napronald opened a new pull request, #19827:
URL: https://github.com/apache/tvm/pull/19827

   ## Summary
   The ONNX Pad operator introduced `mode="wrap"` (circular padding) in opset 
19. Currently, the Relax ONNX frontend has no support for opset 19, which raises
   
   ```text
   OpAttributeInvalid(tvm.error.OpAttributeInvalid: Value wrap in attribute 
"mode" is invalid for operator Pad.
   ```
   ## Changes
   Add opset 19 handling to the Pad converter that dispatches `mode="wrap"` to 
topi.nn.circular_pad, which already implements circular padding but was never 
wired up to the ONNX frontend. Existing behavior for earlier Pad opsets is 
unchanged.
   
   ## Reproduce
   ```python
   import numpy as np
   import onnx
   from onnx import TensorProto, helper, numpy_helper
   
   import tvm
   from tvm import relax
   from tvm.relax.frontend.onnx import from_onnx
   
   def make_model():
       x = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 3, 4])
       y = helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 3, 8])
   
       pads = numpy_helper.from_array(
           np.array([0, 0, 2, 0, 0, 2], dtype=np.int64),
           name="pads",
       )
   
       node = helper.make_node(
           "Pad",
           inputs=["input", "pads"],
           outputs=["output"],
           mode="wrap",
       )
   
       graph = helper.make_graph([node], "pad_wrap_graph", [x], [y], 
initializer=[pads])
       model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 
19)])
       onnx.checker.check_model(model)
       return model
   
   def run_tvm(model, x_np):
       mod = from_onnx(model, shape_dict={"input": list(x_np.shape)})
   
       target = tvm.target.Target("llvm")
       dev = tvm.cpu(0)
   
       with tvm.transform.PassContext(opt_level=3):
           ex = relax.build(mod, target)
   
       vm = relax.VirtualMachine(ex, dev)
       out = vm["main"](tvm.runtime.tensor(x_np, dev))
       return out.numpy() if hasattr(out, "numpy") else out.asnumpy()
   
   x_np = np.array(
       [[[1, 2, 3, 4],
         [5, 6, 7, 8],
         [9, 10, 11, 12]]],
       dtype=np.float32,
   )
   
   expected = np.pad(x_np, [[0, 0], [0, 0], [2, 2]], mode="wrap")
   actual = run_tvm(make_model(), x_np)
   
   print("Expected:")
   print(expected[0])
   print("Actual:")
   print(actual[0])
   print("Matches expected:", np.allclose(actual, expected))
   ```


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