Update flashinfer CUTLASS NVFP4 MoE Kernel to use per expert global scaling factor

[wenscarl]

Essential Elements of an Effective PR Description Checklist

• Update the block_scale_interleave API
• Use per expert global scaling factor for gemm1 and gemm2.

Before the change:
INFO:lm_eval.loggers.evaluation_tracker:Output path not provided, skipping saving results aggregated
vllm (pretrained=nvidia/DeepSeek-R1-FP4,quantization=modelopt_fp4,tensor_parallel_size=4,enforce_eager=True,max_model_len=2048,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.8734	±	0.0092
		strict-match	5	exact_match	↑	0.8726	±	0.0092

After:
INFO:lm_eval.loggers.evaluation_tracker:Output path not provided, skipping saving results aggregated
vllm (pretrained=nvidia/DeepSeek-R1-FP4,quantization=modelopt_fp4,tensor_parallel_size=4,enforce_eager=True,max_model_len=2048,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.9409	±	0.0065
		strict-match	5	exact_match	↑	0.9378	±	0.0067

Purpose

Test Plan

Test Result

(Optional) Documentation Update

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[gemini-code-assist]

Code Review

This pull request updates the FlashInfer CUTLASS MoE kernel usage. The changes mostly involve renaming block_scale_interleave to nvfp4_block_scale_interleave to align with an API update. However, there's a potential issue in how activation scales are handled for FP4 MoE layers. A per-expert scale tensor is being incorrectly used to quantize the entire input activation tensor, which requires a single scalar scale. I've suggested a fix to restore the correct behavior.

[gemini-code-assist]

The change from torch.min(layer.w13_input_scale_quant) to layer.w13_input_scale_quant may be incorrect. The a1_gscale variable is used in extra_prepare_args and passed to FlashInferCutlassMoEPrepareAndFinalize.prepare. This method uses the scale to quantize the input activation tensor (hidden_states) before the tokens are routed to the experts. The input activation tensor has a shape of (num_tokens, hidden_dim). To quantize this tensor, a single scalar scale is required. The original code, torch.min(layer.w13_input_scale_quant), correctly produced this scalar by selecting the most conservative scale among all per-expert scales. The new code passes layer.w13_input_scale_quant, which is a tensor of per-expert scales with shape (num_experts,). Using per-expert scales to quantize the entire activation tensor before expert routing is logically incorrect and may cause a runtime error or incorrect results from flashinfer.fp4_quantize. Please revert this change to use torch.min to ensure a correct scalar scale is used for the initial input quantization.

Suggested change

	a1_gscale = layer.w13_input_scale_quant
	a2_gscale = layer.w2_input_scale_quant
	a1_gscale = torch.min(layer.w13_input_scale_quant)
	a2_gscale = torch.min(layer.w2_input_scale_quant)

[alexm-redhat]

Verified locally that works, LGTM!