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Fix merge conflicts in nn_parser from beam stuff
This commit is contained in:
Matthew Honnibal
parent
69bcacdc09
commit
1cec1efca7
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@ -37,14 +37,11 @@ from preshed.maps cimport MapStruct
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from preshed.maps cimport map_get
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from thinc.api import layerize, chain, noop, clone
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<<<<<<< HEAD
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from thinc.neural import Model, Affine, ELU, ReLu, Maxout
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=======
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from thinc.neural import Model, Affine, ReLu, Maxout
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from thinc.neural._classes.batchnorm import BatchNorm as BN
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from thinc.neural._classes.selu import SELU
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from thinc.neural._classes.layernorm import LayerNorm
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>>>>>>> feature/nn-beam-parser
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from thinc.neural.ops import NumpyOps, CupyOps
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from thinc.neural.util import get_array_module
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@ -68,10 +65,6 @@ from ..strings cimport StringStore
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from ..gold cimport GoldParse
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from ..attrs cimport TAG, DEP
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<<<<<<< HEAD
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=======
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USE_FINE_TUNE = True
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>>>>>>> feature/nn-beam-parser
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def get_templates(*args, **kwargs):
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return []
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@ -259,7 +252,6 @@ cdef class Parser:
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nI=token_vector_width)
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with Model.use_device('cpu'):
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<<<<<<< HEAD
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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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@ -269,12 +261,6 @@ cdef class Parser:
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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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=======
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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, drop_factor=0.0))
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)
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>>>>>>> feature/nn-beam-parser
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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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@ -422,7 +408,6 @@ cdef class Parser:
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c_is_valid = <int*>is_valid.data
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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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<<<<<<< HEAD
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if not has_hidden:
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for i in cython.parallel.prange(
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next_step.size(), num_threads=6, nogil=True):
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@ -442,21 +427,6 @@ cdef class Parser:
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&c_scores[i*nr_class], &c_is_valid[i*nr_class], nr_class)
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action = self.moves.c[guess]
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action.do(st, action.label)
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=======
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for i in range(next_step.size()):
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st = next_step[i]
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st.set_context_tokens(&c_token_ids[i*nr_feat], nr_feat)
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self.moves.set_valid(&c_is_valid[i*nr_class], st)
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vectors = state2vec(token_ids[:next_step.size()])
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scores = vec2scores(vectors)
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c_scores = <float*>scores.data
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for i in range(next_step.size()):
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st = next_step[i]
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guess = arg_max_if_valid(
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&c_scores[i*nr_class], &c_is_valid[i*nr_class], nr_class)
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action = self.moves.c[guess]
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action.do(st, action.label)
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>>>>>>> feature/nn-beam-parser
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this_step, next_step = next_step, this_step
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next_step.clear()
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for st in this_step:
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@ -526,13 +496,10 @@ cdef class Parser:
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free(token_ids)
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def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
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<<<<<<< HEAD
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=======
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if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.5:
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return self.update_beam(docs_tokvecs, golds,
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self.cfg['beam_width'], self.cfg['beam_density'],
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drop=drop, sgd=sgd, losses=losses)
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>>>>>>> feature/nn-beam-parser
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if losses is not None and self.name not in losses:
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losses[self.name] = 0.
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docs, tokvec_lists = docs_tokvecs
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@ -589,9 +556,6 @@ cdef class Parser:
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break
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self._make_updates(d_tokvecs,
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backprops, sgd, cuda_stream)
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<<<<<<< HEAD
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return self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
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=======
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d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
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if USE_FINE_TUNE:
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bp_my_tokvecs(d_tokvecs, sgd=sgd)
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@ -646,7 +610,6 @@ cdef class Parser:
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if USE_FINE_TUNE:
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bp_my_tokvecs(d_tokvecs, sgd=sgd)
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return d_tokvecs
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>>>>>>> feature/nn-beam-parser
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def _init_gold_batch(self, whole_docs, whole_golds):
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"""Make a square batch, of length equal to the shortest doc. A long
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@ -691,21 +654,10 @@ cdef class Parser:
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xp = get_array_module(d_tokvecs)
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for ids, d_vector, bp_vector in backprops:
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d_state_features = bp_vector(d_vector, sgd=sgd)
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<<<<<<< HEAD
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active_feats = ids * (ids >= 0)
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active_feats = active_feats.reshape((ids.shape[0], ids.shape[1], 1))
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if hasattr(xp, 'scatter_add'):
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xp.scatter_add(d_tokvecs,
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ids, d_state_features * active_feats)
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else:
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xp.add.at(d_tokvecs,
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ids, d_state_features * active_feats)
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=======
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mask = ids >= 0
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d_state_features *= mask.reshape(ids.shape + (1,))
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self.model[0].ops.scatter_add(d_tokvecs, ids * mask,
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d_state_features)
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>>>>>>> feature/nn-beam-parser
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@property
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def move_names(self):
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