210 lines
7.9 KiB
Python
210 lines
7.9 KiB
Python
import torch
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import bisect
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import pickle as pkl
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from abc import ABC, abstractmethod
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from torch import Tensor
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from torch.utils.data import Dataset
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from typing import Callable, List, Dict, Literal, Union
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MutiSeg = Dict[str, List[Tensor]]
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Seg = Dict[str, Tensor]
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def load_pkl_files(paths: List[str]):
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segments: MutiSeg = {}
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total_samples = 0
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for path in paths:
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with open(path, "rb") as f:
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pkl_file: Seg = pkl.load(f)
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for key, value in pkl_file.items():
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if key not in segments:
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segments[key] = []
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segments[key].append(value)
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first_key = list(pkl_file.keys())[0]
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total_samples += pkl_file[first_key].numel()
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return segments, total_samples
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class BaseSegmentFetcher:
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def __init__(self, segments: List[Tensor]):
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self.segments = segments
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self.cum_lengths = []
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total = 0
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for seg in segments:
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total += len(seg)
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self.cum_lengths.append(total)
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self.total_length = total if segments else 0
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def fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
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if not (0 <= begin_idx < self.total_length and 0 <= end_idx <= self.total_length):
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raise ValueError("begin_idx or end_idx out of bounds")
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if begin_idx >= end_idx:
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return torch.tensor([], dtype=torch.long)
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seg_start_idx = bisect.bisect_right(self.cum_lengths, begin_idx - 1)
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seg_end_idx = bisect.bisect_left(self.cum_lengths, end_idx - 1)
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result_segments = []
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for i in range(seg_start_idx, seg_end_idx + 1):
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prev_cum = self.cum_lengths[i - 1] if i > 0 else 0
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start = max(begin_idx - prev_cum, 0)
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end = min(end_idx - prev_cum, len(self.segments[i]))
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result_segments.append(self.segments[i][start:end])
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return torch.cat(result_segments, dim=0)
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class MutiSegmentFetcher:
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def __init__(self, muti_segments: MutiSeg):
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self.muti_keys = list(muti_segments.keys())
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self.muti_fetchers = {
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key: BaseSegmentFetcher(segments)
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for key, segments in muti_segments.items()
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}
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def key_fetch(self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]) -> Union[Tensor, Seg]:
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fetch_dict = {}
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keys = [keys] if isinstance(keys, str) else keys
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for key in keys:
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fetcher = self.muti_fetchers[key]
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fetch_tensor = fetcher.fetch_data(begin_idx, end_idx)
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fetch_dict[key] = fetch_tensor
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return fetch_dict if len(keys) > 1 else fetch_dict[keys[0]]
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def fetch_data(self, begin_idx: int, end_idx: int) -> Union[Tensor, Seg]:
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return self.key_fetch(begin_idx, end_idx, self.muti_keys)
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class BaseDataset(Dataset, ABC):
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def __init__(self, chunk_size: int, device: str):
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super().__init__()
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self.segments: MutiSeg = {}
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self.chunk_size = chunk_size
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self.total_samples = 0
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self.device = device
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def save(self, save_path: str):
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first_item = self.segments[keys[0]]
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segment_size = len(first_item)
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keys = list(self.segments.keys())
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for i in range(segment_size):
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formated_segment = {key: self.segments[key][i] for key in keys}
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pkl.dump(formated_segment, open(f"{save_path}_{i}.pkl", "wb"))
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def load(self, load_path: Union[str, List[str]]):
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paths = [load_path] if isinstance(load_path, str) else load_path
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self.segments, self.total_samples = load_pkl_files(paths)
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self.fetcher = MutiSegmentFetcher(self.segments)
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@abstractmethod
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def __getitem__(self, index: int):
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raise NotImplementedError
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def __len__(self) -> int:
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assert self.total_samples // self.chunk_size > 0
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return self.total_samples // self.chunk_size
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class SeqDataset(BaseDataset):
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def __init__(self, chunk_size , device='cuda'):
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super().__init__(chunk_size, device)
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self.fetcher = MutiSegmentFetcher(self.segments)
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def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
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return self.fetcher.key_fetch(begin_idx, end_idx, "sequence")
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def __getitem__(self, index):
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begin_idx = index * self.chunk_size
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end_idx = min(begin_idx + self.chunk_size, self.total_samples - 1)
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x = self._fetch_data(begin_idx, end_idx).to(device=self.device, dtype=torch.long)
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y = self._fetch_data(begin_idx + 1, end_idx + 1).to(device=self.device, dtype=torch.long)
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return x, y
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class SftDataset(BaseDataset):
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def __init__(self, chunk_size, device='cuda'):
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super().__init__(chunk_size, device)
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self.fetcher = MutiSegmentFetcher(self.segments)
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def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
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return self.fetcher.key_fetch(begin_idx, end_idx, key)
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def __getitem__(self, index):
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begin_idx = index * self.chunk_size
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end_idx = min(begin_idx + self.chunk_size, self.total_samples - 1)
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x = self._fetch_data(begin_idx, end_idx, "sequence").to(device=self.device, dtype=torch.long)
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y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence").to(device=self.device, dtype=torch.long)
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loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "mask").to(device=self.device, dtype=torch.bool)
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return x, y, loss_mask
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class DpoDataset(BaseDataset):
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def __init__(self, chunk_size: int, device="cuda"):
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super().__init__(chunk_size, device)
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self.fetcher = MutiSegmentFetcher(self.segments)
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def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
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return self.fetcher.key_fetch(begin_idx, end_idx, key)
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def __getitem__(self, index: int):
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start_idx = index * self.chunk_size
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end_idx = min(start_idx + self.chunk_size, self.total_samples - 1)
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chosen = self._fetch_data(start_idx, end_idx, "chosen").to(device=self.device, dtype=torch.long)
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rejected = self._fetch_data(start_idx, end_idx, "rejected").to(device=self.device, dtype=torch.long)
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chosen_mask = self._fetch_data(start_idx, end_idx, "chosen_mask").to(device=self.device, dtype=torch.bool)
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rejected_mask = self._fetch_data(start_idx, end_idx, "rejected_mask").to(device=self.device, dtype=torch.bool)
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return chosen, rejected, chosen_mask, rejected_mask
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class PpoDataset(BaseDataset):
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def __init__(self, chunk_size: int, device="cuda"):
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super().__init__(chunk_size, device)
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self.fetcher = MutiSegmentFetcher(self.segments)
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def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
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return self.fetcher.key_fetch(begin_idx, end_idx, key)
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def __getitem__(self, index: int) -> Dict[str, Tensor]:
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begin_idx = index * self.chunk_size
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end_idx = min(begin_idx + self.chunk_size, self.total_samples - 1)
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input_ids = self._fetch_data(begin_idx, end_idx, "input_ids").to(self.device),
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actions = self._fetch_data(begin_idx, end_idx, "actions").to(self.device),
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logprobs = self._fetch_data(begin_idx, end_idx, "logprobs").to(self.device),
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rewards = self._fetch_data(begin_idx, end_idx, "rewards").to(self.device)
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return input_ids, actions, logprobs, rewards
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class DatasetLoader:
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@staticmethod
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def load(
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train_type: Literal["seq", "sft", "dpo"],
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load_path: Union[str, List[str]],
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max_len: int,
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device: str
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) -> BaseDataset:
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dataset_router: Dict[str, Callable[[int, torch.device], BaseDataset]] = {
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"seq": lambda m_len, device: SeqDataset(m_len, device=device),
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"sft": lambda m_len, device: SftDataset(m_len, device=device),
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"dpo": lambda m_len, device: DpoDataset(m_len, device=device),
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}
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dataset = dataset_router[train_type](max_len, device)
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dataset.load(load_path)
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return dataset |