arg_utils.py

import argparse
import dataclasses
from dataclasses import dataclass
from typing import List, Optional, Union

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


def nullable_str(val: str):
    if not val or val == "None":
        return None
    return val


@dataclass
class EngineArgs:
    """Arguments for vLLM engine."""
    model: str
    served_model_name: Optional[Union[List[str]]] = None
    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: Optional[int] = None
    max_seq_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
    fully_sharded_loras: bool = False
    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
    speculative_max_model_len: Optional[int] = None
    ngram_prompt_lookup_max: Optional[int] = None
    ngram_prompt_lookup_min: 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=nullable_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=nullable_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=nullable_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=nullable_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=nullable_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=nullable_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],
                            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=nullable_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. '
                            '(DEPRECATED. Use --max-seq_len-to-capture instead'
                            ')')
        parser.add_argument('--max-seq_len-to-capture',
                            type=int,
                            default=EngineArgs.max_seq_len_to_capture,
                            help='Maximum sequence 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=nullable_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(
            '--fully-sharded-loras',
            action='store_true',
            help=('By default, only half of the LoRA computation is '
                  'sharded with tensor parallelism. '
                  'Enabling this will use the fully sharded layers. '
                  'At high sequence length, max rank or '
                  'tensor parallel size, this is likely faster.'))
        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=nullable_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=nullable_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=nullable_str,
            default=EngineArgs.speculative_model,
            help=
            'The name of the draft model to be used in speculative decoding.')

        parser.add_argument(
            '--num-speculative-tokens',
            type=int,
            default=EngineArgs.num_speculative_tokens,
            help='The number of speculative tokens to sample from '
            'the draft model in speculative decoding.')

        parser.add_argument(
            '--speculative-max-model-len',
            type=int,
            default=EngineArgs.speculative_max_model_len,
            help='The maximum sequence length supported by the '
            'draft model. Sequences over this length will skip '
            'speculation.')

        parser.add_argument(
            '--ngram-prompt-lookup-max',
            type=int,
            default=EngineArgs.ngram_prompt_lookup_max,
            help='Max size of window for ngram prompt lookup in speculative '
            'decoding.')

        parser.add_argument(
            '--ngram-prompt-lookup-min',
            type=int,
            default=EngineArgs.ngram_prompt_lookup_min,
            help='Min size of window for ngram prompt lookup in speculative '
            'decoding.')

        parser.add_argument('--model-loader-extra-config',
                            type=nullable_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.')

        parser.add_argument(
            "--served-model-name",
            nargs="+",
            type=str,
            default=None,
            help="The model name(s) used in the API. If multiple "
            "names are provided, the server will respond to any "
            "of the provided names. The model name in the model "
            "field of a response will be the first name in this "
            "list. If not specified, the model name will be the "
            "same as the `--model` argument. Noted that this name(s)"
            "will also be used in `model_name` tag content of "
            "prometheus metrics, if multiple names provided, metrics"
            "tag will take the first one.")

        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_seq_len_to_capture, self.max_logprobs,
            self.skip_tokenizer_init, self.served_model_name)
        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,
            speculative_max_model_len=self.speculative_max_model_len,
            enable_chunked_prefill=self.enable_chunked_prefill,
            use_v2_block_manager=self.use_v2_block_manager,
            ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
            ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
        )

        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,
            fully_sharded_loras=self.fully_sharded_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)