[Fix][Relax][Frontend][ONNX] Support Pad mode="wrap" and axes input for op…

[siyiweigeHEW]

Fixes: #20150

Summary

The Relax ONNX frontend rejected legal opset-18 Pad models using
mode="wrap" (circular padding) or the optional axes input. Both are
ONNX Pad-18 features, are accepted by onnx.checker / onnx.reference /
onnxruntime, and topi.nn.circular_pad already implements circular
padding — this is purely a frontend dispatch gap.

Root cause

Upstream #19827 added Pad._impl_v19 with wrap/axes support, but
get_converter dispatches on the highest _impl_v{N} with N <= opset,
so that method is only reached for models with opset >= 19. A model
with opset 18 — the version that actually introduced wrap and
axes — still resolves to _impl_v11, which:

1. has a whitelist ["constant", "edge", "reflect"], so mode="wrap"
   raises OpAttributeInvalid("Value wrap ... is invalid for operator Pad.");
2. never reads inputs[3] (the axes input), so an axes model is padded
   on the full rank instead of the specified axes and fails with
   ValueError("Input dimension and pad_before dismatch ...").

Fix

Add Pad._impl_v18, mirroring #19827's _impl_v19 but for opset 18:
expand the axes input into full-rank pads via
_get_known_tensor_rank / _normalize_constant_axes, extend the mode
whitelist to include "wrap", and dispatch wrap to
topi.nn.circular_pad:

@classmethod
def _impl_v18(cls, bb, inputs, attr, params):
    # ONNX Pad-18 introduces mode="wrap" and the optional axes input ...
    ...
    axes_input = inputs[3] if len(inputs) > 3 else None
    if axes_input is not None:
        ...
        rank = _get_known_tensor_rank(inputs[0])
        axes = _normalize_constant_axes([int(a) for a in axes], rank, "Pad")
        full_before = [0] * rank
        full_after = [0] * rank
        for i, ax in enumerate(axes):
            full_before[ax] = pad_before[i]
            full_after[ax] = pad_after[i]
        pad_before, pad_after = full_before, full_after

    pad_mode = attr.get("mode", b"constant").decode("utf-8")
    if pad_mode not in ["constant", "edge", "reflect", "wrap"]:
        raise tvm.error.OpAttributeInvalid(...)
    ...
    elif pad_mode == "wrap":
        return bb.emit_te(topi.nn.circular_pad, inputs[0], pad_before, pad_after)

_impl_v2 (opset 2, pads as attribute) and _impl_v11 (opset 11-17,
neither wrap nor axes legal) are left untouched.

Validation

Differential test (Relax from_onnx + relax.build + VirtualMachine
vs onnxruntime) over 81 legal Pad models: 3 input shapes × all modes ×
positive/negative pads, plus axes cases. Verified on the familyfuzz
locked build 262c6d2e0 via runtime monkey-patch
(results/.../onnx_Pad/verify_patch.py, no source files modified).

Category	Cases	Before	After
constant / edge / reflect (opset 11/13)	41	match onnxrt	match onnxrt (no regression)
constant / edge opset-18, no axes	8	match	match
wrap positive pads (opset 18, 19)	14	rejected (OpAttributeInvalid)	match onnxrt, max|diff| = 0
constant + axes (opset 18, incl. negative axis)	5	rejected (ValueError)	match onnxrt, max|diff| = 0
negative pads (crop)	8	match	match
Total	81	59 match / 22 rejected	76 match / 0 rejected / 5 documented deviation

The 5 documented deviations are wrap with negative pads, where the
implementations disagree: onnx.reference (np.pad mode "wrap") errors
out entirely, onnxruntime uses its own crop-window semantics, and
topi.nn.circular_pad follows the ONNX mod formula
(out[i] = in[(i - pad_before) mod dim]), matching upstream #19827's
identical implementation. Positive-pad wrap (the actual use case)
agrees exactly across onnxrt / onnx.reference / TVM.

Run:

/home/shenqingchao/miniconda3/envs/tvm23/bin/python3 \
  results/TVM/deepseek-v4-flash/prove_hum/onnx_Pad/verify_patch.py

Files changed

• python/tvm/relax/frontend/onnx/onnx_frontend.py — add Pad._impl_v18
  with wrap/axes support for opset 18 (same handling as [Relax][Frontend][ONNX] Add support for Pad mode="wrap" for opset 19 #19827's
  _impl_v19), closing the opset-18 gap.