TensorRT-LLM ===========================

A TensorRT Toolbox for Optimized Large Language Model Inference

[![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://nvidia.github.io/TensorRT-LLM/) [![python](https://img.shields.io/badge/python-3.10.12-green)](https://www.python.org/downloads/release/python-31012/) [![cuda](https://img.shields.io/badge/cuda-12.2-green)](https://developer.nvidia.com/cuda-downloads) [![trt](https://img.shields.io/badge/TRT-9.2-green)](https://developer.nvidia.com/tensorrt) [![version](https://img.shields.io/badge/release-0.7.1-green)](./setup.py) [![license](https://img.shields.io/badge/license-Apache%202-blue)](./LICENSE) [Architecture](./docs/source/architecture.md) | [Results](./docs/source/performance.md) | [Examples](./examples/) | [Documentation](./docs/source/) ---

## Latest News * [2024/02/06] [🚀 Speed up inference with SOTA quantization techniques in TRT-LLM](./docs/source/blogs/quantization-in-TRT-LLM.md) * [2024/01/30] [ New **XQA-kernel** provides **2.4x more Llama-70B throughput** within the same latency budget](./docs/source/blogs/XQA-kernel.md) * [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) * [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/) * [2023/11/13] [H200 achieves nearly 12,000 tok/sec on Llama2-13B](./docs/source/blogs/H200launch.md) * [2023/10/22] [🚀 RAG on Windows using TensorRT-LLM and LlamaIndex 🦙](https://github.com/NVIDIA/trt-llm-rag-windows#readme) * [2023/10/19] Getting Started Guide - [Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available ](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/) * [2023/10/17] [Large Language Models up to 4x Faster on RTX With TensorRT-LLM for Windows ](https://blogs.nvidia.com/blog/2023/10/17/tensorrt-llm-windows-stable-diffusion-rtx/) [2023/11/27 - Amazon Sagemaker](https://aws.amazon.com/blogs/machine-learning/boost-inference-performance-for-llms-with-new-amazon-sagemaker-containers/) [2023/11/17 - Perplexity](https://blog.perplexity.ai/blog/turbocharging-llama-2-70b-with-nvidia-h100) ; [2023/10/31 - Phind](https://www.phind.com/blog/phind-model-beats-gpt4-fast) ; [2023/10/12 - Databricks (MosaicML)](https://www.databricks.com/blog/llm-inference-performance-engineering-best-practices) ; [2023/10/04 - Perplexity](https://blog.perplexity.ai/blog/introducing-pplx-api) ; [2023/09/27 - CloudFlare](https://www.cloudflare.com/press-releases/2023/cloudflare-powers-hyper-local-ai-inference-with-nvidia/); ## Table of Contents - [TensorRT-LLM Overview](#tensorrt-llm-overview) - [Installation](#installation) - [Quick Start](#quick-start) - [Support Matrix](#support-matrix) - [Devices](#devices) - [Precision](#precision) - [Key Features](#key-features) - [Models](#models) - [Performance](#performance) - [Advanced Topics](#advanced-topics) - [Quantization](#quantization) - [In-flight Batching](#in-flight-batching) - [Attention](#attention) - [Graph Rewriting](#graph-rewriting) - [Benchmark](#benchmark) - [Troubleshooting](#troubleshooting) - [Release notes](#release-notes) - [Change Log](#change-log) - [Known Issues](#known-issues) - [Report Issues](#report-issues) ## TensorRT-LLM Overview TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build [TensorRT](https://developer.nvidia.com/tensorrt) engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines. It also includes a [backend](https://github.com/triton-inference-server/tensorrtllm_backend) for integration with the [NVIDIA Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server); a production-quality system to serve LLMs. Models built with TensorRT-LLM can be executed on a wide range of configurations going from a single GPU to multiple nodes with multiple GPUs (using [Tensor Parallelism](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/parallelisms.html#tensor-parallelism) and/or [Pipeline Parallelism](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/parallelisms.html#pipeline-parallelism)). The Python API of TensorRT-LLM is architectured to look similar to the [PyTorch](https://pytorch.org) API. It provides users with a [functional](./tensorrt_llm/functional.py) module containing functions like `einsum`, `softmax`, `matmul` or `view`. The [layers](./tensorrt_llm/layers) module bundles useful building blocks to assemble LLMs; like an `Attention` block, a `MLP` or the entire `Transformer` layer. Model-specific components, like `GPTAttention` or `BertAttention`, can be found in the [models](./tensorrt_llm/models) module. TensorRT-LLM comes with several popular models pre-defined. They can easily be modified and extended to fit custom needs. See below for a list of supported [models](#Models). To maximize performance and reduce memory footprint, TensorRT-LLM allows the models to be executed using different quantization modes (see [`examples/gpt`](./examples/gpt) for concrete examples). TensorRT-LLM supports INT4 or INT8 weights (and FP16 activations; a.k.a. INT4/INT8 weight-only) as well as a complete implementation of the [SmoothQuant](https://arxiv.org/abs/2211.10438) technique. For a more detailed presentation of the software architecture and the key concepts used in TensorRT-LLM, we recommend you to read the following [document](./docs/source/architecture.md). ## Installation After installing the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit), please run the following commands to install TensorRT-LLM for x86_64 users. ```bash # Obtain and start the basic docker image environment. docker run --rm --runtime=nvidia --gpus all --entrypoint /bin/bash -it nvidia/cuda:12.1.0-devel-ubuntu22.04 # Install dependencies, TensorRT-LLM requires Python 3.10 apt-get update && apt-get -y install python3.10 python3-pip openmpi-bin libopenmpi-dev # Install the latest preview version (corresponding to the main branch) of TensorRT-LLM. # If you want to install the stable version (corresponding to the release branch), please # remove the `--pre` option. pip3 install tensorrt_llm -U --pre --extra-index-url https://pypi.nvidia.com # Check installation python3 -c "import tensorrt_llm" ``` For developers who have the best performance requirements, debugging needs, or use the aarch64 architecture, please refer to the instructions for [building from source code](docs/source/build_from_source.md). For Windows installation, see [`Windows`](windows/README.md). ## Quick Start Please be sure to complete the [installation steps](#installation) before proceeding with the following steps. To create a TensorRT engine for an existing model, there are 3 steps: 1. Download pre-trained weights, 2. Build a fully-optimized engine of the model, 3. Deploy the engine, in other words, run the fully-optimized model. The following sections show how to use TensorRT-LLM to run the [BLOOM-560m](https://huggingface.co/bigscience/bloom-560m) model. ***0. In the BLOOM folder*** Inside the Docker container, you have to install the requirements: ```bash pip install -r examples/bloom/requirements.txt git lfs install ``` ***1. Download the model weights from HuggingFace*** From the BLOOM example folder, you must download the weights of the model. ```bash cd examples/bloom rm -rf ./bloom/560M mkdir -p ./bloom/560M && git clone https://huggingface.co/bigscience/bloom-560m ./bloom/560M ``` ***2. Build the engine*** ```bash # Single GPU on BLOOM 560M python convert_checkpoint.py --model_dir ./bloom/560M/ \ --dtype float16 \ --output_dir ./bloom/560M/trt_ckpt/fp16/1-gpu/ # May need to add trtllm-build to PATH, export PATH=/usr/local/bin:$PATH trtllm-build --checkpoint_dir ./bloom/560M/trt_ckpt/fp16/1-gpu/ \ --gemm_plugin float16 \ --output_dir ./bloom/560M/trt_engines/fp16/1-gpu/ ``` See the BLOOM [example](examples/bloom) for more details and options regarding the `trtllm-build` command. ***3. Run*** The `../summarize.py` script can be used to perform the summarization of articles from the CNN Daily dataset: ```bash python ../summarize.py --test_trt_llm \ --hf_model_dir ./bloom/560M/ \ --data_type fp16 \ --engine_dir ./bloom/560M/trt_engines/fp16/1-gpu/ ``` More details about the script and how to run the BLOOM model can be found in the example [folder](examples/bloom). Many more [models](#models) than BLOOM are implemented in TensorRT-LLM. They can be found in the [examples](./examples/) directory. Beyond local execution, you can also use the NVIDIA Triton Inference Server to create a production-ready deployment of your LLM as described in this [blog](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/). ## Support Matrix TensorRT-LLM optimizes the performance of a range of well-known models on NVIDIA GPUs. The following sections provide a list of supported GPU architectures as well as important features implemented in TensorRT-LLM. ### Devices TensorRT-LLM is rigorously tested on the following GPUs: * [H100](https://www.nvidia.com/en-us/data-center/h100/) * [L40S](https://www.nvidia.com/en-us/data-center/l40s/) * [A100](https://www.nvidia.com/en-us/data-center/a100/) * [A30](https://www.nvidia.com/en-us/data-center/products/a30-gpu/) * [V100](https://www.nvidia.com/en-us/data-center/v100/) (experimental) If a GPU is not listed above, it is important to note that TensorRT-LLM is expected to work on GPUs based on the Volta, Turing, Ampere, Hopper and Ada Lovelace architectures. Certain limitations may, however, apply. ### Precision Various numerical precisions are supported in TensorRT-LLM. The support for some of those numerical features require specific architectures: | | FP32 | FP16 | BF16 | FP8 | INT8 | INT4 | | :------------------ | :--- | :--- | :--- | :--- | :---- | :---- | | Volta (SM70) | Y | Y | N | N | Y (1) | Y (2) | | Turing (SM75) | Y | Y | N | N | Y (1) | Y (2) | | Ampere (SM80, SM86) | Y | Y | Y | N | Y | Y (3) | | Ada-Lovelace (SM89) | Y | Y | Y | Y | Y | Y | | Hopper (SM90) | Y | Y | Y | Y | Y | Y | (1) INT8 SmoothQuant is not supported on SM70 and SM75.
(2) INT4 AWQ and GPTQ are not supported on SM < 80.
(3) INT4 AWQ and GPTQ with FP8 activations require SM >= 89. In this release of TensorRT-LLM, the support for FP8 and quantized data types (INT8 or INT4) is not implemented for all the models. See the [precision](./docs/source/precision.md) document and the [examples](./examples/.) folder for additional details. ### Key Features TensorRT-LLM contains examples that implement the following features. * Multi-head Attention([MHA](https://arxiv.org/abs/1706.03762)) * Multi-query Attention ([MQA](https://arxiv.org/abs/1911.02150)) * Group-query Attention([GQA](https://arxiv.org/abs/2307.09288)) * In-flight Batching * Paged KV Cache for the Attention * Tensor Parallelism * Pipeline Parallelism * INT4/INT8 Weight-Only Quantization (W4A16 & W8A16) * [SmoothQuant](https://arxiv.org/abs/2211.10438) * [GPTQ](https://arxiv.org/abs/2210.17323) * [AWQ](https://arxiv.org/abs/2306.00978) * [FP8](https://arxiv.org/abs/2209.05433) * Greedy-search * Beam-search * RoPE In this release of TensorRT-LLM, some of the features are not enabled for all the models listed in the [examples](examples/.) folder. ### Models The list of supported models is: * [Baichuan](examples/baichuan) * [BART](examples/enc_dec) * [BERT](examples/bert) * [Blip2](examples/blip2) * [BLOOM](examples/bloom) * [ChatGLM](examples/chatglm) * [FairSeq NMT](examples/nmt) * [Falcon](examples/falcon) * [Flan-T5](examples/enc_dec) * [GPT](examples/gpt) * [GPT-J](examples/gptj) * [GPT-Nemo](examples/gpt) * [GPT-NeoX](examples/gptneox) * [InternLM](examples/internlm) * [LLaMA](examples/llama) * [LLaMA-v2](examples/llama) * [mBART](examples/enc_dec) * [Mistral](examples/llama#mistral-v01) * [MPT](examples/mpt) * [mT5](examples/enc_dec) * [OPT](examples/opt) * [Phi-1.5/Phi-2](examples/phi) * [Qwen](examples/qwen) * [Replit Code](examples/mpt) * [RoBERTa](examples/bert) * [SantaCoder](examples/gpt) * [StarCoder](examples/gpt) * [T5](examples/enc_dec) * [Whisper](examples/whisper) Note: [Encoder-Decoder](examples/enc_dec/) provides general encoder-decoder functionality that supports many encoder-decoder models such as T5 family, BART family, Whisper family, NMT family, etc. We unroll the exact model names in the list above to let users find specific models easier. The list of supported multi-modal models is: * [BLIP2 w/ OPT-2.7B](examples/multimodal) * [BLIP2 w/ T5-XL](examples/multimodal) * [LLaVA-v1.5-7B](examples/multimodal) * [Nougat family](examples/multimodal) Nougat-small, Nougat-base Note: Multi-modal provides general multi-modal functionality that supports many multi-modal architectures such as BLIP family, LLaVA family, etc. We unroll the exact model names in the list above to let users find specific models easier. ## Performance Please refer to the [performance](./docs/source/performance.md) page for performance numbers. That page contains measured numbers for four variants of popular models (GPT-J, LLAMA-7B, LLAMA-70B, Falcon-180B), measured on the H100, L40S and A100 GPU(s). ## Advanced Topics ### Quantization This [document](./docs/source/precision.md) describes the different quantization methods implemented in TensorRT-LLM and contains a support matrix for the different models. ### In-flight Batching TensorRT-LLM supports in-flight batching of requests (also known as continuous batching or iteration-level batching). It's a [technique](./docs/source/batch_manager.md) that aims at reducing wait times in queues, eliminating the need for padding requests and allowing for higher GPU utilization. ### Attention TensorRT-LLM implements several variants of the Attention mechanism that appears in most the Large Language Models. This [document](./docs/source/gpt_attention.md) summarizes those implementations and how they are optimized in TensorRT-LLM. ### Graph Rewriting TensorRT-LLM uses a declarative approach to define neural networks and contains techniques to optimize the underlying graph. For more details, please refer to [doc](./docs/source/graph-rewriting.md) ### Benchmark TensorRT-LLM provides [C++](./benchmarks/cpp/README.md) and [Python](./benchmarks/python/README.md) tools to perform benchmarking. Note, however, that it is recommended to use the C++ version. ## Troubleshooting * It's recommended to add options `–shm-size=1g –ulimit memlock=-1` to the docker or nvidia-docker run command. Otherwise you may see NCCL errors when running multiple GPU inferences. See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/troubleshooting.html#errors for details. * When building models, memory-related issues such as ``` [09/23/2023-03:13:00] [TRT] [E] 9: GPTLMHeadModel/layers/0/attention/qkv/PLUGIN_V2_Gemm_0: could not find any supported formats consistent with input/output data types [09/23/2023-03:13:00] [TRT] [E] 9: [pluginV2Builder.cpp::reportPluginError::24] Error Code 9: Internal Error (GPTLMHeadModel/layers/0/attention/qkv/PLUGIN_V2_Gemm_0: could not find any supported formats consistent with input/output data types) ``` may happen. One possible solution is to reduce the amount of memory needed by reducing the maximum batch size, input and output lengths. Another option is to enable plugins, for example: `--gpt_attention_plugin`. * MPI + Slurm TensorRT-LLM is a [MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface)-aware package that uses [`mpi4py`](https://mpi4py.readthedocs.io/en/stable/). If you are running scripts in a [Slurm](https://slurm.schedmd.com/) environment, you might encounter interferences: ``` -------------------------------------------------------------------------- PMI2_Init failed to initialize. Return code: 14 -------------------------------------------------------------------------- -------------------------------------------------------------------------- The application appears to have been direct launched using "srun", but OMPI was not built with SLURM's PMI support and therefore cannot execute. There are several options for building PMI support under SLURM, depending upon the SLURM version you are using: version 16.05 or later: you can use SLURM's PMIx support. This requires that you configure and build SLURM --with-pmix. Versions earlier than 16.05: you must use either SLURM's PMI-1 or PMI-2 support. SLURM builds PMI-1 by default, or you can manually install PMI-2. You must then build Open MPI using --with-pmi pointing to the SLURM PMI library location. Please configure as appropriate and try again. -------------------------------------------------------------------------- ``` As a rule of thumb, if you are running TensorRT-LLM interactively on a Slurm node, prefix your commands with `mpirun -n 1` to run TensorRT-LLM in a dedicated MPI environment, not the one provided by your Slurm allocation. For example: `mpirun -n 1 python3 examples/gpt/build.py ...` ## Release notes * TensorRT-LLM requires TensorRT 9.2 and 23.10 containers. ### Change Log #### Versions 0.7.0 / 0.7.1 * Models - BART and mBART support in encoder-decoder models - FairSeq Neural Machine Translation (NMT) family - Mixtral-8x7B model - Support weight loading for HuggingFace Mixtral model - OpenAI Whisper - Mixture of Experts support - MPT - Int4 AWQ / SmoothQuant support - Baichuan FP8 quantization support * Features - [Preview] Speculative decoding - Add Python binding for `GptManager` - Add a Python class `ModelRunnerCpp` that wraps C++ `gptSession` - System prompt caching - Enable split-k for weight-only cutlass kernels - FP8 KV cache support for XQA kernel - New Python builder API and `trtllm-build` command(already applied to [blip2](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/blip2) and [OPT](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/opt#3-build-tensorrt-engines) ) - Support `StoppingCriteria` and `LogitsProcessor` in Python generate API (thanks to the contribution from @zhang-ge-hao) - fMHA support for chunked attention and paged kv cache * Bug fixes - Fix tokenizer usage in quantize.py #288, thanks to the contribution from @0xymoro - Fix LLaMa with LoRA error #637 - Fix LLaMA GPTQ failure #580 - Fix Python binding for InferenceRequest issue #528 - Fix CodeLlama SQ accuracy issue #453 * Performance - MMHA optimization for MQA and GQA - LoRA optimization: cutlass grouped gemm - Optimize Hopper warp specialized kernels - Optimize AllReduce for parallel attention on Falcon and GPT-J - Enable split-k for weight-only cutlass kernel when SM>=75 * Documentation - Add [documentation for new builder workflow](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/new_workflow.md) #### For history change log, please see [CHANGELOG.md](./CHANGELOG.md). ### Known Issues * The hang reported in issue [#149](https://github.com/triton-inference-server/tensorrtllm_backend/issues/149) has not been reproduced by the TensorRT-LLM team. If it is caused by a bug in TensorRT-LLM, that bug may be present in that release ### Report Issues You can use GitHub issues to report issues with TensorRT-LLM.

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