[vamsi8213]

This PR adds support for the ONNX LpNormalization operator in the Relax ONNX frontend.

LpNormalization normalizes the input along axis by its L1 or L2 norm:

p = 1:  y = x / sum(|x|, axis, keepdims=True)
p = 2:  y = x / sqrt(sum(x * x, axis, keepdims=True))

Changes

• Added an LpNormalization converter (_impl_v1) in python/tvm/relax/frontend/onnx/onnx_frontend.py, supporting the axis (default -1) and p (default 2) attributes, and registered it in the converter map.
  • Only p=1 and p=2 are accepted, as defined by the ONNX spec (onnxruntime also rejects other values); other values raise a ValueError.
  • axis is normalized and range-checked against the input rank.
  • Slices whose norm is zero produce zeros instead of NaN, matching onnxruntime. This is done with where(norm == 0, 1, norm) rather than clamping with an epsilon, so non-zero norms are never altered and float16 inputs don't underflow to 0 / 0.
• Added test_lp_norm in tests/python/relax/test_frontend_onnx.py:
  • structural IR checks for the L2 (default) and L1 lowerings,
  • numerical correctness against onnxruntime for p in {1, 2} across positive and negative axes,
  • zero-norm slices (float32 and float16),
  • invalid p and out-of-range axis.
• Enabled the ONNX backend node test (test_lpnormalization_default) by adding lpnormalization to _INCLUDE_OPS in tests/python/relax/test_frontend_onnx_backend.py.

[vamsi8213]

Hi @tqchen, @mshr-h

Could you please review this PR?