1803 lines
69 KiB
Python
1803 lines
69 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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"""A scheduler that manages a tensor parallel GPU worker."""
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import faulthandler
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import logging
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import os
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import signal
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import threading
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import time
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import warnings
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from collections import deque
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from concurrent import futures
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from dataclasses import dataclass
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from http import HTTPStatus
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from types import SimpleNamespace
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from typing import Dict, List, Optional, Tuple, Union
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import psutil
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import setproctitle
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import torch
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import zmq
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from sglang.global_config import global_config
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.constrained.base_grammar_backend import create_grammar_backend
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from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
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from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
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from sglang.srt.layers.logits_processor import LogitsProcessorOutput
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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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BatchTokenIDOut,
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CloseSessionReqInput,
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FlushCacheReq,
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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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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.managers.schedule_batch import (
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FINISH_ABORT,
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BaseFinishReason,
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ImageInputs,
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Req,
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ScheduleBatch,
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global_server_args_dict,
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)
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from sglang.srt.managers.schedule_policy import (
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AddReqResult,
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PrefillAdder,
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SchedulePolicy,
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)
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from sglang.srt.managers.session_controller import Session
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from sglang.srt.managers.tp_worker import TpModelWorker
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from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient
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from sglang.srt.managers.utils import validate_input_length
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from sglang.srt.mem_cache.chunk_cache import ChunkCache
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from sglang.srt.mem_cache.radix_cache import RadixCache
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from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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from sglang.srt.server_args import PortArgs, ServerArgs
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from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
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from sglang.srt.utils import (
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broadcast_pyobj,
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configure_logger,
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crash_on_warnings,
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get_bool_env_var,
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get_zmq_socket,
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set_gpu_proc_affinity,
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set_random_seed,
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suppress_other_loggers,
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)
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from sglang.utils import TypeBasedDispatcher, get_exception_traceback
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logger = logging.getLogger(__name__)
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# Test retract decode for debugging purposes
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test_retract = get_bool_env_var("SGLANG_TEST_RETRACT")
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@dataclass
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class GenerationBatchResult:
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logits_output: LogitsProcessorOutput
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next_token_ids: List[int]
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bid: int
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@dataclass
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class EmbeddingBatchResult:
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embeddings: torch.Tensor
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bid: int
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class Scheduler:
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"""A scheduler that manages a tensor parallel GPU worker."""
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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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gpu_id: int,
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tp_rank: int,
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dp_rank: Optional[int],
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):
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# Parse args
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self.server_args = server_args
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self.tp_rank = tp_rank
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self.tp_size = server_args.tp_size
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self.schedule_policy = server_args.schedule_policy
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self.disable_jump_forward = server_args.disable_jump_forward
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self.lora_paths = server_args.lora_paths
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self.max_loras_per_batch = server_args.max_loras_per_batch
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self.enable_overlap = not server_args.disable_overlap_schedule
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self.skip_tokenizer_init = server_args.skip_tokenizer_init
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self.enable_metrics = server_args.enable_metrics
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self.spec_algorithm = SpeculativeAlgorithm.from_string(
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server_args.speculative_algorithm
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)
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self.decode_mem_cache_buf_multiplier = (
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self.server_args.speculative_num_draft_tokens
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if not self.spec_algorithm.is_none()
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else 1
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)
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self.enable_hierarchical_cache = server_args.enable_hierarchical_cache
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# Distributed rank info
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self.dp_size = server_args.dp_size
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self.attn_tp_rank, self.attn_tp_size, self.dp_rank = (
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compute_dp_attention_world_info(
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server_args.enable_dp_attention,
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self.tp_rank,
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self.tp_size,
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self.dp_size,
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)
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)
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# Init inter-process communication
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context = zmq.Context(2)
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if self.attn_tp_rank == 0:
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self.recv_from_tokenizer = get_zmq_socket(
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context, zmq.PULL, port_args.scheduler_input_ipc_name, False
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)
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self.send_to_tokenizer = get_zmq_socket(
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context, zmq.PUSH, port_args.tokenizer_ipc_name, False
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)
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if server_args.skip_tokenizer_init:
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# Directly send to the TokenizerManager
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self.send_to_detokenizer = get_zmq_socket(
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context, zmq.PUSH, port_args.tokenizer_ipc_name, False
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)
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else:
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# Send to the DetokenizerManager
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self.send_to_detokenizer = get_zmq_socket(
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context, zmq.PUSH, port_args.detokenizer_ipc_name, False
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)
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else:
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self.recv_from_tokenizer = None
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self.send_to_tokenizer = SimpleNamespace(send_pyobj=lambda x: None)
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self.send_to_detokenizer = SimpleNamespace(send_pyobj=lambda x: None)
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# Init tokenizer
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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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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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revision=server_args.revision,
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)
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self.tokenizer = self.processor.tokenizer
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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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revision=server_args.revision,
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)
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# Check whether overlap can be enabled
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if not self.is_generation:
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self.enable_overlap = False
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logger.info("Overlap scheduler is disabled for embedding models.")
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if self.model_config.is_multimodal:
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self.enable_overlap = False
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logger.info("Overlap scheduler is disabled for multimodal models.")
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if self.enable_overlap:
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self.disable_jump_forward = True
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# Launch a tensor parallel worker
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if self.enable_overlap:
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TpWorkerClass = TpModelWorkerClient
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else:
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TpWorkerClass = TpModelWorker
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self.tp_worker = TpWorkerClass(
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server_args=server_args,
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gpu_id=gpu_id,
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tp_rank=tp_rank,
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dp_rank=dp_rank,
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nccl_port=port_args.nccl_port,
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)
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# Launch a worker for speculative decoding if needed
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if self.spec_algorithm.is_eagle():
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from sglang.srt.speculative.eagle_worker import EAGLEWorker
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self.draft_worker = EAGLEWorker(
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gpu_id=gpu_id,
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tp_rank=tp_rank,
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server_args=server_args,
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nccl_port=port_args.nccl_port,
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target_worker=self.tp_worker,
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dp_rank=dp_rank,
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)
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else:
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self.draft_worker = None
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# Get token and memory info from the model worker
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(
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self.max_total_num_tokens,
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self.max_prefill_tokens,
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self.max_running_requests,
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self.max_req_len,
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self.max_req_input_len,
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self.random_seed,
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self.device,
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worker_global_server_args_dict,
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_,
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_,
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_,
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) = self.tp_worker.get_worker_info()
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self.tp_cpu_group = self.tp_worker.get_tp_cpu_group()
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self.attn_tp_cpu_group = self.tp_worker.get_attention_tp_cpu_group()
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self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func()
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global_server_args_dict.update(worker_global_server_args_dict)
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set_random_seed(self.random_seed)
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# Print debug info
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logger.info(
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f"max_total_num_tokens={self.max_total_num_tokens}, "
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f"chunked_prefill_size={server_args.chunked_prefill_size}, "
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f"max_prefill_tokens={self.max_prefill_tokens}, "
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f"max_running_requests={self.max_running_requests}, "
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f"context_len={self.model_config.context_len}"
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)
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# Init memory pool and cache
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self.req_to_token_pool, self.token_to_kv_pool = self.tp_worker.get_memory_pool()
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if (
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server_args.chunked_prefill_size is not None
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and server_args.disable_radix_cache
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):
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self.tree_cache = ChunkCache(
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req_to_token_pool=self.req_to_token_pool,
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token_to_kv_pool=self.token_to_kv_pool,
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)
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else:
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self.tree_cache = RadixCache(
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req_to_token_pool=self.req_to_token_pool,
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token_to_kv_pool=self.token_to_kv_pool,
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disable=server_args.disable_radix_cache,
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)
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self.tree_cache_metrics = {"total": 0, "hit": 0}
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self.policy = SchedulePolicy(self.schedule_policy, self.tree_cache)
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# Init running status
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self.waiting_queue: List[Req] = []
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# The running decoding batch for continuous batching
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self.running_batch: Optional[ScheduleBatch] = None
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# The current forward batch
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self.cur_batch: Optional[ScheduleBatch] = None
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# The current forward batch
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self.last_batch: Optional[ScheduleBatch] = None
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self.forward_ct = 0
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self.forward_ct_decode = 0
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self.num_generated_tokens = 0
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self.spec_num_total_accepted_tokens = 0
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self.spec_num_total_forward_ct = 0
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self.last_decode_stats_tic = time.time()
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self.stream_interval = server_args.stream_interval
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self.current_stream = torch.get_device_module(self.device).current_stream()
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if self.device == "cpu":
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self.current_stream.synchronize = lambda: None # No-op for CPU
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# Session info
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self.sessions: Dict[str, Session] = {}
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# Init chunked prefill
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self.chunked_prefill_size = server_args.chunked_prefill_size
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if self.chunked_prefill_size <= 0: # -1 means disable
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self.chunked_prefill_size = None
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self.being_chunked_req = None
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self.is_mixed_chunk = (
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self.chunked_prefill_size is not None and server_args.enable_mixed_chunk
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)
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# Init the grammar backend for constrained generation
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self.grammar_queue: List[Req] = []
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if not server_args.skip_tokenizer_init:
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self.grammar_backend = create_grammar_backend(
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server_args, self.tokenizer, self.model_config.vocab_size
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)
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else:
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self.grammar_backend = None
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# Init new token estimation
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assert (
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server_args.schedule_conservativeness >= 0
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), "Invalid schedule_conservativeness"
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self.init_new_token_ratio = min(
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global_config.default_init_new_token_ratio
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* server_args.schedule_conservativeness,
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1.0,
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)
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self.min_new_token_ratio = min(
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self.init_new_token_ratio
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* global_config.default_min_new_token_ratio_factor,
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1.0,
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)
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self.new_token_ratio_decay = (
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self.init_new_token_ratio - self.min_new_token_ratio
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) / global_config.default_new_token_ratio_decay_steps
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self.new_token_ratio = self.init_new_token_ratio
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# Tells whether the current running batch is full so that we can skip
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# the check of whether to prefill new requests.
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# This is an optimization to reduce the overhead of the prefill check.
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self.batch_is_full = False
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# Init watchdog thread
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self.watchdog_timeout = server_args.watchdog_timeout
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t = threading.Thread(target=self.watchdog_thread, daemon=True)
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t.start()
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self.parent_process = psutil.Process().parent()
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self.memory_saver_adapter = TorchMemorySaverAdapter.create(
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enable=server_args.enable_memory_saver
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)
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# Init profiler
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if os.getenv("SGLANG_TORCH_PROFILER_DIR", "") == "":
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self.profiler = None
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else:
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self.torch_profiler_trace_dir = os.getenv("SGLANG_TORCH_PROFILER_DIR")
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logger.info(
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"Profiling enabled. Traces will be saved to: %s",
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self.torch_profiler_trace_dir,
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)
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self.profiler = torch.profiler.profile(
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activities=[
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torch.profiler.ProfilerActivity.CPU,
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torch.profiler.ProfilerActivity.CUDA,
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],
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with_stack=True,
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)
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# Init metrics stats
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self.stats = SchedulerStats()
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if self.enable_metrics:
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self.metrics_collector = SchedulerMetricsCollector(
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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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# The largest prefill length of a single request
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self._largest_prefill_len: int = 0
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# The largest context length (prefill + generation) of a single request
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self._largest_prefill_decode_len: int = 0
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# Init request dispatcher
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self._request_dispatcher = TypeBasedDispatcher(
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[
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(TokenizedGenerateReqInput, self.handle_generate_request),
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(TokenizedEmbeddingReqInput, self.handle_embedding_request),
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(FlushCacheReq, self.flush_cache_wrapped),
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(AbortReq, self.abort_request),
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(UpdateWeightFromDiskReqInput, self.update_weights_from_disk),
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(InitWeightsUpdateGroupReqInput, self.init_weights_update_group),
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(
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UpdateWeightsFromDistributedReqInput,
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self.update_weights_from_distributed,
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),
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(UpdateWeightsFromTensorReqInput, self.update_weights_from_tensor),
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(GetWeightsByNameReqInput, self.get_weights_by_name),
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(ProfileReq, self.profile),
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(OpenSessionReqInput, self.open_session),
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(CloseSessionReqInput, self.close_session),
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(
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ReleaseMemoryOccupationReqInput,
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lambda _: self.release_memory_occupation(),
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),
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(
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ResumeMemoryOccupationReqInput,
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lambda _: self.resume_memory_occupation(),
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),
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]
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)
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def watchdog_thread(self):
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"""A watch dog thread that will try to kill the server itself if one batch takes too long."""
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self.watchdog_last_forward_ct = 0
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self.watchdog_last_time = time.time()
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while True:
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current = time.time()
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if self.cur_batch is not None:
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if self.watchdog_last_forward_ct == self.forward_ct:
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if current > self.watchdog_last_time + self.watchdog_timeout:
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logger.error(f"Watchdog timeout ({self.watchdog_timeout=})")
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break
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else:
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self.watchdog_last_forward_ct = self.forward_ct
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self.watchdog_last_time = current
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time.sleep(self.watchdog_timeout // 2)
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# Wait sometimes so that the parent process can print the error.
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time.sleep(5)
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self.parent_process.send_signal(signal.SIGQUIT)
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@torch.no_grad()
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def event_loop_normal(self):
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"""A normal scheduler loop."""
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while True:
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recv_reqs = self.recv_requests()
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self.process_input_requests(recv_reqs)
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batch = self.get_next_batch_to_run()
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self.cur_batch = batch
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if batch:
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result = self.run_batch(batch)
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self.process_batch_result(batch, result)
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else:
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# When the server is idle, so self-check and re-init some states
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self.check_memory()
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self.new_token_ratio = self.init_new_token_ratio
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self.last_batch = batch
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@torch.no_grad()
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|
def event_loop_overlap(self):
|
|
"""A scheduler loop that overlaps the CPU processing and GPU computation."""
|
|
self.result_queue = deque()
|
|
|
|
while True:
|
|
recv_reqs = self.recv_requests()
|
|
self.process_input_requests(recv_reqs)
|
|
|
|
batch = self.get_next_batch_to_run()
|
|
self.cur_batch = batch
|
|
|
|
if batch:
|
|
result = self.run_batch(batch)
|
|
self.result_queue.append((batch.copy(), result))
|
|
|
|
if self.last_batch is None:
|
|
# Create a dummy first batch to start the pipeline for overlap schedule.
|
|
# It is now used for triggering the sampling_info_done event.
|
|
tmp_batch = ScheduleBatch(
|
|
reqs=None,
|
|
forward_mode=ForwardMode.DUMMY_FIRST,
|
|
next_batch_sampling_info=self.tp_worker.cur_sampling_info,
|
|
)
|
|
self.process_batch_result(tmp_batch, None)
|
|
|
|
if self.last_batch:
|
|
# Process the results of the last batch
|
|
tmp_batch, tmp_result = self.result_queue.popleft()
|
|
tmp_batch.next_batch_sampling_info = (
|
|
self.tp_worker.cur_sampling_info if batch else None
|
|
)
|
|
self.process_batch_result(tmp_batch, tmp_result)
|
|
elif batch is None:
|
|
# When the server is idle, so self-check and re-init some states
|
|
self.check_memory()
|
|
self.new_token_ratio = self.init_new_token_ratio
|
|
|
|
self.last_batch = batch
|
|
|
|
def recv_requests(self) -> List[Req]:
|
|
"""Receive results at tp_rank = 0 and broadcast it to all other TP ranks."""
|
|
if self.attn_tp_rank == 0:
|
|
recv_reqs = []
|
|
|
|
while True:
|
|
try:
|
|
recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
|
|
except zmq.ZMQError:
|
|
break
|
|
recv_reqs.append(recv_req)
|
|
else:
|
|
recv_reqs = None
|
|
|
|
if self.server_args.enable_dp_attention:
|
|
if self.attn_tp_rank == 0:
|
|
work_reqs = [
|
|
req
|
|
for req in recv_reqs
|
|
if isinstance(
|
|
req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput)
|
|
)
|
|
]
|
|
control_reqs = [
|
|
req
|
|
for req in recv_reqs
|
|
if not isinstance(
|
|
req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput)
|
|
)
|
|
]
|
|
else:
|
|
work_reqs = None
|
|
control_reqs = None
|
|
|
|
if self.attn_tp_size != 1:
|
|
attn_tp_rank_0 = self.dp_rank * self.attn_tp_size
|
|
work_reqs = broadcast_pyobj(
|
|
work_reqs,
|
|
self.attn_tp_rank,
|
|
self.attn_tp_cpu_group,
|
|
src=attn_tp_rank_0,
|
|
)
|
|
if self.tp_size != 1:
|
|
control_reqs = broadcast_pyobj(
|
|
control_reqs, self.tp_rank, self.tp_cpu_group
|
|
)
|
|
recv_reqs = work_reqs + control_reqs
|
|
elif self.tp_size != 1:
|
|
recv_reqs = broadcast_pyobj(recv_reqs, self.tp_rank, self.tp_cpu_group)
|
|
return recv_reqs
|
|
|
|
def process_input_requests(self, recv_reqs: List):
|
|
for recv_req in recv_reqs:
|
|
output = self._request_dispatcher(recv_req)
|
|
if output is not None:
|
|
self.send_to_tokenizer.send_pyobj(output)
|
|
|
|
def handle_generate_request(
|
|
self,
|
|
recv_req: TokenizedGenerateReqInput,
|
|
):
|
|
# Create a new request
|
|
if (
|
|
recv_req.session_params is None
|
|
or recv_req.session_params.id is None
|
|
or recv_req.session_params.id not in self.sessions
|
|
):
|
|
|
|
if recv_req.input_embeds is not None:
|
|
# Generate fake input_ids based on the length of input_embeds
|
|
seq_length = len(recv_req.input_embeds)
|
|
fake_input_ids = [1] * seq_length
|
|
recv_req.input_ids = fake_input_ids
|
|
|
|
# Handle custom logit processor passed to the request
|
|
custom_logit_processor = recv_req.custom_logit_processor
|
|
if (
|
|
not self.server_args.enable_custom_logit_processor
|
|
and custom_logit_processor is not None
|
|
):
|
|
logger.warning(
|
|
"The SGLang server is not configured to enable custom logit processor."
|
|
"The custom logit processor passed in will be ignored."
|
|
"Please set --enable-custom-logits-processor to enable this feature."
|
|
)
|
|
custom_logit_processor = None
|
|
|
|
req = Req(
|
|
recv_req.rid,
|
|
recv_req.input_text,
|
|
recv_req.input_ids,
|
|
recv_req.sampling_params,
|
|
return_logprob=recv_req.return_logprob,
|
|
top_logprobs_num=recv_req.top_logprobs_num,
|
|
stream=recv_req.stream,
|
|
lora_path=recv_req.lora_path,
|
|
input_embeds=recv_req.input_embeds,
|
|
custom_logit_processor=custom_logit_processor,
|
|
eos_token_ids=self.model_config.hf_eos_token_id,
|
|
)
|
|
req.tokenizer = self.tokenizer
|
|
|
|
if (
|
|
recv_req.session_params is not None
|
|
and recv_req.session_params.id is not None
|
|
):
|
|
req.finished_reason = FINISH_ABORT(
|
|
f"Invalid request: session id {recv_req.session_params.id} does not exist"
|
|
)
|
|
self.waiting_queue.append(req)
|
|
return
|
|
else:
|
|
# Create a new request from a previous session
|
|
session = self.sessions[recv_req.session_params.id]
|
|
req = session.create_req(recv_req, self.tokenizer)
|
|
if isinstance(req.finished_reason, FINISH_ABORT):
|
|
self.waiting_queue.append(req)
|
|
return
|
|
|
|
# Handle multimodal inputs
|
|
if recv_req.image_inputs is not None:
|
|
image_inputs = ImageInputs.from_dict(recv_req.image_inputs)
|
|
# Expand a single image token into multiple dummy tokens for receiving image embeddings
|
|
req.origin_input_ids = self.pad_input_ids_func(
|
|
req.origin_input_ids, image_inputs
|
|
)
|
|
req.extend_image_inputs(image_inputs)
|
|
|
|
if len(req.origin_input_ids) >= self.max_req_input_len:
|
|
error_msg = (
|
|
"Multimodal prompt is too long after expanding multimodal tokens. "
|
|
f"After expanding {len(req.origin_input_ids_unpadded)=} => {len(req.origin_input_ids)} >= {self.max_req_input_len}."
|
|
)
|
|
logger.error(error_msg)
|
|
req.origin_input_ids = [0]
|
|
req.image_inputs = None
|
|
req.sampling_params.max_new_tokens = 0
|
|
req.finished_reason = FINISH_ABORT(
|
|
error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError"
|
|
)
|
|
self.waiting_queue.append(req)
|
|
return
|
|
|
|
# Validate prompts length
|
|
error_msg = validate_input_length(
|
|
req,
|
|
self.max_req_input_len,
|
|
self.server_args.allow_auto_truncate,
|
|
)
|
|
if error_msg:
|
|
self.waiting_queue.append(req)
|
|
return
|
|
|
|
# Copy more attributes
|
|
if recv_req.logprob_start_len == -1:
|
|
# By default, only return the logprobs for output tokens
|
|
req.logprob_start_len = len(req.origin_input_ids) - 1
|
|
else:
|
|
req.logprob_start_len = recv_req.logprob_start_len
|
|
|
|
req.sampling_params.max_new_tokens = min(
|
|
(
|
|
req.sampling_params.max_new_tokens
|
|
if req.sampling_params.max_new_tokens is not None
|
|
else 1 << 30
|
|
),
|
|
self.max_req_len - len(req.origin_input_ids) - 1,
|
|
)
|
|
|
|
# Init grammar cache for this request
|
|
add_to_grammar_queue = False
|
|
if (
|
|
req.sampling_params.json_schema is not None
|
|
or req.sampling_params.regex is not None
|
|
or req.sampling_params.ebnf is not None
|
|
):
|
|
assert self.grammar_backend is not None
|
|
if req.sampling_params.json_schema is not None:
|
|
key = ("json", req.sampling_params.json_schema)
|
|
elif req.sampling_params.regex is not None:
|
|
key = ("regex", req.sampling_params.regex)
|
|
elif req.sampling_params.ebnf is not None:
|
|
key = ("ebnf", req.sampling_params.ebnf)
|
|
|
|
req.grammar = self.grammar_backend.get_cached_value(key)
|
|
if not req.grammar:
|
|
req.grammar = self.grammar_backend.get_future_value(key)
|
|
add_to_grammar_queue = True
|
|
|
|
if add_to_grammar_queue:
|
|
self.grammar_queue.append(req)
|
|
else:
|
|
self.waiting_queue.append(req)
|
|
|
|
def handle_embedding_request(
|
|
self,
|
|
recv_req: TokenizedEmbeddingReqInput,
|
|
):
|
|
req = Req(
|
|
recv_req.rid,
|
|
recv_req.input_text,
|
|
recv_req.input_ids,
|
|
recv_req.sampling_params,
|
|
)
|
|
req.tokenizer = self.tokenizer
|
|
|
|
# Validate prompts length
|
|
error_msg = validate_input_length(
|
|
req,
|
|
self.max_req_input_len,
|
|
self.server_args.allow_auto_truncate,
|
|
)
|
|
if error_msg:
|
|
self.waiting_queue.append(req)
|
|
return
|
|
|
|
# Copy more attributes
|
|
req.logprob_start_len = len(req.origin_input_ids) - 1
|
|
self.waiting_queue.append(req)
|
|
|
|
def log_prefill_stats(
|
|
self,
|
|
adder: PrefillAdder,
|
|
can_run_list: List[Req],
|
|
running_bs: ScheduleBatch,
|
|
has_being_chunked: bool,
|
|
):
|
|
self.tree_cache_metrics["total"] += (
|
|
adder.log_input_tokens + adder.log_hit_tokens
|
|
) / 10**9
|
|
self.tree_cache_metrics["hit"] += (adder.log_hit_tokens) / 10**9
|
|
tree_cache_hit_rate = (
|
|
self.tree_cache_metrics["hit"] / self.tree_cache_metrics["total"]
|
|
)
|
|
|
|
num_used = self.max_total_num_tokens - (
|
|
self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
|
|
)
|
|
|
|
logger.info(
|
|
f"Prefill batch. "
|
|
f"#new-seq: {len(can_run_list)}, "
|
|
f"#new-token: {adder.log_input_tokens}, "
|
|
f"#cached-token: {adder.log_hit_tokens}, "
|
|
f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
|
|
f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
|
|
f"#running-req: {running_bs}, "
|
|
f"#queue-req: {len(self.waiting_queue) + has_being_chunked}"
|
|
)
|
|
|
|
if self.enable_metrics:
|
|
self.stats.num_running_reqs = running_bs
|
|
self.stats.num_used_tokens = num_used
|
|
self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2)
|
|
self.stats.num_queue_reqs = len(self.waiting_queue) + has_being_chunked
|
|
self.stats.cache_hit_rate = tree_cache_hit_rate
|
|
self.metrics_collector.log_stats(self.stats)
|
|
|
|
def log_decode_stats(self):
|
|
num_used = self.max_total_num_tokens - (
|
|
self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
|
|
)
|
|
gen_throughput = self.num_generated_tokens / (
|
|
time.time() - self.last_decode_stats_tic
|
|
)
|
|
self.num_generated_tokens = 0
|
|
self.last_decode_stats_tic = time.time()
|
|
num_running_reqs = len(self.running_batch.reqs) if self.running_batch else 0
|
|
|
|
if self.spec_algorithm.is_none():
|
|
msg = (
|
|
f"Decode batch. "
|
|
f"#running-req: {num_running_reqs}, "
|
|
f"#token: {num_used}, "
|
|
f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
|
|
f"gen throughput (token/s): {gen_throughput:.2f}, "
|
|
f"#queue-req: {len(self.waiting_queue)}"
|
|
)
|
|
spec_accept_length = 0
|
|
else:
|
|
spec_accept_length = (
|
|
self.spec_num_total_accepted_tokens / self.spec_num_total_forward_ct
|
|
)
|
|
self.spec_num_total_accepted_tokens = self.spec_num_total_forward_ct = 0
|
|
msg = (
|
|
f"Decode batch. "
|
|
f"#running-req: {num_running_reqs}, "
|
|
f"#token: {num_used}, "
|
|
f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
|
|
f"accept len: {spec_accept_length:.2f}, "
|
|
f"gen throughput (token/s): {gen_throughput:.2f}, "
|
|
f"#queue-req: {len(self.waiting_queue)}"
|
|
)
|
|
|
|
logger.info(msg)
|
|
if self.enable_metrics:
|
|
self.stats.num_running_reqs = num_running_reqs
|
|
self.stats.num_used_tokens = num_used
|
|
self.stats.token_usage = num_used / self.max_total_num_tokens
|
|
self.stats.gen_throughput = gen_throughput
|
|
self.stats.num_queue_reqs = len(self.waiting_queue)
|
|
self.stats.spec_accept_length = spec_accept_length
|
|
self.metrics_collector.log_stats(self.stats)
|
|
|
|
def check_memory(self):
|
|
available_size = (
|
|
self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
|
|
)
|
|
protected_size = self.tree_cache.protected_size()
|
|
memory_leak = available_size != (
|
|
self.max_total_num_tokens
|
|
if not self.enable_hierarchical_cache
|
|
else self.max_total_num_tokens - protected_size
|
|
)
|
|
if memory_leak:
|
|
msg = (
|
|
"KV cache pool leak detected!"
|
|
f"{available_size=}, {protected_size=}, {self.max_total_num_tokens=}\n"
|
|
)
|
|
warnings.warn(msg)
|
|
if crash_on_warnings():
|
|
raise ValueError(msg)
|
|
|
|
if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size:
|
|
msg = (
|
|
"Memory pool leak detected!"
|
|
f"available_size={len(self.req_to_token_pool.free_slots)}, "
|
|
f"total_size={self.req_to_token_pool.size}\n"
|
|
)
|
|
warnings.warn(msg)
|
|
if crash_on_warnings():
|
|
raise ValueError(msg)
|
|
|
|
def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
|
|
# Merge the prefill batch into the running batch
|
|
if self.last_batch and self.last_batch.forward_mode.is_extend():
|
|
if self.being_chunked_req:
|
|
# Move the chunked request out of the batch
|
|
self.last_batch.filter_batch(being_chunked_req=self.being_chunked_req)
|
|
self.tree_cache.cache_unfinished_req(self.being_chunked_req)
|
|
# being chunked request keeps its rid but will get a new req_pool_idx
|
|
self.req_to_token_pool.free(self.being_chunked_req.req_pool_idx)
|
|
self.batch_is_full = False
|
|
|
|
if not self.last_batch.is_empty():
|
|
if self.running_batch is None:
|
|
self.running_batch = self.last_batch
|
|
else:
|
|
self.running_batch.merge_batch(self.last_batch)
|
|
|
|
new_batch = self.get_new_batch_prefill()
|
|
if new_batch is not None:
|
|
# Run prefill first if possible
|
|
ret = new_batch
|
|
else:
|
|
# Run decode
|
|
if self.running_batch is None:
|
|
ret = None
|
|
else:
|
|
self.running_batch = self.update_running_batch(self.running_batch)
|
|
ret = self.running_batch
|
|
|
|
# Handle DP attention
|
|
if self.server_args.enable_dp_attention:
|
|
ret = self.prepare_dp_attn_batch(ret)
|
|
|
|
return ret
|
|
|
|
def get_new_batch_prefill(self) -> Optional[ScheduleBatch]:
|
|
# Check if the grammar is ready in the grammar queue
|
|
if self.grammar_queue:
|
|
self.move_ready_grammar_requests()
|
|
|
|
# Handle the cases where prefill is not allowed
|
|
if (
|
|
self.batch_is_full or len(self.waiting_queue) == 0
|
|
) and self.being_chunked_req is None:
|
|
return None
|
|
|
|
running_bs = len(self.running_batch.reqs) if self.running_batch else 0
|
|
if running_bs >= self.max_running_requests:
|
|
self.batch_is_full = True
|
|
return None
|
|
|
|
# Get priority queue
|
|
prefix_computed = self.policy.calc_priority(self.waiting_queue)
|
|
|
|
# Prefill policy
|
|
adder = PrefillAdder(
|
|
self.tree_cache,
|
|
self.token_to_kv_pool,
|
|
self.running_batch,
|
|
self.new_token_ratio,
|
|
self.max_prefill_tokens,
|
|
self.chunked_prefill_size,
|
|
running_bs if self.is_mixed_chunk else 0,
|
|
)
|
|
|
|
has_being_chunked = self.being_chunked_req is not None
|
|
if has_being_chunked:
|
|
self.being_chunked_req.init_next_round_input()
|
|
self.being_chunked_req = adder.add_being_chunked_req(self.being_chunked_req)
|
|
|
|
if self.lora_paths:
|
|
lora_set = (
|
|
set([req.lora_path for req in self.running_batch.reqs])
|
|
if self.running_batch is not None
|
|
else set([])
|
|
)
|
|
|
|
# Get requests from the waiting queue to a new prefill batch
|
|
for req in self.waiting_queue:
|
|
if (
|
|
self.lora_paths
|
|
and len(
|
|
lora_set
|
|
| set([req.lora_path for req in adder.can_run_list])
|
|
| set([req.lora_path])
|
|
)
|
|
> self.max_loras_per_batch
|
|
):
|
|
self.batch_is_full = True
|
|
break
|
|
|
|
if running_bs + len(adder.can_run_list) >= self.max_running_requests:
|
|
self.batch_is_full = True
|
|
break
|
|
|
|
req.init_next_round_input(None if prefix_computed else self.tree_cache)
|
|
res = adder.add_one_req(req)
|
|
if res != AddReqResult.CONTINUE:
|
|
if res == AddReqResult.NO_TOKEN:
|
|
if self.enable_hierarchical_cache:
|
|
# Set batch_is_full after making sure there are requests that can be served
|
|
self.batch_is_full = len(adder.can_run_list) > 0 or (
|
|
self.running_batch is not None
|
|
and not self.running_batch.is_empty()
|
|
)
|
|
else:
|
|
self.batch_is_full = True
|
|
break
|
|
if self.server_args.prefill_only_one_req:
|
|
break
|
|
|
|
# Update waiting queue
|
|
can_run_list = adder.can_run_list
|
|
if len(can_run_list) == 0:
|
|
return None
|
|
self.waiting_queue = [
|
|
x for x in self.waiting_queue if x not in set(can_run_list)
|
|
]
|
|
|
|
if adder.new_being_chunked_req is not None:
|
|
assert self.being_chunked_req is None
|
|
self.being_chunked_req = adder.new_being_chunked_req
|
|
|
|
if self.being_chunked_req:
|
|
self.being_chunked_req.is_being_chunked += 1
|
|
|
|
# Print stats
|
|
if self.attn_tp_rank == 0:
|
|
self.log_prefill_stats(adder, can_run_list, running_bs, has_being_chunked)
|
|
|
|
# Create a new batch
|
|
new_batch = ScheduleBatch.init_new(
|
|
can_run_list,
|
|
self.req_to_token_pool,
|
|
self.token_to_kv_pool,
|
|
self.tree_cache,
|
|
self.model_config,
|
|
self.enable_overlap,
|
|
self.spec_algorithm,
|
|
self.server_args.enable_custom_logit_processor,
|
|
)
|
|
new_batch.prepare_for_extend()
|
|
|
|
# Mixed-style chunked prefill
|
|
if (
|
|
self.is_mixed_chunk
|
|
and self.running_batch is not None
|
|
and not (new_batch.return_logprob or self.running_batch.return_logprob)
|
|
):
|
|
# TODO (lianmin): support return_logprob + mixed chunked prefill
|
|
self.running_batch.filter_batch()
|
|
if not self.running_batch.is_empty():
|
|
self.running_batch.prepare_for_decode()
|
|
new_batch.mix_with_running(self.running_batch)
|
|
new_batch.decoding_reqs = self.running_batch.reqs
|
|
self.running_batch = None
|
|
else:
|
|
new_batch.decoding_reqs = None
|
|
|
|
return new_batch
|
|
|
|
def update_running_batch(self, batch: ScheduleBatch) -> Optional[ScheduleBatch]:
|
|
"""Update the current running decoding batch."""
|
|
global test_retract
|
|
|
|
initial_bs = batch.batch_size()
|
|
|
|
batch.filter_batch()
|
|
if batch.is_empty():
|
|
self.batch_is_full = False
|
|
return None
|
|
|
|
# Check if decode out of memory
|
|
if not batch.check_decode_mem(self.decode_mem_cache_buf_multiplier) or (
|
|
test_retract and batch.batch_size() > 10
|
|
):
|
|
old_ratio = self.new_token_ratio
|
|
|
|
retracted_reqs, new_token_ratio = batch.retract_decode()
|
|
self.new_token_ratio = new_token_ratio
|
|
if self.draft_worker:
|
|
self.draft_worker.finish_request(retracted_reqs)
|
|
|
|
logger.info(
|
|
"Decode out of memory happened. "
|
|
f"#retracted_reqs: {len(retracted_reqs)}, "
|
|
f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
|
|
)
|
|
self.waiting_queue.extend(retracted_reqs)
|
|
else:
|
|
self.new_token_ratio = max(
|
|
self.new_token_ratio - self.new_token_ratio_decay,
|
|
self.min_new_token_ratio,
|
|
)
|
|
|
|
# Check for jump-forward
|
|
if not self.disable_jump_forward and batch.has_grammar:
|
|
jump_forward_reqs = batch.check_for_jump_forward(self.pad_input_ids_func)
|
|
self.waiting_queue.extend(jump_forward_reqs)
|
|
if batch.is_empty():
|
|
self.batch_is_full = False
|
|
return None
|
|
|
|
if batch.batch_size() < initial_bs:
|
|
self.batch_is_full = False
|
|
|
|
# Update batch tensors
|
|
batch.prepare_for_decode()
|
|
return batch
|
|
|
|
def run_batch(
|
|
self, batch: ScheduleBatch
|
|
) -> Union[GenerationBatchResult, EmbeddingBatchResult]:
|
|
"""Run a batch."""
|
|
self.forward_ct += 1
|
|
|
|
if self.is_generation:
|
|
if self.spec_algorithm.is_none():
|
|
model_worker_batch = batch.get_model_worker_batch()
|
|
logits_output, next_token_ids = self.tp_worker.forward_batch_generation(
|
|
model_worker_batch
|
|
)
|
|
else:
|
|
(
|
|
logits_output,
|
|
next_token_ids,
|
|
model_worker_batch,
|
|
num_accepted_tokens,
|
|
) = self.draft_worker.forward_batch_speculative_generation(batch)
|
|
self.spec_num_total_accepted_tokens += (
|
|
num_accepted_tokens + batch.batch_size()
|
|
)
|
|
self.spec_num_total_forward_ct += batch.batch_size()
|
|
self.num_generated_tokens += num_accepted_tokens
|
|
batch.output_ids = next_token_ids
|
|
|
|
ret = GenerationBatchResult(
|
|
logits_output=logits_output,
|
|
next_token_ids=next_token_ids,
|
|
bid=model_worker_batch.bid,
|
|
)
|
|
else: # embedding or reward model
|
|
model_worker_batch = batch.get_model_worker_batch()
|
|
embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
|
|
ret = EmbeddingBatchResult(
|
|
embeddings=embeddings, bid=model_worker_batch.bid
|
|
)
|
|
return ret
|
|
|
|
def process_batch_result(
|
|
self,
|
|
batch: ScheduleBatch,
|
|
result: Union[GenerationBatchResult, EmbeddingBatchResult],
|
|
):
|
|
if batch.forward_mode.is_decode():
|
|
self.process_batch_result_decode(batch, result)
|
|
if batch.is_empty():
|
|
self.running_batch = None
|
|
elif batch.forward_mode.is_extend():
|
|
self.process_batch_result_prefill(batch, result)
|
|
elif batch.forward_mode.is_idle():
|
|
if self.enable_overlap:
|
|
self.tp_worker.resolve_batch_result(result.bid)
|
|
elif batch.forward_mode.is_dummy_first():
|
|
batch.next_batch_sampling_info.update_regex_vocab_mask()
|
|
self.current_stream.synchronize()
|
|
batch.next_batch_sampling_info.sampling_info_done.set()
|
|
|
|
def process_batch_result_prefill(
|
|
self,
|
|
batch: ScheduleBatch,
|
|
result: Union[GenerationBatchResult, EmbeddingBatchResult],
|
|
):
|
|
skip_stream_req = None
|
|
|
|
if self.is_generation:
|
|
(
|
|
logits_output,
|
|
next_token_ids,
|
|
bid,
|
|
) = (
|
|
result.logits_output,
|
|
result.next_token_ids,
|
|
result.bid,
|
|
)
|
|
|
|
if self.enable_overlap:
|
|
logits_output, next_token_ids = self.tp_worker.resolve_batch_result(bid)
|
|
else:
|
|
# Move next_token_ids and logprobs to cpu
|
|
next_token_ids = next_token_ids.tolist()
|
|
if batch.return_logprob:
|
|
logits_output.next_token_logprobs = (
|
|
logits_output.next_token_logprobs.tolist()
|
|
)
|
|
logits_output.input_token_logprobs = (
|
|
logits_output.input_token_logprobs.tolist()
|
|
)
|
|
|
|
# Check finish conditions
|
|
logprob_pt = 0
|
|
for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
|
|
if req.is_retracted:
|
|
continue
|
|
|
|
if self.is_mixed_chunk and self.enable_overlap and req.finished():
|
|
# Free the one delayed token for the mixed decode batch
|
|
j = len(batch.out_cache_loc) - len(batch.reqs) + i
|
|
self.token_to_kv_pool.free(batch.out_cache_loc[j : j + 1])
|
|
continue
|
|
|
|
if req.is_being_chunked <= 0:
|
|
req.output_ids.append(next_token_id)
|
|
req.check_finished()
|
|
|
|
if req.finished():
|
|
self.tree_cache.cache_finished_req(req)
|
|
elif not batch.decoding_reqs or req not in batch.decoding_reqs:
|
|
self.tree_cache.cache_unfinished_req(req)
|
|
|
|
if req.return_logprob:
|
|
logprob_pt += self.add_logprob_return_values(
|
|
i, req, logprob_pt, next_token_ids, logits_output
|
|
)
|
|
|
|
if req.grammar is not None:
|
|
req.grammar.accept_token(next_token_id)
|
|
req.grammar.finished = req.finished()
|
|
else:
|
|
# being chunked reqs' prefill is not finished
|
|
req.is_being_chunked -= 1
|
|
# There is only at most one request being currently chunked.
|
|
# Because this request does not finish prefill,
|
|
# we don't want to stream the request currently being chunked.
|
|
skip_stream_req = req
|
|
|
|
if batch.next_batch_sampling_info:
|
|
batch.next_batch_sampling_info.update_regex_vocab_mask()
|
|
self.current_stream.synchronize()
|
|
batch.next_batch_sampling_info.sampling_info_done.set()
|
|
|
|
else: # embedding or reward model
|
|
embeddings, bid = result.embeddings, result.bid
|
|
embeddings = embeddings.tolist()
|
|
|
|
# Check finish conditions
|
|
for i, req in enumerate(batch.reqs):
|
|
if req.is_retracted:
|
|
continue
|
|
|
|
req.embedding = embeddings[i]
|
|
if req.is_being_chunked <= 0:
|
|
# Dummy output token for embedding models
|
|
req.output_ids.append(0)
|
|
req.check_finished()
|
|
|
|
if req.finished():
|
|
self.tree_cache.cache_finished_req(req)
|
|
else:
|
|
self.tree_cache.cache_unfinished_req(req)
|
|
else:
|
|
# being chunked reqs' prefill is not finished
|
|
req.is_being_chunked -= 1
|
|
|
|
self.stream_output(batch.reqs, batch.return_logprob, skip_stream_req)
|
|
|
|
def process_batch_result_decode(
|
|
self,
|
|
batch: ScheduleBatch,
|
|
result: GenerationBatchResult,
|
|
):
|
|
logits_output, next_token_ids, bid = (
|
|
result.logits_output,
|
|
result.next_token_ids,
|
|
result.bid,
|
|
)
|
|
self.num_generated_tokens += len(batch.reqs)
|
|
|
|
if self.enable_overlap:
|
|
logits_output, next_token_ids = self.tp_worker.resolve_batch_result(bid)
|
|
next_token_logprobs = logits_output.next_token_logprobs
|
|
else:
|
|
next_token_ids = next_token_ids.tolist()
|
|
if batch.return_logprob:
|
|
next_token_logprobs = logits_output.next_token_logprobs.tolist()
|
|
|
|
self.token_to_kv_pool.free_group_begin()
|
|
|
|
# Check finish condition
|
|
for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
|
|
if req.is_retracted:
|
|
continue
|
|
|
|
if self.enable_overlap and req.finished():
|
|
# Free the one delayed token
|
|
self.token_to_kv_pool.free(batch.out_cache_loc[i : i + 1])
|
|
continue
|
|
|
|
if batch.spec_algorithm.is_none():
|
|
# speculative worker will solve the output_ids in speculative decoding
|
|
req.output_ids.append(next_token_id)
|
|
|
|
req.check_finished()
|
|
|
|
if req.finished():
|
|
self.tree_cache.cache_finished_req(req)
|
|
|
|
if req.return_logprob:
|
|
req.output_token_logprobs_val.append(next_token_logprobs[i])
|
|
req.output_token_logprobs_idx.append(next_token_id)
|
|
if req.top_logprobs_num > 0:
|
|
req.output_top_logprobs_val.append(
|
|
logits_output.next_token_top_logprobs_val[i]
|
|
)
|
|
req.output_top_logprobs_idx.append(
|
|
logits_output.next_token_top_logprobs_idx[i]
|
|
)
|
|
|
|
if req.grammar is not None:
|
|
req.grammar.accept_token(next_token_id)
|
|
req.grammar.finished = req.finished()
|
|
|
|
if batch.next_batch_sampling_info:
|
|
batch.next_batch_sampling_info.update_regex_vocab_mask()
|
|
self.current_stream.synchronize()
|
|
batch.next_batch_sampling_info.sampling_info_done.set()
|
|
|
|
self.stream_output(batch.reqs, batch.return_logprob)
|
|
|
|
self.token_to_kv_pool.free_group_end()
|
|
|
|
self.forward_ct_decode = (self.forward_ct_decode + 1) % (1 << 30)
|
|
if (
|
|
self.attn_tp_rank == 0
|
|
and self.forward_ct_decode % self.server_args.decode_log_interval == 0
|
|
):
|
|
self.log_decode_stats()
|
|
|
|
def add_logprob_return_values(
|
|
self,
|
|
i: int,
|
|
req: Req,
|
|
pt: int,
|
|
next_token_ids: List[int],
|
|
output: LogitsProcessorOutput,
|
|
):
|
|
"""Attach logprobs to the return values."""
|
|
req.output_token_logprobs_val.append(output.next_token_logprobs[i])
|
|
req.output_token_logprobs_idx.append(next_token_ids[i])
|
|
|
|
# If logprob_start_len > 0, then first logprob_start_len prompt tokens will be ignored.
|
|
num_input_logprobs = req.extend_input_len - req.extend_logprob_start_len
|
|
|
|
if req.input_token_logprobs_val is None:
|
|
input_token_logprobs_val = output.input_token_logprobs[
|
|
pt : pt + num_input_logprobs - 1 - req.last_update_decode_tokens
|
|
]
|
|
|
|
input_token_logprobs_idx = req.fill_ids[
|
|
len(req.fill_ids)
|
|
- num_input_logprobs
|
|
+ 1 : len(req.fill_ids)
|
|
- req.last_update_decode_tokens
|
|
]
|
|
# Clip the padded hash values from image tokens.
|
|
# Otherwise, it will lead to detokenization errors.
|
|
input_token_logprobs_idx = [
|
|
x if x < self.model_config.vocab_size - 1 else 0
|
|
for x in input_token_logprobs_idx
|
|
]
|
|
|
|
if (
|
|
req.logprob_start_len == 0
|
|
): # The first token does not have logprob, pad it.
|
|
input_token_logprobs_val = [None] + input_token_logprobs_val
|
|
input_token_logprobs_idx = [req.fill_ids[0]] + input_token_logprobs_idx
|
|
|
|
req.input_token_logprobs_val = input_token_logprobs_val
|
|
req.input_token_logprobs_idx = input_token_logprobs_idx
|
|
|
|
if req.last_update_decode_tokens != 0:
|
|
# Some decode tokens are re-computed in an extend batch
|
|
req.output_token_logprobs_val.extend(
|
|
output.input_token_logprobs[
|
|
pt
|
|
+ num_input_logprobs
|
|
- 1
|
|
- req.last_update_decode_tokens : pt
|
|
+ num_input_logprobs
|
|
- 1
|
|
],
|
|
)
|
|
req.output_token_logprobs_idx.extend(
|
|
req.fill_ids[
|
|
len(req.fill_ids)
|
|
- req.last_update_decode_tokens : len(req.fill_ids)
|
|
]
|
|
)
|
|
|
|
if req.top_logprobs_num > 0:
|
|
if req.input_top_logprobs_val is None:
|
|
req.input_top_logprobs_val = output.input_top_logprobs_val[i]
|
|
req.input_top_logprobs_idx = output.input_top_logprobs_idx[i]
|
|
if req.logprob_start_len == 0:
|
|
req.input_top_logprobs_val = [None] + req.input_top_logprobs_val
|
|
req.input_top_logprobs_idx = [None] + req.input_top_logprobs_idx
|
|
|
|
if req.last_update_decode_tokens != 0:
|
|
req.output_top_logprobs_val.extend(
|
|
output.input_top_logprobs_val[i][-req.last_update_decode_tokens :]
|
|
)
|
|
req.output_top_logprobs_idx.extend(
|
|
output.input_top_logprobs_idx[i][-req.last_update_decode_tokens :]
|
|
)
|
|
|
|
req.output_top_logprobs_val.append(output.next_token_top_logprobs_val[i])
|
|
req.output_top_logprobs_idx.append(output.next_token_top_logprobs_idx[i])
|
|
|
|
return num_input_logprobs
|
|
|
|
def stream_output(
|
|
self, reqs: List[Req], return_logprob: bool, skip_req: Optional[Req] = None
|
|
):
|
|
"""Stream the output to detokenizer."""
|
|
rids = []
|
|
finished_reasons: List[BaseFinishReason] = []
|
|
|
|
if self.is_generation:
|
|
vids = []
|
|
decoded_texts = []
|
|
decode_ids_list = []
|
|
read_offsets = []
|
|
output_ids = []
|
|
|
|
skip_special_tokens = []
|
|
spaces_between_special_tokens = []
|
|
no_stop_trim = []
|
|
prompt_tokens = []
|
|
completion_tokens = []
|
|
cached_tokens = []
|
|
spec_verify_ct = []
|
|
|
|
if return_logprob:
|
|
input_token_logprobs_val = []
|
|
input_token_logprobs_idx = []
|
|
output_token_logprobs_val = []
|
|
output_token_logprobs_idx = []
|
|
input_top_logprobs_val = []
|
|
input_top_logprobs_idx = []
|
|
output_top_logprobs_val = []
|
|
output_top_logprobs_idx = []
|
|
else:
|
|
input_token_logprobs_val = input_token_logprobs_idx = (
|
|
output_token_logprobs_val
|
|
) = output_token_logprobs_idx = input_top_logprobs_val = (
|
|
input_top_logprobs_idx
|
|
) = output_top_logprobs_val = output_top_logprobs_idx = None
|
|
|
|
for req in reqs:
|
|
if req is skip_req:
|
|
continue
|
|
|
|
# TODO(lianmin): revisit this for overlap + retract + stream
|
|
if (
|
|
req.finished()
|
|
# If stream, follow the given stream_interval
|
|
or (req.stream and len(req.output_ids) % self.stream_interval == 0)
|
|
# If not stream, we still want to output some tokens to get the benefit of incremental decoding.
|
|
or (not req.stream and len(req.output_ids) % 50 == 0)
|
|
):
|
|
if self.draft_worker and req.finished():
|
|
self.draft_worker.finish_request(req)
|
|
|
|
rids.append(req.rid)
|
|
finished_reasons.append(
|
|
req.finished_reason.to_json() if req.finished_reason else None
|
|
)
|
|
vids.append(req.vid)
|
|
decoded_texts.append(req.decoded_text)
|
|
decode_ids, read_offset = req.init_incremental_detokenize()
|
|
decode_ids_list.append(decode_ids)
|
|
read_offsets.append(read_offset)
|
|
if self.skip_tokenizer_init:
|
|
output_ids.append(req.output_ids)
|
|
skip_special_tokens.append(req.sampling_params.skip_special_tokens)
|
|
spaces_between_special_tokens.append(
|
|
req.sampling_params.spaces_between_special_tokens
|
|
)
|
|
no_stop_trim.append(req.sampling_params.no_stop_trim)
|
|
|
|
prompt_tokens.append(len(req.origin_input_ids))
|
|
completion_tokens.append(len(req.output_ids))
|
|
cached_tokens.append(req.cached_tokens)
|
|
|
|
if not self.spec_algorithm.is_none():
|
|
spec_verify_ct.append(req.spec_verify_ct)
|
|
|
|
if return_logprob:
|
|
input_token_logprobs_val.append(req.input_token_logprobs_val)
|
|
input_token_logprobs_idx.append(req.input_token_logprobs_idx)
|
|
output_token_logprobs_val.append(req.output_token_logprobs_val)
|
|
output_token_logprobs_idx.append(req.output_token_logprobs_idx)
|
|
input_top_logprobs_val.append(req.input_top_logprobs_val)
|
|
input_top_logprobs_idx.append(req.input_top_logprobs_idx)
|
|
output_top_logprobs_val.append(req.output_top_logprobs_val)
|
|
output_top_logprobs_idx.append(req.output_top_logprobs_idx)
|
|
|
|
# Send to detokenizer
|
|
if rids:
|
|
self.send_to_detokenizer.send_pyobj(
|
|
BatchTokenIDOut(
|
|
rids,
|
|
finished_reasons,
|
|
vids,
|
|
decoded_texts,
|
|
decode_ids_list,
|
|
read_offsets,
|
|
output_ids,
|
|
skip_special_tokens,
|
|
spaces_between_special_tokens,
|
|
no_stop_trim,
|
|
prompt_tokens,
|
|
completion_tokens,
|
|
cached_tokens,
|
|
spec_verify_ct,
|
|
input_token_logprobs_val,
|
|
input_token_logprobs_idx,
|
|
output_token_logprobs_val,
|
|
output_token_logprobs_idx,
|
|
input_top_logprobs_val,
|
|
input_top_logprobs_idx,
|
|
output_top_logprobs_val,
|
|
output_top_logprobs_idx,
|
|
)
|
|
)
|
|
else: # embedding or reward model
|
|
embeddings = []
|
|
prompt_tokens = []
|
|
for req in reqs:
|
|
if req.finished():
|
|
rids.append(req.rid)
|
|
finished_reasons.append(req.finished_reason.to_json())
|
|
embeddings.append(req.embedding)
|
|
prompt_tokens.append(len(req.origin_input_ids))
|
|
self.send_to_detokenizer.send_pyobj(
|
|
BatchEmbeddingOut(rids, finished_reasons, embeddings, prompt_tokens)
|
|
)
|
|
|
|
def prepare_dp_attn_batch(self, local_batch: ScheduleBatch):
|
|
# Check if other DP workers have running batches
|
|
if local_batch is None:
|
|
num_tokens = 0
|
|
elif local_batch.forward_mode.is_decode():
|
|
num_tokens = local_batch.batch_size()
|
|
else:
|
|
num_tokens = local_batch.extend_num_tokens
|
|
|
|
local_num_tokens = torch.tensor([num_tokens], dtype=torch.int64)
|
|
global_num_tokens = torch.empty(self.tp_size, dtype=torch.int64)
|
|
torch.distributed.all_gather_into_tensor(
|
|
global_num_tokens,
|
|
local_num_tokens,
|
|
group=self.tp_cpu_group,
|
|
)
|
|
|
|
if local_batch is None and global_num_tokens.max().item() > 0:
|
|
local_batch = self.get_idle_batch()
|
|
|
|
if local_batch is not None:
|
|
local_batch.global_num_tokens = global_num_tokens.tolist()
|
|
|
|
# Check forward mode for cuda graph
|
|
if not self.server_args.disable_cuda_graph:
|
|
forward_mode_state = torch.tensor(
|
|
(1 if local_batch.forward_mode.is_decode_or_idle() else 0),
|
|
dtype=torch.int32,
|
|
)
|
|
torch.distributed.all_reduce(
|
|
forward_mode_state,
|
|
op=torch.distributed.ReduceOp.MIN,
|
|
group=self.tp_cpu_group,
|
|
)
|
|
local_batch.can_run_dp_cuda_graph = forward_mode_state.item() == 1
|
|
|
|
return local_batch
|
|
|
|
def get_idle_batch(self):
|
|
idle_batch = ScheduleBatch.init_new(
|
|
[],
|
|
self.req_to_token_pool,
|
|
self.token_to_kv_pool,
|
|
self.tree_cache,
|
|
self.model_config,
|
|
self.enable_overlap,
|
|
self.spec_algorithm,
|
|
self.server_args.enable_custom_logit_processor,
|
|
)
|
|
idle_batch.prepare_for_idle()
|
|
return idle_batch
|
|
|
|
def move_ready_grammar_requests(self):
|
|
"""Move requests whose grammar objects are ready from grammar_queue to waiting_queue."""
|
|
num_ready_reqs = 0
|
|
for req in self.grammar_queue:
|
|
try:
|
|
req.grammar = req.grammar.result(timeout=0.05)
|
|
num_ready_reqs += 1
|
|
except futures._base.TimeoutError:
|
|
break
|
|
|
|
if self.tp_size > 1:
|
|
# Sync across TP ranks to make sure they have the same number of ready requests
|
|
tensor = torch.tensor(num_ready_reqs, dtype=torch.int32)
|
|
torch.distributed.all_reduce(
|
|
tensor, op=torch.distributed.ReduceOp.MAX, group=self.tp_cpu_group
|
|
)
|
|
num_ready_reqs_max = tensor.item()
|
|
for i in range(num_ready_reqs, num_ready_reqs_max):
|
|
self.grammar_queue[i].grammar = self.grammar_queue[i].grammar.result()
|
|
num_ready_reqs = num_ready_reqs_max
|
|
|
|
self.waiting_queue.extend(self.grammar_queue[:num_ready_reqs])
|
|
self.grammar_queue = self.grammar_queue[num_ready_reqs:]
|
|
|
|
def flush_cache_wrapped(self, recv_req: FlushCacheReq):
|
|
self.flush_cache()
|
|
|
|
def flush_cache(self):
|
|
"""Flush the memory pool and cache."""
|
|
if len(self.waiting_queue) == 0 and (
|
|
self.running_batch is None or len(self.running_batch.reqs) == 0
|
|
):
|
|
self.tree_cache.reset()
|
|
self.tree_cache_metrics = {"total": 0, "hit": 0}
|
|
if self.grammar_backend:
|
|
self.grammar_backend.reset()
|
|
self.req_to_token_pool.clear()
|
|
self.token_to_kv_pool.clear()
|
|
|
|
if not self.spec_algorithm.is_none():
|
|
self.draft_worker.model_runner.req_to_token_pool.clear()
|
|
self.draft_worker.model_runner.token_to_kv_pool.clear()
|
|
|
|
self.num_generated_tokens = 0
|
|
self.forward_ct_decode = 0
|
|
self.spec_num_total_accepted_tokens = 0
|
|
self.spec_num_total_forward_ct = 0
|
|
torch.cuda.empty_cache()
|
|
logger.info("Cache flushed successfully!")
|
|
if_success = True
|
|
else:
|
|
logging.warning(
|
|
f"Cache not flushed because there are pending requests. "
|
|
f"#queue-req: {len(self.waiting_queue)}, "
|
|
f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
|
|
)
|
|
if_success = False
|
|
return if_success
|
|
|
|
def abort_request(self, recv_req: AbortReq):
|
|
# Delete requests in the waiting queue
|
|
to_del = None
|
|
for i, req in enumerate(self.waiting_queue):
|
|
if req.rid == recv_req.rid:
|
|
to_del = i
|
|
break
|
|
|
|
if to_del is not None:
|
|
del self.waiting_queue[to_del]
|
|
logger.debug(f"Abort queued request. {req.rid=}")
|
|
return
|
|
|
|
# Delete requests in the running batch
|
|
if self.running_batch:
|
|
for req in self.running_batch.reqs:
|
|
if req.rid == recv_req.rid and not req.finished():
|
|
logger.debug(f"Abort running request. {req.rid=}")
|
|
req.to_abort = True
|
|
break
|
|
|
|
def update_weights_from_disk(self, recv_req: UpdateWeightFromDiskReqInput):
|
|
"""In-place update of the weights from disk."""
|
|
success, message = self.tp_worker.update_weights_from_disk(recv_req)
|
|
if success:
|
|
flash_cache_success = self.flush_cache()
|
|
assert flash_cache_success, "Cache flush failed after updating weights"
|
|
else:
|
|
logger.error(message)
|
|
return UpdateWeightFromDiskReqOutput(success, message)
|
|
|
|
def init_weights_update_group(self, recv_req: InitWeightsUpdateGroupReqInput):
|
|
"""Initialize the online model parameter update group."""
|
|
success, message = self.tp_worker.init_weights_update_group(recv_req)
|
|
return InitWeightsUpdateGroupReqOutput(success, message)
|
|
|
|
def update_weights_from_distributed(
|
|
self,
|
|
recv_req: UpdateWeightsFromDistributedReqInput,
|
|
) -> Tuple[bool, str]:
|
|
"""Update the online model parameter."""
|
|
success, message = self.tp_worker.update_weights_from_distributed(recv_req)
|
|
if success:
|
|
flash_cache_success = self.flush_cache()
|
|
assert flash_cache_success, "Cache flush failed after updating weights"
|
|
else:
|
|
logger.error(message)
|
|
return UpdateWeightsFromDistributedReqOutput(success, message)
|
|
|
|
def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
|
|
"""Update the online model parameter from tensors."""
|
|
success, message = self.tp_worker.update_weights_from_tensor(recv_req)
|
|
# TODO extract common code b/t update_weights_from_distributed and update_weights_from_tensor later
|
|
if success:
|
|
flash_cache_success = self.flush_cache()
|
|
assert flash_cache_success, "Cache flush failed after updating weights"
|
|
else:
|
|
logger.error(message)
|
|
return UpdateWeightsFromTensorReqOutput(success, message)
|
|
|
|
def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
|
|
parameter = self.tp_worker.get_weights_by_name(recv_req)
|
|
return GetWeightsByNameReqOutput(parameter)
|
|
|
|
def release_memory_occupation(self):
|
|
self.stashed_model_static_state = _export_static_state(
|
|
self.tp_worker.worker.model_runner.model
|
|
)
|
|
self.memory_saver_adapter.pause()
|
|
self.flush_cache()
|
|
return ReleaseMemoryOccupationReqOutput()
|
|
|
|
def resume_memory_occupation(self):
|
|
self.memory_saver_adapter.resume()
|
|
_import_static_state(
|
|
self.tp_worker.worker.model_runner.model, self.stashed_model_static_state
|
|
)
|
|
del self.stashed_model_static_state
|
|
return ResumeMemoryOccupationReqOutput()
|
|
|
|
def profile(self, recv_req: ProfileReq):
|
|
if recv_req == ProfileReq.START_PROFILE:
|
|
self.start_profile()
|
|
else:
|
|
self.stop_profile()
|
|
|
|
def start_profile(self) -> None:
|
|
if self.profiler is None:
|
|
raise RuntimeError("Profiler is not enabled.")
|
|
self.profiler.start()
|
|
|
|
def stop_profile(self) -> None:
|
|
if self.profiler is None:
|
|
raise RuntimeError("Profiler is not enabled.")
|
|
self.profiler.stop()
|
|
self.profiler.export_chrome_trace(
|
|
self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
|
|
)
|
|
logger.info("Profiler is done")
|
|
|
|
def open_session(self, recv_req: OpenSessionReqInput):
|
|
# handle error
|
|
session_id = recv_req.session_id
|
|
if session_id in self.sessions:
|
|
logger.warning(f"session id {session_id} already exist, cannot open.")
|
|
return OpenSessionReqOutput(session_id, False)
|
|
elif session_id is None:
|
|
logger.warning(f"session id is None, cannot open.")
|
|
return OpenSessionReqOutput(session_id, False)
|
|
else:
|
|
self.sessions[session_id] = Session(
|
|
recv_req.capacity_of_str_len, session_id
|
|
)
|
|
return OpenSessionReqOutput(session_id, True)
|
|
|
|
def close_session(self, recv_req: CloseSessionReqInput):
|
|
# handle error
|
|
session_id = recv_req.session_id
|
|
if session_id not in self.sessions:
|
|
logger.warning(f"session id {session_id} does not exist, cannot delete.")
|
|
else:
|
|
del self.sessions[session_id]
|
|
|
|
|
|
def _export_static_state(model):
|
|
return dict(
|
|
buffers=[
|
|
(name, buffer.detach().clone()) for name, buffer in model.named_buffers()
|
|
]
|
|
)
|
|
|
|
|
|
def _import_static_state(model, static_params):
|
|
self_named_buffers = dict(model.named_buffers())
|
|
for name, tensor in static_params["buffers"]:
|
|
self_named_buffers[name][...] = tensor
|
|
|
|
|
|
def run_scheduler_process(
|
|
server_args: ServerArgs,
|
|
port_args: PortArgs,
|
|
gpu_id: int,
|
|
tp_rank: int,
|
|
dp_rank: Optional[int],
|
|
pipe_writer,
|
|
):
|
|
setproctitle.setproctitle("sglang::scheduler")
|
|
faulthandler.enable()
|
|
|
|
# [For Router] if env var "SGLANG_DP_RANK" exist, set dp_rank to the value of the env var
|
|
if dp_rank is None and "SGLANG_DP_RANK" in os.environ:
|
|
dp_rank = int(os.environ["SGLANG_DP_RANK"])
|
|
|
|
# Configue the logger
|
|
if dp_rank is None:
|
|
configure_logger(server_args, prefix=f" TP{tp_rank}")
|
|
else:
|
|
configure_logger(server_args, prefix=f" DP{dp_rank} TP{tp_rank}")
|
|
suppress_other_loggers()
|
|
|
|
# Set cpu affinity to this gpu process
|
|
if get_bool_env_var("SGLANG_SET_CPU_AFFINITY"):
|
|
set_gpu_proc_affinity(server_args.tp_size, server_args.nnodes, gpu_id)
|
|
|
|
parent_process = psutil.Process().parent()
|
|
|
|
# Create a scheduler and run the event loop
|
|
try:
|
|
scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, dp_rank)
|
|
pipe_writer.send(
|
|
{
|
|
"status": "ready",
|
|
"max_total_num_tokens": scheduler.max_total_num_tokens,
|
|
"max_req_input_len": scheduler.max_req_input_len,
|
|
}
|
|
)
|
|
if scheduler.enable_overlap:
|
|
scheduler.event_loop_overlap()
|
|
else:
|
|
scheduler.event_loop_normal()
|
|
except Exception:
|
|
traceback = get_exception_traceback()
|
|
logger.error(f"Scheduler hit an exception: {traceback}")
|
|
parent_process.send_signal(signal.SIGQUIT)
|