arg_utils.py

import argparse
import dataclasses
import io
import os
from dataclasses import dataclass
from typing import BinaryIO, Optional, Union

from vllm.config import (CacheConfig, DeviceConfig, EngineConfig, LoRAConfig,
                         ModelConfig, ParallelConfig, SchedulerConfig,
                         SpeculativeConfig, TensorizerConfig,
                         TokenizerPoolConfig, VisionLanguageConfig)
from vllm.model_executor.tensorizer_loader import TensorizerArgs
from vllm.utils import str_to_int_tuple


@dataclass
class EngineArgs:
    """Arguments for vLLM engine."""
    model: str
    tokenizer: Optional[str] = None
    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

    # Tensorizer configuration parameters
    tensorizer_uri: Union[io.BufferedIOBase, io.RawIOBase, BinaryIO, str,
                          bytes, os.PathLike, int] = None
    vllm_tensorized: bool = False
    verify_hash: Optional[bool] = False
    num_readers: Optional[int] = 1
    encryption_keyfile: Optional[str] = None
    s3_access_key_id: Optional[str] = None
    s3_secret_access_key: Optional[str] = None
    s3_endpoint: Optional[str] = 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

    # 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."""

        # NOTE: If you update any of the arguments below, please also
        # make sure to update docs/source/models/engine_args.rst

        # 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(
            '--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='tokenizer mode. "auto" will use the fast '
                            'tokenizer if available, and "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. '
            '"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. '
            '"pt" will load the weights in the pytorch bin format. '
            '"safetensors" will load the weights in the safetensors format. '
            '"npcache" will load the weights in pytorch format and store '
            'a numpy cache to speed up the loading. '
            '"dummy" will initialize the weights with random values, '
            'which is mainly for profiling.'
            '"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. '
            'The "auto" option will use FP16 precision '
            'for FP32 and FP16 models, and BF16 precision '
            'for BF16 models.')
        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.')
        # 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')

        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')
        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.'
            '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=['awq', 'gptq', 'squeezellm', 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 = TensorizerArgs.add_cli_args(parser)
        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.download_dir, self.load_format,
            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)
        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

        tensorizer_config = TensorizerConfig(
            tensorizer_uri=self.tensorizer_uri,
            vllm_tensorized=self.vllm_tensorized,
            verify_hash=self.verify_hash,
            num_readers=self.num_readers,
            encryption_keyfile=self.encryption_keyfile,
            s3_access_key_id=self.s3_access_key_id,
            s3_secret_access_key=self.s3_secret_access_key,
            s3_endpoint=self.s3_endpoint,
        )

        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

        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,
                            tensorizer_config=tensorizer_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) -> argparse.ArgumentParser:
        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. '
                            'Default: unlimited.')
        return parser