import argparse import dataclasses import json from dataclasses import dataclass from typing import List, Optional, Tuple, Union from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoadConfig, LoRAConfig, ModelConfig, MultiModalConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig, TokenizerPoolConfig) from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.utils import FlexibleArgumentParser 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 distributed_executor_backend: Optional[str] = None 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 disable_sliding_window: bool = False use_v2_block_manager: bool = False swap_space: int = 4 # GiB cpu_offload_gb: int = 0 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None max_num_seqs: int = 256 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] = None rope_theta: Optional[float] = 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 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: str = '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 preemption_mode: Optional[str] = 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 speculative_draft_tensor_parallel_size: Optional[int] = None num_speculative_tokens: Optional[int] = None 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 otlp_traces_endpoint: Optional[str] = None def __post_init__(self): if self.tokenizer is None: self.tokenizer = self.model @staticmethod def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: """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. ' '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') 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'], 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', 'bitsandbytes' ], 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' '* "bitsandbytes" will load the weights using bitsandbytes ' 'quantization.\n') 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( '--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( '--distributed-executor-backend', choices=['ray', 'mp'], default=EngineArgs.distributed_executor_backend, help='Backend to use for distributed serving. When more than 1 GPU ' 'is used, will be automatically set to "ray" if installed ' 'or "mp" (multiprocessing) otherwise.') parser.add_argument( '--worker-use-ray', action='store_true', help='Deprecated, use --distributed-executor-backend=ray.') 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('--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', 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( '--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.') 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('--rope-scaling', default=None, type=json.loads, help='RoPE scaling configuration in JSON format. ' 'For example, {"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('--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( '--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_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('--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=[ "auto", "cuda", "neuron", "cpu", "openvino", "tpu", "xpu" ], help='Device type for vLLM execution.') 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-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('--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( '--preemption-mode', type=str, default=None, help='If \'recompute\', the engine performs preemption by block ' 'swapping; 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( '--otlp-traces-endpoint', type=str, default=None, help='Target URL to which OpenTelemetry traces will be sent.') 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_engine_config(self, ) -> EngineConfig: # 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}") assert self.cpu_offload_gb >= 0, ( "CPU offload space must be non-negative" f", but got {self.cpu_offload_gb}") multimodal_config = MultiModalConfig() device_config = DeviceConfig(device=self.device) model_config = ModelConfig( model=self.model, tokenizer=self.tokenizer, tokenizer_mode=self.tokenizer_mode, trust_remote_code=self.trust_remote_code, dtype=self.dtype, seed=self.seed, revision=self.revision, code_revision=self.code_revision, rope_scaling=self.rope_scaling, rope_theta=self.rope_theta, tokenizer_revision=self.tokenizer_revision, max_model_len=self.max_model_len, quantization=self.quantization, quantization_param_path=self.quantization_param_path, enforce_eager=self.enforce_eager, max_context_len_to_capture=self.max_context_len_to_capture, 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, multimodal_config=multimodal_config) 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, 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, ) parallel_config = ParallelConfig( pipeline_parallel_size=self.pipeline_parallel_size, tensor_parallel_size=self.tensor_parallel_size, worker_use_ray=self.worker_use_ray, 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) 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_draft_tensor_parallel_size = \ self.speculative_draft_tensor_parallel_size, num_speculative_tokens=self.num_speculative_tokens, 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, 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, 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, ) scheduler_config = SchedulerConfig( max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, use_v2_block_manager=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, embedding_mode=model_config.embedding_mode, preemption_mode=self.preemption_mode, ) 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, 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 = LoadConfig( load_format=self.load_format, download_dir=self.download_dir, model_loader_extra_config=self.model_loader_extra_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) observability_config = ObservabilityConfig( otlp_traces_endpoint=self.otlp_traces_endpoint) if (model_config.get_sliding_window() is not None and scheduler_config.chunked_prefill_enabled and not scheduler_config.use_v2_block_manager): raise ValueError( "Chunked prefill is not supported with sliding window. " "Set --disable-sliding-window to disable sliding window.") 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, multimodal_config=multimodal_config, speculative_config=speculative_config, load_config=load_config, decoding_config=decoding_config, observability_config=observability_config, prompt_adapter_config=prompt_adapter_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: FlexibleArgumentParser, async_args_only: bool = False) -> FlexibleArgumentParser: 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(FlexibleArgumentParser()) def _async_engine_args_parser(): return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(), async_args_only=True)