[Relax][Frontend][ONNX] Add GroupNormalization support

[napronald]

Summary

Adds ONNX frontend support for GroupNormalization by mapping it to the existing relax.op.nn.group_norm.

Supports opset 18 per-group scale/bias expansion, opset 21 per-channel scale/bias, and stash_type cast behavior.

Testing

Includes structural checks for opset 18, opset 21, rank-3 inputs, and fp16 stash_type paths.

[gemini-code-assist]

Code Review

This pull request adds support for converting ONNX GroupNormalization nodes (opsets 18 and 21) into equivalent Relax expressions, along with comprehensive unit tests to verify correctness. The feedback suggests optimizing the opset 18 implementation by resolving scale and bias to constants at import time using get_constant. If they are constants, they can be expanded directly using NumPy (_np.repeat), which avoids adding redundant reshape and broadcast_to operators to the generated Relax graph.

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[gemini-code-assist]

We can use get_constant to resolve scale and bias to constants if they are initializers. This allows us to perform the per-group to per-channel expansion at import time using NumPy, avoiding redundant reshape and broadcast_to operators in the Relax graph.

Suggested change

	data = inputs[0]
	scale = inputs[1]
	bias = inputs[2]
	data = inputs[0]
	scale = get_constant(inputs[1], params)
	bias = get_constant(inputs[2], params)

[gemini-code-assist]

If scale and bias are resolved to constants, we can expand them directly using _np.repeat at import time. This simplifies the generated Relax graph by eliminating unnecessary reshape and broadcast_to operations.

Suggested change

	scale = relax.op.reshape(scale, [num_groups, 1])
	scale = relax.op.broadcast_to(scale, [num_groups, channels_per_group])
	scale = relax.op.reshape(scale, [channels])
	bias = relax.op.reshape(bias, [num_groups, 1])
	bias = relax.op.broadcast_to(bias, [num_groups, channels_per_group])
	bias = relax.op.reshape(bias, [channels])
	if isinstance(scale, relax.Constant):
	scale = relax.const(_np.repeat(scale.data.numpy(), channels_per_group), scale.ty.dtype)
	else:
	scale = relax.op.reshape(scale, [num_groups, 1])
	scale = relax.op.broadcast_to(scale, [num_groups, channels_per_group])
	scale = relax.op.reshape(scale, [channels])
	if isinstance(bias, relax.Constant):
	bias = relax.const(_np.repeat(bias.data.numpy(), channels_per_group), bias.ty.dtype)
	else:
	bias = relax.op.reshape(bias, [num_groups, 1])
	bias = relax.op.broadcast_to(bias, [num_groups, channels_per_group])
	bias = relax.op.reshape(bias, [channels])