mirror of https://github.com/hpcaitech/ColossalAI
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
from typing import Callable
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from .utils import is_rank_0
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# Dahoas/rm-static
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class RmStaticDataset(Dataset):
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"""
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Dataset for reward model
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Args:
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dataset: dataset for reward model
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tokenizer: tokenizer for reward model
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max_length: max length of input
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special_token: special token at the end of sentence
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"""
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def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None:
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super().__init__()
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self.chosen = []
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self.reject = []
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if special_token is None:
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self.end_token = tokenizer.eos_token
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else:
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self.end_token = special_token
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for data in tqdm(dataset, disable=not is_rank_0()):
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prompt = data['prompt']
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chosen = prompt + data['chosen'] + self.end_token
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chosen_token = tokenizer(chosen,
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max_length=max_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt")
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self.chosen.append({
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"input_ids": chosen_token['input_ids'],
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"attention_mask": chosen_token['attention_mask']
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})
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reject = prompt + data['rejected'] + self.end_token
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reject_token = tokenizer(reject,
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max_length=max_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt")
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self.reject.append({
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"input_ids": reject_token['input_ids'],
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"attention_mask": reject_token['attention_mask']
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})
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def __len__(self):
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length = len(self.chosen)
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return length
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def __getitem__(self, idx):
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return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][
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"input_ids"], self.reject[idx]["attention_mask"]
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# Anthropic/hh-rlhf
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class HhRlhfDataset(Dataset):
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"""
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Dataset for reward model
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Args:
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dataset: dataset for reward model
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tokenizer: tokenizer for reward model
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max_length: max length of input
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special_token: special token at the end of sentence
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"""
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def __init__(self, dataset, tokenizer: Callable, max_length: int, special_token=None) -> None:
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super().__init__()
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self.chosen = []
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self.reject = []
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if special_token is None:
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self.end_token = tokenizer.eos_token
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else:
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self.end_token = special_token
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for data in tqdm(dataset, disable=not is_rank_0()):
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chosen = data['chosen'] + self.end_token
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chosen_token = tokenizer(chosen,
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max_length=max_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt")
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self.chosen.append({
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"input_ids": chosen_token['input_ids'],
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"attention_mask": chosen_token['attention_mask']
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})
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reject = data['rejected'] + self.end_token
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reject_token = tokenizer(reject,
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max_length=max_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt")
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self.reject.append({
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"input_ids": reject_token['input_ids'],
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"attention_mask": reject_token['attention_mask']
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})
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def __len__(self):
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length = len(self.chosen)
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return length
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def __getitem__(self, idx):
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return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][
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"input_ids"], self.reject[idx]["attention_mask"]
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