实现BertConfig类

archive/bert_base_chinese/config.json

@@ -0,0 +1,20 @@
+{
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 512,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

archive/bert_base_uncased_english/config.json

@@ -0,0 +1,18 @@
+{
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "pooler_type": "first_token_transform",
+  "vocab_size": 30522
+}

model/BERT/bert_config.py

@@ -0,0 +1,102 @@
+import json
+import copy
+
+# import six
+import logging
+
+
+class BertConfig(object):
+    """BertModel的配置类"""
+
+    def __init__(
+        self,
+        vocab_size=21128,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        pad_token_id=0,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+    ):
+        """
+        Args:
+          vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
+          hidden_size: Size of the encoder layers and the pooler layer.
+          num_hidden_layers: Number of hidden layers in the Transformer encoder.
+          num_attention_heads: Number of attention heads for each attention\
+            layer in the Transformer encoder.
+          intermediate_size: The size of the `intermediate` (i.e., feed-forward)\
+            layer in the Transformer encoder.
+          hidden_act: The non-linear activation function (function or string)\
+            in the encoder and pooler.
+          hidden_dropout_prob: The dropout probability for all fully connected\
+            layers in the embeddings, encoder, and pooler.
+          attention_probs_dropout_prob: The dropout ratio for the attention\
+            probabilities.
+          max_position_embeddings: The maximum sequence length that this model might\
+            ever be used with. Typically set this to something large just in case\
+            (e.g., 512 or 1024 or 2048).
+          type_vocab_size: The vocabulary size of the `token_type_ids` passed into\
+            `BertModel`.
+          initializer_range: The stdev of the `truncated_normal_initializer` for\
+            initializing all weight matrices.
+        """
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.pad_token_id = pad_token_id
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """从json格式配置文件读取配置信息"""
+        with open(json_file, "r") as reader:
+            json_obj = reader.read()
+
+        logging.info(f"成功导入BERT配置文件 {json_file}")
+        return cls.from_dict(json.loads(json_obj))
+
+    @classmethod
+    def from_dict(cls, dict_obj):
+        """从Python字典中读取配置信息"""
+        config = BertConfig(vocab_size=None)  # 创建Config对象
+        for key, value in dict_obj.items():  # 从字典中读取配置信息
+            config.__dict__[key] = value
+
+        return config
+
+    def to_json_str(self):
+        """把对象转换为json格式字符串"""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+    def to_dict(self):
+        """把对象转换为字典"""
+        out = copy.deepcopy(self.__dict__)
+        return out
+
+
+if __name__ == "__main__":
+    import sys, os
+
+    sys.path.append(os.getcwd())
+
+    json_file = "./archive/bert_base_chinese/config.json"
+    config = BertConfig.from_json_file(json_file)
+
+    for key, value in config.__dict__.items():
+        print(f"{key} = {value}")
+
+    print("=" * 20)
+    print(config.to_json_str())