TensorRT-LLMs/examples/medusa

Medusa Decoding

This document shows how to build and run a model using Medusa decoding(Github, BLOG) in TensorRT-LLM on single GPU, single node multiple GPU.

Overview

Different from other models, Medusa decoding need a base model and Medusa heads.

The TensorRT-LLM Medusa Decoding implementation can be found in tensorrt_llm/models/medusa/model.py, which actually adds MedusaHeads to a base model.

For more info about Medusa visit speculative decoding documentation.

Support Matrix

• GPU Compute Capability >= 8.0 (Ampere or newer)
• FP16
• BF16
• PAGED_KV_CACHE
• Tensor Parallel

Usage

The TensorRT-LLM Medusa example code is located in examples/medusa. There is one convert_checkpoint.py file to convert and build the TensorRT engine(s) needed to run models with Medusa decoding support. In our example, we use the model from huggingface FasterDecoding/medusa-vicuna-7b-v1.3, which is a LLAMA based model.

Build TensorRT engine(s)

Get the weights by downloading base model vicuna-7b-v1.3 and Medusa Heads medusa-vicuna-7b-v1.3 from HF.

pip install -r requirements.txt

git lfs install
git clone https://huggingface.co/lmsys/vicuna-7b-v1.3
https://huggingface.co/FasterDecoding/medusa-vicuna-7b-v1.3

We use convert_checkpoint.py script to convert the model for Medusa decoding into TensorRT-LLM checkpoint format. Here we also add --fixed_num_medusa_heads 4 as medusa_num_heads is 2 in config.json of medusa-vicuna-7b-v1.3 but it actually has 4.

Here is the example:

# Convert and Build Medusa decoding support for vicuna-7b-v1.3
python convert_checkpoint.py --model_dir ./vicuna-7b-v1.3 \
                            --medusa_model_dir medusa-vicuna-7b-v1.3 \
                            --output_dir ./tllm_checkpoint_1gpu_medusa \
                            --dtype float16 \
                            --fixed_num_medusa_heads 4

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_medusa \
             --output_dir ./tmp/medusa/7B/trt_engines/fp16/1-gpu/ \
             --gemm_plugin float16 \
             --speculative_decoding_mode medusa \
             --max_batch_size 8

# Convert and Build Medusa decoding support for vicuna-13b-v1.3 with 4-way tensor parallelism.
python convert_checkpoint.py --model_dir ./vicuna-7b-v1.3 \
                            --medusa_model_dir medusa-vicuna-7b-v1.3 \
                            --output_dir ./tllm_checkpoint_1gpu_medusa \
                            --dtype float16 \
                            --fixed_num_medusa_heads 4 \
                            --tp_size 4 \
                            --workers 4

trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_medusa \
             --output_dir ./tmp/medusa/7B/trt_engines/fp16/1-gpu/ \
             --gemm_plugin float16 \
             --speculative_decoding_mode medusa \
             --max_batch_size 8

Run

To run a TensorRT-LLM model with Medusa decoding support, we can use ../run.py script, with an additional argument --medusa_choices. The --medusa_choices is of type list[list[int]], And also the built engine with Medusa decoding support.

Medusa decoding is supported by Python runtime and C++ runtime with inflight-batching. C++ runtime is recommended for performance. For Python runtime use --use_py_session flag to run.py.

Note: Medusa decoding only supporting greedy decoding temperature=1.0 now. So also need --temperature 1.0.

# Medusa decoding using vicuna-7b-v1.3 model with 1 GPU
python ../run.py --engine_dir ./tmp/medusa/7B/trt_engines/fp16/1-gpu/ \
                 --tokenizer_dir ./vicuna-7b-v1.3/ \
                 --max_output_len=100 \
                 --medusa_choices="[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]" \
                 --temperature 1.0 \
                 --input_text "Once upon"

# Medusa decoding using vicuna-13b-v1.3 with 4 GPUs
mpirun -np 4 --allow-run-as-root --oversubscribe \
    python ../run.py --engine_dir ./tmp/medusa/13B/trt_engines/fp16/4-gpu/ \
                     --tokenizer_dir ./vicuna-13b-v1.3/ \
                     --max_output_len=100 \
                     --medusa_choices="[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]" \
                     --temperature 1.0 \
                     --input_text "Once upon"

And you will see output like this if run successfully:

......
Input [Text 0]: "<s> Once upon"
Output [Text 0 Beam 0]: "a time, there was a young girl who loved to read. She would spend hours in the library, devouring books of all genres. She had a special love for fairy tales, and would often dream of living in a magical world where she could meet princes and princesses, and have adventures with talking animals.
One day, while she was reading a book, she came across a passage that spoke to her heart. It said, "You are the author of"

Summarization using Medusa decoding

# Medusa decoding using vicuna-7b-v1.3 model with 1 GPU
python ../summarize.py --engine_dir ./tmp/medusa/7B/trt_engines/fp16/1-gpu/ \
                       --hf_model_dir ./vicuna-7b-v1.3/ \
                       --tokenizer_dir ./vicuna-7b-v1.3/ \
                       --test_trt_llm \
                       --data_type fp16 \
                       --medusa_choices="[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]" \
                       --use_py_session \
                       --temperature 1.0 \
                       --batch_size 1

# Medusa decoding using vicuna-13b-v1.3 with 4 GPUs
mpirun -np 4 --allow-run-as-root --oversubscribe \
    python ../summarize.py --engine_dir ./tmp/medusa/13B/trt_engines/fp16/4-gpu/ \
                           --hf_model_dir ./vicuna-13b-v1.3/ \
                           --tokenizer_dir ./vicuna-13b-v1.3/ \
                           --test_trt_llm \
                           --data_type fp16 \
                           --medusa_choices="[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]" \
                           --use_py_session \
                           --temperature 1.0 \
                           --batch_size 1