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https://git.datalinker.icu/vllm-project/vllm.git
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9090bf02e7
Co-authored-by: zhaoyang <zhao.yang16@zte.com.cn> Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
327 lines
15 KiB
Python
327 lines
15 KiB
Python
import argparse
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import dataclasses
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from dataclasses import dataclass
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from typing import Optional, Tuple
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from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
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SchedulerConfig, LoRAConfig)
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@dataclass
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class EngineArgs:
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"""Arguments for vLLM engine."""
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model: str
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tokenizer: Optional[str] = None
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tokenizer_mode: str = 'auto'
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trust_remote_code: bool = False
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download_dir: Optional[str] = None
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load_format: str = 'auto'
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dtype: str = 'auto'
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kv_cache_dtype: str = 'auto'
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seed: int = 0
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max_model_len: Optional[int] = None
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worker_use_ray: bool = False
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pipeline_parallel_size: int = 1
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tensor_parallel_size: int = 1
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max_parallel_loading_workers: Optional[int] = None
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block_size: int = 16
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swap_space: int = 4 # GiB
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gpu_memory_utilization: float = 0.90
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max_num_batched_tokens: Optional[int] = None
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max_num_seqs: int = 256
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max_paddings: int = 256
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disable_log_stats: bool = False
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revision: Optional[str] = None
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tokenizer_revision: Optional[str] = None
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quantization: Optional[str] = None
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enforce_eager: bool = False
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max_context_len_to_capture: int = 8192
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disable_custom_all_reduce: bool = False
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enable_lora: bool = False
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max_loras: int = 1
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max_lora_rank: int = 16
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lora_extra_vocab_size: int = 256
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lora_dtype = 'auto'
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max_cpu_loras: Optional[int] = None
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def __post_init__(self):
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if self.tokenizer is None:
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self.tokenizer = self.model
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@staticmethod
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def add_cli_args(
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parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
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"""Shared CLI arguments for vLLM engine."""
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# NOTE: If you update any of the arguments below, please also
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# make sure to update docs/source/models/engine_args.rst
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# Model arguments
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parser.add_argument(
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'--model',
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type=str,
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default='facebook/opt-125m',
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help='name or path of the huggingface model to use')
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parser.add_argument(
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'--tokenizer',
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type=str,
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default=EngineArgs.tokenizer,
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help='name or path of the huggingface tokenizer to use')
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parser.add_argument(
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'--revision',
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type=str,
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default=None,
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help='the specific model version to use. It can be a branch '
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'name, a tag name, or a commit id. If unspecified, will use '
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'the default version.')
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parser.add_argument(
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'--tokenizer-revision',
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type=str,
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default=None,
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help='the specific tokenizer version to use. It can be a branch '
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'name, a tag name, or a commit id. If unspecified, will use '
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'the default version.')
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parser.add_argument('--tokenizer-mode',
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type=str,
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default=EngineArgs.tokenizer_mode,
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choices=['auto', 'slow'],
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help='tokenizer mode. "auto" will use the fast '
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'tokenizer if available, and "slow" will '
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'always use the slow tokenizer.')
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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('--download-dir',
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type=str,
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default=EngineArgs.download_dir,
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help='directory to download and load the weights, '
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'default to the default cache dir of '
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'huggingface')
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parser.add_argument(
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'--load-format',
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type=str,
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default=EngineArgs.load_format,
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choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'],
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help='The format of the model weights to load. '
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'"auto" will try to load the weights in the safetensors format '
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'and fall back to the pytorch bin format if safetensors format '
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'is not available. '
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'"pt" will load the weights in the pytorch bin format. '
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'"safetensors" will load the weights in the safetensors format. '
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'"npcache" will load the weights in pytorch format and store '
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'a numpy cache to speed up the loading. '
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'"dummy" will initialize the weights with random values, '
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'which is mainly for profiling.')
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parser.add_argument(
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'--dtype',
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type=str,
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default=EngineArgs.dtype,
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choices=[
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'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
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],
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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(
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'--kv-cache-dtype',
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type=str,
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choices=['auto', 'fp8_e5m2'],
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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. Note FP8 is not supported when cuda version is '
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'lower than 11.8.')
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parser.add_argument('--max-model-len',
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type=int,
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default=None,
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help='model context length. If unspecified, '
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'will be automatically derived from the model.')
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# Parallel arguments
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parser.add_argument('--worker-use-ray',
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action='store_true',
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help='use Ray for distributed serving, will be '
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'automatically set when using more than 1 GPU')
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parser.add_argument('--pipeline-parallel-size',
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'-pp',
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type=int,
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default=EngineArgs.pipeline_parallel_size,
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help='number of pipeline stages')
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parser.add_argument('--tensor-parallel-size',
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'-tp',
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type=int,
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default=EngineArgs.tensor_parallel_size,
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help='number of tensor parallel replicas')
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parser.add_argument(
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'--max-parallel-loading-workers',
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type=int,
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help='load model sequentially in multiple batches, '
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'to avoid RAM OOM when using tensor '
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'parallel and large models')
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# KV cache arguments
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parser.add_argument('--block-size',
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type=int,
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default=EngineArgs.block_size,
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choices=[8, 16, 32],
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help='token block size')
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# TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
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parser.add_argument('--seed',
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type=int,
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default=EngineArgs.seed,
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help='random seed')
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parser.add_argument('--swap-space',
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type=int,
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default=EngineArgs.swap_space,
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help='CPU swap space size (GiB) per GPU')
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parser.add_argument(
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'--gpu-memory-utilization',
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type=float,
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default=EngineArgs.gpu_memory_utilization,
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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('--max-num-batched-tokens',
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type=int,
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default=EngineArgs.max_num_batched_tokens,
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help='maximum number of batched tokens per '
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'iteration')
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parser.add_argument('--max-num-seqs',
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type=int,
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default=EngineArgs.max_num_seqs,
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help='maximum number of sequences per iteration')
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parser.add_argument('--max-paddings',
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type=int,
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default=EngineArgs.max_paddings,
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help='maximum number of paddings in a batch')
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parser.add_argument('--disable-log-stats',
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action='store_true',
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help='disable logging statistics')
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# Quantization settings.
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parser.add_argument('--quantization',
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'-q',
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type=str,
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choices=['awq', 'gptq', 'squeezellm', None],
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default=None,
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help='Method used to quantize the weights. If '
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'None, we first check the `quantization_config` '
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'attribute in the model config file. If that is '
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'None, we assume the model weights are not '
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'quantized and use `dtype` to determine the data '
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'type of the weights.')
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parser.add_argument('--enforce-eager',
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action='store_true',
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help='Always use eager-mode PyTorch. If False, '
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'will use eager mode and CUDA graph in hybrid '
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'for maximal performance and flexibility.')
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parser.add_argument('--max-context-len-to-capture',
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type=int,
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default=EngineArgs.max_context_len_to_capture,
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help='maximum context length covered by CUDA '
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'graphs. When a sequence has context length '
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'larger than this, we fall back to eager mode.')
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parser.add_argument('--disable-custom-all-reduce',
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action='store_true',
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default=EngineArgs.disable_custom_all_reduce,
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help='See ParallelConfig')
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# LoRA related configs
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parser.add_argument('--enable-lora',
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action='store_true',
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help='If True, enable handling of LoRA adapters.')
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parser.add_argument('--max-loras',
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type=int,
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default=EngineArgs.max_loras,
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help='Max number of LoRAs in a single batch.')
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parser.add_argument('--max-lora-rank',
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type=int,
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default=EngineArgs.max_lora_rank,
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help='Max LoRA rank.')
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parser.add_argument(
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'--lora-extra-vocab-size',
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type=int,
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default=EngineArgs.lora_extra_vocab_size,
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help=('Maximum size of extra vocabulary that can be '
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'present in a LoRA adapter (added to the base '
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'model vocabulary).'))
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parser.add_argument(
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'--lora-dtype',
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type=str,
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default=EngineArgs.lora_dtype,
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choices=['auto', 'float16', 'bfloat16', 'float32'],
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help=('Data type for LoRA. If auto, will default to '
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'base model dtype.'))
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parser.add_argument(
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'--max-cpu-loras',
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type=int,
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default=EngineArgs.max_cpu_loras,
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help=('Maximum number of LoRAs to store in CPU memory. '
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'Must be >= than max_num_seqs. '
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'Defaults to max_num_seqs.'))
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return parser
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@classmethod
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def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
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# Get the list of attributes of this dataclass.
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attrs = [attr.name for attr in dataclasses.fields(cls)]
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# Set the attributes from the parsed arguments.
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engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
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return engine_args
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def create_engine_configs(
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self,
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) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig,
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Optional[LoRAConfig]]:
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model_config = ModelConfig(self.model, self.tokenizer,
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self.tokenizer_mode, self.trust_remote_code,
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self.download_dir, self.load_format,
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self.dtype, self.seed, self.revision,
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self.tokenizer_revision, self.max_model_len,
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self.quantization, self.enforce_eager,
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self.max_context_len_to_capture)
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cache_config = CacheConfig(self.block_size,
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self.gpu_memory_utilization,
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self.swap_space, self.kv_cache_dtype,
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model_config.get_sliding_window())
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parallel_config = ParallelConfig(self.pipeline_parallel_size,
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self.tensor_parallel_size,
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self.worker_use_ray,
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self.max_parallel_loading_workers,
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self.disable_custom_all_reduce)
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scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
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self.max_num_seqs,
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model_config.max_model_len,
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self.max_paddings)
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lora_config = LoRAConfig(
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max_lora_rank=self.max_lora_rank,
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max_loras=self.max_loras,
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lora_extra_vocab_size=self.lora_extra_vocab_size,
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lora_dtype=self.lora_dtype,
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max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
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and self.max_cpu_loras > 0 else None) if self.enable_lora else None
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return model_config, cache_config, parallel_config, scheduler_config, lora_config
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@dataclass
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class AsyncEngineArgs(EngineArgs):
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"""Arguments for asynchronous vLLM engine."""
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engine_use_ray: bool = False
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disable_log_requests: bool = False
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max_log_len: Optional[int] = None
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@staticmethod
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def add_cli_args(
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parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
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parser = EngineArgs.add_cli_args(parser)
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parser.add_argument('--engine-use-ray',
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action='store_true',
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help='use Ray to start the LLM engine in a '
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'separate process as the server process.')
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parser.add_argument('--disable-log-requests',
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action='store_true',
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help='disable logging requests')
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parser.add_argument('--max-log-len',
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type=int,
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default=None,
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help='max number of prompt characters or prompt '
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'ID numbers being printed in log. '
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'Default: unlimited.')
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return parser
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