fix missing device in fake operator

Summary:
# context
* when we have the input tensors on the meta device, it calls the fake operator
* however the device information is unintentionally missed so the output tensor is on the default device (cpu)
* this is an incorrect behavior

Reviewed By: gnahzg, iamzainhuda

Differential Revision: D57077813

Author: TroyGarden

torchrec/ir/tests/test_serializer.py

@@ -295,6 +295,23 @@ def test_dynamic_shape_ebc(self) -> None:
             self.assertEqual(eager_out[i].shape, tensor.shape)
             assert torch.allclose(eager_out[i], tensor)
 
+    def test_ir_custom_op_device(self) -> None:
+        model = self.generate_model()
+        model.fpebc1 = copy.deepcopy(model.ebc1)
+        model.fpebc2 = copy.deepcopy(model.ebc1)
+        feature1 = KeyedJaggedTensor.from_offsets_sync(
+            keys=["f1", "f2", "f3"],
+            values=torch.tensor([0, 1, 2, 3, 2, 3]),
+            offsets=torch.tensor([0, 2, 2, 3, 4, 5, 6]),
+        )
+
+        model, sparse_fqns = encapsulate_ir_modules(model, JsonSerializer)
+        for device in ["cpu", "cuda", "meta"]:
+            device = torch.device(device)
+            outputs = model.to(device)(feature1.to(device))
+            for output in outputs:
+                self.assertEqual(output.device.type, device.type)
+
     def test_deserialized_device(self) -> None:
         model = self.generate_model()
         id_list_features = KeyedJaggedTensor.from_offsets_sync(

torchrec/ir/utils.py

@@ -16,7 +16,7 @@
 import torch
 
 from torch import nn
-from torch.export import Dim, ExportedProgram, ShapesCollection
+from torch.export import Dim, ShapesCollection
 from torch.export.dynamic_shapes import _Dim as DIM
 from torchrec.ir.types import SerializerInterface
 from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
@@ -37,16 +37,21 @@ def ir_custom_op_impl(
         if t is not None:
             device = t.device
             break
-    logger.info(f"torch.ops.torchrec.ir_custom_op -> ({batch_size}, {dim})")
+    logger.info(f"torch.ops.torchrec.ir_custom_op -> ({batch_size}, {dim}) {device}")
     return torch.empty(batch_size, dim, device=device)
 
 
 @torch.library.register_fake("torchrec::ir_custom_op")
 def ir_custom_op_fake(
     tensors: List[Optional[torch.Tensor]], batch_size: int, dim: int
 ) -> torch.Tensor:
-    logger.info(f"ir_custom_op_fake -> ({batch_size}, {dim})")
-    return torch.empty(batch_size, dim)
+    device = None
+    for t in tensors:
+        if t is not None:
+            device = t.device
+            break
+    logger.info(f"ir_custom_op_fake -> ({batch_size}, {dim}) {device}")
+    return torch.empty(batch_size, dim, device=device)
 
 
 def encapsulate_ir_modules(