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Merge branch 'develop' of https://github.com/explosion/spaCy into develop

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Matthew Honnibal 2017-05-23 18:47:32 +02:00
commit 48eef94f92
2 changed files with 76 additions and 14 deletions
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2
spacy/syntax/nn_parser.pxd
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@ -16,6 +16,6 @@ cdef class Parser:
cdef void _parse_step(self, StateC* state,
const float* feat_weights,
int nr_class, int nr_feat) nogil
int nr_class, int nr_feat, int nr_piece) nogil
#cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil

88
spacy/syntax/nn_parser.pyx
View File

@ -87,7 +87,7 @@ cdef class precompute_hiddens:
we can do all our hard maths up front, packed into large multiplications,
and do the hard-to-program parsing on the CPU.
'''
cdef int nF, nO
cdef int nF, nO, nP
cdef bint _is_synchronized
cdef public object ops
cdef np.ndarray _features
@ -107,8 +107,9 @@ cdef class precompute_hiddens:
cached = gpu_cached
self.nF = cached.shape[1]
self.nO = cached.shape[2]
self.nP = getattr(lower_model, 'nP', 1)
self.ops = lower_model.ops
self._features = numpy.zeros((batch_size, self.nO), dtype='f')
self._features = numpy.zeros((batch_size, self.nO*self.nP), dtype='f')
self._is_synchronized = False
self._cuda_stream = cuda_stream
self._cached = cached
@ -138,9 +139,12 @@ cdef class precompute_hiddens:
cdef int[:, ::1] ids = token_ids
sum_state_features(<float*>state_vector.data,
feat_weights, &ids[0,0],
token_ids.shape[0], self.nF, self.nO)
token_ids.shape[0], self.nF, self.nO*self.nP)
state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
def backward(d_state_vector, sgd=None):
if bp_nonlinearity is not None:
d_state_vector = bp_nonlinearity(d_state_vector, sgd)
# This will usually be on GPU
if isinstance(d_state_vector, numpy.ndarray):
d_state_vector = self.ops.xp.array(d_state_vector)
@ -148,6 +152,17 @@ cdef class precompute_hiddens:
return d_tokens
return state_vector, backward
def _nonlinearity(self, state_vector):
if self.nP == 1:
return state_vector, None
state_vector = state_vector.reshape(
(state_vector.shape[0], state_vector.shape[1]//self.nP, self.nP))
best, which = self.ops.maxout(state_vector)
def backprop(d_best, sgd=None):
return self.ops.backprop_maxout(d_best, which, self.nP)
return best, backprop
cdef void sum_state_features(float* output,
const float* cached, const int* token_ids, int B, int F, int O) nogil:
cdef int idx, b, f, i
@ -220,9 +235,16 @@ cdef class Parser:
depth = util.env_opt('parser_hidden_depth', depth)
token_vector_width = util.env_opt('token_vector_width', token_vector_width)
hidden_width = util.env_opt('hidden_width', hidden_width)
lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature,
nI=token_vector_width)
parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
if parser_maxout_pieces == 1:
lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature,
nI=token_vector_width)
else:
lower = PrecomputableMaxouts(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature,
nP=parser_maxout_pieces,
nI=token_vector_width)
with Model.use_device('cpu'):
if depth == 0:
@ -314,7 +336,7 @@ cdef class Parser:
const float* feat_weights
StateC* st
vector[StateC*] next_step, this_step
int nr_class, nr_feat, nr_dim, nr_state
int nr_class, nr_feat, nr_piece, nr_dim, nr_state
if isinstance(docs, Doc):
docs = [docs]
@ -328,6 +350,7 @@ cdef class Parser:
cuda_stream = get_cuda_stream()
state2vec, vec2scores = self.get_batch_model(nr_state, tokvecs,
cuda_stream, 0.0)
nr_piece = state2vec.nP
states = self.moves.init_batch(docs)
for state in states:
@ -336,9 +359,32 @@ cdef class Parser:
feat_weights = state2vec.get_feat_weights()
cdef int i
cdef np.ndarray token_ids = numpy.zeros((nr_state, nr_feat), dtype='i')
cdef np.ndarray is_valid = numpy.zeros((nr_state, nr_class), dtype='i')
cdef np.ndarray scores
c_token_ids = <int*>token_ids.data
c_is_valid = <int*>is_valid.data
cdef int has_hidden = hasattr(vec2scores, 'W')
while not next_step.empty():
for i in cython.parallel.prange(next_step.size(), num_threads=4, nogil=True):
self._parse_step(next_step[i], feat_weights, nr_class, nr_feat)
if not has_hidden:
for i in cython.parallel.prange(
next_step.size(), num_threads=6, nogil=True):
self._parse_step(next_step[i],
feat_weights, nr_class, nr_feat, nr_piece)
else:
for i in range(next_step.size()):
st = next_step[i]
st.set_context_tokens(&c_token_ids[i*nr_feat], nr_feat)
self.moves.set_valid(&c_is_valid[i*nr_class], st)
vectors = state2vec(token_ids[:next_step.size()])
scores = vec2scores(vectors)
c_scores = <float*>scores.data
for i in range(next_step.size()):
st = next_step[i]
guess = arg_max_if_valid(
&c_scores[i*nr_class], &c_is_valid[i*nr_class], nr_class)
action = self.moves.c[guess]
action.do(st, action.label)
this_step, next_step = next_step, this_step
next_step.clear()
for st in this_step:
@ -348,16 +394,19 @@ cdef class Parser:
cdef void _parse_step(self, StateC* state,
const float* feat_weights,
int nr_class, int nr_feat) nogil:
int nr_class, int nr_feat, int nr_piece) nogil:
'''This only works with no hidden layers -- fast but inaccurate'''
#for i in cython.parallel.prange(next_step.size(), num_threads=4, nogil=True):
# self._parse_step(next_step[i], feat_weights, nr_class, nr_feat)
token_ids = <int*>calloc(nr_feat, sizeof(int))
scores = <float*>calloc(nr_class, sizeof(float))
scores = <float*>calloc(nr_class * nr_piece, sizeof(float))
is_valid = <int*>calloc(nr_class, sizeof(int))
state.set_context_tokens(token_ids, nr_feat)
sum_state_features(scores,
feat_weights, token_ids, 1, nr_feat, nr_class)
feat_weights, token_ids, 1, nr_feat, nr_class * nr_piece)
self.moves.set_valid(is_valid, state)
guess = arg_max_if_valid(scores, is_valid, nr_class)
guess = arg_maxout_if_valid(scores, is_valid, nr_class, nr_piece)
action = self.moves.c[guess]
action.do(state, action.label)
@ -571,6 +620,19 @@ cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) no
return best
cdef int arg_maxout_if_valid(const weight_t* scores, const int* is_valid,
int n, int nP) nogil:
cdef int best = -1
cdef float best_score = 0
for i in range(n):
if is_valid[i] >= 1:
for j in range(nP):
if best == -1 or scores[i*nP+j] > best_score:
best = i
best_score = scores[i*nP+j]
return best
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
int nr_class) except -1:
cdef weight_t score = 0