218 lines
9.3 KiB
Python
218 lines
9.3 KiB
Python
from torch.utils.data import Dataset
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import torch
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import os
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import random
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from datasets import load_dataset
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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def pre_processing_chat(conversations, add_system_ratio=0.2):
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SYSTEM_PROMPTS = [
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"你是一个知识丰富的AI，尽力为用户提供准确的信息。",
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"你是minimind，一个小巧但有用的语言模型。",
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"你是一个专业的AI助手，请提供有价值的回答。",
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"你是minimind，请尽力帮助用户解决问题。",
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"你是一个可靠的AI，请给出准确的回答。",
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"You are a helpful AI assistant.",
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"You are minimind, a lightweight intelligent assistant.",
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"You are a friendly chatbot. Please answer the user's questions carefully.",
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"You are a knowledgeable AI. Try your best to provide accurate information.",
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"You are minimind, a small but useful language model."
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]
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if conversations and conversations[0].get('role') != 'system':
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if random.random() < add_system_ratio:
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return [{'role': 'system', 'content': random.choice(SYSTEM_PROMPTS)}] + conversations
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return conversations
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def post_processing_chat(prompt_content, empty_think_ratio=0.1):
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if '<think>\n\n</think>\n\n' in prompt_content and random.random() > empty_think_ratio:
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prompt_content = prompt_content.replace('<think>\n\n</think>\n\n', '')
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return prompt_content
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class PretrainDataset(Dataset):
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def __init__(self, data_path, tokenizer, max_length=512):
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super().__init__()
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self.tokenizer = tokenizer
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self.max_length = max_length
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self.samples = load_dataset('json', data_files=data_path, split='train')
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, index):
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sample = self.samples[index]
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tokens = self.tokenizer(str(sample['text']), add_special_tokens=False, max_length=self.max_length - 2, truncation=True).input_ids
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tokens = [self.tokenizer.bos_token_id] + tokens + [self.tokenizer.eos_token_id]
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input_ids = tokens + [self.tokenizer.pad_token_id] * (self.max_length - len(tokens))
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input_ids = torch.tensor(input_ids, dtype=torch.long)
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labels = input_ids.clone()
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labels[input_ids == self.tokenizer.pad_token_id] = -100
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return input_ids, labels
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class SFTDataset(Dataset):
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def __init__(self, jsonl_path, tokenizer, max_length=1024):
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super().__init__()
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self.tokenizer = tokenizer
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self.max_length = max_length
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self.samples = load_dataset('json', data_files=jsonl_path, split='train')
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self.bos_id = tokenizer(f'{tokenizer.bos_token}assistant\n', add_special_tokens=False).input_ids
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self.eos_id = tokenizer(f'{tokenizer.eos_token}\n', add_special_tokens=False).input_ids
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def __len__(self):
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return len(self.samples)
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def create_chat_prompt(self, conversations):
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messages = conversations.copy()
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tools = conversations[0]["functions"] if (conversations and conversations[0]["role"] == "system" and conversations[0].get("functions")) else None
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return self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=False,
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tools=tools
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)
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def generate_labels(self, input_ids):
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labels = [-100] * len(input_ids)
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i = 0
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while i < len(input_ids):
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if input_ids[i:i + len(self.bos_id)] == self.bos_id:
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start = i + len(self.bos_id)
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end = start
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while end < len(input_ids):
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if input_ids[end:end + len(self.eos_id)] == self.eos_id:
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break
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end += 1
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for j in range(start, min(end + len(self.eos_id), self.max_length)):
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labels[j] = input_ids[j]
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i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)
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else:
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i += 1
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return labels
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def __getitem__(self, index):
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sample = self.samples[index]
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conversations = pre_processing_chat(sample['conversations'])
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prompt = self.create_chat_prompt(conversations)
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prompt = post_processing_chat(prompt)
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input_ids = self.tokenizer(prompt).input_ids[:self.max_length]
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input_ids += [self.tokenizer.pad_token_id] * (self.max_length - len(input_ids))
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labels = self.generate_labels(input_ids)
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# # === 调试打印 ===
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# print(f"\n--- Sample {index} ---")
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# for i, (x, y) in enumerate(zip(input_ids[:-1], labels[1:])):
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# print(f"{i:3d}: X={self.tokenizer.decode([x])!r:16s} ---> Y={self.tokenizer.decode([input_ids[i+1]])!r:16s} label={y}")
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# # ================
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return torch.tensor(input_ids, dtype=torch.long), torch.tensor(labels, dtype=torch.long)
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class DPODataset(Dataset):
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def __init__(self, file_path, tokenizer, max_length=4096):
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super().__init__()
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self.tokenizer = tokenizer
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self.max_length = max_length
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self.padding = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
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self.bos_id = tokenizer(f'{tokenizer.bos_token}assistant\n', add_special_tokens=False).input_ids
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self.eos_id = tokenizer(f'{tokenizer.eos_token}\n', add_special_tokens=False).input_ids
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self.samples = load_dataset('json', data_files=file_path, split='train')
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, index):
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sample = self.samples[index]
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chosen = sample['chosen'] # 是一个 list，里面包含若干 {role, content}
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rejected = sample['rejected'] # 同上
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chosen_prompt = self.tokenizer.apply_chat_template(
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chosen, tokenize=False, add_generation_prompt=False
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)
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chosen_prompt = post_processing_chat(chosen_prompt)
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rejected_prompt = self.tokenizer.apply_chat_template(
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rejected, tokenize=False, add_generation_prompt=False
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)
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rejected_prompt = post_processing_chat(rejected_prompt)
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chosen_encoding = self.tokenizer(
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chosen_prompt, truncation=True, max_length=self.max_length, padding='max_length'
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)
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rejected_encoding = self.tokenizer(
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rejected_prompt, truncation=True, max_length=self.max_length, padding='max_length'
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)
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chosen_input_ids = chosen_encoding['input_ids']
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chosen_loss_mask = self.generate_loss_mask(chosen_input_ids)
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rejected_input_ids = rejected_encoding['input_ids']
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rejected_loss_mask = self.generate_loss_mask(rejected_input_ids)
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x_chosen = torch.tensor(chosen_input_ids[:-1], dtype=torch.long)
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y_chosen = torch.tensor(chosen_input_ids[1:], dtype=torch.long)
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mask_chosen = torch.tensor(chosen_loss_mask[1:], dtype=torch.long)
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x_rejected = torch.tensor(rejected_input_ids[:-1], dtype=torch.long)
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y_rejected = torch.tensor(rejected_input_ids[1:], dtype=torch.long)
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mask_rejected = torch.tensor(rejected_loss_mask[1:], dtype=torch.long)
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return {
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'x_chosen': x_chosen,
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'y_chosen': y_chosen,
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'mask_chosen': mask_chosen,
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'x_rejected': x_rejected,
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'y_rejected': y_rejected,
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'mask_rejected': mask_rejected
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}
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def generate_loss_mask(self, input_ids):
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loss_mask = [0] * len(input_ids)
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i = 0
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while i < len(input_ids):
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if input_ids[i:i + len(self.bos_id)] == self.bos_id:
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start = i + len(self.bos_id)
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end = start
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while end < len(input_ids):
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if input_ids[end:end + len(self.eos_id)] == self.eos_id:
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break
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end += 1
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for j in range(start, min(end + len(self.eos_id), self.max_length)):
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loss_mask[j] = 1
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i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)
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else:
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i += 1
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return loss_mask
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class RLAIFDataset(Dataset):
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def __init__(self, jsonl_path, tokenizer, max_length=1024):
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super().__init__()
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self.tokenizer = tokenizer
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self.max_length = max_length
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self.samples = load_dataset('json', data_files=jsonl_path, split='train')
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self.bos_id = tokenizer(f'{tokenizer.bos_token}assistant', add_special_tokens=False).input_ids
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self.eos_id = tokenizer(f'{tokenizer.eos_token}', add_special_tokens=False).input_ids
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def __len__(self):
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return len(self.samples)
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def create_chat_prompt(self, conversations):
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messages = []
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answer = ''
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for i, turn in enumerate(conversations):
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role = 'user' if i % 2 == 0 else 'assistant'
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messages.append({"role": role, "content": turn['content']})
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answer = turn['content']
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prompt = self.tokenizer.apply_chat_template(
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messages[:-1],
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tokenize=False,
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add_generation_prompt=True # 这里需要True
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)
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prompt = post_processing_chat(prompt)
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return prompt, answer
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def __getitem__(self, index):
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sample = self.samples[index]
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prompt, answer = self.create_chat_prompt(sample['conversations'])
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return {
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'prompt': prompt,
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'answer': answer
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}
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if __name__ == "__main__":
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pass |