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Eliminate dead code in parser, and raise errors for obsolete options

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This commit is contained in:
Matthew Honnibal 2017-10-19 00:42:34 +02:00
parent bbfd7d8d5d
commit 960788aaa2
1 changed files with 16 additions and 63 deletions
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79
spacy/syntax/nn_parser.pyx
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@ -241,54 +241,32 @@ cdef class Parser:
@classmethod @classmethod
def Model(cls, nr_class, **cfg): def Model(cls, nr_class, **cfg):
depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1)) depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
if depth != 1:
raise ValueError("Currently parser depth is hard-coded to 1.")
parser_maxout_pieces = util.env_opt('parser_maxout_pieces', cfg.get('maxout_pieces', 2))
if parser_maxout_pieces != 2:
raise ValueError("Currently parser_maxout_pieces is hard-coded to 2")
token_vector_width = util.env_opt('token_vector_width', cfg.get('token_vector_width', 128)) token_vector_width = util.env_opt('token_vector_width', cfg.get('token_vector_width', 128))
hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 200)) hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 200))
parser_maxout_pieces = util.env_opt('parser_maxout_pieces', cfg.get('maxout_pieces', 2))
embed_size = util.env_opt('embed_size', cfg.get('embed_size', 7000)) embed_size = util.env_opt('embed_size', cfg.get('embed_size', 7000))
hist_size = util.env_opt('history_feats', cfg.get('hist_size', 0)) hist_size = util.env_opt('history_feats', cfg.get('hist_size', 0))
hist_width = util.env_opt('history_width', cfg.get('hist_width', 0)) hist_width = util.env_opt('history_width', cfg.get('hist_width', 0))
if hist_size >= 1 and depth == 0: if hist_size != 0:
raise ValueError("Inconsistent hyper-params: " raise ValueError("Currently history size is hard-coded to 0")
"history_feats >= 1 but parser_hidden_depth==0") if hist_width != 0:
raise ValueError("Currently history width is hard-coded to 0")
tok2vec = Tok2Vec(token_vector_width, embed_size, tok2vec = Tok2Vec(token_vector_width, embed_size,
pretrained_dims=cfg.get('pretrained_dims', 0)) pretrained_dims=cfg.get('pretrained_dims', 0))
tok2vec = chain(tok2vec, flatten) tok2vec = chain(tok2vec, flatten)
if parser_maxout_pieces == 1: lower = PrecomputableMaxouts(hidden_width if depth >= 1 else nr_class,
lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class, nF=cls.nr_feature, nP=parser_maxout_pieces,
nF=cls.nr_feature, nI=token_vector_width)
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'): with Model.use_device('cpu'):
if depth == 0: upper = chain(
upper = chain() clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
upper.is_noop = True zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
elif hist_size and depth == 1: )
upper = chain(
HistoryFeatures(nr_class=nr_class, hist_size=hist_size,
nr_dim=hist_width),
zero_init(Affine(nr_class, hidden_width+hist_size*hist_width,
drop_factor=0.0)))
upper.is_noop = False
elif hist_size:
upper = chain(
HistoryFeatures(nr_class=nr_class, hist_size=hist_size,
nr_dim=hist_width),
LayerNorm(Maxout(hidden_width, hidden_width+hist_size*hist_width)),
clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-2),
zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
)
upper.is_noop = False
else:
upper = chain(
clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
)
upper.is_noop = False
# TODO: This is an unfortunate hack atm! # TODO: This is an unfortunate hack atm!
# Used to set input dimensions in network. # Used to set input dimensions in network.
@ -959,31 +937,6 @@ cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) no
return best 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
cdef int mode = -1
cdef int i
for i in range(nr_class):
if actions[i].move == move and (mode == -1 or scores[i] >= score):
mode = i
score = scores[i]
return mode
# These are passed as callbacks to thinc.search.Beam # These are passed as callbacks to thinc.search.Beam
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1: cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
dest = <StateClass>_dest dest = <StateClass>_dest