2700 lines
108 KiB
Python
2700 lines
108 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 sys
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import threading
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import time
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from collections import defaultdict, deque
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from concurrent import futures
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from dataclasses import dataclass
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from pathlib import Path
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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 torch.distributed import barrier
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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.constants import GPU_MEMORY_TYPE_KV_CACHE, GPU_MEMORY_TYPE_WEIGHTS
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from sglang.srt.constrained.base_grammar_backend import (
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INVALID_GRAMMAR_OBJ,
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create_grammar_backend,
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)
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from sglang.srt.disaggregation.decode import (
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DecodePreallocQueue,
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DecodeTransferQueue,
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SchedulerDisaggregationDecodeMixin,
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)
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from sglang.srt.disaggregation.kv_events import EventPublisherFactory, KVEventBatch
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from sglang.srt.disaggregation.prefill import (
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PrefillBootstrapQueue,
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SchedulerDisaggregationPrefillMixin,
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)
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from sglang.srt.disaggregation.utils import (
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DisaggregationMode,
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MetadataBuffers,
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ReqToMetadataIdxAllocator,
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TransferBackend,
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prepare_abort,
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)
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from sglang.srt.distributed import get_pp_group, get_world_group
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from sglang.srt.hf_transformers_utils import (
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get_processor,
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get_tokenizer,
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get_tokenizer_from_processor,
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)
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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.expert_distribution import (
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get_global_expert_distribution_recorder,
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)
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from sglang.srt.managers.io_struct import (
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AbortReq,
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CloseSessionReqInput,
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ExpertDistributionReq,
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ExpertDistributionReqOutput,
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FlushCacheReqInput,
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FlushCacheReqOutput,
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GetInternalStateReq,
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GetInternalStateReqOutput,
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GetWeightsByNameReqInput,
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GetWeightsByNameReqOutput,
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HealthCheckOutput,
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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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ProfileReqOutput,
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ProfileReqType,
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ReleaseMemoryOccupationReqInput,
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ReleaseMemoryOccupationReqOutput,
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ResumeMemoryOccupationReqInput,
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ResumeMemoryOccupationReqOutput,
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RpcReqInput,
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RpcReqOutput,
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SetInternalStateReq,
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SetInternalStateReqOutput,
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SlowDownReqInput,
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SlowDownReqOutput,
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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.mm_utils import init_embedding_cache
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from sglang.srt.managers.schedule_batch import (
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FINISH_ABORT,
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MultimodalInputs,
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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.scheduler_output_processor_mixin import (
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SchedulerOutputProcessorMixin,
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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.hiradix_cache import HiRadixCache
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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, PPProxyTensors
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from sglang.srt.reasoning_parser import ReasoningParser
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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.two_batch_overlap import TboDPAttentionPreparer
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from sglang.srt.utils import (
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DeepEPMode,
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DynamicGradMode,
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broadcast_pyobj,
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configure_gc_logger,
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configure_logger,
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disable_request_logging,
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get_available_gpu_memory,
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get_bool_env_var,
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get_zmq_socket,
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kill_itself_when_parent_died,
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point_to_point_pyobj,
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pyspy_dump_schedulers,
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require_mlp_sync,
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require_mlp_tp_gather,
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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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RECORD_STEP_TIME = get_bool_env_var("SGLANG_RECORD_STEP_TIME")
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GRAMMAR_TIMEOUT = float(os.environ.get("SGLANG_GRAMMAR_TIMEOUT", 300))
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@dataclass
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class GenerationBatchResult:
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logits_output: Optional[LogitsProcessorOutput]
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pp_hidden_states_proxy_tensors: Optional[torch.Tensor]
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next_token_ids: Optional[List[int]]
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extend_input_len_per_req: List[int]
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extend_logprob_start_len_per_req: List[int]
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bid: int
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can_run_cuda_graph: bool
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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 KvMetrics:
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def __init__(self):
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self.request_active_slots = None
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self.request_total_slots = None
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self.kv_active_blocks = None
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self.kv_total_blocks = None
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self.num_requests_waiting = None
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self.gpu_cache_usage_perc = None
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self.gpu_prefix_cache_hit_rate = None
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self.data_parallel_rank = None
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class IdleSleeper:
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"""
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In setups which have long inactivity periods it is desirable to reduce
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system power consumption when sglang does nothing. This would lead not only
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to power savings, but also to more CPU thermal headroom when a request
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eventually comes. This is important in cases when multiple GPUs are connected
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as each GPU would otherwise pin one thread at 100% CPU usage.
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The simplest solution is to use zmq.Poller on all sockets that may receive
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data that needs handling immediately.
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"""
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def __init__(self, sockets):
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self.poller = zmq.Poller()
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for s in sockets:
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self.poller.register(s, zmq.POLLIN)
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def maybe_sleep(self):
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self.poller.poll(1000)
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class Scheduler(
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SchedulerOutputProcessorMixin,
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SchedulerDisaggregationDecodeMixin,
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SchedulerDisaggregationPrefillMixin,
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):
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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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pp_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.pp_rank = pp_rank
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self.dp_rank = dp_rank
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self.tp_size = server_args.tp_size
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self.pp_size = server_args.pp_size
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self.dp_size = server_args.dp_size
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self.schedule_policy = server_args.schedule_policy
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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.enable_kv_cache_events = server_args.kv_events_config is not None
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self.stream_interval = server_args.stream_interval
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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.gpu_id = gpu_id
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self.enable_hierarchical_cache = server_args.enable_hierarchical_cache
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self.page_size = server_args.page_size
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self.dp_size = server_args.dp_size
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self.attn_tp_rank, self.attn_tp_size, self.attn_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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self.idle_sleeper = None
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if self.pp_rank == 0 and 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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self.send_metrics_from_scheduler = get_zmq_socket(
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context, zmq.PUSH, port_args.metrics_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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self.recv_from_rpc = get_zmq_socket(
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context, zmq.DEALER, port_args.rpc_ipc_name, False
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)
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if self.server_args.sleep_on_idle:
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self.idle_sleeper = IdleSleeper(
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[
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self.recv_from_tokenizer,
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self.recv_from_rpc,
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]
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)
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else:
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self.recv_from_tokenizer = None
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self.recv_from_rpc = None
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self.send_metrics_from_scheduler = 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.init_tokenizer()
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# Set reasoning_parser and think_end_id if --reasoning_parser is enabled
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if self.server_args.reasoning_parser and self.tokenizer:
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reasoning_parser = ReasoningParser(
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model_type=self.server_args.reasoning_parser, stream_reasoning=False
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)
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self.tokenizer.think_end_id = self.tokenizer.encode(
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reasoning_parser.detector.think_end_token, add_special_tokens=False
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)[0]
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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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# 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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pp_rank=pp_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 draft worker for speculative decoding
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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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if global_server_args_dict["max_micro_batch_size"] is None:
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global_server_args_dict["max_micro_batch_size"] = max(
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self.max_running_requests // server_args.pp_size, 1
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)
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self.tp_group = self.tp_worker.get_tp_group()
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self.tp_cpu_group = self.tp_group.cpu_group
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self.attn_tp_group = self.tp_worker.get_attention_tp_group()
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self.attn_tp_cpu_group = self.tp_worker.get_attention_tp_cpu_group()
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self.pp_group = get_pp_group()
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self.world_group = get_world_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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if tp_rank == 0:
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avail_mem = get_available_gpu_memory(
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self.device, self.gpu_id, empty_cache=False
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)
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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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f"available_gpu_mem={avail_mem:.2f} GB"
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)
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# Init memory pool and cache
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self.init_memory_pool_and_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: ScheduleBatch = ScheduleBatch(reqs=[], batch_is_full=False)
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# The current forward batch
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self.cur_batch: Optional[ScheduleBatch] = None
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# The last 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.last_prefill_tokens = 0
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self.last_decode_stats_tic = time.perf_counter()
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self.last_prefill_stats_tic = time.perf_counter()
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self.return_health_check_ct = 0
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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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self.forward_sleep_time = None
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# Init 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.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 schedule policy and new token estimation
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self.policy = SchedulePolicy(
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self.schedule_policy,
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self.tree_cache,
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self.enable_hierarchical_cache,
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)
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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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# 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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self.torch_profiler = None
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self.torch_profiler_output_dir: Optional[str] = None
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self.profiler_activities: Optional[List[str]] = None
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self.profile_id: Optional[str] = None
|
|
self.profiler_target_forward_ct: Optional[int] = None
|
|
self.profiler_target_prefill_ct: Optional[int] = None
|
|
self.profiler_target_decode_ct: Optional[int] = None
|
|
self.profiler_prefill_ct: Optional[int] = None
|
|
self.profiler_decode_ct: Optional[int] = None
|
|
self.profile_by_stage: bool = False
|
|
self.profile_steps: Optional[int] = None
|
|
self.profile_in_progress: bool = False
|
|
self.rpd_profiler = None
|
|
|
|
# Init metrics stats
|
|
self.init_metrics()
|
|
self.init_kv_events(server_args.kv_events_config)
|
|
|
|
# Init request dispatcher
|
|
self._request_dispatcher = TypeBasedDispatcher(
|
|
[
|
|
(TokenizedGenerateReqInput, self.handle_generate_request),
|
|
(TokenizedEmbeddingReqInput, self.handle_embedding_request),
|
|
(FlushCacheReqInput, self.flush_cache_wrapped),
|
|
(AbortReq, self.abort_request),
|
|
(OpenSessionReqInput, self.open_session),
|
|
(CloseSessionReqInput, self.close_session),
|
|
(UpdateWeightFromDiskReqInput, self.update_weights_from_disk),
|
|
(InitWeightsUpdateGroupReqInput, self.init_weights_update_group),
|
|
(
|
|
UpdateWeightsFromDistributedReqInput,
|
|
self.update_weights_from_distributed,
|
|
),
|
|
(UpdateWeightsFromTensorReqInput, self.update_weights_from_tensor),
|
|
(GetWeightsByNameReqInput, self.get_weights_by_name),
|
|
(ReleaseMemoryOccupationReqInput, self.release_memory_occupation),
|
|
(ResumeMemoryOccupationReqInput, self.resume_memory_occupation),
|
|
(SlowDownReqInput, self.slow_down),
|
|
(ProfileReq, self.profile),
|
|
(GetInternalStateReq, self.get_internal_state),
|
|
(SetInternalStateReq, self.set_internal_state),
|
|
(RpcReqInput, self.handle_rpc_request),
|
|
(ExpertDistributionReq, self.expert_distribution_handle),
|
|
]
|
|
)
|
|
|
|
self.disaggregation_mode = DisaggregationMode(
|
|
self.server_args.disaggregation_mode
|
|
)
|
|
self.init_disaggregation()
|
|
|
|
if get_bool_env_var("SGLANG_GC_LOG"):
|
|
configure_gc_logger()
|
|
|
|
def maybe_sleep_on_idle(self):
|
|
if self.idle_sleeper is not None:
|
|
self.idle_sleeper.maybe_sleep()
|
|
|
|
def init_tokenizer(self):
|
|
server_args = self.server_args
|
|
|
|
self.model_config = ModelConfig.from_server_args(server_args)
|
|
self.is_generation = self.model_config.is_generation
|
|
|
|
if server_args.skip_tokenizer_init:
|
|
self.tokenizer = self.processor = None
|
|
else:
|
|
if self.model_config.is_multimodal:
|
|
self.processor = get_processor(
|
|
server_args.tokenizer_path,
|
|
tokenizer_mode=server_args.tokenizer_mode,
|
|
trust_remote_code=server_args.trust_remote_code,
|
|
revision=server_args.revision,
|
|
use_fast=not server_args.disable_fast_image_processor,
|
|
)
|
|
self.tokenizer = get_tokenizer_from_processor(self.processor)
|
|
else:
|
|
self.tokenizer = get_tokenizer(
|
|
server_args.tokenizer_path,
|
|
tokenizer_mode=server_args.tokenizer_mode,
|
|
trust_remote_code=server_args.trust_remote_code,
|
|
revision=server_args.revision,
|
|
)
|
|
|
|
def init_memory_pool_and_cache(self):
|
|
server_args = self.server_args
|
|
|
|
self.req_to_token_pool, self.token_to_kv_pool_allocator = (
|
|
self.tp_worker.get_memory_pool()
|
|
)
|
|
|
|
if (
|
|
server_args.chunked_prefill_size is not None
|
|
and server_args.disable_radix_cache
|
|
):
|
|
self.tree_cache = ChunkCache(
|
|
req_to_token_pool=self.req_to_token_pool,
|
|
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
|
|
page_size=self.page_size,
|
|
)
|
|
else:
|
|
if self.enable_hierarchical_cache:
|
|
self.tree_cache = HiRadixCache(
|
|
req_to_token_pool=self.req_to_token_pool,
|
|
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
|
|
tp_cache_group=(
|
|
self.attn_tp_cpu_group
|
|
if self.server_args.enable_dp_attention
|
|
else self.tp_cpu_group
|
|
),
|
|
page_size=self.page_size,
|
|
hicache_ratio=server_args.hicache_ratio,
|
|
hicache_size=server_args.hicache_size,
|
|
hicache_write_policy=server_args.hicache_write_policy,
|
|
)
|
|
self.tp_worker.register_hicache_layer_transfer_counter(
|
|
self.tree_cache.cache_controller.layer_done_counter
|
|
)
|
|
|
|
else:
|
|
self.tree_cache = RadixCache(
|
|
req_to_token_pool=self.req_to_token_pool,
|
|
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
|
|
page_size=self.page_size,
|
|
disable=server_args.disable_radix_cache,
|
|
enable_kv_cache_events=self.enable_kv_cache_events,
|
|
)
|
|
|
|
self.decode_mem_cache_buf_multiplier = (
|
|
1
|
|
if self.spec_algorithm.is_none()
|
|
else (
|
|
server_args.speculative_num_draft_tokens
|
|
+ (
|
|
server_args.speculative_eagle_topk
|
|
* server_args.speculative_num_steps
|
|
)
|
|
)
|
|
)
|
|
|
|
def init_metrics(self):
|
|
self.last_gen_throughput: float = 0.0
|
|
self.last_input_throughput: float = 0.0
|
|
self.step_time_dict = defaultdict(list) # Dict[batch size -> step time]
|
|
self.spec_num_total_accepted_tokens = 0
|
|
self.spec_num_total_forward_ct = 0
|
|
self.cum_spec_accept_length = 0
|
|
self.cum_spec_accept_count = 0
|
|
self.stats = SchedulerStats()
|
|
if self.enable_metrics:
|
|
engine_type = "unified"
|
|
self.metrics_collector = SchedulerMetricsCollector(
|
|
labels={
|
|
"model_name": self.server_args.served_model_name,
|
|
"engine_type": engine_type,
|
|
},
|
|
)
|
|
|
|
def init_kv_events(self, kv_events_config: Optional[str]):
|
|
if self.enable_kv_cache_events:
|
|
self.kv_event_publisher = EventPublisherFactory.create(
|
|
kv_events_config, self.attn_dp_rank
|
|
)
|
|
|
|
def init_disaggregation(self):
|
|
self.transfer_backend = TransferBackend(
|
|
self.server_args.disaggregation_transfer_backend
|
|
)
|
|
|
|
if (
|
|
self.disaggregation_mode == DisaggregationMode.DECODE
|
|
): # *2 for the headroom.
|
|
buffer_size = (self.req_to_token_pool.size) * 2
|
|
self.req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
|
|
buffer_size
|
|
)
|
|
self.disagg_metadata_buffers = MetadataBuffers(
|
|
buffer_size,
|
|
hidden_size=self.model_config.hf_text_config.hidden_size,
|
|
dtype=self.model_config.dtype,
|
|
custom_mem_pool=self.token_to_kv_pool_allocator.get_kvcache().maybe_get_custom_mem_pool(),
|
|
)
|
|
|
|
# The decode requests polling kv cache
|
|
self.disagg_decode_transfer_queue = DecodeTransferQueue(
|
|
gloo_group=self.attn_tp_cpu_group,
|
|
req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
|
|
tp_rank=self.tp_rank,
|
|
metadata_buffers=self.disagg_metadata_buffers,
|
|
scheduler=self,
|
|
tree_cache=self.tree_cache,
|
|
)
|
|
|
|
# The decode requests pending for pre-allocation
|
|
self.disagg_decode_prealloc_queue = DecodePreallocQueue(
|
|
req_to_token_pool=self.req_to_token_pool,
|
|
token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
|
|
draft_token_to_kv_pool=(
|
|
None
|
|
if self.draft_worker is None
|
|
else self.draft_worker.model_runner.token_to_kv_pool
|
|
),
|
|
req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
|
|
metadata_buffers=self.disagg_metadata_buffers,
|
|
scheduler=self,
|
|
transfer_queue=self.disagg_decode_transfer_queue,
|
|
tree_cache=self.tree_cache,
|
|
gloo_group=self.attn_tp_cpu_group,
|
|
tp_rank=self.tp_rank,
|
|
tp_size=self.tp_size,
|
|
dp_size=self.server_args.dp_size,
|
|
gpu_id=self.gpu_id,
|
|
bootstrap_port=self.server_args.disaggregation_bootstrap_port,
|
|
max_total_num_tokens=self.max_total_num_tokens,
|
|
prefill_pp_size=self.server_args.disaggregation_prefill_pp,
|
|
num_reserved_decode_tokens=self.server_args.num_reserved_decode_tokens,
|
|
transfer_backend=self.transfer_backend,
|
|
)
|
|
|
|
# Metric for pre-allocation
|
|
self.num_tokens_pre_allocated = 0
|
|
|
|
elif self.disaggregation_mode == DisaggregationMode.PREFILL:
|
|
# *2 for the headroom.
|
|
buffer_size = self.max_running_requests * 2
|
|
self.req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator(
|
|
buffer_size
|
|
)
|
|
self.disagg_metadata_buffers = MetadataBuffers(
|
|
buffer_size,
|
|
hidden_size=self.model_config.hf_text_config.hidden_size,
|
|
dtype=self.model_config.dtype,
|
|
custom_mem_pool=self.token_to_kv_pool_allocator.get_kvcache().maybe_get_custom_mem_pool(),
|
|
)
|
|
|
|
self.disagg_prefill_bootstrap_queue = PrefillBootstrapQueue(
|
|
token_to_kv_pool=self.token_to_kv_pool_allocator.get_kvcache(),
|
|
draft_token_to_kv_pool=(
|
|
None
|
|
if self.draft_worker is None
|
|
else self.draft_worker.model_runner.token_to_kv_pool
|
|
),
|
|
req_to_metadata_buffer_idx_allocator=self.req_to_metadata_buffer_idx_allocator,
|
|
metadata_buffers=self.disagg_metadata_buffers,
|
|
tp_rank=self.tp_rank,
|
|
tp_size=self.tp_size,
|
|
gpu_id=self.gpu_id,
|
|
bootstrap_port=self.server_args.disaggregation_bootstrap_port,
|
|
gloo_group=self.attn_tp_cpu_group,
|
|
max_total_num_tokens=self.max_total_num_tokens,
|
|
decode_tp_size=self.server_args.disaggregation_decode_tp,
|
|
decode_dp_size=self.server_args.disaggregation_decode_dp,
|
|
scheduler=self,
|
|
pp_rank=self.pp_rank,
|
|
pp_size=self.pp_size,
|
|
transfer_backend=self.transfer_backend,
|
|
)
|
|
# The prefill requests that are in the middle of kv sending
|
|
self.disagg_prefill_inflight_queue: List[Req] = []
|
|
|
|
@DynamicGradMode()
|
|
def event_loop_normal(self):
|
|
"""A normal scheduler loop."""
|
|
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.process_batch_result(batch, result)
|
|
else:
|
|
# When the server is idle, do self-check and re-init some states
|
|
self.check_memory()
|
|
self.new_token_ratio = self.init_new_token_ratio
|
|
self.maybe_sleep_on_idle()
|
|
|
|
self.last_batch = batch
|
|
|
|
@DynamicGradMode()
|
|
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:
|
|
batch.launch_done = threading.Event()
|
|
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, batch.launch_done)
|
|
|
|
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
|
|
)
|
|
# NOTE: we should use current launched batch's launch_done event Instead of the last batch's
|
|
self.process_batch_result(
|
|
tmp_batch, tmp_result, batch.launch_done if batch else None
|
|
)
|
|
elif batch is None:
|
|
# When the server is idle, do self-check and re-init some states
|
|
self.check_memory()
|
|
self.new_token_ratio = self.init_new_token_ratio
|
|
self.maybe_sleep_on_idle()
|
|
|
|
self.last_batch = batch
|
|
|
|
@DynamicGradMode()
|
|
def event_loop_pp(self):
|
|
"""A non-overlap scheduler loop for pipeline parallelism."""
|
|
mbs = [None] * self.pp_size
|
|
last_mbs = [None] * self.pp_size
|
|
self.running_mbs = [
|
|
ScheduleBatch(reqs=[], batch_is_full=False) for _ in range(self.pp_size)
|
|
]
|
|
bids = [None] * self.pp_size
|
|
pp_outputs: Optional[PPProxyTensors] = None
|
|
while True:
|
|
server_is_idle = True
|
|
for mb_id in range(self.pp_size):
|
|
self.running_batch = self.running_mbs[mb_id]
|
|
self.last_batch = last_mbs[mb_id]
|
|
|
|
recv_reqs = self.recv_requests()
|
|
self.process_input_requests(recv_reqs)
|
|
mbs[mb_id] = self.get_next_batch_to_run()
|
|
self.running_mbs[mb_id] = self.running_batch
|
|
|
|
self.cur_batch = mbs[mb_id]
|
|
if self.cur_batch:
|
|
server_is_idle = False
|
|
result = self.run_batch(self.cur_batch)
|
|
|
|
# (last rank) send the outputs to the next step
|
|
if self.pp_group.is_last_rank:
|
|
if self.cur_batch:
|
|
next_token_ids, bids[mb_id] = (
|
|
result.next_token_ids,
|
|
result.bid,
|
|
)
|
|
if self.cur_batch.return_logprob:
|
|
pp_outputs = PPProxyTensors(
|
|
{
|
|
"next_token_ids": next_token_ids,
|
|
"extend_input_len_per_req": result.extend_input_len_per_req,
|
|
"extend_logprob_start_len_per_req": result.extend_logprob_start_len_per_req,
|
|
}
|
|
| (
|
|
{
|
|
f"logits_output.{k}": v
|
|
for k, v in result.logits_output.__dict__.items()
|
|
}
|
|
if result.logits_output is not None
|
|
else {}
|
|
)
|
|
)
|
|
else:
|
|
pp_outputs = PPProxyTensors(
|
|
{
|
|
"next_token_ids": next_token_ids,
|
|
}
|
|
)
|
|
# send the output from the last round to let the next stage worker run post processing
|
|
self.pp_group.send_tensor_dict(
|
|
pp_outputs.tensors,
|
|
all_gather_group=self.attn_tp_group,
|
|
)
|
|
|
|
# receive outputs and post-process (filter finished reqs) the coming microbatch
|
|
next_mb_id = (mb_id + 1) % self.pp_size
|
|
next_pp_outputs = None
|
|
if mbs[next_mb_id] is not None:
|
|
next_pp_outputs: Optional[PPProxyTensors] = PPProxyTensors(
|
|
self.pp_group.recv_tensor_dict(
|
|
all_gather_group=self.attn_tp_group
|
|
)
|
|
)
|
|
mbs[next_mb_id].output_ids = next_pp_outputs["next_token_ids"]
|
|
logits_output_args = {
|
|
k[len("logits_output.") :]: v
|
|
for k, v in next_pp_outputs.tensors.items()
|
|
if k.startswith("logits_output.")
|
|
}
|
|
if len(logits_output_args) > 0:
|
|
logits_output = LogitsProcessorOutput(**logits_output_args)
|
|
else:
|
|
logits_output = None
|
|
output_result = GenerationBatchResult(
|
|
logits_output=logits_output,
|
|
pp_hidden_states_proxy_tensors=None,
|
|
next_token_ids=next_pp_outputs["next_token_ids"],
|
|
extend_input_len_per_req=next_pp_outputs.tensors.get(
|
|
"extend_input_len_per_req", None
|
|
),
|
|
extend_logprob_start_len_per_req=next_pp_outputs.tensors.get(
|
|
"extend_logprob_start_len_per_req", None
|
|
),
|
|
bid=bids[next_mb_id],
|
|
can_run_cuda_graph=result.can_run_cuda_graph,
|
|
)
|
|
self.process_batch_result(mbs[next_mb_id], output_result)
|
|
last_mbs[next_mb_id] = mbs[next_mb_id]
|
|
|
|
# (not last rank)
|
|
if not self.pp_group.is_last_rank:
|
|
if self.cur_batch:
|
|
bids[mb_id] = result.bid
|
|
# carry the outputs to the next stage
|
|
# send the outputs from the last round to let the next stage worker run post processing
|
|
if pp_outputs:
|
|
self.pp_group.send_tensor_dict(
|
|
pp_outputs.tensors,
|
|
all_gather_group=self.attn_tp_group,
|
|
)
|
|
|
|
# send out reqs to the next stage
|
|
dp_offset = self.attn_dp_rank * self.attn_tp_size
|
|
if self.attn_tp_rank == 0:
|
|
point_to_point_pyobj(
|
|
recv_reqs,
|
|
self.pp_rank * self.tp_size + dp_offset,
|
|
self.world_group.cpu_group,
|
|
self.pp_rank * self.tp_size + dp_offset,
|
|
(self.pp_rank + 1) * self.tp_size + dp_offset,
|
|
)
|
|
|
|
# send out proxy tensors to the next stage
|
|
if self.cur_batch:
|
|
self.pp_group.send_tensor_dict(
|
|
result.pp_hidden_states_proxy_tensors,
|
|
all_gather_group=self.attn_tp_group,
|
|
)
|
|
|
|
pp_outputs = next_pp_outputs
|
|
|
|
# When the server is idle, self-check and re-init some states
|
|
if server_is_idle:
|
|
self.check_memory()
|
|
self.new_token_ratio = self.init_new_token_ratio
|
|
self.maybe_sleep_on_idle()
|
|
|
|
def recv_requests(self) -> List[Req]:
|
|
"""Receive results at tp_rank = 0 and broadcast it to all other TP ranks."""
|
|
if self.pp_rank == 0:
|
|
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)
|
|
|
|
while True:
|
|
try:
|
|
recv_rpc = self.recv_from_rpc.recv_pyobj(zmq.NOBLOCK)
|
|
except zmq.ZMQError:
|
|
break
|
|
recv_reqs.append(recv_rpc)
|
|
else:
|
|
recv_reqs = None
|
|
else:
|
|
if self.attn_tp_rank == 0:
|
|
dp_offset = self.attn_dp_rank * self.attn_tp_size
|
|
recv_reqs = point_to_point_pyobj(
|
|
[],
|
|
self.pp_rank * self.tp_size + dp_offset,
|
|
self.world_group.cpu_group,
|
|
(self.pp_rank - 1) * self.tp_size + dp_offset,
|
|
self.pp_rank * self.tp_size + dp_offset,
|
|
)
|
|
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:
|
|
work_reqs = broadcast_pyobj(
|
|
work_reqs,
|
|
self.attn_tp_group.rank,
|
|
self.attn_tp_cpu_group,
|
|
src=self.attn_tp_group.ranks[0],
|
|
)
|
|
if self.tp_size != 1:
|
|
control_reqs = broadcast_pyobj(
|
|
control_reqs,
|
|
self.tp_group.rank,
|
|
self.tp_cpu_group,
|
|
src=self.tp_group.ranks[0],
|
|
)
|
|
recv_reqs = work_reqs + control_reqs
|
|
elif self.tp_size != 1:
|
|
recv_reqs = broadcast_pyobj(
|
|
recv_reqs,
|
|
self.tp_group.rank,
|
|
self.tp_cpu_group,
|
|
src=self.tp_group.ranks[0],
|
|
)
|
|
return recv_reqs
|
|
|
|
def process_input_requests(self, recv_reqs: List):
|
|
for recv_req in recv_reqs:
|
|
# If it is a health check generation request and there are running requests, ignore it.
|
|
if is_health_check_generate_req(recv_req) and (
|
|
self.chunked_req is not None or not self.running_batch.is_empty()
|
|
):
|
|
self.return_health_check_ct += 1
|
|
continue
|
|
|
|
output = self._request_dispatcher(recv_req)
|
|
if output is not None:
|
|
if isinstance(output, RpcReqOutput):
|
|
if self.recv_from_rpc is not None:
|
|
self.recv_from_rpc.send_pyobj(output)
|
|
else:
|
|
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
|
|
|
|
if recv_req.bootstrap_port is None:
|
|
# Use default bootstrap port
|
|
recv_req.bootstrap_port = self.server_args.disaggregation_bootstrap_port
|
|
|
|
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,
|
|
token_ids_logprob=recv_req.token_ids_logprob,
|
|
stream=recv_req.stream,
|
|
lora_path=recv_req.lora_path,
|
|
input_embeds=recv_req.input_embeds,
|
|
custom_logit_processor=recv_req.custom_logit_processor,
|
|
return_hidden_states=recv_req.return_hidden_states,
|
|
eos_token_ids=self.model_config.hf_eos_token_id,
|
|
bootstrap_host=recv_req.bootstrap_host,
|
|
bootstrap_port=recv_req.bootstrap_port,
|
|
bootstrap_room=recv_req.bootstrap_room,
|
|
data_parallel_rank=recv_req.data_parallel_rank,
|
|
)
|
|
req.tokenizer = self.tokenizer
|
|
|
|
if self.disaggregation_mode != DisaggregationMode.NULL:
|
|
# Invalid request for disaggregated mode
|
|
if recv_req.bootstrap_room is None:
|
|
error_msg = (
|
|
f"Invalid request: Disaggregated request received without "
|
|
f"boostrap room id. {req.rid=}"
|
|
)
|
|
logger.error(error_msg)
|
|
prepare_abort(req, error_msg)
|
|
self.stream_output([req], req.return_logprob)
|
|
return
|
|
|
|
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._add_request_to_queue(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._add_request_to_queue(req)
|
|
return
|
|
|
|
# Handle multimodal inputs
|
|
if recv_req.mm_inputs is not None:
|
|
image_inputs = MultimodalInputs.from_dict(recv_req.mm_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:
|
|
req.set_finish_with_abort(
|
|
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}."
|
|
)
|
|
)
|
|
self._add_request_to_queue(req)
|
|
return
|
|
|
|
# Validate prompt length
|
|
error_msg = validate_input_length(
|
|
req,
|
|
self.max_req_input_len,
|
|
self.server_args.allow_auto_truncate,
|
|
)
|
|
if error_msg:
|
|
req.set_finish_with_abort(error_msg)
|
|
self._add_request_to_queue(req)
|
|
return
|
|
|
|
# Copy more attributes
|
|
if recv_req.logprob_start_len == -1 or not recv_req.return_logprob:
|
|
# 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
|
|
|
|
if req.logprob_start_len >= len(req.origin_input_ids):
|
|
error_msg = f"{req.logprob_start_len=} is higher than the number of input tokens {len(req.origin_input_ids)=}. Please use a smaller logprob_start_len."
|
|
req.logprob_start_len = len(req.origin_input_ids) - 1
|
|
req.set_finish_with_abort(error_msg)
|
|
self._add_request_to_queue(req)
|
|
return
|
|
|
|
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
|
|
or req.sampling_params.structural_tag 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)
|
|
elif req.sampling_params.structural_tag:
|
|
key = ("structural_tag", req.sampling_params.structural_tag)
|
|
|
|
value, cache_hit = self.grammar_backend.get_cached_or_future_value(key)
|
|
req.grammar = value
|
|
|
|
if not cache_hit:
|
|
req.grammar_key = key
|
|
add_to_grammar_queue = True
|
|
else:
|
|
if value is INVALID_GRAMMAR_OBJ: # We hit a cached invalid grammar.
|
|
error_msg = f"Invalid grammar request with cache hit: {key=}"
|
|
req.set_finish_with_abort(error_msg)
|
|
|
|
if add_to_grammar_queue:
|
|
req.queue_time_start = time.perf_counter()
|
|
self.grammar_queue.append(req)
|
|
else:
|
|
self._add_request_to_queue(req)
|
|
|
|
def _add_request_to_queue(self, req: Req):
|
|
req.queue_time_start = time.perf_counter()
|
|
if self.disaggregation_mode == DisaggregationMode.PREFILL:
|
|
self.disagg_prefill_bootstrap_queue.add(
|
|
req, self.model_config.num_key_value_heads
|
|
)
|
|
elif self.disaggregation_mode == DisaggregationMode.DECODE:
|
|
self.disagg_decode_prealloc_queue.add(req)
|
|
else:
|
|
self.waiting_queue.append(req)
|
|
|
|
def _extend_requests_to_queue(self, reqs: List[Req], is_retracted: bool = False):
|
|
if self.disaggregation_mode == DisaggregationMode.PREFILL:
|
|
self.disagg_prefill_bootstrap_queue.extend(
|
|
reqs, self.model_config.num_key_value_heads
|
|
)
|
|
elif self.disaggregation_mode == DisaggregationMode.DECODE:
|
|
# If this is a decode server, we put the request to the decode pending prealloc queue
|
|
self.disagg_decode_prealloc_queue.extend(reqs, is_retracted)
|
|
else:
|
|
self.waiting_queue.extend(reqs)
|
|
|
|
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,
|
|
token_type_ids=recv_req.token_type_ids,
|
|
)
|
|
req.tokenizer = self.tokenizer
|
|
|
|
# Handle multimodal inputs
|
|
if recv_req.image_inputs is not None:
|
|
image_inputs = MultimodalInputs.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:
|
|
req.set_finish_with_abort(
|
|
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}."
|
|
)
|
|
)
|
|
self._add_request_to_queue(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._add_request_to_queue(req)
|
|
return
|
|
|
|
# Copy more attributes
|
|
req.logprob_start_len = len(req.origin_input_ids) - 1
|
|
self._add_request_to_queue(req)
|
|
|
|
def _emit_kv_metrics(self):
|
|
kv_metrics = KvMetrics()
|
|
kv_metrics.request_active_slots = self.stats.num_running_reqs
|
|
kv_metrics.request_total_slots = self.max_running_requests
|
|
kv_metrics.kv_active_blocks = int(
|
|
self.stats.token_usage * self.max_total_num_tokens
|
|
)
|
|
kv_metrics.kv_total_blocks = self.max_total_num_tokens
|
|
kv_metrics.num_requests_waiting = self.stats.num_queue_reqs
|
|
kv_metrics.gpu_cache_usage_perc = self.stats.token_usage
|
|
kv_metrics.gpu_prefix_cache_hit_rate = self.stats.cache_hit_rate
|
|
kv_metrics.data_parallel_rank = self.dp_rank if self.dp_rank is not None else 0
|
|
|
|
if not self.send_metrics_from_scheduler.closed:
|
|
self.send_metrics_from_scheduler.send_pyobj(kv_metrics)
|
|
|
|
def log_prefill_stats(
|
|
self,
|
|
adder: PrefillAdder,
|
|
can_run_list: List[Req],
|
|
running_bs: int,
|
|
):
|
|
gap_latency = time.perf_counter() - self.last_prefill_stats_tic
|
|
self.last_prefill_stats_tic = time.perf_counter()
|
|
self.last_input_throughput = self.last_prefill_tokens / gap_latency
|
|
self.last_prefill_tokens = adder.log_input_tokens
|
|
|
|
num_used = self.max_total_num_tokens - (
|
|
self.token_to_kv_pool_allocator.available_size()
|
|
+ self.tree_cache.evictable_size()
|
|
)
|
|
|
|
num_new_seq = len(can_run_list)
|
|
f = (
|
|
f"Prefill batch. "
|
|
f"#new-seq: {num_new_seq}, "
|
|
f"#new-token: {adder.log_input_tokens}, "
|
|
f"#cached-token: {adder.log_hit_tokens}, "
|
|
f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
|
|
)
|
|
|
|
if self.disaggregation_mode == DisaggregationMode.PREFILL:
|
|
f += f"#unbootstrapped-req: {len(self.disagg_prefill_bootstrap_queue.queue)}, "
|
|
f += f"#queue-req: {len(self.waiting_queue)}, "
|
|
f += f"#transferring-req: {len(self.disagg_prefill_inflight_queue)}, "
|
|
f += f"input throughput (token/s): {self.last_input_throughput:.2f} "
|
|
else:
|
|
f += f"#running-req: {running_bs}, "
|
|
f += f"#queue-req: {len(self.waiting_queue)}"
|
|
|
|
logger.info(f)
|
|
|
|
if self.enable_metrics:
|
|
cache_hit_rate = adder.log_hit_tokens / (
|
|
adder.log_input_tokens + adder.log_hit_tokens
|
|
)
|
|
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)
|
|
self.stats.cache_hit_rate = cache_hit_rate
|
|
|
|
total_queue_latency = 0
|
|
for req in can_run_list:
|
|
total_queue_latency += req.queue_time_end - req.queue_time_start
|
|
self.stats.avg_request_queue_latency = total_queue_latency / num_new_seq
|
|
|
|
self.metrics_collector.log_stats(self.stats)
|
|
self._emit_kv_metrics()
|
|
self._publish_kv_events()
|
|
|
|
def log_decode_stats(
|
|
self, can_run_cuda_graph: bool, running_batch: ScheduleBatch = None
|
|
):
|
|
batch = running_batch or self.running_batch
|
|
|
|
gap_latency = time.perf_counter() - self.last_decode_stats_tic
|
|
self.last_decode_stats_tic = time.perf_counter()
|
|
self.last_gen_throughput = self.num_generated_tokens / gap_latency
|
|
self.num_generated_tokens = 0
|
|
num_running_reqs = len(batch.reqs)
|
|
num_used = self.max_total_num_tokens - (
|
|
self.token_to_kv_pool_allocator.available_size()
|
|
+ self.tree_cache.evictable_size()
|
|
)
|
|
|
|
if RECORD_STEP_TIME:
|
|
self.step_time_dict[num_running_reqs].append(
|
|
gap_latency / self.server_args.decode_log_interval
|
|
)
|
|
|
|
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}, "
|
|
)
|
|
|
|
if self.spec_algorithm.is_none():
|
|
spec_accept_length = 0
|
|
else:
|
|
spec_accept_length = (
|
|
self.spec_num_total_accepted_tokens / self.spec_num_total_forward_ct
|
|
)
|
|
self.cum_spec_accept_length += self.spec_num_total_accepted_tokens
|
|
self.cum_spec_accept_count += self.spec_num_total_forward_ct
|
|
self.spec_num_total_accepted_tokens = self.spec_num_total_forward_ct = 0
|
|
msg += f"accept len: {spec_accept_length:.2f}, "
|
|
|
|
if self.disaggregation_mode == DisaggregationMode.DECODE:
|
|
msg += f"pre-allocated usage: {self.num_tokens_pre_allocated / self.max_total_num_tokens:.2f}, "
|
|
msg += f"#retracted-req: {len(self.disagg_decode_prealloc_queue.retracted_queue)}, "
|
|
|
|
msg += (
|
|
f"cuda graph: {can_run_cuda_graph}, "
|
|
f"gen throughput (token/s): {self.last_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.cache_hit_rate = 0.0
|
|
self.stats.gen_throughput = self.last_gen_throughput
|
|
self.stats.num_queue_reqs = len(self.waiting_queue)
|
|
self.stats.num_grammar_queue_reqs = len(self.grammar_queue)
|
|
self.stats.spec_accept_length = spec_accept_length
|
|
self.metrics_collector.log_stats(self.stats)
|
|
self._emit_kv_metrics()
|
|
self._publish_kv_events()
|
|
|
|
def check_memory(self):
|
|
available_size = (
|
|
self.token_to_kv_pool_allocator.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 = (
|
|
"token_to_kv_pool_allocator memory leak detected! "
|
|
f"{available_size=}, {protected_size=}, {self.max_total_num_tokens=}\n"
|
|
f"{self.token_to_kv_pool_allocator.available_size()=}\n"
|
|
f"{self.tree_cache.evictable_size()=}\n"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
if self.disaggregation_mode == DisaggregationMode.DECODE:
|
|
req_total_size = (
|
|
self.req_to_token_pool.size + self.req_to_token_pool.pre_alloc_size
|
|
)
|
|
else:
|
|
req_total_size = self.req_to_token_pool.size
|
|
|
|
if len(self.req_to_token_pool.free_slots) != req_total_size:
|
|
msg = (
|
|
"req_to_token_pool memory leak detected!"
|
|
f"available_size={len(self.req_to_token_pool.free_slots)}, "
|
|
f"total_size={self.req_to_token_pool.size}\n"
|
|
)
|
|
raise ValueError(msg)
|
|
|
|
if (
|
|
self.enable_metrics
|
|
and self.attn_tp_rank == 0
|
|
and time.perf_counter() > self.metrics_collector.last_log_time + 30
|
|
):
|
|
# During idle time, also collect metrics every 30 seconds.
|
|
num_used = self.max_total_num_tokens - (
|
|
self.token_to_kv_pool_allocator.available_size()
|
|
+ self.tree_cache.evictable_size()
|
|
)
|
|
num_running_reqs = len(self.running_batch.reqs)
|
|
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 = 0
|
|
self.stats.num_queue_reqs = len(self.waiting_queue)
|
|
self.stats.num_grammar_queue_reqs = len(self.grammar_queue)
|
|
self.metrics_collector.log_stats(self.stats)
|
|
self._publish_kv_events()
|
|
|
|
def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
|
|
# Merge the prefill batch into the running batch
|
|
chunked_req_to_exclude = set()
|
|
if self.chunked_req:
|
|
# Move the chunked request out of the batch so that we can merge
|
|
# only finished requests to running_batch.
|
|
chunked_req_to_exclude.add(self.chunked_req)
|
|
self.tree_cache.cache_unfinished_req(self.chunked_req)
|
|
# chunked request keeps its rid but will get a new req_pool_idx
|
|
self.req_to_token_pool.free(self.chunked_req.req_pool_idx)
|
|
if self.last_batch and self.last_batch.forward_mode.is_extend():
|
|
if self.last_batch.chunked_req is not None:
|
|
# In the context pipeline parallelism, after the last chunk, the current microbatch still track outdated chunked_req.
|
|
# We need to discard it.
|
|
chunked_req_to_exclude.add(self.last_batch.chunked_req)
|
|
|
|
# Filter batch
|
|
last_bs = self.last_batch.batch_size()
|
|
self.last_batch.filter_batch(
|
|
chunked_req_to_exclude=list(chunked_req_to_exclude)
|
|
)
|
|
if self.last_batch.batch_size() < last_bs:
|
|
self.running_batch.batch_is_full = False
|
|
|
|
# Merge the new batch into the running batch
|
|
if not self.last_batch.is_empty():
|
|
if self.running_batch.is_empty():
|
|
self.running_batch = self.last_batch
|
|
else:
|
|
# Merge running_batch with prefill batch
|
|
self.running_batch.merge_batch(self.last_batch)
|
|
|
|
new_batch = self.get_new_batch_prefill()
|
|
|
|
need_dp_attn_preparation = require_mlp_sync(self.server_args)
|
|
|
|
if need_dp_attn_preparation and not self.spec_algorithm.is_none():
|
|
# In speculative decoding, prefill batches and decode batches cannot be processed in the same DP attention group.
|
|
# We prepare idle batches in advance to skip preparing decode batches when there are prefill batches in the group.
|
|
new_batch, _ = self.prepare_mlp_sync_batch(new_batch)
|
|
need_dp_attn_preparation = new_batch is None
|
|
|
|
if new_batch is not None:
|
|
# Run prefill first if possible
|
|
ret = new_batch
|
|
else:
|
|
# Run decode
|
|
if not self.running_batch.is_empty():
|
|
self.running_batch = self.update_running_batch(self.running_batch)
|
|
ret = self.running_batch if not self.running_batch.is_empty() else None
|
|
else:
|
|
ret = None
|
|
|
|
# Handle DP attention
|
|
if need_dp_attn_preparation:
|
|
ret, _ = self.prepare_mlp_sync_batch(ret)
|
|
|
|
return ret
|
|
|
|
def get_num_allocatable_reqs(self, running_bs):
|
|
res = global_server_args_dict["max_micro_batch_size"] - running_bs
|
|
if self.pp_size > 1:
|
|
res = min(res, self.req_to_token_pool.available_size())
|
|
return res
|
|
|
|
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.running_batch.batch_is_full or len(self.waiting_queue) == 0
|
|
) and self.chunked_req is None:
|
|
return None
|
|
|
|
running_bs = len(self.running_batch.reqs)
|
|
# Ignore the check if self.chunked_req is not None.
|
|
# In the non-PP case, when self.chunked_req is not None, num_allocatable_reqs should always be greater than 0,
|
|
# as the space for the chunked request has just been released.
|
|
# In PP case, a chunked req can start in one microbatch and end in another microbatch, so the max_running_requests per microbatch should not be strict.
|
|
# Instead, we should always allow chunked request to be added, otherwise, there will be a memory leak.
|
|
if self.get_num_allocatable_reqs(running_bs) <= 0 and not self.chunked_req:
|
|
self.running_batch.batch_is_full = True
|
|
return None
|
|
|
|
if self.enable_hierarchical_cache:
|
|
self.tree_cache.check_hicache_events()
|
|
|
|
# Get priority queue
|
|
self.policy.calc_priority(self.waiting_queue)
|
|
|
|
# Prefill policy
|
|
adder = PrefillAdder(
|
|
self.page_size,
|
|
self.tree_cache,
|
|
self.token_to_kv_pool_allocator,
|
|
self.running_batch,
|
|
self.new_token_ratio,
|
|
self.max_prefill_tokens,
|
|
self.chunked_prefill_size,
|
|
running_bs if self.is_mixed_chunk else 0,
|
|
)
|
|
|
|
if self.chunked_req is not None:
|
|
self.chunked_req.init_next_round_input()
|
|
self.chunked_req = adder.add_chunked_req(self.chunked_req)
|
|
|
|
if self.lora_paths:
|
|
lora_set = set([req.lora_path for req in self.running_batch.reqs])
|
|
|
|
# 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.running_batch.batch_is_full = True
|
|
break
|
|
|
|
if len(adder.can_run_list) >= self.get_num_allocatable_reqs(running_bs):
|
|
self.running_batch.batch_is_full = True
|
|
break
|
|
|
|
if self.disaggregation_mode == DisaggregationMode.PREFILL:
|
|
# In prefill mode, prealloc queue and transfer queue can also take memory,
|
|
# so we need to check if the available size for the actual available size.
|
|
if len(adder.can_run_list) >= self.req_to_token_pool.available_size():
|
|
self.running_batch.batch_is_full = True
|
|
break
|
|
|
|
req.init_next_round_input(self.tree_cache)
|
|
res = adder.add_one_req(req, has_chunked_req=(self.chunked_req is not None))
|
|
|
|
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.running_batch.batch_is_full = len(
|
|
adder.can_run_list
|
|
) > 0 or (not self.running_batch.is_empty())
|
|
else:
|
|
self.running_batch.batch_is_full = True
|
|
break
|
|
|
|
# Update waiting queue
|
|
can_run_list: List[Req] = adder.can_run_list
|
|
if len(can_run_list) == 0:
|
|
return None
|
|
|
|
if self.enable_metrics:
|
|
# only record queue time when enable_metrics is True to avoid overhead
|
|
for req in can_run_list:
|
|
req.queue_time_end = time.perf_counter()
|
|
|
|
self.waiting_queue = [
|
|
x for x in self.waiting_queue if x not in set(can_run_list)
|
|
]
|
|
|
|
if adder.new_chunked_req is not None:
|
|
assert self.chunked_req is None
|
|
self.chunked_req = adder.new_chunked_req
|
|
|
|
if self.chunked_req:
|
|
self.chunked_req.is_chunked += 1
|
|
|
|
# Print stats
|
|
if self.attn_tp_rank == 0:
|
|
self.log_prefill_stats(adder, can_run_list, running_bs)
|
|
|
|
# Create a new batch
|
|
new_batch = ScheduleBatch.init_new(
|
|
can_run_list,
|
|
self.req_to_token_pool,
|
|
self.token_to_kv_pool_allocator,
|
|
self.tree_cache,
|
|
self.model_config,
|
|
self.enable_overlap,
|
|
self.spec_algorithm,
|
|
self.server_args.enable_custom_logit_processor,
|
|
chunked_req=self.chunked_req,
|
|
)
|
|
if self.enable_hierarchical_cache:
|
|
# todo (zhiqiang): disable cuda graph execution if hicache loading triggered
|
|
new_batch.hicache_consumer_index = (
|
|
self.tree_cache.ready_to_load_host_cache()
|
|
)
|
|
|
|
new_batch.prepare_for_extend()
|
|
|
|
# Mixed-style chunked prefill
|
|
if (
|
|
self.is_mixed_chunk
|
|
and not self.running_batch.is_empty()
|
|
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 = ScheduleBatch(
|
|
reqs=[], batch_is_full=self.running_batch.batch_is_full
|
|
)
|
|
else:
|
|
new_batch.decoding_reqs = None
|
|
|
|
return new_batch
|
|
|
|
def update_running_batch(self, batch: ScheduleBatch) -> Optional[ScheduleBatch]:
|
|
"""Update the current running decoding batch."""
|
|
initial_bs = batch.batch_size()
|
|
|
|
batch.filter_batch()
|
|
if batch.is_empty():
|
|
batch.batch_is_full = False
|
|
return batch
|
|
|
|
# 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.server_args)
|
|
self.new_token_ratio = new_token_ratio
|
|
|
|
logger.info(
|
|
"KV cache pool is full. Retract requests. "
|
|
f"#retracted_reqs: {len(retracted_reqs)}, "
|
|
f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
|
|
)
|
|
self._extend_requests_to_queue(retracted_reqs, is_retracted=True)
|
|
else:
|
|
self.new_token_ratio = max(
|
|
self.new_token_ratio - self.new_token_ratio_decay,
|
|
self.min_new_token_ratio,
|
|
)
|
|
|
|
if batch.batch_size() < initial_bs:
|
|
batch.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
|
|
|
|
# Whether to run the profiler
|
|
self._profile_batch_predicate(batch)
|
|
if self.forward_sleep_time is not None:
|
|
logger.info(f"Scheduler.run_batch sleep {self.forward_sleep_time}s")
|
|
time.sleep(self.forward_sleep_time)
|
|
|
|
# Run forward
|
|
if self.is_generation:
|
|
if self.spec_algorithm.is_none():
|
|
model_worker_batch = batch.get_model_worker_batch()
|
|
|
|
# update the consumer index of hicache to the running batch
|
|
self.tp_worker.set_hicache_consumer(
|
|
model_worker_batch.hicache_consumer_index
|
|
)
|
|
if self.pp_group.is_last_rank:
|
|
logits_output, next_token_ids, can_run_cuda_graph = (
|
|
self.tp_worker.forward_batch_generation(model_worker_batch)
|
|
)
|
|
else:
|
|
pp_hidden_states_proxy_tensors, _, can_run_cuda_graph = (
|
|
self.tp_worker.forward_batch_generation(model_worker_batch)
|
|
)
|
|
bid = model_worker_batch.bid
|
|
else:
|
|
(
|
|
logits_output,
|
|
next_token_ids,
|
|
bid,
|
|
num_accepted_tokens,
|
|
can_run_cuda_graph,
|
|
) = self.draft_worker.forward_batch_speculative_generation(batch)
|
|
bs = batch.batch_size()
|
|
self.spec_num_total_accepted_tokens += num_accepted_tokens + bs
|
|
self.spec_num_total_forward_ct += bs
|
|
self.num_generated_tokens += num_accepted_tokens
|
|
|
|
if self.pp_group.is_last_rank:
|
|
batch.output_ids = next_token_ids
|
|
|
|
# These 2 values are needed for processing the output, but the values can be
|
|
# modified by overlap schedule. So we have to copy them here so that
|
|
# we can use the correct values in output processing.
|
|
if batch.return_logprob or self.spec_algorithm.is_eagle():
|
|
extend_input_len_per_req = [req.extend_input_len for req in batch.reqs]
|
|
else:
|
|
extend_input_len_per_req = None
|
|
if batch.return_logprob:
|
|
extend_logprob_start_len_per_req = [
|
|
req.extend_logprob_start_len for req in batch.reqs
|
|
]
|
|
else:
|
|
extend_logprob_start_len_per_req = None
|
|
|
|
ret = GenerationBatchResult(
|
|
logits_output=logits_output if self.pp_group.is_last_rank else None,
|
|
pp_hidden_states_proxy_tensors=(
|
|
pp_hidden_states_proxy_tensors
|
|
if not self.pp_group.is_last_rank
|
|
else None
|
|
),
|
|
next_token_ids=next_token_ids if self.pp_group.is_last_rank else None,
|
|
extend_input_len_per_req=extend_input_len_per_req,
|
|
extend_logprob_start_len_per_req=extend_logprob_start_len_per_req,
|
|
bid=bid,
|
|
can_run_cuda_graph=can_run_cuda_graph,
|
|
)
|
|
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],
|
|
launch_done: Optional[threading.Event] = None,
|
|
):
|
|
if batch.forward_mode.is_decode():
|
|
self.process_batch_result_decode(batch, result, launch_done)
|
|
elif batch.forward_mode.is_extend():
|
|
self.process_batch_result_prefill(batch, result, launch_done)
|
|
elif batch.forward_mode.is_idle():
|
|
if self.enable_overlap:
|
|
self.tp_worker.resolve_last_batch_result(launch_done)
|
|
self.set_next_batch_sampling_info_done(batch)
|
|
elif batch.forward_mode.is_dummy_first():
|
|
self.set_next_batch_sampling_info_done(batch)
|
|
|
|
if self.return_health_check_ct:
|
|
# Return some signal for the health check.
|
|
# This is used to prevent the health check signal being blocked by long context prefill.
|
|
# However, one minor issue is that this code path does not check the status of detokenizer manager.
|
|
self.return_health_check_ct -= 1
|
|
self.send_to_tokenizer.send_pyobj(HealthCheckOutput())
|
|
|
|
def prepare_mlp_sync_batch(self, local_batch: ScheduleBatch):
|
|
return self.prepare_mlp_sync_batch_raw(
|
|
local_batch,
|
|
dp_size=self.server_args.dp_size,
|
|
attn_tp_size=self.attn_tp_size,
|
|
tp_cpu_group=self.tp_cpu_group,
|
|
get_idle_batch=self.get_idle_batch,
|
|
disable_cuda_graph=self.server_args.disable_cuda_graph,
|
|
spec_algorithm=self.spec_algorithm,
|
|
speculative_num_draft_tokens=self.server_args.speculative_num_draft_tokens,
|
|
enable_two_batch_overlap=self.server_args.enable_two_batch_overlap,
|
|
enable_deepep_moe=self.server_args.enable_deepep_moe,
|
|
deepep_mode=DeepEPMode[self.server_args.deepep_mode],
|
|
require_mlp_tp_gather=require_mlp_tp_gather(self.server_args),
|
|
)
|
|
|
|
@staticmethod
|
|
def prepare_mlp_sync_batch_raw(
|
|
local_batch: ScheduleBatch,
|
|
dp_size,
|
|
attn_tp_size: int,
|
|
tp_cpu_group,
|
|
get_idle_batch,
|
|
disable_cuda_graph: bool,
|
|
spec_algorithm,
|
|
speculative_num_draft_tokens,
|
|
enable_two_batch_overlap: bool,
|
|
enable_deepep_moe: bool,
|
|
deepep_mode: DeepEPMode,
|
|
require_mlp_tp_gather: bool,
|
|
):
|
|
# Check if other DP workers have running batches
|
|
if local_batch is None:
|
|
num_tokens = 0
|
|
num_tokens_for_logprob = 0
|
|
elif local_batch.forward_mode.is_decode():
|
|
num_tokens = local_batch.batch_size()
|
|
num_tokens_for_logprob = num_tokens
|
|
else:
|
|
num_tokens = local_batch.extend_num_tokens
|
|
num_tokens_for_logprob = sum(
|
|
[
|
|
# We should have at least 1 token for sample in every case.
|
|
max(extend_len - logprob_start_len, 1)
|
|
for logprob_start_len, extend_len in zip(
|
|
local_batch.extend_logprob_start_lens, local_batch.extend_lens
|
|
)
|
|
]
|
|
)
|
|
|
|
if local_batch is None or local_batch.forward_mode.is_decode_or_idle():
|
|
can_cuda_graph = 1
|
|
else:
|
|
can_cuda_graph = 0
|
|
|
|
is_extend_in_batch = (
|
|
local_batch.forward_mode.is_extend() if local_batch else False
|
|
)
|
|
|
|
tbo_preparer = TboDPAttentionPreparer()
|
|
|
|
local_info = torch.tensor(
|
|
[
|
|
num_tokens,
|
|
can_cuda_graph,
|
|
num_tokens_for_logprob,
|
|
is_extend_in_batch,
|
|
*tbo_preparer.prepare_all_gather(
|
|
local_batch,
|
|
deepep_mode,
|
|
enable_deepep_moe,
|
|
enable_two_batch_overlap,
|
|
),
|
|
],
|
|
dtype=torch.int64,
|
|
)
|
|
global_info = torch.empty(
|
|
(dp_size, attn_tp_size, 6),
|
|
dtype=torch.int64,
|
|
)
|
|
torch.distributed.all_gather_into_tensor(
|
|
global_info.flatten(),
|
|
local_info,
|
|
group=tp_cpu_group,
|
|
)
|
|
global_num_tokens = global_info[:, 0, 0].tolist()
|
|
can_cuda_graph = min(global_info[:, 0, 1].tolist())
|
|
global_num_tokens_for_logprob = global_info[:, 0, 2].tolist()
|
|
is_extend_in_batch = global_info[:, 0, 3].tolist()
|
|
|
|
tbo_split_seq_index, global_forward_mode = tbo_preparer.compute_output(
|
|
global_info[:, :, 4:6]
|
|
)
|
|
|
|
if local_batch is None and max(global_num_tokens) > 0:
|
|
local_batch = get_idle_batch()
|
|
|
|
if local_batch is not None:
|
|
# TODO: handle the case when moe_dense_tp_size != 1
|
|
if not require_mlp_tp_gather:
|
|
local_batch.global_num_tokens = [num_tokens]
|
|
local_batch.global_num_tokens_for_logprob = [num_tokens_for_logprob]
|
|
else:
|
|
local_batch.global_num_tokens = global_num_tokens
|
|
local_batch.global_num_tokens_for_logprob = (
|
|
global_num_tokens_for_logprob
|
|
)
|
|
local_batch.is_extend_in_batch = any(is_extend_in_batch)
|
|
local_batch.tbo_split_seq_index = tbo_split_seq_index
|
|
local_batch.global_forward_mode = global_forward_mode
|
|
|
|
# Check forward mode for cuda graph
|
|
if not disable_cuda_graph:
|
|
local_batch.can_run_dp_cuda_graph = can_cuda_graph
|
|
|
|
# TODO(ch-wan): refactor: any(is_extend_in_batch) now is a part of local_batch. Remove it from here.
|
|
return local_batch, any(is_extend_in_batch)
|
|
|
|
def get_idle_batch(self):
|
|
idle_batch = ScheduleBatch.init_new(
|
|
[],
|
|
self.req_to_token_pool,
|
|
self.token_to_kv_pool_allocator,
|
|
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
|
|
num_timeout_reqs = 0
|
|
for req in self.grammar_queue:
|
|
try:
|
|
if req.finished(): # It is aborted by AbortReq
|
|
num_ready_reqs += 1
|
|
continue
|
|
req.grammar = req.grammar.result(timeout=0.03)
|
|
self.grammar_backend.set_cache(req.grammar_key, req.grammar.copy())
|
|
if req.grammar is INVALID_GRAMMAR_OBJ:
|
|
req.set_finish_with_abort(
|
|
f"Invalid grammar request: {req.grammar_key=}"
|
|
)
|
|
num_ready_reqs += 1
|
|
except futures._base.TimeoutError:
|
|
req.grammar_wait_ct += 1
|
|
# NOTE(lianmin): this timeout is the waiting time of the above line. It is
|
|
# not the waiting time from it enters the grammar queue.
|
|
if req.grammar_wait_ct > GRAMMAR_TIMEOUT / 0.03:
|
|
num_timeout_reqs = 1
|
|
break
|
|
|
|
if self.server_args.enable_dp_attention:
|
|
tp_size = self.attn_tp_size
|
|
tp_group = self.attn_tp_cpu_group
|
|
else:
|
|
tp_size = self.tp_size
|
|
tp_group = self.tp_cpu_group
|
|
|
|
if tp_size > 1:
|
|
# Sync across TP ranks to make sure they have the same number of ready requests
|
|
tensor = torch.tensor([num_ready_reqs, num_timeout_reqs], dtype=torch.int32)
|
|
torch.distributed.all_reduce(
|
|
tensor, op=torch.distributed.ReduceOp.MAX, group=tp_group
|
|
)
|
|
num_ready_reqs_max, num_timeout_reqs_max = tensor.tolist()
|
|
|
|
for i in range(num_ready_reqs, num_ready_reqs_max):
|
|
req = self.grammar_queue[i]
|
|
if req.finished(): # It is aborted by AbortReq
|
|
continue
|
|
req.grammar = req.grammar.result()
|
|
self.grammar_backend.set_cache(req.grammar_key, req.grammar.copy())
|
|
if req.grammar is INVALID_GRAMMAR_OBJ:
|
|
req.set_finish_with_abort(
|
|
f"Invalid grammar request: {req.grammar_key=}"
|
|
)
|
|
else:
|
|
num_ready_reqs_max = num_ready_reqs
|
|
num_timeout_reqs_max = num_timeout_reqs
|
|
|
|
for i in range(num_ready_reqs, num_ready_reqs + num_timeout_reqs_max):
|
|
req = self.grammar_queue[i]
|
|
req.grammar.cancel()
|
|
error_msg = f"Grammar preprocessing timed out for {req.grammar_key=}"
|
|
req.set_finish_with_abort(error_msg)
|
|
self.grammar_backend.set_cache(req.grammar_key, INVALID_GRAMMAR_OBJ)
|
|
num_ready_reqs = num_ready_reqs_max + num_timeout_reqs_max
|
|
|
|
self._extend_requests_to_queue(self.grammar_queue[:num_ready_reqs])
|
|
self.grammar_queue = self.grammar_queue[num_ready_reqs:]
|
|
|
|
def set_next_batch_sampling_info_done(self, batch: ScheduleBatch):
|
|
if batch.next_batch_sampling_info:
|
|
if batch.next_batch_sampling_info.grammars is not None:
|
|
batch.next_batch_sampling_info.update_regex_vocab_mask()
|
|
self.current_stream.synchronize()
|
|
batch.next_batch_sampling_info.sampling_info_done.set()
|
|
|
|
def watchdog_thread(self):
|
|
"""A watch dog thread that will try to kill the server itself if one forward batch takes too long."""
|
|
self.watchdog_last_forward_ct = 0
|
|
self.watchdog_last_time = time.perf_counter()
|
|
|
|
while True:
|
|
current = time.perf_counter()
|
|
if self.cur_batch is not None:
|
|
if self.watchdog_last_forward_ct == self.forward_ct:
|
|
if current > self.watchdog_last_time + self.watchdog_timeout:
|
|
break
|
|
else:
|
|
self.watchdog_last_forward_ct = self.forward_ct
|
|
self.watchdog_last_time = current
|
|
time.sleep(self.watchdog_timeout // 2)
|
|
|
|
if not disable_request_logging():
|
|
# Print batch size and memory pool info to check whether there are de-sync issues.
|
|
logger.error(
|
|
f"{self.cur_batch.batch_size()=}, "
|
|
f"{self.cur_batch.reqs=}, "
|
|
f"{self.token_to_kv_pool_allocator.available_size()=}, "
|
|
f"{self.tree_cache.evictable_size()=}, "
|
|
)
|
|
|
|
pyspy_dump_schedulers()
|
|
logger.error(f"Watchdog timeout ({self.watchdog_timeout=})")
|
|
print(file=sys.stderr, flush=True)
|
|
print(file=sys.stdout, flush=True)
|
|
|
|
# Wait for some time so that the parent process can print the error.
|
|
time.sleep(5)
|
|
self.parent_process.send_signal(signal.SIGQUIT)
|
|
|
|
def flush_cache_wrapped(self, recv_req: FlushCacheReqInput):
|
|
success = self.flush_cache()
|
|
return FlushCacheReqOutput(success=success)
|
|
|
|
def flush_cache(self):
|
|
"""Flush the memory pool and cache."""
|
|
if (
|
|
len(self.waiting_queue) == 0
|
|
and self.running_batch.is_empty()
|
|
and (self.pp_size == 1 or all(x.is_empty() for x in self.running_mbs))
|
|
):
|
|
self.cur_batch = None
|
|
self.last_batch = None
|
|
self.tree_cache.reset()
|
|
if self.grammar_backend:
|
|
self.grammar_backend.reset()
|
|
self.req_to_token_pool.clear()
|
|
self.token_to_kv_pool_allocator.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_allocator.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
|
|
self.cum_spec_accept_length = 0
|
|
self.cum_spec_accept_count = 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: {len(self.running_batch.reqs)}"
|
|
)
|
|
if_success = False
|
|
return if_success
|
|
|
|
def get_load(self):
|
|
# TODO(lsyin): use dynamically maintained num_waiting_tokens
|
|
load = (
|
|
self.max_total_num_tokens
|
|
- self.token_to_kv_pool_allocator.available_size()
|
|
- self.tree_cache.evictable_size()
|
|
)
|
|
load += sum(len(req.origin_input_ids) for req in self.waiting_queue)
|
|
if self.disaggregation_mode == DisaggregationMode.PREFILL:
|
|
load += sum(
|
|
len(req.origin_input_ids)
|
|
for req in self.disagg_prefill_bootstrap_queue.queue
|
|
)
|
|
elif self.disaggregation_mode == DisaggregationMode.DECODE:
|
|
load += sum(
|
|
len(req.req.origin_input_ids)
|
|
for req in self.disagg_decode_prealloc_queue.queue
|
|
)
|
|
|
|
return load
|
|
|
|
def get_internal_state(self, recv_req: GetInternalStateReq):
|
|
ret = dict(global_server_args_dict)
|
|
ret["last_gen_throughput"] = self.last_gen_throughput
|
|
if not self.spec_algorithm.is_none() and self.cum_spec_accept_count > 0:
|
|
ret["avg_spec_accept_length"] = (
|
|
self.cum_spec_accept_length / self.cum_spec_accept_count
|
|
)
|
|
if RECORD_STEP_TIME:
|
|
ret["step_time_dict"] = self.step_time_dict
|
|
|
|
ret["load"] = self.get_load()
|
|
|
|
return GetInternalStateReqOutput(internal_state=ret)
|
|
|
|
def set_internal_state(self, recv_req: SetInternalStateReq):
|
|
server_args_dict = recv_req.server_args
|
|
args_allow_update = set(
|
|
[
|
|
"max_micro_batch_size",
|
|
"speculative_accept_threshold_single",
|
|
"speculative_accept_threshold_acc",
|
|
]
|
|
)
|
|
if_success = True
|
|
for k, v in server_args_dict.items():
|
|
if k not in args_allow_update:
|
|
logging.warning(f"Updating {k} is not supported.")
|
|
if_success = False
|
|
break
|
|
elif k == "max_micro_batch_size" and (
|
|
v > self.max_running_requests // self.pp_size or v < 1
|
|
):
|
|
logging.warning(
|
|
f"Updating {k} to {v} is rejected because it is out of the valid range [1, {self.max_running_requests // self.pp_size}]."
|
|
)
|
|
if_success = False
|
|
break
|
|
if if_success:
|
|
if not self.spec_algorithm.is_none() and self.cum_spec_accept_count > 0:
|
|
avg_spec_accept_length = (
|
|
self.cum_spec_accept_length / self.cum_spec_accept_count
|
|
)
|
|
logger.info(f"{avg_spec_accept_length=}")
|
|
self.cum_spec_accept_length = self.cum_spec_accept_count = 0
|
|
for k, v in server_args_dict.items():
|
|
global_server_args_dict[k] = v
|
|
logger.info(f"Global server args updated! {global_server_args_dict=}")
|
|
return SetInternalStateReqOutput(
|
|
updated=True,
|
|
server_args=global_server_args_dict,
|
|
)
|
|
|
|
def handle_rpc_request(self, recv_req: RpcReqInput):
|
|
# Handle RPC requests
|
|
logger.info(
|
|
f"handle_rpc_request: {recv_req.method}, param: {recv_req.parameters}"
|
|
)
|
|
|
|
success = True
|
|
exec = None
|
|
try:
|
|
func = getattr(self, recv_req.method)
|
|
func(recv_req.parameters)
|
|
except Exception as e:
|
|
success = False
|
|
exec = e
|
|
logger.error(f"Failed to call rpc {recv_req.method}: {str(e)}")
|
|
|
|
barrier()
|
|
return RpcReqOutput(success, "" if not exec else str(exec))
|
|
|
|
def save_remote_model(self, params):
|
|
url = params["url"]
|
|
|
|
worker = self.tp_worker.worker
|
|
|
|
worker.model_runner.save_remote_model(url)
|
|
|
|
def save_sharded_model(self, params):
|
|
worker = self.tp_worker.worker
|
|
|
|
worker.model_runner.save_sharded_model(
|
|
path=params["path"],
|
|
pattern=params["pattern"],
|
|
max_size=params["max_size"],
|
|
)
|
|
|
|
def abort_request(self, recv_req: AbortReq):
|
|
# Delete requests in the waiting queue
|
|
to_del = []
|
|
for i, req in enumerate(self.waiting_queue):
|
|
if req.rid.startswith(recv_req.rid):
|
|
to_del.append(i)
|
|
|
|
# Sort in reverse order to avoid index issues when deleting
|
|
for i in reversed(to_del):
|
|
# Abort method 1: directly pop from the queue
|
|
# This only works for requests that have not started anything.
|
|
# We still need to send something back to TokenizerManager to clean up the state.
|
|
req = self.waiting_queue.pop(i)
|
|
self.send_to_tokenizer.send_pyobj(AbortReq(req.rid))
|
|
logger.debug(f"Abort queued request. {req.rid=}")
|
|
|
|
# Delete the requests in the grammar queue
|
|
for req in self.grammar_queue:
|
|
# Abort method 2: call `set_finish_with_abort`
|
|
# The request will still run one prefill forward pass.
|
|
# In this case, we change the input_ids to be only one token to make this prefill cheap.
|
|
if req.rid.startswith(recv_req.rid):
|
|
logger.debug(f"Abort grammar queue request. {req.rid=}")
|
|
if req.grammar:
|
|
req.grammar.cancel()
|
|
req.set_finish_with_abort("Aborted by AbortReq.")
|
|
|
|
# Delete requests in the running batch
|
|
if self.cur_batch is self.running_batch or self.cur_batch is None:
|
|
reqs = self.running_batch.reqs
|
|
else:
|
|
reqs = self.running_batch.reqs + self.cur_batch.reqs
|
|
|
|
for req in reqs:
|
|
if req.rid.startswith(recv_req.rid) and not req.finished():
|
|
# Abort method 3: set `to_abort=True`
|
|
# The request will still run one decode forward pass.
|
|
# Then we reuse all existing code to clean up the KV cache allocation.
|
|
logger.debug(f"Abort running request. {req.rid=}")
|
|
req.to_abort = True
|
|
|
|
def _pause_engine(self) -> Tuple[List[Req], int]:
|
|
raise NotImplementedError()
|
|
|
|
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:
|
|
flush_cache_success = self.flush_cache()
|
|
assert flush_cache_success, "Cache flush failed after updating weights"
|
|
else:
|
|
logger.error(message)
|
|
return UpdateWeightFromDiskReqOutput(success, message, 0)
|
|
|
|
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:
|
|
flush_cache_success = self.flush_cache()
|
|
assert flush_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:
|
|
if recv_req.flush_cache:
|
|
flush_cache_success = self.flush_cache()
|
|
assert flush_cache_success, "Cache flush failed after updating weights"
|
|
else:
|
|
logger.error(message)
|
|
barrier(group=self.tp_cpu_group)
|
|
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, recv_req: ReleaseMemoryOccupationReqInput):
|
|
tags = recv_req.tags
|
|
import subprocess
|
|
|
|
if tags is None:
|
|
tags = [GPU_MEMORY_TYPE_WEIGHTS, GPU_MEMORY_TYPE_KV_CACHE]
|
|
|
|
if GPU_MEMORY_TYPE_KV_CACHE in tags:
|
|
self.memory_saver_adapter.pause(GPU_MEMORY_TYPE_KV_CACHE)
|
|
self.flush_cache()
|
|
|
|
if GPU_MEMORY_TYPE_WEIGHTS in tags:
|
|
self.stashed_model_static_state = _export_static_state(
|
|
self.tp_worker.worker.model_runner.model
|
|
)
|
|
self.memory_saver_adapter.pause(GPU_MEMORY_TYPE_WEIGHTS)
|
|
|
|
return ReleaseMemoryOccupationReqOutput()
|
|
|
|
def resume_memory_occupation(self, recv_req: ResumeMemoryOccupationReqInput):
|
|
tags = recv_req.tags
|
|
if tags is None or len(tags) == 0:
|
|
tags = [GPU_MEMORY_TYPE_WEIGHTS, GPU_MEMORY_TYPE_KV_CACHE]
|
|
|
|
if GPU_MEMORY_TYPE_WEIGHTS in tags:
|
|
self.memory_saver_adapter.resume(GPU_MEMORY_TYPE_WEIGHTS)
|
|
_import_static_state(
|
|
self.tp_worker.worker.model_runner.model,
|
|
self.stashed_model_static_state,
|
|
)
|
|
del self.stashed_model_static_state
|
|
|
|
if GPU_MEMORY_TYPE_KV_CACHE in tags:
|
|
self.memory_saver_adapter.resume(GPU_MEMORY_TYPE_KV_CACHE)
|
|
|
|
return ResumeMemoryOccupationReqOutput()
|
|
|
|
def slow_down(self, recv_req: SlowDownReqInput):
|
|
t = recv_req.forward_sleep_time
|
|
if t is not None and t <= 0:
|
|
t = None
|
|
self.forward_sleep_time = t
|
|
return SlowDownReqOutput()
|
|
|
|
def profile(self, recv_req: ProfileReq):
|
|
if recv_req.type == ProfileReqType.START_PROFILE:
|
|
if recv_req.profile_by_stage:
|
|
return self.init_profile(
|
|
recv_req.output_dir,
|
|
recv_req.num_steps,
|
|
recv_req.activities,
|
|
recv_req.with_stack,
|
|
recv_req.record_shapes,
|
|
recv_req.profile_by_stage,
|
|
recv_req.profile_id,
|
|
)
|
|
else:
|
|
self.init_profile(
|
|
recv_req.output_dir,
|
|
recv_req.num_steps,
|
|
recv_req.activities,
|
|
recv_req.with_stack,
|
|
recv_req.record_shapes,
|
|
recv_req.profile_by_stage,
|
|
recv_req.profile_id,
|
|
)
|
|
return self.start_profile(True)
|
|
else:
|
|
return self.stop_profile()
|
|
|
|
def init_profile(
|
|
self,
|
|
output_dir: Optional[str],
|
|
num_steps: Optional[int],
|
|
activities: Optional[List[str]],
|
|
with_stack: Optional[bool],
|
|
record_shapes: Optional[bool],
|
|
profile_by_stage: bool,
|
|
profile_id: str,
|
|
) -> ProfileReqOutput:
|
|
if self.profile_in_progress:
|
|
return ProfileReqOutput(
|
|
success=False,
|
|
message="Profiling is already in progress. Call /stop_profile first.",
|
|
)
|
|
|
|
self.profile_by_stage = profile_by_stage
|
|
|
|
if output_dir is None:
|
|
output_dir = os.getenv("SGLANG_TORCH_PROFILER_DIR", "/tmp")
|
|
if activities is None:
|
|
activities = ["CPU", "GPU"]
|
|
|
|
self.torch_profiler_output_dir = output_dir
|
|
self.torch_profiler_with_stack = with_stack
|
|
self.torch_profiler_record_shapes = record_shapes
|
|
self.profiler_activities = activities
|
|
self.profile_id = profile_id
|
|
|
|
if num_steps:
|
|
self.profile_steps = num_steps
|
|
if self.profile_by_stage:
|
|
self.profiler_target_prefill_ct = num_steps
|
|
self.profiler_target_decode_ct = num_steps
|
|
self.profiler_prefill_ct = 0
|
|
self.profiler_decode_ct = 0
|
|
else:
|
|
self.profiler_target_forward_ct = self.forward_ct + num_steps
|
|
# The caller will be notified when reaching profiler_target_forward_ct
|
|
else:
|
|
self.profiler_target_forward_ct = None
|
|
|
|
return ProfileReqOutput(success=True, message="Succeeded")
|
|
|
|
def start_profile(
|
|
self, stage: Optional[ForwardMode] = None
|
|
) -> ProfileReqOutput | None:
|
|
stage_str = f" for {stage.__str__()}" if stage else ""
|
|
logger.info(
|
|
f"Profiling starts{stage_str}. Traces will be saved to: {self.torch_profiler_output_dir} (with profile id: {self.profile_id})",
|
|
)
|
|
|
|
activities = self.profiler_activities
|
|
with_stack = self.torch_profiler_with_stack
|
|
record_shapes = self.torch_profiler_record_shapes
|
|
|
|
activity_map = {
|
|
"CPU": torch.profiler.ProfilerActivity.CPU,
|
|
"GPU": torch.profiler.ProfilerActivity.CUDA,
|
|
}
|
|
torchprof_activities = [
|
|
activity_map[a] for a in activities if a in activity_map
|
|
]
|
|
|
|
if "RPD" in activities:
|
|
from rpdTracerControl import rpdTracerControl
|
|
|
|
rpdTracerControl.skipCreate()
|
|
|
|
self.rpd_profile_path = os.path.join(
|
|
self.torch_profiler_output_dir,
|
|
"rpd-" + str(time.time()) + f"-TP-{self.tp_rank}" + ".trace.json.gz",
|
|
)
|
|
|
|
if self.tp_rank == 0:
|
|
import sqlite3
|
|
|
|
from rocpd.schema import RocpdSchema
|
|
|
|
if os.path.exists("trace.rpd"):
|
|
os.unlink("trace.rpd")
|
|
schema = RocpdSchema()
|
|
connection = sqlite3.connect("trace.rpd")
|
|
schema.writeSchema(connection)
|
|
connection.commit()
|
|
del connection
|
|
torch.distributed.barrier(self.tp_cpu_group)
|
|
|
|
self.rpd_profiler = rpdTracerControl()
|
|
self.rpd_profiler.setPythonTrace(True)
|
|
self.rpd_profiler.start()
|
|
self.rpd_profiler.rangePush("", "rpd profile range", "")
|
|
self.profile_in_progress = True
|
|
elif torchprof_activities:
|
|
self.torch_profiler = torch.profiler.profile(
|
|
activities=torchprof_activities,
|
|
with_stack=with_stack if with_stack is not None else True,
|
|
record_shapes=record_shapes if record_shapes is not None else False,
|
|
)
|
|
self.torch_profiler.start()
|
|
self.profile_in_progress = True
|
|
|
|
if "MEM" in activities:
|
|
torch.cuda.memory._record_memory_history(max_entries=100000)
|
|
self.profile_in_progress = True
|
|
|
|
if "CUDA_PROFILER" in activities:
|
|
torch.cuda.cudart().cudaProfilerStart()
|
|
|
|
return ProfileReqOutput(success=True, message="Succeeded")
|
|
|
|
def stop_profile(
|
|
self, stage: Optional[ForwardMode] = None
|
|
) -> ProfileReqOutput | None:
|
|
if not self.profile_in_progress:
|
|
return ProfileReqOutput(
|
|
success=False,
|
|
message="Profiling is not in progress. Call /start_profile first.",
|
|
)
|
|
|
|
if not Path(self.torch_profiler_output_dir).exists():
|
|
Path(self.torch_profiler_output_dir).mkdir(parents=True, exist_ok=True)
|
|
|
|
stage_suffix = f"-{stage.__str__()}" if stage else ""
|
|
logger.info("Stop profiling" + stage_suffix + "...")
|
|
if self.torch_profiler is not None:
|
|
self.torch_profiler.stop()
|
|
self.torch_profiler.export_chrome_trace(
|
|
os.path.join(
|
|
self.torch_profiler_output_dir,
|
|
self.profile_id
|
|
+ f"-TP-{self.tp_rank}"
|
|
+ stage_suffix
|
|
+ ".trace.json.gz",
|
|
)
|
|
)
|
|
torch.distributed.barrier(self.tp_cpu_group)
|
|
|
|
if self.rpd_profiler is not None:
|
|
self.rpd_profiler.rangePop()
|
|
self.rpd_profiler.stop()
|
|
self.rpd_profiler.flush()
|
|
|
|
torch.distributed.barrier(self.tp_cpu_group)
|
|
if self.tp_rank == 0:
|
|
from sglang.srt.utils import rpd_to_chrome_trace
|
|
|
|
rpd_to_chrome_trace("trace.rpd", self.rpd_profile_path)
|
|
self.rpd_profiler = None
|
|
self.rpd_profiler_path = None
|
|
|
|
if self.profiler_activities is not None and "MEM" in self.profiler_activities:
|
|
memory_profile_path = os.path.join(
|
|
self.torch_profiler_output_dir,
|
|
str(time.time())
|
|
+ f"-TP-{self.tp_rank}-memory"
|
|
+ stage_suffix
|
|
+ ".pickle",
|
|
)
|
|
torch.cuda.memory._dump_snapshot(memory_profile_path)
|
|
torch.cuda.memory._record_memory_history(enabled=None)
|
|
|
|
if "CUDA_PROFILER" in self.profiler_activities:
|
|
torch.cuda.cudart().cudaProfilerStop()
|
|
|
|
logger.info(
|
|
"Profiling done. Traces are saved to: %s",
|
|
self.torch_profiler_output_dir,
|
|
)
|
|
self.torch_profiler = None
|
|
self.profile_in_progress = False
|
|
|
|
return ProfileReqOutput(success=True, message="Succeeded.")
|
|
|
|
def _profile_batch_predicate(self, batch):
|
|
if self.profile_by_stage:
|
|
if batch.forward_mode.is_prefill():
|
|
if self.profiler_prefill_ct == 0:
|
|
self.start_profile(batch.forward_mode)
|
|
self.profiler_prefill_ct += 1
|
|
if self.profiler_prefill_ct > self.profiler_target_prefill_ct:
|
|
if self.profile_in_progress:
|
|
self.stop_profile(stage=ForwardMode.EXTEND)
|
|
elif batch.forward_mode.is_decode():
|
|
if self.profiler_decode_ct == 0:
|
|
if self.profile_in_progress:
|
|
# force trace flush
|
|
self.stop_profile(ForwardMode.EXTEND)
|
|
self.start_profile(batch.forward_mode)
|
|
self.profiler_decode_ct += 1
|
|
if self.profiler_decode_ct > self.profiler_target_decode_ct:
|
|
if self.profile_in_progress:
|
|
self.stop_profile(stage=ForwardMode.DECODE)
|
|
elif batch.forward_mode.is_idle():
|
|
pass
|
|
else:
|
|
raise RuntimeError(f"unsupported profile stage: {batch.forward_mode}")
|
|
else:
|
|
# Check profiler
|
|
if (
|
|
self.profiler_target_forward_ct
|
|
and self.profiler_target_forward_ct <= self.forward_ct
|
|
):
|
|
self.stop_profile()
|
|
|
|
def expert_distribution_handle(self, recv_req: ExpertDistributionReq):
|
|
if recv_req == ExpertDistributionReq.START_RECORD:
|
|
get_global_expert_distribution_recorder().start_record()
|
|
elif recv_req == ExpertDistributionReq.STOP_RECORD:
|
|
get_global_expert_distribution_recorder().stop_record()
|
|
elif recv_req == ExpertDistributionReq.DUMP_RECORD:
|
|
get_global_expert_distribution_recorder().dump_record()
|
|
else:
|
|
raise ValueError("Unrecognized ExpertDistributionReq value")
|
|
return ExpertDistributionReqOutput()
|
|
|
|
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("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 get_print_prefix(self):
|
|
prefix = ""
|
|
if self.attn_dp_rank is not None:
|
|
prefix += f" DP{self.attn_dp_rank}"
|
|
if self.server_args.tp_size > 1:
|
|
prefix += f" TP{self.tp_rank}"
|
|
if self.pp_size > 1:
|
|
prefix += f" PP{self.pp_rank}"
|
|
return prefix
|
|
|
|
def _publish_kv_events(self):
|
|
if self.enable_kv_cache_events:
|
|
events = self.tree_cache.take_events()
|
|
if events:
|
|
batch = KVEventBatch(ts=time.time(), events=events)
|
|
self.kv_event_publisher.publish(batch)
|
|
|
|
|
|
def is_health_check_generate_req(recv_req):
|
|
return getattr(recv_req, "rid", "").startswith("HEALTH_CHECK")
|
|
|
|
|
|
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,
|
|
pp_rank: int,
|
|
dp_rank: Optional[int],
|
|
pipe_writer,
|
|
):
|
|
# Generate the prefix
|
|
prefix = ""
|
|
if dp_rank is not None:
|
|
prefix += f" DP{dp_rank}"
|
|
if server_args.tp_size > 1:
|
|
prefix += f" TP{tp_rank}"
|
|
if server_args.pp_size > 1:
|
|
prefix += f" PP{pp_rank}"
|
|
|
|
# Config the process
|
|
kill_itself_when_parent_died()
|
|
setproctitle.setproctitle(f"sglang::scheduler{prefix.replace(' ', '_')}")
|
|
faulthandler.enable()
|
|
parent_process = psutil.Process().parent()
|
|
|
|
# [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"])
|
|
|
|
# Configure the logger
|
|
configure_logger(server_args, prefix=prefix)
|
|
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)
|
|
|
|
embedding_cache_size = 100
|
|
if "SGLANG_VLM_CACHE_SIZE_MB" in os.environ:
|
|
embedding_cache_size = int(os.environ["SGLANG_VLM_CACHE_SIZE_MB"])
|
|
init_embedding_cache(embedding_cache_size * 1024 * 1024)
|
|
# Create a scheduler and run the event loop
|
|
try:
|
|
scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, pp_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,
|
|
}
|
|
)
|
|
disaggregation_mode: DisaggregationMode = scheduler.disaggregation_mode
|
|
|
|
if disaggregation_mode == DisaggregationMode.NULL:
|
|
if server_args.pp_size > 1:
|
|
scheduler.event_loop_pp()
|
|
elif scheduler.enable_overlap:
|
|
scheduler.event_loop_overlap()
|
|
else:
|
|
scheduler.event_loop_normal()
|
|
elif disaggregation_mode == DisaggregationMode.PREFILL:
|
|
if scheduler.enable_overlap:
|
|
scheduler.event_loop_overlap_disagg_prefill()
|
|
else:
|
|
scheduler.event_loop_normal_disagg_prefill()
|
|
|
|
elif disaggregation_mode == DisaggregationMode.DECODE:
|
|
if scheduler.enable_overlap:
|
|
scheduler.event_loop_overlap_disagg_decode()
|
|
else:
|
|
scheduler.event_loop_normal_disagg_decode()
|
|
|
|
except Exception:
|
|
traceback = get_exception_traceback()
|
|
logger.error(f"Scheduler hit an exception: {traceback}")
|
|
parent_process.send_signal(signal.SIGQUIT)
|