【Hackathon 9th No.93】【RFC】为 Fastdeploy 新增 MiniMax-M1 模型#1156
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luotao1 merged 1 commit intoPaddlePaddle:masterfrom Sep 19, 2025
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【Hackathon 9th No.93】【RFC】为 Fastdeploy 新增 MiniMax-M1 模型#1156luotao1 merged 1 commit intoPaddlePaddle:masterfrom
luotao1 merged 1 commit intoPaddlePaddle:masterfrom
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| **核心技术路径**: | ||
| 1. **复用**: 最大化复用 GLM-4.5 PR 中已有的 Partial RoPE 和标准 GQA Attention 组件。 | ||
| 2. **翻译与开发**: 将 vLLM 的 `lightning_attn.py` (Triton) 翻译为高性能的 CUDA C++ 算子，以支持 MiniMax-M1 的线性注意力层。 |
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可以先用triton算子快速验证，同步开发高性能cuda kernel，FD目前支持使用triton算子
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| ### **Phase 1: [核心开发] 实现 Mamba/线性注意力 CUDA 算子 (2-4 周)** |
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目前看主要开发工作在线性注意力，可以先尝试接入triton算子快速验证下，如果attention性能不佳可以尝试实现cuda kenrel优化端到端性能。
chang-wenbin
approved these changes
Sep 19, 2025
luotao1
approved these changes
Sep 19, 2025
This was referenced Mar 20, 2026
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基于本 RFC 完成了 MiniMax-M1 模型的 FastDeploy 实现，代码 PR：PaddlePaddle/FastDeploy#6994（2022 行新增，32/32 测试通过，CI 绿）。 实现与原设计的主要技术偏差：
设计文档已按实际实现更新：#1252 。感谢原 RFC 提供的架构框架。 |
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本文档为新增 MiniMax-M1 模型的 RFC，规划了从 CUDA 算子开发到模型整体集成的技术方案。