arg_utils.py

import argparse
import dataclasses
from dataclasses import dataclass
from typing import Optional

from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
                         EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
                         TokenizerPoolConfig, VisionLanguageConfig)
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from vllm.utils import str_to_int_tuple


@dataclass
class EngineArgs:
    """Arguments for vLLM engine."""
    model: str
    tokenizer: Optional[str] = None
    skip_tokenizer_init: bool = False
    tokenizer_mode: str = 'auto'
    trust_remote_code: bool = False
    download_dir: Optional[str] = None
    load_format: str = 'auto'
    dtype: str = 'auto'
    kv_cache_dtype: str = 'auto'
    quantization_param_path: Optional[str] = None
    seed: int = 0
    max_model_len: Optional[int] = None
    worker_use_ray: bool = False
    pipeline_parallel_size: int = 1
    tensor_parallel_size: int = 1
    max_parallel_loading_workers: Optional[int] = None
    block_size: int = 16
    enable_prefix_caching: bool = False
    use_v2_block_manager: bool = False
    swap_space: int = 4  # GiB
    gpu_memory_utilization: float = 0.90
    max_num_batched_tokens: Optional[int] = None
    max_num_seqs: int = 256
    max_logprobs: int = 5  # OpenAI default value
    disable_log_stats: bool = False
    revision: Optional[str] = None
    code_revision: Optional[str] = None
    tokenizer_revision: Optional[str] = None
    quantization: Optional[str] = None
    enforce_eager: bool = False
    max_context_len_to_capture: int = 8192
    disable_custom_all_reduce: bool = False
    tokenizer_pool_size: int = 0
    tokenizer_pool_type: str = "ray"
    tokenizer_pool_extra_config: Optional[dict] = None
    enable_lora: bool = False
    max_loras: int = 1
    max_lora_rank: int = 16
    lora_extra_vocab_size: int = 256
    lora_dtype = 'auto'
    max_cpu_loras: Optional[int] = None
    device: str = 'auto'
    ray_workers_use_nsight: bool = False
    num_gpu_blocks_override: Optional[int] = None
    num_lookahead_slots: int = 0
    model_loader_extra_config: Optional[dict] = None

    # Related to Vision-language models such as llava
    image_input_type: Optional[str] = None
    image_token_id: Optional[int] = None
    image_input_shape: Optional[str] = None
    image_feature_size: Optional[int] = None
    scheduler_delay_factor: float = 0.0
    enable_chunked_prefill: bool = False

    guided_decoding_backend: str = 'outlines'
    # Speculative decoding configuration.
    speculative_model: Optional[str] = None
    num_speculative_tokens: Optional[int] = None

    def __post_init__(self):
        if self.tokenizer is None:
            self.tokenizer = self.model

    @staticmethod
    def add_cli_args(
            parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
        """Shared CLI arguments for vLLM engine."""

        # Model arguments
        parser.add_argument(
            '--model',
            type=str,
            default='facebook/opt-125m',
            help='Name or path of the huggingface model to use.')
        parser.add_argument(
            '--tokenizer',
            type=str,
            default=EngineArgs.tokenizer,
            help='Name or path of the huggingface tokenizer to use.')
        parser.add_argument(
            '--skip-tokenizer-init',
            action='store_true',
            help='Skip initialization of tokenizer and detokenizer')
        parser.add_argument(
            '--revision',
            type=str,
            default=None,
            help='The specific model version to use. It can be a branch '
            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
        parser.add_argument(
            '--code-revision',
            type=str,
            default=None,
            help='The specific revision to use for the model code on '
            'Hugging Face Hub. It can be a branch name, a tag name, or a '
            'commit id. If unspecified, will use the default version.')
        parser.add_argument(
            '--tokenizer-revision',
            type=str,
            default=None,
            help='The specific tokenizer version to use. It can be a branch '
            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
        parser.add_argument(
            '--tokenizer-mode',
            type=str,
            default=EngineArgs.tokenizer_mode,
            choices=['auto', 'slow'],
            help='The tokenizer mode.\n\n* "auto" will use the '
            'fast tokenizer if available.\n* "slow" will '
            'always use the slow tokenizer.')
        parser.add_argument('--trust-remote-code',
                            action='store_true',
                            help='Trust remote code from huggingface.')
        parser.add_argument('--download-dir',
                            type=str,
                            default=EngineArgs.download_dir,
                            help='Directory to download and load the weights, '
                            'default to the default cache dir of '
                            'huggingface.')
        parser.add_argument(
            '--load-format',
            type=str,
            default=EngineArgs.load_format,
            choices=[
                'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer'
            ],
            help='The format of the model weights to load.\n\n'
            '* "auto" will try to load the weights in the safetensors format '
            'and fall back to the pytorch bin format if safetensors format '
            'is not available.\n'
            '* "pt" will load the weights in the pytorch bin format.\n'
            '* "safetensors" will load the weights in the safetensors format.\n'
            '* "npcache" will load the weights in pytorch format and store '
            'a numpy cache to speed up the loading.\n'
            '* "dummy" will initialize the weights with random values, '
            'which is mainly for profiling.\n'
            '* "tensorizer" will load the weights using tensorizer from '
            'CoreWeave which assumes tensorizer_uri is set to the location of '
            'the serialized weights.')
        parser.add_argument(
            '--dtype',
            type=str,
            default=EngineArgs.dtype,
            choices=[
                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
            ],
            help='Data type for model weights and activations.\n\n'
            '* "auto" will use FP16 precision for FP32 and FP16 models, and '
            'BF16 precision for BF16 models.\n'
            '* "half" for FP16. Recommended for AWQ quantization.\n'
            '* "float16" is the same as "half".\n'
            '* "bfloat16" for a balance between precision and range.\n'
            '* "float" is shorthand for FP32 precision.\n'
            '* "float32" for FP32 precision.')
        parser.add_argument(
            '--kv-cache-dtype',
            type=str,
            choices=['auto', 'fp8'],
            default=EngineArgs.kv_cache_dtype,
            help='Data type for kv cache storage. If "auto", will use model '
            'data type. FP8_E5M2 (without scaling) is only supported on cuda '
            'version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
            'supported for common inference criteria.')
        parser.add_argument(
            '--quantization-param-path',
            type=str,
            default=None,
            help='Path to the JSON file containing the KV cache '
            'scaling factors. This should generally be supplied, when '
            'KV cache dtype is FP8. Otherwise, KV cache scaling factors '
            'default to 1.0, which may cause accuracy issues. '
            'FP8_E5M2 (without scaling) is only supported on cuda version'
            'greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
            'supported for common inference criteria.')
        parser.add_argument('--max-model-len',
                            type=int,
                            default=EngineArgs.max_model_len,
                            help='Model context length. If unspecified, will '
                            'be automatically derived from the model config.')
        parser.add_argument(
            '--guided-decoding-backend',
            type=str,
            default='outlines',
            choices=['outlines', 'lm-format-enforcer'],
            help='Which engine will be used for guided decoding'
            ' (JSON schema / regex etc) by default. Currently support '
            'https://github.com/outlines-dev/outlines and '
            'https://github.com/noamgat/lm-format-enforcer.'
            ' Can be overridden per request via guided_decoding_backend'
            ' parameter.')
        # Parallel arguments
        parser.add_argument('--worker-use-ray',
                            action='store_true',
                            help='Use Ray for distributed serving, will be '
                            'automatically set when using more than 1 GPU.')
        parser.add_argument('--pipeline-parallel-size',
                            '-pp',
                            type=int,
                            default=EngineArgs.pipeline_parallel_size,
                            help='Number of pipeline stages.')
        parser.add_argument('--tensor-parallel-size',
                            '-tp',
                            type=int,
                            default=EngineArgs.tensor_parallel_size,
                            help='Number of tensor parallel replicas.')
        parser.add_argument(
            '--max-parallel-loading-workers',
            type=int,
            default=EngineArgs.max_parallel_loading_workers,
            help='Load model sequentially in multiple batches, '
            'to avoid RAM OOM when using tensor '
            'parallel and large models.')
        parser.add_argument(
            '--ray-workers-use-nsight',
            action='store_true',
            help='If specified, use nsight to profile Ray workers.')
        # KV cache arguments
        parser.add_argument('--block-size',
                            type=int,
                            default=EngineArgs.block_size,
                            choices=[8, 16, 32, 128],
                            help='Token block size for contiguous chunks of '
                            'tokens.')

        parser.add_argument('--enable-prefix-caching',
                            action='store_true',
                            help='Enables automatic prefix caching.')
        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
                            help='Use BlockSpaceMangerV2.')
        parser.add_argument(
            '--num-lookahead-slots',
            type=int,
            default=EngineArgs.num_lookahead_slots,
            help='Experimental scheduling config necessary for '
            'speculative decoding. This will be replaced by '
            'speculative config in the future; it is present '
            'to enable correctness tests until then.')

        parser.add_argument('--seed',
                            type=int,
                            default=EngineArgs.seed,
                            help='Random seed for operations.')
        parser.add_argument('--swap-space',
                            type=int,
                            default=EngineArgs.swap_space,
                            help='CPU swap space size (GiB) per GPU.')
        parser.add_argument(
            '--gpu-memory-utilization',
            type=float,
            default=EngineArgs.gpu_memory_utilization,
            help='The fraction of GPU memory to be used for the model '
            'executor, which can range from 0 to 1. For example, a value of '
            '0.5 would imply 50%% GPU memory utilization. If unspecified, '
            'will use the default value of 0.9.')
        parser.add_argument(
            '--num-gpu-blocks-override',
            type=int,
            default=None,
            help='If specified, ignore GPU profiling result and use this number'
            'of GPU blocks. Used for testing preemption.')
        parser.add_argument('--max-num-batched-tokens',
                            type=int,
                            default=EngineArgs.max_num_batched_tokens,
                            help='Maximum number of batched tokens per '
                            'iteration.')
        parser.add_argument('--max-num-seqs',
                            type=int,
                            default=EngineArgs.max_num_seqs,
                            help='Maximum number of sequences per iteration.')
        parser.add_argument(
            '--max-logprobs',
            type=int,
            default=EngineArgs.max_logprobs,
            help=('Max number of log probs to return logprobs is specified in'
                  ' SamplingParams.'))
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
        # Quantization settings.
        parser.add_argument('--quantization',
                            '-q',
                            type=str,
                            choices=[*QUANTIZATION_METHODS, None],
                            default=EngineArgs.quantization,
                            help='Method used to quantize the weights. If '
                            'None, we first check the `quantization_config` '
                            'attribute in the model config file. If that is '
                            'None, we assume the model weights are not '
                            'quantized and use `dtype` to determine the data '
                            'type of the weights.')
        parser.add_argument('--enforce-eager',
                            action='store_true',
                            help='Always use eager-mode PyTorch. If False, '
                            'will use eager mode and CUDA graph in hybrid '
                            'for maximal performance and flexibility.')
        parser.add_argument('--max-context-len-to-capture',
                            type=int,
                            default=EngineArgs.max_context_len_to_capture,
                            help='Maximum context length covered by CUDA '
                            'graphs. When a sequence has context length '
                            'larger than this, we fall back to eager mode.')
        parser.add_argument('--disable-custom-all-reduce',
                            action='store_true',
                            default=EngineArgs.disable_custom_all_reduce,
                            help='See ParallelConfig.')
        parser.add_argument('--tokenizer-pool-size',
                            type=int,
                            default=EngineArgs.tokenizer_pool_size,
                            help='Size of tokenizer pool to use for '
                            'asynchronous tokenization. If 0, will '
                            'use synchronous tokenization.')
        parser.add_argument('--tokenizer-pool-type',
                            type=str,
                            default=EngineArgs.tokenizer_pool_type,
                            help='Type of tokenizer pool to use for '
                            'asynchronous tokenization. Ignored '
                            'if tokenizer_pool_size is 0.')
        parser.add_argument('--tokenizer-pool-extra-config',
                            type=str,
                            default=EngineArgs.tokenizer_pool_extra_config,
                            help='Extra config for tokenizer pool. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary. Ignored if '
                            'tokenizer_pool_size is 0.')
        # LoRA related configs
        parser.add_argument('--enable-lora',
                            action='store_true',
                            help='If True, enable handling of LoRA adapters.')
        parser.add_argument('--max-loras',
                            type=int,
                            default=EngineArgs.max_loras,
                            help='Max number of LoRAs in a single batch.')
        parser.add_argument('--max-lora-rank',
                            type=int,
                            default=EngineArgs.max_lora_rank,
                            help='Max LoRA rank.')
        parser.add_argument(
            '--lora-extra-vocab-size',
            type=int,
            default=EngineArgs.lora_extra_vocab_size,
            help=('Maximum size of extra vocabulary that can be '
                  'present in a LoRA adapter (added to the base '
                  'model vocabulary).'))
        parser.add_argument(
            '--lora-dtype',
            type=str,
            default=EngineArgs.lora_dtype,
            choices=['auto', 'float16', 'bfloat16', 'float32'],
            help=('Data type for LoRA. If auto, will default to '
                  'base model dtype.'))
        parser.add_argument(
            '--max-cpu-loras',
            type=int,
            default=EngineArgs.max_cpu_loras,
            help=('Maximum number of LoRAs to store in CPU memory. '
                  'Must be >= than max_num_seqs. '
                  'Defaults to max_num_seqs.'))
        parser.add_argument("--device",
                            type=str,
                            default=EngineArgs.device,
                            choices=["auto", "cuda", "neuron", "cpu"],
                            help='Device type for vLLM execution.')
        # Related to Vision-language models such as llava
        parser.add_argument(
            '--image-input-type',
            type=str,
            default=None,
            choices=[
                t.name.lower() for t in VisionLanguageConfig.ImageInputType
            ],
            help=('The image input type passed into vLLM. '
                  'Should be one of "pixel_values" or "image_features".'))
        parser.add_argument('--image-token-id',
                            type=int,
                            default=None,
                            help=('Input id for image token.'))
        parser.add_argument(
            '--image-input-shape',
            type=str,
            default=None,
            help=('The biggest image input shape (worst for memory footprint) '
                  'given an input type. Only used for vLLM\'s profile_run.'))
        parser.add_argument(
            '--image-feature-size',
            type=int,
            default=None,
            help=('The image feature size along the context dimension.'))
        parser.add_argument(
            '--scheduler-delay-factor',
            type=float,
            default=EngineArgs.scheduler_delay_factor,
            help='Apply a delay (of delay factor multiplied by previous'
            'prompt latency) before scheduling next prompt.')
        parser.add_argument(
            '--enable-chunked-prefill',
            action='store_true',
            help='If set, the prefill requests can be chunked based on the '
            'max_num_batched_tokens.')

        parser.add_argument(
            '--speculative-model',
            type=str,
            default=None,
            help=
            'The name of the draft model to be used in speculative decoding.')

        parser.add_argument(
            '--num-speculative-tokens',
            type=int,
            default=None,
            help='The number of speculative tokens to sample from '
            'the draft model in speculative decoding.')

        parser.add_argument('--model-loader-extra-config',
                            type=str,
                            default=EngineArgs.model_loader_extra_config,
                            help='Extra config for model loader. '
                            'This will be passed to the model loader '
                            'corresponding to the chosen load_format. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')

        return parser

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args

    def create_engine_config(self, ) -> EngineConfig:
        device_config = DeviceConfig(self.device)
        model_config = ModelConfig(
            self.model, self.tokenizer, self.tokenizer_mode,
            self.trust_remote_code, self.dtype, self.seed, self.revision,
            self.code_revision, self.tokenizer_revision, self.max_model_len,
            self.quantization, self.quantization_param_path,
            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)