feat: support bmm converter in dynamo

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -245,6 +245,7 @@ def aten_ops_hard_sigmoid(
 @dynamo_tensorrt_converter(torch.ops.aten.matmul)  # type: ignore[misc]
 @dynamo_tensorrt_converter(torch.ops.aten.mm.default)  # type: ignore[misc]
 @dynamo_tensorrt_converter(torch.ops.aten.mv.default)  # type: ignore[misc]
+@dynamo_tensorrt_converter(torch.ops.aten.bmm.default)  # type: ignore[misc]
 def aten_ops_matmul(
     network: TRTNetwork,
     target: Target,

tests/py/dynamo/conversion/test_bmm.py

@@ -0,0 +1,36 @@
+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 TestBmmConverter(DispatchTestCase):
+    @parameterized.expand(
+        [
+            ("10_3_5", (10, 3, 4), (10, 4, 5)),
+            ("1_10_1", (1, 10, 1), (1, 1, 1)),
+            ("1_1_1", (1, 1, 1), (1, 1, 1)),
+        ]
+    )
+    def test_bmm(self, _, input_shape, mat2_shape):
+        class BMM(nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, input, mat2):
+                return torch.bmm(input, mat2)
+
+        inputs = [torch.randn(*input_shape), torch.randn(*mat2_shape)]
+
+        self.run_test(
+            BMM(),
+            inputs,
+            disable_passes=True,
+            expected_ops={torch.ops.aten.bmm.default},
+        )
+
+
+if __name__ == "__main__":
+    run_tests()