+        @staticmethod
+        # pyre-ignore
+        def backward(ctx, grad):
+            # TODO(ivankobzarev): Support codecs(quantization) on backward
+            grad = torch.distributed._functional_collectives.all_to_all_single(
+                grad,
+                ctx.output_split_sizes,
+                ctx.input_split_sizes,
+                ctx.group_name,
+            )
+            if ctx.gradient_division:
+                grad.div_(ctx.group_size)
 
-    torch.library.register_autograd(
-        "torchrec::all_to_all_single",
-        all_to_all_single_backward,
-        setup_context=all_to_all_single_setup_context,
-    )
+            return grad, None, None, None, None, None
 
     # torchrec::reduce_scatter_tensor
     @torch.library.custom_op("torchrec::reduce_scatter_tensor", mutates_args=())