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* Update TensorRT-LLM --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: Tlntin <TlntinDeng01@Gmail.com> Co-authored-by: ZHENG, Zhen <zhengzhen.z@qq.com> Co-authored-by: Pham Van Ngoan <ngoanpham1196@gmail.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Tushar Goel <tushar.goel.ml@gmail.com> Co-authored-by: Mati <132419219+matichon-vultureprime@users.noreply.github.com>
81 lines
5.4 KiB
Markdown
81 lines
5.4 KiB
Markdown
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TensorRT-LLM
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===========================
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<h4> A TensorRT Toolbox for Optimized Large Language Model Inference</h4>
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[](https://nvidia.github.io/TensorRT-LLM/)
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[](https://www.python.org/downloads/release/python-31012/)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://developer.nvidia.com/tensorrt)
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[](./tensorrt_llm/version.py)
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[](./LICENSE)
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[Architecture](./docs/source/architecture/overview.md) | [Results](./docs/source/performance/perf-overview.md) | [Examples](./examples/) | [Documentation](./docs/source/)
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---
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<div align="left">
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## Latest News
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* [*Weekly*] Check out **[@NVIDIAAIDev](https://twitter.com/nvidiaaidev?lang=en)** & **[NVIDIA AI](https://www.linkedin.com/showcase/nvidia-ai/)** LinkedIn for the latest updates!
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* [2024/02/06] [🚀 Speed up inference with SOTA quantization techniques in TRT-LLM](./docs/source/blogs/quantization-in-TRT-LLM.md)
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* [2024/01/30] [ New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget](./docs/source/blogs/XQA-kernel.md)
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* [2023/12/04] [Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100](./docs/source/blogs/Falcon180B-H200.md)
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* [2023/11/27] [SageMaker LMI now supports TensorRT-LLM - improves throughput by 60%, compared to previous version](https://aws.amazon.com/blogs/machine-learning/boost-inference-performance-for-llms-with-new-amazon-sagemaker-containers/)
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* [2023/11/13] [H200 achieves nearly 12,000 tok/sec on Llama2-13B](./docs/source/blogs/H200launch.md)
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* [2023/10/22] [🚀 RAG on Windows using TensorRT-LLM and LlamaIndex 🦙](https://github.com/NVIDIA/trt-llm-rag-windows#readme)
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* [2023/10/19] Getting Started Guide - [Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available
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](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/)
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* [2023/10/17] [Large Language Models up to 4x Faster on RTX With TensorRT-LLM for Windows
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](https://blogs.nvidia.com/blog/2023/10/17/tensorrt-llm-windows-stable-diffusion-rtx/)
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## TensorRT-LLM Overview
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TensorRT-LLM is an easy-to-use Python API to define Large
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Language Models (LLMs) and build
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[TensorRT](https://developer.nvidia.com/tensorrt) engines that contain
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state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
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TensorRT-LLM contains components to create Python and C++ runtimes that
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execute those TensorRT engines. It also includes a
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[backend](https://github.com/triton-inference-server/tensorrtllm_backend)
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for integration with the
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[NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server);
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a production-quality system to serve LLMs. Models built with TensorRT-LLM can
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be executed on a wide range of configurations going from a single GPU to
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multiple nodes with multiple GPUs (using
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[Tensor Parallelism](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/parallelisms.html#tensor-parallelism)
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and/or
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[Pipeline Parallelism](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/parallelisms.html#pipeline-parallelism)).
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The TensorRT-LLM Python API architecture looks similar to the
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[PyTorch](https://pytorch.org) API. It provides a
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[functional](./tensorrt_llm/functional.py) module containing functions like
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`einsum`, `softmax`, `matmul` or `view`. The [layers](./tensorrt_llm/layers)
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module bundles useful building blocks to assemble LLMs; like an `Attention`
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block, a `MLP` or the entire `Transformer` layer. Model-specific components,
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like `GPTAttention` or `BertAttention`, can be found in the
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[models](./tensorrt_llm/models) module.
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TensorRT-LLM comes with several popular models pre-defined. They can easily be
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modified and extended to fit custom needs. Refer to the [Support Matrix](https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html) for a list of supported models.
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To maximize performance and reduce memory footprint, TensorRT-LLM allows the
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models to be executed using different quantization modes (refer to
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[`support matrix`](https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html#software)). TensorRT-LLM supports
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INT4 or INT8 weights (and FP16 activations; a.k.a. INT4/INT8 weight-only) as
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well as a complete implementation of the
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[SmoothQuant](https://arxiv.org/abs/2211.10438) technique.
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## Getting Started
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To get started with TensorRT-LLM, visit our documentation:
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- [Quick Start Guide](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html)
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- [Release Notes](https://nvidia.github.io/TensorRT-LLM/release-notes.html)
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- [Installation Guide for Linux](https://nvidia.github.io/TensorRT-LLM/installation/linux.html)
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- [Installation Guide for Windows](https://nvidia.github.io/TensorRT-LLM/installation/windows.html)
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- [Supported Hardware, Models, and other Software](https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html)
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## Community
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- [Model zoo](https://huggingface.co/TheFloat16) (generated by TRT-LLM rel 0.9 a9356d4b7610330e89c1010f342a9ac644215c52)
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