Start rigging beam back up

spacy/pipeline/transition_parser.pyx

@@ -7,6 +7,7 @@ from libcpp.vector cimport vector
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport calloc, free
 import random
+import contextlib
 
 import srsly
 from thinc.api import set_dropout_rate, CupyOps, get_array_module
@@ -210,14 +211,21 @@ class Parser(TrainablePipe):
         with self.model.use_params(params):
             yield
 
+    def __call__(self, Doc doc):
+        """Apply the parser or entity recognizer, setting the annotations onto
+        the `Doc` object.
+
+        doc (Doc): The document to be processed.
+        """
+        states = self.predict([doc])
+        self.set_annotations([doc], states)
+        return doc
+
     def pipe(self, docs, *, int batch_size=256):
         """Process a stream of documents.
 
         stream: The sequence of documents to process.
         batch_size (int): Number of documents to accumulate into a working set.
-        error_handler (Callable[[str, List[Doc], Exception], Any]): Function that
-            deals with a failing batch of documents. The default function just reraises
-            the exception.
 
         YIELDS (Doc): Documents, in order.
         """
@@ -242,27 +250,23 @@ class Parser(TrainablePipe):
         if not any(len(doc) for doc in docs):
             result = self.moves.init_batch(docs)
             return result
-        if self.cfg["beam_width"] == 1:
-            return self.greedy_parse(docs, drop=0.0)
-        else:
-            return self.beam_parse(
-                docs,
-                drop=0.0,
-                beam_width=self.cfg["beam_width"],
-                beam_density=self.cfg["beam_density"]
-            )
+        with _change_attrs(self.model, beam_width=self.cfg["beam_width"], beam_density=self.cfg["beam_density"]):
+            states_or_beams, _ = self.model.predict((docs, self.moves))
+        return states_or_beams
 
     def greedy_parse(self, docs, drop=0.):
-        set_dropout_rate(self.model, drop)
-        # This is pretty dirty, but the NER can resize itself in init_batch,
-        # if labels are missing. We therefore have to check whether we need to
-        # expand our model output.
+        # Deprecated
         self._resize()
-        states, scores = self.model.predict((docs, self.moves))
+        with _change_attrs(self.model, beam_width=1):
+            states, _ = self.model.predict((docs, self.moves))
         return states
 
     def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
-        raise NotImplementedError
+        # Deprecated
+        self._resize()
+        with _change_attrs(self.model, beam_width=self.cfg["beam_width"], beam_density=self.cfg["beam_density"]):
+            beams, _ = self.model.predict((docs, self.moves))
+        return beams
 
     def set_annotations(self, docs, states_or_beams):
         cdef StateClass state
@@ -461,3 +465,19 @@ class Parser(TrainablePipe):
                 except AttributeError:
                     raise ValueError(Errors.E149) from None
         return self
+
+
+@contextlib.contextmanager
+def _change_attrs(model, **kwargs):
+    """Temporarily modify a thinc model's attributes."""
+    unset = object()
+    old_attrs = {}
+    for key, value in kwargs.items():
+        old_attrs[key] = model.attrs.get(key, unset)
+        model.attrs[key] = value
+    yield model
+    for key, value in old_attrs.items():
+        if value is unset:
+            model.attrs.pop(key)
+        else:
+            model.attrs[key] = value