# Copyright 2023-2024 SGLang Team # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A scheduler that manages a tensor parallel GPU worker.""" import faulthandler import logging import os import signal import sys import threading import time import warnings from collections import defaultdict, deque from concurrent import futures from dataclasses import dataclass from http import HTTPStatus from types import SimpleNamespace from typing import Dict, List, Optional, Tuple, Union import psutil import setproctitle import torch import zmq from torch.distributed import barrier from sglang.global_config import global_config from sglang.srt.configs.model_config import ModelConfig from sglang.srt.constrained.base_grammar_backend import create_grammar_backend from sglang.srt.disaggregation.decode import ( DecodePreallocQueue, DecodeTransferQueue, SchedulerDisaggregationDecodeMixin, ) from sglang.srt.disaggregation.prefill import ( PrefillBootstrapQueue, SchedulerDisaggregationPrefillMixin, ) from sglang.srt.disaggregation.utils import ( DisaggregationMode, ReqToMetadataIdxAllocator, ) from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer from sglang.srt.layers.dp_attention import compute_dp_attention_world_info from sglang.srt.layers.logits_processor import LogitsProcessorOutput from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder from sglang.srt.managers.io_struct import ( AbortReq, CloseSessionReqInput, ExpertDistributionReq, ExpertDistributionReqOutput, FlushCacheReq, GetInternalStateReq, GetInternalStateReqOutput, GetWeightsByNameReqInput, GetWeightsByNameReqOutput, HealthCheckOutput, InitWeightsUpdateGroupReqInput, InitWeightsUpdateGroupReqOutput, OpenSessionReqInput, OpenSessionReqOutput, ProfileReq, ProfileReqOutput, ProfileReqType, ReleaseMemoryOccupationReqInput, ReleaseMemoryOccupationReqOutput, ResumeMemoryOccupationReqInput, ResumeMemoryOccupationReqOutput, RpcReqInput, RpcReqOutput, SetInternalStateReq, SetInternalStateReqOutput, TokenizedEmbeddingReqInput, TokenizedGenerateReqInput, UpdateWeightFromDiskReqInput, UpdateWeightFromDiskReqOutput, UpdateWeightsFromDistributedReqInput, UpdateWeightsFromDistributedReqOutput, UpdateWeightsFromTensorReqInput, UpdateWeightsFromTensorReqOutput, ) from sglang.srt.managers.schedule_batch import ( FINISH_ABORT, MultimodalInputs, Req, ScheduleBatch, global_server_args_dict, ) from sglang.srt.managers.schedule_policy import ( AddReqResult, PrefillAdder, SchedulePolicy, ) from sglang.srt.managers.scheduler_output_processor_mixin import ( SchedulerOutputProcessorMixin, ) from sglang.srt.managers.session_controller import Session from sglang.srt.managers.tp_worker import TpModelWorker from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient from sglang.srt.managers.utils import validate_input_length from sglang.srt.mem_cache.chunk_cache import ChunkCache from sglang.srt.mem_cache.hiradix_cache import HiRadixCache from sglang.srt.mem_cache.radix_cache import RadixCache from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode from sglang.srt.server_args import PortArgs, ServerArgs from sglang.srt.speculative.spec_info import SpeculativeAlgorithm from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter from sglang.srt.utils import ( DynamicGradMode, broadcast_pyobj, configure_logger, crash_on_warnings, get_bool_env_var, get_zmq_socket, kill_itself_when_parent_died, pyspy_dump_schedulers, set_gpu_proc_affinity, set_random_seed, suppress_other_loggers, ) from sglang.utils import TypeBasedDispatcher, get_exception_traceback expert_distribution_recorder = ExpertDistributionRecorder() logger = logging.getLogger(__name__) # Test retract decode for debugging purposes TEST_RETRACT = get_bool_env_var("SGLANG_TEST_RETRACT") RECORD_STEP_TIME = get_bool_env_var("SGLANG_RECORD_STEP_TIME") @dataclass class GenerationBatchResult: logits_output: LogitsProcessorOutput next_token_ids: List[int] extend_input_len_per_req: List[int] extend_logprob_start_len_per_req: List[int] bid: int @dataclass class EmbeddingBatchResult: embeddings: torch.Tensor bid: int class Scheduler( SchedulerOutputProcessorMixin, SchedulerDisaggregationDecodeMixin, SchedulerDisaggregationPrefillMixin, ): """A scheduler that manages a tensor parallel GPU worker.""" def __init__( self, server_args: ServerArgs, port_args: PortArgs, gpu_id: int, tp_rank: int, dp_rank: Optional[int], ): # Parse args self.server_args = server_args self.tp_rank = tp_rank self.tp_size = server_args.tp_size self.schedule_policy = server_args.schedule_policy self.lora_paths = server_args.lora_paths self.max_loras_per_batch = server_args.max_loras_per_batch self.enable_overlap = not server_args.disable_overlap_schedule self.skip_tokenizer_init = server_args.skip_tokenizer_init self.enable_metrics = server_args.enable_metrics self.stream_interval = server_args.stream_interval self.spec_algorithm = SpeculativeAlgorithm.from_string( server_args.speculative_algorithm ) self.gpu_id = gpu_id self.enable_hierarchical_cache = server_args.enable_hierarchical_cache self.page_size = server_args.page_size # Distributed rank info self.dp_size = server_args.dp_size self.attn_tp_rank, self.attn_tp_size, self.dp_rank = ( compute_dp_attention_world_info( server_args.enable_dp_attention, self.tp_rank, self.tp_size, self.dp_size, ) ) # Init inter-process communication context = zmq.Context(2) if self.attn_tp_rank == 0: self.recv_from_tokenizer = get_zmq_socket( context, zmq.PULL, port_args.scheduler_input_ipc_name, False ) self.send_to_tokenizer = get_zmq_socket( context, zmq.PUSH, port_args.tokenizer_ipc_name, False ) if server_args.skip_tokenizer_init: # Directly send to the TokenizerManager self.send_to_detokenizer = get_zmq_socket( context, zmq.PUSH, port_args.tokenizer_ipc_name, False ) else: # Send to the DetokenizerManager self.send_to_detokenizer = get_zmq_socket( context, zmq.PUSH, port_args.detokenizer_ipc_name, False ) self.recv_from_rpc = get_zmq_socket( context, zmq.DEALER, port_args.rpc_ipc_name, False ) else: self.recv_from_tokenizer = None self.recv_from_rpc = None self.send_to_tokenizer = SimpleNamespace(send_pyobj=lambda x: None) self.send_to_detokenizer = SimpleNamespace(send_pyobj=lambda x: None) # Init tokenizer self.init_tokenizer() # Check whether overlap can be enabled if not self.is_generation: self.enable_overlap = False logger.info("Overlap scheduler is disabled for embedding models.") if self.model_config.is_multimodal: self.enable_overlap = False logger.info("Overlap scheduler is disabled for multimodal models.") # Launch a tensor parallel worker if self.enable_overlap: TpWorkerClass = TpModelWorkerClient else: TpWorkerClass = TpModelWorker self.tp_worker = TpWorkerClass( server_args=server_args, gpu_id=gpu_id, tp_rank=tp_rank, dp_rank=dp_rank, nccl_port=port_args.nccl_port, ) # Launch a draft worker for speculative decoding if self.spec_algorithm.is_eagle(): from sglang.srt.speculative.eagle_worker import EAGLEWorker self.draft_worker = EAGLEWorker( gpu_id=gpu_id, tp_rank=tp_rank, server_args=server_args, nccl_port=port_args.nccl_port, target_worker=self.tp_worker, dp_rank=dp_rank, ) else: self.draft_worker = None # Get token and memory info from the model worker ( self.max_total_num_tokens, self.max_prefill_tokens, self.max_running_requests, self.max_req_len, self.max_req_input_len, self.random_seed, self.device, worker_global_server_args_dict, _, _, _, ) = self.tp_worker.get_worker_info() self.tp_cpu_group = self.tp_worker.get_tp_cpu_group() self.attn_tp_cpu_group = self.tp_worker.get_attention_tp_cpu_group() self.pad_input_ids_func = self.tp_worker.get_pad_input_ids_func() global_server_args_dict.update(worker_global_server_args_dict) set_random_seed(self.random_seed) # Print debug info logger.info( f"max_total_num_tokens={self.max_total_num_tokens}, " f"chunked_prefill_size={server_args.chunked_prefill_size}, " f"max_prefill_tokens={self.max_prefill_tokens}, " f"max_running_requests={self.max_running_requests}, " f"context_len={self.model_config.context_len}" ) # Init memory pool and cache self.init_memory_pool_and_cache() # Init running status self.waiting_queue: List[Req] = [] # The running decoding batch for continuous batching self.running_batch: ScheduleBatch = ScheduleBatch(reqs=[], batch_is_full=False) # The current forward batch self.cur_batch: Optional[ScheduleBatch] = None # The last forward batch self.last_batch: Optional[ScheduleBatch] = None self.forward_ct = 0 self.forward_ct_decode = 0 self.num_generated_tokens = 0 self.num_prefill_tokens = 0 self.last_decode_stats_tic = time.time() self.last_prefill_stats_tic = time.time() self.return_health_check_ct = 0 self.current_stream = torch.get_device_module(self.device).current_stream() if self.device == "cpu": self.current_stream.synchronize = lambda: None # No-op for CPU # Init session info self.sessions: Dict[str, Session] = {} # Init chunked prefill self.chunked_prefill_size = server_args.chunked_prefill_size if self.chunked_prefill_size <= 0: # -1 means disable self.chunked_prefill_size = None self.chunked_req = None self.is_mixed_chunk = ( self.chunked_prefill_size is not None and server_args.enable_mixed_chunk ) # Init the grammar backend for constrained generation self.grammar_queue: List[Req] = [] if not server_args.skip_tokenizer_init: self.grammar_backend = create_grammar_backend( server_args, self.tokenizer, self.model_config.vocab_size ) else: self.grammar_backend = None # Init schedule policy and new token estimation self.policy = SchedulePolicy( self.schedule_policy, self.tree_cache, self.enable_hierarchical_cache, ) assert ( server_args.schedule_conservativeness >= 0 ), "Invalid schedule_conservativeness" self.init_new_token_ratio = min( global_config.default_init_new_token_ratio * server_args.schedule_conservativeness, 1.0, ) self.min_new_token_ratio = min( self.init_new_token_ratio * global_config.default_min_new_token_ratio_factor, 1.0, ) self.new_token_ratio_decay = ( self.init_new_token_ratio - self.min_new_token_ratio ) / global_config.default_new_token_ratio_decay_steps self.new_token_ratio = self.init_new_token_ratio # Init watchdog thread self.watchdog_timeout = server_args.watchdog_timeout t = threading.Thread(target=self.watchdog_thread, daemon=True) t.start() self.parent_process = psutil.Process().parent() # Init memory saver self.memory_saver_adapter = TorchMemorySaverAdapter.create( enable=server_args.enable_memory_saver ) # Init profiler self.torch_profiler = None self.torch_profiler_output_dir: Optional[str] = None self.profiler_activities: Optional[List[str]] = None self.profiler_target_forward_ct: Optional[int] = None # Init metrics stats self.init_metrics() # Init request dispatcher self._request_dispatcher = TypeBasedDispatcher( [ (TokenizedGenerateReqInput, self.handle_generate_request), (TokenizedEmbeddingReqInput, self.handle_embedding_request), (FlushCacheReq, 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), (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() def init_tokenizer(self): server_args = self.server_args self.model_config = ModelConfig( server_args.model_path, trust_remote_code=server_args.trust_remote_code, revision=server_args.revision, context_length=server_args.context_length, model_override_args=server_args.json_model_override_args, is_embedding=server_args.is_embedding, dtype=server_args.dtype, quantization=server_args.quantization, ) 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, ) self.tokenizer = self.processor.tokenizer 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, ) 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.tp_worker.get_tp_cpu_group(), page_size=self.page_size, hicache_ratio=server_args.hicache_ratio, ) 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, ) 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): # The largest prefill length of a single request self._largest_prefill_len: int = 0 # The largest context length (prefill + generation) of a single request self._largest_prefill_decode_len: int = 0 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_disaggregation(self): if ( self.disaggregation_mode == DisaggregationMode.DECODE ): # *2 for the headroom. buffer_size = (self.req_to_token_pool.size) * 2 req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator( buffer_size ) aux_dtype = torch.int32 # A list of metadata buffers. The shape is (b, metadata_size) where # b corresponds to a max running requests. The last shape * dtype.itemsize # should be larger than 64 bytes to work with RDMA, so we pad it. output_id_buffer = torch.zeros( (buffer_size, 16), dtype=aux_dtype, device="cpu" ) metadata_buffers = [output_id_buffer] # The decode requests polling kv cache self.disagg_decode_transfer_queue = DecodeTransferQueue( gloo_group=self.tp_worker.get_attention_tp_cpu_group(), req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator, metadata_buffers=metadata_buffers, ) # 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, req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator, metadata_buffers=metadata_buffers, aux_dtype=aux_dtype, scheduler=self, transfer_queue=self.disagg_decode_transfer_queue, tree_cache=self.tree_cache, gloo_group=self.tp_worker.get_attention_tp_cpu_group(), tp_rank=self.tp_rank, tp_size=self.tp_size, bootstrap_port=self.server_args.disaggregation_bootstrap_port, ) elif self.disaggregation_mode == DisaggregationMode.PREFILL: # *2 for the headroom. buffer_size = self.max_running_requests * 2 req_to_metadata_buffer_idx_allocator = ReqToMetadataIdxAllocator( buffer_size ) aux_dtype = torch.int32 # A list of metadata buffers. The shape is (b, metadata_size) where # b corresponds to a max running requests. The last shape * dtype.itemsize # should be larger than 64 bytes to work with RDMA, so we pad it. output_id_buffer = torch.zeros( (buffer_size, 16), dtype=aux_dtype, device="cpu" ) metadata_buffers = [output_id_buffer] self.disagg_prefill_pending_queue = PrefillBootstrapQueue( token_to_kv_pool=self.token_to_kv_pool_allocator.get_kvcache(), req_to_metadata_buffer_idx_allocator=req_to_metadata_buffer_idx_allocator, metadata_buffers=metadata_buffers, aux_dtype=aux_dtype, tp_rank=self.tp_rank, tp_size=self.tp_size, bootstrap_port=self.server_args.disaggregation_bootstrap_port, gloo_group=self.tp_worker.get_attention_tp_cpu_group(), ) # The prefill requests that are in the middle of kv sending self.disagg_prefill_infight_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.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: result = self.run_batch(batch) self.result_queue.append((batch.copy(), result)) if self.last_batch is None: # Create a dummy first batch to start the pipeline for overlap schedule. # It is now used for triggering the sampling_info_done event. tmp_batch = ScheduleBatch( reqs=None, forward_mode=ForwardMode.DUMMY_FIRST, next_batch_sampling_info=self.tp_worker.cur_sampling_info, ) self.process_batch_result(tmp_batch, None) if self.last_batch: # Process the results of the last batch tmp_batch, tmp_result = self.result_queue.popleft() tmp_batch.next_batch_sampling_info = ( self.tp_worker.cur_sampling_info if batch else None ) self.process_batch_result(tmp_batch, tmp_result) elif batch is None: # When the server is idle, do self-check and re-init some states self.check_memory() self.new_token_ratio = self.init_new_token_ratio self.last_batch = batch @torch.no_grad() def event_loop_normal_disagg_prefill(self): """A normal scheduler loop for prefill worker in disaggregation mode.""" while True: recv_reqs = self.recv_requests() self.process_input_requests(recv_reqs) self.waiting_queue.extend( self.disagg_prefill_pending_queue.pop_bootstrapped() ) self.process_prefill_chunk() batch = self.get_new_batch_prefill() self.cur_batch = batch if batch: result = self.run_batch(batch) self.process_batch_result_disagg_prefill(batch, result) if len(self.disagg_prefill_infight_queue) > 0: self.process_disagg_prefill_infight_queue() if batch is None and len(self.disagg_prefill_infight_queue) == 0: self.check_memory() self.new_token_ratio = self.init_new_token_ratio self.last_batch = batch # HACK (byronhsu): reset the batch_is_full flag because we never enter update_running_batch which resets it # Otherwise, it hangs under high concurrency self.running_batch.batch_is_full = False @torch.no_grad() def event_loop_normal_disagg_decode(self): """A normal scheduler loop for decode worker in disaggregation mode.""" while True: recv_reqs = self.recv_requests() self.process_input_requests(recv_reqs) # polling and allocating kv cache self.process_decode_queue() batch = self.get_next_disagg_decode_batch_to_run() self.cur_batch = batch if batch: # Generate fake extend output. if batch.forward_mode.is_extend(): # Note: Logprobs should be handled on the prefill engine. self.stream_output( batch.reqs, [False for _ in range(len(batch.reqs))] ) else: result = self.run_batch(batch) self.process_batch_result(batch, result) if batch is None and ( len(self.disagg_decode_transfer_queue.queue) + len(self.disagg_decode_prealloc_queue.queue) == 0 ): # 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.last_batch = batch def recv_requests(self) -> List[Req]: """Receive results at tp_rank = 0 and broadcast it to all other TP ranks.""" if self.attn_tp_rank == 0: recv_reqs = [] while True: try: recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK) except zmq.ZMQError: break recv_reqs.append(recv_req) 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 if self.server_args.enable_dp_attention: if self.attn_tp_rank == 0: work_reqs = [ req for req in recv_reqs if isinstance( req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput) ) ] control_reqs = [ req for req in recv_reqs if not isinstance( req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput) ) ] else: work_reqs = None control_reqs = None if self.attn_tp_size != 1: attn_tp_rank_0 = self.dp_rank * self.attn_tp_size work_reqs = broadcast_pyobj( work_reqs, self.attn_tp_rank, self.attn_tp_cpu_group, src=attn_tp_rank_0, ) if self.tp_size != 1: control_reqs = broadcast_pyobj( control_reqs, self.tp_rank, self.tp_cpu_group ) recv_reqs = work_reqs + control_reqs elif self.tp_size != 1: recv_reqs = broadcast_pyobj(recv_reqs, self.tp_rank, self.tp_cpu_group) return recv_reqs def process_input_requests(self, recv_reqs: List): for recv_req in recv_reqs: # 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 # Handle custom logit processor passed to the request custom_logit_processor = recv_req.custom_logit_processor if ( not self.server_args.enable_custom_logit_processor and custom_logit_processor is not None ): logger.warning( "The SGLang server is not configured to enable custom logit processor." "The custom logit processor passed in will be ignored." "Please set --enable-custom-logits-processor to enable this feature." ) custom_logit_processor = None req = Req( recv_req.rid, recv_req.input_text, recv_req.input_ids, recv_req.sampling_params, return_logprob=recv_req.return_logprob, top_logprobs_num=recv_req.top_logprobs_num, 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=custom_logit_processor, return_hidden_states=recv_req.return_hidden_states, eos_token_ids=self.model_config.hf_eos_token_id, ) req.tokenizer = self.tokenizer if ( recv_req.session_params is not None and recv_req.session_params.id is not None ): req.finished_reason = FINISH_ABORT( f"Invalid request: session id {recv_req.session_params.id} does not exist" ) self._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: error_msg = ( "Multimodal prompt is too long after expanding multimodal tokens. " f"After expanding {len(req.origin_input_ids_unpadded)=} => {len(req.origin_input_ids)} >= {self.max_req_input_len}." ) logger.error(error_msg) req.origin_input_ids = [0] req.multimodal_inputs = None req.sampling_params.max_new_tokens = 0 req.finished_reason = FINISH_ABORT( error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError" ) 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: req.origin_input_ids = [0] req.sampling_params.max_new_tokens = 0 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): req.finished_reason = FINISH_ABORT( f"logprob_start_len, ({req.logprob_start_len}) is higher than the number of input tokens ({len(req.origin_input_ids)}). Request with a lower logprob_start_len.", HTTPStatus.BAD_REQUEST, "BadRequestError", ) req.logprob_start_len = len(req.origin_input_ids) - 1 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) req.grammar = self.grammar_backend.get_cached_value(key) if not req.grammar: req.grammar = self.grammar_backend.get_future_value(key) add_to_grammar_queue = True if add_to_grammar_queue: self.grammar_queue.append(req) else: self._add_request_to_queue(req) def _add_request_to_queue(self, req: Req): if self.disaggregation_mode == DisaggregationMode.PREFILL: self.disagg_prefill_pending_queue.add(req) 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.DECODE: self.disagg_decode_prealloc_queue.extend(reqs) 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, ) 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: error_msg = ( "Multimodal prompt is too long after expanding multimodal tokens. " f"After expanding {len(req.origin_input_ids_unpadded)=} => {len(req.origin_input_ids)} >= {self.max_req_input_len}." ) logger.error(error_msg) req.origin_input_ids = [0] req.multimodal_inputs = None req.sampling_params.max_new_tokens = 0 req.finished_reason = FINISH_ABORT( error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError" ) self.waiting_queue.append(req) return # Validate prompts length error_msg = validate_input_length( req, self.max_req_input_len, self.server_args.allow_auto_truncate, ) if error_msg: self._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 log_prefill_stats( self, adder: PrefillAdder, can_run_list: List[Req], running_bs: int, ): gap_latency = time.time() - self.last_prefill_stats_tic self.last_prefill_stats_tic = time.time() self.last_input_throughput = self.num_prefill_tokens / gap_latency self.num_prefill_tokens = 0 num_used = self.max_total_num_tokens - ( self.token_to_kv_pool_allocator.available_size() + self.tree_cache.evictable_size() ) self._largest_prefill_len = max( self._largest_prefill_len, adder.log_input_tokens ) f = ( f"Prefill batch. " f"#new-seq: {len(can_run_list)}, " f"#new-token: {adder.log_input_tokens}, " f"#cached-token: {adder.log_hit_tokens}, " f"token usage: {num_used / self.max_total_num_tokens:.2f}, " f"#running-req: {running_bs}, " 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 self.metrics_collector.log_stats(self.stats) def log_decode_stats(self): gap_latency = time.time() - self.last_decode_stats_tic self.last_decode_stats_tic = time.time() self.last_gen_throughput = self.num_generated_tokens / gap_latency self.num_generated_tokens = 0 num_running_reqs = len(self.running_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 ) if self.spec_algorithm.is_none(): msg = ( f"Decode batch. " f"#running-req: {num_running_reqs}, " f"#token: {num_used}, " f"token usage: {num_used / self.max_total_num_tokens:.2f}, " f"gen throughput (token/s): {self.last_gen_throughput:.2f}, " f"#queue-req: {len(self.waiting_queue)}, " ) spec_accept_length = 0 else: spec_accept_length = ( self.spec_num_total_accepted_tokens / self.spec_num_total_forward_ct ) self.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"Decode batch. " f"#running-req: {num_running_reqs}, " f"#token: {num_used}, " f"token usage: {num_used / self.max_total_num_tokens:.2f}, " f"accept len: {spec_accept_length:.2f}, " f"gen throughput (token/s): {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.spec_accept_length = spec_accept_length self.metrics_collector.log_stats(self.stats) 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 = ( "KV cache pool 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" ) warnings.warn(msg) if crash_on_warnings(): raise ValueError(msg) if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size: msg = ( "Memory pool leak detected!" f"available_size={len(self.req_to_token_pool.free_slots)}, " f"total_size={self.req_to_token_pool.size}\n" ) warnings.warn(msg) if crash_on_warnings(): raise ValueError(msg) if ( self.enable_metrics and self.attn_tp_rank == 0 and time.time() > 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.metrics_collector.log_stats(self.stats) def get_next_batch_to_run(self) -> Optional[ScheduleBatch]: # Merge the prefill batch into the running batch if self.last_batch and self.last_batch.forward_mode.is_extend(): if self.chunked_req: # Move the chunked request out of the batch so that we can merge # only finished requests to running_batch. self.last_batch.filter_batch(chunked_req_to_exclude=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) self.running_batch.batch_is_full = False # Filter batch last_bs = self.last_batch.batch_size() self.last_batch.filter_batch() 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() 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 self.server_args.enable_dp_attention or self.server_args.enable_sp_layernorm: ret, _ = self.prepare_dp_attn_batch(ret) return ret def get_new_batch_prefill(self) -> Optional[ScheduleBatch]: # Check if the grammar is ready in the grammar queue if self.grammar_queue: self.move_ready_grammar_requests() # Handle the cases where prefill is not allowed if ( self.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) if running_bs >= self.max_running_requests: self.running_batch.batch_is_full = True return None if self.enable_hierarchical_cache: # check for completion of hierarchical cache activities to release memory self.tree_cache.writing_check() self.tree_cache.loading_check() # Get priority queue prefix_computed = self.policy.calc_priority(self.waiting_queue) # Prefill policy adder = PrefillAdder( 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 running_bs + len(adder.can_run_list) >= self.max_running_requests: self.running_batch.batch_is_full = True break req.init_next_round_input( None if prefix_computed else self.tree_cache, self.enable_hierarchical_cache, ) res = adder.add_one_req( req, self.chunked_req, self.enable_hierarchical_cache ) 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 ( self.running_batch is not None and 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 self.waiting_queue = [ x for x in self.waiting_queue if x not in set(can_run_list) ] if self.enable_hierarchical_cache: self.tree_cache.read_to_load_cache() 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, ) 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( "Decode out of memory happened. " 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) 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 # Check profiler if ( self.profiler_target_forward_ct and self.profiler_target_forward_ct <= self.forward_ct ): self.stop_profile() # Run forward if self.is_generation: if self.spec_algorithm.is_none(): model_worker_batch = batch.get_model_worker_batch() logits_output, next_token_ids = self.tp_worker.forward_batch_generation( model_worker_batch ) bid = model_worker_batch.bid else: ( logits_output, next_token_ids, bid, num_accepted_tokens, ) = self.draft_worker.forward_batch_speculative_generation(batch) self.spec_num_total_accepted_tokens += ( num_accepted_tokens + batch.batch_size() ) self.spec_num_total_forward_ct += batch.batch_size() self.num_generated_tokens += num_accepted_tokens batch.output_ids = next_token_ids # 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: extend_input_len_per_req = [req.extend_input_len for req in batch.reqs] extend_logprob_start_len_per_req = [ req.extend_logprob_start_len for req in batch.reqs ] else: extend_input_len_per_req = None extend_logprob_start_len_per_req = None ret = GenerationBatchResult( logits_output=logits_output, next_token_ids=next_token_ids, extend_input_len_per_req=extend_input_len_per_req, extend_logprob_start_len_per_req=extend_logprob_start_len_per_req, bid=bid, ) else: # embedding or reward model model_worker_batch = batch.get_model_worker_batch() embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch) ret = EmbeddingBatchResult( embeddings=embeddings, bid=model_worker_batch.bid ) return ret def process_batch_result( self, batch: ScheduleBatch, result: Union[GenerationBatchResult, EmbeddingBatchResult], ): if batch.forward_mode.is_decode(): self.process_batch_result_decode(batch, result) elif batch.forward_mode.is_extend(): self.process_batch_result_prefill(batch, result) elif batch.forward_mode.is_idle(): if self.enable_overlap: self.tp_worker.resolve_batch_result(result.bid) if batch.next_batch_sampling_info: batch.next_batch_sampling_info.update_regex_vocab_mask() self.current_stream.synchronize() batch.next_batch_sampling_info.sampling_info_done.set() elif batch.forward_mode.is_dummy_first(): batch.next_batch_sampling_info.update_regex_vocab_mask() self.current_stream.synchronize() batch.next_batch_sampling_info.sampling_info_done.set() 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_dp_attn_batch(self, local_batch: ScheduleBatch): # Check if other DP workers have running batches if local_batch is None: num_tokens = 0 global_num_tokens_for_logprob = 0 elif local_batch.forward_mode.is_decode(): num_tokens = local_batch.batch_size() if not self.spec_algorithm.is_none() and self.spec_algorithm.is_eagle(): num_tokens = num_tokens * self.server_args.speculative_num_draft_tokens global_num_tokens_for_logprob = num_tokens else: num_tokens = local_batch.extend_num_tokens global_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 if not self.spec_algorithm.is_none(): # TODO(sang): Support cuda graph when idle batch is there. if local_batch is None or local_batch.forward_mode.is_idle(): can_cuda_graph = 0 is_extend_in_batch = ( local_batch.forward_mode.is_extend() if local_batch else False ) local_info = torch.tensor( [ num_tokens, can_cuda_graph, global_num_tokens_for_logprob, is_extend_in_batch, ], dtype=torch.int64, ) global_info = torch.empty( (self.server_args.dp_size, self.attn_tp_size, 4), dtype=torch.int64, ) torch.distributed.all_gather_into_tensor( global_info.flatten(), local_info, group=self.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() if local_batch is None and max(global_num_tokens) > 0: local_batch = self.get_idle_batch() if local_batch is not None: local_batch.global_num_tokens = global_num_tokens local_batch.global_num_tokens_for_logprob = global_num_tokens_for_logprob # Check forward mode for cuda graph if not self.server_args.disable_cuda_graph: local_batch.can_run_dp_cuda_graph = can_cuda_graph 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 for req in self.grammar_queue: try: req.grammar = req.grammar.result(timeout=0.05) num_ready_reqs += 1 except futures._base.TimeoutError: break if self.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, dtype=torch.int32) torch.distributed.all_reduce( tensor, op=torch.distributed.ReduceOp.MAX, group=tp_group ) num_ready_reqs_max = tensor.item() for i in range(num_ready_reqs, num_ready_reqs_max): self.grammar_queue[i].grammar = self.grammar_queue[i].grammar.result() num_ready_reqs = num_ready_reqs_max self._extend_requests_to_queue(self.grammar_queue[:num_ready_reqs]) self.grammar_queue = self.grammar_queue[num_ready_reqs:] 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.time() while True: current = time.time() 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: logger.error(f"Watchdog timeout ({self.watchdog_timeout=})") break else: self.watchdog_last_forward_ct = self.forward_ct self.watchdog_last_time = current time.sleep(self.watchdog_timeout // 2) # 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()=}, " ) # Wait for some time so that the parent process can print the error. pyspy_dump_schedulers() print(file=sys.stderr, flush=True) print(file=sys.stdout, flush=True) time.sleep(5) self.parent_process.send_signal(signal.SIGQUIT) def flush_cache_wrapped(self, recv_req: FlushCacheReq): self.flush_cache() def flush_cache(self): """Flush the memory pool and cache.""" if len(self.waiting_queue) == 0 and self.running_batch.is_empty(): 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_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 return GetInternalStateReqOutput( internal_state=ret, ) def set_internal_state(self, recv_req: SetInternalStateReq): server_args_dict = recv_req.server_args args_allow_update = set( [ "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 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! " f"{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) break # Sort in reverse order to avoid index issues when deleting for i in sorted(to_del, reverse=True): req = self.waiting_queue.pop(i) logger.debug(f"Abort queued request. {req.rid=}") return # Delete requests in the running batch for req in self.running_batch.reqs: if req.rid.startswith(recv_req.rid) and not req.finished(): logger.debug(f"Abort running request. {req.rid=}") req.to_abort = True return 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: flash_cache_success = self.flush_cache() assert flash_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: flash_cache_success = self.flush_cache() assert flash_cache_success, "Cache flush failed after updating weights" else: logger.error(message) return UpdateWeightsFromDistributedReqOutput(success, message) def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput): """Update the online model parameter from tensors.""" success, message = self.tp_worker.update_weights_from_tensor(recv_req) # TODO extract common code b/t update_weights_from_distributed and update_weights_from_tensor later if success: if recv_req.flush_cache: flash_cache_success = self.flush_cache() assert flash_cache_success, "Cache flush failed after updating weights" else: logger.error(message) return UpdateWeightsFromTensorReqOutput(success, message) def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput): parameter = self.tp_worker.get_weights_by_name(recv_req) return GetWeightsByNameReqOutput(parameter) def release_memory_occupation(self, recv_req: ReleaseMemoryOccupationReqInput): self.memory_saver_adapter.check_validity( caller_name="release_memory_occupation" ) self.stashed_model_static_state = _export_static_state( self.tp_worker.worker.model_runner.model ) self.memory_saver_adapter.pause() self.flush_cache() return ReleaseMemoryOccupationReqOutput() def resume_memory_occupation(self, recv_req: ResumeMemoryOccupationReqInput): self.memory_saver_adapter.check_validity(caller_name="resume_memory_occupation") self.memory_saver_adapter.resume() _import_static_state( self.tp_worker.worker.model_runner.model, self.stashed_model_static_state ) del self.stashed_model_static_state return ResumeMemoryOccupationReqOutput() def profile(self, recv_req: ProfileReq): if recv_req.type == ProfileReqType.START_PROFILE: return self.start_profile( recv_req.output_dir, recv_req.num_steps, recv_req.activities, recv_req.with_stack, recv_req.record_shapes, ) else: return self.stop_profile() def start_profile( self, output_dir: Optional[str], num_steps: Optional[int], activities: Optional[List[str]], with_stack: Optional[bool], record_shapes: Optional[bool], ) -> None: if self.profiler_activities: return ProfileReqOutput( success=False, message="Profiling is already in progress. Call /stop_profile first.", ) 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.profiler_activities = activities logger.info( "Profiling starts. Traces will be saved to: %s", self.torch_profiler_output_dir, ) 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 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() if "MEM" in activities: torch.cuda.memory._record_memory_history(max_entries=100000) if "CUDA_PROFILER" in activities: torch.cuda.cudart().cudaProfilerStart() if num_steps: 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 stop_profile(self) -> None: if self.profiler_activities is None: return logger.info("Stop profiling...") 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, str(time.time()) + f"-TP-{self.tp_rank}" + ".trace.json.gz", ) ) if "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" + ".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.torch_profiler_output_dir = None self.profiler_activities = None if self.profiler_target_forward_ct: self.send_to_tokenizer.send_pyobj( ProfileReqOutput(success=True, message="Succeeded.") ) def expert_distribution_handle(self, recv_req: ExpertDistributionReq): if recv_req == ExpertDistributionReq.START_RECORD: expert_distribution_recorder.start_record() elif recv_req == ExpertDistributionReq.STOP_RECORD: expert_distribution_recorder.stop_record() elif recv_req == ExpertDistributionReq.DUMP_RECORD: 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 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, dp_rank: Optional[int], pipe_writer, ): # Generate the prefix if dp_rank is None: prefix = f" TP{tp_rank}" else: prefix = f" DP{dp_rank} TP{tp_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) # Create a scheduler and run the event loop try: scheduler = Scheduler(server_args, port_args, gpu_id, tp_rank, dp_rank) pipe_writer.send( { "status": "ready", "max_total_num_tokens": scheduler.max_total_num_tokens, "max_req_input_len": scheduler.max_req_input_len, } ) disaggregation_mode: DisaggregationMode = scheduler.disaggregation_mode if disaggregation_mode == DisaggregationMode.NULL: if scheduler.enable_overlap: scheduler.event_loop_overlap() else: scheduler.event_loop_normal() elif disaggregation_mode == DisaggregationMode.PREFILL: scheduler.event_loop_normal_disagg_prefill() elif disaggregation_mode == DisaggregationMode.DECODE: 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)