296 lines
11 KiB
Python
296 lines
11 KiB
Python
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"""Benchmark offline prioritization."""
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import argparse
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import json
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import random
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import time
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from typing import List, Optional, Tuple
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from transformers import AutoTokenizer, PreTrainedTokenizerBase
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from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
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def sample_requests(
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dataset_path: str,
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num_requests: int,
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tokenizer: PreTrainedTokenizerBase,
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fixed_output_len: Optional[int],
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) -> List[Tuple[str, int, int]]:
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if fixed_output_len is not None and fixed_output_len < 4:
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raise ValueError("output_len too small")
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# Load the dataset.
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with open(dataset_path) as f:
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dataset = json.load(f)
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# Filter out the conversations with less than 2 turns.
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dataset = [data for data in dataset if len(data["conversations"]) >= 2]
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# Only keep the first two turns of each conversation.
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dataset = [(data["conversations"][0]["value"],
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data["conversations"][1]["value"]) for data in dataset]
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# Shuffle the dataset.
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random.shuffle(dataset)
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# Filter out sequences that are too long or too short
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filtered_dataset: List[Tuple[str, int, int]] = []
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for i in range(len(dataset)):
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if len(filtered_dataset) == num_requests:
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break
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# Tokenize the prompts and completions.
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prompt = dataset[i][0]
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prompt_token_ids = tokenizer(prompt).input_ids
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completion = dataset[i][1]
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completion_token_ids = tokenizer(completion).input_ids
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prompt_len = len(prompt_token_ids)
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output_len = len(completion_token_ids
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) if fixed_output_len is None else fixed_output_len
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if prompt_len < 4 or output_len < 4:
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# Prune too short sequences.
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continue
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if prompt_len > 1024 or prompt_len + output_len > 2048:
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# Prune too long sequences.
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continue
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#Select a equi-probable random priority
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priority = 0 if random.random() < 0.5 else 1
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filtered_dataset.append((prompt, prompt_len, output_len, priority))
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return filtered_dataset
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def run_vllm(
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requests: List[Tuple[str, int, int]],
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model: str,
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tokenizer: str,
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quantization: Optional[str],
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tensor_parallel_size: int,
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seed: int,
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n: int,
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use_beam_search: bool,
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trust_remote_code: bool,
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dtype: str,
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max_model_len: Optional[int],
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enforce_eager: bool,
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kv_cache_dtype: str,
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quantization_param_path: Optional[str],
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device: str,
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enable_prefix_caching: bool,
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enable_chunked_prefill: bool,
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max_num_batched_tokens: int,
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gpu_memory_utilization: float = 0.9,
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download_dir: Optional[str] = None,
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) -> float:
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from vllm import LLM, SamplingParams
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llm = LLM(
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model=model,
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tokenizer=tokenizer,
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quantization=quantization,
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tensor_parallel_size=tensor_parallel_size,
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seed=seed,
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trust_remote_code=trust_remote_code,
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dtype=dtype,
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max_model_len=max_model_len,
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gpu_memory_utilization=gpu_memory_utilization,
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enforce_eager=enforce_eager,
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kv_cache_dtype=kv_cache_dtype,
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quantization_param_path=quantization_param_path,
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device=device,
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enable_prefix_caching=enable_prefix_caching,
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download_dir=download_dir,
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enable_chunked_prefill=enable_chunked_prefill,
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max_num_batched_tokens=max_num_batched_tokens,
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disable_log_stats=False,
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)
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# Add the requests to the engine.
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prompts = []
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sampling_params = []
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priority = []
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for prompt, _, output_len, _priority in requests:
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prompts.append(prompt)
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priority.append(_priority)
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sampling_params.append(
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SamplingParams(
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n=n,
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temperature=0.0 if use_beam_search else 1.0,
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top_p=1.0,
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use_beam_search=use_beam_search,
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ignore_eos=True,
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max_tokens=output_len,
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))
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start = time.perf_counter()
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llm.generate(prompts, sampling_params, priority=priority, use_tqdm=True)
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end = time.perf_counter()
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return end - start
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def main(args: argparse.Namespace):
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print(args)
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random.seed(args.seed)
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# Sample the requests.
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tokenizer = AutoTokenizer.from_pretrained(
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args.tokenizer, trust_remote_code=args.trust_remote_code)
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if args.dataset is None:
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# Synthesize a prompt with the given input length.
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prompt = "hi" * (args.input_len - 1)
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requests = [(prompt, args.input_len, args.output_len)
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for _ in range(args.num_prompts)]
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else:
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requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
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args.output_len)
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if args.backend == "vllm":
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elapsed_time = run_vllm(
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requests, args.model, args.tokenizer, args.quantization,
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args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
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args.trust_remote_code, args.dtype, args.max_model_len,
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args.enforce_eager, args.kv_cache_dtype,
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args.quantization_param_path, args.device,
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args.enable_prefix_caching, args.enable_chunked_prefill,
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args.max_num_batched_tokens, args.gpu_memory_utilization,
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args.download_dir)
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else:
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raise ValueError(f"Unknown backend: {args.backend}")
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total_num_tokens = sum(prompt_len + output_len
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for _, prompt_len, output_len, priority in requests)
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print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
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f"{total_num_tokens / elapsed_time:.2f} tokens/s")
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# Output JSON results if specified
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if args.output_json:
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results = {
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"elapsed_time": elapsed_time,
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"num_requests": len(requests),
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"total_num_tokens": total_num_tokens,
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"requests_per_second": len(requests) / elapsed_time,
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"tokens_per_second": total_num_tokens / elapsed_time,
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}
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with open(args.output_json, "w") as f:
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json.dump(results, f, indent=4)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Benchmark the throughput.")
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parser.add_argument("--backend",
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type=str,
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choices=["vllm", "hf", "mii"],
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default="vllm")
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parser.add_argument("--dataset",
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type=str,
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default=None,
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help="Path to the dataset.")
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parser.add_argument("--input-len",
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type=int,
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default=None,
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help="Input prompt length for each request")
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parser.add_argument("--output-len",
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type=int,
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default=None,
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help="Output length for each request. Overrides the "
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"output length from the dataset.")
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parser.add_argument("--model", type=str, default="facebook/opt-125m")
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parser.add_argument("--tokenizer", type=str, default=None)
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parser.add_argument('--quantization',
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'-q',
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choices=[*QUANTIZATION_METHODS, None],
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default=None)
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parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
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parser.add_argument("--n",
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type=int,
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default=1,
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help="Number of generated sequences per prompt.")
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parser.add_argument("--use-beam-search", action="store_true")
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parser.add_argument("--num-prompts",
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type=int,
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default=200,
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help="Number of prompts to process.")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument('--trust-remote-code',
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action='store_true',
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help='trust remote code from huggingface')
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parser.add_argument(
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'--max-model-len',
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type=int,
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default=None,
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help='Maximum length of a sequence (including prompt and output). '
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'If None, will be derived from the model.')
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parser.add_argument(
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'--dtype',
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type=str,
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default='auto',
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choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
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help='data type for model weights and activations. '
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'The "auto" option will use FP16 precision '
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'for FP32 and FP16 models, and BF16 precision '
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'for BF16 models.')
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parser.add_argument('--gpu-memory-utilization',
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type=float,
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default=0.9,
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help='the fraction of GPU memory to be used for '
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'the model executor, which can range from 0 to 1.'
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'If unspecified, will use the default value of 0.9.')
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parser.add_argument("--enforce-eager",
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action="store_true",
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help="enforce eager execution")
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parser.add_argument(
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'--kv-cache-dtype',
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type=str,
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choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
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default="auto",
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help='Data type for kv cache storage. If "auto", will use model '
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'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
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'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
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parser.add_argument(
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'--quantization-param-path',
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type=str,
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default=None,
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help='Path to the JSON file containing the KV cache scaling factors. '
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'This should generally be supplied, when KV cache dtype is FP8. '
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'Otherwise, KV cache scaling factors default to 1.0, which may cause '
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'accuracy issues. FP8_E5M2 (without scaling) is only supported on '
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'cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is '
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'instead supported for common inference criteria.')
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parser.add_argument(
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"--device",
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type=str,
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default="cuda",
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choices=["cuda", "cpu"],
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help='device type for vLLM execution, supporting CUDA and CPU.')
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parser.add_argument(
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"--enable-prefix-caching",
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action='store_true',
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help="enable automatic prefix caching for vLLM backend.")
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parser.add_argument("--enable-chunked-prefill",
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action='store_true',
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help="enable chunked prefill for vLLM backend.")
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parser.add_argument('--max-num-batched-tokens',
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type=int,
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default=None,
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help='maximum number of batched tokens per '
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'iteration')
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parser.add_argument('--download-dir',
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type=str,
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default=None,
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help='directory to download and load the weights, '
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'default to the default cache dir of huggingface')
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parser.add_argument(
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'--output-json',
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type=str,
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default=None,
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help='Path to save the throughput results in JSON format.')
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args = parser.parse_args()
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if args.tokenizer is None:
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args.tokenizer = args.model
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if args.dataset is None:
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assert args.input_len is not None
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assert args.output_len is not None
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else:
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assert args.input_len is None
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main(args)
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