undoing the empty changes, keeping the cat different case handling

Author: apbose

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -249,19 +249,9 @@ def parse_cat_args(
     return input_tensors, dim
 
 
-def cat_validator(node: Node, settings: Optional[CompilationSettings] = None) -> bool:
-    # empty tensor in cat input as ITensor leads to [RemoveDeadLayers] Input Tensor y is unused or used only at compile-time, but is not being removed.
-    inputs, _ = parse_cat_args(node.args, node.kwargs)
-    for each_input in inputs:
-        if isinstance(each_input, TRTTensor) and any(s == 0 for s in each_input.shape):
-            return False
-    return True
-
-
 @dynamo_tensorrt_converter(
     torch.ops.aten.cat.default,
     supports_dynamic_shapes=True,
-    capability_validator=cat_validator,
 )
 def aten_ops_cat(
     ctx: ConversionContext,

py/torch_tensorrt/dynamo/conversion/impl/cat.py

@@ -30,13 +30,6 @@ def cat(
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
     trt_inputs = []
     for i, each_input in enumerate(input):
-        if isinstance(each_input, torch.Tensor) and each_input.numel() == 0:
-            logger.warning(
-                f"Warning: empty tensor in cat input {i}, replacing with zeros"
-            )
-            # ITensor with same condition leads to [RemoveDeadLayers] Input Tensor y is unused or used only at compile-time, but is not being removed.
-            # hence the validator
-            continue
         if not isinstance(each_input, TRTTensor):
             each_input = get_trt_tensor(ctx, each_input, f"{name}_tensor_{i}")
         if cast_dtype:

tests/py/dynamo/conversion/test_cat_aten.py

@@ -60,42 +60,6 @@ def forward(self, x, y):
         inputs = [x, y]
         self.run_test(Cat(), inputs)
 
-    @parameterized.expand(
-        [
-            ("pos", 0),
-            ("neg", -3),
-        ]
-    )
-    def test_cat_with_empty_tensor(self, _, dim):
-        # Handle empty tensor in concat
-        class Cat(nn.Module):
-            def forward(self, x):
-                y = torch.empty(0, 2, 3, device="cuda")
-                return torch.ops.aten.cat.default((x, y), dim)
-
-        inputs = [
-            torch.randn(1, 2, 3, device="cuda"),
-        ]
-        self.run_test(Cat(), inputs)
-
-    @parameterized.expand(
-        [
-            ("pos", 2),
-            ("neg", -1),
-        ]
-    )
-    def test_cat_with_different_dtypes(self, _, dim):
-        # check dtype promotion path in concat
-        class Cat(nn.Module):
-            def forward(self, x, y):
-                return torch.ops.aten.cat.default((x, y), dim)
-
-        inputs = [
-            torch.ones(1, 2, 3, dtype=torch.float32, device="cuda"),
-            torch.ones(1, 2, 3, dtype=torch.float16, device="cuda"),
-        ]
-        self.run_test(Cat(), inputs)
-
     @parameterized.expand(
         [
             ("pos", 1),