Write moves costs directly into numpy array (#10163)

This avoids elementwise indexing and the allocation of an additional
array.

Gives a ~15% speed improvement when using batch_by_sequence with size
32.

spacy/pipeline/transition_parser.pyx

@@ -23,6 +23,7 @@ from ._parser_internals cimport _beam_utils
 from ._parser_internals import _beam_utils
 from ..vocab cimport Vocab
 from ._parser_internals.transition_system cimport TransitionSystem
+from ..typedefs cimport weight_t
 
 from ..training import validate_examples, validate_get_examples
 from ..errors import Errors, Warnings
@@ -335,8 +336,8 @@ class Parser(TrainablePipe):
         cdef int nO = moves.n_moves
         cdef int nS = sum([len(history) for history in histories])
         cdef Pool mem = Pool()
+        cdef np.ndarray costs_i
         is_valid = <int*>mem.alloc(nO, sizeof(int))
-        c_costs = <float*>mem.alloc(nO, sizeof(float))
         states = moves.init_batch([eg.x for eg in examples])
         batch = []
         for eg, s, h in zip(examples, states, histories):
@@ -347,13 +348,12 @@ class Parser(TrainablePipe):
         while batch:
             costs = numpy.zeros((len(batch), nO), dtype="f")
             for i, (eg, state, history, gold) in enumerate(batch):
+                costs_i = costs[i]
                 clas = history.pop(0)
-                moves.set_costs(is_valid, c_costs, state.c, gold)
+                moves.set_costs(is_valid, <weight_t*>costs_i.data, state.c, gold)
                 action = moves.c[clas]
                 action.do(state.c, action.label)
                 state.c.history.push_back(clas)
-                for j in range(nO):
-                    costs[i, j] = c_costs[j]
             output.append(costs)
             batch = [(eg, s, h, g) for eg, s, h, g in batch if len(h) != 0]
         return self.model.ops.xp.vstack(output)