mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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a37d815b83
Co-authored-by: Yun Ding <yunding@nvidia.com> Co-authored-by: Roger Wang <ywang@roblox.com>
578 lines
26 KiB
Python
578 lines
26 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
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from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
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EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
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ParallelConfig, SchedulerConfig, SpeculativeConfig,
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TokenizerPoolConfig, VisionLanguageConfig)
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from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
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from vllm.utils import str_to_int_tuple
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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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skip_tokenizer_init: bool = False
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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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quantization_param_path: Optional[str] = None
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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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enable_prefix_caching: bool = False
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use_v2_block_manager: bool = False
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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_logprobs: int = 5 # OpenAI default value
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disable_log_stats: bool = False
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revision: Optional[str] = None
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code_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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tokenizer_pool_size: int = 0
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tokenizer_pool_type: str = "ray"
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tokenizer_pool_extra_config: Optional[dict] = None
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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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device: str = 'auto'
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ray_workers_use_nsight: bool = False
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num_gpu_blocks_override: Optional[int] = None
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num_lookahead_slots: int = 0
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model_loader_extra_config: Optional[dict] = None
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# Related to Vision-language models such as llava
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image_input_type: Optional[str] = None
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image_token_id: Optional[int] = None
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image_input_shape: Optional[str] = None
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image_feature_size: Optional[int] = None
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scheduler_delay_factor: float = 0.0
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enable_chunked_prefill: bool = False
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guided_decoding_backend: str = 'outlines'
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# Speculative decoding configuration.
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speculative_model: Optional[str] = None
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num_speculative_tokens: 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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# 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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'--skip-tokenizer-init',
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action='store_true',
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help='Skip initialization of tokenizer and detokenizer')
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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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'--code-revision',
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type=str,
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default=None,
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help='The specific revision to use for the model code on '
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'Hugging Face Hub. It can be a branch name, a tag name, or a '
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'commit id. If unspecified, will use 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(
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'--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='The tokenizer mode.\n\n* "auto" will use the '
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'fast tokenizer if available.\n* "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=[
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'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer'
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],
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help='The format of the model weights to load.\n\n'
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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.\n'
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'* "pt" will load the weights in the pytorch bin format.\n'
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'* "safetensors" will load the weights in the safetensors format.\n'
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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.\n'
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'* "dummy" will initialize the weights with random values, '
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'which is mainly for profiling.\n'
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'* "tensorizer" will load the weights using tensorizer from '
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'CoreWeave which assumes tensorizer_uri is set to the location of '
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'the serialized weights.')
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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.\n\n'
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'* "auto" will use FP16 precision for FP32 and FP16 models, and '
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'BF16 precision for BF16 models.\n'
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'* "half" for FP16. Recommended for AWQ quantization.\n'
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'* "float16" is the same as "half".\n'
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'* "bfloat16" for a balance between precision and range.\n'
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'* "float" is shorthand for FP32 precision.\n'
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'* "float32" for FP32 precision.')
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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'],
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default=EngineArgs.kv_cache_dtype,
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help='Data type for kv cache storage. If "auto", will use model '
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'data type. FP8_E5M2 (without scaling) is only supported on cuda '
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'version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
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'supported for common inference criteria.')
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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 '
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'scaling factors. This should generally be supplied, when '
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'KV cache dtype is FP8. Otherwise, KV cache scaling factors '
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'default to 1.0, which may cause accuracy issues. '
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'FP8_E5M2 (without scaling) is only supported on cuda version'
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'greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
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'supported for common inference criteria.')
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parser.add_argument('--max-model-len',
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type=int,
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default=EngineArgs.max_model_len,
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help='Model context length. If unspecified, will '
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'be automatically derived from the model config.')
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parser.add_argument(
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'--guided-decoding-backend',
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type=str,
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default='outlines',
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choices=['outlines', 'lm-format-enforcer'],
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help='Which engine will be used for guided decoding'
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' (JSON schema / regex etc) by default. Currently support '
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'https://github.com/outlines-dev/outlines and '
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'https://github.com/noamgat/lm-format-enforcer.'
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' Can be overridden per request via guided_decoding_backend'
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' parameter.')
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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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default=EngineArgs.max_parallel_loading_workers,
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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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parser.add_argument(
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'--ray-workers-use-nsight',
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action='store_true',
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help='If specified, use nsight to profile Ray workers.')
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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, 128],
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help='Token block size for contiguous chunks of '
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'tokens.')
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parser.add_argument('--enable-prefix-caching',
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action='store_true',
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help='Enables automatic prefix caching.')
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parser.add_argument('--use-v2-block-manager',
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action='store_true',
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help='Use BlockSpaceMangerV2.')
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parser.add_argument(
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'--num-lookahead-slots',
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type=int,
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default=EngineArgs.num_lookahead_slots,
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help='Experimental scheduling config necessary for '
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'speculative decoding. This will be replaced by '
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'speculative config in the future; it is present '
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'to enable correctness tests until then.')
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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 for operations.')
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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 the model '
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'executor, which can range from 0 to 1. For example, a value of '
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'0.5 would imply 50%% GPU memory utilization. If unspecified, '
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'will use the default value of 0.9.')
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parser.add_argument(
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'--num-gpu-blocks-override',
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type=int,
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default=None,
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help='If specified, ignore GPU profiling result and use this number'
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'of GPU blocks. Used for testing preemption.')
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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(
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'--max-logprobs',
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type=int,
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default=EngineArgs.max_logprobs,
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help=('Max number of log probs to return logprobs is specified in'
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' SamplingParams.'))
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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=[*QUANTIZATION_METHODS, None],
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default=EngineArgs.quantization,
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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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parser.add_argument('--tokenizer-pool-size',
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type=int,
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default=EngineArgs.tokenizer_pool_size,
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help='Size of tokenizer pool to use for '
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'asynchronous tokenization. If 0, will '
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'use synchronous tokenization.')
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parser.add_argument('--tokenizer-pool-type',
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type=str,
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default=EngineArgs.tokenizer_pool_type,
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help='Type of tokenizer pool to use for '
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'asynchronous tokenization. Ignored '
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'if tokenizer_pool_size is 0.')
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parser.add_argument('--tokenizer-pool-extra-config',
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type=str,
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default=EngineArgs.tokenizer_pool_extra_config,
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help='Extra config for tokenizer pool. '
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'This should be a JSON string that will be '
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'parsed into a dictionary. Ignored if '
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'tokenizer_pool_size is 0.')
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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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parser.add_argument("--device",
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type=str,
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default=EngineArgs.device,
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choices=["auto", "cuda", "neuron", "cpu"],
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help='Device type for vLLM execution.')
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# Related to Vision-language models such as llava
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parser.add_argument(
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'--image-input-type',
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type=str,
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default=None,
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choices=[
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t.name.lower() for t in VisionLanguageConfig.ImageInputType
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],
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help=('The image input type passed into vLLM. '
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'Should be one of "pixel_values" or "image_features".'))
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parser.add_argument('--image-token-id',
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type=int,
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default=None,
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help=('Input id for image token.'))
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parser.add_argument(
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'--image-input-shape',
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type=str,
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default=None,
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help=('The biggest image input shape (worst for memory footprint) '
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'given an input type. Only used for vLLM\'s profile_run.'))
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parser.add_argument(
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'--image-feature-size',
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type=int,
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default=None,
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help=('The image feature size along the context dimension.'))
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parser.add_argument(
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'--scheduler-delay-factor',
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type=float,
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default=EngineArgs.scheduler_delay_factor,
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help='Apply a delay (of delay factor multiplied by previous'
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'prompt latency) before scheduling next prompt.')
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parser.add_argument(
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'--enable-chunked-prefill',
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action='store_true',
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help='If set, the prefill requests can be chunked based on the '
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'max_num_batched_tokens.')
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parser.add_argument(
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'--speculative-model',
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type=str,
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default=None,
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help=
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'The name of the draft model to be used in speculative decoding.')
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parser.add_argument(
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'--num-speculative-tokens',
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type=int,
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default=None,
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help='The number of speculative tokens to sample from '
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'the draft model in speculative decoding.')
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parser.add_argument('--model-loader-extra-config',
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type=str,
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default=EngineArgs.model_loader_extra_config,
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help='Extra config for model loader. '
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'This will be passed to the model loader '
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'corresponding to the chosen load_format. '
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'This should be a JSON string that will be '
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'parsed into a dictionary.')
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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_config(self, ) -> EngineConfig:
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device_config = DeviceConfig(self.device)
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model_config = ModelConfig(
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self.model, self.tokenizer, self.tokenizer_mode,
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self.trust_remote_code, self.dtype, self.seed, self.revision,
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self.code_revision, self.tokenizer_revision, self.max_model_len,
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self.quantization, self.quantization_param_path,
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self.enforce_eager, self.max_context_len_to_capture,
|
|
self.max_logprobs, self.skip_tokenizer_init)
|
|
cache_config = CacheConfig(self.block_size,
|
|
self.gpu_memory_utilization,
|
|
self.swap_space, self.kv_cache_dtype,
|
|
self.num_gpu_blocks_override,
|
|
model_config.get_sliding_window(),
|
|
self.enable_prefix_caching)
|
|
parallel_config = ParallelConfig(
|
|
self.pipeline_parallel_size, self.tensor_parallel_size,
|
|
self.worker_use_ray, self.max_parallel_loading_workers,
|
|
self.disable_custom_all_reduce,
|
|
TokenizerPoolConfig.create_config(
|
|
self.tokenizer_pool_size,
|
|
self.tokenizer_pool_type,
|
|
self.tokenizer_pool_extra_config,
|
|
), self.ray_workers_use_nsight)
|
|
|
|
speculative_config = SpeculativeConfig.maybe_create_spec_config(
|
|
target_model_config=model_config,
|
|
target_parallel_config=parallel_config,
|
|
target_dtype=self.dtype,
|
|
speculative_model=self.speculative_model,
|
|
num_speculative_tokens=self.num_speculative_tokens,
|
|
)
|
|
|
|
scheduler_config = SchedulerConfig(
|
|
self.max_num_batched_tokens,
|
|
self.max_num_seqs,
|
|
model_config.max_model_len,
|
|
self.use_v2_block_manager,
|
|
num_lookahead_slots=(self.num_lookahead_slots
|
|
if speculative_config is None else
|
|
speculative_config.num_lookahead_slots),
|
|
delay_factor=self.scheduler_delay_factor,
|
|
enable_chunked_prefill=self.enable_chunked_prefill,
|
|
)
|
|
lora_config = LoRAConfig(
|
|
max_lora_rank=self.max_lora_rank,
|
|
max_loras=self.max_loras,
|
|
lora_extra_vocab_size=self.lora_extra_vocab_size,
|
|
lora_dtype=self.lora_dtype,
|
|
max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
|
|
and self.max_cpu_loras > 0 else None) if self.enable_lora else None
|
|
|
|
load_config = LoadConfig(
|
|
load_format=self.load_format,
|
|
download_dir=self.download_dir,
|
|
model_loader_extra_config=self.model_loader_extra_config,
|
|
)
|
|
|
|
if self.image_input_type:
|
|
if (not self.image_token_id or not self.image_input_shape
|
|
or not self.image_feature_size):
|
|
raise ValueError(
|
|
'Specify `image_token_id`, `image_input_shape` and '
|
|
'`image_feature_size` together with `image_input_type`.')
|
|
vision_language_config = VisionLanguageConfig(
|
|
image_input_type=VisionLanguageConfig.
|
|
get_image_input_enum_type(self.image_input_type),
|
|
image_token_id=self.image_token_id,
|
|
image_input_shape=str_to_int_tuple(self.image_input_shape),
|
|
image_feature_size=self.image_feature_size,
|
|
)
|
|
else:
|
|
vision_language_config = None
|
|
|
|
decoding_config = DecodingConfig(
|
|
guided_decoding_backend=self.guided_decoding_backend)
|
|
|
|
return EngineConfig(model_config=model_config,
|
|
cache_config=cache_config,
|
|
parallel_config=parallel_config,
|
|
scheduler_config=scheduler_config,
|
|
device_config=device_config,
|
|
lora_config=lora_config,
|
|
vision_language_config=vision_language_config,
|
|
speculative_config=speculative_config,
|
|
load_config=load_config,
|
|
decoding_config=decoding_config)
|
|
|
|
|
|
@dataclass
|
|
class AsyncEngineArgs(EngineArgs):
|
|
"""Arguments for asynchronous vLLM engine."""
|
|
engine_use_ray: bool = False
|
|
disable_log_requests: bool = False
|
|
max_log_len: Optional[int] = None
|
|
|
|
@staticmethod
|
|
def add_cli_args(parser: argparse.ArgumentParser,
|
|
async_args_only: bool = False) -> argparse.ArgumentParser:
|
|
if not async_args_only:
|
|
parser = EngineArgs.add_cli_args(parser)
|
|
parser.add_argument('--engine-use-ray',
|
|
action='store_true',
|
|
help='Use Ray to start the LLM engine in a '
|
|
'separate process as the server process.')
|
|
parser.add_argument('--disable-log-requests',
|
|
action='store_true',
|
|
help='Disable logging requests.')
|
|
parser.add_argument('--max-log-len',
|
|
type=int,
|
|
default=None,
|
|
help='Max number of prompt characters or prompt '
|
|
'ID numbers being printed in log.'
|
|
'\n\nDefault: Unlimited')
|
|
return parser
|
|
|
|
|
|
# These functions are used by sphinx to build the documentation
|
|
def _engine_args_parser():
|
|
return EngineArgs.add_cli_args(argparse.ArgumentParser())
|
|
|
|
|
|
def _async_engine_args_parser():
|
|
return AsyncEngineArgs.add_cli_args(argparse.ArgumentParser(),
|
|
async_args_only=True)
|