* `Language.update`: ensure that tok2vec gets updated
The components in a pipeline can be updated independently. However,
tok2vec implementations are an exception to this, since they depend on
listeners for their gradients. The update method of a tok2vec
implementation computes the tok2vec forward and passes this along with a
backprop function to the listeners. This backprop function accumulates
gradients for all the listeners. There are two ways in which the
accumulated gradients can be used to update the tok2vec weights:
1. Call the `finish_update` method of tok2vec *after* the `update`
method is called on all of the pipes that use a tok2vec listener.
2. Pass an optimizer to the `update` method of tok2vec. In this
case, tok2vec will give the last listener a special backprop
function that calls `finish_update` on the tok2vec.
Unfortunately, `Language.update` did neither of these. Instead, it
immediately called `finish_update` on every pipe after `update`. As a
result, the tok2vec weights are updated when no gradients have been
accumulated from listeners yet. And the gradients of the listeners are
only used in the next call to `Language.update` (when `finish_update` is
called on tok2vec again).
This change fixes this issue by passing the optimizer to the `update`
method of trainable pipes, leading to use of the second strategy
outlined above.
The main updating loop in `Language.update` is also simplified by using
the `TrainableComponent` protocol consistently.
* Train loop: `sgd` is `Optional[Optimizer]`, do not pass false
* Language.update: call pipe finish_update after all pipe updates
This does correct and fast updates if multiple components update the
same parameters.
* Add comment why we moved `finish_update` to a separate loop
* Add training option to set annotations on update
Add a `[training]` option called `set_annotations_on_update` to specify
a list of components for which the predicted annotations should be set
on `example.predicted` immediately after that component has been
updated. The predicted annotations can be accessed by later components
in the pipeline during the processing of the batch in the same `update`
call.
* Rename to annotates / annotating_components
* Add test for `annotating_components` when training from config
* Add documentation
