mirror of https://github.com/vllm-project/vllm.git
138 lines
5.0 KiB
Python
138 lines
5.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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from vllm.benchmarks.datasets import add_dataset_parser, get_samples
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from vllm.v1.metrics.reader import Counter, Vector
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try:
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from vllm.utils import FlexibleArgumentParser
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except ImportError:
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from argparse import ArgumentParser as FlexibleArgumentParser
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def parse_args():
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parser = FlexibleArgumentParser()
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add_dataset_parser(parser)
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parser.add_argument(
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"--dataset",
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type=str,
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default="./examples/data/gsm8k.jsonl",
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help="downloaded from the eagle repo "
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"https://github.com/SafeAILab/EAGLE/blob/main/eagle/data/",
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)
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parser.add_argument(
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"--method", type=str, default="eagle", choices=["ngram", "eagle", "eagle3"]
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)
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parser.add_argument("--max-num-seqs", type=int, default=8)
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parser.add_argument("--num-spec-tokens", type=int, default=2)
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parser.add_argument("--prompt-lookup-max", type=int, default=5)
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parser.add_argument("--prompt-lookup-min", type=int, default=2)
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parser.add_argument("--tp", type=int, default=1)
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parser.add_argument("--draft-tp", type=int, default=1)
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parser.add_argument("--enforce-eager", action="store_true")
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parser.add_argument("--enable-chunked-prefill", action="store_true")
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parser.add_argument("--max-num-batched-tokens", type=int, default=2048)
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parser.add_argument("--temp", type=float, default=0)
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parser.add_argument("--top-p", type=float, default=1.0)
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parser.add_argument("--top-k", type=int, default=-1)
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parser.add_argument("--print-output", action="store_true")
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parser.add_argument("--output-len", type=int, default=256)
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return parser.parse_args()
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def main():
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args = parse_args()
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args.endpoint_type = "openai-chat"
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model_dir = "meta-llama/Llama-3.1-8B-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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max_model_len = 2048
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prompts = get_samples(args, tokenizer)
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# add_special_tokens is False to avoid adding bos twice when using chat templates
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prompt_ids = [
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tokenizer.encode(prompt.prompt, add_special_tokens=False) for prompt in prompts
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]
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if args.method == "eagle" or args.method == "eagle3":
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if args.method == "eagle":
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eagle_dir = "yuhuili/EAGLE-LLaMA3.1-Instruct-8B"
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elif args.method == "eagle3":
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eagle_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
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speculative_config = {
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"method": args.method,
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"model": eagle_dir,
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"num_speculative_tokens": args.num_spec_tokens,
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"draft_tensor_parallel_size": args.draft_tp,
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"max_model_len": max_model_len,
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}
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elif args.method == "ngram":
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speculative_config = {
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"method": "ngram",
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"num_speculative_tokens": args.num_spec_tokens,
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"prompt_lookup_max": args.prompt_lookup_max,
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"prompt_lookup_min": args.prompt_lookup_min,
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"max_model_len": max_model_len,
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}
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else:
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raise ValueError(f"unknown method: {args.method}")
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llm = LLM(
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model=model_dir,
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trust_remote_code=True,
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tensor_parallel_size=args.tp,
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enable_chunked_prefill=args.enable_chunked_prefill,
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max_num_batched_tokens=args.max_num_batched_tokens,
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enforce_eager=args.enforce_eager,
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max_model_len=max_model_len,
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max_num_seqs=args.max_num_seqs,
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gpu_memory_utilization=0.8,
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speculative_config=speculative_config,
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disable_log_stats=False,
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)
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sampling_params = SamplingParams(temperature=args.temp, max_tokens=args.output_len)
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outputs = llm.generate(prompt_token_ids=prompt_ids, sampling_params=sampling_params)
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# print the generated text
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if args.print_output:
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for output in outputs:
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print("-" * 50)
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print(f"prompt: {output.prompt}")
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print(f"generated text: {output.outputs[0].text}")
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print("-" * 50)
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try:
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metrics = llm.get_metrics()
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except AssertionError:
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print("Metrics are not supported in the V0 engine.")
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return
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num_drafts = num_accepted = 0
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acceptance_counts = [0] * args.num_spec_tokens
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for metric in metrics:
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if metric.name == "vllm:spec_decode_num_drafts":
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assert isinstance(metric, Counter)
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num_drafts += metric.value
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elif metric.name == "vllm:spec_decode_num_accepted_tokens":
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assert isinstance(metric, Counter)
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num_accepted += metric.value
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elif metric.name == "vllm:spec_decode_num_accepted_tokens_per_pos":
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assert isinstance(metric, Vector)
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for pos in range(len(metric.values)):
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acceptance_counts[pos] += metric.values[pos]
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print("-" * 50)
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print(f"mean acceptance length: {1 + (num_accepted / num_drafts):.2f}")
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print("-" * 50)
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# print acceptance at each token position
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for i in range(len(acceptance_counts)):
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print(f"acceptance at token {i}:{acceptance_counts[i] / num_drafts:.2f}")
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if __name__ == "__main__":
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main()
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