from typing import List import torch from transformers.configuration_utils import PretrainedConfig from colossalai.inference.config import InferenceConfig from colossalai.inference.flash_decoding_utils import FDIntermTensors from colossalai.inference.kv_cache import KVCacheManager from colossalai.inference.logit_processors import logit_processor from colossalai.inference.sampler import * from colossalai.inference.struct import BatchInfo, RequestStatus, Sequence from colossalai.logging import get_dist_logger __all__ = ["RunningList", "RequestHandler"] logger = get_dist_logger(__name__) class RunningList: """ RunningList is an structure for recording the running sequences, contains prefill and decoding list. Prefilling samples will be hold until the actual ratio of prefill samples versus decoding samples exceeds ratio. Args: prefill_ratio: (float) A ratio for determing whether to perform prefill or not. prefill: (List) List that contains default inputs, defaults to []. """ def __init__(self, prefill_ratio: str, prefill: List[Sequence] = None): self.prefill_ratio = prefill_ratio self.decoding: List[Sequence] = [] self.prefill: List[Sequence] = prefill if prefill is not None else [] def append(self, seq: Sequence): # add seq to prefilling list first. self.prefill.append(seq) def find_seq(self, request_id): for seq in self.decoding: if request_id == seq.request_id: return seq for seq in self.prefill: if request_id == seq.request_id: return seq return None def remove(self, seq: Sequence): if seq in self.decoding: self.decoding.remove(seq) elif seq in self.prefill: self.prefill.remove(seq) else: raise ValueError(f"sequence {seq.request_id} is not in running list") def ready_for_prefill(self): if not self.decoding: return len(self.prefill) > 0 return len(self.prefill) / len(self.decoding) >= self.prefill_ratio def is_empty(self): return not self.decoding and not self.prefill def total_seq_num(self): return len(self.decoding) + len(self.prefill) class RequestHandler: """ RequestHandler is the core for handling existing requests and updating current batch. During generation process, we call schedule function each iteration to update current batch. Args: inference_config: Configuration for initialize and manage kv cache. model_config: Configuration for model dtype (torch.dtype): The data type for weights and activations. """ def __init__(self, inference_config: InferenceConfig, model_config: PretrainedConfig) -> None: self.inference_config = inference_config self.running_list: RunningList = RunningList(inference_config.prefill_ratio) self.waiting_list: List[List] = [[], [], []] self.done_list: List[Sequence] = [] self.dtype = inference_config.dtype self.max_batch_size = inference_config.max_batch_size # initialize cache self._init_cache(model_config) # initialize batch device = torch.cuda.current_device() kv_max_split_num = ( inference_config.max_input_len + inference_config.max_output_len + inference_config.block_size - 1 ) // inference_config.block_size head_dim = model_config.hidden_size // model_config.num_attention_heads fd_inter_tensor = FDIntermTensors() fd_inter_tensor.initialize( max_batch_size=self.max_batch_size, num_attn_heads=model_config.num_attention_heads, kv_max_split_num=kv_max_split_num, head_dim=head_dim, dtype=self.dtype, device=device, ) # TODO In the continuous batching scenario, the batch size may be greater than max_batch_size, # which may cause bugs and this issue should be fixed later. self.running_batch = BatchInfo( max_batch_size=self.max_batch_size, kv_max_split_num=kv_max_split_num, num_heads=model_config.num_attention_heads, head_dim=head_dim, is_prompts=False, device=device, dtype=self.dtype, fd_inter_tensor=fd_inter_tensor, ) self.prefill_batch = BatchInfo( max_batch_size=self.max_batch_size, kv_max_split_num=kv_max_split_num, num_heads=model_config.num_attention_heads, head_dim=head_dim, is_prompts=True, device=device, dtype=self.dtype, fd_inter_tensor=fd_inter_tensor, ) def _init_cache(self, model_config): self.cache_manager = KVCacheManager(self.inference_config, model_config) def _has_waiting(self) -> bool: return any(lst for lst in self.waiting_list) def get_kvcache(self): return self.cache_manager.get_kv_cache() def schedule(self): """ The main logic of request handler. """ if self._has_waiting(): # Try to allocate cache blocks for the sequence using a priority of prompt length. for lst in reversed(self.waiting_list): if lst: remove_list = [] for seq in lst: if seq.input_len > self.inference_config.max_input_len: # If the prompt length is longer than max_input_len, abort the sequence. logger.warning( f"the prompt(Request id = {seq.request_id}) length is longer than max_input_len, abort this sequence." ) self.abort_sequence(seq.request_id) remove_list.append(seq) break # stop feeding new sequence into running list to assure if self.cache_manager.num_available_blocks <= self.running_list.total_seq_num(): break # Try to allocate cache blocks for the sequence. if ( self.cache_manager.check_allocation(seq) and (len(self.running_list.prefill) + len(self.running_list.decoding)) < self.max_batch_size # There some bugs in continous batching, so we disable it here. ): # If succeed, add the sequence to running list. remove_list.append(seq) self.running_list.append(seq) self.cache_manager.allocate_context_from_block_table(seq.block_table, seq.sentence_len) for seq in remove_list: lst.remove(seq) if self.running_list.ready_for_prefill(): for seq in self.running_list.prefill: seq.mark_running() self.prefill_batch.add_seqs(self.running_list.prefill) return self.prefill_batch if not self.running_batch.is_empty: for seq in self.running_batch.sequences_set: recycle = self.cache_manager.allocate_token_from_block_table(seq.block_table, seq.sentence_len) if recycle: seq.recycle() self.running_batch.del_seq(seq) self.running_list.remove(seq) self.waiting_list[-1].append(seq) # the recycled sequences are handled with highest priority. return self.running_batch def add_sequence(self, req: Sequence): """ Add the request to waiting list. """ assert not self._find_sequence(req.request_id), f"Sequence {req.request_id} already exists." assert ( req.input_len <= self.inference_config.max_input_len ), f"Sequence {req.request_id} exceeds input length limit" self.waiting_list[req.input_len * 3 // (self.inference_config.max_input_len + 1)].append(req) def abort_sequence(self, request_id: str): """ Abort the request. """ seq, priority = self._find_sequence(request_id) if seq.status == RequestStatus.WAITING: seq.mark_aborted() self.waiting_list[priority].remove(seq) elif seq.status.is_running(): self.cache_manager.free_block_table(seq.block_table) self.running_list.remove(seq) else: try: self.done_list.remove(seq) except: return def _find_sequence(self, request_id: str) -> Sequence: """ Find the request by request_id. """ for priority, lst in enumerate(self.waiting_list): for seq in lst: if seq.request_id == request_id: return seq, priority if self.running_list.find_seq(request_id): return seq, None return None def _sample(self, probs: torch.Tensor, logprobs: torch.Tensor, generation_config): if generation_config.num_beams == 1: if generation_config.do_sample: sample_tokens = multinomial_sample(generation_config, probs) else: sample_tokens = greedy_sample(generation_config, logprobs) else: sample_tokens = beam_search_sample(generation_config, logprobs, is_prompt=not self.prefill_batch.is_empty) return sample_tokens def mark_finished(self, sequence: Sequence, generation_config): if ( sequence.output_token_id[-1] == generation_config.eos_id or sequence.output_len >= generation_config.max_output_len ): sequence.mark_finished() def check_unfinished_seqs(self) -> bool: return self._has_waiting() or not self.running_list.is_empty() def search_tokens(self, generation_config, logits): """ Sample tokens for finished requests. """ # do logit processor # NOTE: need to decide the granularity to process logits (sequence or batch) for type in ["top_k", "top_p", "min_p"]: config_dict = generation_config.to_dict() if type in config_dict and config_dict[type] is not None: logits = logit_processor(type, logits, config_dict[type]) # calculate probs probs = torch.softmax(logits, dim=-1, dtype=torch.float) logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float) # sample the next tokens sample_tokens = self._sample(probs, logprobs, generation_config) if not self.prefill_batch.is_empty: self.prefill_batch.update_batch_tokens(sample_tokens) else: self.running_batch.update_batch_tokens(sample_tokens) def update(self): """ Update current running list and done list """ if not self.prefill_batch.is_empty: self.running_list.decoding.extend(self.running_list.prefill) self.running_batch.add_seqs(self.running_list.prefill) self.running_list.prefill.clear() self.prefill_batch.clear_batch() finish_seqs = self.running_batch.fliter_batch() for seq in finish_seqs: self.running_list.remove(seq) self.cache_manager.free_block_table(seq.block_table) self.done_list.extend(finish_seqs) return finish_seqs