fix: conv parameter check failure

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -2468,13 +2468,14 @@ def aten_ops_convolution(
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
     is_transposed = args[6]
+    is_conv1d = len(args[0].shape) == 3
     if not is_transposed:
         return impl.conv.convNd(
             ctx,
             target,
             source_ir=SourceIR.ATEN,
             name=name,
-            is_conv1d=len(args[3]) == 1,
+            is_conv1d=is_conv1d,
             input=args[0],
             weight=args[1],
             bias=args_bounds_check(args, 2, None),
@@ -2489,7 +2490,7 @@ def aten_ops_convolution(
             target,
             source_ir=SourceIR.ATEN,
             name=name,
-            is_deconv1d=len(args[3]) == 1,
+            is_deconv1d=is_conv1d,
             input=args[0],
             weight=args[1],
             bias=args_bounds_check(args, 2, None),

py/torch_tensorrt/dynamo/conversion/impl/conv.py

@@ -6,7 +6,6 @@
 import tensorrt as trt
 import torch
 from torch.fx.node import Target
-
 from torch_tensorrt.dynamo.conversion import impl
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion.converter_utils import (
@@ -45,6 +44,8 @@ def convNd(
     if has_dynamic_shape(input.shape):
         assert input.shape[1] != -1, "Channel dim can't be dynamic for convolution."
 
+    num_dims = len(input.shape) - 2
+
     if is_conv1d:
         # Apply an unsqueeze operation to transform the conv1d problem into conv2d
         input = impl.unsqueeze.unsqueeze(
@@ -104,9 +105,26 @@ def convNd(
         conv_layer.set_input(2, bias)
 
     # Cast certain fields to tuples, in accordance with TRT requirements
-    padding = (padding,) if isinstance(padding, int) else padding
-    stride = (stride,) if isinstance(stride, int) else stride
-    dilation = (dilation,) if isinstance(dilation, int) else dilation
+    if isinstance(padding, int):
+        padding = (padding,) * num_dims
+    elif isinstance(padding, (list, tuple)):
+        padding = tuple(padding)
+        if len(padding) == 1:
+            padding = (padding[0],) * num_dims
+
+    if isinstance(stride, int):
+        stride = (stride,) * num_dims
+    elif isinstance(stride, (list, tuple)):
+        stride = tuple(stride)
+        if len(stride) == 1:
+            stride = (stride[0],) * num_dims
+
+    if isinstance(dilation, int):
+        dilation = (dilation,) * num_dims
+    elif isinstance(dilation, (list, tuple)):
+        dilation = tuple(dilation)
+        if len(dilation) == 1:
+            dilation = (dilation[0],) * num_dims
 
     # Expand parameters manually for Conv1D computations
     if is_conv1d:

tests/py/dynamo/conversion/test_convolution_aten.py

@@ -1,7 +1,6 @@
 import torch
 from parameterized import param, parameterized
 from torch.testing._internal.common_utils import run_tests
-
 from torch_tensorrt import Input
 
 from .harness import DispatchTestCase
@@ -133,8 +132,13 @@ def forward(self, x):
             ("default", 1),
             param("no_bias", 1, bias=False),
             ("tuple_parameters", 1, (1, 1), (1, 1)),
+            param("list_stride", 2, stride=[2]),
             param("non_zero_padding", 1, padding=1),
-            param("dilation", 1, dilation=2),
+            param("list_zero_padding", 1, padding=[0]),
+            param("list_non_padding", 1, padding=[1]),
+            param("dilation", 2, dilation=3),
+            param("tuple_dilation", 2, dilation=(3, 3)),
+            param("list_dilation", 2, dilation=[3]),
             param("groups", 1, groups=3),
         ]
     )
@@ -204,8 +208,12 @@ def forward(self, x):
             ("default", 1),
             param("no_bias", 1, bias=False),
             ("tuple_parameters", 1, (1, 1, 1), (1, 1, 1)),
+            param("list_stride", 2, stride=[2]),
             param("non_zero_padding", 1, padding=1),
-            param("dilation", 1, dilation=2),
+            param("list_zero_padding", 1, padding=[0]),
+            param("list_non_padding", 1, padding=[1]),
+            param("dilation", 2, dilation=2),
+            param("list_dilation", 2, dilation=[2]),
             ## TODO TRT 8.4.1 will trigger issue with this test. T127981773
             # param("groups", 1, groups=3),
         ]