import copy import enum import hashlib import json import warnings from dataclasses import dataclass, field, replace from pathlib import Path from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Counter, Dict, Final, List, Literal, Mapping, Optional, Set, Tuple, Type, Union) import torch from pydantic import BaseModel, Field, PrivateAttr from transformers import PretrainedConfig import vllm.envs as envs from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass from vllm.logger import init_logger from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS, get_quantization_config) from vllm.model_executor.models import ModelRegistry from vllm.platforms import current_platform from vllm.tracing import is_otel_available, otel_import_error_traceback from vllm.transformers_utils.config import ( ConfigFormat, get_config, get_hf_image_processor_config, get_hf_text_config, get_pooling_config, get_sentence_transformer_tokenizer_config, is_encoder_decoder, uses_mrope) from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory, identity, print_warning_once, resolve_obj_by_qualname) if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup from vllm.executor.executor_base import ExecutorBase from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.model_loader.loader import BaseModelLoader from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import ( BaseTokenizerGroup) else: QuantizationConfig = None logger = init_logger(__name__) _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120 TaskOption = Literal["auto", "generate", "embedding"] # "draft" is only used internally for speculative decoding _Task = Literal["generate", "embedding", "draft"] HfOverrides = Union[Dict[str, Any], Callable[[PretrainedConfig], PretrainedConfig]] class ModelConfig: """Configuration for the model. Args: model: Name or path of the huggingface model to use. It is also used as the content for `model_name` tag in metrics output when `served_model_name` is not specified. task: 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. tokenizer: Name or path of the huggingface tokenizer to use. tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if available, "slow" will always use the slow tokenizer, and "mistral" will always use the tokenizer from `mistral_common`. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. allowed_local_media_path: 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. dtype: Data type for model weights and activations. The "auto" option will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models. seed: Random seed for reproducibility. revision: 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. code_revision: 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. tokenizer_revision: 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. max_model_len: Maximum length of a sequence (including prompt and output). If None, will be derived from the model. quantization: Quantization method that was used to quantize the model weights. If None, we assume the model weights are not quantized. quantization_param_path: Path to JSON file containing scaling factors. Used to load KV cache scaling factors into the model when KV cache type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also be used to load activation and weight scaling factors when the model dtype is FP8_E4M3 on ROCm. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. If None, the user did not specify, so default to False. max_seq_len_to_capture: Maximum sequence len 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. disable_sliding_window: Whether to disable sliding window. If True, we will disable the sliding window functionality of the model. If the model does not support sliding window, this argument is ignored. skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. served_model_name: The model name used in metrics tag `model_name`, matches the model name exposed via the APIs. If multiple model names provided, the first name will be used. If not specified, the model name will be the same as `model`. limit_mm_per_prompt: Maximum number of data items per modality per prompt. Only applicable for multimodal models. config_format: The config format which shall be loaded. Defaults to 'auto' which defaults to 'hf'. hf_overrides: If a dictionary, contains arguments to be forwarded to the HuggingFace config. If a callable, it is called to update the HuggingFace config. mm_processor_kwargs: Arguments to be forwarded to the model's processor for multi-modal data, e.g., image processor. override_neuron_config: Initialize non default neuron config or override default neuron config that are specific to Neuron devices, this argument will be used to configure the neuron config that can not be gathered from the vllm arguments. override_pooling_config: Initialize non default pooling config or override default pooling config for the embedding model. """ def __init__( self, model: str, task: Union[TaskOption, _Task], tokenizer: str, tokenizer_mode: str, trust_remote_code: bool, dtype: Union[str, torch.dtype], seed: int, allowed_local_media_path: str = "", revision: Optional[str] = None, code_revision: Optional[str] = None, rope_scaling: Optional[Dict[str, Any]] = None, rope_theta: Optional[float] = None, tokenizer_revision: Optional[str] = None, max_model_len: Optional[int] = None, spec_target_max_model_len: Optional[int] = None, quantization: Optional[str] = None, quantization_param_path: Optional[str] = None, enforce_eager: Optional[bool] = None, max_seq_len_to_capture: Optional[int] = None, max_logprobs: int = 20, disable_sliding_window: bool = False, skip_tokenizer_init: bool = False, served_model_name: Optional[Union[str, List[str]]] = None, limit_mm_per_prompt: Optional[Mapping[str, int]] = None, use_async_output_proc: bool = True, config_format: ConfigFormat = ConfigFormat.AUTO, hf_overrides: Optional[HfOverrides] = None, mm_processor_kwargs: Optional[Dict[str, Any]] = None, override_neuron_config: Optional[Dict[str, Any]] = None, override_pooler_config: Optional["PoolerConfig"] = None) -> None: self.model = model self.tokenizer = tokenizer self.tokenizer_mode = tokenizer_mode self.trust_remote_code = trust_remote_code self.allowed_local_media_path = allowed_local_media_path self.seed = seed self.revision = revision self.code_revision = code_revision if hf_overrides is None: hf_overrides = {} if callable(hf_overrides): hf_overrides_kw = {} hf_overrides_fn = hf_overrides else: hf_overrides_kw = hf_overrides hf_overrides_fn = identity if rope_scaling is not None: hf_override: Dict[str, Any] = {"rope_scaling": rope_scaling} hf_overrides_kw.update(hf_override) msg = ("`--rope-scaling` will be removed in a future release. " f"'Please instead use `--hf-overrides '{hf_override!r}'`") warnings.warn(DeprecationWarning(msg), stacklevel=2) if rope_theta is not None: hf_override = {"rope_theta": rope_theta} hf_overrides_kw.update(hf_override) msg = ("`--rope-theta` will be removed in a future release. " f"'Please instead use `--hf-overrides '{hf_override!r}'`") warnings.warn(DeprecationWarning(msg), stacklevel=2) # The tokenizer version is consistent with the model version by default. if tokenizer_revision is None: self.tokenizer_revision = revision else: self.tokenizer_revision = tokenizer_revision self.quantization = quantization self.quantization_param_path = quantization_param_path self.enforce_eager = enforce_eager self.max_seq_len_to_capture = max_seq_len_to_capture self.max_logprobs = max_logprobs self.disable_sliding_window = disable_sliding_window self.skip_tokenizer_init = skip_tokenizer_init hf_config = get_config(self.model, trust_remote_code, revision, code_revision, config_format, **hf_overrides_kw) hf_config = hf_overrides_fn(hf_config) self.hf_config = hf_config self.hf_text_config = get_hf_text_config(self.hf_config) self.encoder_config = self._get_encoder_config() self.hf_image_processor_config = get_hf_image_processor_config( self.model, revision) self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype) self.use_async_output_proc = use_async_output_proc self.mm_processor_kwargs = mm_processor_kwargs # Set enforce_eager to False if the value is unset. if self.enforce_eager is None: self.enforce_eager = False sliding_window = getattr(self.hf_text_config, "sliding_window", None) has_interleaved_attention = (sliding_window is not None) and ( isinstance(sliding_window, list) or (self.hf_text_config.model_type in ["gemma2"])) if (not self.disable_sliding_window and has_interleaved_attention): if envs.VLLM_ATTENTION_BACKEND == "XFORMERS": sliding_window_len_min = get_min_sliding_window( self.hf_text_config.sliding_window) print_warning_once( f"{self.hf_text_config.model_type} has interleaved " "attention, which is currently not supported by the " "XFORMERS backend. Disabling sliding window and capping " "the max length to the sliding window size " f"({sliding_window_len_min}).") self.disable_sliding_window = True else: # for a model with interleaved attention, # the scheduler and the model treat it as full attention # (i.e., not dropping any tokens outside the window). # only the attention layer itself is aware of the sliding # window, and use the window size to compute the attention. self.hf_text_config.interleaved_sliding_window = sliding_window delattr(self.hf_text_config, "sliding_window") sliding_window = None self.max_model_len = _get_and_verify_max_len( hf_config=self.hf_text_config, max_model_len=max_model_len, disable_sliding_window=self.disable_sliding_window, sliding_window_len=self.get_hf_config_sliding_window(), spec_target_max_model_len=spec_target_max_model_len, encoder_config=self.encoder_config) self.served_model_name = get_served_model_name(model, served_model_name) self.multimodal_config = self._init_multimodal_config( limit_mm_per_prompt) if not self.skip_tokenizer_init: self._verify_tokenizer_mode() self.is_attention_free = self._init_attention_free() self.has_inner_state = self._init_has_inner_state() if current_platform.is_neuron(): self.override_neuron_config = override_neuron_config else: self.override_neuron_config = None supported_tasks, task = self._resolve_task(task, self.hf_config) self.supported_tasks = supported_tasks self.task: Final = task self.pooler_config = self._init_pooler_config(override_pooler_config) self._verify_quantization() self._verify_cuda_graph() self._verify_bnb_config() def _init_multimodal_config( self, limit_mm_per_prompt: Optional[Mapping[str, int]] ) -> Optional["MultiModalConfig"]: architectures = getattr(self.hf_config, "architectures", []) if ModelRegistry.is_multimodal_model(architectures): return MultiModalConfig(limit_per_prompt=limit_mm_per_prompt or {}) if limit_mm_per_prompt: raise ValueError("`limit_mm_per_prompt` is only supported for " "multimodal models.") return None def _get_encoder_config(self): return get_sentence_transformer_tokenizer_config( self.model, self.revision) def _init_pooler_config( self, override_pooler_config: Optional["PoolerConfig"], ) -> Optional["PoolerConfig"]: if self.task == "embedding": user_config = override_pooler_config or PoolerConfig() base_config = get_pooling_config(self.model, self.revision) if base_config is not None: # Only set values that are not overridden by the user for k, v in base_config.items(): if getattr(user_config, k) is None: setattr(user_config, k, v) return user_config return None def _init_attention_free(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.is_attention_free_model(architectures) def _init_has_inner_state(self) -> bool: architectures = getattr(self.hf_config, "architectures", []) return ModelRegistry.model_has_inner_state(architectures) def _verify_tokenizer_mode(self) -> None: tokenizer_mode = self.tokenizer_mode.lower() if tokenizer_mode not in ["auto", "slow", "mistral"]: raise ValueError( f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be " "either 'auto', 'slow' or 'mistral'.") self.tokenizer_mode = tokenizer_mode def _resolve_task( self, task_option: Union[TaskOption, _Task], hf_config: PretrainedConfig, ) -> Tuple[Set[_Task], _Task]: if task_option == "draft": return {"draft"}, "draft" architectures = getattr(hf_config, "architectures", []) task_support: Dict[_Task, bool] = { # NOTE: Listed from highest to lowest priority, # in case the model supports multiple of them "generate": ModelRegistry.is_text_generation_model(architectures), "embedding": ModelRegistry.is_embedding_model(architectures), } supported_tasks_lst: List[_Task] = [ task for task, is_supported in task_support.items() if is_supported ] supported_tasks = set(supported_tasks_lst) if task_option == "auto": selected_task = next(iter(supported_tasks_lst)) if len(supported_tasks) > 1: logger.info( "This model supports multiple tasks: %s. " "Defaulting to '%s'.", supported_tasks, selected_task) else: if task_option not in supported_tasks: msg = ( f"This model does not support the '{task_option}' task. " f"Supported tasks: {supported_tasks}") raise ValueError(msg) selected_task = task_option return supported_tasks, selected_task def _parse_quant_hf_config(self): quant_cfg = getattr(self.hf_config, "quantization_config", None) if quant_cfg is None: # compressed-tensors uses a "compression_config" key quant_cfg = getattr(self.hf_config, "compression_config", None) return quant_cfg def _verify_quantization(self) -> None: supported_quantization = QUANTIZATION_METHODS rocm_supported_quantization = [ "awq", "gptq", "fp8", "compressed_tensors", "compressed-tensors", "fbgemm_fp8" ] optimized_quantization_methods = [ "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin", "awq_marlin", "fbgemm_fp8", "compressed_tensors", "compressed-tensors", "experts_int8" ] tpu_supported_quantization = ["tpu_int8"] neuron_supported_quantization = ["neuron_quant"] if self.quantization is not None: self.quantization = self.quantization.lower() # Parse quantization method from the HF model config, if available. quant_cfg = self._parse_quant_hf_config() if quant_cfg is not None: quant_method = quant_cfg.get("quant_method", "").lower() # Detect which checkpoint is it for name in QUANTIZATION_METHODS: method = get_quantization_config(name) quantization_override = method.override_quantization_method( quant_cfg, self.quantization) if quantization_override: quant_method = quantization_override self.quantization = quantization_override break # Verify quantization configurations. if self.quantization is None: self.quantization = quant_method elif self.quantization != quant_method: raise ValueError( "Quantization method specified in the model config " f"({quant_method}) does not match the quantization " f"method specified in the `quantization` argument " f"({self.quantization}).") if self.quantization is not None: if self.quantization not in supported_quantization: raise ValueError( f"Unknown quantization method: {self.quantization}. Must " f"be one of {supported_quantization}.") if current_platform.is_rocm( ) and self.quantization not in rocm_supported_quantization: raise ValueError( f"{self.quantization} quantization is currently not " f"supported in ROCm.") if current_platform.is_tpu( ) and self.quantization not in tpu_supported_quantization: raise ValueError( f"{self.quantization} quantization is currently not " f"supported in TPU Backend.") if self.quantization not in optimized_quantization_methods: logger.warning( "%s quantization is not fully " "optimized yet. The speed can be slower than " "non-quantized models.", self.quantization) if (self.quantization == "awq" and current_platform.is_rocm() and not envs.VLLM_USE_TRITON_AWQ): logger.warning( "Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ" " is not set, enabling VLLM_USE_TRITON_AWQ.") envs.VLLM_USE_TRITON_AWQ = True if current_platform.is_neuron( ) and self.quantization not in neuron_supported_quantization: raise ValueError( f"{self.quantization} quantization is currently not " f"supported in Neuron Backend.") def _verify_cuda_graph(self) -> None: if self.max_seq_len_to_capture is None: self.max_seq_len_to_capture = self.max_model_len self.max_seq_len_to_capture = min(self.max_seq_len_to_capture, self.max_model_len) def _verify_bnb_config(self) -> None: """ The current version of bitsandbytes (0.44.0) with 8-bit models does not yet support CUDA graph. """ is_bitsandbytes = self.quantization == "bitsandbytes" has_quantization_config = (getattr(self.hf_config, "quantization_config", None) is not None) is_8bit = (self.hf_config.quantization_config.get( "load_in_8bit", False) if has_quantization_config else False) if all([ is_bitsandbytes, has_quantization_config, is_8bit, not self.enforce_eager, ]): logger.warning( "CUDA graph is not supported on BitAndBytes 8bit yet, " "fallback to the eager mode.") self.enforce_eager = True def verify_async_output_proc(self, parallel_config, speculative_config, device_config) -> None: if not self.use_async_output_proc: # Nothing to check return if parallel_config.pipeline_parallel_size > 1: logger.warning("Async output processing can not be enabled " "with pipeline parallel") self.use_async_output_proc = False return # Reminder: Please update docs/source/serving/compatibility_matrix.rst # If the feature combo become valid if device_config.device_type not in ("cuda", "tpu", "xpu", "hpu"): logger.warning( "Async output processing is only supported for CUDA, TPU, XPU " "and HPU." "Disabling it for other platforms.") self.use_async_output_proc = False return if envs.VLLM_USE_RAY_SPMD_WORKER: logger.warning( "Async output processing can not be enabled with ray spmd") self.use_async_output_proc = False return # Reminder: Please update docs/source/serving/compatibility_matrix.rst # If the feature combo become valid if device_config.device_type == "cuda" and self.enforce_eager: logger.warning( "To see benefits of async output processing, enable CUDA " "graph. Since, enforce-eager is enabled, async output " "processor cannot be used") self.use_async_output_proc = not self.enforce_eager return # Async postprocessor is not necessary with embedding mode # since there is no token generation if self.task == "embedding": self.use_async_output_proc = False # Reminder: Please update docs/source/serving/compatibility_matrix.rst # If the feature combo become valid if speculative_config: logger.warning("Async output processing is not supported with" " speculative decoding currently.") self.use_async_output_proc = False def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: total_num_attention_heads = getattr(self.hf_text_config, "num_attention_heads", 0) tensor_parallel_size = parallel_config.tensor_parallel_size if total_num_attention_heads % tensor_parallel_size != 0: raise ValueError( f"Total number of attention heads ({total_num_attention_heads})" " must be divisible by tensor parallel size " f"({tensor_parallel_size}).") pipeline_parallel_size = parallel_config.pipeline_parallel_size if pipeline_parallel_size > 1: architectures = getattr(self.hf_config, "architectures", []) if not ModelRegistry.is_pp_supported_model(architectures): raise NotImplementedError( "Pipeline parallelism is not supported for this model. " "Supported models implement the `SupportsPP` interface.") if self.use_async_output_proc: logger.warning("Async output processor is not supported with " "pipeline parallelism currently. Disabling it.") self.use_async_output_proc = False def get_hf_config_sliding_window( self) -> Union[Optional[int], List[Optional[int]]]: """Get the sliding window size, or None if disabled.""" # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in # addition to sliding window size. We check if that field is present # and if it's False, return None. if (hasattr(self.hf_text_config, "use_sliding_window") and not self.hf_text_config.use_sliding_window): return None return getattr(self.hf_text_config, "sliding_window", None) def get_sliding_window(self) -> Optional[Union[int, List[Optional[int]]]]: """Get the sliding window size, or None if disabled. """ # If user disables sliding window, return None. if self.disable_sliding_window: return None # Otherwise get the value from the hf config. return self.get_hf_config_sliding_window() def get_vocab_size(self) -> int: return self.hf_text_config.vocab_size def get_hidden_size(self) -> int: return self.hf_text_config.hidden_size def get_head_size(self) -> int: # TODO remove hard code if hasattr(self.hf_text_config, "model_type" ) and self.hf_text_config.model_type == 'deepseek_v2': # FlashAttention supports only head_size 32, 64, 128, 256, # we need to pad head_size 192 to 256 return 256 if self.is_attention_free: return 0 if hasattr(self.hf_text_config, "head_dim"): return self.hf_text_config.head_dim # FIXME(woosuk): This may not be true for all models. return (self.hf_text_config.hidden_size // self.hf_text_config.num_attention_heads) def get_total_num_kv_heads(self) -> int: """Returns the total number of KV heads.""" # For GPTBigCode & Falcon: # NOTE: for falcon, when new_decoder_architecture is True, the # multi_query flag is ignored and we use n_head_kv for the number of # KV heads. falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"] new_decoder_arch_falcon = ( self.hf_config.model_type in falcon_model_types and getattr(self.hf_config, "new_decoder_architecture", False)) if not new_decoder_arch_falcon and getattr(self.hf_text_config, "multi_query", False): # Multi-query attention, only one KV head. # Currently, tensor parallelism is not supported in this case. return 1 # For DBRX and MPT if self.hf_config.model_type == "mpt": if "kv_n_heads" in self.hf_config.attn_config: return self.hf_config.attn_config["kv_n_heads"] return self.hf_config.num_attention_heads if self.hf_config.model_type == "dbrx": return getattr(self.hf_config.attn_config, "kv_n_heads", self.hf_config.num_attention_heads) if self.is_attention_free: return 0 attributes = [ # For Falcon: "n_head_kv", "num_kv_heads", # For LLaMA-2: "num_key_value_heads", # For ChatGLM: "multi_query_group_num", ] for attr in attributes: num_kv_heads = getattr(self.hf_text_config, attr, None) if num_kv_heads is not None: return num_kv_heads # For non-grouped-query attention models, the number of KV heads is # equal to the number of attention heads. return self.hf_text_config.num_attention_heads def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int: """Returns the number of KV heads per GPU.""" total_num_kv_heads = self.get_total_num_kv_heads() # If tensor parallelism is used, we divide the number of KV heads by # the tensor parallel size. We will replicate the KV heads in the # case where the number of KV heads is smaller than the tensor # parallel size so each GPU has at least one KV head. return max(1, total_num_kv_heads // parallel_config.tensor_parallel_size) def get_num_attention_heads(self, parallel_config: "ParallelConfig") -> int: num_heads = getattr(self.hf_text_config, "num_attention_heads", 0) return num_heads // parallel_config.tensor_parallel_size def get_num_layers(self, parallel_config: "ParallelConfig") -> int: from vllm.distributed.utils import get_pp_indices total_num_hidden_layers = getattr(self.hf_text_config, "num_hidden_layers", 0) pp_rank = parallel_config.rank // parallel_config.tensor_parallel_size pp_size = parallel_config.pipeline_parallel_size start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size) return end - start def get_num_attention_layers(self, parallel_config: "ParallelConfig") -> int: if self.is_attention_free: return 0 num_layers = self.get_num_layers(parallel_config) # Transformers supports layers_block_type @property layers = getattr(self.hf_config, "layers_block_type", ["attention"] * num_layers) return len([t for t in layers if t == "attention"]) def get_multimodal_config(self) -> "MultiModalConfig": """ Get the multimodal configuration of the model. Raises: ValueError: If the model is not multimodal. """ if self.multimodal_config is None: raise ValueError("The model is not multimodal.") return self.multimodal_config @property def is_encoder_decoder(self) -> bool: """Extract the HF encoder/decoder model flag.""" return is_encoder_decoder(self.hf_config) @property def uses_mrope(self) -> bool: return uses_mrope(self.hf_config) @property def is_multimodal_model(self) -> bool: return self.multimodal_config is not None class CacheConfig: """Configuration for the KV cache. Args: block_size: Size of a cache block in number of tokens. gpu_memory_utilization: Fraction of GPU memory to use for the vLLM execution. swap_space: Size of the CPU swap space per GPU (in GiB). cache_dtype: Data type for kv cache storage. num_gpu_blocks_override: Number of GPU blocks to use. This overrides the profiled num_gpu_blocks if specified. Does nothing if None. """ def __init__( self, block_size: int, gpu_memory_utilization: float, swap_space: float, cache_dtype: str, is_attention_free: bool = False, num_gpu_blocks_override: Optional[int] = None, sliding_window: Optional[int] = None, enable_prefix_caching: bool = False, cpu_offload_gb: float = 0, ) -> None: self.block_size = block_size self.gpu_memory_utilization = gpu_memory_utilization self.swap_space_bytes = swap_space * GiB_bytes self.num_gpu_blocks_override = num_gpu_blocks_override self.cache_dtype = cache_dtype self.is_attention_free = is_attention_free self.sliding_window = sliding_window self.enable_prefix_caching = enable_prefix_caching self.cpu_offload_gb = cpu_offload_gb self._verify_args() self._verify_cache_dtype() self._verify_prefix_caching() # Will be set after profiling. self.num_gpu_blocks: Optional[int] = None self.num_cpu_blocks: Optional[int] = None def metrics_info(self): # convert cache_config to dict(key: str, value: str) for prometheus # metrics info return {key: str(value) for key, value in self.__dict__.items()} def _verify_args(self) -> None: if self.gpu_memory_utilization > 1.0: raise ValueError( "GPU memory utilization must be less than 1.0. Got " f"{self.gpu_memory_utilization}.") def _verify_cache_dtype(self) -> None: if self.cache_dtype == "auto": pass elif self.cache_dtype in ("fp8", "fp8_e4m3", "fp8_e5m2"): logger.info( "Using fp8 data type to store kv cache. It reduces the GPU " "memory footprint and boosts the performance. " "Meanwhile, it may cause accuracy drop without a proper " "scaling factor") else: raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}") def _verify_prefix_caching(self) -> None: if not self.enable_prefix_caching: return if self.sliding_window is not None: raise NotImplementedError( "Prefix caching is not supported with sliding window. " "Run with --disable-sliding-window to use prefix caching.") def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: total_cpu_memory = get_cpu_memory() # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel # group are in the same node. However, the GPUs may span multiple nodes. num_gpus_per_node = parallel_config.tensor_parallel_size cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node msg = (f"{cpu_memory_usage / GiB_bytes:.2f} GiB out of the " f"{total_cpu_memory / GiB_bytes:.2f} GiB total CPU memory " "is allocated for the swap space.") if cpu_memory_usage > 0.7 * total_cpu_memory: raise ValueError("Too large swap space. " + msg) elif cpu_memory_usage > 0.4 * total_cpu_memory: logger.warning("Possibly too large swap space. %s", msg) @dataclass class TokenizerPoolConfig: """Configuration for the tokenizer pool. Args: pool_size: Number of tokenizer workers in the pool. pool_type: Type of the pool. extra_config: Additional config for the pool. The way the config will be used depends on the pool type. """ pool_size: int pool_type: Union[str, Type["BaseTokenizerGroup"]] extra_config: dict def __post_init__(self): if self.pool_type not in ("ray", ) and not isinstance( self.pool_type, type): raise ValueError(f"Unknown pool type: {self.pool_type}") if not isinstance(self.extra_config, dict): raise ValueError("extra_config must be a dictionary.") @classmethod def create_config( cls, tokenizer_pool_size: int, tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]], tokenizer_pool_extra_config: Optional[Union[str, dict]] ) -> Optional["TokenizerPoolConfig"]: """Create a TokenizerPoolConfig from the given parameters. If tokenizer_pool_size is 0, return None. Args: tokenizer_pool_size: Number of tokenizer workers in the pool. tokenizer_pool_type: Type of the pool. tokenizer_pool_extra_config: Additional config for the pool. The way the config will be used depends on the pool type. This can be a JSON string (will be parsed). """ if tokenizer_pool_size: if isinstance(tokenizer_pool_extra_config, str): tokenizer_pool_extra_config_parsed = json.loads( tokenizer_pool_extra_config) else: tokenizer_pool_extra_config_parsed = ( tokenizer_pool_extra_config or {}) tokenizer_pool_config = cls(tokenizer_pool_size, tokenizer_pool_type, tokenizer_pool_extra_config_parsed) else: tokenizer_pool_config = None return tokenizer_pool_config class LoadFormat(str, enum.Enum): AUTO = "auto" PT = "pt" SAFETENSORS = "safetensors" NPCACHE = "npcache" DUMMY = "dummy" TENSORIZER = "tensorizer" SHARDED_STATE = "sharded_state" GGUF = "gguf" BITSANDBYTES = "bitsandbytes" MISTRAL = "mistral" @dataclass class LoadConfig: """ download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. "tensorizer" will use CoreWeave's tensorizer library for fast weight loading. "bitsandbytes" will load nf4 type weights. ignore_patterns: The list of patterns to ignore when loading the model. Default to "original/**/*" to avoid repeated loading of llama's checkpoints. """ load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO download_dir: Optional[str] = None model_loader_extra_config: Optional[Union[str, dict]] = field( default_factory=dict) ignore_patterns: Optional[Union[List[str], str]] = None def __post_init__(self): model_loader_extra_config = self.model_loader_extra_config or {} if isinstance(model_loader_extra_config, str): self.model_loader_extra_config = json.loads( model_loader_extra_config) self._verify_load_format() if self.ignore_patterns is not None and len(self.ignore_patterns) > 0: logger.info( "Ignoring the following patterns when downloading weights: %s", self.ignore_patterns) else: self.ignore_patterns = ["original/**/*"] def _verify_load_format(self) -> None: if not isinstance(self.load_format, str): return load_format = self.load_format.lower() self.load_format = LoadFormat(load_format) rocm_not_supported_load_format: List[str] = [] if current_platform.is_rocm( ) and load_format in rocm_not_supported_load_format: rocm_supported_load_format = [ f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format) ] raise ValueError( f"load format '{load_format}' is not supported in ROCm. " f"Supported load formats are " f"{rocm_supported_load_format}") @dataclass class ParallelConfig: """Configuration for the distributed execution.""" pipeline_parallel_size: int = 1 # Number of pipeline parallel groups. tensor_parallel_size: int = 1 # Number of tensor parallel groups. # Deprecated, use distributed_executor_backend instead. worker_use_ray: Optional[bool] = None # Maximum number of multiple batches # when load model sequentially. To avoid RAM OOM when using tensor # parallel and large models. max_parallel_loading_workers: Optional[int] = None # Disable the custom all-reduce kernel and fall back to NCCL. disable_custom_all_reduce: bool = False # Config for the tokenizer pool. If None, will use synchronous tokenization. tokenizer_pool_config: Optional[TokenizerPoolConfig] = None # Whether to profile Ray workers with nsight, see https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler. ray_workers_use_nsight: bool = False # ray distributed model workers placement group. placement_group: Optional["PlacementGroup"] = None # 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 # and hpu only support Ray for distributed inference. distributed_executor_backend: Optional[Union[str, Type["ExecutorBase"]]] = None # the full name of the worker class to use. If "auto", the worker class # will be determined based on the platform. worker_cls: str = "auto" world_size: int = field(init=False) rank: int = 0 def __post_init__(self) -> None: self.world_size = self.pipeline_parallel_size * \ self.tensor_parallel_size if self.worker_use_ray: if self.distributed_executor_backend is None: self.distributed_executor_backend = "ray" elif not self.use_ray: raise ValueError(f"worker-use-ray can't be used with " f"distributed executor backend " f"'{self.distributed_executor_backend}'.") if current_platform.is_tpu() and self.world_size > 1: if self.distributed_executor_backend is None: self.distributed_executor_backend = "ray" if self.distributed_executor_backend != "ray": raise ValueError( "TPU backend only supports Ray for distributed inference.") if current_platform.is_hpu() and self.world_size > 1: if self.distributed_executor_backend is None: self.distributed_executor_backend = "ray" if self.distributed_executor_backend != "ray": raise ValueError( "HPU backend only supports Ray for distributed inference.") if self.distributed_executor_backend is None and self.world_size > 1: # We use multiprocessing by default if world_size fits on the # current node and we aren't in a ray placement group. from vllm.executor import ray_utils backend = "mp" ray_found = ray_utils.ray_is_available() if (current_platform.is_cuda() and cuda_device_count_stateless() < self.world_size): if not ray_found: raise ValueError("Unable to load Ray which is " "required for multi-node inference, " "please install Ray with `pip install " "ray`.") from ray_utils.ray_import_err backend = "ray" elif ray_found: if self.placement_group: backend = "ray" else: from ray import is_initialized as ray_is_initialized if ray_is_initialized(): from ray.util import get_current_placement_group if get_current_placement_group(): backend = "ray" self.distributed_executor_backend = backend logger.info("Defaulting to use %s for distributed inference", backend) self._verify_args() @property def use_ray(self) -> bool: return self.distributed_executor_backend == "ray" or ( isinstance(self.distributed_executor_backend, type) and self.distributed_executor_backend.uses_ray) def _verify_args(self) -> None: # Lazy import to avoid circular import from vllm.executor.executor_base import ExecutorBase if self.distributed_executor_backend not in ( "ray", "mp", None) and not (isinstance( self.distributed_executor_backend, type) and issubclass( self.distributed_executor_backend, ExecutorBase)): raise ValueError( "Unrecognized distributed executor backend " f"{self.distributed_executor_backend}. Supported " "values are 'ray', 'mp' or custom ExecutorBase subclass.") if self.use_ray: from vllm.executor import ray_utils ray_utils.assert_ray_available() if current_platform.is_rocm(): self.disable_custom_all_reduce = True logger.info( "Disabled the custom all-reduce kernel because it is not " "supported on AMD GPUs.") if self.ray_workers_use_nsight and not self.use_ray: raise ValueError("Unable to use nsight profiling unless workers " "run with Ray.") @dataclass class SchedulerConfig: """Scheduler configuration.""" task: str = "generate" # The task to use the model for. # Maximum number of tokens to be processed in a single iteration. max_num_batched_tokens: int = field(default=None) # type: ignore # Maximum number of sequences to be processed in a single iteration. max_num_seqs: int = 128 # Maximum length of a sequence (including prompt and generated text). max_model_len: int = 8192 # The number of slots to allocate per sequence per # step, beyond the known token ids. This is used in speculative # decoding to store KV activations of tokens which may or may not be # accepted. num_lookahead_slots: int = 0 # Apply a delay (of delay factor multiplied by previous # prompt latency) before scheduling next prompt. delay_factor: float = 0.0 # If True, prefill requests can be chunked based # on the remaining max_num_batched_tokens. enable_chunked_prefill: bool = False is_multimodal_model: bool = False # Whether to perform preemption by swapping or # recomputation. If not specified, we determine the mode as follows: # We use recomputation by default since it incurs lower overhead than # swapping. However, when the sequence group has multiple sequences # (e.g., beam search), recomputation is not currently supported. In # such a case, we use swapping instead. preemption_mode: Optional[str] = None num_scheduler_steps: int = 1 multi_step_stream_outputs: bool = False # Private API. If used, scheduler sends delta data to # workers instead of an entire data. It should be enabled only # when SPMD worker architecture is enabled. I.e., # VLLM_USE_RAY_SPMD_WORKER=1 send_delta_data: bool = False # The scheduling policy to use. "fcfs" (default) or "priority". policy: str = "fcfs" chunked_prefill_enabled: bool = field(init=False) def __post_init__(self) -> None: if self.max_num_batched_tokens is None: if self.enable_chunked_prefill: if self.num_scheduler_steps > 1: # Multi-step Chunked-Prefill doesn't allow prompt-chunking # for now. Have max_num_batched_tokens set to max_model_len # so we don't reject sequences on account of a short # max_num_batched_tokens. self.max_num_batched_tokens = max(self.max_model_len, 2048) else: # This value is chosen to have a balance between ITL # and TTFT. Note it is not optimized for throughput. self.max_num_batched_tokens = 2048 else: # If max_model_len is too short, use 2048 as the default value # for higher throughput. self.max_num_batched_tokens = max(self.max_model_len, 2048) if self.task == "embedding": # For embedding, choose specific value for higher throughput self.max_num_batched_tokens = max( self.max_num_batched_tokens, _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS, ) if self.is_multimodal_model: # The value needs to be at least the number of multimodal tokens self.max_num_batched_tokens = max( self.max_num_batched_tokens, _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS, ) if self.enable_chunked_prefill: logger.info( "Chunked prefill is enabled with max_num_batched_tokens=%d.", self.max_num_batched_tokens) self.chunked_prefill_enabled = self.enable_chunked_prefill self._verify_args() def _verify_args(self) -> None: if (self.max_num_batched_tokens < self.max_model_len and not self.chunked_prefill_enabled): raise ValueError( f"max_num_batched_tokens ({self.max_num_batched_tokens}) is " f"smaller than max_model_len ({self.max_model_len}). " "This effectively limits the maximum sequence length to " "max_num_batched_tokens and makes vLLM reject longer " "sequences. Please increase max_num_batched_tokens or " "decrease max_model_len.") if self.max_num_batched_tokens < self.max_num_seqs: raise ValueError( f"max_num_batched_tokens ({self.max_num_batched_tokens}) must " "be greater than or equal to max_num_seqs " f"({self.max_num_seqs}).") if self.num_lookahead_slots < 0: raise ValueError( "num_lookahead_slots " f"({self.num_lookahead_slots}) must be greater than or " "equal to 0.") if self.num_scheduler_steps < 1: raise ValueError( "num_scheduler_steps " f"({self.num_scheduler_steps}) must be greater than or " "equal to 1.") @property def is_multi_step(self) -> bool: return self.num_scheduler_steps > 1 class DeviceConfig: device: Optional[torch.device] device_type: str def __init__(self, device: str = "auto") -> None: if device == "auto": # Automated device type detection self.device_type = current_platform.device_type if not self.device_type: raise RuntimeError("Failed to infer device type") else: # Device type is assigned explicitly self.device_type = device # Some device types require processing inputs on CPU if self.device_type in ["neuron", "openvino"]: self.device = torch.device("cpu") elif self.device_type in ["tpu"]: self.device = None else: # Set device with device type self.device = torch.device(self.device_type) class SpeculativeConfig: """Configuration for speculative decoding. The configuration is currently specialized to draft-model speculative decoding with top-1 proposals. """ @staticmethod def maybe_create_spec_config( target_model_config: ModelConfig, target_parallel_config: ParallelConfig, target_dtype: str, speculative_model: Optional[str], speculative_model_quantization: Optional[str], speculative_draft_tensor_parallel_size: Optional[int], num_speculative_tokens: Optional[int], speculative_disable_mqa_scorer: Optional[bool], speculative_max_model_len: Optional[int], enable_chunked_prefill: bool, disable_log_stats: bool, speculative_disable_by_batch_size: Optional[int], ngram_prompt_lookup_max: Optional[int], ngram_prompt_lookup_min: Optional[int], draft_token_acceptance_method: str, typical_acceptance_sampler_posterior_threshold: Optional[float], typical_acceptance_sampler_posterior_alpha: Optional[float], disable_logprobs: Optional[bool], ) -> Optional["SpeculativeConfig"]: """Create a SpeculativeConfig if possible, else return None. This function attempts to create a SpeculativeConfig object based on the provided parameters. If the necessary conditions are met, it returns an instance of SpeculativeConfig. Otherwise, it returns None. Args: target_model_config (ModelConfig): The configuration of the target model. target_parallel_config (ParallelConfig): The parallel configuration for the target model. target_dtype (str): The data type used for the target model. speculative_model (Optional[str]): The name of the speculative model, if provided. speculative_model_quantization (Optional[str]): Quantization method that was used to quantize the speculative model weights. If None, we assume the model weights are not quantized. speculative_draft_tensor_parallel_size (Optional[int]): The degree of the tensor parallelism for the draft model. num_speculative_tokens (Optional[int]): The number of speculative tokens, if provided. Will default to the number in the draft model config if present, otherwise is required. speculative_disable_mqa_scorer (Optional[bool]): Disable the MQA scorer for the speculative model and fall back to batch expansion for scoring. speculative_max_model_len (Optional[int]): The maximum model len of the speculative model. Used when testing the ability to skip speculation for some sequences. enable_chunked_prefill (bool): Whether vLLM is configured to use chunked prefill or not. Used for raising an error since its not yet compatible with spec decode. speculative_disable_by_batch_size (Optional[int]): Disable speculative decoding for new incoming requests when the number of enqueue requests is larger than this value, if provided. ngram_prompt_lookup_max (Optional[int]): Max size of ngram token window, if provided. ngram_prompt_lookup_min (Optional[int]): Min size of ngram token window, if provided. draft_token_acceptance_method (str): The method to use for accepting draft tokens. This can take two possible values 'rejection_sampler' and 'typical_acceptance_sampler' for RejectionSampler and TypicalAcceptanceSampler respectively. typical_acceptance_sampler_posterior_threshold (Optional[float]): A threshold value that sets a lower bound on the posterior probability of a token in the target model for it to be accepted. This threshold is used only when we use the TypicalAcceptanceSampler for token acceptance. typical_acceptance_sampler_posterior_alpha (Optional[float]): A scaling factor for the entropy-based threshold in the TypicalAcceptanceSampler. disable_logprobs (Optional[bool]): If set to True, token log probabilities are not returned during speculative decoding. If set to False, token log probabilities are returned according to the log probability settings in SamplingParams. If not specified, it defaults to True. Returns: Optional["SpeculativeConfig"]: An instance of SpeculativeConfig if the necessary conditions are met, else None. """ if speculative_model is None: if num_speculative_tokens is not None: raise ValueError("num_speculative_tokens was provided without " "speculative_model.") return None if (speculative_disable_by_batch_size is not None and speculative_disable_by_batch_size < 2): raise ValueError("Expect the batch size threshold of disabling " "speculative decoding is > 1, but got " f"{speculative_disable_by_batch_size=}") # TODO: The user should be able to specify revision/max model len # for the draft model. It is not currently supported. draft_revision = None draft_code_revision = None draft_quantization = speculative_model_quantization if speculative_model == "[ngram]": if ngram_prompt_lookup_min is None: ngram_prompt_lookup_min = 1 if ngram_prompt_lookup_max is None or ngram_prompt_lookup_max < 1: raise ValueError(f"{ngram_prompt_lookup_max=} must be > 0") if ngram_prompt_lookup_min < 1: raise ValueError(f"{ngram_prompt_lookup_min=} must be > 0") if ngram_prompt_lookup_min > ngram_prompt_lookup_max: raise ValueError(f"{ngram_prompt_lookup_min=} cannot be " f"larger than {ngram_prompt_lookup_max=}") # TODO: current we still need extract vocab_size from target model # config, in future, we may try refactor it out, and set # draft related config as None here. draft_model_config = target_model_config draft_parallel_config = target_parallel_config else: ngram_prompt_lookup_max = 0 ngram_prompt_lookup_min = 0 draft_model_config = ModelConfig( model=speculative_model, task="draft", tokenizer=target_model_config.tokenizer, tokenizer_mode=target_model_config.tokenizer_mode, trust_remote_code=target_model_config.trust_remote_code, allowed_local_media_path=target_model_config. allowed_local_media_path, dtype=target_model_config.dtype, seed=target_model_config.seed, revision=draft_revision, code_revision=draft_code_revision, tokenizer_revision=target_model_config.tokenizer_revision, max_model_len=None, spec_target_max_model_len=target_model_config.max_model_len, quantization=draft_quantization, enforce_eager=target_model_config.enforce_eager, max_seq_len_to_capture=target_model_config. max_seq_len_to_capture, max_logprobs=target_model_config.max_logprobs, ) draft_hf_config = draft_model_config.hf_config if (num_speculative_tokens is not None and hasattr(draft_hf_config, "num_lookahead_tokens")): draft_hf_config.num_lookahead_tokens = num_speculative_tokens n_predict = getattr(draft_hf_config, "n_predict", None) if n_predict is not None: if num_speculative_tokens is None: # Default to max value defined in draft model config. num_speculative_tokens = n_predict elif num_speculative_tokens > n_predict: # Verify provided value doesn't exceed the maximum # supported by the draft model. raise ValueError( "This speculative model supports a maximum of " f"num_speculative_tokens={n_predict}, but " f"{num_speculative_tokens=} was provided.") if enable_chunked_prefill and draft_hf_config.model_type in ( "medusa", "mlp_speculator", "eagle"): raise ValueError( "Chunked prefill and hidden-state based draft models are " "not compatible.") speculative_draft_tensor_parallel_size = \ SpeculativeConfig._verify_and_get_draft_model_tensor_parallel_size( target_parallel_config, speculative_draft_tensor_parallel_size, draft_hf_config ) if (enable_chunked_prefill and \ speculative_draft_tensor_parallel_size != 1): # TODO - Investigate why the error reported in # https://github.com/vllm-project/vllm/pull/9291#issuecomment-2463266258 # is happening and re-enable it. raise ValueError( "Chunked prefill and speculative decoding can be enabled " "simultaneously only for draft models with tensor " "parallel size 1.") draft_model_config.max_model_len = ( SpeculativeConfig._maybe_override_draft_max_model_len( speculative_max_model_len, draft_model_config.max_model_len, target_model_config.max_model_len, )) draft_parallel_config = ( SpeculativeConfig.create_draft_parallel_config( target_parallel_config, speculative_draft_tensor_parallel_size, draft_hf_config)) if num_speculative_tokens is None: raise ValueError( "num_speculative_tokens must be provided with " "speculative_model unless the draft model config contains an " "n_predict parameter.") if typical_acceptance_sampler_posterior_threshold is None: typical_acceptance_sampler_posterior_threshold = 0.09 if typical_acceptance_sampler_posterior_alpha is None: typical_acceptance_sampler_posterior_alpha = 0.3 if disable_logprobs is None: disable_logprobs = True return SpeculativeConfig( draft_model_config, draft_parallel_config, num_speculative_tokens, speculative_disable_mqa_scorer, speculative_disable_by_batch_size, ngram_prompt_lookup_max, ngram_prompt_lookup_min, draft_token_acceptance_method=draft_token_acceptance_method, typical_acceptance_sampler_posterior_threshold=\ typical_acceptance_sampler_posterior_threshold, typical_acceptance_sampler_posterior_alpha=\ typical_acceptance_sampler_posterior_alpha, disable_logprobs=disable_logprobs, disable_log_stats=disable_log_stats, ) @staticmethod def _maybe_override_draft_max_model_len( speculative_max_model_len: Optional[int], draft_max_model_len: int, target_max_model_len: int, ) -> int: """Determine the max sequence len for the draft model. This is usually the draft_max_model_len, but may be the target_max_model_len if it is less than the draft_max_model_len, or may be speculative_max_model_len if it is specified. This is necessary so that sequences do not exceed the capacity of the draft model or the target model. speculative_max_model_len is mainly used for testing that sequences can skip speculation. """ if speculative_max_model_len is not None: if speculative_max_model_len > draft_max_model_len: raise ValueError(f"{speculative_max_model_len=} cannot be " f"larger than {draft_max_model_len=}") if speculative_max_model_len > target_max_model_len: raise ValueError(f"{speculative_max_model_len=} cannot be " f"larger than {target_max_model_len=}") return speculative_max_model_len return min( draft_max_model_len, target_max_model_len, ) @staticmethod def _verify_and_get_draft_model_tensor_parallel_size( target_parallel_config: ParallelConfig, speculative_draft_tensor_parallel_size: Optional[int], draft_hf_config: PretrainedConfig) -> int: """ Verifies and adjusts the tensor parallel size for a draft model specified using speculative_draft_tensor_parallel_size. """ # If speculative_draft_tensor_parallel_size is unset then set it # appropriately else verify that it is set correctly. if speculative_draft_tensor_parallel_size is None: if draft_hf_config.model_type == "mlp_speculator": speculative_draft_tensor_parallel_size = 1 if target_parallel_config.tensor_parallel_size > 1: logger.warning( "MLPSpeculator cannot currently be run with tp>1; " "setting speculative_draft_tensor_parallel_size=1") else: speculative_draft_tensor_parallel_size = \ target_parallel_config.tensor_parallel_size elif speculative_draft_tensor_parallel_size not in ( 1, target_parallel_config.tensor_parallel_size): raise ValueError( f"{speculative_draft_tensor_parallel_size=} cannot be " f"other value than 1 or target model tensor_parallel_size") return speculative_draft_tensor_parallel_size @staticmethod def create_draft_parallel_config( target_parallel_config: ParallelConfig, speculative_draft_tensor_parallel_size: int, draft_hf_config: PretrainedConfig, ) -> ParallelConfig: """Create a parallel config for use by the draft worker. This is mostly a copy of the target parallel config, except the tp_size. """ draft_parallel_config = ParallelConfig( pipeline_parallel_size=target_parallel_config. pipeline_parallel_size, tensor_parallel_size=speculative_draft_tensor_parallel_size, distributed_executor_backend=target_parallel_config. distributed_executor_backend, max_parallel_loading_workers=target_parallel_config. max_parallel_loading_workers, disable_custom_all_reduce=target_parallel_config. disable_custom_all_reduce, tokenizer_pool_config=target_parallel_config.tokenizer_pool_config, ray_workers_use_nsight=target_parallel_config. ray_workers_use_nsight, placement_group=target_parallel_config.placement_group, ) return draft_parallel_config def __init__( self, draft_model_config: ModelConfig, draft_parallel_config: ParallelConfig, num_speculative_tokens: int, speculative_disable_mqa_scorer: Optional[bool], speculative_disable_by_batch_size: Optional[int], ngram_prompt_lookup_max: Optional[int], ngram_prompt_lookup_min: Optional[int], draft_token_acceptance_method: str, typical_acceptance_sampler_posterior_threshold: float, typical_acceptance_sampler_posterior_alpha: float, disable_logprobs: bool, disable_log_stats: bool, ): """Create a SpeculativeConfig object. Args: draft_model_config: ModelConfig for the draft model. draft_parallel_config: ParallelConfig for the draft model. num_speculative_tokens: The number of tokens to sample from the draft model before scoring with the target model. speculative_disable_by_batch_size: Disable speculative decoding for new incoming requests when the number of enqueue requests is larger than this value. ngram_prompt_lookup_max: Max size of ngram token window. ngram_prompt_lookup_min: Min size of ngram token window. draft_token_acceptance_method (str): The method to use for accepting draft tokens. This can take two possible values 'rejection_sampler' and 'typical_acceptance_sampler' for RejectionSampler and TypicalAcceptanceSampler respectively. typical_acceptance_sampler_posterior_threshold (Optional[float]): A threshold value that sets a lower bound on the posterior probability of a token in the target model for it to be accepted. This threshold is used only when we use the TypicalAcceptanceSampler for token acceptance. typical_acceptance_sampler_posterior_alpha (Optional[float]): A scaling factor for the entropy-based threshold in the TypicalAcceptanceSampler. disable_logprobs: If set to True, token log probabilities will not be returned even if requested by sampling parameters. This reduces latency by skipping logprob calculation in proposal sampling, target sampling, and after accepted tokens are determined. If set to False, log probabilities will be returned. disable_log_stats: Whether to disable periodic printing of stage times in speculative decoding. """ self.draft_model_config = draft_model_config self.draft_parallel_config = draft_parallel_config self.num_speculative_tokens = num_speculative_tokens self.speculative_disable_mqa_scorer = speculative_disable_mqa_scorer self.speculative_disable_by_batch_size = \ speculative_disable_by_batch_size self.ngram_prompt_lookup_max = ngram_prompt_lookup_max or 0 self.ngram_prompt_lookup_min = ngram_prompt_lookup_min or 0 self.draft_token_acceptance_method = draft_token_acceptance_method self.typical_acceptance_sampler_posterior_threshold = \ typical_acceptance_sampler_posterior_threshold self.typical_acceptance_sampler_posterior_alpha = \ typical_acceptance_sampler_posterior_alpha self.disable_logprobs = disable_logprobs self.disable_log_stats = disable_log_stats self._verify_args() def _verify_args(self) -> None: if self.num_speculative_tokens <= 0: raise ValueError("Expected num_speculative_tokens to be greater " f"than zero ({self.num_speculative_tokens}).") if self.draft_model_config: self.draft_model_config.verify_with_parallel_config( self.draft_parallel_config) # Validate and set draft token acceptance related settings. if (self.draft_token_acceptance_method is None): raise ValueError("draft_token_acceptance_method is not set. " "Expected values are rejection_sampler or " "typical_acceptance_sampler.") if (self.draft_token_acceptance_method != 'rejection_sampler' and self.draft_token_acceptance_method != 'typical_acceptance_sampler'): raise ValueError( "Expected draft_token_acceptance_method to be either " "rejection_sampler or typical_acceptance_sampler. Instead it " f"is {self.draft_token_acceptance_method}") if (self.typical_acceptance_sampler_posterior_threshold < 0 or self.typical_acceptance_sampler_posterior_alpha < 0): raise ValueError( "Expected typical_acceptance_sampler_posterior_threshold " "and typical_acceptance_sampler_posterior_alpha to be > 0. " "Instead found " f"typical_acceptance_sampler_posterior_threshold = " f"{self.typical_acceptance_sampler_posterior_threshold} and " f"typical_acceptance_sampler_posterior_alpha = " f"{self.typical_acceptance_sampler_posterior_alpha}") @property def num_lookahead_slots(self) -> int: """The number of additional slots the scheduler should allocate per step, in addition to the slots allocated for each known token. This is equal to the number of speculative tokens, as each speculative token must be scored. """ return self.num_speculative_tokens def __repr__(self) -> str: if self.ngram_prompt_lookup_max > 0: draft_model = "[ngram]" else: draft_model = self.draft_model_config.model num_spec_tokens = self.num_speculative_tokens return f"SpeculativeConfig({draft_model=}, {num_spec_tokens=})" @dataclass class LoRAConfig: max_lora_rank: int max_loras: int fully_sharded_loras: bool = False max_cpu_loras: Optional[int] = None lora_dtype: Optional[Union[torch.dtype, str]] = None lora_extra_vocab_size: int = 256 # This is a constant. lora_vocab_padding_size: ClassVar[int] = 256 long_lora_scaling_factors: Optional[Tuple[float]] = None bias_enabled: bool = False def __post_init__(self): # Setting the maximum rank to 256 should be able to satisfy the vast # majority of applications. possible_max_ranks = (8, 16, 32, 64, 128, 256) possible_lora_extra_vocab_size = (0, 256, 512) if self.max_lora_rank not in possible_max_ranks: raise ValueError( f"max_lora_rank ({self.max_lora_rank}) must be one of " f"{possible_max_ranks}.") if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size: raise ValueError( f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) " f"must be one of {possible_lora_extra_vocab_size}.") if self.max_loras < 1: raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.") if self.max_cpu_loras is None: self.max_cpu_loras = self.max_loras elif self.max_cpu_loras < self.max_loras: raise ValueError( f"max_cpu_loras ({self.max_cpu_loras}) must be >= " f"max_loras ({self.max_loras})") def verify_with_model_config(self, model_config: ModelConfig): if self.lora_dtype in (None, "auto"): self.lora_dtype = model_config.dtype elif isinstance(self.lora_dtype, str): self.lora_dtype = getattr(torch, self.lora_dtype) if model_config.quantization and model_config.quantization not in [ "awq", "gptq" ]: # TODO support marlin logger.warning("%s quantization is not tested with LoRA yet.", model_config.quantization) def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig): # Reminder: Please update docs/source/serving/compatibility_matrix.rst # If the feature combo become valid if scheduler_config.chunked_prefill_enabled: raise ValueError("LoRA is not supported with chunked prefill yet.") @dataclass class PromptAdapterConfig: max_prompt_adapters: int max_prompt_adapter_token: int max_cpu_prompt_adapters: Optional[int] = None prompt_adapter_dtype: Optional[torch.dtype] = None def __post_init__(self): if self.max_prompt_adapters < 1: raise ValueError(f"max_prompt_adapters " f"({self.max_prompt_adapters}) must be >= 1.") if self.max_prompt_adapter_token == 0: raise ValueError("max_prompt_adapter_token must be set.") if self.max_cpu_prompt_adapters is None: self.max_cpu_prompt_adapters = self.max_prompt_adapters def verify_with_model_config(self, model_config: ModelConfig): if self.prompt_adapter_dtype in (None, "auto"): self.prompt_adapter_dtype = model_config.dtype elif isinstance(self.prompt_adapter_dtype, str): self.prompt_adapter_dtype = getattr(torch, self.prompt_adapter_dtype) @dataclass class MultiModalConfig: """Controls the behavior of multimodal models.""" limit_per_prompt: Mapping[str, int] = field(default_factory=dict) """ The maximum number of multi-modal input instances allowed per prompt for each :class:`~vllm.multimodal.MultiModalPlugin`. """ # TODO: Add configs to init vision tower or not. @dataclass class PoolerConfig: """Controls the behavior of output pooling in embedding models.""" pooling_type: Optional[str] = None """ The pooling method of the embedding model. This should be a key in :class:`vllm.model_executor.layers.pooler.PoolingType`. """ normalize: Optional[bool] = None """ Whether to normalize the pooled outputs. Usually, this should be set to ``True`` for embedding outputs. """ softmax: Optional[bool] = None """ Whether to apply softmax to the pooled outputs. Usually, this should be set to ``True`` for classification outputs. """ step_tag_id: Optional[int] = None """ If set, only the score corresponding to the ``step_tag_id`` in the generated sentence should be returned. Otherwise, the scores for all tokens are returned. """ returned_token_ids: Optional[List[int]] = None """ A list of indices for the vocabulary dimensions to be extracted, such as the token IDs of ``good_token`` and ``bad_token`` in the ``math-shepherd-mistral-7b-prm`` model. """ @staticmethod def from_json(json_str: str) -> "PoolerConfig": return PoolerConfig(**json.loads(json_str)) _STR_DTYPE_TO_TORCH_DTYPE = { "half": torch.float16, "float16": torch.float16, "float": torch.float32, "float32": torch.float32, "bfloat16": torch.bfloat16, } _ROCM_NOT_SUPPORTED_DTYPE: List[str] = [] # def _get_and_verify_dtype( config: PretrainedConfig, dtype: Union[str, torch.dtype], ) -> torch.dtype: # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct # because config.torch_dtype can be None. config_dtype = getattr(config, "torch_dtype", None) if config_dtype is None: config_dtype = torch.float32 if isinstance(dtype, str): dtype = dtype.lower() if dtype == "auto": if config_dtype == torch.float32: if config.model_type == "gemma2": logger.info( "For Gemma 2, we downcast float32 to bfloat16 instead " "of float16 by default. Please specify `dtype` if you " "want to use float16.") torch_dtype = torch.bfloat16 else: # Following the common practice, we use float16 for float32 # models. torch_dtype = torch.float16 else: torch_dtype = config_dtype if current_platform.is_hpu() and config_dtype == torch.float16: logger.info( "For HPU, we cast models to bfloat16 instead of" "using float16 by default. Please specify `dtype` if you " "want to use float16.") torch_dtype = torch.bfloat16 else: if dtype not in _STR_DTYPE_TO_TORCH_DTYPE: raise ValueError(f"Unknown dtype: {dtype}") torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype] elif isinstance(dtype, torch.dtype): torch_dtype = dtype else: raise ValueError(f"Unknown dtype: {dtype}") # Verify the dtype. if torch_dtype != config_dtype: if torch_dtype == torch.float32: # Upcasting to float32 is allowed. logger.info("Upcasting %s to %s.", config_dtype, torch_dtype) pass elif config_dtype == torch.float32: # Downcasting from float32 to float16 or bfloat16 is allowed. logger.info("Downcasting %s to %s.", config_dtype, torch_dtype) pass else: # Casting between float16 and bfloat16 is allowed with a warning. logger.warning("Casting %s to %s.", config_dtype, torch_dtype) return torch_dtype def _get_and_verify_max_len( hf_config: PretrainedConfig, max_model_len: Optional[int], disable_sliding_window: bool, sliding_window_len: Optional[Union[int, List[Optional[int]]]], spec_target_max_model_len: Optional[int] = None, encoder_config: Optional[Any] = None, ) -> int: """Get and verify the model's maximum length.""" derived_max_model_len = float("inf") possible_keys = [ # OPT "max_position_embeddings", # GPT-2 "n_positions", # MPT "max_seq_len", # ChatGLM2 "seq_length", # Command-R "model_max_length", # Others "max_sequence_length", "max_seq_length", "seq_len", ] # Choose the smallest "max_length" from the possible keys. max_len_key = None for key in possible_keys: max_len = getattr(hf_config, key, None) if max_len is not None: max_len_key = key if max_len < derived_max_model_len \ else max_len_key derived_max_model_len = min(derived_max_model_len, max_len) # If sliding window is manually disabled, max_length should be less # than the sliding window length in the model config. if disable_sliding_window and sliding_window_len is not None: sliding_window_len_min = get_min_sliding_window(sliding_window_len) max_len_key = "sliding_window" \ if sliding_window_len_min < derived_max_model_len else max_len_key derived_max_model_len = min(derived_max_model_len, sliding_window_len_min) # If none of the keys were found in the config, use a default and # log a warning. if derived_max_model_len == float("inf"): if max_model_len is not None: # If max_model_len is specified, we use it. return max_model_len if spec_target_max_model_len is not None: # If this is a speculative draft model, we use the max model len # from the target model. return spec_target_max_model_len default_max_len = 2048 logger.warning( "The model's config.json does not contain any of the following " "keys to determine the original maximum length of the model: " "%s. Assuming the model's maximum length is %d.", possible_keys, default_max_len) derived_max_model_len = default_max_len rope_scaling = getattr(hf_config, "rope_scaling", None) if rope_scaling is not None: # No need to consider "type" key because of patch_rope_scaling when # loading HF config rope_type = rope_scaling["rope_type"] if rope_type not in ("su", "longrope", "llama3"): if disable_sliding_window: # TODO(robertgshaw): Find a model that supports rope_scaling # with sliding window to see if this case should be allowed. raise NotImplementedError( "Disabling sliding window is not supported for models " "with rope_scaling. Please raise an issue so we can " "investigate.") # NOTE: rope_type == "default" does not define factor # https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/modeling_rope_utils.py scaling_factor = rope_scaling.get("factor", 1.0) if rope_type == "yarn": derived_max_model_len = rope_scaling[ "original_max_position_embeddings"] derived_max_model_len *= scaling_factor if encoder_config and "max_seq_length" in encoder_config: derived_max_model_len = encoder_config["max_seq_length"] # If the user specified a max length, make sure it is smaller than the # derived length from the HF model config. if max_model_len is None: max_model_len = int(derived_max_model_len) elif max_model_len > derived_max_model_len: # Some models might have a separate key for specifying model_max_length # that will be bigger than derived_max_model_len. We compare user input # with model_max_length and allow this override when it's smaller. model_max_length = getattr(hf_config, "model_max_length", None) if model_max_length is not None and max_model_len <= model_max_length: if disable_sliding_window: # TODO(robertgshaw): Find a model that has model_max_length # with sliding window to see if this case should be allowed. raise NotImplementedError( "Disabling sliding window is not supported for models " "model_max_length in the config. Please raise an issue " "so we can investigate.") else: msg = ( f"User-specified max_model_len ({max_model_len}) is greater " f"than the derived max_model_len ({max_len_key}=" f"{derived_max_model_len} or model_max_length=" f"{model_max_length} in model's config.json). This may lead " "to incorrect model outputs or CUDA errors.") if envs.VLLM_ALLOW_LONG_MAX_MODEL_LEN: logger.warning( "%s Make sure the value is correct and within the " "model context size.", msg) else: raise ValueError( f"{msg} To allow overriding this maximum, set " "the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN=1") return int(max_model_len) def get_min_sliding_window( sliding_window: Union[int, List[Optional[int]]]) -> int: if isinstance(sliding_window, list): return min(s for s in sliding_window if s is not None) return sliding_window def get_served_model_name(model: str, served_model_name: Optional[Union[str, List[str]]]): """ If the input is a non-empty list, the first model_name in `served_model_name` is taken. If the input is a non-empty string, it is used directly. For cases where the input is either an empty string or an empty list, the fallback is to use `self.model`. """ if not served_model_name: return model if isinstance(served_model_name, list): return served_model_name[0] return served_model_name @dataclass class DecodingConfig: """Dataclass which contains the decoding strategy of the engine""" # Which guided decoding algo to use. 'outlines' / 'lm-format-enforcer' guided_decoding_backend: str = 'outlines' def __post_init__(self): valid_guided_backends = ['outlines', 'lm-format-enforcer'] backend = self.guided_decoding_backend if backend not in valid_guided_backends: raise ValueError(f"Invalid guided_decoding_backend '{backend}," f"must be one of {valid_guided_backends}") @dataclass class ObservabilityConfig: """Configuration for observability.""" otlp_traces_endpoint: Optional[str] = None # Collecting detailed timing information for each request can be expensive. # If set, collects the model forward time for the request. collect_model_forward_time: bool = False # If set, collects the model execute time for the request. collect_model_execute_time: bool = False def __post_init__(self): if not is_otel_available() and self.otlp_traces_endpoint is not None: raise ValueError( "OpenTelemetry is not available. Unable to configure " "'otlp_traces_endpoint'. Ensure OpenTelemetry packages are " f"installed. Original error:\n{otel_import_error_traceback}") class CompilationLevel: # constants for the levels of the compilation process NO_COMPILATION = 0 DYNAMO_AS_IS = 1 DYNAMO_ONCE = 2 PIECEWISE = 3 class CompilationConfig(BaseModel): """ Configuration for compilation. It has three parts: - Top-level Compilation control: - level: the level of compilation. - 0: no compilation. - 1: dynamo as is. - 2: dynamo once. - 3: piecewise compilation. - backend: the backend for compilation. It needs to be a string. - "" (empty string): use the default backend. - "eager"/"openxla"/...: use the specified backend registered in PyTorch. - "full.module.name": a qualified name which can be used to import the backend function. We use string to avoid serialization issues when using compilation in a distributed setting. When the compilation level is 1 or 2, the backend is used for the compilation directly (it sees the whole graph). When the compilation level is 3, the backend is used for the piecewise compilation (it sees a part of the graph). - custom_ops: fine-grained control over which custom ops to enable/disable. Use 'all' to enable all, 'none' to disable all. Also specify a list of custom op names to enable (prefixed with a '+'), or disable (prefixed with a '-'). Examples: - 'all,-op1' to enable all except op1 - 'none,+op1,+op2' to enable only op1 and op2 By default, all custom ops are enabled when running without Inductor and disabled when running with Inductor (compile_level >= Inductor). - splitting_ops: a list of ops to split the full graph into subgraphs, used in piecewise compilation. - CudaGraph capture: - use_cudagraph: whether to use cudagraph inside compilation. - False: cudagraph inside compilation is not used. - True: cudagraph inside compilation is used. It requires that all input buffers have fixed addresses, and all splitting ops write their outputs to input buffers. Note that this is orthogonal to the cudagraph capture logic outside of compilation. TODO: move outside cudagraph logic into compilation. torch.compile will handle cudagraph capture logic in the future. - cudagraph_capture_sizes: sizes to capture cudagraph. - None: capture sizes are inferred from compilation context. - List[int]: capture sizes are specified. - cudagraph_num_of_warmups: number of warmup runs for cudagraph. It means the first several runs will be treated as warmup runs. Only after that, the execution will be recorded, and the recorded cudagraph will be used for subsequent runs. - cudagraph_copy_inputs: whether to copy input tensors for cudagraph. If the caller can guarantee that the same input buffers are always used, it can set this to False. Otherwise, it should set this to True, and the compiler will copy the input to an internally managed buffer. Default is False. - Inductor compilation: - use_inductor: whether to use inductor compilation. - False: inductor compilation is not used. graph runs in eager. - True: inductor compilation is used. one graph for symbolic shape is compiled. In addition, compile for different sizes specified in inductor_compile_sizes, using configurations in inductor_compile_config. - inductor_compile_sizes: sizes to compile for inductor. - inductor_specialize_for_cudagraph_no_more_than: an optional integer to specialize inductor for cudagraph sizes no more than the specified size. It is useful when we want to specialize inductor with a subset of cudagraph sizes. - inductor_compile_config: additional configurations for inductor. - None: use default configurations. - inductor_passes: additional passes for inductor. It is a dictionary from pass name to pass function qualified name. We use function name because the config uses json format. If we pass the config from Python, functions can also be passed directly via Python object constructor, e.g. `CompilationConfig(inductor_passes={"a": func})` - custom inductor passes: see PassConfig for more details Why we have different sizes for cudagraph and inductor: - cudagraph: a cudagraph captured for a specific size can only be used for the same size. We need to capture all the sizes we want to use. - inductor: a graph compiled by inductor for a general shape can be used for different sizes. Inductor can also compile for specific sizes, where it can have more information to optimize the graph with fully static shapes. However, we find the general shape compilation is sufficient for most cases. It might be beneficial to compile for certain small batchsizes, where inductor is good at optimizing. """ # noqa level: int = 0 backend: str = "" custom_ops: List[str] = Field(default_factory=list) splitting_ops: List[str] = Field(default_factory=lambda: [ "vllm.unified_attention", "vllm.unified_v1_flash_attention", ]) use_inductor: bool = True inductor_specialize_for_cudagraph_no_more_than: Optional[int] = None inductor_compile_sizes: Optional[List[int]] = Field(default_factory=dict) inductor_compile_config: Dict = Field(default_factory=dict) inductor_passes: Dict[str, str] = Field(default_factory=dict) use_cudagraph: bool = False cudagraph_num_of_warmups: int = 0 cudagraph_capture_sizes: Optional[List[int]] = None cudagraph_copy_inputs: bool = False class PassConfig(BaseModel): """ Configuration for custom Inductor passes. This is separate from general CompilationConfig so that inductor passes don't all have access to full configuration - that would create a cycle as the PassManager is set as a property of config. - dump_graph_stages: list of stages for which we want to dump the graph. Each pass defines its own stages (before, after, maybe in-between). - dump_graph_dir: directory to dump the graphs. Default is . - enable_fusion: whether to enable the custom fusion pass. - enable_reshape: whether to enable the custom reshape elimination pass. TODO better pass enabling system. """ dump_graph_stages: List[str] = Field(default_factory=list) dump_graph_dir: Path = Field(default=Path(".")) enable_fusion: bool = True enable_reshape: bool = True def uuid(self): """ Produces a hash unique to the pass configuration. Any new fields that affect compilation should be added to the hash. Do not include dump_graph_* in the hash - they don't affect compilation. """ dict_ = self.model_dump( include={"enable_fusion", "enable_reshape"}) encoded = json.dumps(dict_, sort_keys=True).encode("utf-8") return hashlib.sha256(encoded).digest() def model_post_init(self, __context: Any) -> None: if not self.enable_reshape and self.enable_fusion: print_warning_once( "Fusion enabled but reshape elimination disabled." "RMSNorm + quant (fp8) fusion might not work") pass_config: PassConfig = Field(default_factory=PassConfig) # not configurable, computed after init compile_sizes: List[int] = PrivateAttr capture_sizes: List[int] = PrivateAttr # keep track of enabled and disabled custom ops enabled_custom_ops: Counter[str] = PrivateAttr disabled_custom_ops: Counter[str] = PrivateAttr # Per-model forward context # Mainly used to store attention cls # Map from layer name to the attention cls static_forward_context: Dict[str, Any] = PrivateAttr @classmethod def from_cli(cls, cli_value: str) -> "CompilationConfig": """Parse the CLI value for the compilation config.""" if cli_value in ["0", "1", "2", "3"]: return cls(level=int(cli_value)) return CompilationConfig.model_validate_json(cli_value) def model_post_init(self, __context: Any) -> None: count_none = self.custom_ops.count("none") count_all = self.custom_ops.count("all") assert count_none + count_all <= 1, "Can only specify 'none' or 'all'" for k, v in self.inductor_passes.items(): if not isinstance(v, str): assert callable(v), ( f"pass {k} should be callable or a qualified name") self.inductor_compile_config[k] = v if isinstance( v, InductorPass) else CallableInductorPass(v) continue # resolve function from qualified name names = v.split(".") module = ".".join(names[:-1]) func_name = names[-1] func = __import__(module).__dict__[func_name] self.inductor_compile_config[k] = func if isinstance( func, InductorPass) else CallableInductorPass(func) self.enabled_custom_ops = Counter() self.disabled_custom_ops = Counter() self.static_forward_context = {} def init_backend(self) -> Union[str, Callable]: if self.level == CompilationLevel.NO_COMPILATION: raise ValueError("No compilation level is set.") from torch._dynamo.backends.registry import list_backends torch_backends = list_backends(exclude_tags=tuple()) if self.level in [ CompilationLevel.DYNAMO_AS_IS, CompilationLevel.DYNAMO_ONCE ]: if self.backend == "": return "eager" if self.backend in torch_backends: return self.backend return resolve_obj_by_qualname(self.backend) # TODO: pass user-specified backend to piecewise compilation # merge with the config use_inductor assert self.level == CompilationLevel.PIECEWISE from vllm.compilation.backends import VllmBackend return VllmBackend(self) def init_during_runtime(self): """To complete the initialization of config, we need to know the compile context, which is only available during the first run of the model. """ from vllm.compilation.compile_context import get_compile_context context = get_compile_context() context = copy.deepcopy(context) if context is not None else [] sizes_to_specialize: List[int] = context if self.cudagraph_capture_sizes is None: self.capture_sizes = sizes_to_specialize else: self.capture_sizes = self.cudagraph_capture_sizes logger.info(("cudagraph sizes specified by model runner" " %s is overridden by config %s"), sizes_to_specialize, self.cudagraph_capture_sizes) if self.inductor_specialize_for_cudagraph_no_more_than is not None: assert self.inductor_compile_sizes is None, ( "inductor_compile_sizes should be None when " "inductor_specialize_for_cudagraph_no_more_than is not None") self.compile_sizes = [ x for x in self.capture_sizes if x <= self.inductor_specialize_for_cudagraph_no_more_than ] else: assert self.inductor_compile_sizes is not None, ( "inductor_compile_sizes should not be None when " "inductor_specialize_for_cudagraph_no_more_than is None") self.compile_sizes = self.inductor_compile_sizes @dataclass class VllmConfig: """Dataclass which contains all vllm-related configuration. This simplifies passing around the distinct configurations in the codebase. """ model_config: ModelConfig = field(default=None, init=True) # type: ignore cache_config: CacheConfig = field(default=None, init=True) # type: ignore parallel_config: ParallelConfig = field(default_factory=ParallelConfig, init=True) scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig, init=True) device_config: DeviceConfig = field(default=None, init=True) # type: ignore load_config: LoadConfig = field(default=None, init=True) # type: ignore lora_config: Optional[LoRAConfig] = None speculative_config: Optional[SpeculativeConfig] = None decoding_config: Optional[DecodingConfig] = None observability_config: Optional[ObservabilityConfig] = None prompt_adapter_config: Optional[PromptAdapterConfig] = None quant_config: Optional[QuantizationConfig] = None compilation_config: CompilationConfig = field(default=None, init=True) # type: ignore @staticmethod def _get_quantization_config( model_config: ModelConfig, load_config: LoadConfig) -> Optional[QuantizationConfig]: """Get the quantization config.""" if model_config.quantization is not None: from vllm.model_executor.model_loader.weight_utils import ( get_quant_config) quant_config = get_quant_config(model_config, load_config) capability_tuple = current_platform.get_device_capability() if capability_tuple is not None: capability = capability_tuple.to_int() if capability < quant_config.get_min_capability(): raise ValueError( f"The quantization method {model_config.quantization} " "is not supported for the current GPU. Minimum " f"capability: {quant_config.get_min_capability()}. " f"Current capability: {capability}.") supported_dtypes = quant_config.get_supported_act_dtypes() if model_config.dtype not in supported_dtypes: raise ValueError( f"{model_config.dtype} is not supported for quantization " f"method {model_config.quantization}. Supported dtypes: " f"{supported_dtypes}") return quant_config return None def with_hf_config(self, hf_config: PretrainedConfig) -> "VllmConfig": model_config = copy.deepcopy(self.model_config) model_config.hf_config = hf_config return replace(self, model_config=model_config) def __post_init__(self): """Verify configs are valid & consistent with each other. """ if self.model_config is not None: self.model_config.verify_async_output_proc(self.parallel_config, self.speculative_config, self.device_config) self.model_config.verify_with_parallel_config(self.parallel_config) if self.cache_config is not None: self.cache_config.verify_with_parallel_config(self.parallel_config) if self.lora_config: self.lora_config.verify_with_model_config(self.model_config) self.lora_config.verify_with_scheduler_config( self.scheduler_config) if self.prompt_adapter_config: self.prompt_adapter_config.verify_with_model_config( self.model_config) if self.quant_config is None and \ self.model_config is not None and self.load_config is not None: self.quant_config = VllmConfig._get_quantization_config( self.model_config, self.load_config) if self.compilation_config is None: self.compilation_config = CompilationConfig() if envs.VLLM_USE_V1 and not self.model_config.enforce_eager: # NOTE(woosuk): Currently, we use inductor because the piecewise # CUDA graphs do not work properly with the custom CUDA kernels. # FIXME(woosuk): Disable inductor to reduce the compilation time # and avoid any potential issues with the inductor. self.compilation_config.custom_ops = ["none"] self.compilation_config.use_cudagraph = True self.compilation_config.use_inductor = True self.compilation_config.pass_config.enable_fusion = False self.compilation_config.pass_config.enable_reshape = False self.compilation_config.level = CompilationLevel.PIECEWISE current_platform.check_and_update_config(self) def __str__(self): return ("model=%r, speculative_config=%r, tokenizer=%r, " "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, " "override_neuron_config=%s, tokenizer_revision=%s, " "trust_remote_code=%s, dtype=%s, max_seq_len=%d, " "download_dir=%r, load_format=%s, tensor_parallel_size=%d, " "pipeline_parallel_size=%d, " "disable_custom_all_reduce=%s, quantization=%s, " "enforce_eager=%s, kv_cache_dtype=%s, " "quantization_param_path=%s, device_config=%s, " "decoding_config=%r, observability_config=%r, " "seed=%d, served_model_name=%s, " "num_scheduler_steps=%d, enable_prefix_caching=%s, " "use_async_output_proc=%s, mm_processor_kwargs=%s") % \ (self.model_config.model, self.speculative_config, self.model_config.tokenizer, self.model_config.skip_tokenizer_init, self.model_config.tokenizer_mode, self.model_config.revision, self.model_config.override_neuron_config, self.model_config.tokenizer_revision, self.model_config.trust_remote_code, self.model_config.dtype, self.model_config.max_model_len, self.load_config.download_dir, self.load_config.load_format, self.parallel_config.tensor_parallel_size, self.parallel_config.pipeline_parallel_size, self.parallel_config.disable_custom_all_reduce, self.model_config.quantization, self.model_config.enforce_eager, self.cache_config.cache_dtype, self.model_config.quantization_param_path, self.device_config.device, self.decoding_config, self.observability_config, self.model_config.seed, self.model_config.served_model_name, self.scheduler_config.num_scheduler_steps, self.cache_config.enable_prefix_caching, self.model_config.use_async_output_proc, self.model_config.mm_processor_kwargs)