Awesome

Update

• Supports flashinfer and paged attention to further accelerate inference.
• Supports SnapKV-based drafting for higher speculation quality.
• Supppots Qwen2.5-[7B,14B,32B], Yi-1.5-[6B,34B], Mistral-7B-v0.1 and Mistral-7B-v0.1.
• Please make sure PyTorch version greater than 2.5 to use the new features like custom all-reduce.

Installation

Environment Set Up

conda create -n magicdec python=3.11
conda activate magicdec
pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/

Prepare Checkpoints

Currently, we support Llama-2-7b and its long context variant Llama-2-7b-32k, Llama-2-13b, Llama-2-70b, Llama-3-8b, Llama-3-70b, llama-68m, TinyLlama, Llama-3.1-8b, Llama-3.1-70b, Llama-3.2-1b, Qwen2.5-[7B,14B,32B], Yi-1.5-[6B,34B], Mistral-7B-v0.1 and Mistral-7B-v0.1.

We can first download the checkpoints we need through download.py. The --repo_id should be set to the repository ID to download from. The --hf_token should be your HuggingFace API token. The --out_dir should be the directory you want to save the checkpoint.

python download.py --repo_id meta-llama/Meta-Llama-3.1-8B --hf_token "YOUR HUGGINGFACE API TOKEN" --out_dir checkpoints/meta-llama/Meta-Llama-3.1-8B

Then we need to convert the downloaded checkpoint. --checkpoint_dir should be set to the directory we just saved the checkpoint. This script will generate a new model.pth file in the configured directory.

python convert_hf_checkpoint.py --checkpoint_dir checkpoints/meta-llama/Meta-Llama-3.1-8B

Evaluations

We conducted all the experiments in the paper on 8xA100, 8xH100 and 8xL40. We used PG-19 as the dataset for all the experiments.

Baseline

We used the new one-shot and two-shot all-reduce of PyTorch 2.5 by setting ENABLE_INTRA_NODE_COMM=1. --nproc_per_node should be set to the number of GPUs you want to do tensor parallelism. --model should be set to the directory of the model.pth, which is the checkpoint we want to serve. --model_name should be set to the repo id of the checkpoint, which is used to load tokenizer. --rank_group should be set to the list of GPU id in tensor parallelism. --B is the batch size, --prefix_len is the prefill length, --max_len is the max number of tokens we want to generate for each sentence. --printoutput is the flag which decides whether or not to print the output after generation finishes. --compile is the flag to decide whether or not use torch.compile to accelerate the generation.

ENABLE_INTRA_NODE_COMM=1 torchrun --standalone --nproc_per_node=8 tests/baseline_benchmark.py --model checkpoints/meta-llama/Meta-Llama-3.1-8B/model.pth --model_name meta-llama/Meta-Llama-3.1-8B --rank_group 0 1 2 3 4 5 6 7 --B 1 --prefix_len 3969 --max_len 4096 --printoutput --compile

Standalone Draft

For standalone draft experiment, we use --target and --model to set the target and draft checkpoint. --model_name should be set to the repo id of target model, which will used to load the corresponding tokenizer. --rank_group should be set to the GPU id we want to do tensor parallelism for the target model, and --draft_rank_group should be set to the GPU id we want to do TP for the draft model. --draft_budget should be set to the KV budget for the draft model. Set --draft_budget of StreamingLLM/longspec_benchmark.py to -1 to disable KV compression of draft model (Use full KV, the original speculative decoding).

SnapKV-based Drafting

ENABLE_INTRA_NODE_COMM=1 torchrun --standalone --nproc_per_node=8 tests/SnapKV/longspec_benchmark.py --target checkpoints/meta-llama/Meta-Llama-3.1-8B/model.pth --model checkpoints/meta-llama/Llama-3.2-1B/model.pth --model_name meta-llama/Meta-Llama-3.1-8B --rank_group 0 1 2 3 4 5 6 7 --draft_rank_group 0 1 2 3 --gamma 3 --B 64 --prefix_len 16032 --max_len 16128 --draft_budget 257 --benchmark --compile

StreamingLLM-based Drafting

ENABLE_INTRA_NODE_COMM=1 torchrun --standalone --nproc_per_node=8 tests/StreamingLLM/longspec_benchmark.py --target checkpoints/meta-llama/Meta-Llama-3.1-8B/model.pth --model checkpoints/meta-llama/Llama-3.2-1B/model.pth --model_name meta-llama/Meta-Llama-3.1-8B --rank_group 0 1 2 3 4 5 6 7 --draft_rank_group 0 1 2 3 --gamma 3 --B 64 --prefix_len 16032 --max_len 16128 --draft_budget 257 --benchmark --compile

Self-Speculation

Similar to the standalone draft, but here we do not need to configure the draft model as it is the target model itself.

SnapKV-based Drafting

ENABLE_INTRA_NODE_COMM=1 torchrun --standalone --nproc_per_node=8 tests/SnapKV/selfspec_benchmark.py --model checkpoints/meta-llama/Meta-Llama-3.1-8B/model.pth --model_name meta-llama/Meta-Llama-3.1-8B --rank_group 0 1 2 3 4 5 6 7 --gamma 3 --B 64 --prefix_len 16032 --max_len 16128 --draft_budget 257 --benchmark --compile

StreamingLLM-based Drafting

ENABLE_INTRA_NODE_COMM=1 torchrun --standalone --nproc_per_node=8 tests/StreamingLLM/selfspec_benchmark.py --model checkpoints/meta-llama/Meta-Llama-3.1-8B/model.pth --model_name meta-llama/Meta-Llama-3.1-8B --rank_group 0 1 2 3 4 5 6 7 --gamma 3 --B 64 --prefix_len 16032 --gen_len 16128 --draft_budget 257 --benchmark --compile

Citation

If you find MagicDec useful or relevant to your project and research, please kindly cite our paper:

@article{chen2024magicdec,
  title={MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding},
  author={Chen, Jian and Tiwari, Vashisth and Sadhukhan, Ranajoy and Chen, Zhuoming and Shi, Jinyuan and Yen, Ian En-Hsu and Chen, Beidi},
  journal={arXiv preprint arXiv:2408.11049},
  year={2024}
}