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cuda on in the examples by default

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thomwolf
2019-02-11 12:15:43 +01:00
parent 0a9860daa7
commit b31ba23913
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@@ -187,8 +187,14 @@ Let's see how to use `BertModel` to get hidden states
model = BertModel.from_pretrained('bert-base-uncased')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
model.to('cuda')
# Predict hidden states features for each layer
encoded_layers, _ = model(tokens_tensor, segments_tensors)
with torch.no_grad():
encoded_layers, _ = model(tokens_tensor, segments_tensors)
# We have a hidden states for each of the 12 layers in model bert-base-uncased
assert len(encoded_layers) == 12
```
@@ -200,8 +206,14 @@ And how to use `BertForMaskedLM`
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
model.to('cuda')
# Predict all tokens
predictions = model(tokens_tensor, segments_tensors)
with torch.no_grad():
predictions = model(tokens_tensor, segments_tensors)
# confirm we were able to predict 'henson'
predicted_index = torch.argmax(predictions[0, masked_index]).item()
@@ -240,8 +252,13 @@ Let's see how to use `OpenAIGPTModel` to get hidden states
model = OpenAIGPTModel.from_pretrained('openai-gpt')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
model.to('cuda')
# Predict hidden states features for each layer
hidden_states = model(tokens_tensor)
with torch.no_grad():
hidden_states = model(tokens_tensor)
```
And how to use `OpenAIGPTLMHeadModel`
@@ -251,19 +268,25 @@ And how to use `OpenAIGPTLMHeadModel`
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
model.to('cuda')
# Predict all tokens
predictions = model(tokens_tensor)
with torch.no_grad():
predictions = model(tokens_tensor)
# get the predicted last token
predicted_index = torch.argmax(predictions[0, masked_index]).item()
predicted_index = torch.argmax(predictions[0, -1, :]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
assert predicted_token == '.</w>'
```
### Transformer-XL
Here is a quick-start example using `OpenAIGPTTokenizer`, `OpenAIGPTModel` and `OpenAIGPTLMHeadModel` class with OpenAI's pre-trained model. See the [doc section](#doc) below for all the details on these classes.
Here is a quick-start example using `TransfoXLTokenizer`, `TransfoXLModel` and `TransfoXLModelLMHeadModel` class with the Transformer-XL model pre-trained on WikiText-103. See the [doc section](#doc) below for all the details on these classes.
First let's prepare a tokenized input with `OpenAIGPTTokenizer`
First let's prepare a tokenized input with `TransfoXLTokenizer`
```python
import torch
@@ -294,27 +317,40 @@ Let's see how to use `TransfoXLModel` to get hidden states
model = TransfoXLModel.from_pretrained('transfo-xl-wt103')
model.eval()
# Predict hidden states features for each layer
hidden_states_1, mems_1 = model(tokens_tensor_1)
# We can re-use the memory cells in a subsequent call to attend a longer context
hidden_states_2, mems_2 = model(tokens_tensor_2, mems_1)
# If you have a GPU, put everything on cuda
tokens_tensor_1 = tokens_tensor_1.to('cuda')
tokens_tensor_2 = tokens_tensor_2.to('cuda')
model.to('cuda')
with torch.no_grad():
# Predict hidden states features for each layer
hidden_states_1, mems_1 = model(tokens_tensor_1)
# We can re-use the memory cells in a subsequent call to attend a longer context
hidden_states_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
```
And how to use `OpenAIGPTLMHeadModel`
And how to use `TransfoXLLMHeadModel`
```python
# Load pre-trained model (weights)
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
model = TransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103')
model.eval()
# Predict all tokens
predictions_1, mems_1 = model(tokens_tensor_1)
# We can re-use the memory cells in a subsequent call to attend a longer context
predictions_2, mems_2 = model(tokens_tensor_2, mems_1)
# If you have a GPU, put everything on cuda
tokens_tensor_1 = tokens_tensor_1.to('cuda')
tokens_tensor_2 = tokens_tensor_2.to('cuda')
model.to('cuda')
with torch.no_grad():
# Predict all tokens
predictions_1, mems_1 = model(tokens_tensor_1)
# We can re-use the memory cells in a subsequent call to attend a longer context
predictions_2, mems_2 = model(tokens_tensor_2, mems=mems_1)
# get the predicted last token
predicted_index = torch.argmax(predictions_1[0, masked_index]).item()
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
assert predicted_token == '.</w>'
```
## Doc