612 lines
23 KiB
Python
612 lines
23 KiB
Python
import asyncio
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import logging
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import multiprocessing
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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
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import numpy as np
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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 rpyc.utils.server import ThreadedServer
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from sglang.srt.constrained.fast_forward import FastForwardCache
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from sglang.srt.constrained.fsm_cache import FSMCache
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from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
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from sglang.srt.managers.io_struct import (
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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.managers.router.infer_batch import Batch, ForwardMode, Req
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from sglang.srt.managers.router.model_runner import ModelRunner
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from sglang.srt.managers.router.radix_cache import RadixCache
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from sglang.srt.managers.router.scheduler import Scheduler
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from sglang.srt.model_config import ModelConfig
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from sglang.srt.server_args import PortArgs, ServerArgs
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from sglang.srt.utils import (
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get_exception_traceback,
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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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)
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logger = logging.getLogger("model_rpc")
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class ModelRpcServer(rpyc.Service):
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def exposed_init_model(
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self,
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tp_rank: int,
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server_args: ServerArgs,
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port_args: PortArgs,
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):
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server_args, port_args = [obtain(x) for x in [server_args, port_args]]
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# Copy arguments
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self.model_mode = server_args.model_mode
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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.schedule_heuristic = server_args.schedule_heuristic
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self.no_regex_fast_forward = server_args.no_regex_fast_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, server_args.trust_remote_code
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)
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self.model_runner = ModelRunner(
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self.model_config,
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server_args.mem_fraction_static,
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tp_rank,
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server_args.tp_size,
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port_args.nccl_port,
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server_args.load_format,
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server_args.trust_remote_code,
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server_args.model_mode,
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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.eos_token_id = self.tokenizer.eos_token_id
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self.max_total_num_token = self.model_runner.max_total_num_token
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self.max_num_running_seq = self.max_total_num_token // 2
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self.max_prefill_num_token = max(
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self.model_config.context_len, self.max_total_num_token // 6
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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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logger.info(
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f"Rank {self.tp_rank}: "
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f"max_total_num_token={self.max_total_num_token}, "
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f"max_prefill_num_token={self.max_prefill_num_token}, "
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f"context_len={self.model_config.context_len}, "
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f"model_mode={self.model_mode}"
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)
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# Init cache
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self.tree_cache = RadixCache(disable="no-cache" in self.model_mode)
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self.tree_cache_metrics = {"total": 0, "hit": 0}
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self.scheduler = Scheduler(
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self.schedule_heuristic,
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self.max_num_running_seq,
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self.max_prefill_num_token,
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self.max_total_num_token,
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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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# 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.fast_forward_cache = FastForwardCache()
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# Init new token estimation
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self.new_token_ratio = min(0.4 * server_args.schedule_conservativeness, 1.0)
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self.min_new_token_ratio = min(0.2 * server_args.schedule_conservativeness, 1.0)
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self.new_token_ratio_step = (0.0001, 0.05) # (down, up)
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def flush_cache(self):
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if len(self.forward_queue) == 0 and (
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self.running_batch is None or len(self.running_batch.reqs) == 0
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):
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self.tree_cache.reset()
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self.tree_cache_metrics = {"total": 0, "hit": 0}
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self.regex_fsm_cache.reset()
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self.req_to_token_pool.clear()
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self.token_to_kv_pool.clear()
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torch.cuda.empty_cache()
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logger.info("Cache flushed successfully!")
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else:
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warnings.warn(
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"Cache not flushed because there are pending requests. "
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f"#queue-req: {len(self.forward_queue)}, "
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f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}"
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)
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def exposed_step(self, recv_reqs):
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if self.tp_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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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 ModelRpcClient:\n" + get_exception_traceback())
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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 new fill batch
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self.forward_fill_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.forward_decode_batch(self.running_batch)
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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.reqs[0].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_token:
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warnings.warn(
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"Warning: "
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f"available_size={available_size}, max_total_num_token={self.max_total_num_token}\n"
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"KV cache pool leak detected!"
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)
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if self.running_batch is not None and self.tp_rank == 0:
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if self.decode_forward_ct % 20 == 0:
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num_used = self.max_total_num_token - (
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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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logger.info(
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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_token:.2f}, "
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f"#queue-req: {len(self.forward_queue)}"
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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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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.input_ids, req.image_offset = self.model_runner.model.pad_input_ids(
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req.input_ids, pad_value, req.pixel_values.shape, req.image_size
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)
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req.image_size = recv_req.image_size
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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.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.no_regex_fast_forward:
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req.fast_forward_map = self.fast_forward_cache.query(
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req.sampling_params.regex
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)
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# Truncate long prompts
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req.input_ids = req.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.input_ids),
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self.max_total_num_token - 128 - len(req.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):
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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_num_running_seq
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):
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return None
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for req in self.forward_queue:
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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:
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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_num_token
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):
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delta = self.tree_cache.inc_ref_counter(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 the insertion
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delta = self.tree_cache.dec_ref_counter(req.last_node)
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available_size += delta
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else:
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# Add this request to the running batch
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self.token_to_kv_pool.add_refs(req.prefix_indices)
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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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if len(can_run_list) == 0:
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return None
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if self.tp_rank == 0:
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running_req = 0 if self.running_batch is None else len(self.running_batch.reqs)
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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"] += (hit_tokens + new_batch_input_tokens) / 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"new fill batch. #seq: {len(can_run_list)}. "
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f"#cached_token: {hit_tokens}. "
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f"#new_token: {new_batch_input_tokens}. "
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f"#remaining_req: {len(self.forward_queue) - len(can_run_list)}. "
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f"#running_req: {running_req}. "
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f"tree_cache_hit_rate: {100.0 * tree_cache_hit_rate:.2f}%."
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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.fast_forward_cache.get_cache_hit_rate():.2f}%. "
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f"ff_cache_avg_init_time: {self.fast_forward_cache.get_avg_init_time():.2f}s. "
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)
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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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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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if batch.extend_num_tokens != 0:
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# Forward
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logits, (logprobs, normalized_logprobs) = self.model_runner.forward(
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batch, ForwardMode.EXTEND, batch.return_logprob
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)
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# print("extend logits", logits)
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if logprobs is not None:
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logprobs = logprobs.cpu().tolist()
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normalized_logprobs = normalized_logprobs.cpu().tolist()
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next_token_ids, next_token_probs = batch.sample(logits)
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next_token_ids = next_token_ids.cpu().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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logprobs = normalized_logprobs = None
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# Check finish condition
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reqs = batch.reqs
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pt = 0
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for i, req in enumerate(reqs):
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req.output_ids = [next_token_ids[i]]
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req.check_finished()
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if logprobs is not None:
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req.logprob = logprobs[pt : pt + req.extend_input_len - 1]
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req.normalized_logprob = normalized_logprobs[i]
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pt += req.extend_input_len
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self.handle_finished_requests(batch)
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def forward_decode_batch(self, batch: Batch):
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# check if decode out of memory
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if not batch.check_decode_mem():
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old_ratio = self.new_token_ratio
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self.new_token_ratio = min(old_ratio + self.new_token_ratio_step[1], 1.0)
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retracted_reqs = batch.retract_decode()
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logger.info(
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"decode out of memory happened, "
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f"#retracted_reqs: {len(retracted_reqs)}, "
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f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}"
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)
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self.forward_queue.extend(retracted_reqs)
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else:
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self.new_token_ratio = max(
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self.new_token_ratio - self.new_token_ratio_step[0],
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self.min_new_token_ratio,
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)
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if not self.no_regex_fast_forward:
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# check for fast forward
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fast_forward_reqs = batch.check_for_fast_forward()
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self.forward_queue.extend(fast_forward_reqs)
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if batch.is_empty():
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return
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# Update batch tensors
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self.decode_forward_ct = (self.decode_forward_ct + 1) % (1 << 30)
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batch.prepare_for_decode()
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# Forward
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logits = self.model_runner.forward(batch, ForwardMode.DECODE)
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next_token_ids, next_token_probs = batch.sample(logits)
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next_token_ids = next_token_ids.cpu().tolist()
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# Check finish condition
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reqs = batch.reqs
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for i in range(len(reqs)):
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reqs[i].output_ids.append(next_token_ids[i])
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reqs[i].check_finished()
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self.handle_finished_requests(batch)
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def handle_finished_requests(self, batch: Batch):
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output_rids = []
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output_tokens = []
|
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output_and_fast_forward_strs = []
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output_hit_stop_str = []
|
|
output_skip_special_tokens = []
|
|
output_meta_info = []
|
|
output_finished = []
|
|
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)
|
|
output_tokens.append(req.output_ids)
|
|
output_and_fast_forward_strs.append(req.output_and_fast_forward_str)
|
|
output_hit_stop_str.append(req.hit_stop_str)
|
|
output_skip_special_tokens.append(
|
|
req.sampling_params.skip_special_tokens
|
|
)
|
|
meta_info = {
|
|
"prompt_tokens": len(req.input_ids),
|
|
"completion_tokens": len(req.output_ids),
|
|
}
|
|
if req.return_logprob:
|
|
meta_info["prompt_logprob"] = req.logprob
|
|
meta_info["normalized_prompt_logprob"] = req.normalized_logprob
|
|
output_meta_info.append(meta_info)
|
|
output_finished.append(req.finished)
|
|
|
|
# Send to detokenizer
|
|
if output_rids:
|
|
self.out_pyobjs.append(
|
|
BatchTokenIDOut(
|
|
output_rids,
|
|
output_tokens,
|
|
output_and_fast_forward_strs,
|
|
output_hit_stop_str,
|
|
output_skip_special_tokens,
|
|
output_meta_info,
|
|
output_finished,
|
|
)
|
|
)
|
|
|
|
# Remove finished reqs
|
|
if finished_indices:
|
|
# Update radix cache
|
|
req_pool_indices_cpu = batch.req_pool_indices.cpu().tolist()
|
|
for i in finished_indices:
|
|
req = batch.reqs[i]
|
|
req_pool_idx = req_pool_indices_cpu[i]
|
|
token_ids = tuple(req.input_ids + req.output_ids)
|
|
seq_len = len(token_ids) - 1
|
|
indices = self.req_to_token_pool.req_to_token[req_pool_idx, :seq_len]
|
|
prefix_len = self.tree_cache.insert(
|
|
token_ids[:seq_len], indices.clone()
|
|
)
|
|
|
|
self.token_to_kv_pool.free(indices[:prefix_len])
|
|
self.req_to_token_pool.free(req_pool_idx)
|
|
self.tree_cache.dec_ref_counter(req.last_node)
|
|
|
|
# Update batch tensors
|
|
if unfinished_indices:
|
|
batch.filter_batch(unfinished_indices)
|
|
else:
|
|
batch.reqs = []
|
|
|
|
|
|
class ModelRpcClient:
|
|
def __init__(self, server_args: ServerArgs, port_args: PortArgs):
|
|
tp_size = server_args.tp_size
|
|
|
|
if tp_size == 1:
|
|
# Init model
|
|
self.model_server = ModelRpcServer()
|
|
self.model_server.exposed_init_model(0, server_args, port_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(tp_size) as executor:
|
|
# Launch model processes
|
|
rets = executor.map(start_model_process, port_args.model_rpc_ports)
|
|
self.model_servers = [x[0] for x in rets]
|
|
self.procs = [x[1] for x in rets]
|
|
|
|
# Init model
|
|
def init_model(i):
|
|
return self.model_servers[i].init_model(i, server_args, port_args)
|
|
|
|
rets = [obtain(x) for x in executor.map(init_model, range(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")
|
|
|
|
|
|
def start_model_process(port):
|
|
def _init_service(port):
|
|
t = ThreadedServer(
|
|
ModelRpcServer(),
|
|
port=port,
|
|
protocol_config={"allow_pickle": True, "sync_request_timeout": 600},
|
|
)
|
|
t.start()
|
|
|
|
proc = multiprocessing.Process(target=_init_service, args=(port,))
|
|
proc.start()
|
|
time.sleep(1)
|
|
|
|
repeat_count = 0
|
|
while repeat_count < 20:
|
|
try:
|
|
con = rpyc.connect(
|
|
"localhost",
|
|
port,
|
|
config={"allow_pickle": True, "sync_request_timeout": 600},
|
|
)
|
|
break
|
|
except ConnectionRefusedError:
|
|
time.sleep(1)
|
|
repeat_count += 1
|
|
if repeat_count == 20:
|
|
raise RuntimeError("init rpc env error!")
|
|
|
|
assert proc.is_alive()
|
|
return con.root, proc
|