add finish_update to Pipe

spacy/language.py

@@ -1094,7 +1094,7 @@ class Language:
                     and hasattr(proc, "model")
                     and proc.model not in (True, False, None)
                 ):
-                    proc.model.finish_update(sgd)
+                    proc.finish_update(sgd)
         return losses
 
     def rehearse(

spacy/pipeline/entity_linker.py

@@ -238,7 +238,7 @@ class EntityLinker(Pipe):
         )
         bp_context(d_scores)
         if sgd is not None:
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         losses[self.name] += loss
         if set_annotations:
             self.set_annotations(docs, predictions)

spacy/pipeline/multitask.pyx

@@ -209,7 +209,7 @@ class ClozeMultitask(Pipe):
         loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions)
         bp_predictions(d_predictions)
         if sgd is not None:
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         if losses is not None:
             losses[self.name] += loss
         return losses

spacy/pipeline/pipe.pyx

@@ -132,7 +132,7 @@ cdef class Pipe:
         loss, d_scores = self.get_loss(examples, scores)
         bp_scores(d_scores)
         if sgd not in (None, False):
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         losses[self.name] += loss
         if set_annotations:
             docs = [eg.predicted for eg in examples]
@@ -245,6 +245,17 @@ cdef class Pipe:
         with self.model.use_params(params):
             yield
 
+    def finish_update(self, sgd):
+        """Update parameters using the current parameter gradients.
+        The Optimizer instance contains the functionality to perform
+        the stochastic gradient descent.
+
+        sgd (thinc.api.Optimizer): The optimizer.
+
+        DOCS: https://nightly.spacy.io/api/pipe#finish_update
+        """
+        self.model.finish_update(sgd)
+
     def score(self, examples, **kwargs):
         """Score a batch of examples.
 

spacy/pipeline/tagger.pyx

@@ -203,7 +203,7 @@ class Tagger(Pipe):
         loss, d_tag_scores = self.get_loss(examples, tag_scores)
         bp_tag_scores(d_tag_scores)
         if sgd not in (None, False):
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
 
         losses[self.name] += loss
         if set_annotations:
@@ -238,7 +238,7 @@ class Tagger(Pipe):
         target = self._rehearsal_model(examples)
         gradient = guesses - target
         backprop(gradient)
-        self.model.finish_update(sgd)
+        self.finish_update(sgd)
         if losses is not None:
             losses.setdefault(self.name, 0.0)
             losses[self.name] += (gradient**2).sum()

spacy/pipeline/textcat.py

@@ -212,7 +212,7 @@ class TextCategorizer(Pipe):
         loss, d_scores = self.get_loss(examples, scores)
         bp_scores(d_scores)
         if sgd is not None:
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         losses[self.name] += loss
         if set_annotations:
             docs = [eg.predicted for eg in examples]
@@ -256,7 +256,7 @@ class TextCategorizer(Pipe):
         gradient = scores - target
         bp_scores(gradient)
         if sgd is not None:
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         if losses is not None:
             losses[self.name] += (gradient ** 2).sum()
         return losses

spacy/pipeline/tok2vec.py

@@ -188,7 +188,7 @@ class Tok2Vec(Pipe):
             accumulate_gradient(one_d_tokvecs)
             d_docs = bp_tokvecs(d_tokvecs)
             if sgd is not None:
-                self.model.finish_update(sgd)
+                self.finish_update(sgd)
             return d_docs
 
         batch_id = Tok2VecListener.get_batch_id(docs)

spacy/pipeline/transition_parser.pyx

@@ -315,7 +315,7 @@ cdef class Parser(Pipe):
 
         backprop_tok2vec(golds)
         if sgd not in (None, False):
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         if set_annotations:
             docs = [eg.predicted for eg in examples]
             self.set_annotations(docs, all_states)
@@ -367,7 +367,7 @@ cdef class Parser(Pipe):
         # Do the backprop
         backprop_tok2vec(docs)
         if sgd is not None:
-            self.model.finish_update(sgd)
+            self.finish_update(sgd)
         losses[self.name] += loss / n_scores
         del backprop
         del backprop_tok2vec

website/docs/api/pipe.md

@@ -294,6 +294,24 @@ context, the original parameters are restored.
 | -------- | -------------------------------------------------- |
 | `params` | The parameter values to use in the model. ~~dict~~ |
 
+## Pipe.finish_update {#finish_update tag="method"}
+
+Update parameters using the current parameter gradients. Defaults to calling
+[`self.model.finish_update`](https://thinc.ai/docs/api-model#finish_update).
+
+> #### Example
+>
+> ```python
+> pipe = nlp.add_pipe("your_custom_pipe")
+> optimizer = nlp.initialize()
+> losses = pipe.update(examples, sgd=None)
+> pipe.finish_update(sgd)
+> ```
+
+| Name  | Description                           |
+| ----- | ------------------------------------- |
+| `sgd` | An optimizer. ~~Optional[Optimizer]~~ |
+
 ## Pipe.add_label {#add_label tag="method"}
 
 > #### Example