fix(onnx): unwrap list/tuple value in Pad op (#13500)

* fix(onnx): unwrap list/tuple value in Pad op

* add regression test for Pad list value

* remove trailing whitespace

* use _resolve_const for Pad constant_value

test/models/test_onnx.py

@@ -58,6 +58,18 @@ class TestOnnxModel(unittest.TestCase):
     print(cls, _LABELS[cls])
     assert "car" in _LABELS[cls] or _LABELS[cls] == "convertible"
 
+  def test_pad_list_value(self):
+    from tinygrad.nn.onnx import onnx_ops
+    from tinygrad import Tensor
+    Pad = onnx_ops['Pad']
+    x = Tensor([1, 2, 3])
+    out = Pad(x, pads=[0, 1], value=[-float('inf')])
+    assert out.shape == (4,)
+    assert out.numpy()[-1] == -float('inf')
+    out2 = Pad(x, pads=[1, 0], constant_value=[5.0])
+    assert out2.shape == (4,)
+    assert out2.numpy()[0] == 5.0
+
 @unittest.skipUnless(Device.DEFAULT == "METAL", "only run on METAL")
 class TestHuggingFaceOnnxModels(unittest.TestCase):
   @classmethod

tinygrad/nn/onnx.py

@@ -710,7 +710,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
 
   def Pad(x:Tensor, pads:list[int], constant_value:ConstType|None=None, axes:list[int]|None=None,
           mode:Literal["constant", "reflect", "edge", "wrap"]="constant", value=0):
-    value = constant_value or value
+    value = _resolve_const(constant_value or value)
     axes = axes or list(range(x.ndim))
     real_pads = [0] * (x.ndim*2)
     for i,axis in enumerate(axes): real_pads[axis%x.ndim], real_pads[axis%x.ndim+x.ndim] = pads[i], pads[i+len(axes)]