This requires the whole model to be able to fit on to one GPU (as per data parallel's usual implementation) 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...
tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `torch_dtype` attribute specified in the mode...
网址:https://www.deepspeed.ai/training/ Deepspeed并行框架介绍:https://github.com/wzzzd/LLM_Learning_Note/blob/main/Parallel/deepspeed.md Deepspeed是微软推出的一个开源分布式工具,其集合了分布式训练、推断、压缩等高效模块。 该工具旨在提高大规模模型训练的效率和可扩展性。它通过多种技术手段来加速训练,包...
tensor_parallel_size=1 张量并行(tensor_parallel_size=4) 数据并行 数据并行 vs 张量并行 背景 chenhuixi:影响VLLM推理速度的重要参数配置45 赞同 · 1 评论文章 VLLM推理还需要继续进阶。 假设现在有4个GPU,模型单个GPU能容纳,需要推理样本数量1700,生成最大长度2048。 tensor_parallel_size=1 llm = LLM( mo...
k在beam search算法中被称为beam_size Sample 随机采样方式。按照词表每个token的概率采样一个token出来。这个方式多样性更强,是目前主流的生成方式。 1. 前言 1.1 重要推理超参数 do_sample:布尔类型。是否使用随机采样方式运行推理,如果设置为False,则使用beam_search方式...
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...
k在beam search算法中被称为beam_size Sample 随机采样方式。按照词表每个token的概率采样一个token出来。这个方式多样性更强,是目前主流的生成方式。 1. 前言 1.1 重要推理超参数 do_sample:布尔类型。是否使用随机采样方式运行推理,如果设置为False,则使用beam_search方式...
] # 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.generate(prompts, sampling_params) # 完成推理 for output in outputs: prompt = output.prompt generated_text =...
vllm serve /data/DeepSeek-R1 --tensor-parallel-size 8 --max-model-len 16384 --port 8102 --trust-remote-code --served-model-name deepseek-r1 --enable-chunked-prefill --max-num-batched-tokens 2048 --gpu-memory-utilization 0.9 #sglang ...
vllm serve gpt2 --tensor-parallel-size 4 --pipeline-parallel-size 2 @DavideHe Let me try to understand what you mean. We were using tp=32, so 18432/32 = 576 which is not divisible by weight quantization block_n = 128. So you are suggesting us to use tp=8 and pp=4 instead (...