# 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. # ============================================================================== """DetokenizerManager is a process that detokenizes the token ids.""" import dataclasses import logging import os import signal from collections import OrderedDict, defaultdict from typing import Dict, List, Optional, Tuple, Union import psutil import pybase64 import setproctitle import torch import zmq from sglang.srt.constants import HEALTH_CHECK_RID_PREFIX from sglang.srt.environ import envs from sglang.srt.managers.io_struct import ( BatchEmbeddingOutput, BatchStrOutput, BatchTokenIDOutput, ConfigureLoggingReq, FreezeGCReq, sock_recv, sock_send, ) from sglang.srt.managers.multi_tokenizer_mixin import MultiHttpWorkerDetokenizerMixin from sglang.srt.observability.cpu_monitor import start_cpu_monitor_thread from sglang.srt.server_args import PortArgs, ServerArgs from sglang.srt.utils import configure_logger, freeze_gc, kill_itself_when_parent_died from sglang.srt.utils.hf_transformers_utils import get_tokenizer from sglang.srt.utils.network import get_zmq_socket from sglang.srt.utils.patch_tokenizer import decode_without_hf_kwargs from sglang.srt.utils.watchdog import Watchdog from sglang.utils import ( TypeBasedDispatcher, find_printable_text, get_exception_traceback, ) logger = logging.getLogger(__name__) # Maximum number of request states that detokenizer can hold. When exceeded, # oldest request states will be evicted. Default: 65536 (1<<16). # For more details, see: https://github.com/sgl-project/sglang/issues/2812 # Use power of 2 values for better memory allocation. DETOKENIZER_MAX_STATES = int(os.environ.get("SGLANG_DETOKENIZER_MAX_STATES", 1 << 16)) @dataclasses.dataclass class DecodeStatus: """Store the status of incremental decoding.""" decoded_text: str decode_ids: List[int] surr_offset: int read_offset: int # Offset that's sent to tokenizer for incremental update. sent_offset: int = 0 decoded_text_len: int = dataclasses.field(init=False) decoded_text_chunks: List[str] = dataclasses.field(default_factory=list) def __post_init__(self): self.decoded_text_len = len(self.decoded_text) def append_decoded_text(self, text: str): if text: self.decoded_text_chunks.append(text) self.decoded_text_len += len(text) def get_decoded_text(self) -> str: if self.decoded_text_chunks: self.decoded_text += "".join(self.decoded_text_chunks) self.decoded_text_chunks.clear() return self.decoded_text class DetokenizerManager(MultiHttpWorkerDetokenizerMixin): """DetokenizerManager is a process that detokenizes the token ids.""" def __init__( self, server_args: ServerArgs, port_args: PortArgs, ): # Init inter-process communication self.init_ipc_channels(port_args, server_args) # Init tokenizer self.init_tokenizer(server_args) # Init running status self.init_running_status(server_args) # Init dispatcher self.init_request_dispatcher() def init_ipc_channels(self, port_args: PortArgs, server_args: ServerArgs): context = zmq.Context(2) self.recv_from_scheduler = get_zmq_socket( context, zmq.PULL, port_args.detokenizer_ipc_name, True ) # In multi-tokenizer mode, results are pushed back to each TokenizerWorker # directly via SocketMapping inside multi_http_worker_event_loop, so the # single send_to_tokenizer socket is unused. if server_args.tokenizer_worker_num == 1: self.send_to_tokenizer = get_zmq_socket( context, zmq.PUSH, port_args.tokenizer_ipc_name, False ) def init_tokenizer(self, server_args: ServerArgs): if server_args.skip_tokenizer_init: self.tokenizer = None 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, tokenizer_backend=server_args.tokenizer_backend, ) def init_running_status(self, server_args: ServerArgs): self.decode_status = LimitedCapacityDict(capacity=DETOKENIZER_MAX_STATES) self.disable_tokenizer_batch_decode = server_args.disable_tokenizer_batch_decode self.is_tool_call_parser_gpt_oss = server_args.tool_call_parser == "gpt-oss" self.soft_watchdog = Watchdog.create( debug_name="DetokenizerManager", watchdog_timeout=server_args.soft_watchdog_timeout, soft=True, test_stuck_time=envs.SGLANG_TEST_STUCK_DETOKENIZER.get(), ) if server_args.enable_metrics: start_cpu_monitor_thread("detokenizer") def init_request_dispatcher(self): self._request_dispatcher = TypeBasedDispatcher( [ (BatchEmbeddingOutput, self.handle_batch_embedding_out), (BatchTokenIDOutput, self.handle_batch_token_id_out), (FreezeGCReq, self.handle_freeze_gc_req), (ConfigureLoggingReq, self.handle_configure_logging_req), ] ) def event_loop(self): """The event loop that handles requests""" while True: with self.soft_watchdog.disable(): recv_obj = sock_recv(self.recv_from_scheduler) output = self._request_dispatcher(recv_obj) if output is not None: sock_send(self.send_to_tokenizer, output) self.soft_watchdog.feed() def trim_matched_stop( self, output: Union[str, List[int]], finished_reason: Dict, no_stop_trim: bool ): if not finished_reason: return output matched = finished_reason.get("matched", None) if not matched: return output # TODO(lmzheng): handle the case where multiple stop strs are hit # Trim stop str. if isinstance(matched, str) and isinstance(output, str): pos = output.find(matched) if pos == -1: return output end = pos + len(matched) return output[:end] if no_stop_trim else output[:pos] # Trim stop token. if isinstance(matched, int) and isinstance(output, list): if no_stop_trim: return output # 200012 <|call|> is the tool call token and one of eos tokens for gpt-oss model if output[-1] == 200012 and self.is_tool_call_parser_gpt_oss: return output assert len(output) > 0 # NOTE: We can always assume the last token is the matched stop token return output[:-1] return output def handle_batch_embedding_out(self, recv_obj: BatchEmbeddingOutput): # If it is embedding model, no detokenization is needed. return recv_obj def _grouped_batch_decode( self, ids_list: List[List[int]], skip_list: List[bool], space_list: List[bool], ) -> List[str]: """Batch decode with grouping by (skip_special_tokens, spaces_between_special_tokens).""" n = len(ids_list) if n == 0: return [] # Empty token spans decode to "" but tokenizer.batch_decode (and the # slow per-row decode_without_hf_kwargs path) still pays per-row # overhead; under high-concurrency streaming this adds up. Filter # empties out, decode the rest, then scatter back. keep_idx: Optional[List[int]] = None if not all(ids_list): keep_idx = [i for i, ids in enumerate(ids_list) if ids] if not keep_idx: return [""] * n ids_list = [ids_list[i] for i in keep_idx] skip_list = [skip_list[i] for i in keep_idx] space_list = [space_list[i] for i in keep_idx] if not getattr(self.tokenizer, "is_fast", False): decoded = [ decode_without_hf_kwargs(self.tokenizer, ids, skip) for ids, skip in zip(ids_list, skip_list) ] else: # fast path: all rows share the same (skip, space) flags. first_skip, first_space = skip_list[0], space_list[0] if all( s == first_skip and sp == first_space for s, sp in zip(skip_list, space_list) ): decoded = self.tokenizer.batch_decode( ids_list, skip_special_tokens=first_skip, spaces_between_special_tokens=first_space, ) else: # Group indices by (skip, space) tuple and decode each group. groups: Dict[Tuple[bool, bool], List[int]] = defaultdict(list) for idx, (skip, space) in enumerate(zip(skip_list, space_list)): groups[(skip, space)].append(idx) decoded = [""] * len(ids_list) for (skip, space), indices in groups.items(): group_decoded = self.tokenizer.batch_decode( [ids_list[idx] for idx in indices], skip_special_tokens=skip, spaces_between_special_tokens=space, ) for idx, text in zip(indices, group_decoded): decoded[idx] = text if keep_idx is None: return decoded results = [""] * n for i, text in zip(keep_idx, decoded): results[i] = text return results def _decode_batch_token_id_output(self, recv_obj: BatchTokenIDOutput): bs = len(recv_obj.rids) # Initialize decode status read_ids, surr_ids = [], [] for i in range(bs): rid = recv_obj.rids[i] if rid not in self.decode_status: s = DecodeStatus( decoded_text=recv_obj.decoded_texts[i], decode_ids=list(recv_obj.decode_ids[i]), surr_offset=0, read_offset=recv_obj.read_offsets[i], ) self.decode_status[rid] = s else: s = self.decode_status[rid] s.decode_ids.extend(recv_obj.decode_ids[i]) read_ids.append( self.trim_matched_stop( s.decode_ids[s.surr_offset :], recv_obj.finished_reasons[i], recv_obj.no_stop_trim[i], ) ) surr_ids.append(s.decode_ids[s.surr_offset : s.read_offset]) # Decode token ids to strings if not self.disable_tokenizer_batch_decode: surr_texts = self._grouped_batch_decode( surr_ids, recv_obj.skip_special_tokens, recv_obj.spaces_between_special_tokens, ) read_texts = self._grouped_batch_decode( read_ids, recv_obj.skip_special_tokens, recv_obj.spaces_between_special_tokens, ) else: # Do not use batch decode to prevent some detokenization edge cases (e.g., gpt-oss). surr_texts = [ self.tokenizer.decode( surr, skip_special_tokens=skip, spaces_between_special_tokens=space ) for surr, skip, space in zip( surr_ids, recv_obj.skip_special_tokens, recv_obj.spaces_between_special_tokens, ) ] read_texts = [ self.tokenizer.decode( read, skip_special_tokens=skip, spaces_between_special_tokens=space ) for read, skip, space in zip( read_ids, recv_obj.skip_special_tokens, recv_obj.spaces_between_special_tokens, ) ] # Incremental decoding output_strs = [] for i in range(bs): rid = recv_obj.rids[i] try: s = self.decode_status[rid] except KeyError: raise RuntimeError( f"Decode status not found for request {rid}. " "It may be due to the request being evicted from the decode status due to memory pressure. " "Please increase the maximum number of requests by setting " "the SGLANG_DETOKENIZER_MAX_STATES environment variable to a bigger value than the default value. " f"The current value is {DETOKENIZER_MAX_STATES}. " "For more details, see: https://github.com/sgl-project/sglang/issues/2812" ) new_text = read_texts[i][len(surr_texts[i]) :] if recv_obj.finished_reasons[i] is None: # Streaming. Invariant: sent_offset >= decoded_text_len. The # gap (`pending`) is "printable but uncommitted" text emitted # in a prior "�" recovery step; we skip it from this step's # emission so we don't double-send. pending = s.sent_offset - s.decoded_text_len if new_text and not new_text.endswith("�"): # Clean text: commit to decoded_text and advance offsets. s.append_decoded_text(new_text) s.surr_offset = s.read_offset s.read_offset = len(s.decode_ids) s.sent_offset = s.decoded_text_len output_strs.append(new_text[pending:] if pending else new_text) else: # Incomplete UTF-8: emit the printable prefix only; do not # commit (token offsets stay so the next iteration retries # with more tokens). printable = find_printable_text(new_text) s.sent_offset = s.decoded_text_len + len(printable) output_strs.append(printable[pending:] if pending else printable) continue if rid in self.decode_status: del self.decode_status[rid] # Finished: materialize once, trim the matched stop, emit the tail. output_str = self.trim_matched_stop( s.get_decoded_text() + new_text, recv_obj.finished_reasons[i], recv_obj.no_stop_trim[i], ) incremental_output = output_str[s.sent_offset :] s.sent_offset = len(output_str) output_strs.append(incremental_output) return output_strs @staticmethod def _b64_encode_per_request( data_list: Optional[List[Optional[torch.Tensor]]], ) -> Optional[List[Optional[str]]]: """Encode a per-request list of tensors as base64 strings, off the tokenizer hot path. Returns None when the input is None; per-item None stays None. """ if data_list is None: return None return [ ( pybase64.b64encode(item.numpy().tobytes()).decode("utf-8") if item is not None else None ) for item in data_list ] def handle_batch_token_id_out(self, recv_obj: BatchTokenIDOutput): # If handling idle batch, set output_strs to []. output_strs = ( self._decode_batch_token_id_output(recv_obj) if len(recv_obj.rids) > 0 else [] ) routed_experts = self._b64_encode_per_request(recv_obj.routed_experts) indexer_topk = self._b64_encode_per_request(recv_obj.indexer_topk) return BatchStrOutput( rids=recv_obj.rids, http_worker_ipcs=recv_obj.http_worker_ipcs, finished_reasons=recv_obj.finished_reasons, output_strs=output_strs, output_ids=recv_obj.output_ids, prompt_tokens=recv_obj.prompt_tokens, reasoning_tokens=recv_obj.reasoning_tokens, completion_tokens=recv_obj.completion_tokens, cached_tokens=recv_obj.cached_tokens, cached_tokens_details=recv_obj.cached_tokens_details, image_tokens=recv_obj.image_tokens, audio_tokens=recv_obj.audio_tokens, video_tokens=recv_obj.video_tokens, spec_verify_ct=recv_obj.spec_verify_ct, spec_num_correct_drafts=recv_obj.spec_num_correct_drafts, spec_num_block_accept_tokens=recv_obj.spec_num_block_accept_tokens, spec_num_cap_tokens=recv_obj.spec_num_cap_tokens, spec_correct_drafts_histogram=recv_obj.spec_correct_drafts_histogram, spec_cap_lens_histogram=recv_obj.spec_cap_lens_histogram, input_token_logprobs_val=recv_obj.input_token_logprobs_val, input_token_logprobs_idx=recv_obj.input_token_logprobs_idx, output_token_logprobs_val=recv_obj.output_token_logprobs_val, output_token_logprobs_idx=recv_obj.output_token_logprobs_idx, input_top_logprobs_val=recv_obj.input_top_logprobs_val, input_top_logprobs_idx=recv_obj.input_top_logprobs_idx, output_top_logprobs_val=recv_obj.output_top_logprobs_val, output_top_logprobs_idx=recv_obj.output_top_logprobs_idx, input_token_ids_logprobs_val=recv_obj.input_token_ids_logprobs_val, input_token_ids_logprobs_idx=recv_obj.input_token_ids_logprobs_idx, output_token_ids_logprobs_val=recv_obj.output_token_ids_logprobs_val, output_token_ids_logprobs_idx=recv_obj.output_token_ids_logprobs_idx, output_token_entropy_val=recv_obj.output_token_entropy_val, output_hidden_states=recv_obj.output_hidden_states, routed_experts=routed_experts, indexer_topk=indexer_topk, customized_info=recv_obj.customized_info, placeholder_tokens_idx=None, placeholder_tokens_val=None, retraction_counts=recv_obj.retraction_counts, token_steps=recv_obj.token_steps, dp_ranks=recv_obj.dp_ranks, time_stats=recv_obj.time_stats, ) def handle_freeze_gc_req(self, recv_req: FreezeGCReq): freeze_gc("Detokenizer Manager") return None def handle_configure_logging_req(self, recv_req: ConfigureLoggingReq): if recv_req.log_level is not None: logging.getLogger().setLevel(recv_req.log_level.upper()) def is_health_check_request(rid: Optional[str]) -> bool: return isinstance(rid, str) and rid.startswith(HEALTH_CHECK_RID_PREFIX) class LimitedCapacityDict(OrderedDict): def __init__(self, capacity: int, *args, **kwargs): super().__init__(*args, **kwargs) self.capacity = capacity def __setitem__(self, key, value): if len(self) >= self.capacity: # Remove the oldest element (first item in the dict) self.popitem(last=False) # Set the new item super().__setitem__(key, value) def run_detokenizer_process( server_args: ServerArgs, port_args: PortArgs, detokenizer_manager_class=DetokenizerManager, ): kill_itself_when_parent_died() setproctitle.setproctitle("sglang::detokenizer") configure_logger(server_args) parent_process = psutil.Process().parent() manager = None try: manager = detokenizer_manager_class(server_args, port_args) if server_args.tokenizer_worker_num == 1: manager.event_loop() else: manager.multi_http_worker_event_loop() except Exception: traceback = get_exception_traceback() logger.error(f"DetokenizerManager hit an exception: {traceback}") if manager is not None: manager.maybe_clear_socket_mapping() parent_process.send_signal(signal.SIGQUIT)