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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. The implementation adds Medusa heads 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
  • FP8 (base model)
  • 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. We could use --num_medusa_heads to set the number of medusa heads that we want to use. If not, num_medusa_heads will be set according to the medusa_num_heads from medusa weights' config.json.

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 \
                            --num_medusa_heads 4

# Note: Increasing the batch size may have a negative impact on performance
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 4

# 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 \
                            --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 4

FP8 Post-Training Quantization for Base Model

The example below quantizes the base model to FP8, while keeping the weight of the medusa head non-quantize.

# Quantize base model into FP8 and export trtllm checkpoint
python ../quantization/quantize.py --model_dir /path/to/base-model-hf/ \
                                   --dtype float16 \
                                   --qformat fp8 \
                                   --kv_cache_dtype fp8 \
                                   --output_dir ./tllm_checkpoint_1gpu_base_model_fp8_medusa_fp16 \
                                   --calib_size 512 \
                                   --tp_size 1 \
                                   --medusa_model_dir /path/to/medusa_head/ \
                                   --num_medusa_heads 4

# Build trtllm engines from the trtllm checkpoint
trtllm-build --checkpoint_dir ./tllm_checkpoint_1gpu_base_model_fp8_medusa_fp16 \
         --output_dir ./trt_engine_1gpu_base_model_fp8_medusa_fp16 \
         --gemm_plugin float16 \
         --gpt_attention_plugin float16 \
         --speculative_decoding_mode medusa \
         --max_batch_size 4

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]].

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.

Medusa decoding only supporting greedy decoding, indicated by temperature=1.0 argument. The output is equivalent to the base model inference with --temperature 0.0 (equivalent to --temperature 1.0 --top-k 1).

# 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

Medusa with Qwen2

To use Medusa with Qwen2 models, specify --model_type qwen2 to convert_checkpoint.py. You have to provide a Qwen2 model checkpoint and the medusa heads. After TRT-LLM checkpoint is generated, trllm-build and ../run.py use the same arguments as for LLaMA models.