forked from EngineX-Cambricon/enginex-mlu370-vllm
add qwen3
This commit is contained in:
Chranos
501
vllm-v0.6.2/vllm/worker/worker_base.py
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501
vllm-v0.6.2/vllm/worker/worker_base.py
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@@ -0,0 +1,501 @@
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import dataclasses
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import importlib
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import os
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import time
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
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import torch
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from vllm.config import ObservabilityConfig, VllmConfig
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from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.model_executor.layers.sampler import SamplerOutput
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from vllm.platforms import current_platform
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from vllm.sequence import ExecuteModelRequest, IntermediateTensors
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from vllm.utils import (enable_trace_function_call_for_thread,
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update_environment_variables)
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from vllm.worker.model_runner_base import (BroadcastableModelInput,
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ModelRunnerBase,
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ModelRunnerInputBase)
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logger = init_logger(__name__)
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class WorkerBase(ABC):
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"""Worker interface that allows vLLM to cleanly separate implementations for
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different hardware. Also abstracts control plane communication, e.g., to
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communicate request metadata to other workers.
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"""
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def __init__(
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self,
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vllm_config: VllmConfig,
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) -> None:
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.cache_config = vllm_config.cache_config
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self.lora_config = vllm_config.lora_config
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self.load_config = vllm_config.load_config
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self.parallel_config = vllm_config.parallel_config
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self.scheduler_config = vllm_config.scheduler_config
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self.device_config = vllm_config.device_config
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self.speculative_config = vllm_config.speculative_config
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self.prompt_adapter_config = vllm_config.prompt_adapter_config
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self.observability_config = vllm_config.observability_config
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@abstractmethod
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def init_device(self) -> None:
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"""Initialize device state, such as loading the model or other on-device
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memory allocations.
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"""
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raise NotImplementedError
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@abstractmethod
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def determine_num_available_blocks(self) -> Tuple[int, int]:
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"""Determine the number of available blocks for the GPU KV cache and
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swappable CPU KV cache.
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The implementation may run profiling or other heuristics to determine
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the size of caches.
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Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
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are blocks that are "active" on the device and can be appended to.
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num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
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appended to.
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"""
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raise NotImplementedError
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@abstractmethod
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def initialize_cache(self, num_gpu_blocks: int,
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num_cpu_blocks: int) -> None:
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"""Initialize the KV cache with the given size in blocks.
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"""
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raise NotImplementedError
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@current_platform.inference_mode()
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def start_worker_execution_loop(self) -> None:
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"""Execute model loop in parallel worker.
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You can stop the loop by executing a driver worker with an empty output.
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See `stop_remote_worker_execution_loop` for more details.
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"""
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while True:
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output = self.execute_model(execute_model_req=None)
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if output is None:
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return None
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@abstractmethod
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def execute_model(
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self,
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execute_model_req: Optional[ExecuteModelRequest] = None
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) -> Optional[List[SamplerOutput]]:
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raise NotImplementedError
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@abstractmethod
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def get_cache_block_size_bytes(self) -> int:
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"""Return the size of a single cache block, in bytes. Used in
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speculative decoding.
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"""
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raise NotImplementedError
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@abstractmethod
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def add_lora(self, lora_request: LoRARequest) -> bool:
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raise NotImplementedError
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@abstractmethod
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def remove_lora(self, lora_id: int) -> bool:
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raise NotImplementedError
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@abstractmethod
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def pin_lora(self, lora_id: int) -> bool:
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raise NotImplementedError
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@abstractmethod
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def list_loras(self) -> Set[int]:
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raise NotImplementedError
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class LoraNotSupportedWorkerBase(WorkerBase):
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"""Partial implementation of WorkerBase that raises exceptions when LoRA
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methods are invoked.
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"""
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def add_lora(self, lora_request: LoRARequest) -> bool:
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raise ValueError(f"{type(self)} does not support LoRA")
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def remove_lora(self, lora_id: int) -> bool:
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raise ValueError(f"{type(self)} does not support LoRA")
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def pin_lora(self, lora_id: int) -> bool:
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return ValueError(
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f"{type(self)} does not support LoRA") # type: ignore
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def list_loras(self) -> Set[int]:
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raise ValueError(f"{type(self)} does not support LoRA")
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@dataclasses.dataclass(frozen=True)
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class WorkerInput:
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"""Local inputs to each worker. May contain device-specific data. These
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fields should be broadcastable to other workers.
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"""
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num_seq_groups: Optional[int] = None
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blocks_to_swap_in: Optional[torch.Tensor] = None
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blocks_to_swap_out: Optional[torch.Tensor] = None
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blocks_to_copy: Optional[torch.Tensor] = None
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virtual_engine: int = 0
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num_steps: int = 1
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@classmethod
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def from_broadcasted_tensor_dict(
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cls: Type["WorkerInput"],
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tensor_dict: Dict[str, Any],
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) -> "WorkerInput":
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"""
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Pop fields from the given tensor_dict and populate a new instance of
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WorkerInput.
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"""
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return cls(
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num_seq_groups=tensor_dict.pop("num_seq_groups"),
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blocks_to_swap_in=tensor_dict.pop("blocks_to_swap_in"),
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blocks_to_swap_out=tensor_dict.pop("blocks_to_swap_out"),
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blocks_to_copy=tensor_dict.pop("blocks_to_copy"),
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virtual_engine=tensor_dict["virtual_engine"],
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num_steps=tensor_dict.pop("num_steps"),
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)
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def as_broadcastable_tensor_dict(
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self) -> Dict[str, Union[int, torch.Tensor]]:
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"""
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Extract broadcastable fields.
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"""
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tensor_dict = {
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"num_seq_groups": self.num_seq_groups,
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"blocks_to_swap_in": self.blocks_to_swap_in,
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"blocks_to_swap_out": self.blocks_to_swap_out,
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"blocks_to_copy": self.blocks_to_copy,
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"virtual_engine": self.virtual_engine,
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"num_steps": self.num_steps,
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}
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return tensor_dict
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class LocalOrDistributedWorkerBase(WorkerBase):
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"""
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Partial implementation of WorkerBase that has a default `execute_model`
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definition to perform metadata transfer between workers when in distributed
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mode. Subclasses of this interface should use model runners that inherit
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from ModelRunnerBase, and should only need to implement worker-local logic.
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If custom control plane logic is needed to transfer metadata, or if the
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model runner cannot inherit from ModelRunnerBase, use WorkerBase instead.
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"""
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is_driver_worker: bool
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model_runner: ModelRunnerBase
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observability_config: Optional[ObservabilityConfig] = None
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@property
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@abstractmethod
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def do_metadata_broadcast(self) -> bool:
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"""
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Used by the default `execute_model` to check whether broadcast is
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needed to transfer request inputs from the driver worker to other
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workers in the TP group. If WorkerBase subclass only supports
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single-worker execution, then this method should return False.
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"""
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raise NotImplementedError
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@property
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@abstractmethod
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def kv_cache(self) -> Optional[List[List[torch.Tensor]]]:
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"""
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Gets the list of kv caches to pass to the worker's model runner. Each
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element in the list is a kv cache corresponding to a particular virtual
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engine (PP stream). Used by the default `execute_model`. If the worker's
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model runner does not follow the ModelRunnerBase interface, then inherit
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from WorkerBase instead.
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"""
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raise NotImplementedError
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@abstractmethod
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def prepare_worker_input(
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self, execute_model_req: ExecuteModelRequest) -> WorkerInput:
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"""
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Prepare the inputs to WorkerBase.execute_worker from an execution
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request. This method may move data to the worker's local device. It is
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not allowed to communicate with other workers or devices.
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"""
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raise NotImplementedError
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@abstractmethod
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def execute_worker(self, worker_input: WorkerInput) -> None:
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"""
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Process an execution request.
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"""
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raise NotImplementedError
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def _get_worker_input_from_broadcast(
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self
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) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[
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str, torch.Tensor]]]:
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""" Get the worker input from the broadcasted tensor dict. """
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assert self.do_metadata_broadcast
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assert not self.is_driver_worker
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broadcast_data = broadcast_tensor_dict(src=0)
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if not broadcast_data:
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return None
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worker_input = WorkerInput.from_broadcasted_tensor_dict(broadcast_data)
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model_input = (
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self.model_runner.make_model_input_from_broadcasted_tensor_dict(
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broadcast_data))
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kwargs = extract_previous_hidden_states(broadcast_data)
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return model_input, worker_input, kwargs
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def _get_driver_input_and_broadcast(
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self, execute_model_req: ExecuteModelRequest
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) -> Tuple[BroadcastableModelInput, WorkerInput, Dict[str, torch.Tensor]]:
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""" Get the driver input and broadcast it to other workers. """
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assert self.is_driver_worker
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worker_input: WorkerInput = self.prepare_worker_input(
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execute_model_req=execute_model_req)
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model_input: ModelRunnerInputBase = (
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self.model_runner.prepare_model_input(
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execute_model_req.seq_group_metadata_list,
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execute_model_req.virtual_engine,
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execute_model_req.finished_requests_ids))
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kwargs = extract_previous_hidden_states(execute_model_req)
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if self.do_metadata_broadcast:
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broadcast_data = worker_input.as_broadcastable_tensor_dict()
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broadcast_data.update(model_input.as_broadcastable_tensor_dict())
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broadcast_data.update(kwargs)
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broadcast_tensor_dict(broadcast_data, src=0)
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if execute_model_req.async_callback:
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model_input = dataclasses.replace( # type: ignore
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model_input,
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async_callback=execute_model_req.async_callback)
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return model_input, worker_input, kwargs
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def prepare_input(
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self,
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execute_model_req: Optional[ExecuteModelRequest] = None
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) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[
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str, torch.Tensor]]]:
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"""
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Prepare the inputs to ModelRunner and workers.
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"""
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if self.is_driver_worker:
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if execute_model_req is None:
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if self.do_metadata_broadcast:
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# This signals that there's no more requests to process for
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# now. All workers are running infinite loop with
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# broadcast_tensor_dict, and it stops the loop when the
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# driver broadcasts an empty input. Send an empty input to
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# notify all other workers to stop their execution loop.
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broadcast_tensor_dict({}, src=0)
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return None
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return self._get_driver_input_and_broadcast(execute_model_req)
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else:
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return self._get_worker_input_from_broadcast()
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def execute_model(
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self,
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execute_model_req: Optional[ExecuteModelRequest] = None,
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) -> Optional[List[SamplerOutput]]:
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"""Executes at least one model step on the given sequences, unless no
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sequences are provided."""
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start_time = time.perf_counter()
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inputs = self.prepare_input(execute_model_req)
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if inputs is None:
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return None
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model_input, worker_input, kwargs = inputs
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num_steps = worker_input.num_steps
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self.execute_worker(worker_input)
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# If there is no input, we don't need to execute the model.
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if worker_input.num_seq_groups == 0:
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return []
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intermediate_tensors = None
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orig_model_execute_time = 0.0
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if not get_pp_group().is_first_rank:
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intermediate_tensors = IntermediateTensors(
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get_pp_group().recv_tensor_dict(
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all_gather_group=get_tp_group()))
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if (self.observability_config is not None
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and self.observability_config.collect_model_execute_time):
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orig_model_execute_time = intermediate_tensors.tensors.get(
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"model_execute_time", torch.tensor(0)).item()
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output = self.model_runner.execute_model(
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model_input=model_input,
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kv_caches=self.kv_cache[worker_input.virtual_engine]
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if self.kv_cache is not None else None,
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intermediate_tensors=intermediate_tensors,
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num_steps=num_steps,
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**kwargs,
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)
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model_execute_time = time.perf_counter() - start_time
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if not get_pp_group().is_last_rank:
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# output is IntermediateTensors
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if (self.observability_config is not None
|
||||
and self.observability_config.collect_model_execute_time):
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output.tensors["model_execute_time"] = torch.tensor(
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model_execute_time + orig_model_execute_time)
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get_pp_group().send_tensor_dict(output.tensors,
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all_gather_group=get_tp_group())
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return [None]
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if (self.observability_config is not None
|
||||
and self.observability_config.collect_model_execute_time
|
||||
and output is not None):
|
||||
for o in output:
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||||
o.model_execute_time = (orig_model_execute_time +
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||||
model_execute_time)
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||||
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||||
# output is List[SamplerOutput]
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||||
return output
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||||
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||||
def _execute_model_spmd(
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||||
self,
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||||
execute_model_req: ExecuteModelRequest,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None
|
||||
) -> Optional[List[SamplerOutput]]:
|
||||
"""
|
||||
Execute model in Single Program Multiple Data (SPMD) fashion.
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||||
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:
|
||||
"""
|
||||
The whole point of this class is to lazily initialize the worker.
|
||||
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`.
|
||||
|
||||
If worker_class_fn is specified, it will be executed to get the worker
|
||||
class.
|
||||
Otherwise, the worker class will be obtained by dynamically importing it
|
||||
using worker_module_name and worker_class_name.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
worker_module_name: str,
|
||||
worker_class_name: str,
|
||||
trust_remote_code: bool = False,
|
||||
worker_class_fn: Optional[Callable[[],
|
||||
Type[WorkerBase]]] = None) -> None:
|
||||
self.worker_module_name = worker_module_name
|
||||
self.worker_class_name = worker_class_name
|
||||
self.worker_class_fn = worker_class_fn
|
||||
self.worker: Optional[WorkerBase] = None
|
||||
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()
|
||||
|
||||
@staticmethod
|
||||
def update_environment_variables(envs: Dict[str, str]) -> None:
|
||||
key = 'MLU_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, *args, **kwargs):
|
||||
"""
|
||||
Here we inject some common logic before initializing the worker.
|
||||
Arguments are passed to the worker class constructor.
|
||||
"""
|
||||
enable_trace_function_call_for_thread()
|
||||
|
||||
# see https://github.com/NVIDIA/nccl/issues/1234
|
||||
os.environ['NCCL_CUMEM_ENABLE'] = '0'
|
||||
|
||||
from vllm.plugins import load_general_plugins
|
||||
load_general_plugins()
|
||||
|
||||
if self.worker_class_fn:
|
||||
worker_class = self.worker_class_fn()
|
||||
else:
|
||||
mod = importlib.import_module(self.worker_module_name)
|
||||
worker_class = getattr(mod, self.worker_class_name)
|
||||
|
||||
self.worker = worker_class(*args, **kwargs)
|
||||
assert self.worker is not None
|
||||
|
||||
def execute_method(self, method, *args, **kwargs):
|
||||
try:
|
||||
target = self if self.worker is None else self.worker
|
||||
executor = getattr(target, method)
|
||||
return executor(*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}. "
|
||||
"This might cause deadlock in distributed execution.")
|
||||
logger.exception(msg)
|
||||
raise e
|
||||
|
||||
|
||||
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
|
||||
Reference in New Issue
Block a user