feat: support aten.arange.start_step dynamo converter

py/torch_tensorrt/dynamo/conversion/ops_evaluators.py

@@ -1,3 +1,4 @@
+import builtins
 import logging
 import operator
 from typing import Dict, Sequence, Tuple, Union
@@ -23,7 +24,9 @@ def getitem_validator(getitem_node: Node) -> bool:
 
 # TODO: Subsequent evaluators should be registered here with their own validators
 @dynamo_tensorrt_converter(operator.getitem, capability_validator=getitem_validator)
+@dynamo_tensorrt_converter(builtins.getattr)
 @dynamo_tensorrt_converter(torch.ops.aten.detach.default)
+@dynamo_tensorrt_converter(torch.ops.aten.arange.start_step)
 def generic_evaluator(
     ctx: ConversionContext,
     target: Target,

tests/py/dynamo/conversion/test_arange_aten.py

@@ -0,0 +1,33 @@
+import torch
+import torch.nn as nn
+from parameterized import parameterized
+from torch.testing._internal.common_utils import run_tests
+
+from .harness import DispatchTestCase
+
+
+class TestArangeConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            (0, 5, 1),
+            (1, 5, 2),
+            (3, 5, 3),
+            (5, 0, -1),
+            (5, 1, -2),
+            (5, 3, -3),
+        ]
+    )
+    def test_arange(self, start, end, step):
+        class Arange(nn.Module):
+            def forward(self, x):
+                return torch.ops.aten.arange.start_step(start, x.shape[0], step)
+
+        inputs = [torch.randn(end, 1)]
+        self.run_test(
+            Arange(),
+            inputs,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()