[depend on flashinfer 0.6.0) change mm_fp4 to auto for cu13

[vincentzed]

Motivation

flashinfer-ai/flashinfer#1979 enables auto for mm_fp4, so we should use it for cu13 (except in the case that user specify flashinfer mm fp4 backend).
If cudnn>=9.15, then we should use cudnn. It is faster

Modifications

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[gemini-code-assist]

Summary of Changes

Hello @vincentzed, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on optimizing FP4 GEMM (General Matrix Multiply) operations by integrating the latest features from FlashInfer 0.6.0. The core change introduces an intelligent, automatic backend selection for FP4 GEMM, specifically targeting CUDA 13 environments. This enhancement aims to improve performance and efficiency by leveraging the most suitable underlying library (cuDNN or Cutlass) for matrix multiplications, without requiring manual configuration, thereby streamlining the execution of quantized models.

Highlights

• FlashInfer Dependency Update: The pull request updates the FlashInfer dependency to version 0.6.0, which introduces enhanced capabilities for automatic backend selection in FP4 matrix multiplication operations.
• Automatic FP4 GEMM Backend Selection for CUDA 13: A new mechanism has been implemented to automatically resolve the mm_fp4 GEMM backend. For CUDA 13 environments, if the FLASHINFER_FP4_GEMM_BACKEND environment variable is not explicitly set, the backend will default to 'auto', allowing FlashInfer to dynamically choose between cuDNN (if version >= 9.15) and Cutlass for optimal performance. For other CUDA versions, it defaults to 'cutlass'.

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[gemini-code-assist]

Code Review

This pull request updates the mm_fp4 backend selection to use auto for CUDA 13, which is a good improvement that leverages new features in flashinfer. The refactoring to centralize the backend selection logic into a new function, resolve_mm_fp4_gemm_backend, improves code clarity and maintainability. I have one suggestion to further simplify this new function to reduce code duplication. Overall, the changes are well-structured and align with the goals of the pull request.

[gemini-code-assist]

This function can be simplified to avoid repeating the check for FLASHINFER_FP4_GEMM_BACKEND. By checking for the user-specified backend first, you can reduce code duplication and make the logic more straightforward.

Suggested change

	if _IS_CUDA_13:
	# auto resolution: if cudnn < 9.15, use cutlass else use cudnn
	# https://github.com/flashinfer-ai/flashinfer/pull/1979
	backend = FLASHINFER_FP4_GEMM_BACKEND if FLASHINFER_FP4_GEMM_BACKEND else "auto"
	else:
	backend = (
	FLASHINFER_FP4_GEMM_BACKEND if FLASHINFER_FP4_GEMM_BACKEND else "cutlass"
	)
	return backend
	if FLASHINFER_FP4_GEMM_BACKEND:
	return FLASHINFER_FP4_GEMM_BACKEND
	if _IS_CUDA_13:
	# auto resolution: if cudnn < 9.15, use cutlass else use cudnn
	# https://github.com/flashinfer-ai/flashinfer/pull/1979
	return "auto"
	return "cutlass"