1007 lines
38 KiB
Python
1007 lines
38 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""TokenizerManager is a process that tokenizes the text."""
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import asyncio
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import copy
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import dataclasses
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import logging
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import os
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import pickle
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import signal
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import sys
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import threading
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import time
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import uuid
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from datetime import datetime
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from http import HTTPStatus
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from typing import Any, Awaitable, Dict, Generic, List, Optional, Tuple, TypeVar, Union
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import fastapi
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import uvloop
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import zmq
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import zmq.asyncio
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from fastapi import BackgroundTasks
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from sglang.srt.aio_rwlock import RWLock
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
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from sglang.srt.managers.image_processor import (
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get_dummy_image_processor,
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get_image_processor,
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)
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from sglang.srt.managers.io_struct import (
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AbortReq,
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BatchEmbeddingOut,
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BatchStrOut,
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BatchTokenIDOut,
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CloseSessionReqInput,
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ConfigureLoggingReq,
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EmbeddingReqInput,
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FlushCacheReq,
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GenerateReqInput,
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GetWeightsByNameReqInput,
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GetWeightsByNameReqOutput,
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InitWeightsUpdateGroupReqInput,
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InitWeightsUpdateGroupReqOutput,
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OpenSessionReqInput,
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OpenSessionReqOutput,
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ProfileReq,
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ReleaseMemoryOccupationReqInput,
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ReleaseMemoryOccupationReqOutput,
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ResumeMemoryOccupationReqInput,
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ResumeMemoryOccupationReqOutput,
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SessionParams,
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TokenizedEmbeddingReqInput,
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TokenizedGenerateReqInput,
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UpdateWeightFromDiskReqInput,
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UpdateWeightFromDiskReqOutput,
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UpdateWeightsFromDistributedReqInput,
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UpdateWeightsFromDistributedReqOutput,
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UpdateWeightsFromTensorReqInput,
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UpdateWeightsFromTensorReqOutput,
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)
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from sglang.srt.metrics.collector import TokenizerMetricsCollector
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from sglang.srt.sampling.sampling_params import SamplingParams
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from sglang.srt.server_args import PortArgs, ServerArgs
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from sglang.srt.utils import (
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dataclass_to_string_truncated,
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get_zmq_socket,
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kill_process_tree,
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)
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from sglang.utils import TypeBasedDispatcher, get_exception_traceback
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asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
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logger = logging.getLogger(__name__)
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@dataclasses.dataclass
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class ReqState:
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"""Store the state a request."""
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out_list: List
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finished: bool
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event: asyncio.Event
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obj: Any
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# For metrics
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created_time: float
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first_token_time: Optional[float] = None
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# For streaming output
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last_output_offset: int = 0
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class TokenizerManager:
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"""TokenizerManager is a process that tokenizes the text."""
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def __init__(
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self,
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server_args: ServerArgs,
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port_args: PortArgs,
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):
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# Parse args
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self.server_args = server_args
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self.enable_metrics = server_args.enable_metrics
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self.log_requests = server_args.log_requests
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self.log_requests_level = 0
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# Init inter-process communication
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context = zmq.asyncio.Context(2)
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self.recv_from_detokenizer = get_zmq_socket(
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context, zmq.PULL, port_args.tokenizer_ipc_name, True
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)
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self.send_to_scheduler = get_zmq_socket(
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context, zmq.PUSH, port_args.scheduler_input_ipc_name, True
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)
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# Read model args
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self.model_path = server_args.model_path
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self.served_model_name = server_args.served_model_name
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self.model_config = ModelConfig(
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server_args.model_path,
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trust_remote_code=server_args.trust_remote_code,
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revision=server_args.revision,
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context_length=server_args.context_length,
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model_override_args=server_args.json_model_override_args,
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is_embedding=server_args.is_embedding,
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dtype=server_args.dtype,
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quantization=server_args.quantization,
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)
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self.is_generation = self.model_config.is_generation
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self.context_len = self.model_config.context_len
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self.image_token_id = self.model_config.image_token_id
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# Create image processor placeholder
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self.image_processor = get_dummy_image_processor()
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# Create tokenizer
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if server_args.skip_tokenizer_init:
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self.tokenizer = self.processor = None
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else:
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if self.model_config.is_multimodal:
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self.processor = get_processor(
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server_args.tokenizer_path,
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tokenizer_mode=server_args.tokenizer_mode,
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trust_remote_code=server_args.trust_remote_code,
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)
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self.tokenizer = self.processor.tokenizer
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# We want to parallelize the image pre-processing so we create an executor for it
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self.image_processor = get_image_processor(
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self.model_config.hf_config, server_args, self.processor
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)
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else:
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self.tokenizer = get_tokenizer(
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server_args.tokenizer_path,
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tokenizer_mode=server_args.tokenizer_mode,
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trust_remote_code=server_args.trust_remote_code,
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)
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# Store states
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self.to_create_loop = True
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self.rid_to_state: Dict[str, ReqState] = {}
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self.dump_requests_folder = "" # By default do not dump
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self.dump_requests_threshold = 1000
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self.dump_request_list: List[Tuple] = []
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# The event to notify the weight sync is finished.
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self.model_update_lock = RWLock()
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self.model_update_result: Optional[Awaitable[UpdateWeightFromDiskReqOutput]] = (
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None
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)
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self.asyncio_tasks = set()
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# For session info
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self.session_futures = {} # session_id -> asyncio event
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# Others
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self.gracefully_exit = False
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self.init_weights_update_group_communicator = _Communicator(
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self.send_to_scheduler, server_args.dp_size
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)
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self.update_weights_from_distributed_communicator = _Communicator(
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self.send_to_scheduler, server_args.dp_size
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)
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self.update_weights_from_tensor_communicator = _Communicator(
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self.send_to_scheduler, server_args.dp_size
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)
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self.get_weights_by_name_communicator = _Communicator(
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self.send_to_scheduler, server_args.dp_size
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)
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self.release_memory_occupation_communicator = _Communicator(
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self.send_to_scheduler, server_args.dp_size
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)
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self.resume_memory_occupation_communicator = _Communicator(
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self.send_to_scheduler, server_args.dp_size
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)
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# Set after scheduler is initialized
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self.max_req_input_len = None
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# Metrics
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if self.enable_metrics:
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self.metrics_collector = TokenizerMetricsCollector(
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labels={
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"model_name": self.server_args.served_model_name,
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# TODO: Add lora name/path in the future,
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},
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)
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self._dispatcher = TypeBasedDispatcher(
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[
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(BatchStrOut, self._handle_batch_output),
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(BatchEmbeddingOut, self._handle_batch_output),
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(BatchTokenIDOut, self._handle_batch_output),
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(OpenSessionReqOutput, self._handle_open_session_req_output),
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(
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UpdateWeightFromDiskReqOutput,
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self._handle_update_weights_from_disk_req_output,
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),
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(
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InitWeightsUpdateGroupReqOutput,
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self.init_weights_update_group_communicator.handle_recv,
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),
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(
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UpdateWeightsFromDistributedReqOutput,
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self.update_weights_from_distributed_communicator.handle_recv,
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),
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(
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UpdateWeightsFromTensorReqOutput,
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self.update_weights_from_tensor_communicator.handle_recv,
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),
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(
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GetWeightsByNameReqOutput,
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self.get_weights_by_name_communicator.handle_recv,
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),
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(
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ReleaseMemoryOccupationReqOutput,
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self.release_memory_occupation_communicator.handle_recv,
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),
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(
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ResumeMemoryOccupationReqOutput,
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self.resume_memory_occupation_communicator.handle_recv,
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),
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]
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)
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async def generate_request(
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self,
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obj: Union[GenerateReqInput, EmbeddingReqInput],
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request: Optional[fastapi.Request] = None,
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):
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created_time = time.time()
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self.auto_create_handle_loop()
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if isinstance(obj, EmbeddingReqInput) and self.is_generation:
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raise ValueError(
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"This model does not appear to be an embedding model by default. "
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"Please add `--is-embedding` when launching the server or try another model."
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)
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obj.normalize_batch_and_arguments()
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if self.log_requests:
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max_length = 2048 if self.log_requests_level == 0 else 1 << 30
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logger.info(
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f"Receive: obj={dataclass_to_string_truncated(obj, max_length)}"
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)
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async with self.model_update_lock.reader_lock:
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is_single = obj.is_single
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if is_single:
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tokenized_obj = await self._tokenize_one_request(obj)
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self._send_one_request(obj, tokenized_obj, created_time)
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async for response in self._wait_one_response(obj, request):
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yield response
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else:
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async for response in self._handle_batch_request(
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obj, request, created_time
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):
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yield response
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async def _tokenize_one_request(
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self,
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obj: Union[GenerateReqInput, EmbeddingReqInput],
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):
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"""Tokenize one request."""
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# Tokenize
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input_embeds = None
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input_text = obj.text
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if obj.input_embeds is not None:
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if not self.server_args.disable_radix_cache:
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raise ValueError(
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"input_embeds is provided while disable_radix_cache is False. "
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"Please add `--disable-radix-cache` when you launch the server "
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"if you want to use input_embeds as inputs."
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)
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input_embeds = obj.input_embeds
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input_ids = obj.input_ids
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elif obj.input_ids is not None:
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input_ids = obj.input_ids
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else:
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if self.tokenizer is None:
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raise ValueError(
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"The engine initialized with skip_tokenizer_init=True cannot "
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"accept text prompts. Please provide input_ids or re-initialize "
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"the engine with skip_tokenizer_init=False."
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)
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input_ids = self.tokenizer.encode(input_text)
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if self.is_generation:
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# TODO: also support getting embeddings for multimodal models
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image_inputs: Dict = await self.image_processor.process_images_async(
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obj.image_data, input_text or input_ids, obj, self.max_req_input_len
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)
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if image_inputs and "input_ids" in image_inputs:
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input_ids = image_inputs["input_ids"]
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return_logprob = obj.return_logprob
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logprob_start_len = obj.logprob_start_len
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top_logprobs_num = obj.top_logprobs_num
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session_params = (
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SessionParams(**obj.session_params) if obj.session_params else None
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)
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input_token_num = len(input_ids) if input_ids is not None else 0
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if input_token_num >= self.context_len:
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raise ValueError(
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f"The input ({input_token_num} tokens) is longer than the "
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f"model's context length ({self.context_len} tokens)."
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)
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if (
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obj.sampling_params.get("max_new_tokens") is not None
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and obj.sampling_params.get("max_new_tokens") + input_token_num
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>= self.context_len
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):
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raise ValueError(
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f"Requested token count exceeds the model's maximum context length "
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f"of {self.context_len} tokens. You requested a total of "
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f"{obj.sampling_params.get('max_new_tokens') + input_token_num} "
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f"tokens: {input_token_num} tokens from the input messages and "
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f"{obj.sampling_params.get('max_new_tokens')} tokens for the "
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f"completion. Please reduce the number of tokens in the input "
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f"messages or the completion to fit within the limit."
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)
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# Parse sampling parameters
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sampling_params = SamplingParams(**obj.sampling_params)
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sampling_params.normalize(self.tokenizer)
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sampling_params.verify()
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# Build return object
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if isinstance(obj, GenerateReqInput):
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tokenized_obj = TokenizedGenerateReqInput(
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obj.rid,
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input_text,
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input_ids,
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image_inputs,
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sampling_params,
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return_logprob,
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logprob_start_len,
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top_logprobs_num,
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obj.stream,
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lora_path=obj.lora_path,
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input_embeds=input_embeds,
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session_params=session_params,
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)
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elif isinstance(obj, EmbeddingReqInput):
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tokenized_obj = TokenizedEmbeddingReqInput(
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obj.rid,
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input_text,
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input_ids,
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sampling_params,
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)
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return tokenized_obj
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def _send_one_request(
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self,
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obj: Union[GenerateReqInput, EmbeddingReqInput],
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tokenized_obj: Union[TokenizedGenerateReqInput, TokenizedEmbeddingReqInput],
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created_time: Optional[float] = None,
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):
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event = asyncio.Event()
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state = ReqState([], False, event, obj, created_time=created_time)
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self.rid_to_state[obj.rid] = state
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self.send_to_scheduler.send_pyobj(tokenized_obj)
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async def _wait_one_response(
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self,
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obj: Union[GenerateReqInput, EmbeddingReqInput],
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request: Optional[fastapi.Request] = None,
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):
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"""Wait for the response of one request."""
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state = self.rid_to_state[obj.rid]
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while True:
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try:
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await asyncio.wait_for(state.event.wait(), timeout=4)
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except asyncio.TimeoutError:
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if request is not None and await request.is_disconnected():
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self.abort_request(obj.rid)
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raise ValueError(f"Abort request {obj.rid}")
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continue
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out = state.out_list[-1]
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state.out_list = []
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if state.finished:
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if self.log_requests:
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max_length = 2048 if self.log_requests_level == 0 else 1 << 30
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msg = f"Finish: obj={dataclass_to_string_truncated(obj, max_length)}, out={dataclass_to_string_truncated(out, max_length)}"
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logger.info(msg)
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del self.rid_to_state[obj.rid]
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# Check if this was an abort/error created by scheduler
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if isinstance(out["meta_info"].get("finish_reason"), dict):
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finish_reason = out["meta_info"]["finish_reason"]
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if (
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finish_reason.get("type") == "abort"
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and finish_reason.get("status_code") == HTTPStatus.BAD_REQUEST
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):
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raise ValueError(finish_reason["message"])
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yield out
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break
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state.event.clear()
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if obj.stream:
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yield out
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else:
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if request is not None and await request.is_disconnected():
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self.abort_request(obj.rid)
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raise ValueError(f"Abort request {obj.rid}")
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async def _handle_batch_request(
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self,
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obj: Union[GenerateReqInput, EmbeddingReqInput],
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request: Optional[fastapi.Request] = None,
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created_time: Optional[float] = None,
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):
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batch_size = obj.batch_size
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generators = []
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rids = []
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if getattr(obj, "parallel_sample_num", 1) == 1:
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# Send all requests
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for i in range(batch_size):
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tmp_obj = obj[i]
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tokenized_obj = await self._tokenize_one_request(tmp_obj)
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self._send_one_request(tmp_obj, tokenized_obj, created_time)
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generators.append(self._wait_one_response(tmp_obj, request))
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rids.append(tmp_obj.rid)
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else:
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# FIXME: When using batch and parallel_sample_num together, the perf is not optimal.
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if batch_size > 128:
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logger.warning(
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"Sending a single large batch with parallel sampling (n > 1) has not been well optimized. "
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"The performance might be better if you just duplicate the requests n times or use "
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"many threads to send them one by one with parallel sampling (n > 1)."
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)
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# Tokenize all requests
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objs = [obj[i] for i in range(batch_size)]
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tokenized_objs = await asyncio.gather(
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*(self._tokenize_one_request(obj) for obj in objs)
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)
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# Cache the common prefix for parallel sampling
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for i in range(batch_size):
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tmp_obj = copy.copy(objs[i])
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tokenized_obj = copy.copy(tokenized_objs[i])
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tokenized_obj.rid = tmp_obj.regenerate_rid()
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tokenized_obj.sampling_params = copy.copy(tokenized_obj.sampling_params)
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tokenized_obj.sampling_params.max_new_tokens = 0
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tokenized_obj.stream = False
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self._send_one_request(tmp_obj, tokenized_obj, created_time)
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await self._wait_one_response(tmp_obj, request).__anext__()
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# Expand requests, assign new rids for them, and send them
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for i in range(batch_size):
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for _ in range(obj.parallel_sample_num):
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tmp_obj = copy.copy(objs[i])
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tokenized_obj = copy.copy(tokenized_objs[i])
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tokenized_obj.rid = tmp_obj.regenerate_rid()
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self._send_one_request(tmp_obj, tokenized_obj, created_time)
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generators.append(self._wait_one_response(tmp_obj, request))
|
|
rids.append(tmp_obj.rid)
|
|
|
|
# Wait for all requests
|
|
is_stream = hasattr(obj, "stream") and obj.stream
|
|
if not is_stream:
|
|
outputs = await asyncio.gather(*(gen.__anext__() for gen in generators))
|
|
yield outputs
|
|
else:
|
|
rid_to_index = {rid: i for i, rid in enumerate(rids)}
|
|
task_map = {asyncio.create_task(gen.__anext__()): gen for gen in generators}
|
|
while task_map:
|
|
done, _ = await asyncio.wait(
|
|
task_map.keys(), return_when=asyncio.FIRST_COMPLETED
|
|
)
|
|
|
|
for task in done:
|
|
gen = task_map.pop(task)
|
|
try:
|
|
result = task.result()
|
|
result["index"] = rid_to_index[result["meta_info"]["id"]]
|
|
yield result
|
|
new_task = asyncio.create_task(gen.__anext__())
|
|
task_map[new_task] = gen
|
|
except StopAsyncIteration:
|
|
pass
|
|
|
|
def flush_cache(self):
|
|
req = FlushCacheReq()
|
|
self.send_to_scheduler.send_pyobj(req)
|
|
|
|
def abort_request(self, rid: str):
|
|
if rid not in self.rid_to_state:
|
|
return
|
|
del self.rid_to_state[rid]
|
|
req = AbortReq(rid)
|
|
self.send_to_scheduler.send_pyobj(req)
|
|
|
|
def start_profile(self):
|
|
req = ProfileReq.START_PROFILE
|
|
self.send_to_scheduler.send_pyobj(req)
|
|
|
|
def stop_profile(self):
|
|
req = ProfileReq.STOP_PROFILE
|
|
self.send_to_scheduler.send_pyobj(req)
|
|
|
|
async def update_weights_from_disk(
|
|
self,
|
|
obj: UpdateWeightFromDiskReqInput,
|
|
request: Optional[fastapi.Request] = None,
|
|
) -> Tuple[bool, str]:
|
|
self.auto_create_handle_loop()
|
|
|
|
# default the load format to the server_args
|
|
if obj.load_format is None:
|
|
obj.load_format = self.server_args.load_format
|
|
logger.info("Start update_weights. Load format=%s", obj.load_format)
|
|
|
|
if True:
|
|
# Hold the lock if it is not async. This means that weight sync
|
|
# cannot run while requests are in progress.
|
|
async with self.model_update_lock.writer_lock:
|
|
return await self._wait_for_model_update_from_disk(obj)
|
|
|
|
async def _wait_for_model_update_from_disk(
|
|
self, obj: UpdateWeightFromDiskReqInput
|
|
) -> Tuple[bool, str]:
|
|
self.send_to_scheduler.send_pyobj(obj)
|
|
self.model_update_result = asyncio.Future()
|
|
if self.server_args.dp_size == 1:
|
|
result = await self.model_update_result
|
|
if result.success:
|
|
self.served_model_name = obj.model_path
|
|
self.server_args.model_path = obj.model_path
|
|
self.server_args.load_format = obj.load_format
|
|
self.model_path = obj.model_path
|
|
return result.success, result.message
|
|
else: # self.server_args.dp_size > 1
|
|
self.model_update_tmp = []
|
|
result = await self.model_update_result
|
|
|
|
all_success = all([r.success for r in result])
|
|
if all_success is True:
|
|
self.server_args.model_path = obj.model_path
|
|
self.server_args.load_format = obj.load_format
|
|
self.model_path = obj.model_path
|
|
all_message = [r.message for r in result]
|
|
all_message = " | ".join(all_message)
|
|
return all_success, all_message
|
|
|
|
async def init_weights_update_group(
|
|
self,
|
|
obj: InitWeightsUpdateGroupReqInput,
|
|
request: Optional[fastapi.Request] = None,
|
|
) -> Tuple[bool, str]:
|
|
self.auto_create_handle_loop()
|
|
assert (
|
|
self.server_args.dp_size == 1
|
|
), "dp_size must be 1 for init parameter update group"
|
|
result = (await self.init_weights_update_group_communicator(obj))[0]
|
|
return result.success, result.message
|
|
|
|
async def update_weights_from_distributed(
|
|
self,
|
|
obj: UpdateWeightsFromDistributedReqInput,
|
|
request: Optional[fastapi.Request] = None,
|
|
) -> Tuple[bool, str]:
|
|
self.auto_create_handle_loop()
|
|
assert (
|
|
self.server_args.dp_size == 1
|
|
), "dp_size must be for update weights from distributed"
|
|
|
|
# This means that weight sync
|
|
# cannot run while requests are in progress.
|
|
async with self.model_update_lock.writer_lock:
|
|
result = (await self.update_weights_from_distributed_communicator(obj))[0]
|
|
return result.success, result.message
|
|
|
|
async def update_weights_from_tensor(
|
|
self,
|
|
obj: UpdateWeightsFromTensorReqInput,
|
|
request: Optional[fastapi.Request] = None,
|
|
) -> Tuple[bool, str]:
|
|
self.auto_create_handle_loop()
|
|
assert (
|
|
self.server_args.dp_size == 1
|
|
), "dp_size must be for update weights from distributed"
|
|
|
|
# This means that weight sync
|
|
# cannot run while requests are in progress.
|
|
async with self.model_update_lock.writer_lock:
|
|
result = (await self.update_weights_from_tensor_communicator(obj))[0]
|
|
return result.success, result.message
|
|
|
|
async def get_weights_by_name(
|
|
self, obj: GetWeightsByNameReqInput, request: Optional[fastapi.Request] = None
|
|
):
|
|
self.auto_create_handle_loop()
|
|
results = await self.get_weights_by_name_communicator(obj)
|
|
all_parameters = [r.parameter for r in results]
|
|
if self.server_args.dp_size == 1:
|
|
return all_parameters[0]
|
|
else:
|
|
return all_parameters
|
|
|
|
async def release_memory_occupation(
|
|
self,
|
|
obj: ReleaseMemoryOccupationReqInput,
|
|
request: Optional[fastapi.Request] = None,
|
|
):
|
|
self.auto_create_handle_loop()
|
|
await self.release_memory_occupation_communicator(obj)
|
|
|
|
async def resume_memory_occupation(
|
|
self,
|
|
obj: ResumeMemoryOccupationReqInput,
|
|
request: Optional[fastapi.Request] = None,
|
|
):
|
|
self.auto_create_handle_loop()
|
|
await self.resume_memory_occupation_communicator(obj)
|
|
|
|
async def open_session(
|
|
self, obj: OpenSessionReqInput, request: Optional[fastapi.Request] = None
|
|
):
|
|
self.auto_create_handle_loop()
|
|
|
|
if obj.session_id is None:
|
|
obj.session_id = uuid.uuid4().hex
|
|
elif obj.session_id in self.session_futures:
|
|
return None
|
|
|
|
self.send_to_scheduler.send_pyobj(obj)
|
|
|
|
self.session_futures[obj.session_id] = asyncio.Future()
|
|
session_id = await self.session_futures[obj.session_id]
|
|
del self.session_futures[obj.session_id]
|
|
return session_id
|
|
|
|
async def close_session(
|
|
self, obj: CloseSessionReqInput, request: Optional[fastapi.Request] = None
|
|
):
|
|
assert not self.to_create_loop, "close session should not be the first request"
|
|
await self.send_to_scheduler.send_pyobj(obj)
|
|
|
|
def configure_logging(self, obj: ConfigureLoggingReq):
|
|
if obj.log_requests is not None:
|
|
self.log_requests = obj.log_requests
|
|
if obj.log_requests_level is not None:
|
|
self.log_requests_level = obj.log_requests_level
|
|
if obj.dump_requests_folder is not None:
|
|
self.dump_requests_folder = obj.dump_requests_folder
|
|
if obj.dump_requests_threshold is not None:
|
|
self.dump_requests_threshold = obj.dump_requests_threshold
|
|
logging.info(f"Config logging: {obj=}")
|
|
|
|
def create_abort_task(self, obj: GenerateReqInput):
|
|
# Abort the request if the client is disconnected.
|
|
async def abort_request():
|
|
await asyncio.sleep(1)
|
|
if obj.is_single:
|
|
self.abort_request(obj.rid)
|
|
else:
|
|
for rid in obj.rid:
|
|
self.abort_request(rid)
|
|
|
|
background_tasks = BackgroundTasks()
|
|
background_tasks.add_task(abort_request)
|
|
return background_tasks
|
|
|
|
def auto_create_handle_loop(self):
|
|
if not self.to_create_loop:
|
|
return
|
|
|
|
self.to_create_loop = False
|
|
loop = asyncio.get_event_loop()
|
|
self.asyncio_tasks.add(
|
|
loop.create_task(print_exception_wrapper(self.handle_loop))
|
|
)
|
|
|
|
# We cannot add signal handler when the tokenizer manager is not in
|
|
# the main thread due to the CPython limitation.
|
|
if threading.current_thread() is threading.main_thread():
|
|
signal_handler = SignalHandler(self)
|
|
loop.add_signal_handler(signal.SIGTERM, signal_handler.signal_handler)
|
|
else:
|
|
logger.warning(
|
|
"Signal handler is not added because the tokenizer manager is "
|
|
"not in the main thread. This disables graceful shutdown of the "
|
|
"tokenizer manager when SIGTERM is received."
|
|
)
|
|
self.asyncio_tasks.add(
|
|
loop.create_task(print_exception_wrapper(self.sigterm_watchdog))
|
|
)
|
|
|
|
async def sigterm_watchdog(self):
|
|
while not self.gracefully_exit:
|
|
await asyncio.sleep(5)
|
|
|
|
# Drain requests
|
|
while True:
|
|
remain_num_req = len(self.rid_to_state)
|
|
logger.info(
|
|
f"Gracefully exiting... remaining number of requests {remain_num_req}"
|
|
)
|
|
if remain_num_req > 0:
|
|
await asyncio.sleep(5)
|
|
else:
|
|
break
|
|
|
|
kill_process_tree(os.getpid(), include_parent=True)
|
|
sys.exit(0)
|
|
|
|
async def handle_loop(self):
|
|
"""The event loop that handles requests"""
|
|
|
|
while True:
|
|
recv_obj = await self.recv_from_detokenizer.recv_pyobj()
|
|
self._dispatcher(recv_obj)
|
|
|
|
def _handle_batch_output(
|
|
self, recv_obj: Union[BatchStrOut, BatchEmbeddingOut, BatchTokenIDOut]
|
|
):
|
|
for i, rid in enumerate(recv_obj.rids):
|
|
state = self.rid_to_state.get(rid, None)
|
|
if state is None:
|
|
continue
|
|
|
|
meta_info = {
|
|
"id": rid,
|
|
"finish_reason": recv_obj.finished_reasons[i],
|
|
"prompt_tokens": recv_obj.prompt_tokens[i],
|
|
}
|
|
|
|
if getattr(state.obj, "return_logprob", False):
|
|
self.convert_logprob_style(
|
|
meta_info,
|
|
state.obj.top_logprobs_num,
|
|
state.obj.return_text_in_logprobs,
|
|
recv_obj,
|
|
i,
|
|
)
|
|
|
|
if not isinstance(recv_obj, BatchEmbeddingOut):
|
|
meta_info.update(
|
|
{
|
|
"completion_tokens": recv_obj.completion_tokens[i],
|
|
"cached_tokens": recv_obj.cached_tokens[i],
|
|
}
|
|
)
|
|
|
|
if isinstance(recv_obj, BatchStrOut):
|
|
out_dict = {
|
|
"text": recv_obj.output_strs[i],
|
|
"meta_info": meta_info,
|
|
}
|
|
elif isinstance(recv_obj, BatchTokenIDOut):
|
|
out_dict = {
|
|
"token_ids": recv_obj.output_ids[i],
|
|
"meta_info": meta_info,
|
|
}
|
|
else:
|
|
assert isinstance(recv_obj, BatchEmbeddingOut)
|
|
out_dict = {
|
|
"embedding": recv_obj.embeddings[i],
|
|
"meta_info": meta_info,
|
|
}
|
|
state.out_list.append(out_dict)
|
|
state.finished = recv_obj.finished_reasons[i] is not None
|
|
state.event.set()
|
|
|
|
if self.enable_metrics and state.obj.log_metrics:
|
|
self.collect_metrics(state, recv_obj, i)
|
|
if self.dump_requests_folder and state.finished and state.obj.log_metrics:
|
|
self.dump_requests(state, out_dict)
|
|
|
|
def convert_logprob_style(
|
|
self,
|
|
meta_info: dict,
|
|
top_logprobs_num: int,
|
|
return_text_in_logprobs: bool,
|
|
recv_obj: BatchStrOut,
|
|
recv_obj_index: int,
|
|
):
|
|
meta_info["input_token_logprobs"] = self.detokenize_logprob_tokens(
|
|
recv_obj.input_token_logprobs_val[recv_obj_index],
|
|
recv_obj.input_token_logprobs_idx[recv_obj_index],
|
|
return_text_in_logprobs,
|
|
)
|
|
meta_info["output_token_logprobs"] = self.detokenize_logprob_tokens(
|
|
recv_obj.output_token_logprobs_val[recv_obj_index],
|
|
recv_obj.output_token_logprobs_idx[recv_obj_index],
|
|
return_text_in_logprobs,
|
|
)
|
|
|
|
if top_logprobs_num > 0:
|
|
meta_info["input_top_logprobs"] = self.detokenize_top_logprobs_tokens(
|
|
recv_obj.input_top_logprobs_val[recv_obj_index],
|
|
recv_obj.input_top_logprobs_idx[recv_obj_index],
|
|
return_text_in_logprobs,
|
|
)
|
|
meta_info["output_top_logprobs"] = self.detokenize_top_logprobs_tokens(
|
|
recv_obj.output_top_logprobs_val[recv_obj_index],
|
|
recv_obj.output_top_logprobs_idx[recv_obj_index],
|
|
return_text_in_logprobs,
|
|
)
|
|
|
|
def detokenize_logprob_tokens(
|
|
self,
|
|
token_logprobs_val: List[float],
|
|
token_logprobs_idx: List[int],
|
|
decode_to_text: bool,
|
|
):
|
|
if not decode_to_text:
|
|
return [
|
|
(logprob, token_id, None)
|
|
for logprob, token_id in zip(token_logprobs_val, token_logprobs_idx)
|
|
]
|
|
else:
|
|
assert self.tokenizer is not None
|
|
token_texts = self.tokenizer.batch_decode(token_logprobs_idx)
|
|
return list(zip(token_logprobs_val, token_logprobs_idx, token_texts))
|
|
|
|
def detokenize_top_logprobs_tokens(
|
|
self,
|
|
token_logprobs_val: List[float],
|
|
token_logprobs_idx: List[int],
|
|
decode_to_text: bool,
|
|
):
|
|
# TODO: The current implementation only batches the detokenization for top-k tokens per single position.
|
|
# We should batch all top-k tokens in all positions.
|
|
ret = []
|
|
for i in range(len(token_logprobs_val)):
|
|
if token_logprobs_val[i]:
|
|
ret.append(
|
|
self.detokenize_logprob_tokens(
|
|
token_logprobs_val[i], token_logprobs_idx[i], decode_to_text
|
|
)
|
|
)
|
|
else:
|
|
ret.append(None)
|
|
return ret
|
|
|
|
def collect_metrics(self, state: ReqState, recv_obj: BatchStrOut, i: int):
|
|
completion_tokens = (
|
|
recv_obj.completion_tokens[i]
|
|
if getattr(recv_obj, "completion_tokens", None)
|
|
else 0
|
|
)
|
|
|
|
if state.first_token_time is None:
|
|
state.first_token_time = time.time()
|
|
self.metrics_collector.observe_time_to_first_token(
|
|
state.first_token_time - state.created_time
|
|
)
|
|
else:
|
|
if completion_tokens >= 2:
|
|
# Compute time_per_output_token for the streaming case
|
|
self.metrics_collector.observe_time_per_output_token(
|
|
(time.time() - state.first_token_time) / (completion_tokens - 1)
|
|
)
|
|
|
|
if state.finished:
|
|
self.metrics_collector.observe_one_finished_request(
|
|
recv_obj.prompt_tokens[i], completion_tokens
|
|
)
|
|
self.metrics_collector.observe_e2e_request_latency(
|
|
time.time() - state.created_time
|
|
)
|
|
# Compute time_per_output_token for the non-streaming case
|
|
if (
|
|
hasattr(state.obj, "stream")
|
|
and not state.obj.stream
|
|
and completion_tokens >= 1
|
|
):
|
|
self.metrics_collector.observe_time_per_output_token(
|
|
(time.time() - state.created_time) / completion_tokens
|
|
)
|
|
|
|
def dump_requests(self, state: ReqState, out_dict: dict):
|
|
self.dump_request_list.append(
|
|
(state.obj, out_dict, state.created_time, time.time())
|
|
)
|
|
|
|
if len(self.dump_request_list) >= self.dump_requests_threshold:
|
|
filename = os.path.join(
|
|
self.dump_requests_folder,
|
|
datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".pkl",
|
|
)
|
|
logger.info(f"Dump {len(self.dump_request_list)} requests to {filename}")
|
|
|
|
to_dump = self.dump_request_list
|
|
self.dump_request_list = []
|
|
|
|
def background_task():
|
|
os.makedirs(self.dump_requests_folder, exist_ok=True)
|
|
with open(filename, "wb") as f:
|
|
pickle.dump(to_dump, f)
|
|
|
|
# Schedule the task to run in the background without awaiting it
|
|
asyncio.create_task(asyncio.to_thread(background_task))
|
|
|
|
def _handle_open_session_req_output(self, recv_obj):
|
|
self.session_futures[recv_obj.session_id].set_result(
|
|
recv_obj.session_id if recv_obj.success else None
|
|
)
|
|
|
|
def _handle_update_weights_from_disk_req_output(self, recv_obj):
|
|
if self.server_args.dp_size == 1:
|
|
self.model_update_result.set_result(recv_obj)
|
|
else: # self.server_args.dp_size > 1
|
|
self.model_update_tmp.append(recv_obj)
|
|
# set future if the all results are recevied
|
|
if len(self.model_update_tmp) == self.server_args.dp_size:
|
|
self.model_update_result.set_result(self.model_update_tmp)
|
|
|
|
|
|
async def print_exception_wrapper(func):
|
|
"""
|
|
Sometimes an asyncio function does not print exception.
|
|
We do another wrapper to handle the exception.
|
|
"""
|
|
try:
|
|
await func()
|
|
except Exception:
|
|
traceback = get_exception_traceback()
|
|
logger.error(f"TokenizerManager hit an exception: {traceback}")
|
|
kill_process_tree(os.getpid(), include_parent=True)
|
|
sys.exit(1)
|
|
|
|
|
|
class SignalHandler:
|
|
def __init__(self, tokenizer_manager):
|
|
self.tokenizer_manager = tokenizer_manager
|
|
|
|
def signal_handler(self, signum=None, frame=None):
|
|
logger.warning(
|
|
f"SIGTERM received. {signum=} {frame=}. Draining requests and shutting down..."
|
|
)
|
|
self.tokenizer_manager.gracefully_exit = True
|
|
|
|
|
|
T = TypeVar("T")
|
|
|
|
|
|
class _Communicator(Generic[T]):
|
|
def __init__(self, sender, fan_out: int):
|
|
self._sender = sender
|
|
self._fan_out = fan_out
|
|
self._result_future: Optional[asyncio.Future] = None
|
|
self._result_values: Optional[List[T]] = None
|
|
|
|
async def __call__(self, obj):
|
|
self._sender.send_pyobj(obj)
|
|
self._result_future = asyncio.Future()
|
|
self._result_values = []
|
|
await self._result_future
|
|
result_values = self._result_values
|
|
self._result_future = self._result_values = None
|
|
return result_values
|
|
|
|
def handle_recv(self, recv_obj: T):
|
|
self._result_values.append(recv_obj)
|
|
if len(self._result_values) == self._fan_out:
|
|
self._result_future.set_result(None)
|