space some code apart

mlx/onnx/ops/op_conv.py

@@ -4,6 +4,9 @@
 
 def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, auto_pad="NOTSET", kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None):
     assert group == 1, f"mlx only supports 1 group, got {group}"
+    if dilations is not None:
+        assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1"
+
     if isinstance(kernel_shape, mx.array):
         kernel_shape = kernel_shape.tolist()
     if isinstance(strides, mx.array):
@@ -14,13 +17,14 @@ def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations
         pads = pads.tolist()
     if pads is None:
         pads = [0] * len(kernel_shape)
+
     if x.ndim < weight.ndim:
         x = mx.expand_dims(x, 0)
+
     if auto_pad != "NOTSET":
         padding = convert_pad(ap(x.shape, auto_pad, strides, kernel_shape))
         x = mx.pad(x, pad_width=[(0,0), (0,0)] + padding, constant_values=0)
-    if dilations is not None:
-        assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1"
+
     if x.ndim == 3:
         c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads[0] if pads is not None else 0, stride=strides[0] if strides is not None else 1)
         c = c + bias if bias is not None else c