Migrating from previous packages

Migrating from pytorch-transformers to 🤗 Transformers

Here is a quick summary of what you should take care of when migrating from pytorch-transformers to 🤗 Transformers.

Positional order of some models’ keywords inputs (attention_mask, token_type_ids…) changed

To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models keywords inputs (attention_mask, token_type_ids…) has been changed.

If you used to call the models with keyword names for keyword arguments, e.g. model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids), this should not cause any change.

If you used to call the models with positional inputs for keyword arguments, e.g. model(inputs_ids, attention_mask, token_type_ids), you may have to double check the exact order of input arguments.

Migrating from pytorch-pretrained-bert

Here is a quick summary of what you should take care of when migrating from pytorch-pretrained-bert to 🤗 Transformers

Models always output tuples

The main breaking change when migrating from pytorch-pretrained-bert to 🤗 Transformers is that the models forward method always outputs a tuple with various elements depending on the model and the configuration parameters.

The exact content of the tuples for each model are detailled in the models’ docstrings and the documentation.

In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in pytorch-pretrained-bert.

Here is a pytorch-pretrained-bert to 🤗 Transformers conversion example for a BertForSequenceClassification classification model:

# Let's load our model
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# If you used to have this line in pytorch-pretrained-bert:
loss = model(input_ids, labels=labels)

# Now just use this line in 🤗 Transformers to extract the loss from the output tuple:
outputs = model(input_ids, labels=labels)
loss = outputs[0]

# In 🤗 Transformers you can also have access to the logits:
loss, logits = outputs[:2]

# And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=True)
outputs = model(input_ids, labels=labels)
loss, logits, attentions = outputs

Serialization

Breaking change in the from_pretrained()method:

  1. Models are now set in evaluation mode by default when instantiated with the from_pretrained() method. To train them don’t forget to set them back in training mode (model.train()) to activate the dropout modules.

  2. The additional *inputs and **kwargs arguments supplied to the from_pretrained() method used to be directly passed to the underlying model’s class __init__() method. They are now used to update the model configuration attribute first which can break derived model classes build based on the previous BertForSequenceClassification examples. More precisely, the positional arguments *inputs provided to from_pretrained() are directly forwarded the model __init__() method while the keyword arguments **kwargs (i) which match configuration class attributes are used to update said attributes (ii) which don’t match any configuration class attributes are forwarded to the model __init__() method.

Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method save_pretrained(save_directory) if you were using any other serialization method before.

Here is an example:

### Let's load a model and tokenizer
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

### Do some stuff to our model and tokenizer
# Ex: add new tokens to the vocabulary and embeddings of our model
tokenizer.add_tokens(['[SPECIAL_TOKEN_1]', '[SPECIAL_TOKEN_2]'])
model.resize_token_embeddings(len(tokenizer))
# Train our model
train(model)

### Now let's save our model and tokenizer to a directory
model.save_pretrained('./my_saved_model_directory/')
tokenizer.save_pretrained('./my_saved_model_directory/')

### Reload the model and the tokenizer
model = BertForSequenceClassification.from_pretrained('./my_saved_model_directory/')
tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')

Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules

The two optimizers previously included, BertAdam and OpenAIAdam, have been replaced by a single AdamW optimizer which has a few differences:

  • it only implements weights decay correction,

  • schedules are now externals (see below),

  • gradient clipping is now also external (see below).

The new optimizer AdamW matches PyTorch Adam optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping.

The schedules are now standard PyTorch learning rate schedulers and not part of the optimizer anymore.

Here is a conversion examples from BertAdam with a linear warmup and decay schedule to AdamW and the same schedule:

# Parameters:
lr = 1e-3
max_grad_norm = 1.0
num_training_steps = 1000
num_warmup_steps = 100
warmup_proportion = float(num_warmup_steps) / float(num_training_steps)  # 0.1

### Previously BertAdam optimizer was instantiated like this:
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
### and used like this:
for batch in train_data:
    loss = model(batch)
    loss.backward()
    optimizer.step()

### In 🤗 Transformers, optimizer and schedules are splitted and instantiated like this:
optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
### and used like this:
for batch in train_data:
    loss = model(batch)
    loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
    optimizer.step()
    scheduler.step()