在vllm上层接口可以直接通过参数tensor_parallel_size设置,来将模型分布在 tensor_parallel_size 个 GPU 上进行并行计算,每个 GPU 负责处理模型的一部分张量。 vllm中与tp并行有关的操作主要在vllm/distributed中。 vllm/distributed/parallel_state.py initialize_model_parallel()函数实现了模型并行分组的初始化(这里...
行并行——RowParallelLinear 完整代码实现以及测试 背景与概述 在LLM推理上,张量并行(Tensor Parallelism, TP)是一种重要的模型加速手段:将模型的权重矩阵按照一定的规则(如列分割或行分割)拆分为多个部分,在每个GPU上分别完成部分计算,从而可以提高计算速度以及降低单个GPU的内存需求。在VLLM中,张量并行主要涉及到进程...
这两天一直被一个问题困扰,用vLLM加载千问时不能开并行(tensor_parallel_size>1),一开就会卡在ray阶段,最初是提示Started a local Ray instance,后来手工启动ray集群,就提示connected to Ray cluster。无论怎样调都无法跑下去,根本不会加载模型,换了各种版本的vllm、transformer、ray、显卡、甚至是用本来可以跑的...
Describe the bug Incorrect vLLM tensor-parallel-size calculated by auto-scheduling and causes inference engine error Steps to reproduce In an A800x4 environment, try to deploy ModelScope/OpenGVLab/InternVL2_5-78B-AWQ with --trust-remote-...
and will doubtless have a higher RAM overhead (I haven't checked, but it shouldn't be massive depending on your text size), but it does run seem to run at roughly N times the speed of running on one GPU (where N=number of GPUs) compared to <N times for the tensor parallel implem...
from vllm import LLM, SamplingParams prompts = [ "Have you followed marsggbo in Zhihu?", "你一键三连了吗?" ] # 输入prompts sampling_params = SamplingParams(temperature=0.8, top_k=50) # 采样策略 llm = LLM(model="facebook/opt-125m", tensor_parallel_size=2) # 初始化 LLM outputs = ...
llm = LLM(model="facebook/opt-125m", tensor_parallel_size=2) # 初始化 LLM outputs = llm.generate(prompts, sampling_params) # 完成推理 for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r...
理论支持论文:Megatron-LM’s tensor parallel algorithm 安装分布式框架 ray 代码语言:shell AI代码解释 pip install ray tensor_parallel_size 可以指定使用 GPU 的数量。 代码语言:shell AI代码解释 from vllm import LLM llm = LLM("facebook/opt-13b", tensor_parallel_size=4) output = llm.generate("San...
vllm 当我设置tensor_parallel_size=2时，发生了一个时间错误,当tensor_parallel_size=2被使用时，输出...
vllm --tensor-parallel-size 2 fails to load on GCP我在qwen72b模型上遇到了同样的问题。