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Fix dropout in parser
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5b67bcbee0
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@ -249,11 +249,13 @@ cdef class Parser:
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with Model.use_device('cpu'):
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if depth == 0:
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upper = chain()
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upper.is_noop = True
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else:
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upper = chain(
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clone(Maxout(hidden_width), (depth-1)),
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zero_init(Affine(nr_class))
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zero_init(Affine(nr_class, drop_factor=0.0))
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)
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upper.is_noop = False
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# TODO: This is an unfortunate hack atm!
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# Used to set input dimensions in network.
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lower.begin_training(lower.ops.allocate((500, token_vector_width)))
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@ -364,7 +366,7 @@ cdef class Parser:
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cdef np.ndarray scores
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c_token_ids = <int*>token_ids.data
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c_is_valid = <int*>is_valid.data
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cdef int has_hidden = hasattr(vec2scores, 'W')
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cdef int has_hidden = not getattr(vec2scores, 'is_noop', False)
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while not next_step.empty():
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if not has_hidden:
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for i in cython.parallel.prange(
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@ -426,7 +428,7 @@ cdef class Parser:
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states = self.moves.init_batch(docs)
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state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
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drop)
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0.0)
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todo = [(s, g) for (s, g) in zip(states, golds)
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if not s.is_final() and g is not None]
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@ -438,11 +440,14 @@ cdef class Parser:
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states, golds = zip(*todo)
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token_ids = self.get_token_ids(states)
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vector, bp_vector = state2vec.begin_update(token_ids, drop=drop)
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vector, bp_vector = state2vec.begin_update(token_ids, drop=0.0)
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mask = vec2scores.ops.get_dropout_mask(vector.shape, drop)
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vector *= mask
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scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
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d_scores = self.get_batch_loss(states, golds, scores)
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d_vector = bp_scores(d_scores, sgd=sgd)
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d_vector *= mask
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if isinstance(self.model[0].ops, CupyOps) \
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and not isinstance(token_ids, state2vec.ops.xp.ndarray):
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