mirror of
https://github.com/jingyaogong/minimind.git
synced 2026-01-13 19:57:20 +08:00
148 lines
8.6 KiB
Python
148 lines
8.6 KiB
Python
import random
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import json
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from tokenizers import (
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decoders,
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models,
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pre_tokenizers,
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trainers,
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Tokenizer,
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)
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import os
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random.seed(42)
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def train_tokenizer():
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# 读取JSONL文件并提取文本数据
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def read_texts_from_jsonl(file_path):
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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data = json.loads(line)
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yield data['text']
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data_path = '../dataset/pretrain_hq.jsonl'
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# 初始化tokenizer
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tokenizer = Tokenizer(models.BPE())
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tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
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# 定义特殊token
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special_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"]
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# 设置训练器并添加特殊token
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trainer = trainers.BpeTrainer(
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vocab_size=6400,
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special_tokens=special_tokens, # 确保这三个token被包含
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show_progress=True,
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initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
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)
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# 读取文本数据
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texts = read_texts_from_jsonl(data_path)
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# 训练tokenizer
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tokenizer.train_from_iterator(texts, trainer=trainer)
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# 设置解码器
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tokenizer.decoder = decoders.ByteLevel()
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# 检查特殊token的索引
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assert tokenizer.token_to_id("<|endoftext|>") == 0
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assert tokenizer.token_to_id("<|im_start|>") == 1
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assert tokenizer.token_to_id("<|im_end|>") == 2
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# 保存tokenizer
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tokenizer_dir = "../model/"
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os.makedirs(tokenizer_dir, exist_ok=True)
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tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
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tokenizer.model.save("../model/")
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# 手动创建配置文件
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config = {
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"add_bos_token": False,
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"add_eos_token": False,
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"add_prefix_space": False,
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"added_tokens_decoder": {
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"0": {
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"content": "<|endoftext|>",
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"lstrip": False,
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"normalized": False,
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"rstrip": False,
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"single_word": False,
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"special": True
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},
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"1": {
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"content": "<|im_start|>",
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"lstrip": False,
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"normalized": False,
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"rstrip": False,
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"single_word": False,
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"special": True
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},
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"2": {
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"content": "<|im_end|>",
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"lstrip": False,
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"normalized": False,
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"rstrip": False,
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"single_word": False,
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"special": True
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}
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},
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"additional_special_tokens": [],
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"bos_token": "<|im_start|>",
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"clean_up_tokenization_spaces": False,
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"eos_token": "<|im_end|>",
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"legacy": True,
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"model_max_length": 32768,
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"pad_token": "<|endoftext|>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": False,
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "<|endoftext|>",
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' -%}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else -%}\n {{- '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
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}
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# 保存配置文件
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with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w", encoding="utf-8") as config_file:
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json.dump(config, config_file, ensure_ascii=False, indent=4)
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print("Tokenizer training completed and saved.")
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def eval_tokenizer():
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from transformers import AutoTokenizer
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# 加载预训练的tokenizer
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tokenizer = AutoTokenizer.from_pretrained("../model/")
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messages = [
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{"role": "system", "content": "你是一个优秀的聊天机器人，总是给我正确的回应！"},
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{"role": "user", "content": '你来自哪里？'},
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{"role": "assistant", "content": '我来自地球'}
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]
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new_prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False
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)
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print(new_prompt)
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# 获取实际词汇表长度（包括特殊符号）
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actual_vocab_size = len(tokenizer)
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print('tokenizer实际词表长度：', actual_vocab_size)
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model_inputs = tokenizer(new_prompt)
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print('encoder长度：', len(model_inputs['input_ids']))
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input_ids = model_inputs['input_ids']
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response = tokenizer.decode(input_ids, skip_special_tokens=False)
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print('decoder和原始文本是否一致：', response == new_prompt)
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def main():
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train_tokenizer()
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eval_tokenizer()
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if __name__ == '__main__':
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main()
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