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85a78d695d
* Support FD block scheduler v1 * Support FD block scheduler v1 * Support FD block scheduler v1 * Fix according to copilot review * Fix according to review * Remove is_dummy * Fix bug when real_bsz=1 * Fix infer first token cost time --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
262 lines
13 KiB
Python
262 lines
13 KiB
Python
import threading
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import time
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from collections import deque
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from collections.abc import Iterable
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from concurrent.futures import ThreadPoolExecutor
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from dataclasses import dataclass
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from typing import Union
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from fastdeploy.engine.request import Request, RequestStatus, RequestType
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from fastdeploy.engine.resource_manager import ResourceManager
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from fastdeploy.utils import llm_logger
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@dataclass
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class ScheduledDecodeTask:
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"""
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Task for allocating new blocks to decode.
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"""
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idx: int
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request_id: str
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block_tables: list[int]
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task_type: RequestType = RequestType.DECODE
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@dataclass
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class ScheduledPreemptTask:
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"""
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Task for terminating inference to recycle resource.
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"""
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idx: int
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request_id: str
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task_type: RequestType = RequestType.PREEMPTED
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class ResourceManagerV1(ResourceManager):
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"""
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Resource manager for scheduler v1.
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In scheduler v1, all gpu blocks are managed by PrefixCacheManager.
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Tasks sent to worker are divided into 3 types, PREFILL、DECODE and PREEMPTED.
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For prefill task, the worker infer with one step and then stopped for this query if not all prompt tokens are computed.
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For decode task, the work continues to decode until allocated blocks are exhausted.
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For preempted task, the work reset all inputs to terminate the inference.
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"""
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def __init__(self, max_num_seqs, config, tensor_parallel_size, splitwise_role, local_data_parallel_id=0):
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super(ResourceManagerV1, self).__init__(
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max_num_seqs, config, tensor_parallel_size, splitwise_role, local_data_parallel_id
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)
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# req_id -> Request
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self.config = config
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self.requests: dict[str, Request] = {}
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# Priority queues for requests.
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self.waiting: deque[Request] = deque()
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self.running: list[Request] = []
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self.finish_execution_pool = ThreadPoolExecutor(max_workers=1)
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self.lock = threading.Lock()
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def allocated_slots(self, request: Request):
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return len(request.block_tables) * self.config.cache_config.block_size
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def get_new_block_nums(self, request: Request, num_new_tokens: int):
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return (
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request.num_computed_tokens + num_new_tokens + self.config.cache_config.block_size - 1
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) // self.config.cache_config.block_size - len(request.block_tables)
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def _prepare_prefill_task(self, request, new_token_num):
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request.prefill_start_index = request.num_computed_tokens
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request.prefill_end_index = request.num_computed_tokens + new_token_num
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request.task_type = RequestType.PREFILL
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return request
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def _prepare_decode_task(self, request):
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return ScheduledDecodeTask(idx=request.idx, request_id=request.request_id, block_tables=request.block_tables)
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def _prepare_preempt_task(self, request):
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return ScheduledPreemptTask(idx=request.idx, request_id=request.request_id)
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def _trigger_preempt(self, request, num_new_blocks, preempted_reqs, scheduled_reqs):
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can_schedule = True
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while True:
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if not self.cache_manager.can_allocate_gpu_blocks(num_new_blocks):
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preempted_req = self.running.pop()
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preempted_req.status = RequestStatus.PREEMPTED
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preempted_req.num_computed_tokens = 0
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self._free_blocks(preempted_req)
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self.waiting.appendleft(preempted_req)
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preempted_reqs.append(preempted_req)
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scheduled_reqs.append(self._prepare_preempt_task(preempted_req))
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if preempted_req == request:
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# No more request to preempt.
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can_schedule = False
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break
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else:
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# The request can be scheduled.
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can_schedule = True
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break
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return can_schedule
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def schedule(self):
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with self.lock:
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scheduled_reqs: list[Request] = []
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preempted_reqs: list[Request] = []
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token_budget = self.config.max_num_batched_tokens
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# First, schedule the RUNNING requests.
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req_index = 0
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num_decoding_req_nums = 0
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while req_index < len(self.running) and token_budget > 0:
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request = self.running[req_index]
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if request.num_computed_tokens >= request.prompt_token_ids_len: # to be decoding
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if request.num_total_tokens > request.prompt_token_ids_len: # has generated tokens
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request.num_computed_tokens = request.num_total_tokens - 1
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if (
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self.allocated_slots(request) - request.num_total_tokens
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<= self.config.cache_config.prealloc_dec_block_slot_num_threshold
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):
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# Allocation for next decoding blocks
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if self.cache_manager.can_allocate_gpu_blocks(self.config.cache_config.enc_dec_block_num):
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llm_logger.debug(
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f"schedule decoding task: {request} request.num_total_tokens {request.num_total_tokens} request.num_computed_tokens {request.num_computed_tokens}"
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)
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request.block_tables.extend(
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self.cache_manager.allocate_gpu_blocks(self.config.cache_config.enc_dec_block_num)
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)
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# Prepare decoding task
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scheduled_reqs.append(self._prepare_decode_task(request))
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else:
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# Not enough blocks to allocate, trigger preemption
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can_schedule = self._trigger_preempt(
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request, self.config.cache_config.enc_dec_block_num, preempted_reqs, scheduled_reqs
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)
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if not can_schedule:
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break
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# Allocation for next decoding blocks
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request.block_tables.extend(
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self.cache_manager.allocate_gpu_blocks(self.config.cache_config.enc_dec_block_num)
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)
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# Prepare decoding task
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scheduled_reqs.append(self._prepare_decode_task(request))
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num_decoding_req_nums += 1
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token_budget -= 1
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else: # need to prefill
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llm_logger.debug(
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f"scheduler prefill task: {request} request.prompt_token_ids_len {request.prompt_token_ids_len} request.num_computed_tokens {request.num_computed_tokens}"
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)
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num_new_tokens = request.prompt_token_ids_len - request.num_computed_tokens
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num_new_tokens = min(num_new_tokens, token_budget)
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num_new_block = self.get_new_block_nums(request, num_new_tokens)
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# Allocate blocks to prefill
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if self.cache_manager.can_allocate_gpu_blocks(num_new_block):
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request.block_tables.extend(self.cache_manager.allocate_gpu_blocks(num_new_block))
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# Prepare prefill task
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scheduled_reqs.append(self._prepare_prefill_task(request, num_new_tokens))
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else:
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can_schedule = self._trigger_preempt(request, num_new_block, preempted_reqs, scheduled_reqs)
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if not can_schedule:
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break
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request.block_tables.extend(self.cache_manager.allocate_gpu_blocks(num_new_block))
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# Prepare prefill task
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scheduled_reqs.append(self._prepare_prefill_task(request, num_new_tokens))
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token_budget -= num_new_tokens
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request.num_computed_tokens += num_new_tokens
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req_index += 1
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# schedule the WAITING requests.
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if not preempted_reqs:
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while self.waiting and token_budget > 0:
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if len(self.running) == self.max_num_seqs:
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break
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request = self.waiting[0]
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if request.status == RequestStatus.WAITING:
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num_new_tokens = request.num_total_tokens - request.num_computed_tokens
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num_new_tokens = min(num_new_tokens, token_budget)
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num_new_block = self.get_new_block_nums(request, num_new_tokens)
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# Allocate blocks to prefill
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if self.cache_manager.can_allocate_gpu_blocks(num_new_block):
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request.block_tables.extend(self.cache_manager.allocate_gpu_blocks(num_new_block))
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self.waiting.popleft()
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self.running.append(request)
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scheduled_reqs.append(self._prepare_prefill_task(request, num_new_tokens))
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request.inference_start_time = time.time()
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request.schedule_start_time = time.time()
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token_budget -= num_new_tokens
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request.num_computed_tokens += num_new_tokens
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request.status = RequestStatus.RUNNING
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allocated_position = self.get_available_position()
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request.idx = allocated_position
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self.tasks_list[allocated_position] = request
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self.stop_flags[allocated_position] = False
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self.req_dict[request.request_id] = allocated_position
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else:
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break
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elif request.status == RequestStatus.PREEMPTED:
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num_new_tokens = request.num_total_tokens - request.num_computed_tokens
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num_new_tokens = min(num_new_tokens, token_budget)
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num_new_block = self.get_new_block_nums(request, num_new_tokens)
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# Allocate blocks to prefill
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if self.cache_manager.can_allocate_gpu_blocks(num_new_block):
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request.block_tables.extend(self.cache_manager.allocate_gpu_blocks(num_new_block))
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self.waiting.popleft()
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self.running.append(request)
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scheduled_reqs.append(self._prepare_prefill_task(request, num_new_tokens))
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token_budget -= num_new_tokens
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request.num_computed_tokens += num_new_tokens
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request.status = RequestStatus.RUNNING
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else:
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break
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else:
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llm_logger.error("Unknown request status type")
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if scheduled_reqs:
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llm_logger.debug(f"schedued_reqs: {scheduled_reqs}")
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return scheduled_reqs
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def get_available_position(self) -> int:
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position = 0
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while position < self.max_num_seqs:
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if self.stop_flags[position] is True:
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return position
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position += 1
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raise RuntimeError("No available position is available for new request")
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def get_real_bsz(self) -> int:
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for i in range(self.max_num_seqs - 1, -1, -1):
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if not self.stop_flags[i]:
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self.real_bsz = i + 1
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break
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return self.real_bsz
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def add_request(self, request: Request) -> None:
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self.waiting.append(request)
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self.requests[request.request_id] = request
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def _free_blocks(self, request: Request):
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self.cache_manager.recycle_gpu_blocks(request.block_tables)
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request.block_tables = []
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def finish_requests_async(self, request_ids: Union[str, Iterable[str]]):
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return self.finish_execution_pool.submit(self.finish_requests, request_ids)
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def finish_requests(self, request_ids: Union[str, Iterable[str]]):
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llm_logger.info(f"recycle resources for requests: {request_ids}")
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try:
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with self.lock:
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if isinstance(request_ids, str):
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request_ids = (request_ids,)
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else:
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request_ids = set(request_ids)
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for req_id in request_ids:
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request = self.requests.get(req_id)
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if request is None:
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# Invalid request ID.
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continue
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request.status = RequestStatus.FINISHED
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self.running.remove(request)
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self._free_blocks(request)
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self.tasks_list[request.idx] = None
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self.stop_flags[request.idx] = True
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del self.requests[req_id]
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except Exception as e:
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llm_logger.error(e)
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