工程实践中,通过输入对齐和固定填充策略可有效缓解,根本解决需依赖模型架构和框架层面的优化。 当使用大语言模型(LLM)进行批量推理(Batch Inference)时,即使关闭随机采样(sampling=False),结果仍可能与单例推理(batch_size=1)不同,核心原因可分为随机性相关因素和确定性解码中的系统性偏差两类: 随机性相关因素(
Anything you want to discuss about vllm. In the vllm docs, there is an example for sending a batch of multi-modal prompts to offline inference # Batch inference image_1 = PIL.Image.open(...) image_2 = PIL.Image.open(...) outputs = llm.ge...
batch inference推理的结果居然会和一条一条推理结果差的很远?!! Batch Decoding/Inference of LLMs will cause different outputs with different batch size?! 并且VLLM等框架都有这个问题,不完全是精度和溢出的问题 行为表现 测试中可以发现,即使是在推理阶段,不是在训练阶段,对于多模态大模型VLLM,如果推理时候为...
Quick vLLM inline batch inference benchmark numbers usinghttps://gist.github.com/yanxi0830/4e424f5cfc9a736af800f662c68d0b76 On Llama3.1-70B w/ 80 prompts, 4 GPUs w/ batch inference: 2297.87 toks/s w/o batch inference: 47.65 toks/s Providers Support Inline vLLM Remote We will need ...
Batch inference example notebooks using Python The following example notebook creates a provisioned throughput endpoint and runs batch LLM inference using Python and the Meta Llama 3.1 70B model. It also has guidance on benchmarking your batch inference workload and creating a provisioned throughput ...
vLLM 提供了两类推理的实现,一类是 offline inference,类似于 HF pipeline 的 batch 推理接口,用于离线批量的推理生成;一类是和 openai api 类似的实时在线推理,用于服务端接收并发推理请求的应用部署,其本身也可以通过命令行拉起一个 web 服务端进行部署。
To get started with batch inference with LLMs on Unity Catalog tables see the notebook examples in Batch inference using Foundation Model APIs provisioned throughput. Requirements See the requirements of the ai_query function. Query permission on the Delta table in Unity Catalog that contains the ...
December 12, 2024/3 min read Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Featured See All Partners Cloud Providers Technology Partners Data Partners Built on Databricks Consulting & System Integrators ...
Step 2:Launch Anyscale as the backend for LLM inference. Step 3:Start an Anyscale Job to run batch inference using Ray Data with RAG. This involves: Launching a vector database (e.g., FAISS) and load embeddings from cloud storage into it. ...
因此,DHelix 的 SI 设计通过使训练路径能够同时容纳两个相邻 Micro Batch,有效隐藏了 LLM 训练关键路径中的通信开销,显著提升整体性能。同时,SI 在现有并行级别之下运行,可以无缝集成于 TP、SP、CP 和 EP。 4.2 模型折叠 这里,作者具体介绍了其模型折叠(Folding)技术。这一关键的 DHelix 技术使得 PP 得以实现,具...