python
复制代码
# 注：不建议再重复训练tokenizer（"词典"），MiniMind已自带，此脚本仅供学习和参考。基于不同词典训练的模型将导致输出完全不统一，降低社区的模型复用性
# Note: It is not recommended to re-train the tokenizer. MiniMind already includes one. This script is for learning and reference only. Training models with different tokenizers will lead to inconsistent outputs and reduce model reusability in the community.
import os
import json
from tokenizers import decoders, models, pre_tokenizers, trainers, Tokenizer
DATA_PATH = '../dataset/pretrain_hq.jsonl'
TOKENIZER_DIR = '../model_learn_tokenizer/'
VOCAB_SIZE = 6400
def get_texts(data_path):
with open(data_path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
if i >= 10000: break # 实验性，可只用前10000行测试
data = json.loads(line)
yield data['text']
def train_tokenizer(data_path, tokenizer_dir, vocab_size):
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
trainer = trainers.BpeTrainer(
vocab_size=vocab_size,
special_tokens=["<|endoftext|>", "<|im_start|>", "<|im_end|>"],
show_progress=True,
initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
)
texts = get_texts(data_path)
tokenizer.train_from_iterator(texts, trainer=trainer)
tokenizer.decoder = decoders.ByteLevel()
assert tokenizer.token_to_id("<|endoftext|>") == 0
assert tokenizer.token_to_id("<|im_start|>") == 1
assert tokenizer.token_to_id("<|im_end|>") == 2
os.makedirs(tokenizer_dir, exist_ok=True)
tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json"))
tokenizer.model.save(tokenizer_dir)
config = {
"add_bos_token": False,
"add_eos_token": False,
"add_prefix_space": False,
"added_tokens_decoder": {
"0": {
"content": "<|endoftext|>",
"lstrip": False,
"normalized": False,
"rstrip": False,
"single_word": False,
"special": True
},
"1": {
"content": "<|im_start|>",
"lstrip": False,
"normalized": False,
"rstrip": False,
"single_word": False,
"special": True
},
"2": {
"content": "<|im_end|>",
"lstrip": False,
"normalized": False,
"rstrip": False,
"single_word": False,
"special": True
}
},
"additional_special_tokens": [],
"bos_token": "<|im_start|>",
"clean_up_tokenization_spaces": False,
"eos_token": "<|im_end|>",
"legacy": True,
"model_max_length": 32768,
"pad_token": "<|endoftext|>",
"sp_model_kwargs": {},
"spaces_between_special_tokens": False,
"tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "<|endoftext|>",
"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 {{- '<|im_start|>' + message.role + '\\n' + content }}\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 %}"
}
with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w", encoding="utf-8") as f:
json.dump(config, f, ensure_ascii=False, indent=4)
print("Tokenizer training completed.")
def eval_tokenizer(tokenizer_dir):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
messages = [
{"role": "system", "content": "你是一个优秀的聊天机器人，总是给我正确的回应！"},
{"role": "user", "content": '你来自哪里？'},
{"role": "assistant", "content": '我来自地球'}
]
new_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False
)
print('-'*100)
print(new_prompt)
print('-'*100)
print('tokenizer词表长度：', len(tokenizer))
model_inputs = tokenizer(new_prompt)
print('encoder长度：', len(model_inputs['input_ids']))
response = tokenizer.decode(model_inputs['input_ids'], skip_special_tokens=False)
print('decoder一致性：', response == new_prompt, "\n")
print('-'*100)
print('流式解码（字节缓冲）测试：')
input_ids = model_inputs['input_ids']
token_cache = []
for tid in input_ids:
token_cache.append(tid)
current_decode = tokenizer.decode(token_cache)
if current_decode and '\ufffd' not in current_decode:
display_ids = token_cache[0] if len(token_cache) == 1 else token_cache
raw_tokens = [tokenizer.convert_ids_to_tokens(int(t)) for t in (token_cache if isinstance(token_cache, list) else [token_cache])]
print(f'Token ID: {str(display_ids):15} -> Raw: {str(raw_tokens):20} -> Decode Str: {current_decode}')
token_cache = []
if __name__ == '__main__':
train_tokenizer(DATA_PATH, TOKENIZER_DIR, VOCAB_SIZE)
eval_tokenizer(TOKENIZER_DIR)