Fix multitask objectives

spacy/pipeline.pyx

@@ -681,13 +681,19 @@ class MultitaskObjective(Tagger):
         return tokvecs, scores
 
     def get_loss(self, docs, golds, scores):
+        assert len(docs) == len(golds)
         cdef int idx = 0
         correct = numpy.zeros((scores.shape[0],), dtype='i')
         guesses = scores.argmax(axis=1)
-        for gold in golds:
-            for i in range(len(gold.labels)):
-                label = self.make_label(i, gold.words, gold.tags, gold.heads,
-                                        gold.labels, gold.ents)
+        for i, gold in enumerate(golds):
+            for j in range(len(docs[i])):
+                # Handes alignment for tokenization differences
+                gold_idx = gold.cand_to_gold[j]
+                if gold_idx is None:
+                    idx += 1
+                    continue
+                label = self.make_label(gold_idx, gold.words, gold.tags,
+                                        gold.heads, gold.labels, gold.ents)
                 if label is None or label not in self.labels:
                     correct[idx] = guesses[idx]
                 else:

spacy/syntax/nn_parser.pyx

@@ -542,6 +542,7 @@ cdef class Parser:
     def update(self, docs, golds, drop=0., sgd=None, losses=None):
         if not any(self.moves.has_gold(gold) for gold in golds):
             return None
+        assert len(docs) == len(golds)
         if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.0:
             return self.update_beam(docs, golds,
                     self.cfg['beam_width'], self.cfg['beam_density'],
@@ -551,6 +552,8 @@ cdef class Parser:
         if isinstance(docs, Doc) and isinstance(golds, GoldParse):
             docs = [docs]
             golds = [golds]
+        for multitask in self._multitasks:
+            multitask.update(docs, golds, drop=drop, sgd=sgd)
         cuda_stream = util.get_cuda_stream()
         states, golds, max_steps = self._init_gold_batch(docs, golds)
         (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
@@ -605,9 +608,7 @@ cdef class Parser:
                 break
         self._make_updates(d_tokvecs,
             bp_tokvecs, backprops, sgd, cuda_stream)
-        for multitask in self._multitasks:
-            multitask.update(docs, golds, drop=drop, sgd=sgd)
-
+    
     def update_beam(self, docs, golds, width=None, density=None,
             drop=0., sgd=None, losses=None):
         if not any(self.moves.has_gold(gold) for gold in golds):