chore: bump ms-swift from 3.5.3 to 3.9.1

[dependabot]

Bumps ms-swift from 3.5.3 to 3.9.1.

Release notes

Sourced from ms-swift's releases.

Patch release v3.9.1

Full Changelog: modelscope/ms-swift@v3.9.0...v3.9.1

v3.9.0

中文版

新特性

1. Megatron-SWIFT a. 支持更多模型架构：Qwen3-VL, Qwen3-Omni, Qwen3-Next, Kimi-VL, InternVL3.5-HF等。完整的模型支持情况，参考支持的模型文档：https://swift.readthedocs.io/en/latest/Instruction/Supported-models-and-datasets.html b. 支持KTO训练，包括全参数/LoRA/MoE/多模态/Packing等训练技术等支持。感谢招商银行技术团队@​kevssim 的贡献。训练脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/megatron/rlhf/kto c. 支持RM训练，包括全参数/LoRA/MoE/多模态/Packing等训练技术等支持。训练脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/megatron/rlhf/rm d. 支持序列分类模型架构，包括三种任务：regression、single_label_classification、multi_label_classification。训练脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/megatron/seq_cls e. 支持VPP并行技术，减少PP并行的计算空泡，提高GPU利用率，但会略微提高通信量。支持异构PP并行 pipeline_model_parallel_layout，自定义流水线并行（PP/VPP）布局。 f. DPO等RLHF技术中的ref_model不初始化 main_grad 降低显存占用。
2. 训练 a. 序列并行优化，ulysses 和 ring-attention 支持混合使用，实现更长的序列处理能力。支持纯文本和多模态模型的SFT/DPO/GRPO训练。训练脚本参考：https://github.com/modelscope/ms-swift/blob/main/examples/train/sequence_parallel/sequence_parallel.sh b. 纯文本及多模态模型Embedding/Reranker/序列分类任务训练支持使用 padding_free 以节约显存资源并加速训练。 c. Embedding和Reranker训练数据集格式重构，具体参考文档：https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html, https://swift.readthedocs.io/en/latest/BestPractices/Reranker.html d. Agent template支持更多模型：deepseek_v3_1, qwen3_coder。（感谢@​gakkiri ,@​ray075hl 的贡献） e. load_from_cache_file 默认值从True改成False，避免因缓存原因导致的未知问题。
3. RLHF a. GRPO支持CHORD算法，在GRPO训练中混合SFT训练，参考文档：https://swift.readthedocs.io/en/latest/Instruction/GRPO/AdvancedResearch/CHORD.html b. KTO支持padding free和packing以节约显存资源并加速训练。 c. GRPO训练 padding_free重构，更好支持多模态模型。 d. GRPO vLLM 支持PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True"环境变量，减小显存碎片。
4. 推理 a. 支持Reranker任务的推理/部署 (pt/vllm)，以及序列分类任务的推理部署（pt/vllm）。脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/deploy/reranker, https://github.com/modelscope/ms-swift/tree/main/examples/deploy/seq_cls

新模型

1. 纯文本模型 a. Qwen/Qwen3-Next-80B-A3B-Instruct系列，训练脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/models/qwen3_next b. ZhipuAI/GLM-4.6 c. inclusionAI/Ling-mini-2.0; inclusionAI/Ring-mini-2.0系列 d. iic/Tongyi-DeepResearch-30B-A3B e. ByteDance-Seed/Seed-OSS-36B-Instruct系列（感谢@​hpsun1109 的贡献） f. deepseek-ai/DeepSeek-V3.1-Terminus g. PaddlePaddle/ERNIE-4.5-21B-A3B-Thinking h. google/embeddinggemma-300m（embedding模型）
2. 多模态模型 a. Qwen/Qwen3-VL-30B-A3B-Instruct系列，训练脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/models/qwen3_vl b. Qwen/Qwen3-Omni-30B-A3B-Instruct系列，训练脚本参考：https://github.com/modelscope/ms-swift/tree/main/examples/models/qwen3_omni c. Kwai-Keye/Keye-VL-1_5-8B（感谢@​hellopahe 的贡献） d. OpenGVLab/InternVL3_5-1B-HF系列 e. BytedanceDouyinContent/SAIL-VL2-2B系列 f. stepfun-ai/Step-Audio-2-mini（感谢@​CJack812 的贡献）

English Version

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Commits

• fd4ea9b [template] Support qwen3 omni mixed data (#6196)
• c232caa [model] support Qwen/Qwen3Guard-Gen-0.6B series (#6189)
• df9508f [bugfix] add tools fileds in grpo inputs2reqeusts (#6054)
• 830f622 [docs] update docs (#6180)
• c27dd1a [template] update qwen3_vl grounding dataset format (#6178)
• a472a9e [bugfix] Fix multimodal lazy_tokenize false (#6172)
• b2c7053 [bugfix] qwen3-vl support mixed data #6161 (#6161)
• 4ecd245 [deploy] update vllm_enable_prefix_caching (#6165)
• b9c2a47 Fix bug of sp/cp (#6163)
• cf073f9 [bugfix] fix qwen3_vl videos (#6162)
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Superseded by #56.