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