强制 BERT 转换器使用 CUDA
Force BERT transformer to use CUDA
我想强制 Huggingface 转换器 (BERT) 使用 CUDA。
nvidia-smi 显示我的所有 CPU 核心在代码执行期间都已达到极限，但我的 GPU 利用率为 0%。不幸的是，我是 Hugginface 库和 PyTorch 的新手，不知道在哪里放置 CUDA 属性 device = cuda:0 或 .to(cuda:0).
下面的代码基本上是 german sentiment BERT working example
的自定义部分
class SentimentModel_t(pt.nn.Module):
def __init__(self, model_name: str = "oliverguhr/german-sentiment-bert"):
DEVICE = "cuda:0" if pt.cuda.is_available() else "cpu"
print(DEVICE)
super(SentimentModel_t,self).__init__()
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(DEVICE)
self.tokenizer = BertTokenizerFast.from_pretrained(model_name)
def predict_sentiment(self, texts: List[str])-> List[str]:
texts = [self.clean_text(text) for text in texts]
# Add special tokens takes care of adding [CLS], [SEP], <s>... tokens in the right way for each model.
input_ids = self.tokenizer.batch_encode_plus(texts,padding=True, add_special_tokens=True, truncation=True, max_length=self.tokenizer.max_len_single_sentence)
input_ids = pt.tensor(input_ids["input_ids"])
with pt.no_grad():
logits = self.model(input_ids)
label_ids = pt.argmax(logits[0], axis=1)
labels = [self.model.config.id2label[label_id] for label_id in label_ids.tolist()]
return labels
编辑：应用@KonstantinosKokos 的建议后（见上面编辑过的代码）我得到了
RuntimeError: Input, output and indices must be on the current device
指向
with pt.no_grad():
logits = self.model(input_ids)
完整的错误代码可以在下面获得：
<ipython-input-15-b843edd87a1a> in predict_sentiment(self, texts)
23
24 with pt.no_grad():
---> 25 logits = self.model(input_ids)
26
27 label_ids = pt.argmax(logits[0], axis=1)
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
1364 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1365
-> 1366 outputs = self.bert(
1367 input_ids,
1368 attention_mask=attention_mask,
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)
859 head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
860
--> 861 embedding_output = self.embeddings(
862 input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
863 )
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
196
197 if inputs_embeds is None:
--> 198 inputs_embeds = self.word_embeddings(input_ids)
199 token_type_embeddings = self.token_type_embeddings(token_type_ids)
200
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/sparse.py in forward(self, input)
122
123 def forward(self, input: Tensor) -> Tensor:
--> 124 return F.embedding(
125 input, self.weight, self.padding_idx, self.max_norm,
126 self.norm_type, self.scale_grad_by_freq, self.sparse)
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1850 # remove once script supports set_grad_enabled
1851 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1852 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1853
1854
您可以像这样使整个 class 继承 torch.nn.Module：
class SentimentModel_t(torch.nn.Module):
def __init___(...)
super(SentimentModel_t, self).__init__()
...
初始化模型后，您可以调用 .to(device) 将其投射到您选择的设备，如下所示：
sentiment_model = SentimentModel_t(...)
sentiment_model.to('cuda')
.to()递归应用于class的所有子模块，model是其中之一（抱脸模型继承torch.nn.Module，从而为to()).
请注意，这使得在 __init__() 中选择设备变得多余：它现在是一个外部上下文，您可以轻松切换 to/from。
或者，您可以通过将包含的 BERT 模型直接转换为 cuda（不太优雅）来对设备进行硬编码：
class SentimentModel_t():
def __init__(self, ...):
DEVICE = "cuda:0" if pt.cuda.is_available() else "cpu"
print(DEVICE)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(DEVICE)
我来晚了一点。我编写的 python 包已经使用了您的 GPU。你可以看看 code to see how it was implemented
只需安装软件包：
pip install germansentiment
和运行代码：
from germansentiment import SentimentModel
model = SentimentModel()
texts = [
"Mit keinem guten Ergebniss","Das ist gar nicht mal so gut",
"Total awesome!","nicht so schlecht wie erwartet",
"Der Test verlief positiv.","Sie fährt ein grünes Auto."]
result = model.predict_sentiment(texts)
print(result)
重要提示：如果您自己编写代码来使用该模型，您还需要运行预处理代码。否则结果可能不对。
我想强制 Huggingface 转换器 (BERT) 使用 CUDA。
nvidia-smi 显示我的所有 CPU 核心在代码执行期间都已达到极限，但我的 GPU 利用率为 0%。不幸的是，我是 Hugginface 库和 PyTorch 的新手，不知道在哪里放置 CUDA 属性 device = cuda:0 或 .to(cuda:0).
下面的代码基本上是 german sentiment BERT working example
的自定义部分class SentimentModel_t(pt.nn.Module):
def __init__(self, model_name: str = "oliverguhr/german-sentiment-bert"):
DEVICE = "cuda:0" if pt.cuda.is_available() else "cpu"
print(DEVICE)
super(SentimentModel_t,self).__init__()
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(DEVICE)
self.tokenizer = BertTokenizerFast.from_pretrained(model_name)
def predict_sentiment(self, texts: List[str])-> List[str]:
texts = [self.clean_text(text) for text in texts]
# Add special tokens takes care of adding [CLS], [SEP], <s>... tokens in the right way for each model.
input_ids = self.tokenizer.batch_encode_plus(texts,padding=True, add_special_tokens=True, truncation=True, max_length=self.tokenizer.max_len_single_sentence)
input_ids = pt.tensor(input_ids["input_ids"])
with pt.no_grad():
logits = self.model(input_ids)
label_ids = pt.argmax(logits[0], axis=1)
labels = [self.model.config.id2label[label_id] for label_id in label_ids.tolist()]
return labels
编辑：应用@KonstantinosKokos 的建议后（见上面编辑过的代码）我得到了
RuntimeError: Input, output and indices must be on the current device
指向
with pt.no_grad():
logits = self.model(input_ids)
完整的错误代码可以在下面获得：
<ipython-input-15-b843edd87a1a> in predict_sentiment(self, texts)
23
24 with pt.no_grad():
---> 25 logits = self.model(input_ids)
26
27 label_ids = pt.argmax(logits[0], axis=1)
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)
1364 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1365
-> 1366 outputs = self.bert(
1367 input_ids,
1368 attention_mask=attention_mask,
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)
859 head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
860
--> 861 embedding_output = self.embeddings(
862 input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
863 )
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds)
196
197 if inputs_embeds is None:
--> 198 inputs_embeds = self.word_embeddings(input_ids)
199 token_type_embeddings = self.token_type_embeddings(token_type_ids)
200
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/modules/sparse.py in forward(self, input)
122
123 def forward(self, input: Tensor) -> Tensor:
--> 124 return F.embedding(
125 input, self.weight, self.padding_idx, self.max_norm,
126 self.norm_type, self.scale_grad_by_freq, self.sparse)
~/PycharmProjects/Test_project/venv/lib/python3.8/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1850 # remove once script supports set_grad_enabled
1851 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1852 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1853
1854
您可以像这样使整个 class 继承 torch.nn.Module：
class SentimentModel_t(torch.nn.Module):
def __init___(...)
super(SentimentModel_t, self).__init__()
...
初始化模型后，您可以调用 .to(device) 将其投射到您选择的设备，如下所示：
sentiment_model = SentimentModel_t(...)
sentiment_model.to('cuda')
.to()递归应用于class的所有子模块，model是其中之一（抱脸模型继承torch.nn.Module，从而为to()).
请注意，这使得在 __init__() 中选择设备变得多余：它现在是一个外部上下文，您可以轻松切换 to/from。
或者，您可以通过将包含的 BERT 模型直接转换为 cuda（不太优雅）来对设备进行硬编码：
class SentimentModel_t():
def __init__(self, ...):
DEVICE = "cuda:0" if pt.cuda.is_available() else "cpu"
print(DEVICE)
self.model = AutoModelForSequenceClassification.from_pretrained(model_name).to(DEVICE)
我来晚了一点。我编写的 python 包已经使用了您的 GPU。你可以看看 code to see how it was implemented
只需安装软件包：
pip install germansentiment
和运行代码：
from germansentiment import SentimentModel
model = SentimentModel()
texts = [
"Mit keinem guten Ergebniss","Das ist gar nicht mal so gut",
"Total awesome!","nicht so schlecht wie erwartet",
"Der Test verlief positiv.","Sie fährt ein grünes Auto."]
result = model.predict_sentiment(texts)
print(result)
重要提示：如果您自己编写代码来使用该模型，您还需要运行预处理代码。否则结果可能不对。