TensorRT-LLM

A TensorRT Toolbox for Optimized Large Language Model Inference

Architecture   |   Results   |   Examples   |   Documentation

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Latest News

• [Weekly] Check out @NVIDIAAIDev & NVIDIA AI LinkedIn for the latest updates!
• [2024/02/06] 🚀 Speed up inference with SOTA quantization techniques in TRT-LLM
• [2024/01/30] New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget
• [2023/12/04] Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100
• [2023/11/27] SageMaker LMI now supports TensorRT-LLM - improves throughput by 60%, compared to previous version
• [2023/11/13] H200 achieves nearly 12,000 tok/sec on Llama2-13B
• [2023/10/22] 🚀 RAG on Windows using TensorRT-LLM and LlamaIndex 🦙
• [2023/10/19] Getting Started Guide - Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM, Now Publicly Available
• [2023/10/17] Large Language Models up to 4x Faster on RTX With TensorRT-LLM for Windows

Table of Contents

• TensorRT-LLM
  • Latest News
  • Table of Contents
  • TensorRT-LLM Overview
  • Installation
  • Quick Start
  • Support Matrix
    • Devices
    • Precision
    • Key Features
    • Models
  • Performance
  • Advanced Topics
    • Quantization
    • In-flight Batching
    • Attention
    • Graph Rewriting
    • Benchmark
  • Troubleshooting
  • Release notes
    • Change Log
      • Versions 0.9.0
      • For history change log, please see CHANGELOG.md.
    • Known 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 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 for integration with the 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 and/or Pipeline Parallelism).

The Python API of TensorRT-LLM is architectured to look similar to the PyTorch API. It provides users with a functional module containing functions like einsum, softmax, matmul or view. The 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 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.

To maximize performance and reduce memory footprint, TensorRT-LLM allows the models to be executed using different quantization modes (see 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 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.

Installation

After installing the NVIDIA Container Toolkit, please run the following commands to install TensorRT-LLM for x86_64 users.

# 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.

For Windows installation, see Windows.

Quick Start

Please be sure to complete the installation steps 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 model.

0. In the BLOOM folder

Inside the Docker container, you have to install the requirements:

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.

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

# 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 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:

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. Many more models than BLOOM are implemented in TensorRT-LLM. They can be found in the 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.

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 supports the following architectures:

• NVIDIA Hopper (SM90), for example, H200, H100, H20
• NVIDIA Ada Lovelace (SM89), for example, L40S, L20, L4
• NVIDIA Ampere (SM80, SM86), for example, A100, A30, A10G
• NVIDIA Turing (SM75), for example, T4
• NVIDIA Volta (SM70 - experimental), for example, V100

It is important to note that TensorRT-LLM is expected to work on all GPUs based on the Volta, Turing, Ampere, Hopper, and Ada Lovelace architectures. Certain limitations may 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 document and the examples folder for additional details.

Key Features

TensorRT-LLM contains examples that implement the following features.

• Multi-head Attention(MHA)
• Multi-query Attention (MQA)
• Group-query Attention(GQA)
• In-flight Batching
• Paged KV Cache for the Attention
• Tensor Parallelism
• Pipeline Parallelism
• INT4/INT8 Weight-Only Quantization (W4A16 & W8A16)
• SmoothQuant
• GPTQ
• AWQ
• FP8
• 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 folder.

Models

The list of supported models is:

• Baichuan
• BART
• BERT
• Blip2
• BLOOM
• ChatGLM
• FairSeq NMT
• Falcon
• Flan-T5
• GPT
• GPT-J
• GPT-Nemo
• GPT-NeoX
• InternLM
• LLaMA
• LLaMA-v2
• Mamba
• mBART
• Medusa
• Mistral
• MPT
• mT5
• OPT
• Phi-1.5/Phi-2
• Qwen
• Replit Code
• RoBERTa
• SantaCoder
• StarCoder1/StarCoder2
• T5
• Whisper

Note: Encoder-Decoder 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
• BLIP2 w/ T5-XL
• LLaVA-v1.5-7B
• Nougat family 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 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 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 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 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

Benchmark

TensorRT-LLM provides C++ and Python tools to perform benchmarking. Note, however, that it is recommended to use the C++ version.

Troubleshooting

• If you encounter accuracy issues in the generated text, you may want to increase the internal precision in the attention layer. For that, pass the --context_fmha_fp32_acc enable to trtllm-build.

• 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-aware package that uses mpi4py. If you are running scripts in a Slurm 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/run.py ...

Release notes

• TensorRT-LLM requires TensorRT 9.3 and 24.02 containers.

Change Log

Versions 0.9.0

• Model Support
  • Support distil-whisper, thanks to the contribution from @Bhuvanesh09 in PR #1061
  • Support HuggingFace StarCoder2
  • Support VILA
  • Support Smaug-72B-v0.1
  • Migrate BLIP-2 examples to examples/multimodal
• Features
  • Add support to context chunking to work with KV cache reuse
  • Enable different rewind tokens per sequence for Medusa
  • BART LoRA support (limited to the Python runtime)
  • Enable multi-LoRA for BART LoRA
  • Support early_stopping=False in beam search for C++ Runtime
  • Add logits post processor to the batch manager (see docs/source/batch_manager.md#logits-post-processor-optional)
  • Support import and convert HuggingFace Gemma checkpoints, thanks for the contribution from @mfuntowicz in #1147
  • Support loading Gemma from HuggingFace
  • Support auto parallelism planner for high-level API and unified builder workflow
  • Support run GptSession without OpenMPI #1220
  • [BREAKING CHANGE] TopP sampling optimization with deterministic AIR TopP algorithm is enabled by default
  • Medusa IFB support
  • [Experimental] Support FP8 FMHA, note that the performance is not optimal, and we will keep optimizing it
  • [BREAKING CHANGE] Support embedding sharing for Gemma
  • More head sizes support for LLaMA-like models
    • Ampere (sm80, sm86), Ada (sm89), Hopper(sm90) all support head sizes [32, 40, 64, 80, 96, 104, 128, 160, 256] now.
  • OOTB functionality support
    • T5
    • Mixtral 8x7B
• API
  • C++ executor API
    • Add Python bindings, see documentation and examples in examples/bindings
    • Add advanced and multi-GPU examples for Python binding of executor C++ API, see examples/bindings/README.md
    • Add documents for C++ executor API, see docs/source/executor.md
  • High-level API (refer to examples/high-level-api/README.md for guidance)
    • [BREAKING CHANGE] Reuse the QuantConfig used in trtllm-build tool, support broader quantization features
    • Support in LLM() API to accept engines built by trtllm-build command
    • Add support for TensorRT-LLM checkpoint as model input
    • Refine SamplingConfig used in LLM.generate or LLM.generate_async APIs, with the support of beam search, a variety of penalties, and more features
    • Add support for the StreamingLLM feature, enable it by setting LLM(streaming_llm=...)
    • Migrate Mixtral to high level API and unified builder workflow
  • [BREAKING CHANGE] Refactored Qwen model to the unified build workflow, see examples/qwen/README.md for the latest commands
  • [BREAKING CHANGE] Move LLaMA convert checkpoint script from examples directory into the core library
  • [BREAKING CHANGE] Refactor GPT with unified building workflow, see examples/gpt/README.md for the latest commands
  • [BREAKING CHANGE] Removed all the lora related flags from convert_checkpoint.py script and the checkpoint content to trtllm-build command, to generalize the feature better to more models
  • [BREAKING CHANGE] Removed the use_prompt_tuning flag and options from convert_checkpoint.py script and the checkpoint content, to generalize the feature better to more models. Use the trtllm-build --max_prompt_embedding_table_size instead.
  • [BREAKING CHANGE] Changed the trtllm-build --world_size flag to --auto_parallel flag, the option is used for auto parallel planner only.
  • [BREAKING CHANGE] AsyncLLMEngine is removed, tensorrt_llm.GenerationExecutor class is refactored to work with both explicitly launching with mpirun in the application level, and accept an MPI communicator created by mpi4py
  • [BREAKING CHANGE] examples/server are removed, see examples/app instead.
  • [BREAKING CHANGE] Remove LoRA related parameters from convert checkpoint scripts
  • [BREAKING CHANGE] Simplify Qwen convert checkpoint script
  • [BREAKING CHANGE] Remove model parameter from gptManagerBenchmark and gptSessionBenchmark
• Bug fixes
  • Fix a weight-only quant bug for Whisper to make sure that the encoder_input_len_range is not 0, thanks to the contribution from @Eddie-Wang1120 in #992
  • Fix the issue that log probabilities in Python runtime are not returned #983
  • Multi-GPU fixes for multimodal examples #1003
  • Fix wrong end_id issue for Qwen #987
  • Fix a non-stopping generation issue #1118 #1123
  • Fix wrong link in examples/mixtral/README.md #1181
  • Fix LLaMA2-7B bad results when int8 kv cache and per-channel int8 weight only are enabled #967
  • Fix wrong head_size when importing Gemma model from HuggingFace Hub, thanks for the contribution from @mfuntowicz in #1148
  • Fix ChatGLM2-6B building failure on INT8 #1239
  • Fix wrong relative path in Baichuan documentation #1242
  • Fix wrong SamplingConfig tensors in ModelRunnerCpp #1183
  • Fix error when converting SmoothQuant LLaMA #1267
  • Fix the issue that examples/run.py only load one line from --input_file
  • Fix the issue that ModelRunnerCpp does not transfer SamplingConfig tensor fields correctly #1183
• Benchmark
  • Add emulated static batching in gptManagerBenchmark
  • Support arbitrary dataset from HuggingFace for C++ benchmarks, see “Prepare dataset” section in benchmarks/cpp/README.md
  • Add percentile latency report to gptManagerBenchmark
• Performance
  • Optimize gptDecoderBatch to support batched sampling
  • Enable FMHA for models in BART, Whisper and NMT family
  • Remove router tensor parallelism to improve performance for MoE models, thanks to the contribution from @megha95 in #1091
  • Improve custom all-reduce kernel
• Infra
  • Base Docker image for TensorRT-LLM is updated to nvcr.io/nvidia/pytorch:24.02-py3
  • Base Docker image for TensorRT-LLM backend is updated to nvcr.io/nvidia/tritonserver:24.02-py3
  • The dependent TensorRT version is updated to 9.3
  • The dependent PyTorch version is updated to 2.2
  • The dependent CUDA version is updated to 12.3.2 (a.k.a. 12.3 Update 2)

For history change log, please see CHANGELOG.md.

Known Issues

• On windows, running context FMHA plugin with FP16 accumulation on LLaMA, Mistral and Phi models suffers from poor accuracy and the resulting inference output may be garbled. The suggestion to workaround these is to enable FP32 accumulation when building the models, i.e. passing the options --context_fmha disable --context_fmha_fp32_acc enable to trtllm-build command as a work-around, and this should be fixed in the next version

• The hang reported in issue #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.