| |
| |
| 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 |
| 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) |
|
|
