Add test case for ITensor weight in convolution and fix related bug (#3327)

Co-authored-by: Hoonkyung Cho <[email protected]>

Author: chohk88

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

@@ -6,6 +6,7 @@
 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 (
@@ -68,10 +69,9 @@ def convNd(
         weight = get_trt_tensor(ctx, weight, f"{name}_weight")
         # Append new dimension (unsqueeze) if the convolution is 1d
         if is_conv1d:
-            input = impl.unsqueeze.unsqueeze(
-                ctx, target, source_ir, name + "_unsqueeze_weight", weight, -1
+            weight = impl.unsqueeze.unsqueeze(
+                ctx, target, source_ir, weight.name + "_unsqueeze_conv1d", weight, -1
             )
-
     elif isinstance(weight, (torch.Tensor, np.ndarray)):
         # Transform the weight constant into a Numpy array
         weight = to_numpy(weight, dtype=input.dtype)

tests/py/dynamo/conversion/test_convolution_aten.py

@@ -1,6 +1,7 @@
 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
@@ -45,6 +46,54 @@ def forward(self, x):
             enable_passes=True,
         )
 
+    @parameterized.expand(
+        [
+            ("default", 1),
+            param("no_bias", 1, bias=False),
+            ("tuple_parameters", 1, (1), (1)),
+            param("non_zero_padding", 1, padding=1),
+            param("dilation", 1, dilation=2),
+        ]
+    )
+    def test_conv1d_TRTTensor_weight(
+        self,
+        _,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+    ):
+        class TestModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+
+            def forward(self, x, w):
+                return torch.ops.aten.convolution.default(
+                    x,
+                    w,
+                    None,
+                    (stride,) if isinstance(stride, int) else stride,
+                    (padding,) if isinstance(padding, int) else padding,
+                    (dilation,) if isinstance(dilation, int) else dilation,
+                    False,
+                    (0,),
+                    groups,
+                )
+
+        inputs = [
+            torch.randn(1, 3, 32),
+            torch.randn(
+                6, 3, 1
+            ),  # Conv1d weight shape: (out_channels, in_channels, kernel_size)
+        ]
+        self.run_test(
+            TestModule(),
+            inputs,
+            use_dynamo_tracer=True,
+        )
+
     def test_conv1d_with_dynamic_shape(
         self,
         kernel_size=1,