# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import dataclasses import os import time from abc import abstractmethod from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union import cloudpickle import torch import torch.nn as nn from vllm.config import (ObservabilityConfig, VllmConfig, set_current_vllm_config) from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import ExecuteModelRequest, IntermediateTensors from vllm.utils import (enable_trace_function_call_for_thread, resolve_obj_by_qualname, run_method, update_environment_variables, warn_for_unimplemented_methods) from vllm.worker.model_runner_base import (BroadcastableModelInput, ModelRunnerBase, ModelRunnerInputBase) logger = init_logger(__name__) @warn_for_unimplemented_methods class WorkerBase: """Worker interface that allows vLLM to cleanly separate implementations for different hardware. Also abstracts control plane communication, e.g., to communicate request metadata to other workers. """ def __init__( self, vllm_config: VllmConfig, ) -> None: self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.cache_config = vllm_config.cache_config self.lora_config = vllm_config.lora_config self.load_config = vllm_config.load_config self.parallel_config = vllm_config.parallel_config self.scheduler_config = vllm_config.scheduler_config self.device_config = vllm_config.device_config self.speculative_config = vllm_config.speculative_config self.observability_config = vllm_config.observability_config self.kv_transfer_config = vllm_config.kv_transfer_config self.compilation_config = vllm_config.compilation_config from vllm.platforms import current_platform self.current_platform = current_platform def init_device(self) -> None: """Initialize device state, such as loading the model or other on-device memory allocations. """ raise NotImplementedError def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: """Initialize the KV cache with the given size in blocks. """ raise NotImplementedError def get_model(self) -> nn.Module: raise NotImplementedError def load_model(self) -> None: """Load model onto target device.""" raise NotImplementedError def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None ) -> Optional[List[SamplerOutput]]: raise NotImplementedError def start_worker_execution_loop(self) -> None: """Execute model loop in parallel worker. You can stop the loop by executing a driver worker with an empty output. See `stop_remote_worker_execution_loop` for more details. """ with self.current_platform.inference_mode(): while True: output = self.execute_model(execute_model_req=None) if output is None: return None def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of available blocks for the GPU KV cache and swappable CPU KV cache. The implementation may run profiling or other heuristics to determine the size of caches. Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks are blocks that are "active" on the device and can be appended to. num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be appended to. """ raise NotImplementedError def get_cache_block_size_bytes(self) -> int: """Return the size of a single cache block, in bytes. Used in speculative decoding. """ raise NotImplementedError def add_lora(self, lora_request: LoRARequest) -> bool: raise NotImplementedError def remove_lora(self, lora_id: int) -> bool: raise NotImplementedError def pin_lora(self, lora_id: int) -> bool: raise NotImplementedError def list_loras(self) -> Set[int]: raise NotImplementedError @property def vocab_size(self) -> int: """Get vocabulary size from model configuration.""" return self.model_config.get_vocab_size() class DelegateWorkerBase(WorkerBase): """ A class that delegates all methods to another WorkerBase instance. This is useful for creating a WorkerBase that wraps another WorkerBase instance, e.g. speculative decoding. """ worker: WorkerBase def __init__( self, *args, **kwargs, ) -> None: vllm_config: VllmConfig = kwargs.get("vllm_config") cls = resolve_obj_by_qualname(vllm_config.parallel_config.worker_cls) self.worker = cls(*args, **kwargs) def init_device(self) -> None: self.worker.init_device() def determine_num_available_blocks(self) -> Tuple[int, int]: return self.worker.determine_num_available_blocks() def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: self.worker.initialize_cache(num_gpu_blocks, num_cpu_blocks) def load_model(self) -> None: """Load model onto target device.""" self.worker.load_model() def get_model(self) -> nn.Module: return self.worker.get_model() def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None ) -> Optional[List[SamplerOutput]]: return self.worker.execute_model(execute_model_req) def get_cache_block_size_bytes(self) -> int: return self.worker.get_cache_block_size_bytes() def add_lora(self, lora_request: LoRARequest) -> bool: return self.worker.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: return self.worker.remove_lora(lora_id) def pin_lora(self, lora_id: int) -> bool: return self.worker.pin_lora(lora_id) def list_loras(self) -> Set[int]: return self.worker.list_loras() def __getattr__(self, attr): return getattr(self.worker, attr) class LoRANotSupportedWorkerBase(WorkerBase): """Partial implementation of WorkerBase that raises exceptions when LoRA methods are invoked. """ def add_lora(self, lora_request: LoRARequest) -> bool: raise ValueError(f"{type(self)} does not support LoRA") def remove_lora(self, lora_id: int) -> bool: raise ValueError(f"{type(self)} does not support LoRA") def pin_lora(self, lora_id: int) -> bool: raise ValueError(f"{type(self)} does not support LoRA") def list_loras(self) -> Set[int]: raise ValueError(f"{type(self)} does not support LoRA") @dataclasses.dataclass(frozen=True) class WorkerInput: """Local inputs to each worker. May contain device-specific data. These fields should be broadcastable to other workers. """ num_seq_groups: Optional[int] = None blocks_to_swap_in: Optional[torch.Tensor] = None blocks_to_swap_out: Optional[torch.Tensor] = None blocks_to_copy: Optional[torch.Tensor] = None virtual_engine: int = 0 num_steps: int = 1 @classmethod def from_broadcasted_tensor_dict( cls: Type["WorkerInput"], tensor_dict: Dict[str, Any], ) -> "WorkerInput": """ Pop fields from the given tensor_dict and populate a new instance of WorkerInput. """ return cls( num_seq_groups=tensor_dict.pop("num_seq_groups"), blocks_to_swap_in=tensor_dict.pop("blocks_to_swap_in"), blocks_to_swap_out=tensor_dict.pop("blocks_to_swap_out"), blocks_to_copy=tensor_dict.pop("blocks_to_copy"), virtual_engine=tensor_dict["virtual_engine"], num_steps=tensor_dict.pop("num_steps"), ) def as_broadcastable_tensor_dict( self) -> Dict[str, Union[int, torch.Tensor]]: """ Extract broadcastable fields. """ tensor_dict = { "num_seq_groups": self.num_seq_groups, "blocks_to_swap_in": self.blocks_to_swap_in, "blocks_to_swap_out": self.blocks_to_swap_out, "blocks_to_copy": self.blocks_to_copy, "virtual_engine": self.virtual_engine, "num_steps": self.num_steps, } return tensor_dict class LocalOrDistributedWorkerBase(WorkerBase): """ Partial implementation of WorkerBase that has a default `execute_model` definition to perform metadata transfer between workers when in distributed mode. Subclasses of this interface should use model runners that inherit from ModelRunnerBase, and should only need to implement worker-local logic. If custom control plane logic is needed to transfer metadata, or if the model runner cannot inherit from ModelRunnerBase, use WorkerBase instead. """ is_driver_worker: bool model_runner: ModelRunnerBase observability_config: Optional[ObservabilityConfig] = None @property @abstractmethod def do_metadata_broadcast(self) -> bool: """ Used by the default `execute_model` to check whether broadcast is needed to transfer request inputs from the driver worker to other workers in the TP group. If WorkerBase subclass only supports single-worker execution, then this method should return False. """ raise NotImplementedError @property @abstractmethod def kv_cache(self) -> Optional[List[List[torch.Tensor]]]: """ Gets the list of kv caches to pass to the worker's model runner. Each element in the list is a kv cache corresponding to a particular virtual engine (PP stream). Used by the default `execute_model`. If the worker's model runner does not follow the ModelRunnerBase interface, then inherit from WorkerBase instead. """ raise NotImplementedError @abstractmethod def prepare_worker_input( self, execute_model_req: ExecuteModelRequest) -> WorkerInput: """ Prepare the inputs to WorkerBase.execute_worker from an execution request. This method may move data to the worker's local device. It is not allowed to communicate with other workers or devices. """ raise NotImplementedError @abstractmethod def execute_worker(self, worker_input: WorkerInput) -> None: """ Process an execution request. """ raise NotImplementedError def _get_worker_input_from_broadcast( self ) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[ str, torch.Tensor]]]: """ Get the worker input from the broadcasted tensor dict. """ assert self.do_metadata_broadcast assert not self.is_driver_worker broadcast_data = broadcast_tensor_dict(src=0) if not broadcast_data: return None worker_input = WorkerInput.from_broadcasted_tensor_dict(broadcast_data) model_input = ( self.model_runner.make_model_input_from_broadcasted_tensor_dict( broadcast_data)) kwargs = extract_previous_hidden_states(broadcast_data) return model_input, worker_input, kwargs def _get_driver_input_and_broadcast( self, execute_model_req: ExecuteModelRequest ) -> Tuple[BroadcastableModelInput, WorkerInput, Dict[str, torch.Tensor]]: """ Get the driver input and broadcast it to other workers. """ assert self.is_driver_worker worker_input: WorkerInput = self.prepare_worker_input( execute_model_req=execute_model_req) model_input: ModelRunnerInputBase = ( self.model_runner.prepare_model_input( execute_model_req.seq_group_metadata_list, execute_model_req.virtual_engine, execute_model_req.finished_requests_ids)) kwargs = extract_previous_hidden_states(execute_model_req) if self.do_metadata_broadcast: broadcast_data = worker_input.as_broadcastable_tensor_dict() broadcast_data.update(model_input.as_broadcastable_tensor_dict()) broadcast_data.update(kwargs) broadcast_tensor_dict(broadcast_data, src=0) if execute_model_req.async_callback: model_input = dataclasses.replace( # type: ignore model_input, async_callback=execute_model_req.async_callback) return model_input, worker_input, kwargs def prepare_input( self, execute_model_req: Optional[ExecuteModelRequest] = None ) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[ str, torch.Tensor]]]: """ Prepare the inputs to ModelRunner and workers. """ if self.is_driver_worker: if execute_model_req is None: if self.do_metadata_broadcast: # This signals that there's no more requests to process for # now. All workers are running infinite loop with # broadcast_tensor_dict, and it stops the loop when the # driver broadcasts an empty input. Send an empty input to # notify all other workers to stop their execution loop. broadcast_tensor_dict({}, src=0) return None return self._get_driver_input_and_broadcast(execute_model_req) else: return self._get_worker_input_from_broadcast() def get_model(self) -> nn.Module: return self.model_runner.get_model() def execute_model( self, execute_model_req: Optional[ExecuteModelRequest] = None, ) -> Optional[List[SamplerOutput]]: """Executes at least one model step on the given sequences, unless no sequences are provided.""" start_time = time.perf_counter() inputs = self.prepare_input(execute_model_req) if inputs is None: return None model_input, worker_input, kwargs = inputs num_steps = worker_input.num_steps self.execute_worker(worker_input) # If there is no input, we don't need to execute the model. if worker_input.num_seq_groups == 0: return [] intermediate_tensors = None orig_model_execute_time = 0.0 if not get_pp_group().is_first_rank: intermediate_tensors = IntermediateTensors( get_pp_group().recv_tensor_dict( all_gather_group=get_tp_group())) if (self.observability_config is not None and self.observability_config.collect_model_execute_time): orig_model_execute_time = intermediate_tensors.tensors.get( "model_execute_time", torch.tensor(0)).item() output = self.model_runner.execute_model( model_input=model_input, kv_caches=self.kv_cache[worker_input.virtual_engine] if self.kv_cache is not None else None, intermediate_tensors=intermediate_tensors, num_steps=num_steps, **kwargs, ) model_execute_time = time.perf_counter() - start_time if not get_pp_group().is_last_rank: # output is IntermediateTensors assert isinstance(output, IntermediateTensors) if (self.observability_config is not None and self.observability_config.collect_model_execute_time): output.tensors["model_execute_time"] = torch.tensor( model_execute_time + orig_model_execute_time) get_pp_group().send_tensor_dict(output.tensors, all_gather_group=get_tp_group()) return [None] if (self.observability_config is not None and self.observability_config.collect_model_execute_time and output is not None): for o in output: o.model_execute_time = (orig_model_execute_time + model_execute_time) # output is List[SamplerOutput] return output def _execute_model_spmd( self, execute_model_req: ExecuteModelRequest, intermediate_tensors: Optional[IntermediateTensors] = None ) -> Optional[List[SamplerOutput]]: """ Execute model in Single Program Multiple Data (SPMD) fashion. All workers take the same request, prepare the input and execute the model. """ assert execute_model_req is not None, ( "_execute_model_spmd() requires each worker to take in an " "ExecuteModelRequest") worker_input: WorkerInput = self.prepare_worker_input( execute_model_req=execute_model_req) model_input: ModelRunnerInputBase = ( self.model_runner.prepare_model_input( execute_model_req.seq_group_metadata_list)) self.execute_worker(worker_input) # If there is no input, we don't need to execute the model. if worker_input.num_seq_groups == 0: return [] kwargs = extract_previous_hidden_states(execute_model_req) return self.model_runner.execute_model( model_input=model_input, kv_caches=self.kv_cache[worker_input.virtual_engine] if self.kv_cache is not None else None, intermediate_tensors=intermediate_tensors, **kwargs, ) class WorkerWrapperBase: """ This class represents one process in an executor/engine. It is responsible for lazily initializing the worker and handling the worker's lifecycle. We first instantiate the WorkerWrapper, which remembers the worker module and class name. Then, when we call `update_environment_variables`, and the real initialization happens in `init_worker`. """ def __init__( self, vllm_config: VllmConfig, rpc_rank: int = 0, ) -> None: """ Initialize the worker wrapper with the given vllm_config and rpc_rank. Note: rpc_rank is the rank of the worker in the executor. In most cases, it is also the rank of the worker in the distributed group. However, when multiple executors work together, they can be different. e.g. in the case of SPMD-style offline inference with TP=2, users can launch 2 engines/executors, each with only 1 worker. All workers have rpc_rank=0, but they have different ranks in the TP group. """ self.rpc_rank = rpc_rank self.worker: Optional[WorkerBase] = None self.vllm_config: Optional[VllmConfig] = None # do not store this `vllm_config`, `init_worker` will set the final # one. TODO: investigate if we can remove this field in # `WorkerWrapperBase`, `init_cached_hf_modules` should be # unnecessary now. if vllm_config.model_config is not None: # it can be None in tests trust_remote_code = vllm_config.model_config.trust_remote_code if trust_remote_code: # note: lazy import to avoid importing torch before initializing from vllm.utils import init_cached_hf_modules init_cached_hf_modules() def adjust_rank(self, rank_mapping: Dict[int, int]) -> None: """ Adjust the rpc_rank based on the given mapping. It is only used during the initialization of the executor, to adjust the rpc_rank of workers after we create all workers. """ if self.rpc_rank in rank_mapping: self.rpc_rank = rank_mapping[self.rpc_rank] def update_environment_variables(self, envs_list: List[Dict[str, str]]) -> None: envs = envs_list[self.rpc_rank] key = 'CUDA_VISIBLE_DEVICES' if key in envs and key in os.environ: # overwriting CUDA_VISIBLE_DEVICES is desired behavior # suppress the warning in `update_environment_variables` del os.environ[key] update_environment_variables(envs) def init_worker(self, all_kwargs: List[Dict[str, Any]]) -> None: """ Here we inject some common logic before initializing the worker. Arguments are passed to the worker class constructor. """ kwargs = all_kwargs[self.rpc_rank] self.vllm_config = kwargs.get("vllm_config", None) assert self.vllm_config is not None, ( "vllm_config is required to initialize the worker") enable_trace_function_call_for_thread(self.vllm_config) from vllm.plugins import load_general_plugins load_general_plugins() if isinstance(self.vllm_config.parallel_config.worker_cls, str): worker_class = resolve_obj_by_qualname( self.vllm_config.parallel_config.worker_cls) else: logger.warning( "passing worker_cls as a class object is strongly deprecated," " as the serialization of class objects can be tricky and" " error-prone. To be safe, please keep the class in a separate" " module and pass the qualified name of the class as a string." ) assert isinstance(self.vllm_config.parallel_config.worker_cls, bytes) worker_class = cloudpickle.loads( self.vllm_config.parallel_config.worker_cls) if self.vllm_config.parallel_config.worker_extension_cls: worker_extension_cls = resolve_obj_by_qualname( self.vllm_config.parallel_config.worker_extension_cls) extended_calls = [] if worker_extension_cls not in worker_class.__bases__: # check any conflicts between worker and worker_extension_cls for attr in dir(worker_extension_cls): if attr.startswith("__"): continue assert not hasattr(worker_class, attr), ( f"Worker class {worker_class} already has an attribute" f" {attr}, which conflicts with the worker" f" extension class {worker_extension_cls}.") if callable(getattr(worker_extension_cls, attr)): extended_calls.append(attr) # dynamically inherit the worker extension class worker_class.__bases__ = worker_class.__bases__ + ( worker_extension_cls, ) logger.info( "Injected %s into %s for extended collective_rpc calls %s", worker_extension_cls, worker_class, extended_calls) with set_current_vllm_config(self.vllm_config): # To make vLLM config available during worker initialization self.worker = worker_class(**kwargs) assert self.worker is not None def initialize_from_config(self, kv_cache_configs: List[Any]) -> None: kv_cache_config = kv_cache_configs[self.rpc_rank] with set_current_vllm_config(self.vllm_config): self.worker.initialize_from_config(kv_cache_config) # type: ignore def init_device(self): with set_current_vllm_config(self.vllm_config): # To make vLLM config available during device initialization self.worker.init_device() # type: ignore def execute_method(self, method: Union[str, bytes], *args, **kwargs): try: # method resolution order: # if a method is defined in this class, it will be called directly. # otherwise, since we define `__getattr__` and redirect attribute # query to `self.worker`, the method will be called on the worker. return run_method(self, method, args, kwargs) except Exception as e: # if the driver worker also execute methods, # exceptions in the rest worker may cause deadlock in rpc like ray # see https://github.com/vllm-project/vllm/issues/3455 # print the error and inform the user to solve the error msg = (f"Error executing method {method!r}. " "This might cause deadlock in distributed execution.") logger.exception(msg) raise e def __getattr__(self, attr): return getattr(self.worker, attr) def extract_previous_hidden_states( data: Union[ExecuteModelRequest, Dict[str, torch.Tensor]]) -> \ Dict[str, torch.Tensor]: """If data contains previous_hidden_states, extract it. This returns a dict which can be used directly as additional kwargs in any following execute_model calls. This is used in draft models like EAGLE.""" output = {} # When called from non-driver worker, data is dict but when called from # driver worker, data is ExecuteModelRequest. if isinstance(data, dict): if "previous_hidden_states" in data: output["previous_hidden_states"] = data["previous_hidden_states"] elif data.previous_hidden_states is not None: output["previous_hidden_states"] = data.previous_hidden_states\ .hidden_states return output