Remove the state argument from Language

spacy/language.py

@@ -145,7 +145,7 @@ class Language(object):
         else:
             self.pipeline = []
 
-    def __call__(self, text, state=None, **disabled):
+    def __call__(self, text, **disabled):
         """
         Apply the pipeline to some text.  The text can span multiple sentences,
         and can contain arbtrary whitespace.  Alignment into the original string
@@ -153,7 +153,6 @@ class Language(object):
 
         Args:
             text (unicode): The text to be processed.
-            state: Arbitrary
 
         Returns:
             doc (Doc): A container for accessing the annotations.
@@ -170,31 +169,28 @@ class Language(object):
             name = getattr(proc, 'name', None)
             if name in disabled and not disabled[name]:
                 continue
-            state = proc(doc, state=state)
+            proc(doc)
         return doc
 
-    def update(self, docs, golds, state=None, drop=0., sgd=None):
+    def update(self, docs, golds, drop=0., sgd=None):
         grads = {}
         def get_grads(W, dW, key=None):
             grads[key] = (W, dW)
-        state = {} if state is None else state
-        for process in self.pipeline:
-            if hasattr(process, 'update'):
-                state = process.update(docs, golds,
-                            state=state,
-                            drop=drop,
-                            sgd=get_grads)
-            else:
-                process(docs, state=state)
-        if sgd is not None:
-            for key, (W, dW) in grads.items():
-                # TODO: Unhack this when thinc improves
-                if isinstance(W, numpy.ndarray):
-                    sgd.ops = NumpyOps()
-                else:
-                    sgd.ops = CupyOps()
-                sgd(W, dW, key=key)
-        return state
+        tok2vec = self.pipeline[0]
+        feats = tok2vec.doc2feats(docs)
+        for proc in self.pipeline[1:]:
+            tokvecs, bp_tokvecs = tok2vec.model.begin_update(feats, drop=drop)
+            grads = {}
+            d_tokvecs = proc.update((docs, tokvecs), golds, sgd=get_grads, drop=drop)
+            bp_tokvecs(d_tokvecs, sgd=get_grads)
+            if sgd is not None:
+                for key, (W, dW) in grads.items():
+                    # TODO: Unhack this when thinc improves
+                    if isinstance(W, numpy.ndarray):
+                        sgd.ops = NumpyOps()
+                    else:
+                        sgd.ops = CupyOps()
+                    sgd(W, dW, key=key)
 
     @contextmanager
     def begin_training(self, gold_tuples, **cfg):
@@ -248,18 +244,18 @@ class Language(object):
             parse (bool)
             entity (bool)
         """
-        #stream = ((self.make_doc(text), None) for text in texts)
-        stream = ((doc, {}) for doc in texts)
+        #docs = (self.make_doc(text) for text in texts)
+        docs = texts
         for proc in self.pipeline:
             name = getattr(proc, 'name', None)
             if name in disabled and not disabled[name]:
                 continue
 
             if hasattr(proc, 'pipe'):
-                stream = proc.pipe(stream, n_threads=n_threads, batch_size=batch_size)
+                docs = proc.pipe(docs, n_threads=n_threads, batch_size=batch_size)
             else:
-                stream = (proc(doc, state) for doc, state in stream)
-        for doc, state in stream:
+                docs = (proc(doc) for doc in docs)
+        for doc in docs:
             yield doc
 
     def to_disk(self, path, **exclude):