TensorRT-LLMs/examples/mixtral/README.md

Mixtral

This document shows how to build and run a Mixtral model in TensorRT-LLM on both single GPU, single node multi-GPU and multi-node multi-GPU.

Overview

The TensorRT-LLM Mixtral implementation is based on the LLaMA model, with Mixture of Experts enabled. The implementation can be found in tensorrt_llm/models/llama/model.py. See the LLaMA example examples/llama for details.

Build TensorRT engine(s)

Get the weights by downloading from HF https://huggingface.co/mistralai/Mixtral-8x7B-v0.1. See also https://huggingface.co/docs/transformers/main/en/model_doc/mixtral

pip install -r requirements.txt # install latest version of transformers, needed for Mixtral

git lfs install
git clone https://huggingface.co/mistralai/Mixtral-8x7B-v0.1

We use the LLaMA convert_checkpoint.py script to convert and build the model. TensorRT-LLM LLaMA builds TensorRT engine(s) from HF checkpoint provided by --model_dir. If no checkpoint directory is specified, TensorRT-LLM will build engine(s) with dummy weights.

trtllm-build uses one GPU by default, but if you have already more GPUs available at build time, you may enable parallel builds to make the engine building process faster by adding the --workers argument.

Here are some examples:

# Build Mixtral8x7B with pipeline parallelism
python convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \
                             --output_dir ./tllm_checkpoint_mixtral_2gpu \
                             --dtype float16 \
                             --pp_size 2
trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \
                 --output_dir ./trt_engines/mixtral/pp2 \
                 --gemm_plugin float16

# Build Mixtral8x7B with tensor parallelism
python convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \
                             --output_dir ./tllm_checkpoint_mixtral_2gpu \
                             --dtype float16 \
                             --tp_size 2
trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \
                 --output_dir ./trt_engines/mixtral/tp2 \
                 --gemm_plugin float16

Then, you can test your engine with the run.py script:

mpirun -n 2 python3 ../run.py --engine_dir ./trt_engines/mixtral/tp2 --tokenizer_dir ./Mixtral-8x7B-v0.1 --max_output_len 8 --input_text "I love french quiche"

For more examples see examples/llama/README.md

OOTB

Mixtral supports OOTB operation without the plugin, however this comes at a significant performance cost. Users should prefer using the plugin path whenever possible