fix: Raise error when registering Packet-keyed converter

py/torch_tensorrt/dynamo/conversion/converter_registry.py

@@ -16,6 +16,7 @@
     cast,
 )
 
+from torch._ops import OpOverloadPacket
 from torch.fx.node import Argument, Node, Target, _get_qualified_name
 from torch_tensorrt.fx.converter_registry import CONVERTERS
 from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
@@ -101,6 +102,19 @@ def register_converter(converter: ConverterImplSignature) -> ConverterImplSignat
                 capability_validator=capability_validator,
             )
 
+        # OpOverloadPackets are only valid if they have a single overload, or
+        # only the ["default", "out"] overloads, due to PyTorch conventions
+        if isinstance(key, OpOverloadPacket) and (
+            len(key.overloads()) >= 3
+            or (len(key.overloads()) == 2 and "out" not in key.overloads())
+        ):
+            raise AssertionError(
+                f"Detected converter for OpOverloadPacket {key}. "
+                "We do not support OpOverloadPacket-keyed converters with multiple overloads. "
+                "Make sure to explicitly specify each converter overload. For instance "
+                "aten.mean is not a valid key, but aten.mean.default is."
+            )
+
         # If a converter for this operator already exists, append the new converter to the list
         # Otherwise, start a new list
         if key in DYNAMO_ATEN_CONVERTERS:

tests/py/dynamo/backend/test_specialized_models.py

@@ -236,5 +236,42 @@ def forward(self, x):
         torch._dynamo.reset()
 
 
+class TestPacketOperator(TestCase):
+    def test_packet_operator(self):
+        class PacketAsOperator(torch.nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.tanh(x)
+
+        # Operations expected to be removed in the traced graph
+        expected_ops = {torch.ops.aten.tanh.default}
+        unexpected_ops = {
+            torch.ops.aten.tanh,
+        }
+
+        inputs = [torch.rand((3, 5)).cuda()]
+
+        fx_graph = torch.fx.symbolic_trace(PacketAsOperator())
+        unexpected_ops_seen, expected_ops_unseen = lower_graph_testing(
+            fx_graph,
+            inputs,
+            expected_ops=expected_ops,
+            unexpected_ops=unexpected_ops,
+            min_block_size=1,
+        )
+
+        self.assertEquals(
+            len(unexpected_ops_seen),
+            0,
+            f"The following unexpected ops were encountered: {unexpected_ops_seen}",
+        )
+
+        self.assertEquals(
+            len(expected_ops_unseen),
+            0,
+            f"The following expected ops were not encountered: {expected_ops_unseen}",
+        )
+        torch._dynamo.reset()
+
+
 if __name__ == "__main__":
     run_tests()