diff --git a/spacy/cli/ud/ud_train.py b/spacy/cli/ud/ud_train.py
index afef6c073..44ecababe 100644
--- a/spacy/cli/ud/ud_train.py
+++ b/spacy/cli/ud/ud_train.py
@@ -299,7 +299,7 @@ def get_token_conllu(token, i):
         head = 0
     else:
         head = i + (token.head.i - token.i) + 1
-    features = token.vocab.morphology.get(token.morph_key)
+    features = list(token.morph)
     feat_str = []
     replacements = {"one": "1", "two": "2", "three": "3"}
     for feat in features:
diff --git a/spacy/morphology.pyx b/spacy/morphology.pyx
index 63d0291ff..2f3e8d1fa 100644
--- a/spacy/morphology.pyx
+++ b/spacy/morphology.pyx
@@ -107,11 +107,11 @@ def get_field_id(feature):
 
 
 def get_field_size(field):
-    return FIELD_SIZES[field]
+    return FIELD_SIZES[FIELDS[field]]
 
 
 def get_field_offset(field):
-    return FIELD_OFFSETS[field]
+    return FIELD_OFFSETS[FIELDS[]]]]field]
 
 
 cdef class Morphology:
@@ -831,6 +831,8 @@ FEATURES = [
    "Aspect_mod",
    "Aspect_none",
    "Aspect_perf",
+   "Aspect_prof",
+   "Aspect_prosp",
    "Case_abe",
    "Case_abl",
    "Case_abs",
@@ -1074,6 +1076,6 @@ _seen_fields = Counter()
 for i, feature in enumerate(FEATURES):
     field = FEATURE_FIELDS[feature]
     FEATURE_OFFSETS[feature] = _seen_fields[field]
-    if _seen_fields == 0:
+    if _seen_fields[field] == 0:
         FIELD_OFFSETS[field] = i
     _seen_fields[field] += 1 
diff --git a/spacy/pipeline/morphologizer.pyx b/spacy/pipeline/morphologizer.pyx
index 9f25ba357..223bb6ec5 100644
--- a/spacy/pipeline/morphologizer.pyx
+++ b/spacy/pipeline/morphologizer.pyx
@@ -81,16 +81,18 @@ class Morphologizer(Pipe):
             doc_scores = batch_scores[i]
             doc_guesses = scores_to_guesses(doc_scores, self.model.softmax.out_sizes)
             # Convert the neuron indices into feature IDs.
-            doc_feat_ids = self.model.ops.allocate((len(doc), len(field_names)), dtype='i')
+            doc_feat_ids = numpy.zeros((len(doc), len(field_names)), dtype='i')
             for j in range(len(doc)):
                 for k, offset in enumerate(offsets):
                     if doc_guesses[j, k] == 0:
                         doc_feat_ids[j, k] = 0
                     else:
                         doc_feat_ids[j, k] = offset + doc_guesses[j, k]
+                # Get the set of feature names.
+                feats = {FEATURES[f] for f in doc_feat_ids[j] if f != 0}
                 # Now add the analysis, and set the hash.
                 try:
-                    doc.c[j].morph = self.vocab.morphology.add(doc_feat_ids[j])
+                    doc.c[j].morph = self.vocab.morphology.add(feats)
                 except:
                     print(offsets)
                     print(doc_guesses[j])
@@ -114,7 +116,12 @@ class Morphologizer(Pipe):
             guesses.append(scores_to_guesses(doc_scores, self.model.softmax.out_sizes))
         guesses = self.model.ops.xp.vstack(guesses)
         scores = self.model.ops.xp.vstack(scores)
+        if not isinstance(scores, numpy.ndarray):
+            scores = scores.get()
+        if not isinstance(guesses, numpy.ndarray):
+            guesses = guesses.get()
         cdef int idx = 0
+        # Do this on CPU, as we can't vectorize easily.
         target = numpy.zeros(scores.shape, dtype='f')
         field_sizes = self.model.softmax.out_sizes
         for gold in golds:
@@ -134,7 +141,8 @@ class Morphologizer(Pipe):
                             target[idx, col_offset] = 1.
                         col_offset += field_size
                 idx += 1
-        target = self.model.ops.xp.array(target, dtype='f')
+        target = self.model.ops.asarray(target, dtype='f')
+        scores = self.model.ops.asarray(scores, dtype='f')
         d_scores = scores - target
         loss = (d_scores**2).sum()
         d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])