Fix Op (convolution) | add nd support to convolution (#2108)

![Screenshot 2025-03-14
171007](https://github.com/user-attachments/assets/fc965055-9a29-44c6-a25d-b0e4a5867d0b)

Ran into a case that aten.convolution.default takes 2D image with [0] as
padding, which broke our assumption of it comes with the same rank of nd
image.

Author: titaiwangms

onnxscript/function_libs/torch_lib/ops/core.py

@@ -2074,16 +2074,30 @@ def aten_convolution(
 ) -> TFloat:
     """convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups) -> Tensor"""
 
+    rank = len(input.shape)
+
+    image_d = rank - 2
+
+    # NOTE: We assume the sequence padding/dilation/stride
+    # from ATen op can only be either len == 1 or
+    # len == rank.
+
     if not isinstance(padding, Sequence):
-        padding = (padding, padding)
+        padding = [padding] * image_d
+    elif len(padding) == 1:
+        padding = [padding[0]] * image_d
     pads = [*padding, *padding]
 
     if not isinstance(dilation, Sequence):
-        dilation = (dilation, dilation)
+        dilation = [dilation] * image_d
+    elif len(dilation) == 1:
+        dilation = [dilation[0]] * image_d
     dilations = list(dilation)
 
     if not isinstance(stride, Sequence):
-        stride = (stride, stride)
+        stride = [stride] * image_d
+    elif len(stride) == 1:
+        stride = [stride[0]] * image_d
     strides = list(stride)
 
     result = _aten_convolution_onnx(

tests/function_libs/torch_lib/extra_opinfo.py

@@ -239,6 +239,19 @@ def sample_inputs_convolution(op_info, device, dtype, requires_grad, **kwargs):
                 "groups": 1,
             },
         ),
+        (
+            (1, 3, 224, 224),
+            (32, 3, 3, 3),
+            None,
+            {
+                "stride": (2,),
+                "padding": (1,),
+                "dilation": (1,),
+                "transposed": False,
+                "output_padding": (0, 0),
+                "groups": 1,
+            },
+        ),
         (
             (1, 3, 3, 224, 224),
             (32, 3, 3, 3, 3),
@@ -252,21 +265,19 @@ def sample_inputs_convolution(op_info, device, dtype, requires_grad, **kwargs):
                 "groups": 1,
             },
         ),
-        # FIXME(jiz): Uncomment out these test data once
-        # torch 2.0 is released.
-        # (
-        #     (1, 3, 224, 224, 224),
-        #     (32, 3, 3, 3, 3),
-        #     (32,),
-        #     {
-        #         "stride": (2, 2, 2),
-        #         "padding": (1, 1, 1),
-        #         "dilation": (1, 1, 1),
-        #         "transposed": False,
-        #         "output_padding": (0, 0, 0),
-        #         "groups": 1,
-        #     },
-        # ),
+        (
+            (1, 3, 224, 224, 224),
+            (32, 3, 3, 3, 3),
+            (32,),
+            {
+                "stride": (2, 2, 2),
+                "padding": (1, 1, 1),
+                "dilation": (1, 1, 1),
+                "transposed": False,
+                "output_padding": (0, 0, 0),
+                "groups": 1,
+            },
+        ),
         (
             (2, 4, 6, 6),
             (4, 1, 3, 3),

tests/function_libs/torch_lib/ops_test_data.py

@@ -1557,7 +1557,7 @@ def _where_input_wrangler(
     TorchLibOpInfo(
         "ops.aten.convolution",
         core_ops.aten_convolution,
-        tolerance={torch.float32: (3.7e-5, 1.8e-4)},
+        tolerance={torch.float32: (2e-4, 9e-4)},
     ),
     TorchLibOpInfo("empty_like", core_ops.aten_empty_like, nondeterministic=True),
     TorchLibOpInfo(