[Relax][ONNX] Fix LayerNormalization no-bias zero tensor shape and dtype

[javierdejesusda]

Root cause

In the ONNX LayerNormalization spec the bias B is optional; when omitted it should behave as
zeros shaped and typed like the scale W. In LayerNormalization._impl_v17, the synthesized zero
bias instead took its shape from data.struct_info.shape[1] (an unrelated data dim) and hardcoded
dtype="float32". For input [2, 3, 4, 8] with scale [8] and axis=-1 this builds a bias of
shape (3,) while gamma is (8,), so relax.op.nn.layer_norm raises a size-mismatch
InternalError. The float32 hardcode also breaks fp16/bf16 no-bias models, since gamma, beta, and
data must share a dtype. PyTorch's nn.LayerNorm(..., bias=False) exports exactly this no-bias form.

Fix

Derive both the shape and dtype of the synthesized zero bias from the scale, matching the ONNX
semantics for an omitted B and the existing torch frontend
(relax.const(np.zeros(shape), x.struct_info.dtype)):

if bias is None:
    bias = relax.const(_np.zeros(gamma_shape, dtype=scale.struct_info.dtype))

gamma_shape and the _np/get_const_tuple imports are already present. Deriving the dtype from
the scale (rather than the issue's float32-only suggestion) is what also fixes the fp16/bf16 case.

Test plan

Added non-square no-bias regression cases to test_frontend_onnx.py::test_layer_norm (the previous
no-bias case was square, which masked the bug): float32 [2,3,4,8]/scale [8] and float16 with
full check_correctness, plus a bf16 importer-only case (ORT's CPU provider has no bf16
LayerNormalization kernel).

Fixes #19691

[gemini-code-assist]

Code Review

This pull request fixes an issue in the ONNX frontend's LayerNormalization importer when no bias is provided. It updates the fallback bias creation to use the shape and data type of the scale (gamma_shape and scale.struct_info.dtype) instead of hardcoding a float32 array based on the input's second dimension. It also adds corresponding unit tests for non-square inputs, float16, and bfloat16 data types. A review comment correctly points out that using bfloat16 directly in _np.zeros will raise a TypeError in standard NumPy environments, and suggests constructing the NumPy array as float32 and letting relax.const handle the conversion to the target dtype.

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