# SPDX-License-Identifier: Apache-2.0 import argparse import dataclasses import json from dataclasses import dataclass from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional, Tuple, Type, Union, cast, get_args) import torch import vllm.envs as envs from vllm import version from vllm.config import (CacheConfig, CompilationConfig, ConfigFormat, DecodingConfig, DeviceConfig, HfOverrides, KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig, ModelConfig, ModelImpl, ObservabilityConfig, ParallelConfig, PoolerConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig, TaskOption, TokenizerPoolConfig, VllmConfig) from vllm.executor.executor_base import ExecutorBase from vllm.logger import init_logger from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.plugins import load_general_plugins from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3 from vllm.transformers_utils.utils import check_gguf_file from vllm.usage.usage_lib import UsageContext from vllm.utils import FlexibleArgumentParser, StoreBoolean if TYPE_CHECKING: from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup logger = init_logger(__name__) ALLOWED_DETAILED_TRACE_MODULES = ["model", "worker", "all"] DEVICE_OPTIONS = [ "auto", "cuda", "neuron", "cpu", "openvino", "tpu", "xpu", "hpu", ] def nullable_str(val: str): if not val or val == "None": return None return val def nullable_kvs(val: str) -> Optional[Mapping[str, int]]: """Parses a string containing comma separate key [str] to value [int] pairs into a dictionary. Args: val: String value to be parsed. Returns: Dictionary with parsed values. """ if len(val) == 0: return None out_dict: Dict[str, int] = {} for item in val.split(","): kv_parts = [part.lower().strip() for part in item.split("=")] if len(kv_parts) != 2: raise argparse.ArgumentTypeError( "Each item should be in the form KEY=VALUE") key, value = kv_parts try: parsed_value = int(value) except ValueError as exc: msg = f"Failed to parse value of item {key}={value}" raise argparse.ArgumentTypeError(msg) from exc if key in out_dict and out_dict[key] != parsed_value: raise argparse.ArgumentTypeError( f"Conflicting values specified for key: {key}") out_dict[key] = parsed_value return out_dict @dataclass class EngineArgs: """Arguments for vLLM engine.""" model: str = 'facebook/opt-125m' served_model_name: Optional[Union[str, List[str]]] = None tokenizer: Optional[str] = None hf_config_path: Optional[str] = None task: TaskOption = "auto" skip_tokenizer_init: bool = False tokenizer_mode: str = 'auto' trust_remote_code: bool = False allowed_local_media_path: str = "" download_dir: Optional[str] = None load_format: str = 'auto' config_format: ConfigFormat = ConfigFormat.AUTO dtype: str = 'auto' kv_cache_dtype: str = 'auto' seed: Optional[int] = None max_model_len: Optional[int] = None # Note: Specifying a custom executor backend by passing a class # is intended for expert use only. The API may change without # notice. distributed_executor_backend: Optional[Union[str, Type[ExecutorBase]]] = None # number of P/D disaggregation (or other disaggregation) workers pipeline_parallel_size: int = 1 tensor_parallel_size: int = 1 enable_expert_parallel: bool = False max_parallel_loading_workers: Optional[int] = None block_size: Optional[int] = None enable_prefix_caching: Optional[bool] = None disable_sliding_window: bool = False use_v2_block_manager: bool = True swap_space: float = 4 # GiB cpu_offload_gb: float = 0 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None max_num_partial_prefills: Optional[int] = 1 max_long_partial_prefills: Optional[int] = 1 long_prefill_token_threshold: Optional[int] = 0 max_num_seqs: Optional[int] = None max_logprobs: int = 20 # Default value for OpenAI Chat Completions API disable_log_stats: bool = False revision: Optional[str] = None code_revision: Optional[str] = None rope_scaling: Optional[Dict[str, Any]] = None rope_theta: Optional[float] = None hf_overrides: Optional[HfOverrides] = None tokenizer_revision: Optional[str] = None quantization: Optional[str] = None enforce_eager: Optional[bool] = None max_seq_len_to_capture: int = 8192 disable_custom_all_reduce: bool = False tokenizer_pool_size: int = 0 # Note: Specifying a tokenizer pool by passing a class # is intended for expert use only. The API may change without # notice. tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray" tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None limit_mm_per_prompt: Optional[Mapping[str, int]] = None mm_processor_kwargs: Optional[Dict[str, Any]] = None disable_mm_preprocessor_cache: bool = False enable_lora: bool = False enable_lora_bias: bool = False max_loras: int = 1 max_lora_rank: int = 16 enable_prompt_adapter: bool = False max_prompt_adapters: int = 1 max_prompt_adapter_token: int = 0 fully_sharded_loras: bool = False lora_extra_vocab_size: int = 256 long_lora_scaling_factors: Optional[Tuple[float]] = None lora_dtype: Optional[Union[str, torch.dtype]] = 'auto' max_cpu_loras: Optional[int] = None device: str = 'auto' num_scheduler_steps: int = 1 multi_step_stream_outputs: bool = True 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 ignore_patterns: Optional[Union[str, List[str]]] = None preemption_mode: Optional[str] = None scheduler_delay_factor: float = 0.0 enable_chunked_prefill: Optional[bool] = None guided_decoding_backend: str = 'xgrammar' logits_processor_pattern: Optional[str] = None # Speculative decoding configuration. speculative_model: Optional[str] = None speculative_model_quantization: Optional[str] = None speculative_draft_tensor_parallel_size: Optional[int] = None num_speculative_tokens: Optional[int] = None speculative_disable_mqa_scorer: Optional[bool] = False speculative_max_model_len: Optional[int] = None speculative_disable_by_batch_size: Optional[int] = None ngram_prompt_lookup_max: Optional[int] = None ngram_prompt_lookup_min: Optional[int] = None spec_decoding_acceptance_method: str = 'rejection_sampler' typical_acceptance_sampler_posterior_threshold: Optional[float] = None typical_acceptance_sampler_posterior_alpha: Optional[float] = None qlora_adapter_name_or_path: Optional[str] = None disable_logprobs_during_spec_decoding: Optional[bool] = None show_hidden_metrics_for_version: Optional[str] = None otlp_traces_endpoint: Optional[str] = None collect_detailed_traces: Optional[str] = None disable_async_output_proc: bool = False scheduling_policy: Literal["fcfs", "priority"] = "fcfs" scheduler_cls: Union[str, Type[object]] = "vllm.core.scheduler.Scheduler" override_neuron_config: Optional[Dict[str, Any]] = None override_pooler_config: Optional[PoolerConfig] = None compilation_config: Optional[CompilationConfig] = None worker_cls: str = "auto" worker_extension_cls: str = "" kv_transfer_config: Optional[KVTransferConfig] = None generation_config: Optional[str] = "auto" override_generation_config: Optional[Dict[str, Any]] = None enable_sleep_mode: bool = False model_impl: str = "auto" calculate_kv_scales: Optional[bool] = None additional_config: Optional[Dict[str, Any]] = None enable_reasoning: Optional[bool] = None reasoning_parser: Optional[str] = None use_tqdm_on_load: bool = True def __post_init__(self): if not self.tokenizer: self.tokenizer = self.model # Override the default value of enable_prefix_caching if it's not set # by user. if self.enable_prefix_caching is None: self.enable_prefix_caching = bool(envs.VLLM_USE_V1) # Override max_num_seqs if it's not set by user. if self.max_num_seqs is None: self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024 # support `EngineArgs(compilation_config={...})` # without having to manually construct a # CompilationConfig object if isinstance(self.compilation_config, (int, dict)): self.compilation_config = CompilationConfig.from_cli( str(self.compilation_config)) # Setup plugins from vllm.plugins import load_general_plugins load_general_plugins() @staticmethod def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: """Shared CLI arguments for vLLM engine.""" # Model arguments parser.add_argument( '--model', type=str, default=EngineArgs.model, help='Name or path of the huggingface model to use.') parser.add_argument( '--task', default=EngineArgs.task, choices=get_args(TaskOption), help='The task to use the model for. Each vLLM instance only ' 'supports one task, even if the same model can be used for ' 'multiple tasks. When the model only supports one task, ``"auto"`` ' 'can be used to select it; otherwise, you must specify explicitly ' 'which task to use.') parser.add_argument( '--tokenizer', type=nullable_str, default=EngineArgs.tokenizer, help='Name or path of the huggingface tokenizer to use. ' 'If unspecified, model name or path will be used.') parser.add_argument( "--hf-config-path", type=nullable_str, default=EngineArgs.hf_config_path, help='Name or path of the huggingface config to use. ' 'If unspecified, model name or path will be used.') parser.add_argument( '--skip-tokenizer-init', action='store_true', help='Skip initialization of tokenizer and detokenizer. ' 'Expects valid prompt_token_ids and None for prompt from ' 'the input. The generated output will contain token ids.') 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='Revision of the huggingface tokenizer 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', 'mistral', 'custom'], help='The tokenizer mode.\n\n* "auto" will use the ' 'fast tokenizer if available.\n* "slow" will ' 'always use the slow tokenizer. \n* ' '"mistral" will always use the `mistral_common` tokenizer. \n* ' '"custom" will use --tokenizer to select the ' 'preregistered tokenizer.') parser.add_argument('--trust-remote-code', action='store_true', help='Trust remote code from huggingface.') parser.add_argument( '--allowed-local-media-path', type=str, help="Allowing API requests to read local images or videos " "from directories specified by the server file system. " "This is a security risk. " "Should only be enabled in trusted environments.") 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=[f.value for f in LoadFormat], 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. See the Tensorize vLLM Model script in the Examples ' 'section for more information.\n' '* "runai_streamer" will load the Safetensors weights using Run:ai' 'Model Streamer \n' '* "bitsandbytes" will load the weights using bitsandbytes ' 'quantization.\n') parser.add_argument( '--config-format', default=EngineArgs.config_format, choices=[f.value for f in ConfigFormat], help='The format of the model config to load.\n\n' '* "auto" will try to load the config in hf format ' 'if available else it will try to load in mistral format ') 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', 'fp8_e5m2', 'fp8_e4m3'], default=EngineArgs.kv_cache_dtype, help='Data type for kv cache storage. If "auto", will use model ' 'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ' 'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)') 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='xgrammar', help='Which engine will be used for guided decoding' ' (JSON schema / regex etc) by default. Currently support ' 'https://github.com/outlines-dev/outlines, ' 'https://github.com/mlc-ai/xgrammar, and ' 'https://github.com/noamgat/lm-format-enforcer.' ' Can be overridden per request via guided_decoding_backend' ' parameter.\n' 'Backend-specific options can be supplied in a comma-separated ' 'list following a colon after the backend name. Valid backends and ' 'all available options are: [xgrammar:no-fallback, ' 'xgrammar:disable-any-whitespace, ' 'outlines:no-fallback, lm-format-enforcer:no-fallback]') parser.add_argument( '--logits-processor-pattern', type=nullable_str, default=None, help='Optional regex pattern specifying valid logits processor ' 'qualified names that can be passed with the `logits_processors` ' 'extra completion argument. Defaults to None, which allows no ' 'processors.') parser.add_argument( '--model-impl', type=str, default=EngineArgs.model_impl, choices=[f.value for f in ModelImpl], help='Which implementation of the model to use.\n\n' '* "auto" will try to use the vLLM implementation if it exists ' 'and fall back to the Transformers implementation if no vLLM ' 'implementation is available.\n' '* "vllm" will use the vLLM model implementation.\n' '* "transformers" will use the Transformers model ' 'implementation.\n') # Parallel arguments parser.add_argument( '--distributed-executor-backend', choices=['ray', 'mp', 'uni', 'external_launcher'], default=EngineArgs.distributed_executor_backend, help='Backend to use for distributed model ' 'workers, either "ray" or "mp" (multiprocessing). If the product ' 'of pipeline_parallel_size and tensor_parallel_size is less than ' 'or equal to the number of GPUs available, "mp" will be used to ' 'keep processing on a single host. Otherwise, this will default ' 'to "ray" if Ray is installed and fail otherwise. Note that tpu ' 'only supports Ray for distributed inference.') 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( '--enable-expert-parallel', action='store_true', help='Use expert parallelism instead of tensor parallelism ' 'for MoE layers.') 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, 64, 128], help='Token block size for contiguous chunks of ' 'tokens. This is ignored on neuron devices and ' 'set to ``--max-model-len``. On CUDA devices, ' 'only block sizes up to 32 are supported. ' 'On HPU devices, block size defaults to 128.') parser.add_argument( "--enable-prefix-caching", action=argparse.BooleanOptionalAction, default=EngineArgs.enable_prefix_caching, help="Enables automatic prefix caching. " "Use ``--no-enable-prefix-caching`` to disable explicitly.", ) parser.add_argument('--disable-sliding-window', action='store_true', help='Disables sliding window, ' 'capping to sliding window size.') parser.add_argument('--use-v2-block-manager', action='store_true', default=True, help='[DEPRECATED] block manager v1 has been ' 'removed and SelfAttnBlockSpaceManager (i.e. ' 'block manager v2) is now the default. ' 'Setting this flag to True or False' ' has no effect on vLLM behavior.') 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=float, default=EngineArgs.swap_space, help='CPU swap space size (GiB) per GPU.') parser.add_argument( '--cpu-offload-gb', type=float, default=0, help='The space in GiB to offload to CPU, per GPU. ' 'Default is 0, which means no offloading. Intuitively, ' 'this argument can be seen as a virtual way to increase ' 'the GPU memory size. For example, if you have one 24 GB ' 'GPU and set this to 10, virtually you can think of it as ' 'a 34 GB GPU. Then you can load a 13B model with BF16 weight, ' 'which requires at least 26GB GPU memory. Note that this ' 'requires fast CPU-GPU interconnect, as part of the model is ' 'loaded from CPU memory to GPU memory on the fly in each ' 'model forward pass.') 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. This is a per-instance ' 'limit, and only applies to the current vLLM instance.' 'It does not matter if you have another vLLM instance running ' 'on the same GPU. For example, if you have two vLLM instances ' 'running on the same GPU, you can set the GPU memory utilization ' 'to 0.5 for each instance.') 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-partial-prefills", type=int, default=EngineArgs.max_num_partial_prefills, help="For chunked prefill, the max number of concurrent \ partial prefills." "Defaults to 1", ) parser.add_argument( "--max-long-partial-prefills", type=int, default=EngineArgs.max_long_partial_prefills, help="For chunked prefill, the maximum number of prompts longer " "than --long-prefill-token-threshold that will be prefilled " "concurrently. Setting this less than --max-num-partial-prefills " "will allow shorter prompts to jump the queue in front of longer " "prompts in some cases, improving latency. Defaults to 1.") parser.add_argument( "--long-prefill-token-threshold", type=float, default=EngineArgs.long_prefill_token_threshold, help="For chunked prefill, a request is considered long if the " "prompt is longer than this number of tokens. Defaults to 4%% of " "the model's context length.", ) 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( '--rope-scaling', default=None, type=json.loads, help='RoPE scaling configuration in JSON format. ' 'For example, ``{"rope_type":"dynamic","factor":2.0}``') parser.add_argument('--rope-theta', default=None, type=float, help='RoPE theta. Use with `rope_scaling`. In ' 'some cases, changing the RoPE theta improves the ' 'performance of the scaled model.') parser.add_argument('--hf-overrides', type=json.loads, default=EngineArgs.hf_overrides, help='Extra arguments for the HuggingFace config. ' 'This should be a JSON string that will be ' 'parsed into a dictionary.') 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-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. ' 'Additionally for encoder-decoder models, if the ' 'sequence length of the encoder input is larger ' 'than this, we fall back to the 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.') # Multimodal related configs parser.add_argument( '--limit-mm-per-prompt', type=nullable_kvs, default=EngineArgs.limit_mm_per_prompt, # The default value is given in # MultiModalRegistry.init_mm_limits_per_prompt help=('For each multimodal plugin, limit how many ' 'input instances to allow for each prompt. ' 'Expects a comma-separated list of items, ' 'e.g.: `image=16,video=2` allows a maximum of 16 ' 'images and 2 videos per prompt. Defaults to 1 for ' 'each modality.')) parser.add_argument( '--mm-processor-kwargs', default=None, type=json.loads, help=('Overrides for the multimodal input mapping/processing, ' 'e.g., image processor. For example: ``{"num_crops": 4}``.')) parser.add_argument( '--disable-mm-preprocessor-cache', action='store_true', help='If true, then disables caching of the multi-modal ' 'preprocessor/mapper. (not recommended)') # LoRA related configs parser.add_argument('--enable-lora', action='store_true', help='If True, enable handling of LoRA adapters.') parser.add_argument('--enable-lora-bias', action='store_true', help='If True, enable bias for 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'], help=('Data type for LoRA. If auto, will default to ' 'base model dtype.')) parser.add_argument( '--long-lora-scaling-factors', type=nullable_str, default=EngineArgs.long_lora_scaling_factors, help=('Specify multiple scaling factors (which can ' 'be different from base model scaling factor ' '- see eg. Long LoRA) to allow for multiple ' 'LoRA adapters trained with those scaling ' 'factors to be used at the same time. If not ' 'specified, only adapters trained with the ' 'base model scaling factor are allowed.')) 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_loras. ' 'Defaults to max_loras.')) 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('--enable-prompt-adapter', action='store_true', help='If True, enable handling of PromptAdapters.') parser.add_argument('--max-prompt-adapters', type=int, default=EngineArgs.max_prompt_adapters, help='Max number of PromptAdapters in a batch.') parser.add_argument('--max-prompt-adapter-token', type=int, default=EngineArgs.max_prompt_adapter_token, help='Max number of PromptAdapters tokens') parser.add_argument("--device", type=str, default=EngineArgs.device, choices=DEVICE_OPTIONS, help='Device type for vLLM execution.') parser.add_argument('--num-scheduler-steps', type=int, default=1, help=('Maximum number of forward steps per ' 'scheduler call.')) parser.add_argument( '--use-tqdm-on-load', dest='use_tqdm_on_load', action=argparse.BooleanOptionalAction, default=EngineArgs.use_tqdm_on_load, help='Whether to enable/disable progress bar ' 'when loading model weights.', ) parser.add_argument( '--multi-step-stream-outputs', action=StoreBoolean, default=EngineArgs.multi_step_stream_outputs, nargs="?", const="True", help='If False, then multi-step will stream outputs at the end ' 'of all steps') 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=StoreBoolean, default=EngineArgs.enable_chunked_prefill, nargs="?", const="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.') # Quantization settings for speculative model. parser.add_argument( '--speculative-model-quantization', type=nullable_str, choices=[*QUANTIZATION_METHODS, None], default=EngineArgs.speculative_model_quantization, help='Method used to quantize the weights of speculative model. ' '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( '--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-disable-mqa-scorer', action='store_true', help= 'If set to True, the MQA scorer will be disabled in speculative ' ' and fall back to batch expansion') parser.add_argument( '--speculative-draft-tensor-parallel-size', '-spec-draft-tp', type=int, default=EngineArgs.speculative_draft_tensor_parallel_size, help='Number of tensor parallel replicas for ' '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( '--speculative-disable-by-batch-size', type=int, default=EngineArgs.speculative_disable_by_batch_size, help='Disable speculative decoding for new incoming requests ' 'if the number of enqueue requests is larger than this value.') 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( '--spec-decoding-acceptance-method', type=str, default=EngineArgs.spec_decoding_acceptance_method, choices=['rejection_sampler', 'typical_acceptance_sampler'], help='Specify the acceptance method to use during draft token ' 'verification in speculative decoding. Two types of acceptance ' 'routines are supported: ' '1) RejectionSampler which does not allow changing the ' 'acceptance rate of draft tokens, ' '2) TypicalAcceptanceSampler which is configurable, allowing for ' 'a higher acceptance rate at the cost of lower quality, ' 'and vice versa.') parser.add_argument( '--typical-acceptance-sampler-posterior-threshold', type=float, default=EngineArgs.typical_acceptance_sampler_posterior_threshold, help='Set the lower bound threshold for the posterior ' 'probability of a token to be accepted. This threshold is ' 'used by the TypicalAcceptanceSampler to make sampling decisions ' 'during speculative decoding. Defaults to 0.09') parser.add_argument( '--typical-acceptance-sampler-posterior-alpha', type=float, default=EngineArgs.typical_acceptance_sampler_posterior_alpha, help='A scaling factor for the entropy-based threshold for token ' 'acceptance in the TypicalAcceptanceSampler. Typically defaults ' 'to sqrt of --typical-acceptance-sampler-posterior-threshold ' 'i.e. 0.3') parser.add_argument( '--disable-logprobs-during-spec-decoding', action=StoreBoolean, default=EngineArgs.disable_logprobs_during_spec_decoding, nargs="?", const="True", help='If set to True, token log probabilities are not returned ' 'during speculative decoding. If set to False, log probabilities ' 'are returned according to the settings in SamplingParams. If ' 'not specified, it defaults to True. Disabling log probabilities ' 'during speculative decoding reduces latency by skipping logprob ' 'calculation in proposal sampling, target sampling, and after ' 'accepted tokens are determined.') 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( '--ignore-patterns', action="append", type=str, default=[], help="The pattern(s) to ignore when loading the model." "Default to `original/**/*` to avoid repeated loading of llama's " "checkpoints.") parser.add_argument( '--preemption-mode', type=str, default=None, help='If \'recompute\', the engine performs preemption by ' 'recomputing; If \'swap\', the engine performs preemption by ' 'block swapping.') 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.") parser.add_argument('--qlora-adapter-name-or-path', type=str, default=None, help='Name or path of the QLoRA adapter.') parser.add_argument('--show-hidden-metrics-for-version', type=str, default=None, help='Enable deprecated Prometheus metrics that ' 'have been hidden since the specified version. ' 'For example, if a previously deprecated metric ' 'has been hidden since the v0.7.0 release, you ' 'use --show-hidden-metrics-for-version=0.7 as a ' 'temporary escape hatch while you migrate to new ' 'metrics. The metric is likely to be removed ' 'completely in an upcoming release.') parser.add_argument( '--otlp-traces-endpoint', type=str, default=None, help='Target URL to which OpenTelemetry traces will be sent.') parser.add_argument( '--collect-detailed-traces', type=str, default=None, help="Valid choices are " + ",".join(ALLOWED_DETAILED_TRACE_MODULES) + ". It makes sense to set this only if ``--otlp-traces-endpoint`` is" " set. If set, it will collect detailed traces for the specified " "modules. This involves use of possibly costly and or blocking " "operations and hence might have a performance impact.") parser.add_argument( '--disable-async-output-proc', action='store_true', default=EngineArgs.disable_async_output_proc, help="Disable async output processing. This may result in " "lower performance.") parser.add_argument( '--scheduling-policy', choices=['fcfs', 'priority'], default="fcfs", help='The scheduling policy to use. "fcfs" (first come first served' ', i.e. requests are handled in order of arrival; default) ' 'or "priority" (requests are handled based on given ' 'priority (lower value means earlier handling) and time of ' 'arrival deciding any ties).') parser.add_argument( '--scheduler-cls', default=EngineArgs.scheduler_cls, help='The scheduler class to use. "vllm.core.scheduler.Scheduler" ' 'is the default scheduler. Can be a class directly or the path to ' 'a class of form "mod.custom_class".') parser.add_argument( '--override-neuron-config', type=json.loads, default=None, help="Override or set neuron device configuration. " "e.g. ``{\"cast_logits_dtype\": \"bloat16\"}``.") parser.add_argument( '--override-pooler-config', type=PoolerConfig.from_json, default=None, help="Override or set the pooling method for pooling models. " "e.g. ``{\"pooling_type\": \"mean\", \"normalize\": false}``.") parser.add_argument('--compilation-config', '-O', type=CompilationConfig.from_cli, default=None, help='torch.compile configuration for the model.' 'When it is a number (0, 1, 2, 3), it will be ' 'interpreted as the optimization level.\n' 'NOTE: level 0 is the default level without ' 'any optimization. level 1 and 2 are for internal ' 'testing only. level 3 is the recommended level ' 'for production.\n' 'To specify the full compilation config, ' 'use a JSON string.\n' 'Following the convention of traditional ' 'compilers, using -O without space is also ' 'supported. -O3 is equivalent to -O 3.') parser.add_argument('--kv-transfer-config', type=KVTransferConfig.from_cli, default=None, help='The configurations for distributed KV cache ' 'transfer. Should be a JSON string.') parser.add_argument( '--worker-cls', type=str, default="auto", help='The worker class to use for distributed execution.') parser.add_argument( '--worker-extension-cls', type=str, default="", help='The worker extension class on top of the worker cls, ' 'it is useful if you just want to add new functions to the worker ' 'class without changing the existing functions.') parser.add_argument( "--generation-config", type=nullable_str, default="auto", help="The folder path to the generation config. " "Defaults to 'auto', the generation config will be loaded from " "model path. If set to 'vllm', no generation config is loaded, " "vLLM defaults will be used. If set to a folder path, the " "generation config will be loaded from the specified folder path. " "If `max_new_tokens` is specified in generation config, then " "it sets a server-wide limit on the number of output tokens " "for all requests.") parser.add_argument( "--override-generation-config", type=json.loads, default=None, help="Overrides or sets generation config in JSON format. " "e.g. ``{\"temperature\": 0.5}``. If used with " "--generation-config=auto, the override parameters will be merged " "with the default config from the model. If generation-config is " "None, only the override parameters are used.") parser.add_argument("--enable-sleep-mode", action="store_true", default=False, help="Enable sleep mode for the engine. " "(only cuda platform is supported)") parser.add_argument( '--calculate-kv-scales', action='store_true', help='This enables dynamic calculation of ' 'k_scale and v_scale when kv-cache-dtype is fp8. ' 'If calculate-kv-scales is false, the scales will ' 'be loaded from the model checkpoint if available. ' 'Otherwise, the scales will default to 1.0.') parser.add_argument( "--additional-config", type=json.loads, default=None, help="Additional config for specified platform in JSON format. " "Different platforms may support different configs. Make sure the " "configs are valid for the platform you are using. The input format" " is like '{\"config_key\":\"config_value\"}'") parser.add_argument( "--enable-reasoning", action="store_true", default=False, help="Whether to enable reasoning_content for the model. " "If enabled, the model will be able to generate reasoning content." ) parser.add_argument( "--reasoning-parser", type=str, choices=["deepseek_r1"], default=None, help= "Select the reasoning parser depending on the model that you're " "using. This is used to parse the reasoning content into OpenAI " "API format. Required for ``--enable-reasoning``.") return parser @classmethod def from_cli_args(cls, args: argparse.Namespace): # 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_model_config(self) -> ModelConfig: # gguf file needs a specific model loader and doesn't use hf_repo if check_gguf_file(self.model): self.quantization = self.load_format = "gguf" # NOTE: This is to allow model loading from S3 in CI if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3 and self.model in MODELS_ON_S3 and self.load_format == LoadFormat.AUTO): # noqa: E501 self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}" self.load_format = LoadFormat.RUNAI_STREAMER return ModelConfig( model=self.model, hf_config_path=self.hf_config_path, task=self.task, # We know this is not None because we set it in __post_init__ tokenizer=cast(str, self.tokenizer), tokenizer_mode=self.tokenizer_mode, trust_remote_code=self.trust_remote_code, allowed_local_media_path=self.allowed_local_media_path, dtype=self.dtype, seed=self.seed, revision=self.revision, code_revision=self.code_revision, rope_scaling=self.rope_scaling, rope_theta=self.rope_theta, hf_overrides=self.hf_overrides, tokenizer_revision=self.tokenizer_revision, max_model_len=self.max_model_len, quantization=self.quantization, enforce_eager=self.enforce_eager, max_seq_len_to_capture=self.max_seq_len_to_capture, max_logprobs=self.max_logprobs, disable_sliding_window=self.disable_sliding_window, skip_tokenizer_init=self.skip_tokenizer_init, served_model_name=self.served_model_name, limit_mm_per_prompt=self.limit_mm_per_prompt, use_async_output_proc=not self.disable_async_output_proc, config_format=self.config_format, mm_processor_kwargs=self.mm_processor_kwargs, disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache, override_neuron_config=self.override_neuron_config, override_pooler_config=self.override_pooler_config, logits_processor_pattern=self.logits_processor_pattern, generation_config=self.generation_config, override_generation_config=self.override_generation_config, enable_sleep_mode=self.enable_sleep_mode, model_impl=self.model_impl, ) def create_load_config(self) -> LoadConfig: # bitsandbytes quantization needs a specific model loader # so we make sure the quant method and the load format are consistent if (self.quantization == "bitsandbytes" or self.qlora_adapter_name_or_path is not None) and \ self.load_format != "bitsandbytes": raise ValueError( "BitsAndBytes quantization and QLoRA adapter only support " f"'bitsandbytes' load format, but got {self.load_format}") if (self.load_format == "bitsandbytes" or self.qlora_adapter_name_or_path is not None) and \ self.quantization != "bitsandbytes": raise ValueError( "BitsAndBytes load format and QLoRA adapter only support " f"'bitsandbytes' quantization, but got {self.quantization}") return LoadConfig( load_format=self.load_format, download_dir=self.download_dir, model_loader_extra_config=self.model_loader_extra_config, ignore_patterns=self.ignore_patterns, use_tqdm_on_load=self.use_tqdm_on_load, ) def create_engine_config(self, usage_context: Optional[UsageContext] = None ) -> VllmConfig: from vllm.platforms import current_platform current_platform.pre_register_and_update() if envs.VLLM_USE_V1: self._override_v1_engine_args(usage_context) device_config = DeviceConfig(device=self.device) model_config = self.create_model_config() if (model_config.is_multimodal_model and not envs.VLLM_USE_V1 and self.enable_prefix_caching): logger.warning("--enable-prefix-caching is currently not " "supported for multimodal models in v0 and " "has been disabled.") self.enable_prefix_caching = False cache_config = CacheConfig( block_size=self.block_size, gpu_memory_utilization=self.gpu_memory_utilization, swap_space=self.swap_space, cache_dtype=self.kv_cache_dtype, is_attention_free=model_config.is_attention_free, num_gpu_blocks_override=self.num_gpu_blocks_override, sliding_window=model_config.get_sliding_window(), enable_prefix_caching=self.enable_prefix_caching, cpu_offload_gb=self.cpu_offload_gb, calculate_kv_scales=self.calculate_kv_scales, ) parallel_config = ParallelConfig( pipeline_parallel_size=self.pipeline_parallel_size, tensor_parallel_size=self.tensor_parallel_size, enable_expert_parallel=self.enable_expert_parallel, max_parallel_loading_workers=self.max_parallel_loading_workers, disable_custom_all_reduce=self.disable_custom_all_reduce, tokenizer_pool_config=TokenizerPoolConfig.create_config( self.tokenizer_pool_size, self.tokenizer_pool_type, self.tokenizer_pool_extra_config, ), ray_workers_use_nsight=self.ray_workers_use_nsight, distributed_executor_backend=self.distributed_executor_backend, worker_cls=self.worker_cls, worker_extension_cls=self.worker_extension_cls, ) max_model_len = model_config.max_model_len use_long_context = max_model_len > 32768 if self.enable_chunked_prefill is None: # If not explicitly set, enable chunked prefill by default for # long context (> 32K) models. This is to avoid OOM errors in the # initial memory profiling phase. # For multimodal models and models with MLA, chunked prefill is # disabled by default in V0, but enabled by design in V1 if model_config.is_multimodal_model or model_config.use_mla: self.enable_chunked_prefill = bool(envs.VLLM_USE_V1) elif use_long_context: is_gpu = device_config.device_type == "cuda" use_sliding_window = (model_config.get_sliding_window() is not None) use_spec_decode = self.speculative_model is not None from vllm.platforms import current_platform if (is_gpu and not use_sliding_window and not use_spec_decode and not self.enable_lora and not self.enable_prompt_adapter and model_config.runner_type != "pooling" and not current_platform.is_rocm()): self.enable_chunked_prefill = True logger.warning( "Chunked prefill is enabled by default for models with " "max_model_len > 32K. Currently, chunked prefill might " "not work with some features or models. If you " "encounter any issues, please disable chunked prefill " "by setting --enable-chunked-prefill=False.") if self.enable_chunked_prefill is None: self.enable_chunked_prefill = False if not self.enable_chunked_prefill and use_long_context: logger.warning( "The model has a long context length (%s). This may cause OOM " "errors during the initial memory profiling phase, or result " "in low performance due to small KV cache space. Consider " "setting --max-model-len to a smaller value.", max_model_len) elif (self.enable_chunked_prefill and model_config.runner_type == "pooling"): msg = "Chunked prefill is not supported for pooling models" raise ValueError(msg) 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, speculative_model_quantization = \ self.speculative_model_quantization, speculative_draft_tensor_parallel_size = \ self.speculative_draft_tensor_parallel_size, num_speculative_tokens=self.num_speculative_tokens, speculative_disable_mqa_scorer=self.speculative_disable_mqa_scorer, speculative_disable_by_batch_size=self. speculative_disable_by_batch_size, speculative_max_model_len=self.speculative_max_model_len, enable_chunked_prefill=self.enable_chunked_prefill, disable_log_stats=self.disable_log_stats, ngram_prompt_lookup_max=self.ngram_prompt_lookup_max, ngram_prompt_lookup_min=self.ngram_prompt_lookup_min, draft_token_acceptance_method=\ self.spec_decoding_acceptance_method, typical_acceptance_sampler_posterior_threshold=self. typical_acceptance_sampler_posterior_threshold, typical_acceptance_sampler_posterior_alpha=self. typical_acceptance_sampler_posterior_alpha, disable_logprobs=self.disable_logprobs_during_spec_decoding, ) # Reminder: Please update docs/source/features/compatibility_matrix.md # If the feature combo become valid if self.num_scheduler_steps > 1: if speculative_config is not None: raise ValueError("Speculative decoding is not supported with " "multi-step (--num-scheduler-steps > 1)") if self.enable_chunked_prefill and self.pipeline_parallel_size > 1: raise ValueError("Multi-Step Chunked-Prefill is not supported " "for pipeline-parallel-size > 1") from vllm.platforms import current_platform if current_platform.is_cpu(): logger.warning("Multi-Step (--num-scheduler-steps > 1) is " "currently not supported for CPUs and has been " "disabled.") self.num_scheduler_steps = 1 # make sure num_lookahead_slots is set the higher value depending on # if we are using speculative decoding or multi-step num_lookahead_slots = max(self.num_lookahead_slots, self.num_scheduler_steps - 1) num_lookahead_slots = num_lookahead_slots \ if speculative_config is None \ else speculative_config.num_lookahead_slots scheduler_config = SchedulerConfig( runner_type=model_config.runner_type, max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, num_lookahead_slots=num_lookahead_slots, delay_factor=self.scheduler_delay_factor, enable_chunked_prefill=self.enable_chunked_prefill, is_multimodal_model=model_config.is_multimodal_model, preemption_mode=self.preemption_mode, num_scheduler_steps=self.num_scheduler_steps, multi_step_stream_outputs=self.multi_step_stream_outputs, send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER and parallel_config.use_ray), policy=self.scheduling_policy, scheduler_cls=self.scheduler_cls, max_num_partial_prefills=self.max_num_partial_prefills, max_long_partial_prefills=self.max_long_partial_prefills, long_prefill_token_threshold=self.long_prefill_token_threshold, ) lora_config = LoRAConfig( bias_enabled=self.enable_lora_bias, 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, long_lora_scaling_factors=self.long_lora_scaling_factors, 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 if self.qlora_adapter_name_or_path is not None and \ self.qlora_adapter_name_or_path != "": if self.model_loader_extra_config is None: self.model_loader_extra_config = {} self.model_loader_extra_config[ "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path load_config = self.create_load_config() prompt_adapter_config = PromptAdapterConfig( max_prompt_adapters=self.max_prompt_adapters, max_prompt_adapter_token=self.max_prompt_adapter_token) \ if self.enable_prompt_adapter else None decoding_config = DecodingConfig( guided_decoding_backend=self.guided_decoding_backend, reasoning_backend=self.reasoning_parser if self.enable_reasoning else None, ) show_hidden_metrics = False if self.show_hidden_metrics_for_version is not None: show_hidden_metrics = version._prev_minor_version_was( self.show_hidden_metrics_for_version) detailed_trace_modules = [] if self.collect_detailed_traces is not None: detailed_trace_modules = self.collect_detailed_traces.split(",") for m in detailed_trace_modules: if m not in ALLOWED_DETAILED_TRACE_MODULES: raise ValueError( f"Invalid module {m} in collect_detailed_traces. " f"Valid modules are {ALLOWED_DETAILED_TRACE_MODULES}") observability_config = ObservabilityConfig( show_hidden_metrics=show_hidden_metrics, otlp_traces_endpoint=self.otlp_traces_endpoint, collect_model_forward_time="model" in detailed_trace_modules or "all" in detailed_trace_modules, collect_model_execute_time="worker" in detailed_trace_modules or "all" in detailed_trace_modules, ) config = VllmConfig( model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, speculative_config=speculative_config, load_config=load_config, decoding_config=decoding_config, observability_config=observability_config, prompt_adapter_config=prompt_adapter_config, compilation_config=self.compilation_config, kv_transfer_config=self.kv_transfer_config, additional_config=self.additional_config, ) if envs.VLLM_USE_V1: self._override_v1_engine_config(config) return config def _override_v1_engine_args(self, usage_context: UsageContext) -> None: """ Override the EngineArgs's args based on the usage context for V1. """ assert envs.VLLM_USE_V1, "V1 is not enabled" # V1 always uses chunked prefills. self.enable_chunked_prefill = True # When no user override, set the default values based on the usage # context. # Use different default values for different hardware. # Try to query the device name on the current platform. If it fails, # it may be because the platform that imports vLLM is not the same # as the platform that vLLM is running on (e.g. the case of scaling # vLLM with Ray) and has no GPUs. In this case we use the default # values for non-H100/H200 GPUs. try: from vllm.platforms import current_platform device_name = current_platform.get_device_name().lower() except Exception: # This is only used to set default_max_num_batched_tokens device_name = "no-device" if "h100" in device_name or "h200" in device_name: # For H100 and H200, we use larger default values. default_max_num_batched_tokens = { UsageContext.LLM_CLASS: 16384, UsageContext.OPENAI_API_SERVER: 8192, } else: # TODO(woosuk): Tune the default values for other hardware. default_max_num_batched_tokens = { UsageContext.LLM_CLASS: 8192, UsageContext.OPENAI_API_SERVER: 2048, } if (self.max_num_batched_tokens is None and usage_context in default_max_num_batched_tokens): self.max_num_batched_tokens = default_max_num_batched_tokens[ usage_context] logger.warning( "Setting max_num_batched_tokens to %d for %s usage context.", self.max_num_batched_tokens, usage_context.value) def _override_v1_engine_config(self, engine_config: VllmConfig) -> None: """ Override the EngineConfig's configs based on the usage context for V1. """ assert envs.VLLM_USE_V1, "V1 is not enabled" @dataclass class AsyncEngineArgs(EngineArgs): """Arguments for asynchronous vLLM engine.""" disable_log_requests: bool = False @staticmethod def add_cli_args(parser: FlexibleArgumentParser, async_args_only: bool = False) -> FlexibleArgumentParser: # Initialize plugin to update the parser, for example, The plugin may # adding a new kind of quantization method to --quantization argument or # a new device to --device argument. load_general_plugins() if not async_args_only: parser = EngineArgs.add_cli_args(parser) parser.add_argument('--disable-log-requests', action='store_true', help='Disable logging requests.') from vllm.platforms import current_platform current_platform.pre_register_and_update(parser) return parser # These functions are used by sphinx to build the documentation def _engine_args_parser(): return EngineArgs.add_cli_args(FlexibleArgumentParser()) def _async_engine_args_parser(): return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(), async_args_only=True)