mirror of https://github.com/hpcaitech/ColossalAI
347 lines
13 KiB
Python
347 lines
13 KiB
Python
# Adapted from https://github.com/ModelTC/lightllm
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import collections
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from dataclasses import dataclass
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from typing import Dict, List, Tuple
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import numpy as np
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import torch
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from colossalai.inference.tensor_parallel import MemoryManager
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# make batch infer state an attr of InferBatch
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class InferSamplingParams:
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def __init__(
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self,
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do_sample: bool = False,
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presence_penalty: float = 0.0,
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frequency_penalty: float = 0.0,
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temperature: float = 1.0,
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top_p: float = 1.0,
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top_k: int = -1,
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vocab_size: int = -1,
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) -> None:
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self.do_sample = do_sample
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self.presence_penalty = presence_penalty
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self.frequency_penalty = frequency_penalty
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self.temperature = temperature
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self.top_p = top_p
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self.top_k = top_k
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if self.top_k == -1:
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self.top_k = vocab_size
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return
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@dataclass
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class InferBatch:
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batch_id: int
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requests: List
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requests_idx_mapping: Dict[int, int]
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input_ids: torch.Tensor
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all_input_ids: List[List[int]]
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input_lengths: List[int]
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out_token_id_counts: List
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sampling_param_list: List[InferSamplingParams]
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nopad_total_token_num: int
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nopad_max_len_in_batch: int
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nopad_b_loc: torch.Tensor
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nopad_b_start_loc: torch.Tensor
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nopad_b_seq_len: torch.Tensor
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cache_manager: MemoryManager
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max_total_len: int
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@classmethod
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@torch.no_grad()
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def init_batch(
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cls,
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batch_id,
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requests,
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dtype: torch.dtype,
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device: torch.device,
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cache_manager: MemoryManager,
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vocab_size: int,
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max_total_len: int,
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) -> "InferBatch":
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input_lengths = []
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all_input_ids = []
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requests_idx_mapping = {}
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out_token_id_counts = []
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sampling_param_list = []
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nopad_total_token_num = 0
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nopad_max_len_in_batch = 0
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nopad_b_loc = torch.empty((len(requests), max_total_len + 12), dtype=torch.long, device="cuda")
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# to avoid memory leak , we pre-allocate 12 more space for each batch.
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nopad_b_start_loc = torch.zeros(len(requests), dtype=torch.int32, device="cuda")
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for i, r in enumerate(requests):
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# request id -> idx in list mapping
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requests_idx_mapping[r["request_id"]] = i
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tokenized_input = r["input_id"]
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input_length = len(tokenized_input)
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input_lengths.append(input_length)
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all_input_ids.append(tokenized_input)
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out_token_id_counts.append(collections.defaultdict(int))
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# postprocessor
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sampling_param = r["sampling_param"]
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sampling_param["vocab_size"] = vocab_size
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sampling_param_list.append(InferSamplingParams(**sampling_param))
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nopad_total_token_num += input_length
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nopad_max_len_in_batch = max(nopad_max_len_in_batch, input_length)
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nopad_b_seq_len = torch.tensor(input_lengths, dtype=torch.int32, device="cuda")
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nopad_b_start_loc[1:] = torch.cumsum(nopad_b_seq_len, dim=0, dtype=torch.int32)[0:-1]
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if len(requests) > 1:
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input_ids = np.concatenate(all_input_ids, dtype=np.int64)
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else:
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input_ids = all_input_ids[0]
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# Create tensors on device
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input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
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return cls(
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batch_id=batch_id,
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requests=requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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input_lengths=input_lengths,
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all_input_ids=all_input_ids,
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nopad_total_token_num=nopad_total_token_num,
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nopad_max_len_in_batch=nopad_max_len_in_batch,
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nopad_b_loc=nopad_b_loc,
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nopad_b_start_loc=nopad_b_start_loc,
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nopad_b_seq_len=nopad_b_seq_len,
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out_token_id_counts=out_token_id_counts,
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sampling_param_list=sampling_param_list,
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cache_manager=cache_manager,
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max_total_len=max_total_len,
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)
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@torch.no_grad()
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def free_self(self) -> None:
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"""
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Free the memory of the InferBatch itself
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"""
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remove_index = []
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for idx in range(len(self)):
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remove_index.append(
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self.nopad_b_loc[
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idx,
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(self.nopad_max_len_in_batch - 1)
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- (self.nopad_b_seq_len[idx] - 1) : (self.nopad_max_len_in_batch - 1),
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]
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)
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remove_index = torch.cat(remove_index, dim=-1)
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self.cache_manager.free(remove_index)
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@torch.no_grad()
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def filter(self, request_ids: List[int]) -> "InferBatch":
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"""
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Filter finished batch and return a new InferBatch with left ones.
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"""
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if len(request_ids) == 0:
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raise ValueError("Batch must have at least one request")
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if len(request_ids) == len(self):
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return self
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requests_idx_mapping = {}
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indices = []
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requests = []
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all_input_ids = []
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input_lengths = []
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nopad_total_token_num = 0
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nopad_max_len_in_batch = 0
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nopad_b_loc = torch.empty((len(request_ids), self.max_total_len + 12), dtype=torch.long, device="cuda")
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nopad_b_start_loc = torch.zeros(len(request_ids), dtype=torch.int32, device="cuda")
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nopad_b_seq_len = torch.zeros(len(request_ids), dtype=torch.int32, device="cuda")
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left_idx = []
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for i, request_id in enumerate(request_ids):
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idx = self.requests_idx_mapping[request_id]
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left_idx.append(idx)
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left_idx_set = set(left_idx)
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remove_index = []
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for idx in range(len(self)):
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if idx not in left_idx_set:
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remove_index.append(
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self.nopad_b_loc[
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idx,
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(self.nopad_max_len_in_batch - 1)
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- (self.nopad_b_seq_len[idx] - 1) : (self.nopad_max_len_in_batch - 1),
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]
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)
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remove_index = torch.cat(remove_index, dim=-1)
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self.cache_manager.free(remove_index)
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nopad_max_len_in_batch = 0
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for i, request_id in enumerate(request_ids):
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idx = self.requests_idx_mapping[request_id]
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indices.append(idx)
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nopad_b_seq_len[:] = self.nopad_b_seq_len[indices]
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nopad_max_len_in_batch = torch.max(nopad_b_seq_len).item()
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nopad_b_start_loc[1:] = torch.cumsum(nopad_b_seq_len, dim=0, dtype=torch.int32)[0:-1]
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nopad_total_token_num = torch.sum(nopad_b_seq_len).item()
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nopad_b_loc[:, 0 : (nopad_max_len_in_batch - 1)] = self.nopad_b_loc[
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indices,
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(self.nopad_max_len_in_batch - 1) - (nopad_max_len_in_batch - 1) : (self.nopad_max_len_in_batch - 1),
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]
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for i, request_id in enumerate(request_ids):
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idx = self.requests_idx_mapping[request_id]
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requests_idx_mapping[request_id] = i
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requests.append(self.requests[idx])
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all_input_ids.append(self.all_input_ids[idx])
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input_lengths.append(self.input_lengths[idx])
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input_ids = self.input_ids[indices]
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return InferBatch(
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batch_id=self.batch_id,
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requests=requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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input_lengths=input_lengths,
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all_input_ids=all_input_ids,
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nopad_total_token_num=nopad_total_token_num,
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nopad_max_len_in_batch=nopad_max_len_in_batch,
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nopad_b_loc=nopad_b_loc,
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nopad_b_start_loc=nopad_b_start_loc,
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nopad_b_seq_len=nopad_b_seq_len,
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out_token_id_counts=[self.out_token_id_counts[_i] for _i in indices],
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sampling_param_list=[self.sampling_param_list[_i] for _i in indices],
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cache_manager=self.cache_manager,
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max_total_len=self.max_total_len,
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)
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@classmethod
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@torch.no_grad()
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def merge(cls, batch1, batch2) -> "InferBatch":
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"""
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Return megerd new InferBatch
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"""
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requests = batch1.requests + batch2.requests
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requests_idx_mapping = {}
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new_batch_size = len(batch1) + len(batch2)
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input_ids = batch1.input_ids.new_empty(new_batch_size)
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all_input_ids = []
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input_lengths = []
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out_token_id_counts = []
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sampling_param_list = []
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cumulative_batch_size = 0
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nopad_total_token_num = batch1.nopad_total_token_num + batch2.nopad_total_token_num
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nopad_max_len_in_batch = max(batch1.nopad_max_len_in_batch, batch2.nopad_max_len_in_batch)
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max_total_len = max(batch1.max_total_len, batch2.max_total_len)
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nopad_b_loc = torch.empty((new_batch_size, batch1.max_total_len + 12), dtype=torch.long, device="cuda")
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nopad_b_start_loc = torch.zeros(new_batch_size, dtype=torch.int32, device="cuda")
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nopad_b_seq_len = torch.zeros(new_batch_size, dtype=torch.int32, device="cuda")
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nopad_start_loc_len_temp = 0
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batches = [batch1, batch2]
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for i, batch in enumerate(batches):
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if i == 0:
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requests_idx_mapping = batch.requests_idx_mapping
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else:
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for k, v in batch.requests_idx_mapping.items():
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requests_idx_mapping[k] = v + cumulative_batch_size
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start_index = cumulative_batch_size
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end_index = cumulative_batch_size + len(batch)
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input_ids[start_index:end_index] = batch.input_ids
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nopad_b_seq_len[start_index:end_index] = batch.nopad_b_seq_len
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nopad_b_start_loc[start_index:end_index] = batch.nopad_b_start_loc + nopad_start_loc_len_temp
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nopad_start_loc_len_temp = nopad_b_start_loc[end_index - 1] + nopad_b_seq_len[end_index - 1]
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nopad_b_loc[
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start_index:end_index,
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nopad_max_len_in_batch - batch.nopad_max_len_in_batch : nopad_max_len_in_batch - 1,
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] = batch.nopad_b_loc[:, : batch.nopad_max_len_in_batch - 1]
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all_input_ids.extend(batch.all_input_ids)
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input_lengths.extend(batch.input_lengths)
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out_token_id_counts.extend(batch.out_token_id_counts)
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sampling_param_list.extend(batch.sampling_param_list)
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# Update
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cumulative_batch_size += len(batch)
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nopad_b_loc[:, nopad_max_len_in_batch - 1] = (
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nopad_total_token_num - new_batch_size + torch.arange(0, new_batch_size, dtype=torch.int32, device="cuda")
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)
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return InferBatch(
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batch_id=batches[0].batch_id,
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requests=requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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input_lengths=input_lengths,
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all_input_ids=all_input_ids,
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nopad_total_token_num=nopad_total_token_num,
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nopad_max_len_in_batch=nopad_max_len_in_batch,
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nopad_b_loc=nopad_b_loc,
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nopad_b_start_loc=nopad_b_start_loc,
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nopad_b_seq_len=nopad_b_seq_len,
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out_token_id_counts=out_token_id_counts,
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sampling_param_list=sampling_param_list,
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cache_manager=batches[0].cache_manager,
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max_total_len=max_total_len,
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)
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def __len__(self):
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return len(self.requests)
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def get_post_sample_tensors(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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presence_penalties: List[float] = []
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frequency_penalties: List[float] = []
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temperatures: List[float] = []
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top_ps: List[float] = []
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top_ks: List[int] = []
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p_token_ids: List[int] = []
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p_token_counts: List[int] = []
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p_seq_len: List[int] = [
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0,
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]
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p_max_len_in_batch: int = 0
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for i, id_to_count in enumerate(self.out_token_id_counts):
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sample_param = self.sampling_param_list[i]
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presence_penalties.append(sample_param.presence_penalty)
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frequency_penalties.append(sample_param.frequency_penalty)
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temperatures.append(sample_param.temperature)
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top_ps.append(sample_param.top_p)
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top_ks.append(sample_param.top_k)
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for token_id, count in id_to_count.items():
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p_token_ids.append(token_id)
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p_token_counts.append(count)
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p_seq_len.append(len(id_to_count))
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p_max_len_in_batch = max(p_max_len_in_batch, len(id_to_count))
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presence_penalties = torch.tensor(presence_penalties, dtype=torch.float, device="cuda")
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frequency_penalties = torch.tensor(frequency_penalties, dtype=torch.float, device="cuda")
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temperatures = torch.tensor(temperatures, dtype=torch.float, device="cuda")
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top_ps = torch.tensor(top_ps, dtype=torch.float, device="cuda")
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top_ks = torch.tensor(top_ks, dtype=torch.int32, device="cuda")
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p_token_ids = torch.tensor(p_token_ids, dtype=torch.int32, device="cuda")
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p_token_counts = torch.tensor(p_token_counts, dtype=torch.int32, device="cuda")
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p_seq_len = torch.tensor(p_seq_len, dtype=torch.int32, device="cuda")
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p_cumsum_seq_len = torch.cumsum(p_seq_len, dim=0, dtype=torch.int32)
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return (
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presence_penalties,
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frequency_penalties,
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temperatures,
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top_ps,
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top_ks,
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p_token_ids,
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p_token_counts,
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p_cumsum_seq_len,
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p_max_len_in_batch,
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)
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