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spacy/ml/tb_framework.pyx

@@ -171,15 +171,18 @@ class TransitionModelInputs:
         "states",
     ]
 
-    def __init__(self, docs: List[Doc], moves: TransitionSystem,
-        actions: Optional[List[Ints1d]]=None, max_moves: int=0,
+    def __init__(
+        self,
+        docs: List[Doc],
+        moves: TransitionSystem,
+        actions: Optional[List[Ints1d]]=None,
+        max_moves: int=0,
         states: Optional[List[State]]=None):
         """
         actions (Optional[List[Ints1d]]): actions to apply for each Doc.
         docs (List[Doc]): Docs to predict transition sequences for.
-        max_moves: (Optional[int]): the maximum number of moves to apply,
-            values less than 1 will apply moves to states until they are
-            final states.
+        max_moves: (int): the maximum number of moves to apply, values less
+            than 1 will apply moves to states until they are final states.
         moves (TransitionSystem): the transition system to use when predicting
             the transition sequences.
         states (Optional[List[States]]): the initial states to predict the
@@ -208,7 +211,7 @@ def forward(model, inputs: TransitionModelInputs, is_train: bool):
     feats, backprop_feats = _forward_precomputable_affine(model, tokvecs, is_train)
     seen_mask = _get_seen_mask(model)
 
-    if beam_width == 1 and not is_train and isinstance(model.ops, NumpyOps):
+    if not is_train and beam_width == 1 and isinstance(model.ops, NumpyOps):
         # Note: max_moves is only used during training, so we don't need to
         #       pass it to the greedy inference path.
         return _forward_greedy_cpu(model, moves, states, feats, seen_mask, actions=actions)
@@ -271,8 +274,17 @@ cdef list _parse_batch(CBlas cblas, TransitionSystem moves, StateC** states,
     return scores
 
 
-def _forward_fallback(model: Model, moves: TransitionSystem, states: List[StateClass], tokvecs, backprop_tok2vec, feats, backprop_feats, seen_mask, is_train: bool,
-        actions: Optional[List[Ints1d]]=None, max_moves=0):
+def _forward_fallback(
+    model: Model,
+    moves: TransitionSystem,
+    states: List[StateClass],
+    tokvecs, backprop_tok2vec,
+    feats,
+    backprop_feats,
+    seen_mask,
+    is_train: bool,
+    actions: Optional[List[Ints1d]]=None,
+    max_moves: int=0):
     nF = model.get_dim("nF")
     output = model.get_ref("output")
     hidden_b = model.get_param("hidden_b")

spacy/pipeline/transition_parser.pyx

@@ -528,10 +528,10 @@ class Parser(TrainablePipe):
 
     def _init_gold_batch(self, examples, max_length):
         """Make a square batch, of length equal to the shortest transition
-        sequence or a cap. A long
-        doc will get multiple states. Let's say we have a doc of length 2*N,
-        where N is the shortest doc. We'll make two states, one representing
-        long_doc[:N], and another representing long_doc[N:]."""
+        sequence or a cap. A long doc will get multiple states. Let's say we
+        have a doc of length 2*N, where N is the shortest doc. We'll make
+        two states, one representing long_doc[:N], and another representing
+        long_doc[N:]."""
         cdef:
             StateClass start_state
             StateClass state