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ReStructuredText
43 lines
1.5 KiB
ReStructuredText
Overview
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========
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Fairseq can be extended through user-supplied `plug-ins
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<https://en.wikipedia.org/wiki/Plug-in_(computing)>`_. We support five kinds of
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plug-ins:
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- :ref:`Models` define the neural network architecture and encapsulate all of the
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learnable parameters.
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- :ref:`Criterions` compute the loss function given the model outputs and targets.
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- :ref:`Tasks` store dictionaries and provide helpers for loading/iterating over
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Datasets, initializing the Model/Criterion and calculating the loss.
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- :ref:`Optimizers` update the Model parameters based on the gradients.
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- :ref:`Learning Rate Schedulers` update the learning rate over the course of
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training.
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**Training Flow**
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Given a ``model``, ``criterion``, ``task``, ``optimizer`` and ``lr_scheduler``,
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fairseq implements the following high-level training flow::
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for epoch in range(num_epochs):
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itr = task.get_batch_iterator(task.dataset('train'))
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for num_updates, batch in enumerate(itr):
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loss = criterion(model, batch)
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optimizer.backward(loss)
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optimizer.step()
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lr_scheduler.step_update(num_updates)
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lr_scheduler.step(epoch)
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**Registering new plug-ins**
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New plug-ins are *registered* through a set of ``@register`` function
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decorators, for example::
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@register_model('my_lstm')
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class MyLSTM(FairseqModel):
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(...)
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Once registered, new plug-ins can be used with the existing :ref:`Command-line
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Tools`. See the Tutorial sections for more detailed walkthroughs of how to add
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new plug-ins.
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