fix: Add support for default dimension in aten.cat

- Add default `dim=0` to concatenation operator for use cases which do
not have a specific concatenation dimension specified
- T5 encounters this error during compilation
- Add test cases to elicit error with default dimension

Author: gs-olive

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -358,7 +358,7 @@ def aten_ops_cat(
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
     kwargs_new = {
         "tensors": args[0],
-        "dim": args[1],
+        "dim": args[1] if len(args) >= 2 else 0,
     }
     return acc_ops_converters.acc_ops_cat(network, target, None, kwargs_new, name)
 

py/torch_tensorrt/fx/test/converters/aten_op/test_cat_aten.py

@@ -9,7 +9,7 @@ class TestCatConverter(DispatchTestCase):
     @parameterized.expand(
         [
             ("pos", 1),
-            # ("neg", -2), #dim can not have dynamic input
+            ("neg", -2),
         ]
     )
     def test_cat(self, _, dim):
@@ -27,7 +27,7 @@ def forward(self, x, y, z):
     @parameterized.expand(
         [
             ("pos", 1),
-            # ("neg", -2), #dim can not have dynamic input
+            ("neg", -2),
         ]
     )
     def test_cat_dynamic_shape(self, _, dim):
@@ -53,6 +53,41 @@ def forward(self, x, y):
             expected_ops={torch.ops.aten.cat.default},
         )
 
+    def test_cat_no_dim(self):
+        class Cat(nn.Module):
+            def forward(self, x, y, z):
+                return torch.cat((x, y, z))
+
+        inputs = [torch.randn(2, 1, 3), torch.randn(1, 1, 3), torch.randn(3, 1, 3)]
+        self.run_test(
+            Cat(),
+            inputs,
+            expected_ops={torch.ops.aten.cat.default},
+        )
+
+    def test_cat_dynamic_shape_no_dim(self):
+        class Cat(nn.Module):
+            def forward(self, x, y):
+                return torch.cat((x, y))
+
+        input_specs = [
+            InputTensorSpec(
+                shape=(-1, 16, 3),
+                dtype=torch.float32,
+                shape_ranges=[((2, 16, 3), (3, 16, 3), (32, 16, 3))],
+            ),
+            InputTensorSpec(
+                shape=(-1, 16, 3),
+                dtype=torch.float32,
+                shape_ranges=[((2, 16, 3), (3, 16, 3), (32, 16, 3))],
+            ),
+        ]
+        self.run_test_with_dynamic_shape(
+            Cat(),
+            input_specs,
+            expected_ops={torch.ops.aten.cat.default},
+        )
+
 
 if __name__ == "__main__":
     run_tests()