eagle.py

# SPDX-License-Identifier: Apache-2.0
import argparse
import json
import os

from transformers import AutoTokenizer

from vllm import LLM, SamplingParams


def load_prompts(dataset_path, num_prompts):
    if os.path.exists(dataset_path):
        prompts = []
        try:
            with open(dataset_path) as f:
                for line in f:
                    data = json.loads(line)
                    prompts.append(data["turns"][0])
        except Exception as e:
            print(f"Error reading dataset: {e}")
            return []
    else:
        prompts = [
            "The future of AI is", "The president of the United States is"
        ]

    return prompts[:num_prompts]


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--dataset",
        type=str,
        default="./examples/data/gsm8k.jsonl",
        help="downloaded from the eagle repo " \
        "https://github.com/SafeAILab/EAGLE/blob/main/eagle/data/"
    )
    parser.add_argument("--max_num_seqs", type=int, default=8)
    parser.add_argument("--num_prompts", type=int, default=80)
    parser.add_argument("--num_spec_tokens", type=int, default=2)
    parser.add_argument("--tp", type=int, default=1)
    parser.add_argument("--draft_tp", type=int, default=1)
    parser.add_argument("--enforce_eager", action='store_true')
    parser.add_argument("--enable_chunked_prefill", action='store_true')
    parser.add_argument("--max_num_batched_tokens", type=int, default=2048)
    parser.add_argument("--temp", type=float, default=0)
    args = parser.parse_args()

    model_dir = "meta-llama/Meta-Llama-3-8B-Instruct"
    eagle_dir = "abhigoyal/EAGLE-LLaMA3-Instruct-8B-vllm"

    max_model_len = 2048

    tokenizer = AutoTokenizer.from_pretrained(model_dir)

    prompts = load_prompts(args.dataset, args.num_prompts)

    prompt_ids = [
        tokenizer.apply_chat_template([{
            "role": "user",
            "content": prompt
        }],
                                      add_generation_prompt=True)
        for prompt in prompts
    ]

    llm = LLM(
        model=model_dir,
        trust_remote_code=True,
        tensor_parallel_size=args.tp,
        enable_chunked_prefill=args.enable_chunked_prefill,
        max_num_batched_tokens=args.max_num_batched_tokens,
        enforce_eager=args.enforce_eager,
        max_model_len=max_model_len,
        max_num_seqs=args.max_num_seqs,
        gpu_memory_utilization=0.8,
        speculative_config={
            "method": "eagle",
            "model": eagle_dir,
            "num_speculative_tokens": args.num_spec_tokens,
            "draft_tensor_parallel_size": args.draft_tp,
            "max_model_len": max_model_len,
        },
        disable_log_stats=False,
    )

    sampling_params = SamplingParams(temperature=args.temp, max_tokens=256)

    outputs = llm.generate(prompt_token_ids=prompt_ids,
                           sampling_params=sampling_params)

    # calculate the average number of accepted tokens per forward pass, +1 is
    # to account for the token from the target model that's always going to be
    # accepted
    acceptance_counts = [0] * (args.num_spec_tokens + 1)
    for output in outputs:
        for step, count in enumerate(
                output.metrics.spec_token_acceptance_counts):
            acceptance_counts[step] += count

    print("-" * 50)
    print(f"mean acceptance length: \
        {sum(acceptance_counts) / acceptance_counts[0]:.2f}")
    print("-" * 50)


if __name__ == "__main__":
    main()