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spaCy
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Let beam forward use minibatches

This commit is contained in:
Matthew Honnibal 2017-11-15 00:51:42 +01:00
parent 855872f872
commit d274d3a3b9
1 changed files with 32 additions and 23 deletions
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51
spacy/syntax/nn_parser.pyx
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@ -17,7 +17,7 @@ from cpython.ref cimport PyObject, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
from libc.math cimport exp
from libcpp.vector cimport vector
from libc.string cimport memset
from libc.string cimport memset, memcpy
from libc.stdlib cimport calloc, free
from cymem.cymem cimport Pool
from thinc.typedefs cimport weight_t, class_t, hash_t
@ -485,14 +485,14 @@ cdef class Parser:
cdef np.ndarray scores
cdef Doc doc
cdef int nr_class = self.moves.n_moves
cdef StateClass stcls, output
cuda_stream = util.get_cuda_stream()
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
docs, cuda_stream, 0.0)
beams = []
cdef int offset = 0
cdef int j = 0
cdef int k
beams = []
for doc in docs:
beam = Beam(nr_class, beam_width, min_density=beam_density)
beam.initialize(self.moves.init_beam_state, doc.length, doc.c)
@ -501,34 +501,43 @@ cdef class Parser:
state.offset = offset
offset += len(doc)
beam.check_done(_check_final_state, NULL)
while not beam.is_done:
states = []
beams.append(beam)
cdef np.ndarray token_ids
token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature),
dtype='i', order='C')
todo = [beam for beam in beams if not beam.is_done]
cdef int* c_ids
cdef int nr_feature = self.nr_feature
cdef int n_states
while todo:
todo = [beam for beam in beams if not beam.is_done]
token_ids.fill(-1)
c_ids = <int*>token_ids.data
n_states = 0
for beam in todo:
for i in range(beam.size):
stcls = StateClass.borrow(<StateC*>beam.at(i))
state = <StateC*>beam.at(i)
# This way we avoid having to score finalized states
# We do have to take care to keep indexes aligned, though
if not stcls.is_final():
states.append(stcls)
token_ids = self.get_token_ids(states)
vectors = state2vec(token_ids)
if self.cfg.get('hist_size', 0):
hists = numpy.asarray([st.history[:self.cfg['hist_size']]
for st in states], dtype='i')
scores = vec2scores((vectors, hists))
else:
if not state.is_final():
state.set_context_tokens(c_ids, nr_feature)
c_ids += nr_feature
n_states += 1
if n_states == 0:
break
vectors = state2vec(token_ids[:n_states])
scores = vec2scores(vectors)
j = 0
c_scores = <float*>scores.data
for beam in todo:
for i in range(beam.size):
state = <StateC*>beam.at(i)
if not state.is_final():
self.moves.set_valid(beam.is_valid[i], state)
for k in range(nr_class):
beam.scores[i][k] = c_scores[j * scores.shape[1] + k]
j += 1
memcpy(beam.scores[i], c_scores, nr_class * sizeof(float))
c_scores += nr_class
beam.advance(_transition_state, NULL, <void*>self.moves.c)
beam.check_done(_check_final_state, NULL)
beams.append(beam)
tokvecs = self.model[0].ops.unflatten(tokvecs,
[len(doc) for doc in docs])
return beams, tokvecs
@ -536,7 +545,7 @@ cdef class Parser:
def update(self, docs, golds, drop=0., sgd=None, losses=None):
if not any(self.moves.has_gold(gold) for gold in golds):
return None
if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.5:
if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.0:
return self.update_beam(docs, golds,
self.cfg['beam_width'], self.cfg['beam_density'],
drop=drop, sgd=sgd, losses=losses)