update tokenizer code, train from local files

src_tokenizer/hf_tokenizer_code/step2_hf_train_from_scratch_BPEandBBPE.py

@@ -1,28 +1,134 @@
-from tokenizers import Tokenizer
-from tokenizers.models import BPE  
-from tokenizers.trainers import BpeTrainer
-from tokenizers.pre_tokenizers import ByteLevel
-from tokenizers.pre_tokenizers import Whitespace
+import os
+import json
+from pathlib import Path
+from tokenizers import (
+    decoders,
+    models,
+    normalizers,
+    pre_tokenizers,
+    processors,
+    trainers,
+    Tokenizer,
+)
 from transformers import PreTrainedTokenizerFast
 
-tokenizer = Tokenizer(BPE(unk_token="[UNK]"))  
-# tokenizer.pre_tokenizer = Whitespace()
-tokenizer.pre_tokenizer = ByteLevel(add_prefix_space=True)
-trainer = BpeTrainer(
-    vocab_size=12800,           
-    special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"],  
-    min_frequency=2,            
-    show_progress=True         
+def get_training_corpus():
+    for file_path in jsonl_files:
+        with open(file_path, "r", encoding="utf-8") as f:
+            for line in f:
+                data = json.loads(line)
+                if "text" in data:
+                    yield data["text"]
+
+# 使用Path对象处理路径
+corpus_dir = Path("./corpus")
+jsonl_files = list(corpus_dir.glob("**/*.jsonl"))
+if not jsonl_files:
+    raise ValueError("No training files found")
+
+# 使用Qwen2官方特殊标记
+special_tokens = [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+]
+
+# 创建BPE tokenizer
+tokenizer = Tokenizer(models.BPE())
+
+# 使用Qwen2的normalizer配置
+tokenizer.normalizer = normalizers.Sequence([
+    normalizers.NFKC(),
+    normalizers.StripAccents(),
+])
+
+# 简化预分词器：使用ByteLevel代替多个Split规则
+tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(
+    add_prefix_space=False,  # 对齐Qwen2配置
+    use_regex=True
 )
 
-tokenizer.train(files=["corpus/wikipedia.txt"], trainer=trainer)
-tokenizer.save("tokenizer_file/tokenizer.json")
-fast_tokenizer = PreTrainedTokenizerFast(
-    tokenizer_file="tokenizer_file/tokenizer.json",
-    unk_token="[UNK]",
-    pad_token="[PAD]",
-    cls_token="[CLS]",
-    sep_token="[SEP]",
-    mask_token="[MASK]",
+# 移除后处理器（Qwen2不需要特殊后处理）
+tokenizer.post_processor = None
+
+# 配置解码器
+tokenizer.decoder = decoders.ByteLevel(
+    use_regex=True,
+    trim_offsets=True,
+    add_prefix_space=False  # 对齐Qwen2的add_prefix_space配置
 )
-fast_tokenizer.save_pretrained("tokenizer_file/hf_tokenizer_BBPE")
+
+# 配置训练器
+trainer = trainers.BpeTrainer(
+    vocab_size=12800,
+    special_tokens=special_tokens,
+    min_frequency=2,  # 添加最小频率阈值
+    show_progress=True  # 显示进度条
+)
+
+print("Training tokenizer...")
+tokenizer.train_from_iterator(
+    get_training_corpus(),
+    trainer=trainer,
+    length=sum(1 for _ in get_training_corpus())  # 添加总长度用于进度显示
+)
+
+# 创建PreTrainedTokenizerFast时添加Qwen2关键配置
+wrapped_tokenizer = PreTrainedTokenizerFast(
+    tokenizer_object=tokenizer,
+    bos_token=None,  # 对齐Qwen2配置
+    eos_token="<|endoftext|>",
+    pad_token="<|endoftext|>",
+    unk_token=None,  # 对齐Qwen2配置
+    additional_special_tokens=["<|im_start|>", "<|im_end|>"],
+    model_max_length=32768,  # 添加model_max_length
+    clean_up_tokenization_spaces=False,  # 对齐Qwen2配置
+    errors="replace",  # 对齐Qwen2配置
+    split_special_tokens=False,  # 对齐Qwen2配置
+)
+
+output_dir = Path("./llm_tokenizer")
+output_dir.mkdir(exist_ok=True, parents=True)
+
+# 保存tokenizer
+wrapped_tokenizer.save_pretrained(output_dir)
+print(f"Tokenizer saved to {output_dir}")
+
+# 使用Qwen2官方chat_template
+QWEN2_CHAT_TEMPLATE = (
+    "{% for message in messages %}"
+    "{% if loop.first and messages[0]['role'] != 'system' %}"
+    "{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}"
+    "{% endif %}"
+    "{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}"
+    "{% endfor %}"
+    "{% if add_generation_prompt %}"
+    "{{ '<|im_start|>assistant\n' }}"
+    "{% endif %}"
+)
+
+# 直接设置chat_template避免重新加载
+wrapped_tokenizer.chat_template = QWEN2_CHAT_TEMPLATE
+
+# 再次保存包含chat_template的配置
+wrapped_tokenizer.save_pretrained(output_dir)
+
+# 测试对话模板
+messages = [
+    {"role": "user", "content": "你好！"},
+    {"role": "assistant", "content": "你好，有什么可以帮您？"},
+    {"role": "user", "content": "Python怎么用？"}
+]
+
+formatted = wrapped_tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+print("\n格式化后的对话:")
+print(formatted)
+
+# 测试tokenization
+test_text = "深度学习是人工智能的一个重要分支。Deep learning is a subset of machine learning."
+print("\nTest tokenization:")
+print(wrapped_tokenizer.tokenize(test_text))