mirror of https://github.com/hpcaitech/ColossalAI
297 lines
11 KiB
Python
297 lines
11 KiB
Python
# Adapted from https://github.com/ModelTC/lightllm
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import time
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from typing import List
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from .dynamic_batching.get_tokenizer import get_tokenizer
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from .dynamic_batching.infer_batch import InferBatch
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from .dynamic_batching.io_struct import Batch, Req
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from .dynamic_batching.req_queue import ReqQueue
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from .dynamic_batching.sampling_params import SamplingParams
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from .dynamic_batching.stats import Stats
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from .tensor_parallel import TPInferEngine
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class DynamicBatchManager:
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def __init__(
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self,
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tp_engine: TPInferEngine,
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max_total_token_num,
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batch_max_tokens,
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model,
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tokenizer=None,
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eos_id=None,
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log_stats=True,
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log_stats_interval=10,
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running_batch: Batch = None,
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waiting_req_list: List = [],
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):
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"""
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Args: tp_engine : The tp engine that dynamic batch manager hold, defined before dynamic batch manager
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max_total_token_num : max_total_token_num for memory manager, default to: max batch size * (max input len + max output len)
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batch_max_tokens : max tokens of one batch, default to (max input + output len) * num_requests
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running_max_req_size : max request size of running batch, equals to MAX_BATCH_SIZE of tp engine
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eos_id : The end token of a seq
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model: the model weight dir path, the app will load config, weights and tokenizer from this dir
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log_stats : whether to log stats
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log_stats_interval : log stats interval
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running_batch : running batch
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waiting_req_list : list of waiting requests, initialized before dynamic batch manager
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"""
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self.engine = tp_engine
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self.max_total_token_num = max_total_token_num
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running_max_req_size = self.engine.max_batch_size if self.engine is not None else 2
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self.req_queue = ReqQueue(max_total_token_num, batch_max_tokens, running_max_req_size, waiting_req_list)
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# all the inputs should be put into req_queue: waiting req list
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assert max_total_token_num >= self.engine.max_batch_size * (
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self.engine.max_input_len + self.engine.max_output_len
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), "max_total_token_num should be greater than max_batch_size * (max_input_len+max_output_len)"
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assert (
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batch_max_tokens >= self.engine.max_input_len + self.engine.max_output_len
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), "batch_max_tokens should be greater than (max_input_len+max_output_len)"
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self.running_batch: Batch = running_batch
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self.eos_id = eos_id
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self.has_wait_tokens = 0
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self.max_wait_tokens = 10
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self.model = model
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self.stats_tool = Stats(log_stats, log_stats_interval)
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self.mem_usage_interval = log_stats_interval * 2
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self.tokenizer = get_tokenizer(tokenizer_name=self.model) if tokenizer is None else tokenizer
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if self.eos_id == None:
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self.eos_id = self.tokenizer.eos_token_id
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def add_req(self, request_id: str, prompt_ids: List[int], sampling_params: SamplingParams, prompts: str = ""):
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"""
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Add new request to req queue, during initialization all requests are held in waiting list.
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"""
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sampling_params.max_new_tokens = (
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self.engine.max_output_len
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if sampling_params.max_new_tokens > self.engine.max_output_len
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else sampling_params.max_new_tokens
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)
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req = Req(request_id, prompt_ids, sampling_params, prompts)
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self.req_queue.append(req)
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return
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def add_input(self, request_id, prompts, sampling_params):
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"""
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Encode and Add new input to req queue. support one sequence input for now.
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"""
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prompt_ids = self.tokenizer.encode(prompts)
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prompt_len = len(prompt_ids)
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if prompt_len > self.engine.max_input_len:
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raise ValueError(f"the input prompt token len {prompt_len} is too long > {self.engine.max_input_len}")
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sampling_params.stop_sentences_to_token_ids(self.tokenizer)
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self.add_req(request_id, prompt_ids, sampling_params, prompts)
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return
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def abort(self, request_id):
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if self.running_batch is not None:
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for req in self.running_batch.reqs:
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if req.request_id == request_id:
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req.has_generate_finished = True
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req.aborted = True
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for req in self.req_queue.waiting_req_list:
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if req.request_id == request_id:
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req.has_generate_finished = True
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req.aborted = True
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return
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def loop_for_fwd(self):
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"""
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The main loop for a dynamic batching process.
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"""
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counter_count = 0
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# self.running_batch is not None or self.req_queue.waiting_req_list
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while self.running_batch is not None or self.req_queue.waiting_req_list:
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yield from self._step()
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counter_count += 1
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if self.running_batch is not None:
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if counter_count % self.mem_usage_interval == 0:
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print(
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"current batch size:",
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len(self.running_batch.reqs),
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"token used ratio:",
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self.running_batch.calcu_used_tokens() / self.max_total_token_num,
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)
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self.stats_tool.print_stats()
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if self.running_batch is None:
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time.sleep(0.1) # 10ms
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def _step(self):
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"""
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Logic for handling requests
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"""
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if self.running_batch is None:
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new_batch = self.req_queue.generate_new_batch(self.running_batch)
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if new_batch is not None:
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self.stats_tool.count_prompt_tokens(new_batch)
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self.running_batch = new_batch
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yield from self._prefill_batch(self.running_batch)
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self._filter_runing_batch()
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self.has_wait_tokens = 0
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return
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if self.has_wait_tokens < self.max_wait_tokens:
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self.stats_tool.count_output_tokens(self.running_batch)
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yield from self._decode_batch(self.running_batch)
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self._filter_runing_batch()
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self.has_wait_tokens += 1
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return
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else:
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new_mini_batch = self.req_queue.generate_new_batch(self.running_batch)
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if new_mini_batch is not None:
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self.stats_tool.count_prompt_tokens(new_mini_batch)
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yield from self._prefill_batch(new_mini_batch)
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if not new_mini_batch.is_clear():
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self._merge_batch(self.running_batch, new_mini_batch)
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self.running_batch.merge(new_mini_batch)
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self.has_wait_tokens = 0
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else:
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self.stats_tool.count_output_tokens(self.running_batch)
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yield from self._decode_batch(self.running_batch)
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self._filter_runing_batch()
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self.has_wait_tokens += 1
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return
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def _init_batch(self, batch: Batch, dtype="fp16"):
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reqs = [r.to_rpc_obj() for r in batch.reqs]
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batch_id = batch.batch_id
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import torch
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if dtype == "fp16":
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dtype = torch.float16
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else:
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assert False, "error dtype"
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batch_data = InferBatch.init_batch(
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batch_id,
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reqs,
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dtype,
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torch.cuda.current_device(),
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self.engine.cache_manager,
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self.engine.model.config.vocab_size,
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self.engine.max_input_len + self.engine.max_output_len,
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)
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self.engine.cache[batch_id] = batch_data
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def _prefill_batch(self, batch):
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"""
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For all batches, no matter it is a new batch or a mini batch, we need to do prefill first.
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"""
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self._init_batch(batch)
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# TODO: figure out if cache and batch id is needed
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ans = self.engine._prefill_batch(batch.batch_id)
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req_to_out_token_id = ans
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self._add_token_id_to_req(batch, req_to_out_token_id)
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has_new_finished_req = batch.mark_finished_req(self.eos_id, self.engine.max_output_len)
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yield from self._handle_finish_req(batch, has_new_finished_req)
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# delete finished reqs
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def _decode_batch(self, batch: Batch):
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"""
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Decoding process
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"""
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ans = self.engine._decode_batch(batch.batch_id)
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req_to_out_token_id = ans
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self._add_token_id_to_req(batch, req_to_out_token_id)
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has_new_finished_req = batch.mark_finished_req(self.eos_id, self.engine.max_output_len)
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yield from self._handle_finish_req(batch, has_new_finished_req)
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def _filter_batch(self, batch: Batch):
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batch_id = batch.batch_id
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req_id_list = [r.request_id for r in batch.reqs]
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batch = self.engine.cache.pop(batch_id)
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filter_batch = batch.filter(req_id_list)
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del batch
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self.engine.cache[batch_id] = filter_batch
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def _merge_batch(self, batch1, batch2):
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"""
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Merge new mini batch into running batch.
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"""
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batch1 = self.engine.cache.pop(batch1.batch_id)
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batch2 = self.engine.cache.pop(batch2.batch_id)
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m_batch = InferBatch.merge(batch1, batch2)
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self.engine.cache[batch1.batch_id] = m_batch
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del batch1
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del batch2
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def _remove_batch(self, batch):
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"""
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Remove finished batch.
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"""
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batch = self.engine.cache.pop(batch.batch_id)
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batch.free_self()
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del batch
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def _handle_finish_req(self, batch: Batch, has_new_finished_req):
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if has_new_finished_req:
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finished_reqs = batch.filter_finished()
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if batch.is_clear():
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self._remove_batch(batch)
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else:
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self._filter_batch(batch)
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yield from self._output_process(finished_reqs)
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def _filter_runing_batch(self):
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if self.running_batch is not None and self.running_batch.is_clear():
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self.running_batch = None
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def _add_token_id_to_req(self, batch: Batch, req_ans):
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for req_id, (new_token_id, new_gen_metadata) in req_ans.items():
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req = batch.id_to_reqs[req_id]
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req.output_ids.append(new_token_id)
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req.output_metadata_list.append(new_gen_metadata)
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return
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def _output_process(self, finished_reqs: List[Req]):
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"""
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Process the output of a batch.
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"""
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for req in finished_reqs:
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output = self.tokenizer.decode(req.output_ids)
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yield req.prompts + output
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def clean_up(self):
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# this logic should be implemented in the future.
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pass
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def generate(self, request_id, prompts, sampling_params):
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"""
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Generate the output of a request.
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"""
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self.add_input(request_id, prompts, sampling_params)
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return self.loop_for_fwd()
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def is_running(self):
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return self.running_batch is not None or self.req_queue.waiting_req_list
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def start_dynamic_batching(args, tp_engine, waiting_req_list):
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try:
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batch_manager = DynamicBatchManager(
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tp_engine=tp_engine,
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max_total_token_num=args.max_total_token_num,
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batch_max_tokens=args.batch_max_tokens,
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eos_id=args.eos_id,
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model=args.model,
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log_stats=not args.disable_log_stats,
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log_stats_interval=args.log_stats_interval,
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waiting_req_list=waiting_req_list,
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)
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except Exception:
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raise Exception
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return batch_manager
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