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Remove use of ExampleC from beam parser

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This commit is contained in:
Matthew Honnibal 2016-07-29 19:58:49 +02:00
parent 6b912731f8
commit 25513b8389
2 changed files with 23 additions and 24 deletions
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6
spacy/syntax/_neural.pxd
View File

@ -1,13 +1,13 @@
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.neural.nn cimport NeuralNet
from thinc.linear.features cimport ConjunctionExtracter
from thinc.structs cimport NeuralNetC, ExampleC
from thinc.structs cimport NeuralNetC, FeatureC
cdef class ParserNeuralNet(NeuralNet):
cdef ConjunctionExtracter extracter
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil
cdef int _set_featuresC(self, FeatureC* feats, const void* _state) nogil
cdef class ParserPerceptron(AveragedPerceptron):
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil
cdef int _set_featuresC(self, FeatureC* feats, const void* _state) nogil

41
spacy/syntax/_neural.pyx
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@ -48,14 +48,14 @@ cdef class ParserPerceptron(AveragedPerceptron):
self.update_weight(feat.key, clas, feat.value * step)
return int(loss)
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil:
cdef int _set_featuresC(self, FeatureC* feats, const void* _state) nogil:
cdef atom_t[CONTEXT_SIZE] context
state = <const StateC*>_state
fill_context(eg.atoms, state)
eg.nr_feat = self.extracter.set_features(eg.features, eg.atoms)
fill_context(context, state)
return self.extracter.set_features(feats, context)
def _update_from_history(self, TransitionSystem moves, Doc doc, history, weight_t grad):
cdef Pool mem = Pool()
cdef atom_t[CONTEXT_SIZE] context
features = <FeatureC*>mem.alloc(self.nr_feat, sizeof(FeatureC))
cdef StateClass stcls = StateClass.init(doc.c, doc.length)
@ -64,8 +64,7 @@ cdef class ParserPerceptron(AveragedPerceptron):
cdef class_t clas
self.time += 1
for clas in history:
fill_context(context, stcls.c)
nr_feat = self.extracter.set_features(features, context)
nr_feat = self._set_featuresC(features, stcls.c)
for feat in features[:nr_feat]:
self.update_weight(feat.key, clas, feat.value * grad)
moves.c[clas].do(stcls.c, moves.c[clas].label)
@ -96,11 +95,10 @@ cdef class ParserNeuralNet(NeuralNet):
def nr_feat(self):
return 2000
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil:
memset(eg.features, 0, 2000 * sizeof(FeatureC))
cdef int _set_featuresC(self, FeatureC* feats, const void* _state) nogil:
memset(feats, 0, 2000 * sizeof(FeatureC))
state = <const StateC*>_state
fill_context(eg.atoms, state)
feats = eg.features
start = feats
feats = _add_token(feats, 0, state.S_(0), 1.0)
feats = _add_token(feats, 4, state.S_(1), 1.0)
@ -132,7 +130,7 @@ cdef class ParserNeuralNet(NeuralNet):
state.R_(state.S(0), 2))
feats = _add_pos_trigram(feats, 75, state.S_(0), state.L_(state.S(0), 1),
state.L_(state.S(0), 2))
eg.nr_feat = feats - eg.features
return feats - start
cdef void _set_delta_lossC(self, weight_t* delta_loss,
const weight_t* cost, const weight_t* scores) nogil:
@ -143,8 +141,11 @@ cdef class ParserNeuralNet(NeuralNet):
pass
def _update_from_history(self, TransitionSystem moves, Doc doc, history, weight_t grad):
cdef Example py_eg = Example(nr_class=moves.n_moves, nr_atom=CONTEXT_SIZE,
nr_feat=self.nr_feat, widths=self.widths)
cdef Pool mem = Pool()
features = <FeatureC*>mem.alloc(self.nr_feat, sizeof(FeatureC))
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
costs = <weight_t*>mem.alloc(self.moves.n_moves, sizeof(weight_t))
stcls = StateClass.init(doc.c, doc.length)
moves.initialize_state(stcls.c)
cdef uint64_t[2] key
@ -152,8 +153,8 @@ cdef class ParserNeuralNet(NeuralNet):
key[1] = 0
cdef uint64_t clas
for clas in history:
self.set_featuresC(py_eg.c, stcls.c)
moves.set_valid(py_eg.c.is_valid, stcls.c)
nr_feat = self._set_featuresC(features, stcls.c)
moves.set_valid(is_valid, stcls.c)
# Update with a sparse gradient: everything's 0, except our class.
# Remember, this is a component of the global update. It's not our
# "job" here to think about the other beam candidates. We just want
@ -162,13 +163,11 @@ cdef class ParserNeuralNet(NeuralNet):
# We therefore have a key that indicates the current sequence, so that
# the model can merge updates that refer to the same state together,
# by summing their gradients.
memset(py_eg.c.costs, 0, self.moves.n_moves)
py_eg.c.costs[clas] = grad
self.updateC(
py_eg.c.features, py_eg.c.nr_feat, True, py_eg.c.costs, py_eg.c.is_valid,
False, key=key[0])
memset(costs, 0, self.moves.n_moves)
costs[clas] = grad
self.updateC(features,
nr_feat, True, costs, is_valid, False, key=key[0])
moves.c[clas].do(stcls.c, self.moves.c[clas].label)
py_eg.c.reset()
# Build a hash of the state sequence.
# Position 0 represents the previous sequence, position 1 the new class.
# So we want to do: