[Relax][ONNX] Make ReduceMax/ReduceMin NaN propagation order-independent(numpy semantics)

[cchung100m]

Hi Committers,

This PR addresses the ReduceMax/ ReduceMin part of issue #19572. Any suggestions would be appreciated if you are available.

Root cause:

The ONNX frontend ReduceMax / ReduceMin converters return relax.op.max / relax.op.min. After legalization these map to topi.max / topi.min, which fold with a commutative reducer whose combiner is Max(x, y) / Min(x, y). In codegen, Max(a, b) lowers to select(a > b, a, b) using an ordered float comparison (fcmp ogt), which is false for NaN. As a left-fold (acc = Max(acc, elem)), NaN propagation becomes position-dependent - a later non-NaN element silently overwrites an earlier NaN.

Solution:

Adopt the well-defined, order-independent numpy/IEEE convention (matching numpy.max/min and torch.amax/amin): the reduction yields NaN whenever any reduced element is NaN. Minimal, ONNX-frontend-only change:

• Add a shared helper _reduce_min_max_preserve_nan(reduce_op, data, axes, keepdims).
• For floating-pint inputs, detect NaN along the reduced axes via sum(astype(isnan(data), dtype), axes, keepdims) > 0 and force those outputs to NaN with where(has_nan, nan, reduce(data)). The mask reduces over the same axes/keepdims, so it aligns in shape with the reduced result.
• Keep non-floating(integer) inputs unchanged.
• Route all reduce paths(_impl_v11and both reduce branches of _impl_v18) through the helper; the noop_with_empty_axes passthrough is left untouched since it performs no reduction.

Note on scope (re: #19589 ):

The underlying NaN behavior of Max/Min is the same family of ops discussed in #19589. Per review comments there, enforcing NaN semantics at the IR / LLVM-IR level is undesirable(backward-compat with older LLVM, and portability to CUDA/OpenCL/Vulkan), and a dedicated portable nanmin/nanmax TIRx intrinsic(like nearbyint) would be the preferred long-term mechanism. This PR deliberately:

• does not touch the IR-level Max/Min lowering, and
• does not rely on the bool reduction of the NaN mask - it uses sum(isnan) > 0, fully sidestepping Max/Min NaN behavior.

[gemini-code-assist]

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