Paper summary: 现有的大型语言模型系统需要大量的内存来存储键-值缓存。由于这些内存是动态变化的,因此可能会因碎片化和冗余复制而浪费大量内存,从而限制了批处理大小。作者提出了PagedAttention来动态管理键-值缓存。在PagedAttention的基础上,他们构建了vLLM。这个LLM服务系统实现了(1)几乎零浪费的KV缓存内存和(2)在...
2、NVIDIA Megatron github: github.com/NVIDIA/Megat 3、torch distributed tutorial: pytorch.org/docs/stable 4、init_process_group: cnblogs.com/rossixyz/p/ 5、DeepSpeed Megatron tutorial: deepspeed.ai/tutorials/ 6、codegeex paper: arxiv.org/abs/2303.1756编辑...
之前主要的大模型训练方式是数据并行,Megatron-LM 比较成熟的支持 LLM 的模型并行和流水并行。 模型并行部分,主要是提供了 transformer 的模型并行的高效实现。结合数据并行和模型并行,Megtron-LM 把 GPT-2 模型扩展到了8B、 512 GPUs的规模,扩展效率为 75%+. 流水并行部分,Megatron-LM 组合了数据、模型、流水并行...
NeMo Megatron NVIDIA 宣布了新的更新 NVIDIA NeMo Megatron ,这是一个培训大型语言模型( LLM )的框架,其参数高达数万亿。基于 Megatron paper 的创新, NeMo Megatron 的研究机构和企业可以培训任何 LLM ,以实现融合。 NeMo Megatron 提供数据预处理、并行(数据、张量和管道)、编排和调度,以及自动精度自适应。 它包...
InstructRetro(Wang et al., 2023b)further scales up the size of Retro to 48B, featuring the largest LLM pretrained with retrieval (as of December 2023). The obtained foundation model, Retro 48B, largely outperforms the GPT counterpart in terms of perplexity. With instruction tuning on Retro, ...
academic_paper_scripts bert gpt3 inference gpt quantization README.md ptq_trtllm_llama_7b.sh ptq_trtllm_nemotron3_8b.sh text_generation_ptq.py trtllm_text_generation.py README.md run_text_generation_server_345M.sh run_text_generation_server_345M_8_tensor_parallel.sh ...
First introduced in 2019, Megatron (1, 2, and 3) sparked a wave of innovation in the AI community, enabling researchers and developers to utilize the underpinnings of this library to further LLM advancements. Today, many of the most popular LLM developer frameworks have been inspired by and ...
Megatron-LM serves as a research-oriented framework leveraging Megatron-Core for large language model (LLM) training. Megatron-Core, on the other hand, is a library of GPU optimized training techniques that comes with formal product support including versioned APIs and regular releases. You can ...
AI Infra论文阅读之LIGHTSEQ(LLM长文本训练的Infra工作) - 知乎 感觉这篇paper有几个亮点,首先把Megatron-LM的Self-Attention模块的模型并行方式变成序列并行,优化了通信量,同时通过计算和通信重叠近一步压缩了训练迭代时间。另外,在使用重计算的时候发现当前Huggingface/Me… 【分布式训练技术分享四】聊聊序列并行Sequence...
the analysis by theHANS paper(opens in new tab), BERT baselines trained onMNLI(opens in new tab)performs near-perfect on half of its subcategories while near-zero on the other half. That indicates that they are strongly depend...