SangBin Cho
2e9a2227ec
Currently we need to call rotary embedding kernel for each LoRA, which makes it hard to serve multiple long context length LoRA. Add batched rotary embedding kernel and pipe it through. It replaces the rotary embedding layer to the one that is aware of multiple cos-sin-cache per scaling factors. Follow up of https://github.com/vllm-project/vllm/pull/3095/files
691 lines
31 KiB
Python
691 lines
31 KiB
Python
import argparse
|
|
import dataclasses
|
|
from dataclasses import dataclass
|
|
from typing import List, Optional, Tuple, Union
|
|
|
|
from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
|
|
EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
|
|
ParallelConfig, SchedulerConfig, SpeculativeConfig,
|
|
TokenizerPoolConfig, VisionLanguageConfig)
|
|
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
|
|
from vllm.utils import str_to_int_tuple
|
|
|
|
|
|
def nullable_str(val: str):
|
|
if not val or val == "None":
|
|
return None
|
|
return val
|
|
|
|
|
|
@dataclass
|
|
class EngineArgs:
|
|
"""Arguments for vLLM engine."""
|
|
model: str
|
|
served_model_name: Optional[Union[List[str]]] = None
|
|
tokenizer: Optional[str] = None
|
|
skip_tokenizer_init: bool = False
|
|
tokenizer_mode: str = 'auto'
|
|
trust_remote_code: bool = False
|
|
download_dir: Optional[str] = None
|
|
load_format: str = 'auto'
|
|
dtype: str = 'auto'
|
|
kv_cache_dtype: str = 'auto'
|
|
quantization_param_path: Optional[str] = None
|
|
seed: int = 0
|
|
max_model_len: Optional[int] = None
|
|
worker_use_ray: bool = False
|
|
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
|
|
use_v2_block_manager: bool = False
|
|
swap_space: int = 4 # GiB
|
|
gpu_memory_utilization: float = 0.90
|
|
max_num_batched_tokens: Optional[int] = None
|
|
max_num_seqs: int = 256
|
|
max_logprobs: int = 5 # OpenAI default value
|
|
disable_log_stats: bool = False
|
|
revision: Optional[str] = None
|
|
code_revision: Optional[str] = None
|
|
tokenizer_revision: Optional[str] = None
|
|
quantization: Optional[str] = None
|
|
enforce_eager: bool = False
|
|
max_context_len_to_capture: Optional[int] = None
|
|
max_seq_len_to_capture: int = 8192
|
|
disable_custom_all_reduce: bool = False
|
|
tokenizer_pool_size: int = 0
|
|
tokenizer_pool_type: str = "ray"
|
|
tokenizer_pool_extra_config: Optional[dict] = None
|
|
enable_lora: bool = False
|
|
max_loras: int = 1
|
|
max_lora_rank: int = 16
|
|
fully_sharded_loras: bool = False
|
|
lora_extra_vocab_size: int = 256
|
|
long_lora_scaling_factors: Optional[Tuple[float]] = None
|
|
lora_dtype = 'auto'
|
|
max_cpu_loras: Optional[int] = None
|
|
device: str = 'auto'
|
|
ray_workers_use_nsight: bool = False
|
|
num_gpu_blocks_override: Optional[int] = None
|
|
num_lookahead_slots: int = 0
|
|
model_loader_extra_config: Optional[dict] = None
|
|
|
|
# Related to Vision-language models such as llava
|
|
image_input_type: Optional[str] = None
|
|
image_token_id: Optional[int] = None
|
|
image_input_shape: Optional[str] = None
|
|
image_feature_size: Optional[int] = None
|
|
scheduler_delay_factor: float = 0.0
|
|
enable_chunked_prefill: bool = False
|
|
|
|
guided_decoding_backend: str = 'outlines'
|
|
# Speculative decoding configuration.
|
|
speculative_model: Optional[str] = None
|
|
num_speculative_tokens: Optional[int] = None
|
|
speculative_max_model_len: Optional[int] = None
|
|
speculative_disable_by_batch_size: Optional[int] = None
|
|
ngram_prompt_lookup_max: Optional[int] = None
|
|
ngram_prompt_lookup_min: Optional[int] = None
|
|
|
|
def __post_init__(self):
|
|
if self.tokenizer is None:
|
|
self.tokenizer = self.model
|
|
|
|
@staticmethod
|
|
def add_cli_args(
|
|
parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
|
|
"""Shared CLI arguments for vLLM engine."""
|
|
|
|
# Model arguments
|
|
parser.add_argument(
|
|
'--model',
|
|
type=str,
|
|
default='facebook/opt-125m',
|
|
help='Name or path of the huggingface model to use.')
|
|
parser.add_argument(
|
|
'--tokenizer',
|
|
type=nullable_str,
|
|
default=EngineArgs.tokenizer,
|
|
help='Name or path of the huggingface tokenizer to use.')
|
|
parser.add_argument(
|
|
'--skip-tokenizer-init',
|
|
action='store_true',
|
|
help='Skip initialization of tokenizer and detokenizer')
|
|
parser.add_argument(
|
|
'--revision',
|
|
type=nullable_str,
|
|
default=None,
|
|
help='The specific model version to use. It can be a branch '
|
|
'name, a tag name, or a commit id. If unspecified, will use '
|
|
'the default version.')
|
|
parser.add_argument(
|
|
'--code-revision',
|
|
type=nullable_str,
|
|
default=None,
|
|
help='The specific revision to use for the model code on '
|
|
'Hugging Face Hub. It can be a branch name, a tag name, or a '
|
|
'commit id. If unspecified, will use the default version.')
|
|
parser.add_argument(
|
|
'--tokenizer-revision',
|
|
type=nullable_str,
|
|
default=None,
|
|
help='The specific tokenizer version to use. It can be a branch '
|
|
'name, a tag name, or a commit id. If unspecified, will use '
|
|
'the default version.')
|
|
parser.add_argument(
|
|
'--tokenizer-mode',
|
|
type=str,
|
|
default=EngineArgs.tokenizer_mode,
|
|
choices=['auto', 'slow'],
|
|
help='The tokenizer mode.\n\n* "auto" will use the '
|
|
'fast tokenizer if available.\n* "slow" will '
|
|
'always use the slow tokenizer.')
|
|
parser.add_argument('--trust-remote-code',
|
|
action='store_true',
|
|
help='Trust remote code from huggingface.')
|
|
parser.add_argument('--download-dir',
|
|
type=nullable_str,
|
|
default=EngineArgs.download_dir,
|
|
help='Directory to download and load the weights, '
|
|
'default to the default cache dir of '
|
|
'huggingface.')
|
|
parser.add_argument(
|
|
'--load-format',
|
|
type=str,
|
|
default=EngineArgs.load_format,
|
|
choices=[
|
|
'auto', 'pt', 'safetensors', 'npcache', 'dummy', 'tensorizer'
|
|
],
|
|
help='The format of the model weights to load.\n\n'
|
|
'* "auto" will try to load the weights in the safetensors format '
|
|
'and fall back to the pytorch bin format if safetensors format '
|
|
'is not available.\n'
|
|
'* "pt" will load the weights in the pytorch bin format.\n'
|
|
'* "safetensors" will load the weights in the safetensors format.\n'
|
|
'* "npcache" will load the weights in pytorch format and store '
|
|
'a numpy cache to speed up the loading.\n'
|
|
'* "dummy" will initialize the weights with random values, '
|
|
'which is mainly for profiling.\n'
|
|
'* "tensorizer" will load the weights using tensorizer from '
|
|
'CoreWeave. See the Tensorize vLLM Model script in the Examples'
|
|
'section for more information.\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'],
|
|
default=EngineArgs.kv_cache_dtype,
|
|
help='Data type for kv cache storage. If "auto", will use model '
|
|
'data type. FP8_E5M2 (without scaling) is only supported on cuda '
|
|
'version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
|
|
'supported for common inference criteria.')
|
|
parser.add_argument(
|
|
'--quantization-param-path',
|
|
type=nullable_str,
|
|
default=None,
|
|
help='Path to the JSON file containing the KV cache '
|
|
'scaling factors. This should generally be supplied, when '
|
|
'KV cache dtype is FP8. Otherwise, KV cache scaling factors '
|
|
'default to 1.0, which may cause accuracy issues. '
|
|
'FP8_E5M2 (without scaling) is only supported on cuda version'
|
|
'greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
|
|
'supported for common inference criteria.')
|
|
parser.add_argument('--max-model-len',
|
|
type=int,
|
|
default=EngineArgs.max_model_len,
|
|
help='Model context length. If unspecified, will '
|
|
'be automatically derived from the model config.')
|
|
parser.add_argument(
|
|
'--guided-decoding-backend',
|
|
type=str,
|
|
default='outlines',
|
|
choices=['outlines', 'lm-format-enforcer'],
|
|
help='Which engine will be used for guided decoding'
|
|
' (JSON schema / regex etc) by default. Currently support '
|
|
'https://github.com/outlines-dev/outlines and '
|
|
'https://github.com/noamgat/lm-format-enforcer.'
|
|
' Can be overridden per request via guided_decoding_backend'
|
|
' parameter.')
|
|
# Parallel arguments
|
|
parser.add_argument(
|
|
'--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('--use-v2-block-manager',
|
|
action='store_true',
|
|
help='Use BlockSpaceMangerV2.')
|
|
parser.add_argument(
|
|
'--num-lookahead-slots',
|
|
type=int,
|
|
default=EngineArgs.num_lookahead_slots,
|
|
help='Experimental scheduling config necessary for '
|
|
'speculative decoding. This will be replaced by '
|
|
'speculative config in the future; it is present '
|
|
'to enable correctness tests until then.')
|
|
|
|
parser.add_argument('--seed',
|
|
type=int,
|
|
default=EngineArgs.seed,
|
|
help='Random seed for operations.')
|
|
parser.add_argument('--swap-space',
|
|
type=int,
|
|
default=EngineArgs.swap_space,
|
|
help='CPU swap space size (GiB) per GPU.')
|
|
parser.add_argument(
|
|
'--gpu-memory-utilization',
|
|
type=float,
|
|
default=EngineArgs.gpu_memory_utilization,
|
|
help='The fraction of GPU memory to be used for the model '
|
|
'executor, which can range from 0 to 1. For example, a value of '
|
|
'0.5 would imply 50%% GPU memory utilization. If unspecified, '
|
|
'will use the default value of 0.9.')
|
|
parser.add_argument(
|
|
'--num-gpu-blocks-override',
|
|
type=int,
|
|
default=None,
|
|
help='If specified, ignore GPU profiling result and use this number'
|
|
'of GPU blocks. Used for testing preemption.')
|
|
parser.add_argument('--max-num-batched-tokens',
|
|
type=int,
|
|
default=EngineArgs.max_num_batched_tokens,
|
|
help='Maximum number of batched tokens per '
|
|
'iteration.')
|
|
parser.add_argument('--max-num-seqs',
|
|
type=int,
|
|
default=EngineArgs.max_num_seqs,
|
|
help='Maximum number of sequences per iteration.')
|
|
parser.add_argument(
|
|
'--max-logprobs',
|
|
type=int,
|
|
default=EngineArgs.max_logprobs,
|
|
help=('Max number of log probs to return logprobs is specified in'
|
|
' SamplingParams.'))
|
|
parser.add_argument('--disable-log-stats',
|
|
action='store_true',
|
|
help='Disable logging statistics.')
|
|
# Quantization settings.
|
|
parser.add_argument('--quantization',
|
|
'-q',
|
|
type=nullable_str,
|
|
choices=[*QUANTIZATION_METHODS, None],
|
|
default=EngineArgs.quantization,
|
|
help='Method used to quantize the weights. If '
|
|
'None, we first check the `quantization_config` '
|
|
'attribute in the model config file. If that is '
|
|
'None, we assume the model weights are not '
|
|
'quantized and use `dtype` to determine the data '
|
|
'type of the weights.')
|
|
parser.add_argument('--enforce-eager',
|
|
action='store_true',
|
|
help='Always use eager-mode PyTorch. If False, '
|
|
'will use eager mode and CUDA graph in hybrid '
|
|
'for maximal performance and flexibility.')
|
|
parser.add_argument('--max-context-len-to-capture',
|
|
type=int,
|
|
default=EngineArgs.max_context_len_to_capture,
|
|
help='Maximum context length covered by CUDA '
|
|
'graphs. When a sequence has context length '
|
|
'larger than this, we fall back to eager mode. '
|
|
'(DEPRECATED. Use --max-seq_len-to-capture instead'
|
|
')')
|
|
parser.add_argument('--max-seq_len-to-capture',
|
|
type=int,
|
|
default=EngineArgs.max_seq_len_to_capture,
|
|
help='Maximum sequence length covered by CUDA '
|
|
'graphs. When a sequence has context length '
|
|
'larger than this, we fall back to eager mode.')
|
|
parser.add_argument('--disable-custom-all-reduce',
|
|
action='store_true',
|
|
default=EngineArgs.disable_custom_all_reduce,
|
|
help='See ParallelConfig.')
|
|
parser.add_argument('--tokenizer-pool-size',
|
|
type=int,
|
|
default=EngineArgs.tokenizer_pool_size,
|
|
help='Size of tokenizer pool to use for '
|
|
'asynchronous tokenization. If 0, will '
|
|
'use synchronous tokenization.')
|
|
parser.add_argument('--tokenizer-pool-type',
|
|
type=str,
|
|
default=EngineArgs.tokenizer_pool_type,
|
|
help='Type of tokenizer pool to use for '
|
|
'asynchronous tokenization. Ignored '
|
|
'if tokenizer_pool_size is 0.')
|
|
parser.add_argument('--tokenizer-pool-extra-config',
|
|
type=nullable_str,
|
|
default=EngineArgs.tokenizer_pool_extra_config,
|
|
help='Extra config for tokenizer pool. '
|
|
'This should be a JSON string that will be '
|
|
'parsed into a dictionary. Ignored if '
|
|
'tokenizer_pool_size is 0.')
|
|
# LoRA related configs
|
|
parser.add_argument('--enable-lora',
|
|
action='store_true',
|
|
help='If True, enable handling of LoRA adapters.')
|
|
parser.add_argument('--max-loras',
|
|
type=int,
|
|
default=EngineArgs.max_loras,
|
|
help='Max number of LoRAs in a single batch.')
|
|
parser.add_argument('--max-lora-rank',
|
|
type=int,
|
|
default=EngineArgs.max_lora_rank,
|
|
help='Max LoRA rank.')
|
|
parser.add_argument(
|
|
'--lora-extra-vocab-size',
|
|
type=int,
|
|
default=EngineArgs.lora_extra_vocab_size,
|
|
help=('Maximum size of extra vocabulary that can be '
|
|
'present in a LoRA adapter (added to the base '
|
|
'model vocabulary).'))
|
|
parser.add_argument(
|
|
'--lora-dtype',
|
|
type=str,
|
|
default=EngineArgs.lora_dtype,
|
|
choices=['auto', 'float16', 'bfloat16', 'float32'],
|
|
help=('Data type for LoRA. If auto, will default to '
|
|
'base model dtype.'))
|
|
parser.add_argument(
|
|
'--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("--device",
|
|
type=str,
|
|
default=EngineArgs.device,
|
|
choices=["auto", "cuda", "neuron", "cpu"],
|
|
help='Device type for vLLM execution.')
|
|
# Related to Vision-language models such as llava
|
|
parser.add_argument(
|
|
'--image-input-type',
|
|
type=nullable_str,
|
|
default=None,
|
|
choices=[
|
|
t.name.lower() for t in VisionLanguageConfig.ImageInputType
|
|
],
|
|
help=('The image input type passed into vLLM. '
|
|
'Should be one of "pixel_values" or "image_features".'))
|
|
parser.add_argument('--image-token-id',
|
|
type=int,
|
|
default=None,
|
|
help=('Input id for image token.'))
|
|
parser.add_argument(
|
|
'--image-input-shape',
|
|
type=nullable_str,
|
|
default=None,
|
|
help=('The biggest image input shape (worst for memory footprint) '
|
|
'given an input type. Only used for vLLM\'s profile_run.'))
|
|
parser.add_argument(
|
|
'--image-feature-size',
|
|
type=int,
|
|
default=None,
|
|
help=('The image feature size along the context dimension.'))
|
|
parser.add_argument(
|
|
'--scheduler-delay-factor',
|
|
type=float,
|
|
default=EngineArgs.scheduler_delay_factor,
|
|
help='Apply a delay (of delay factor multiplied by previous'
|
|
'prompt latency) before scheduling next prompt.')
|
|
parser.add_argument(
|
|
'--enable-chunked-prefill',
|
|
action='store_true',
|
|
help='If set, the prefill requests can be chunked based on the '
|
|
'max_num_batched_tokens.')
|
|
|
|
parser.add_argument(
|
|
'--speculative-model',
|
|
type=nullable_str,
|
|
default=EngineArgs.speculative_model,
|
|
help=
|
|
'The name of the draft model to be used in speculative decoding.')
|
|
|
|
parser.add_argument(
|
|
'--num-speculative-tokens',
|
|
type=int,
|
|
default=EngineArgs.num_speculative_tokens,
|
|
help='The number of speculative tokens to sample from '
|
|
'the draft model in speculative decoding.')
|
|
|
|
parser.add_argument(
|
|
'--speculative-max-model-len',
|
|
type=int,
|
|
default=EngineArgs.speculative_max_model_len,
|
|
help='The maximum sequence length supported by the '
|
|
'draft model. Sequences over this length will skip '
|
|
'speculation.')
|
|
|
|
parser.add_argument(
|
|
'--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('--model-loader-extra-config',
|
|
type=nullable_str,
|
|
default=EngineArgs.model_loader_extra_config,
|
|
help='Extra config for model loader. '
|
|
'This will be passed to the model loader '
|
|
'corresponding to the chosen load_format. '
|
|
'This should be a JSON string that will be '
|
|
'parsed into a dictionary.')
|
|
|
|
parser.add_argument(
|
|
"--served-model-name",
|
|
nargs="+",
|
|
type=str,
|
|
default=None,
|
|
help="The model name(s) used in the API. If multiple "
|
|
"names are provided, the server will respond to any "
|
|
"of the provided names. The model name in the model "
|
|
"field of a response will be the first name in this "
|
|
"list. If not specified, the model name will be the "
|
|
"same as the `--model` argument. Noted that this name(s)"
|
|
"will also be used in `model_name` tag content of "
|
|
"prometheus metrics, if multiple names provided, metrics"
|
|
"tag will take the first one.")
|
|
|
|
return parser
|
|
|
|
@classmethod
|
|
def from_cli_args(cls, args: argparse.Namespace):
|
|
# Get the list of attributes of this dataclass.
|
|
attrs = [attr.name for attr in dataclasses.fields(cls)]
|
|
# Set the attributes from the parsed arguments.
|
|
engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
|
|
return engine_args
|
|
|
|
def create_engine_config(self, ) -> EngineConfig:
|
|
device_config = DeviceConfig(self.device)
|
|
model_config = ModelConfig(
|
|
self.model, self.tokenizer, self.tokenizer_mode,
|
|
self.trust_remote_code, self.dtype, self.seed, self.revision,
|
|
self.code_revision, self.tokenizer_revision, self.max_model_len,
|
|
self.quantization, self.quantization_param_path,
|
|
self.enforce_eager, self.max_context_len_to_capture,
|
|
self.max_seq_len_to_capture, self.max_logprobs,
|
|
self.skip_tokenizer_init, self.served_model_name)
|
|
cache_config = CacheConfig(self.block_size,
|
|
self.gpu_memory_utilization,
|
|
self.swap_space, self.kv_cache_dtype,
|
|
self.num_gpu_blocks_override,
|
|
model_config.get_sliding_window(),
|
|
self.enable_prefix_caching)
|
|
parallel_config = ParallelConfig(
|
|
self.pipeline_parallel_size,
|
|
self.tensor_parallel_size,
|
|
self.worker_use_ray,
|
|
self.max_parallel_loading_workers,
|
|
self.disable_custom_all_reduce,
|
|
TokenizerPoolConfig.create_config(
|
|
self.tokenizer_pool_size,
|
|
self.tokenizer_pool_type,
|
|
self.tokenizer_pool_extra_config,
|
|
),
|
|
self.ray_workers_use_nsight,
|
|
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,
|
|
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,
|
|
)
|
|
|
|
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,
|
|
embedding_mode=model_config.embedding_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
|
|
|
|
load_config = LoadConfig(
|
|
load_format=self.load_format,
|
|
download_dir=self.download_dir,
|
|
model_loader_extra_config=self.model_loader_extra_config,
|
|
)
|
|
|
|
if self.image_input_type:
|
|
if (not self.image_token_id or not self.image_input_shape
|
|
or not self.image_feature_size):
|
|
raise ValueError(
|
|
'Specify `image_token_id`, `image_input_shape` and '
|
|
'`image_feature_size` together with `image_input_type`.')
|
|
vision_language_config = VisionLanguageConfig(
|
|
image_input_type=VisionLanguageConfig.
|
|
get_image_input_enum_type(self.image_input_type),
|
|
image_token_id=self.image_token_id,
|
|
image_input_shape=str_to_int_tuple(self.image_input_shape),
|
|
image_feature_size=self.image_feature_size,
|
|
)
|
|
else:
|
|
vision_language_config = None
|
|
|
|
decoding_config = DecodingConfig(
|
|
guided_decoding_backend=self.guided_decoding_backend)
|
|
|
|
if (model_config.get_sliding_window() is not None
|
|
and scheduler_config.chunked_prefill_enabled):
|
|
raise ValueError(
|
|
"Chunked prefill is not supported with 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,
|
|
vision_language_config=vision_language_config,
|
|
speculative_config=speculative_config,
|
|
load_config=load_config,
|
|
decoding_config=decoding_config)
|
|
|
|
|
|
@dataclass
|
|
class AsyncEngineArgs(EngineArgs):
|
|
"""Arguments for asynchronous vLLM engine."""
|
|
engine_use_ray: bool = False
|
|
disable_log_requests: bool = False
|
|
max_log_len: Optional[int] = None
|
|
|
|
@staticmethod
|
|
def add_cli_args(parser: argparse.ArgumentParser,
|
|
async_args_only: bool = False) -> argparse.ArgumentParser:
|
|
if not async_args_only:
|
|
parser = EngineArgs.add_cli_args(parser)
|
|
parser.add_argument('--engine-use-ray',
|
|
action='store_true',
|
|
help='Use Ray to start the LLM engine in a '
|
|
'separate process as the server process.')
|
|
parser.add_argument('--disable-log-requests',
|
|
action='store_true',
|
|
help='Disable logging requests.')
|
|
parser.add_argument('--max-log-len',
|
|
type=int,
|
|
default=None,
|
|
help='Max number of prompt characters or prompt '
|
|
'ID numbers being printed in log.'
|
|
'\n\nDefault: Unlimited')
|
|
return parser
|
|
|
|
|
|
# These functions are used by sphinx to build the documentation
|
|
def _engine_args_parser():
|
|
return EngineArgs.add_cli_args(argparse.ArgumentParser())
|
|
|
|
|
|
def _async_engine_args_parser():
|
|
return AsyncEngineArgs.add_cli_args(argparse.ArgumentParser(),
|
|
async_args_only=True)
|