921 lines
35 KiB
Python
921 lines
35 KiB
Python
"""
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 tensor parallel worker."""
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import logging
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import multiprocessing
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import os
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import pickle
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import time
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import warnings
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from typing import Any, List, Optional, Union
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import torch
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import torch.distributed
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import torch.distributed as dist
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from sglang.global_config import global_config
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from sglang.srt.constrained.fsm_cache import FSMCache
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from sglang.srt.constrained.jump_forward import JumpForwardCache
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from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
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from sglang.srt.layers.logits_processor import LogitProcessorOutput
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from sglang.srt.managers.io_struct import (
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AbortReq,
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BatchEmbeddingOut,
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BatchTokenIDOut,
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FlushCacheReq,
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TokenizedEmbeddingReqInput,
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TokenizedGenerateReqInput,
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UpdateWeightReqInput,
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UpdateWeightReqOutput,
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)
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from sglang.srt.managers.policy_scheduler import PolicyScheduler, PrefillAdder
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from sglang.srt.managers.schedule_batch import (
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FINISH_ABORT,
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BaseFinishReason,
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Req,
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ScheduleBatch,
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)
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from sglang.srt.mem_cache.chunk_cache import ChunkCache
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from sglang.srt.mem_cache.radix_cache import RadixCache
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from sglang.srt.model_config import ModelConfig
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from sglang.srt.model_executor.forward_batch_info import ForwardMode
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.utils import (
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is_multimodal_model,
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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 get_exception_traceback
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logger = logging.getLogger(__name__)
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crash_on_warning = os.getenv("SGLANG_IS_IN_CI", "false") == "true"
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class ModelTpServer:
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def __init__(
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self,
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gpu_id: int,
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tp_rank: int,
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server_args: ServerArgs,
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nccl_port: int,
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model_overide_args: dict,
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):
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suppress_other_loggers()
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# Copy arguments
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self.gpu_id = gpu_id
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self.tp_rank = tp_rank
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self.tp_size = server_args.tp_size
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self.dp_size = server_args.dp_size
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self.schedule_policy = server_args.schedule_policy
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self.disable_regex_jump_forward = server_args.disable_regex_jump_forward
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# Init model and tokenizer
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self.model_config = ModelConfig(
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server_args.model_path,
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server_args.trust_remote_code,
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context_length=server_args.context_length,
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model_overide_args=model_overide_args,
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)
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self.model_runner = ModelRunner(
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model_config=self.model_config,
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mem_fraction_static=server_args.mem_fraction_static,
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gpu_id=gpu_id,
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tp_rank=tp_rank,
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tp_size=server_args.tp_size,
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nccl_port=nccl_port,
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server_args=server_args,
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)
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if server_args.skip_tokenizer_init:
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self.tokenizer = self.processor = None
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else:
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if is_multimodal_model(server_args.model_path):
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self.processor = get_processor(
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server_args.tokenizer_path,
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tokenizer_mode=server_args.tokenizer_mode,
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trust_remote_code=server_args.trust_remote_code,
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)
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self.tokenizer = self.processor.tokenizer
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else:
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self.tokenizer = get_tokenizer(
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server_args.tokenizer_path,
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tokenizer_mode=server_args.tokenizer_mode,
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trust_remote_code=server_args.trust_remote_code,
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)
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self.max_total_num_tokens = self.model_runner.max_total_num_tokens
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self.max_prefill_tokens = server_args.max_prefill_tokens
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self.max_running_requests = min(
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(
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self.max_total_num_tokens // 2
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if server_args.max_running_requests is None
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else server_args.max_running_requests
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),
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self.model_runner.req_to_token_pool.size - 1,
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)
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self.max_req_input_len = min(
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self.model_config.context_len - 1,
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self.max_total_num_tokens - 1,
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)
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# Sync random seed
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server_args.random_seed = broadcast_recv_input(
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[server_args.random_seed],
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self.tp_rank,
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self.model_runner.tp_group.cpu_group,
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)[0]
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set_random_seed(server_args.random_seed)
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# Print info
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logger.info(
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f"[gpu={self.gpu_id}] "
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f"max_total_num_tokens={self.max_total_num_tokens}, "
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f"max_prefill_tokens={self.max_prefill_tokens}, "
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f"max_running_requests={self.max_running_requests}, "
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f"context_len={self.model_config.context_len}"
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)
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# Init cache
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if (
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server_args.chunked_prefill_size is not None
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and server_args.disable_radix_cache
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):
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self.tree_cache = ChunkCache(
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req_to_token_pool=self.model_runner.req_to_token_pool,
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token_to_kv_pool=self.model_runner.token_to_kv_pool,
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)
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else:
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self.tree_cache = RadixCache(
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req_to_token_pool=self.model_runner.req_to_token_pool,
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token_to_kv_pool=self.model_runner.token_to_kv_pool,
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disable=server_args.disable_radix_cache,
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)
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self.tree_cache_metrics = {"total": 0, "hit": 0}
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self.scheduler = PolicyScheduler(self.schedule_policy, self.tree_cache)
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self.req_to_token_pool = self.model_runner.req_to_token_pool
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self.token_to_kv_pool = self.model_runner.token_to_kv_pool
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# Init running status
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self.waiting_queue: List[Req] = []
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self.running_batch: ScheduleBatch = None
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self.out_pyobjs = []
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self.decode_forward_ct = 0
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self.stream_interval = server_args.stream_interval
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self.num_generated_tokens = 0
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self.last_stats_tic = time.time()
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# Chunked prefill
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self.chunked_prefill_size = server_args.chunked_prefill_size
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self.current_inflight_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 FSM cache for constrained generation
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if not server_args.skip_tokenizer_init:
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self.regex_fsm_cache = FSMCache(
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server_args.tokenizer_path,
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{
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"tokenizer_mode": server_args.tokenizer_mode,
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"trust_remote_code": server_args.trust_remote_code,
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},
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skip_tokenizer_init=server_args.skip_tokenizer_init,
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)
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self.jump_forward_cache = JumpForwardCache()
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# Init new token estimation
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assert (
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server_args.schedule_conservativeness >= 0
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), "Invalid schedule_conservativeness"
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self.min_new_token_ratio = min(
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global_config.base_min_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.new_token_ratio = self.min_new_token_ratio
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self.new_token_ratio_decay = global_config.new_token_ratio_decay
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def exposed_step(self, recv_reqs: List):
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try:
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# Recv requests
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for recv_req in recv_reqs:
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if isinstance(
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recv_req, (TokenizedGenerateReqInput, TokenizedEmbeddingReqInput)
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):
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self.handle_generate_request(recv_req)
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elif isinstance(recv_req, FlushCacheReq):
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self.flush_cache()
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elif isinstance(recv_req, AbortReq):
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self.abort_request(recv_req)
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elif isinstance(recv_req, UpdateWeightReqInput):
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success, message = self.update_weights(recv_req)
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self.out_pyobjs.append(UpdateWeightReqOutput(success, message))
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else:
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raise ValueError(f"Invalid request: {recv_req}")
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# Forward
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self.forward_step()
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except Exception:
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logger.error("Exception in ModelTpServer:\n" + get_exception_traceback())
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raise
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# Return results
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ret = self.out_pyobjs
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self.out_pyobjs = []
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return ret
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@torch.inference_mode()
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def forward_step(self):
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new_batch = self.get_new_prefill_batch()
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if new_batch is not None:
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# Run a new prefill batch
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self.forward_prefill_batch(new_batch)
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if not new_batch.is_empty():
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if self.running_batch is None:
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self.running_batch = new_batch
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else:
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self.running_batch.merge(new_batch)
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else:
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# Run a decode batch
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if self.running_batch is not None:
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# Run a few decode batches continuously for reducing overhead
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for _ in range(global_config.num_continue_decode_steps):
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self.num_generated_tokens += len(self.running_batch.reqs)
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self.forward_decode_batch(self.running_batch)
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# Print stats
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if self.tp_rank == 0 and self.decode_forward_ct % 40 == 0:
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self.print_decode_stats()
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if self.running_batch.is_empty():
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self.running_batch = None
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break
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if self.out_pyobjs and self.running_batch.has_stream():
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break
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else:
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self.check_memory()
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self.new_token_ratio = global_config.init_new_token_ratio
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def print_decode_stats(self):
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num_used = self.max_total_num_tokens - (
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self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
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)
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throughput = self.num_generated_tokens / (time.time() - self.last_stats_tic)
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self.num_generated_tokens = 0
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self.last_stats_tic = time.time()
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logger.info(
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f"[gpu={self.gpu_id}] Decode batch. "
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f"#running-req: {len(self.running_batch.reqs)}, "
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f"#token: {num_used}, "
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f"token usage: {num_used / self.max_total_num_tokens:.2f}, "
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f"gen throughput (token/s): {throughput:.2f}, "
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f"#queue-req: {len(self.waiting_queue)}"
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)
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def check_memory(self):
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available_size = (
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self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size()
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)
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if available_size != self.max_total_num_tokens:
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warnings.warn(
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"Warning: "
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f"available_size={available_size}, max_total_num_tokens={self.max_total_num_tokens}\n"
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"KV cache pool leak detected!"
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)
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exit(1) if crash_on_warning else None
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if len(self.req_to_token_pool.free_slots) != self.req_to_token_pool.size:
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warnings.warn(
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"Warning: "
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f"available req slots={len(self.req_to_token_pool.free_slots)}, "
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f"total slots={self.req_to_token_pool.size}\n"
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"Memory pool leak detected!"
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)
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exit(1) if crash_on_warning else None
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def handle_generate_request(
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self,
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recv_req: Union[TokenizedGenerateReqInput, TokenizedEmbeddingReqInput],
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):
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req = Req(recv_req.rid, recv_req.input_text, recv_req.input_ids)
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req.tokenizer = self.tokenizer
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req.sampling_params = recv_req.sampling_params
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if self.model_runner.is_generation:
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req.pixel_values = recv_req.pixel_values
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if req.pixel_values is not None:
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image_hash = (
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hash(tuple(recv_req.image_hash))
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if isinstance(recv_req.image_hash, list)
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else recv_req.image_hash
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)
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req.pad_value = [
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(image_hash) % self.model_config.vocab_size,
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(image_hash >> 16) % self.model_config.vocab_size,
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(image_hash >> 32) % self.model_config.vocab_size,
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(image_hash >> 64) % self.model_config.vocab_size,
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]
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req.image_size = recv_req.image_size
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(
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req.origin_input_ids,
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req.image_offset,
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) = self.model_runner.model.pad_input_ids(
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req.origin_input_ids_unpadded,
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req.pad_value,
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req.pixel_values.shape,
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req.image_size,
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)
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req.return_logprob = recv_req.return_logprob
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req.logprob_start_len = recv_req.logprob_start_len
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req.top_logprobs_num = recv_req.top_logprobs_num
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req.stream = recv_req.stream
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# Init regex fsm
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if req.sampling_params.regex is not None:
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req.regex_fsm = self.regex_fsm_cache.query(req.sampling_params.regex)
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if not self.disable_regex_jump_forward:
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req.jump_forward_map = self.jump_forward_cache.query(
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req.sampling_params.regex
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)
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# Truncate prompts that are too long
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if len(req.origin_input_ids) >= self.max_req_input_len:
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logger.warn(
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"Request length is longer than the KV cache pool size or "
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"the max context length. Truncated!!!"
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)
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req.origin_input_ids = req.origin_input_ids[: self.max_req_input_len]
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if self.model_runner.is_generation:
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req.sampling_params.max_new_tokens = min(
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(
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req.sampling_params.max_new_tokens
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if req.sampling_params.max_new_tokens is not None
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else 1 << 30
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),
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self.max_req_input_len - 1 - len(req.origin_input_ids),
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)
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self.waiting_queue.append(req)
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def get_new_prefill_batch(self) -> Optional[ScheduleBatch]:
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running_bs = (
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len(self.running_batch.reqs) if self.running_batch is not None else 0
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)
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if running_bs >= self.max_running_requests:
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return None
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# Get priority queue
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prefix_computed = self.scheduler.calc_priority(self.waiting_queue)
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num_mixed_running = running_bs if self.is_mixed_chunk else 0
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adder = PrefillAdder(
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self.tree_cache,
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self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size(),
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self.max_prefill_tokens,
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self.chunked_prefill_size,
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num_mixed_running,
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)
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if self.running_batch is not None:
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adder.remove_running_tokens(self.running_batch, self.new_token_ratio)
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has_inflight = self.current_inflight_req is not None
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if self.current_inflight_req is not None:
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self.current_inflight_req.init_next_round_input(
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None if prefix_computed else self.tree_cache
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)
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self.current_inflight_req = adder.add_inflight_req(
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self.current_inflight_req
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)
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for req in self.waiting_queue:
|
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req.init_next_round_input(None if prefix_computed else self.tree_cache)
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res = adder.add_one_req(req)
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if (
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not res
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or adder.no_remaining_tokens()
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or running_bs + len(adder.can_run_list) >= self.max_running_requests
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):
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break
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|
can_run_list = adder.can_run_list
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|
|
|
if adder.new_inflight_req is not None:
|
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assert self.current_inflight_req is None
|
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self.current_inflight_req = adder.new_inflight_req
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|
|
|
if len(can_run_list) == 0:
|
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return None
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|
|
# Print stats
|
|
if self.tp_rank == 0:
|
|
if isinstance(self.tree_cache, RadixCache):
|
|
self.tree_cache_metrics["total"] += (
|
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adder.log_input_tokens + adder.log_hit_tokens
|
|
) / 10**9
|
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self.tree_cache_metrics["hit"] += (adder.log_hit_tokens) / 10**9
|
|
tree_cache_hit_rate = (
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|
self.tree_cache_metrics["hit"] / self.tree_cache_metrics["total"]
|
|
)
|
|
else:
|
|
tree_cache_hit_rate = 0.0
|
|
|
|
if num_mixed_running > 0:
|
|
logger.info(
|
|
f"[gpu={self.gpu_id}] Prefill batch"
|
|
f"(mixed #running-req: {num_mixed_running}). "
|
|
f"#new-seq: {len(can_run_list)}, "
|
|
f"#new-token: {adder.log_input_tokens}, "
|
|
f"#cached-token: {adder.log_hit_tokens}, "
|
|
f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
|
|
f"#queue-req: {len(self.waiting_queue) - len(can_run_list) + has_inflight}"
|
|
)
|
|
else:
|
|
logger.info(
|
|
f"[gpu={self.gpu_id}] Prefill batch. "
|
|
f"#new-seq: {len(can_run_list)}, "
|
|
f"#new-token: {adder.log_input_tokens}, "
|
|
f"#cached-token: {adder.log_hit_tokens}, "
|
|
f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
|
|
f"#running-req: {running_bs}, "
|
|
f"#queue-req: {len(self.waiting_queue) - len(can_run_list) + has_inflight}"
|
|
)
|
|
|
|
# Return the new batch
|
|
new_batch = ScheduleBatch.init_new(
|
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can_run_list,
|
|
self.req_to_token_pool,
|
|
self.token_to_kv_pool,
|
|
self.tree_cache,
|
|
)
|
|
self.waiting_queue = [x for x in self.waiting_queue if x not in can_run_list]
|
|
return new_batch
|
|
|
|
def forward_prefill_batch(self, batch: ScheduleBatch):
|
|
# Build batch tensors
|
|
batch.prepare_for_extend(self.model_config.vocab_size)
|
|
|
|
decoding_reqs = []
|
|
if self.is_mixed_chunk and self.running_batch is not None:
|
|
self.running_batch.prepare_for_decode()
|
|
batch.mix_with_running(self.running_batch)
|
|
decoding_reqs = self.running_batch.reqs
|
|
self.running_batch = None
|
|
|
|
if self.model_runner.is_generation:
|
|
# Forward and sample the next tokens
|
|
if batch.extend_num_tokens != 0:
|
|
output = self.model_runner.forward(batch, ForwardMode.EXTEND)
|
|
next_token_ids = batch.sample(output.next_token_logits)
|
|
batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
|
|
next_token_ids
|
|
)
|
|
|
|
# Move logprobs to cpu
|
|
if output.next_token_logprobs is not None:
|
|
output.next_token_logprobs = output.next_token_logprobs[
|
|
torch.arange(len(next_token_ids), device=next_token_ids.device),
|
|
next_token_ids,
|
|
].tolist()
|
|
output.input_token_logprobs = output.input_token_logprobs.tolist()
|
|
output.normalized_prompt_logprobs = (
|
|
output.normalized_prompt_logprobs.tolist()
|
|
)
|
|
|
|
next_token_ids = next_token_ids.tolist()
|
|
else:
|
|
if self.tokenizer is None:
|
|
next_token_ids = []
|
|
for req in batch.reqs:
|
|
next_token_ids.append(
|
|
next(iter(req.sampling_params.stop_token_ids))
|
|
)
|
|
else:
|
|
next_token_ids = [self.tokenizer.eos_token_id] * len(batch.reqs)
|
|
|
|
# Check finish conditions
|
|
pt = 0
|
|
for i, req in enumerate(batch.reqs):
|
|
if req is not self.current_inflight_req:
|
|
# Inflight reqs' prefill is not finished
|
|
req.completion_tokens_wo_jump_forward += 1
|
|
req.output_ids.append(next_token_ids[i])
|
|
req.check_finished()
|
|
|
|
if req.regex_fsm is not None:
|
|
req.regex_fsm_state = req.regex_fsm.get_next_state(
|
|
req.regex_fsm_state, next_token_ids[i]
|
|
)
|
|
|
|
if req.finished():
|
|
self.tree_cache.cache_finished_req(req)
|
|
elif req not in decoding_reqs:
|
|
# To reduce overhead, only cache prefill reqs
|
|
self.tree_cache.cache_unfinished_req(req)
|
|
|
|
if req is self.current_inflight_req:
|
|
# Inflight request would get a new req idx
|
|
self.req_to_token_pool.free(req.req_pool_idx)
|
|
|
|
if req.return_logprob:
|
|
self.add_logprob_return_values(i, req, pt, next_token_ids, output)
|
|
pt += req.extend_input_len
|
|
else:
|
|
assert batch.extend_num_tokens != 0
|
|
output = self.model_runner.forward(batch, ForwardMode.EXTEND)
|
|
embeddings = output.embeddings.tolist()
|
|
|
|
# Check finish conditions
|
|
for i, req in enumerate(batch.reqs):
|
|
req.embedding = embeddings[i]
|
|
if req is not self.current_inflight_req:
|
|
# Inflight reqs' prefill is not finished
|
|
# dummy output token for embedding models
|
|
req.output_ids.append(0)
|
|
req.check_finished()
|
|
|
|
if req.finished():
|
|
self.tree_cache.cache_finished_req(req)
|
|
else:
|
|
self.tree_cache.cache_unfinished_req(req)
|
|
|
|
if req is self.current_inflight_req:
|
|
# Inflight request would get a new req idx
|
|
self.req_to_token_pool.free(req.req_pool_idx)
|
|
|
|
self.handle_finished_requests(batch)
|
|
|
|
def add_logprob_return_values(
|
|
self,
|
|
i,
|
|
req: Req,
|
|
pt: int,
|
|
next_token_ids: List[int],
|
|
output: LogitProcessorOutput,
|
|
):
|
|
if req.normalized_prompt_logprob is None:
|
|
req.normalized_prompt_logprob = output.normalized_prompt_logprobs[i]
|
|
|
|
if req.input_token_logprobs is None:
|
|
# If logprob_start_len > 0, then first logprob_start_len prompt tokens will be ignored.
|
|
req.input_token_logprobs = list(
|
|
zip(
|
|
output.input_token_logprobs[pt : pt + req.extend_input_len - 1],
|
|
req.fill_ids[-req.extend_input_len + 1 :],
|
|
)
|
|
)
|
|
if req.logprob_start_len == 0:
|
|
req.input_token_logprobs = [
|
|
(None, req.fill_ids[0])
|
|
] + req.input_token_logprobs
|
|
|
|
if req.last_update_decode_tokens != 0:
|
|
req.output_token_logprobs.extend(
|
|
list(
|
|
zip(
|
|
output.input_token_logprobs[
|
|
pt
|
|
+ req.extend_input_len
|
|
- req.last_update_decode_tokens : pt
|
|
+ req.extend_input_len
|
|
- 1
|
|
],
|
|
req.fill_ids[-req.last_update_decode_tokens + 1 :],
|
|
)
|
|
)
|
|
)
|
|
|
|
req.output_token_logprobs.append(
|
|
(output.next_token_logprobs[i], next_token_ids[i])
|
|
)
|
|
|
|
if req.top_logprobs_num > 0:
|
|
if req.input_top_logprobs is None:
|
|
req.input_top_logprobs = output.input_top_logprobs[i]
|
|
if req.logprob_start_len == 0:
|
|
req.input_top_logprobs = [None] + req.input_top_logprobs
|
|
|
|
if req.last_update_decode_tokens != 0:
|
|
req.output_top_logprobs.extend(
|
|
output.input_top_logprobs[i][-req.last_update_decode_tokens + 1 :]
|
|
)
|
|
req.output_top_logprobs.append(output.output_top_logprobs[i])
|
|
|
|
def forward_decode_batch(self, batch: ScheduleBatch):
|
|
# Check if decode out of memory
|
|
if not batch.check_decode_mem():
|
|
old_ratio = self.new_token_ratio
|
|
|
|
retracted_reqs, new_token_ratio = batch.retract_decode()
|
|
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.waiting_queue.extend(retracted_reqs)
|
|
else:
|
|
self.new_token_ratio = max(
|
|
self.new_token_ratio - self.new_token_ratio_decay,
|
|
self.min_new_token_ratio,
|
|
)
|
|
|
|
if not self.disable_regex_jump_forward:
|
|
# Check for jump-forward
|
|
jump_forward_reqs = batch.check_for_jump_forward(self.model_runner)
|
|
self.waiting_queue.extend(jump_forward_reqs)
|
|
if batch.is_empty():
|
|
return
|
|
|
|
# Update batch tensors
|
|
self.decode_forward_ct = (self.decode_forward_ct + 1) % (1 << 30)
|
|
batch.prepare_for_decode()
|
|
|
|
# Forward and sample the next tokens
|
|
output = self.model_runner.forward(batch, ForwardMode.DECODE)
|
|
next_token_ids = batch.sample(output.next_token_logits)
|
|
batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
|
|
next_token_ids
|
|
)
|
|
|
|
# Move logprobs to cpu
|
|
if output.next_token_logprobs is not None:
|
|
next_token_logprobs = output.next_token_logprobs[
|
|
torch.arange(len(next_token_ids), device=next_token_ids.device),
|
|
next_token_ids,
|
|
].tolist()
|
|
|
|
next_token_ids = next_token_ids.tolist()
|
|
|
|
# Check finish condition
|
|
for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):
|
|
req.completion_tokens_wo_jump_forward += 1
|
|
req.output_ids.append(next_token_id)
|
|
req.check_finished()
|
|
|
|
if req.regex_fsm is not None:
|
|
req.regex_fsm_state = req.regex_fsm.get_next_state(
|
|
req.regex_fsm_state, next_token_id
|
|
)
|
|
|
|
if req.finished():
|
|
self.tree_cache.cache_finished_req(req)
|
|
|
|
if req.return_logprob:
|
|
req.output_token_logprobs.append(
|
|
(next_token_logprobs[i], next_token_id)
|
|
)
|
|
if req.top_logprobs_num > 0:
|
|
req.output_top_logprobs.append(output.output_top_logprobs[i])
|
|
|
|
self.handle_finished_requests(batch)
|
|
|
|
def handle_finished_requests(self, batch: ScheduleBatch):
|
|
output_rids = []
|
|
output_meta_info = []
|
|
output_finished_reason: List[BaseFinishReason] = []
|
|
if self.model_runner.is_generation:
|
|
output_vids = []
|
|
decoded_texts = []
|
|
output_read_ids = []
|
|
output_read_offsets = []
|
|
output_skip_special_tokens = []
|
|
output_spaces_between_special_tokens = []
|
|
else: # for embedding model
|
|
output_embeddings = []
|
|
unfinished_indices = []
|
|
|
|
for i, req in enumerate(batch.reqs):
|
|
if not req.finished() and req is not self.current_inflight_req:
|
|
unfinished_indices.append(i)
|
|
|
|
if req.finished() or (
|
|
(
|
|
req.stream
|
|
and (
|
|
self.decode_forward_ct % self.stream_interval == 0
|
|
or len(req.output_ids) == 1
|
|
)
|
|
)
|
|
):
|
|
output_rids.append(req.rid)
|
|
output_finished_reason.append(req.finished_reason)
|
|
if self.model_runner.is_generation:
|
|
output_vids.append(req.vid)
|
|
decoded_texts.append(req.decoded_text)
|
|
read_ids, read_offset = req.init_incremental_detokenize()
|
|
output_read_ids.append(read_ids)
|
|
output_read_offsets.append(read_offset)
|
|
output_skip_special_tokens.append(
|
|
req.sampling_params.skip_special_tokens
|
|
)
|
|
output_spaces_between_special_tokens.append(
|
|
req.sampling_params.spaces_between_special_tokens
|
|
)
|
|
|
|
meta_info = {
|
|
"prompt_tokens": len(req.origin_input_ids),
|
|
"completion_tokens": len(req.output_ids),
|
|
"completion_tokens_wo_jump_forward": req.completion_tokens_wo_jump_forward,
|
|
"finish_reason": str(req.finished_reason),
|
|
}
|
|
if req.return_logprob:
|
|
(
|
|
meta_info["input_token_logprobs"],
|
|
meta_info["output_token_logprobs"],
|
|
meta_info["input_top_logprobs"],
|
|
meta_info["output_top_logprobs"],
|
|
meta_info["normalized_prompt_logprob"],
|
|
) = (
|
|
req.input_token_logprobs,
|
|
req.output_token_logprobs,
|
|
req.input_top_logprobs,
|
|
req.output_top_logprobs,
|
|
req.normalized_prompt_logprob,
|
|
)
|
|
output_meta_info.append(meta_info)
|
|
else: # for embedding model
|
|
output_embeddings.append(req.embedding)
|
|
meta_info = {
|
|
"prompt_tokens": len(req.origin_input_ids),
|
|
}
|
|
output_meta_info.append(meta_info)
|
|
|
|
# Send to detokenizer
|
|
if output_rids:
|
|
if self.model_runner.is_generation:
|
|
self.out_pyobjs.append(
|
|
BatchTokenIDOut(
|
|
output_rids,
|
|
output_vids,
|
|
decoded_texts,
|
|
output_read_ids,
|
|
output_read_offsets,
|
|
output_skip_special_tokens,
|
|
output_spaces_between_special_tokens,
|
|
output_meta_info,
|
|
output_finished_reason,
|
|
)
|
|
)
|
|
else: # for embedding model
|
|
self.out_pyobjs.append(
|
|
BatchEmbeddingOut(
|
|
output_rids,
|
|
output_embeddings,
|
|
output_meta_info,
|
|
output_finished_reason,
|
|
)
|
|
)
|
|
|
|
# Remove finished reqs: update batch tensors
|
|
batch.filter_batch(unfinished_indices)
|
|
|
|
def flush_cache(self):
|
|
if len(self.waiting_queue) == 0 and (
|
|
self.running_batch is None or len(self.running_batch.reqs) == 0
|
|
):
|
|
self.tree_cache.reset()
|
|
self.tree_cache_metrics = {"total": 0, "hit": 0}
|
|
self.regex_fsm_cache.reset()
|
|
self.req_to_token_pool.clear()
|
|
self.token_to_kv_pool.clear()
|
|
torch.cuda.empty_cache()
|
|
logger.info("Cache flushed successfully!")
|
|
if_success = True
|
|
else:
|
|
logging.warning(
|
|
f"Cache not flushed because there are pending requests. "
|
|
f"#queue-req: {len(self.waiting_queue)}, "
|
|
f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
|
|
)
|
|
if_success = False
|
|
return if_success
|
|
|
|
def abort_request(self, recv_req):
|
|
# Delete requests in the waiting queue
|
|
to_del = None
|
|
for i, req in enumerate(self.waiting_queue):
|
|
if req.rid == recv_req.rid:
|
|
to_del = i
|
|
break
|
|
|
|
if to_del is not None:
|
|
del self.waiting_queue[to_del]
|
|
|
|
# Delete requests in the running batch
|
|
if self.running_batch:
|
|
for req in self.running_batch.reqs:
|
|
if req.rid == recv_req.rid:
|
|
req.finished_reason = FINISH_ABORT()
|
|
break
|
|
|
|
def update_weights(self, recv_req):
|
|
success, message = self.model_runner.update_weights(
|
|
recv_req.model_path, recv_req.load_format
|
|
)
|
|
if success:
|
|
flash_cache_success = self.flush_cache()
|
|
assert flash_cache_success, "Cache flush failed after updating weights"
|
|
return success, message
|
|
|
|
|
|
def run_tp_server(
|
|
gpu_id: int,
|
|
tp_rank: int,
|
|
server_args: ServerArgs,
|
|
nccl_port: int,
|
|
model_overide_args: dict,
|
|
):
|
|
"""Run a tensor parallel server."""
|
|
try:
|
|
model_server = ModelTpServer(
|
|
gpu_id,
|
|
tp_rank,
|
|
server_args,
|
|
nccl_port,
|
|
model_overide_args,
|
|
)
|
|
tp_cpu_group = model_server.model_runner.tp_group.cpu_group
|
|
|
|
while True:
|
|
recv_reqs = broadcast_recv_input(None, tp_rank, tp_cpu_group)
|
|
model_server.exposed_step(recv_reqs)
|
|
except Exception:
|
|
logger.error("Exception in run_tp_server:\n" + get_exception_traceback())
|
|
raise
|
|
|
|
|
|
def launch_tp_servers(
|
|
gpu_ids: List[int],
|
|
tp_rank_range: List[int],
|
|
server_args: ServerArgs,
|
|
nccl_port: int,
|
|
model_overide_args: dict,
|
|
):
|
|
"""Launch multiple tensor parallel servers."""
|
|
procs = []
|
|
for i in tp_rank_range:
|
|
proc = multiprocessing.Process(
|
|
target=run_tp_server,
|
|
args=(gpu_ids[i], i, server_args, nccl_port, model_overide_args),
|
|
)
|
|
proc.start()
|
|
procs.append(proc)
|
|
|
|
return procs
|
|
|
|
|
|
def broadcast_recv_input(
|
|
data: Any, rank: int, dist_group: torch.distributed.ProcessGroup
|
|
):
|
|
"""Broadcast inputs from rank=0 to all other ranks with torch.dist backend."""
|
|
|
|
if rank == 0:
|
|
if len(data) == 0:
|
|
tensor_size = torch.tensor([0], dtype=torch.long)
|
|
dist.broadcast(tensor_size, src=0, group=dist_group)
|
|
else:
|
|
serialized_data = pickle.dumps(data)
|
|
size = len(serialized_data)
|
|
tensor_data = torch.ByteTensor(list(serialized_data))
|
|
tensor_size = torch.tensor([size], dtype=torch.long)
|
|
|
|
dist.broadcast(tensor_size, src=0, group=dist_group)
|
|
dist.broadcast(tensor_data, src=0, group=dist_group)
|
|
return data
|
|
else:
|
|
tensor_size = torch.tensor([0], dtype=torch.long)
|
|
dist.broadcast(tensor_size, src=0, group=dist_group)
|
|
size = tensor_size.item()
|
|
|
|
if size == 0:
|
|
return []
|
|
|
|
tensor_data = torch.empty(size, dtype=torch.uint8)
|
|
dist.broadcast(tensor_data, src=0, group=dist_group)
|
|
|
|
serialized_data = bytes(tensor_data.tolist())
|
|
data = pickle.loads(serialized_data)
|
|
return data
|