799 lines
31 KiB
Python
799 lines
31 KiB
Python
"""A tensor parallel worker."""
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import asyncio
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import logging
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import time
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import warnings
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from concurrent.futures import ThreadPoolExecutor
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from typing import List, Optional
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import rpyc
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import torch
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from rpyc.utils.classic import obtain
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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.managers.controller.infer_batch import (
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FINISH_ABORT,
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BaseFinishReason,
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Batch,
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ForwardMode,
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Req,
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)
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from sglang.srt.managers.controller.model_runner import ModelRunner
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from sglang.srt.managers.controller.radix_cache import RadixCache
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from sglang.srt.managers.controller.schedule_heuristic import ScheduleHeuristic
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from sglang.srt.managers.io_struct import (
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AbortReq,
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BatchTokenIDOut,
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FlushCacheReq,
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TokenizedGenerateReqInput,
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)
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from sglang.srt.model_config import ModelConfig
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from sglang.srt.server_args import ModelPortArgs, ServerArgs
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from sglang.srt.utils import (
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get_int_token_logit_bias,
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is_multimodal_model,
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set_random_seed,
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start_rpyc_service_process,
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connect_rpyc_service,
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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("srt.tp_worker")
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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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model_port_args: ModelPortArgs,
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model_overide_args,
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):
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server_args, model_port_args = obtain(server_args), obtain(model_port_args)
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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_heuristic = server_args.schedule_heuristic
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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=model_port_args.nccl_port,
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server_args=server_args,
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)
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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 = (
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max(
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self.model_config.context_len,
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min(self.max_total_num_tokens // 6, 32768),
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)
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if server_args.max_prefill_tokens is None
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else server_args.max_prefill_tokens
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)
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self.max_running_requests = (
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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.int_token_logit_bias = torch.tensor(
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get_int_token_logit_bias(self.tokenizer, self.model_config.vocab_size)
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)
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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_id={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"context_len={self.model_config.context_len}"
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)
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if self.tp_rank == 0:
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logger.info(
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f"[gpu_id={self.gpu_id}] "
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f"server_args: {server_args.print_mode_args()}"
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)
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# Init cache
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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 = ScheduleHeuristic(
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self.schedule_heuristic,
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self.max_running_requests,
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self.max_prefill_tokens,
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self.max_total_num_tokens,
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self.tree_cache,
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)
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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.forward_queue: List[Req] = []
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self.running_batch: Batch = 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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# Init the FSM cache for constrained generation
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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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)
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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.new_token_ratio = min(
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global_config.base_new_token_ratio * server_args.schedule_conservativeness,
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1.0,
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)
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self.min_new_token_ratio = min(
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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_decay = global_config.new_token_ratio_decay
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self.new_token_ratio_recovery = global_config.new_token_ratio_recovery
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def exposed_step(self, recv_reqs):
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if self.tp_size * self.dp_size != 1:
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recv_reqs = obtain(recv_reqs)
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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(recv_req, TokenizedGenerateReqInput):
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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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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_fill_batch()
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if new_batch is not None:
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# Run a new fill batch
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self.forward_fill_batch(new_batch)
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self.cache_filled_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 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(10):
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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:
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if self.decode_forward_ct % 40 == 0:
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num_used = self.max_total_num_tokens - (
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self.token_to_kv_pool.available_size()
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+ self.tree_cache.evictable_size()
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)
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throughput = self.num_generated_tokens / (
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time.time() - self.last_stats_tic
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)
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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_id={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.forward_queue)}"
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)
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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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# Check the available size
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available_size = (
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self.token_to_kv_pool.available_size()
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+ 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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def handle_generate_request(
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self,
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recv_req: TokenizedGenerateReqInput,
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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.pixel_values = recv_req.pixel_values
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if req.pixel_values is not None:
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req.pad_value = [
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(recv_req.image_hash) % self.model_config.vocab_size,
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(recv_req.image_hash >> 16) % self.model_config.vocab_size,
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(recv_req.image_hash >> 32) % self.model_config.vocab_size,
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(recv_req.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.sampling_params = recv_req.sampling_params
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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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req.tokenizer = self.tokenizer
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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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req.origin_input_ids = req.origin_input_ids[: self.model_config.context_len - 1]
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req.sampling_params.max_new_tokens = min(
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req.sampling_params.max_new_tokens,
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self.model_config.context_len - 1 - len(req.origin_input_ids),
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self.max_total_num_tokens - 128 - len(req.origin_input_ids),
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)
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self.forward_queue.append(req)
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def get_new_fill_batch(self) -> Optional[Batch]:
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if (
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self.running_batch is not None
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and len(self.running_batch.reqs) > self.max_running_requests
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):
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return None
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# Compute matched prefix length
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for req in self.forward_queue:
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req.input_ids = req.origin_input_ids + req.output_ids
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prefix_indices, last_node = self.tree_cache.match_prefix(req.input_ids)
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if req.return_logprob:
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prefix_indices = prefix_indices[: req.logprob_start_len]
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req.extend_input_len = len(req.input_ids) - len(prefix_indices)
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req.prefix_indices = prefix_indices
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req.last_node = last_node
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# Get priority queue
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self.forward_queue = self.scheduler.get_priority_queue(self.forward_queue)
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# Add requests if there is available space
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can_run_list = []
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new_batch_total_tokens = 0
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new_batch_input_tokens = 0
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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 self.running_batch:
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available_size -= sum(
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[
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(r.max_new_tokens() - len(r.output_ids)) * self.new_token_ratio
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for r in self.running_batch.reqs
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]
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)
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for req in self.forward_queue:
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if req.return_logprob and req.normalized_prompt_logprob is None:
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# Need at least two tokens to compute normalized logprob
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if req.extend_input_len < 2:
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delta = 2 - req.extend_input_len
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req.extend_input_len += delta
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req.prefix_indices = req.prefix_indices[:-delta]
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if req.image_offset is not None:
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req.image_offset += delta
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if req.extend_input_len == 0 and req.max_new_tokens() > 0:
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# Need at least one token to compute logits
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req.extend_input_len = 1
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req.prefix_indices = req.prefix_indices[:-1]
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if req.image_offset is not None:
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req.image_offset += 1
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if (
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req.extend_input_len + req.max_new_tokens() + new_batch_total_tokens
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< available_size
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and req.extend_input_len + new_batch_input_tokens
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< self.max_prefill_tokens
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):
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delta = self.tree_cache.inc_lock_ref(req.last_node)
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available_size += delta
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if not (
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req.extend_input_len + req.max_new_tokens() + new_batch_total_tokens
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< available_size
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):
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# Undo locking
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delta = self.tree_cache.dec_lock_ref(req.last_node)
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available_size += delta
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break
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else:
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# Add this request to the running batch
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can_run_list.append(req)
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new_batch_total_tokens += (
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req.extend_input_len + req.max_new_tokens()
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)
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new_batch_input_tokens += req.extend_input_len
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else:
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break
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if len(can_run_list) == 0:
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return None
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|
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# Print stats
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if self.tp_rank == 0:
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running_req = (
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0 if self.running_batch is None else len(self.running_batch.reqs)
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)
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hit_tokens = sum(len(x.prefix_indices) for x in can_run_list)
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self.tree_cache_metrics["total"] += (
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hit_tokens + new_batch_input_tokens
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) / 10**9
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self.tree_cache_metrics["hit"] += hit_tokens / 10**9
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tree_cache_hit_rate = (
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self.tree_cache_metrics["hit"] / self.tree_cache_metrics["total"]
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)
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logger.info(
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f"[gpu_id={self.gpu_id}] Prefill batch. "
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f"#new-seq: {len(can_run_list)}, "
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f"#new-token: {new_batch_input_tokens}, "
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f"#cached-token: {hit_tokens}, "
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f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, "
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f"#running-req: {running_req}, "
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f"#queue-req: {len(self.forward_queue) - len(can_run_list)}"
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)
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# logger.debug(
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# f"fsm_cache_hit_rate: {100.0 * self.regex_fsm_cache.get_cache_hit_rate():.2f}%. "
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# f"fsm_cache_avg_init_time: {self.regex_fsm_cache.get_avg_init_time():.2f}s. "
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# f"ff_cache_hit_rate: {100.0 * self.jump_forward_cache.get_cache_hit_rate():.2f}%. "
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# f"ff_cache_avg_init_time: {self.jump_forward_cache.get_avg_init_time():.2f}s. "
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# )
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|
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# Return the new batch
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new_batch = Batch.init_new(
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can_run_list,
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self.req_to_token_pool,
|
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self.token_to_kv_pool,
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self.tree_cache,
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)
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self.forward_queue = [x for x in self.forward_queue if x not in can_run_list]
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return new_batch
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|
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def forward_fill_batch(self, batch: Batch):
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# Build batch tensors
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batch.prepare_for_extend(
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self.model_config.vocab_size, self.int_token_logit_bias
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)
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|
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# Forward and sample the next tokens
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if batch.extend_num_tokens != 0:
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output = self.model_runner.forward(batch, ForwardMode.EXTEND)
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next_token_ids, _ = batch.sample(output.next_token_logits)
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|
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# Move logprobs to cpu
|
|
if output.next_token_logprobs is not None:
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output.next_token_logprobs = output.next_token_logprobs[
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torch.arange(len(next_token_ids), device=next_token_ids.device),
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next_token_ids,
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].tolist()
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output.prefill_token_logprobs = output.prefill_token_logprobs.tolist()
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|
output.normalized_prompt_logprobs = output.normalized_prompt_logprobs.tolist()
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next_token_ids = next_token_ids.tolist()
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else:
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next_token_ids = [self.tokenizer.eos_token_id] * len(batch.reqs)
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|
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# Check finish conditions
|
|
pt = 0
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for i, req in enumerate(batch.reqs):
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req.completion_tokens_wo_jump_forward += 1
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req.output_ids.append(next_token_ids[i])
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req.check_finished()
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|
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if req.return_logprob:
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self.add_logprob_return_values(i, req, pt, next_token_ids, output)
|
|
pt += req.extend_input_len
|
|
|
|
self.handle_finished_requests(batch)
|
|
|
|
def add_logprob_return_values(self, i, req, pt, next_token_ids, output):
|
|
if req.normalized_prompt_logprob is None:
|
|
req.normalized_prompt_logprob = output.normalized_prompt_logprobs[i]
|
|
|
|
if req.prefill_token_logprobs is None:
|
|
# If logprob_start_len > 0, then first logprob_start_len prompt tokens will be ignored.
|
|
req.prefill_token_logprobs = list(
|
|
zip(
|
|
output.prefill_token_logprobs[pt : pt + req.extend_input_len - 1],
|
|
req.input_ids[-req.extend_input_len + 1 :],
|
|
)
|
|
)
|
|
if req.logprob_start_len == 0:
|
|
req.prefill_token_logprobs = [
|
|
(None, req.input_ids[0])
|
|
] + req.prefill_token_logprobs
|
|
|
|
if req.last_update_decode_tokens != 0:
|
|
req.decode_token_logprobs.extend(
|
|
list(
|
|
zip(
|
|
output.prefill_token_logprobs[
|
|
pt
|
|
+ req.extend_input_len
|
|
- req.last_update_decode_tokens : pt
|
|
+ req.extend_input_len
|
|
- 1
|
|
],
|
|
req.input_ids[-req.last_update_decode_tokens + 1 :],
|
|
)
|
|
)
|
|
)
|
|
|
|
req.decode_token_logprobs.append(
|
|
(output.next_token_logprobs[i], next_token_ids[i])
|
|
)
|
|
|
|
if req.top_logprobs_num > 0:
|
|
if req.prefill_top_logprobs is None:
|
|
req.prefill_top_logprobs = output.prefill_top_logprobs[i]
|
|
if req.logprob_start_len == 0:
|
|
req.prefill_top_logprobs = [None] + req.prefill_top_logprobs
|
|
|
|
if req.last_update_decode_tokens != 0:
|
|
req.decode_top_logprobs.extend(
|
|
output.prefill_top_logprobs[i][-req.last_update_decode_tokens + 1 :]
|
|
)
|
|
req.decode_top_logprobs.append(output.decode_top_logprobs[i])
|
|
|
|
def cache_filled_batch(self, batch: Batch):
|
|
req_pool_indices_cpu = batch.req_pool_indices.cpu().numpy()
|
|
for i, req in enumerate(batch.reqs):
|
|
new_prefix_indices, new_last_node = self.tree_cache.cache_req(
|
|
token_ids=tuple(req.origin_input_ids + req.output_ids)[:-1],
|
|
last_uncached_pos=len(req.prefix_indices),
|
|
req_pool_idx=req_pool_indices_cpu[i],
|
|
del_in_memory_pool=False,
|
|
old_last_node=req.last_node,
|
|
)
|
|
req.prefix_indices, req.last_node = new_prefix_indices, new_last_node
|
|
|
|
def forward_decode_batch(self, batch: Batch):
|
|
# Check if decode out of memory
|
|
if not batch.check_decode_mem():
|
|
old_ratio = self.new_token_ratio
|
|
self.new_token_ratio = min(old_ratio + self.new_token_ratio_recovery, 1.0)
|
|
|
|
retracted_reqs = batch.retract_decode()
|
|
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.forward_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.forward_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)
|
|
|
|
# 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.return_logprob:
|
|
req.decode_token_logprobs.append((next_token_logprobs[i], next_token_id))
|
|
if req.top_logprobs_num > 0:
|
|
req.decode_top_logprobs.append(output.decode_top_logprobs[i])
|
|
|
|
self.handle_finished_requests(batch)
|
|
|
|
def handle_finished_requests(self, batch: Batch):
|
|
output_rids = []
|
|
decoded_texts = []
|
|
surr_output_ids = []
|
|
read_output_ids = []
|
|
output_skip_special_tokens = []
|
|
output_spaces_between_special_tokens = []
|
|
output_meta_info = []
|
|
output_finished_reason: List[BaseFinishReason] = []
|
|
finished_indices = []
|
|
unfinished_indices = []
|
|
for i, req in enumerate(batch.reqs):
|
|
if req.finished():
|
|
finished_indices.append(i)
|
|
else:
|
|
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)
|
|
decoded_texts.append(req.decoded_text)
|
|
surr_ids, read_ids, _ = req.init_detokenize_incrementally()
|
|
surr_output_ids.append(surr_ids)
|
|
read_output_ids.append(read_ids)
|
|
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["prefill_token_logprobs"],
|
|
meta_info["decode_token_logprobs"],
|
|
meta_info["prefill_top_logprobs"],
|
|
meta_info["decode_top_logprobs"],
|
|
meta_info["normalized_prompt_logprob"],
|
|
) = (
|
|
req.prefill_token_logprobs,
|
|
req.decode_token_logprobs,
|
|
req.prefill_top_logprobs,
|
|
req.decode_top_logprobs,
|
|
req.normalized_prompt_logprob,
|
|
)
|
|
output_meta_info.append(meta_info)
|
|
output_finished_reason.append(req.finished_reason)
|
|
|
|
# Send to detokenizer
|
|
if output_rids:
|
|
self.out_pyobjs.append(
|
|
BatchTokenIDOut(
|
|
output_rids,
|
|
decoded_texts,
|
|
surr_output_ids,
|
|
read_output_ids,
|
|
output_skip_special_tokens,
|
|
output_spaces_between_special_tokens,
|
|
output_meta_info,
|
|
output_finished_reason,
|
|
)
|
|
)
|
|
|
|
# Remove finished reqs
|
|
if finished_indices:
|
|
# Update radix cache
|
|
req_pool_indices_cpu = batch.req_pool_indices.tolist()
|
|
for i in finished_indices:
|
|
req = batch.reqs[i]
|
|
self.tree_cache.cache_req(
|
|
token_ids=tuple(req.origin_input_ids + req.output_ids)[:-1],
|
|
last_uncached_pos=len(req.prefix_indices),
|
|
req_pool_idx=req_pool_indices_cpu[i],
|
|
)
|
|
|
|
self.tree_cache.dec_lock_ref(req.last_node)
|
|
|
|
# Update batch tensors
|
|
if unfinished_indices:
|
|
batch.filter_batch(unfinished_indices)
|
|
else:
|
|
batch.reqs = []
|
|
|
|
def flush_cache(self):
|
|
if len(self.forward_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!")
|
|
else:
|
|
warnings.warn(
|
|
f"Cache not flushed because there are pending requests. "
|
|
f"#queue-req: {len(self.forward_queue)}, "
|
|
f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
|
|
)
|
|
|
|
def abort_request(self, recv_req):
|
|
# Delete requests in the waiting queue
|
|
to_del = None
|
|
for i, req in enumerate(self.forward_queue):
|
|
if req.rid == recv_req.rid:
|
|
to_del = i
|
|
break
|
|
|
|
if to_del is not None:
|
|
del self.forward_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
|
|
|
|
|
|
class ModelTpService(rpyc.Service):
|
|
exposed_ModelTpServer = ModelTpServer
|
|
|
|
|
|
class ModelTpClient:
|
|
def __init__(
|
|
self,
|
|
gpu_ids: List[int],
|
|
server_args: ServerArgs,
|
|
model_port_args: ModelPortArgs,
|
|
model_overide_args,
|
|
):
|
|
server_args, model_port_args = obtain(server_args), obtain(model_port_args)
|
|
self.tp_size = server_args.tp_size
|
|
|
|
if self.tp_size * server_args.dp_size == 1:
|
|
# Init model
|
|
assert len(gpu_ids) == 1
|
|
self.model_server = ModelTpService().exposed_ModelTpServer(
|
|
0,
|
|
gpu_ids[0],
|
|
server_args,
|
|
model_port_args,
|
|
model_overide_args,
|
|
)
|
|
|
|
# Wrap functions
|
|
def async_wrap(f):
|
|
async def _func(*args, **kwargs):
|
|
return f(*args, **kwargs)
|
|
|
|
return _func
|
|
|
|
self.step = async_wrap(self.model_server.exposed_step)
|
|
else:
|
|
with ThreadPoolExecutor(self.tp_size) as executor:
|
|
# Launch model processes
|
|
if server_args.nnodes == 1:
|
|
self.procs = list(executor.map(
|
|
lambda args: start_rpyc_service_process(*args),
|
|
[(ModelTpService, p) for p in model_port_args.model_tp_ports],
|
|
))
|
|
addrs = [("localhost", p) for p in model_port_args.model_tp_ports]
|
|
else:
|
|
addrs = [(ip, port) for ip, port in zip(model_port_args.model_tp_ips, model_port_args.model_tp_ports)]
|
|
|
|
self.model_services = list(executor.map(
|
|
lambda args: connect_rpyc_service(*args), addrs))
|
|
|
|
# Init model
|
|
def init_model(i):
|
|
return self.model_services[i].ModelTpServer(
|
|
gpu_ids[i],
|
|
i,
|
|
server_args,
|
|
model_port_args,
|
|
model_overide_args,
|
|
)
|
|
|
|
self.model_servers = list(executor.map(init_model, range(self.tp_size)))
|
|
|
|
# Wrap functions
|
|
def async_wrap(func_name):
|
|
fs = [rpyc.async_(getattr(m, func_name)) for m in self.model_servers]
|
|
|
|
async def _func(*args, **kwargs):
|
|
tasks = [f(*args, **kwargs) for f in fs]
|
|
await asyncio.gather(*[asyncio.to_thread(t.wait) for t in tasks])
|
|
return obtain(tasks[0].value)
|
|
|
|
return _func
|
|
|
|
self.step = async_wrap("step")
|