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TensorRT-LLM on Windows Deployment - with merged HF checkpoint

TensorRT-LLM is NVIDIA's library for high-performance LLM inference across on-device and data center platforms.

To deploy a quantized model using TensorRT-LLM, you first need to quantize the model using NVIDIA's TensorRT Model Optimizer library. This guide provides a step-by-step process for quantizing and deploying LLMs trained with the RTX AI Toolkit.

Note

Follow this guide to deploy a merged HF checkpoint using TensorRT-LLM. To deploy models with (multiple) LoRA adapters at runtime, follow the instructions here.

Model Quantization using TensorRT Model Optimizer - INT4_AWQ

This guide assumes you have exported a merged HF checkpoint (not just LoRA adapters) using the Llama-Factory project in the LLM fine-tuning tutorial. Make a note of the merged checkpoints directory, for example: /project/data/scratch/merged.

The Llama-Factory Workbench project includes a short Jupyter Notebook to quantize the HF checkpoint using TensorRT Model Optimizer (ModelOpt). We use the INT4_AWQ method for quantization. To use the Notebook, start the Jupyterlab app from AI Workbench using the app dropdown on top-right corner.

Once the jupyterlab app has started, navigate to and start the quantization Notebook - code\quantization.ipynb. Execute all the cells in this notebook to generate the quantized TRT-LLM checkpoint. Next, move the generated quantized checkpoint to your Windows filesystem, for ex. in your 'Downloads' directory. Note this directory path, for ex: C:\Users\NVIDIA\Downloads\merged-int4.

Also make sure to copy the tokenizer and HF configs from merged HF checkpoint directory into a Windows location.

config.json, generation_config.json, tokenizer.json, tokenizer_config.json, special_tokens_map.json

Note this directory path, for ex: C:\Users\NVIDIA\Downloads\merged-hf. These files are necessary for TRT-LLM to run inference. The screenshot shows all the files you need to copy:

TensorRT-LLM Windows Deployment

0. Pre-requisites

Install TensorRT-LLM v0.10 for Windows by following the instructions here.

1. TRT Engine Building

  1. Clone the TensorRT-LLM repo - v0.10.0 tag.
git clone https://github.com/NVIDIA/TensorRT-LLM -b v0.10.0
  1. Build the TRT-LLM Engine using trtllm-build. Here, we have provided the sample engine building scripts for llama models. trtllm-build commands for other model architectures are documented in the TRT-LLM examples README.
trtllm-build --checkpoint_dir_ <quantized_ckpt_dir>  --output_dir <trt_engine_dir> --gemm_plugin float16

For example:

trtllm-build --checkpoint_dir C:\Users\NVIDIA\Downloads\merged-int4  --output_dir C:\Users\NVIDIA\Downloads\trtllm_engine --gemm_plugin float16
  1. Run inference using the provided sample scripts.
cd TensorRT-LLM\examples

Use the run.py script as follows (replace with your own engine and tokenizer paths):

python-- run.py --max_output_len=100 \
                  --tokenizer_dir C:\NVIDI \
                  --engine_dir=./tmp/llama/7B/trt_engines/fp16/1-gpu/