[Aharrypotter]

Summary

This PR extends Relax quantize and dequantize type inference to support
rank-zero inputs when their scale and zero point describe per-tensor
quantization.

The gap was exposed by the INT8 dynamic-shape BiRefNeXt anime segmentation
model. Its opset-18 graph contains scalar-input DequantizeLinear nodes with
scalar scale and zero-point initializers. This is valid ONNX per-tensor
dequantization, but the current Relax operator rejects the canonicalized axis
because a rank-zero tensor has no axis.

Goal

Import and execute valid scalar ONNX QuantizeLinear and DequantizeLinear
graphs without weakening validation for per-axis quantization.

What changed

• Treat the axis as irrelevant for rank-zero Relax quantize/dequantize inputs.
• Require rank-zero inputs to use scalar or singleton scale and zero-point
  tensors.
• Keep the existing axis and parameter-size checks unchanged for inputs with
  rank one or greater.
• Add symmetric core type-inference coverage for scalar quantize and
  dequantize.
• Add ONNX Runtime-backed numerical and dtype coverage for scalar opset-18
  QuantizeLinear and DequantizeLinear graphs.

Design

ONNX uses scalar scale and zero-point tensors for per-tensor quantization. In
that mode the axis attribute is ignored. The Relax ONNX frontend already
canonicalizes the default axis to 0 for inputs with rank at most one, but the
Relax operator type relation previously rejected every axis for a rank-zero
input:

rank(input) = 0
valid axis range = [0, rank(input) - 1] = [0, -1]

For rank-zero input, this change therefore skips the axis-range check and
instead validates the condition that makes the axis irrelevant: both scale and
zero point must be scalar or singleton tensors. The legalization and output
shape rules already handle rank-zero tensors, so no converter or lowering
special case is needed.

Updated behavior

Input / quantization parameters	Behavior
Rank zero + scalar/singleton scale and zero point	Accept as per-tensor QDQ; preserve scalar output shape
Rank zero + non-singleton scale or zero point	Reject with an explicit error
Rank one or greater	Preserve the existing axis and parameter-size validation

Safety checks

• Scalar support is implemented symmetrically for quantize and dequantize.
• A parametrized core test verifies that non-singleton per-axis parameters are
  still rejected for rank-zero input.
• The ONNX tests compare TVM execution and output dtypes with ONNX Runtime.
• The full Relax ONNX frontend test file passes in the validation environment,
  apart from five pre-existing Float8 saturate baseline cases that were
  excluded explicitly.

Out of scope / non-goals

• Claiming full BiRefNeXt import or execution; this PR removes only its first
  observed scalar-QDQ blocker, and the model still has an independent dynamic
  shape Concat blocker.
• Adding the external 117 MB BiRefNeXt model to the TVM test suite.
• Changing per-axis QDQ behavior for non-scalar inputs.
• Addressing the existing Float8 saturate test baseline.

Results

Before this change, minimal scalar ONNX graphs fail during import with:

ValueError: relax.quantize: axis param is out of range (0)
ValueError: relax.dequantize: axis param is out of range (0)

With this change, opset-18 scalar graphs import, legalize, compile, and execute
against ONNX Runtime:

QuantizeLinear:   x = 1.25, scale = 0.25, zero_point = 2 -> uint8 scalar 7
DequantizeLinear: x = 7,    scale = 0.25, zero_point = 2 -> float32 scalar 1.25

Tests

• pre-commit run --files src/relax/op/tensor/qdq.cc tests/python/relax/test_frontend_onnx.py tests/python/relax/test_op_qdq.py
• Fresh CPU-only H20 Release build: passed
• Relax QDQ operator tests: 9 passed
• Focused scalar ONNX Runtime-backed tests: 2 passed, 500 deselected
• Relax ONNX frontend H20 run: 484 passed, 9 skipped, 5 deselected, 4 xfailed

References

• ONNX QuantizeLinear specification
• ONNX DequantizeLinear specification
• BiRefNeXt anime segmentation ONNX model

[tlopex]

LGTM Sorry the CI is broken