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TensorRT-LLM七日谈 Day3

今天主要是结合理论进一步熟悉TensorRT-LLM的内容

从下面的分享可以看出,TensorRT-LLM是在TensorRT的基础上进行了进一步封装,提供拼batch,量化等推理加速实现方式。

下面的图片更好的展示了TensorRT-LLM的流程,包含权重转换,构建Engine,以及推理,评估等内容。总结一下就是三步。

不想看图的话,可以看看AI的总结,我放在附录中。

下图也很好的展示的trt-llm推理的全流程。

多卡并行

值得注意的是,trt-llm特意考虑了多卡部署的使用场景。通过tp-size参数来控制张量并行的程度,pp-size来控制溧水县并行的程度。

流水线并行

量化

权重&激活值量化

KV Cache量化

量化精度影响

从下图可以看出,使用FP8进行量化,量化精度较高。

性能调优

关于性能调优,trt-llm中也使用了类似于vllm中xontinuous batching的策略。

附录

The image describes an overview of the TensorRT-LLM (Large Language Model) workflow. Here's a summary of the key steps and elements involved:

  1. Input Models:
  • Various external models from frameworks like HuggingFace, NeMo, AMMO, and Jax can be used as inputs.
  1. TRT-LLM Checkpoint:
  • These external models are converted into a format defined by TRT-LLM using scripts like convert_checkpoint.py or quantize.py.
  • This conversion determines several key backward layer parameters, including:
    • Quantization method
    • Parallelization method
    • And more...
  1. TRT-LLM Engines:
  • After converting to the checkpoint format, the trtllm-build command is used to further convert and optimize the checkpoint into TensorRT Engines.
  • During this step, important inference parameters are set, such as:
    • Max batch size
    • Max input length
    • Max output length
    • Max beam width
    • Plugin configuration
    • And others...
  • Most of the automatic optimizations occur at this stage.
  1. Application Development:
  • Using C++/Python APIs, developers can build applications with these optimized engines.
  • TensorRT-LLM comes with several built-in tools to help with secondary development:
    • summarize.py for text summarization
    • mmlu.py for accuracy testing
    • run.py for a dry run to verify the model
    • benchmark for benchmarking
  • The runtime options include:
    • Temperature (for sampling)
    • Top K (for top K sampling)
    • Top P (for nucleus sampling)

This workflow outlines how to integrate and optimize models for efficient inference with TensorRT-LLM and leverage its tools for application development and performance testing.

NVIDIA AI 加速精讲堂-TensorRT-LLM 应用与部署_哔哩哔哩_bilibili

标签: 人工智能

本文转载自: https://blog.csdn.net/qq_19859865/article/details/142907826
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