[Quant][Prototype] Hoist static FP8 input quant into model code

[mgoin]

Prototype for the torch.compile-removal migration: model code calls a small helper that runs RMSNorm and, when the downstream linear opts in, returns a QuantizedActivation produced by the fused rms_norm_static_fp8_quant C++ kernel. The linear's kernel passes it through to the matmul without re-quantizing.

Scope: static-per-tensor FP8 (kFp8StaticTensorSym) via compressed-tensors, wired through Llama.

• quant_fusion.py: QuantizedActivation + rms_norm_input_quant. Dispatches on layer.input_quant_key; no scheme/method indirection.
• CompressedTensorsW8A8Fp8: sets layer.input_quant_key for static-tensor; apply_weights is a passthrough.
• FP8ScaledMMLinearKernel.apply_weights: unwraps QuantizedActivation and skips internal quant.
• LlamaDecoderLayer.forward: calls the helper before qkv_proj and gate_up_proj.

Tested on RedHatAI/Llama-3.2-1B-FP8 in both eager and torch.compile modes; output matches main. AI-assisted.

[gemini-code-assist]

Code Review

This pull request introduces input quantization fusion for NVFP4 linear kernels. Key changes include refactoring the CUTLASS kernel to support pre-quantized activations via a new apply_quantized method, adding a QuantizedActivation dataclass, and implementing the rms_norm_input_quant utility to combine RMS normalization with quantization. The Qwen3 model has been updated to leverage these improvements. Feedback was provided regarding the safety of attribute access in the rms_norm_input_quant function, suggesting the use of getattr and hasattr to prevent potential AttributeError when accessing quant_method on a torch.nn.Module.

[gemini-code-assist]

The access to linear.quant_method is unsafe here. linear is typed as torch.nn.Module, which does not have a quant_method attribute in the standard PyTorch API. While vLLM linear layers typically have this attribute, it's safer to use getattr to avoid potential AttributeError. Additionally, quant_method could be None for unquantized layers, or it might not implement quantize_input if it's a different quantization method.

Suggested change

	if getattr(linear, "input_quant_key", None) is None:
	return out, residual
	return linear.quant_method.quantize_input(linear, out), residual
	if getattr(linear, "input_quant_key", None) is not None:
	quant_method = getattr(linear, "quant_method", None)
	if quant_method is not None and hasattr(quant_method, "quantize_input"):
	return quant_method.quantize_input(linear, out), residual
	return out, residual

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @mgoin.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork