trtllmgen-moe-fp8

[aleozlx]

📌 Description

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Reviewer Notes

[gemini-code-assist]

Summary of Changes

Hello @aleozlx, 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 introduces a significant new capability: fused Mixture-of-Experts operations with FP8 precision, specifically tailored for NVIDIA Blackwell GPUs. It integrates highly optimized batched GEMM kernels and custom CUDA logic for key MoE stages, exposed through PyTorch custom operators. The changes aim to enhance performance and memory efficiency for large-scale language models utilizing MoE architectures.

Highlights

• FP8 Mixture-of-Experts (MoE) Implementation: Introduced a comprehensive implementation of fused Mixture-of-Experts (MoE) operations with support for FP8 (Float 8) precision, including both per-tensor and block-scale quantization. This enables more memory-efficient and potentially faster inference for large language models.
• NVIDIA Blackwell (SM100a) Optimization: The entire MoE pipeline is heavily optimized for NVIDIA Blackwell (SM100a) architecture, leveraging advanced hardware features like TMA (Tensor Memory Access) for efficient data movement and programmatic stream serialization (PDL) for improved kernel launch efficiency.
• Modular C++/CUDA Architecture: The MoE pipeline is designed with a modular C++/CUDA architecture, breaking down the complex operation into distinct stages: batched GEMM, expert routing, activation, permutation, and finalization. This modularity allows for fine-grained optimization and easier maintenance.
• PyTorch Integration: The optimized MoE operations are exposed as PyTorch custom operators, providing a seamless and high-performance interface for integration into existing PyTorch-based deep learning frameworks.
• Extensive Validation and Testing: Comprehensive unit tests are included, featuring detailed Python reference implementations for various MoE stages and FP8 quantization schemes. These tests ensure the correctness and numerical accuracy of the new CUDA kernels.

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[gemini-code-assist]

Code Review

This pull request introduces significant new functionality for FP8 Mixture of Experts (MoE) by integrating kernels from TensorRT-LLM. The changes are extensive and add considerable value.

My review focused on ensuring the correctness and maintainability of the new code. I have identified a few issues, including a critical bug in the kernel selection logic that could prevent the system from functioning correctly, as well as some high-severity issues related to performance and logical correctness. Additionally, there are opportunities to improve code quality by reducing duplication and replacing estimated values with precise calculations.

I've provided detailed comments and suggestions for each of these points. Addressing them will be important for the stability and performance of this new feature.