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Fix parser set_annotations during update
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@ -290,9 +290,6 @@ cdef class Parser(TrainablePipe):
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cdef void c_transition_batch(self, StateC** states, const float* scores,
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int nr_class, int batch_size) nogil:
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# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
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with gil:
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assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
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is_valid = <int*>calloc(self.moves.n_moves, sizeof(int))
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cdef int i, guess
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cdef Transition action
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@ -310,6 +307,7 @@ cdef class Parser(TrainablePipe):
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def update(self, examples, *, drop=0., sgd=None, losses=None):
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cdef StateClass state
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cdef Transition action
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if losses is None:
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losses = {}
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losses.setdefault(self.name, 0.)
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@ -351,6 +349,9 @@ cdef class Parser(TrainablePipe):
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all_states = list(states)
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states_golds = list(zip(states, golds, state2doc))
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n_moves = 0
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mem = Pool()
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is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
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cdef float[::1] scores_row
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while states_golds:
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states, golds, state2doc = zip(*states_golds)
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scores, backprop = model.begin_update(states)
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@ -360,10 +361,20 @@ cdef class Parser(TrainablePipe):
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# can't normalize by the number of states either, as then we'd
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# be getting smaller gradients for states in long sequences.
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backprop(d_scores)
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# Follow the predicted action
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actions = self.transition_states(states, scores)
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for i, action in enumerate(actions):
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histories[i].append(action)
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# Ugh, we need to get the actions for the histories, so we're
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# duplicating work that's being done in transition_states. This
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# should be refactored.
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scores_view = scores
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for i, state in enumerate(states):
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self.moves.set_valid(is_valid, state.c)
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scores_row = scores[i]
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guess = arg_max_if_valid(&scores_row[0], is_valid, scores.shape[1])
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if guess == -1:
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raise ValueError("Could not find valid transition")
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histories[state2doc[i]].append(guess)
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# Follow the predicted action
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action = self.moves.c[guess]
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action.do(state.c, action.label)
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states_golds = [
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s for s in zip(states, golds, state2doc)
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if not s[0].is_final()
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