fix: Add generic evaluator function

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -14,7 +14,7 @@
 from torch_tensorrt.fx.converters import acc_ops_converters
 from torch_tensorrt.fx.types import TRTNetwork, TRTTensor
 
-from .converter_registry import dynamo_tensorrt_converter
+from .converter_registry import ConverterRegistry, dynamo_tensorrt_converter
 
 _LOGGER: logging.Logger = logging.getLogger(__name__)
 
@@ -421,22 +421,26 @@ def aten_ops_to_copy_dtype(
     )
 
 
-@dynamo_tensorrt_converter(operator.getitem)
-def operator_getitem(
+def getitem_validator(getitem_node: Node) -> bool:
+    from torch_tensorrt.dynamo.conversion.converter_registry import DYNAMO_CONVERTERS
+
+    # Getitem nodes can only be converted if their parent node also can
+    return getitem_node.args[0] in DYNAMO_CONVERTERS
+
+
+# TODO: Subsequent evaluators should be registered here with their own validators
+@dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)
+def generic_evaluator(
     network: TRTNetwork,
     target: Target,
     args: Tuple[Argument, ...],
     kwargs: Dict[str, Argument],
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-    return impl.evaluators.getitem(
-        network,
-        target,
-        SourceIR.ATEN,
-        name,
-        args[0],
-        args[1],
+    _LOGGER.debug(
+        f"Evaluating {ConverterRegistry.qualified_name_or_str(target)} on object with name: {name}"
     )
+    return target(*args, **kwargs)
 
 
 @dynamo_tensorrt_converter(torch.ops.aten.clone.default)

py/torch_tensorrt/dynamo/conversion/converter_registry.py

@@ -64,7 +64,7 @@ def dynamo_tensorrt_converter(
     enabled: bool = True,
     capability_validator: Optional[Callable[[Node], bool]] = None,
     priority: ConverterPriority = ConverterPriority.STANDARD,
-) -> Callable[[Any], Union[TRTTensor, Sequence[TRTTensor]]]:
+) -> Callable[[Any], TRTTensor | Sequence[TRTTensor]]:
     """Decorator for Dynamo TensorRT Converter
 
     Registers the decorated function in the DYNAMO_ATEN_CONVERTERS registry
@@ -347,8 +347,8 @@ def unique_targets(self) -> Set[Target]:
         """Returns the set of unique converter targets stored across all registries"""
         return set.union(*[set(registry.keys()) for registry in self.registries])
 
-    # TODO: Make this a static method since it does not need state
-    def qualified_name_or_str(self, target: Target) -> str:
+    @staticmethod
+    def qualified_name_or_str(target: Target) -> str:
         """Returns string representation of an FX Node target"""
         if isinstance(target, str):
             return target

py/torch_tensorrt/dynamo/conversion/impl/__init__.py

@@ -6,7 +6,6 @@
     condition,
     elementwise,
     embedding,
-    evaluators,
     matmul,
     normalization,
     permutation,

py/torch_tensorrt/dynamo/conversion/impl/cast.py

@@ -1,10 +1,13 @@
+import logging
 from typing import Optional
 
 from torch.fx.node import Target
 from torch_tensorrt.dynamo._SourceIR import SourceIR
 from torch_tensorrt.dynamo.conversion.converter_utils import cast_trt_tensor
 from torch_tensorrt.fx.types import TRTDataType, TRTNetwork, TRTTensor
 
+LOGGER: logging.Logger = logging.getLogger(__name__)
+
 
 def to_copy(
     network: TRTNetwork,
@@ -21,3 +24,20 @@ def to_copy(
 
     casted_tensor = cast_trt_tensor(network, input, dtype, name, target, source_ir)
     return casted_tensor
+
+
+def clone(
+    network: TRTNetwork,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+) -> TRTTensor:
+    if not isinstance(input, TRTTensor):
+        raise RuntimeError(
+            f"clone received input {input} that is not a TensorRT ITensor"
+        )
+
+    LOGGER.debug(f"Evaluating clone on object with name: {name}")
+
+    return input

tests/py/dynamo/converters/test_casts.py

@@ -5,6 +5,36 @@
 from torch_tensorrt.dynamo.conversion import UnsupportedOperatorException
 
 
+class TestCloneConverter(DispatchTestCase):
+    def test_clone_contiguous(self):
+        class Clone(nn.Module):
+            def forward(self, x):
+                y = torch.clone(x, memory_format=torch.contiguous_format)
+                return y + 1
+
+        inputs = [torch.randn((1, 3, 10))]
+        self.run_test(
+            Clone(),
+            inputs,
+            expected_ops={torch.ops.aten.clone.default},
+            disable_passes=True,
+        )
+
+    def test_clone_regular(self):
+        class Clone(nn.Module):
+            def forward(self, x):
+                y = torch.clone(x)
+                return y + 1
+
+        inputs = [torch.randn((8, 2, 10))]
+        self.run_test(
+            Clone(),
+            inputs,
+            expected_ops={torch.ops.aten.clone.default},
+            disable_passes=True,
+        )
+
+
 class TestToCopyConverter(DispatchTestCase):
     def test_to_copy_half(self):
         class ToCopyHalf(nn.Module):

tests/py/dynamo/converters/test_evaluators.py

@@ -7,36 +7,6 @@
 from torch.testing._internal.common_utils import run_tests
 
 
-class TestCloneConverter(DispatchTestCase):
-    def test_clone_contiguous(self):
-        class Clone(nn.Module):
-            def forward(self, x):
-                y = torch.clone(x, memory_format=torch.contiguous_format)
-                return y + 1
-
-        inputs = [torch.randn((1, 3, 10))]
-        self.run_test(
-            Clone(),
-            inputs,
-            expected_ops={torch.ops.aten.clone.default},
-            disable_passes=True,
-        )
-
-    def test_clone_regular(self):
-        class Clone(nn.Module):
-            def forward(self, x):
-                y = torch.clone(x)
-                return y + 1
-
-        inputs = [torch.randn((8, 2, 10))]
-        self.run_test(
-            Clone(),
-            inputs,
-            expected_ops={torch.ops.aten.clone.default},
-            disable_passes=True,
-        )
-
-
 # TODO: Switch this test back to self.run_test once an implementation exists
 # for a converter that returns a list, such as aten.split
 @unittest.skip("Pending aten.split converter. Currently tested by E2E")

tests/py/dynamo/models/test_models.py

@@ -27,7 +27,6 @@ def test_resnet18(ir):
         "ir": ir,
         "pass_through_build_failures": True,
         "optimization_level": 1,
-        "min_block_size": 10,
         "ir": "torch_compile",
     }
 
@@ -176,7 +175,6 @@ def test_resnet18_half(ir):
         "ir": ir,
         "pass_through_build_failures": True,
         "optimization_level": 1,
-        "min_block_size": 10,
         "ir": "torch_compile",
     }