loading state_dict for BertModel
AwsomeName opened this issue · 12 comments
您好，非常感谢您的代码：
我在调试的时候，下载了谷歌的chinese_base压缩包，解压后，用https://github.com/huggingface/pytorch-pretrained-BERT/tree/1de35b624b9d7998feb4d518e4f7e8e53abac4e1的方法转化成bin。或者是用https://github.com/NLPScott/bert-Chinese-classification-task/issues/13这里提供的chinese版本，都会遇到模型载入的错误。
RuntimeError: Error(s) in loading state_dict for BertModel:
Missing key(s) in state_dict: "embeddings.word_embeddings.weight",
可以发现是模型的名字对应错误，应该是名字有了调整，这里我解决不了，您能帮忙看看吗？
请问一下这个问题 这边解决了吗？我也遇到同样的错误，想请教一下呢
@AwsomeName 同问，我这边也是这个问题
自己手动改一下字典key值就好了
Missing key(s) in state_dict:
"embeddings.word_embeddings.weight",
"embeddings.position_embeddings.weight",
"embeddings.token_type_embeddings.weight",
"embeddings.LayerNorm.gamma",
"embeddings.LayerNorm.beta",......
Unexpected key(s) in state_dict:
"bert.embeddings.word_embeddings.weight",
"bert.embeddings.position_embeddings.weight",
"bert.embeddings.token_type_embeddings.weight",
"bert.embeddings.LayerNorm.weight",
"bert.embeddings.LayerNorm.bias"......
就是当前模型和state_dict字典key名字不同，可参考以下代码更改key值
state_dict = torch.load(pretrained_model_file, map_location='cpu')
old_keys = []
new_keys = []
for key in list(state_dict.keys()):
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta', 'bias')
if 'bert.' in key:
new_key = key.replace('bert.', '')
if 'cls.' in key:
state_dict.pop(key)
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
model.bert.load_state_dict(state_dict)Missing key(s) in state_dict:
"embeddings.word_embeddings.weight",
"embeddings.position_embeddings.weight",
"embeddings.token_type_embeddings.weight",
"embeddings.LayerNorm.gamma",
"embeddings.LayerNorm.beta",......Unexpected key(s) in state_dict:
"bert.embeddings.word_embeddings.weight",
"bert.embeddings.position_embeddings.weight",
"bert.embeddings.token_type_embeddings.weight",
"bert.embeddings.LayerNorm.weight",
"bert.embeddings.LayerNorm.bias"......就是当前模型和state_dict字典key名字不同，可参考以下代码更改key值
state_dict = torch.load(pretrained_model_file, map_location='cpu') old_keys = [] new_keys = [] for key in list(state_dict.keys()): new_key = None if 'gamma' in key: new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') if 'bert.' in key: new_key = key.replace('bert.', '') if 'cls.' in key: state_dict.pop(key) if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): state_dict[new_key] = state_dict.pop(old_key) model.bert.load_state_dict(state_dict)
感谢大佬，正解，解决了
#25 感谢大佬，正解，解决了
你好，请问你的解决了吗？
我按照上面那个人的方法，又出现一个新的错误：state_dict[new_key] = state_dict.pop(old_key) kyError: 'cls.predictions.transform.LayerNorm.gamma'
按照你的方法出现了新的错误，我在你的基础上又改了改，代码如下：
state_dict = torch.load(path, map_location='cpu')
old_keys = []
new_keys = []
for key in list(state_dict.keys()):
# print(key)
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta', 'bias')
if 'bert.' in key:
new_key = key.replace('bert.', '')
if 'cls.' in key:
new_key = key.replace('cls.', '')
# state_dict.pop(key)
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
#print('old_key',old_key)
#print ('new_key',new_key)
state_dict[new_key] = state_dict.pop(old_key)
if args.init_checkpoint is not None:
model.bert.load_state_dict(state_dict,strict=False)
主要是strict=False，我找了很久，
我是这样处理的： state_dict = torch.load(args.init_checkpoint, map_location='cpu') new_keys = ["embeddings.word_embeddings.weight", "embeddings.position_embeddings.weight", "embeddings.token_type_embeddings.weight", "embeddings.LayerNorm.gamma", "embeddings.LayerNorm.beta", "encoder.layer.0.attention.self.query.weight", "encoder.layer.0.attention.self.query.bias", "encoder.layer.0.attention.self.key.weight", "encoder.layer.0.attention.self.key.bias", "encoder.layer.0.attention.self.value.weight", "encoder.layer.0.attention.self.value.bias", "encoder.layer.0.attention.output.dense.weight", "encoder.layer.0.attention.output.dense.bias", "encoder.layer.0.attention.output.LayerNorm.gamma", "encoder.layer.0.attention.output.LayerNorm.beta", "encoder.layer.0.intermediate.dense.weight", "encoder.layer.0.intermediate.dense.bias", "encoder.layer.0.output.dense.weight", "encoder.layer.0.output.dense.bias", "encoder.layer.0.output.LayerNorm.gamma", "encoder.layer.0.output.LayerNorm.beta", "encoder.layer.1.attention.self.query.weight", "encoder.layer.1.attention.self.query.bias", "encoder.layer.1.attention.self.key.weight", "encoder.layer.1.attention.self.key.bias", "encoder.layer.1.attention.self.value.weight", "encoder.layer.1.attention.self.value.bias", "encoder.layer.1.attention.output.dense.weight", "encoder.layer.1.attention.output.dense.bias", "encoder.layer.1.attention.output.LayerNorm.gamma", "encoder.layer.1.attention.output.LayerNorm.beta", "encoder.layer.1.intermediate.dense.weight", "encoder.layer.1.intermediate.dense.bias", "encoder.layer.1.output.dense.weight", "encoder.layer.1.output.dense.bias", "encoder.layer.1.output.LayerNorm.gamma", "encoder.layer.1.output.LayerNorm.beta", "encoder.layer.2.attention.self.query.weight", "encoder.layer.2.attention.self.query.bias", "encoder.layer.2.attention.self.key.weight", "encoder.layer.2.attention.self.key.bias", "encoder.layer.2.attention.self.value.weight", "encoder.layer.2.attention.self.value.bias", "encoder.layer.2.attention.output.dense.weight", "encoder.layer.2.attention.output.dense.bias", "encoder.layer.2.attention.output.LayerNorm.gamma", "encoder.layer.2.attention.output.LayerNorm.beta", "encoder.layer.2.intermediate.dense.weight", "encoder.layer.2.intermediate.dense.bias", "encoder.layer.2.output.dense.weight", "encoder.layer.2.output.dense.bias", "encoder.layer.2.output.LayerNorm.gamma", "encoder.layer.2.output.LayerNorm.beta", "encoder.layer.3.attention.self.query.weight", "encoder.layer.3.attention.self.query.bias", "encoder.layer.3.attention.self.key.weight", "encoder.layer.3.attention.self.key.bias", "encoder.layer.3.attention.self.value.weight", "encoder.layer.3.attention.self.value.bias", "encoder.layer.3.attention.output.dense.weight", "encoder.layer.3.attention.output.dense.bias", "encoder.layer.3.attention.output.LayerNorm.gamma", "encoder.layer.3.attention.output.LayerNorm.beta", "encoder.layer.3.intermediate.dense.weight", "encoder.layer.3.intermediate.dense.bias", "encoder.layer.3.output.dense.weight", "encoder.layer.3.output.dense.bias", "encoder.layer.3.output.LayerNorm.gamma", "encoder.layer.3.output.LayerNorm.beta", "encoder.layer.4.attention.self.query.weight", "encoder.layer.4.attention.self.query.bias", "encoder.layer.4.attention.self.key.weight", "encoder.layer.4.attention.self.key.bias", "encoder.layer.4.attention.self.value.weight", "encoder.layer.4.attention.self.value.bias", "encoder.layer.4.attention.output.dense.weight", "encoder.layer.4.attention.output.dense.bias", "encoder.layer.4.attention.output.LayerNorm.gamma", "encoder.layer.4.attention.output.LayerNorm.beta", "encoder.layer.4.intermediate.dense.weight", "encoder.layer.4.intermediate.dense.bias", "encoder.layer.4.output.dense.weight", "encoder.layer.4.output.dense.bias", "encoder.layer.4.output.LayerNorm.gamma", "encoder.layer.4.output.LayerNorm.beta", "encoder.layer.5.attention.self.query.weight", 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"encoder.layer.10.attention.output.LayerNorm.gamma", "encoder.layer.10.attention.output.LayerNorm.beta", "encoder.layer.10.intermediate.dense.weight", "encoder.layer.10.intermediate.dense.bias", "encoder.layer.10.output.dense.weight", "encoder.layer.10.output.dense.bias", "encoder.layer.10.output.LayerNorm.gamma", "encoder.layer.10.output.LayerNorm.beta", "encoder.layer.11.attention.self.query.weight", "encoder.layer.11.attention.self.query.bias", "encoder.layer.11.attention.self.key.weight", "encoder.layer.11.attention.self.key.bias", "encoder.layer.11.attention.self.value.weight", "encoder.layer.11.attention.self.value.bias", "encoder.layer.11.attention.output.dense.weight", "encoder.layer.11.attention.output.dense.bias", "encoder.layer.11.attention.output.LayerNorm.gamma", "encoder.layer.11.attention.output.LayerNorm.beta", "encoder.layer.11.intermediate.dense.weight", "encoder.layer.11.intermediate.dense.bias", "encoder.layer.11.output.dense.weight", "encoder.layer.11.output.dense.bias", "encoder.layer.11.output.LayerNorm.gamma", "encoder.layer.11.output.LayerNorm.beta", "pooler.dense.weight", "pooler.dense.bias"] old_keys = ["bert.embeddings.word_embeddings.weight", "bert.embeddings.position_embeddings.weight", "bert.embeddings.token_type_embeddings.weight", "bert.embeddings.LayerNorm.gamma", "bert.embeddings.LayerNorm.beta", "bert.encoder.layer.0.attention.self.query.weight", "bert.encoder.layer.0.attention.self.query.bias", "bert.encoder.layer.0.attention.self.key.weight", "bert.encoder.layer.0.attention.self.key.bias", "bert.encoder.layer.0.attention.self.value.weight", "bert.encoder.layer.0.attention.self.value.bias", "bert.encoder.layer.0.attention.output.dense.weight", "bert.encoder.layer.0.attention.output.dense.bias", "bert.encoder.layer.0.attention.output.LayerNorm.gamma", "bert.encoder.layer.0.attention.output.LayerNorm.beta", "bert.encoder.layer.0.intermediate.dense.weight", "bert.encoder.layer.0.intermediate.dense.bias", "bert.encoder.layer.0.output.dense.weight", "bert.encoder.layer.0.output.dense.bias", "bert.encoder.layer.0.output.LayerNorm.gamma", "bert.encoder.layer.0.output.LayerNorm.beta", "bert.encoder.layer.1.attention.self.query.weight", "bert.encoder.layer.1.attention.self.query.bias", "bert.encoder.layer.1.attention.self.key.weight", "bert.encoder.layer.1.attention.self.key.bias", "bert.encoder.layer.1.attention.self.value.weight", "bert.encoder.layer.1.attention.self.value.bias", "bert.encoder.layer.1.attention.output.dense.weight", "bert.encoder.layer.1.attention.output.dense.bias", "bert.encoder.layer.1.attention.output.LayerNorm.gamma", "bert.encoder.layer.1.attention.output.LayerNorm.beta", "bert.encoder.layer.1.intermediate.dense.weight", "bert.encoder.layer.1.intermediate.dense.bias", "bert.encoder.layer.1.output.dense.weight", "bert.encoder.layer.1.output.dense.bias", "bert.encoder.layer.1.output.LayerNorm.gamma", "bert.encoder.layer.1.output.LayerNorm.beta", "bert.encoder.layer.2.attention.self.query.weight", "bert.encoder.layer.2.attention.self.query.bias", "bert.encoder.layer.2.attention.self.key.weight", "bert.encoder.layer.2.attention.self.key.bias", "bert.encoder.layer.2.attention.self.value.weight", "bert.encoder.layer.2.attention.self.value.bias", "bert.encoder.layer.2.attention.output.dense.weight", "bert.encoder.layer.2.attention.output.dense.bias", "bert.encoder.layer.2.attention.output.LayerNorm.gamma", "bert.encoder.layer.2.attention.output.LayerNorm.beta", "bert.encoder.layer.2.intermediate.dense.weight", "bert.encoder.layer.2.intermediate.dense.bias", "bert.encoder.layer.2.output.dense.weight", "bert.encoder.layer.2.output.dense.bias", "bert.encoder.layer.2.output.LayerNorm.gamma", "bert.encoder.layer.2.output.LayerNorm.beta", "bert.encoder.layer.3.attention.self.query.weight", "bert.encoder.layer.3.attention.self.query.bias", "bert.encoder.layer.3.attention.self.key.weight", 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"bert.encoder.layer.10.attention.output.LayerNorm.gamma", "bert.encoder.layer.10.attention.output.LayerNorm.beta", "bert.encoder.layer.10.intermediate.dense.weight", "bert.encoder.layer.10.intermediate.dense.bias", "bert.encoder.layer.10.output.dense.weight", "bert.encoder.layer.10.output.dense.bias", "bert.encoder.layer.10.output.LayerNorm.gamma", "bert.encoder.layer.10.output.LayerNorm.beta", "bert.encoder.layer.11.attention.self.query.weight", "bert.encoder.layer.11.attention.self.query.bias", "bert.encoder.layer.11.attention.self.key.weight", "bert.encoder.layer.11.attention.self.key.bias", "bert.encoder.layer.11.attention.self.value.weight", "bert.encoder.layer.11.attention.self.value.bias", "bert.encoder.layer.11.attention.output.dense.weight", "bert.encoder.layer.11.attention.output.dense.bias", "bert.encoder.layer.11.attention.output.LayerNorm.gamma", "bert.encoder.layer.11.attention.output.LayerNorm.beta", "bert.encoder.layer.11.intermediate.dense.weight", "bert.encoder.layer.11.intermediate.dense.bias", "bert.encoder.layer.11.output.dense.weight", "bert.encoder.layer.11.output.dense.bias", "bert.encoder.layer.11.output.LayerNorm.gamma", "bert.encoder.layer.11.output.LayerNorm.beta", "bert.pooler.dense.weight", "bert.pooler.dense.bias", "cls.predictions.bias", "cls.predictions.transform.dense.weight", "cls.predictions.transform.dense.bias", "cls.predictions.transform.LayerNorm.gamma", "cls.predictions.transform.LayerNorm.beta", "cls.predictions.decoder.weight", "cls.seq_relationship.weight", "cls.seq_relationship.bias"] for key in list(state_dict.keys()): # new_key = None # if 'gamma' in key: # new_key = key.replace('gamma', 'weight') # if 'beta' in key: # new_key = key.replace('beta', 'bias') # if 'bert.' in key: # new_key = key.replace('bert.', '') if 'cls.' in key: state_dict.pop(key) # if new_key: # old_keys.append(key) # new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): state_dict[new_key] = state_dict.pop(old_key) model.bert.load_state_dict(state_dict)
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------------------ 原始邮件 ------------------ 发件人: "754563116"notifications@github.com; 发送时间: 2019年10月12日(星期六) 下午3:32 收件人: "NLPScott/bert-Chinese-classification-task"bert-Chinese-classification-task@noreply.github.com; 抄送: "572915766"572915766@qq.com; "Comment"comment@noreply.github.com; 主题: Re: [NLPScott/bert-Chinese-classification-task] loading state_dictfor BertModel (#25) 按照你的方法出现了新的错误，我在你的基础上又改了改，代码如下： state_dict = torch.load(path, map_location='cpu') old_keys = [] new_keys = [] for key in list(state_dict.keys()): # print(key) new_key = None if 'gamma' in key: new_key = key.replace('gamma', 'weight') if 'beta' in key: new_key = key.replace('beta', 'bias') if 'bert.' in key: new_key = key.replace('bert.', '') if 'cls.' in key: new_key = key.replace('cls.', '') # state_dict.pop(key) if new_key: old_keys.append(key) new_keys.append(new_key) for old_key, new_key in zip(old_keys, new_keys): #print('old_key',old_key) #print ('new_key',new_key) state_dict[new_key] = state_dict.pop(old_key) if args.init_checkpoint is not None: model.bert.load_state_dict(state_dict,strict=False) 主要是strict=False，我找了很久， — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.
我像你这样处理，仍然会报错
按照你的方法出现了新的错误，我在你的基础上又改了改，代码如下：
state_dict = torch.load(path, map_location='cpu')
old_keys = []
new_keys = []
for key in list(state_dict.keys()):print(key)
new_key = None
if 'gamma' in key:
new_key = key.replace('gamma', 'weight')
if 'beta' in key:
new_key = key.replace('beta', 'bias')
if 'bert.' in key:
new_key = key.replace('bert.', '')
if 'cls.' in key:
new_key = key.replace('cls.', '')state_dict.pop(key)
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
#print('old_key',old_key)
#print ('new_key',new_key)
state_dict[new_key] = state_dict.pop(old_key)
if args.init_checkpoint is not None:
model.bert.load_state_dict(state_dict,strict=False)主要是strict=False，我找了很久，
我按这个又出现了新问题：
RuntimeError: CUDA error: invalid device ordinal