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Get pre-computed version working

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This commit is contained in:
Matthew Honnibal 2017-05-08 00:38:35 +02:00
parent 35458987e8
commit 10682d35ab
4 changed files with 106 additions and 91 deletions
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9
bin/parser/train_ud.py
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@ -144,7 +144,6 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
docs = list(Xs)
for doc in docs:
encoder(doc)
parser.begin_training(docs, ys)
nn_loss = [0.]
def track_progress():
scorer = score_model(vocab, encoder, tagger, parser, dev_Xs, dev_ys)
@ -153,7 +152,7 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
nn_loss.append(0.)
trainer.each_epoch.append(track_progress)
trainer.batch_size = 12
trainer.nb_epoch = 2
trainer.nb_epoch = 20
for docs, golds in trainer.iterate(Xs, ys, progress_bar=False):
docs = [Doc(vocab, words=[w.text for w in doc]) for doc in docs]
tokvecs, upd_tokvecs = encoder.begin_update(docs)
@ -161,9 +160,9 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
doc.tensor = tokvec
for doc, gold in zip(docs, golds):
tagger.update(doc, gold)
d_tokvecs, loss = parser.update(docs, golds, sgd=optimizer)
d_tokvecs = parser.update(docs, golds, sgd=optimizer)
upd_tokvecs(d_tokvecs, sgd=optimizer)
nn_loss[-1] += loss
#nn_loss[-1] += loss
nlp = LangClass(vocab=vocab, tagger=tagger, parser=parser)
#nlp.end_training(model_dir)
#scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
@ -173,7 +172,7 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
if __name__ == '__main__':
import cProfile
import pstats
if 0:
if 1:
plac.call(main)
else:
cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")

85
spacy/_ml.py
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@ -51,47 +51,6 @@ def doc2feats(cols):
model = layerize(forward)
return model
def build_feature_precomputer(model, feat_maps):
'''Allow a model to be "primed" by pre-computing input features in bulk.
This is used for the parser, where we want to take a batch of documents,
and compute vectors for each (token, position) pair. These vectors can then
be reused, especially for beam-search.
Let's say we're using 12 features for each state, e.g. word at start of
buffer, three words on stack, their children, etc. In the normal arc-eager
system, a document of length N is processed in 2*N states. This means we'll
create 2*N*12 feature vectors --- but if we pre-compute, we only need
N*12 vector computations. The saving for beam-search is much better:
if we have a beam of k, we'll normally make 2*N*12*K computations --
so we can save the factor k. This also gives a nice CPU/GPU division:
we can do all our hard maths up front, packed into large multiplications,
and do the hard-to-program parsing on the CPU.
'''
def precompute(input_vectors):
cached, backprops = zip(*[lyr.begin_update(input_vectors)
for lyr in feat_maps)
def forward(batch_token_ids, drop=0.):
output = ops.allocate((batch_size, output_width))
# i: batch index
# j: position index (i.e. N0, S0, etc
# tok_i: Index of the token within its document
for i, token_ids in enumerate(batch_token_ids):
for j, tok_i in enumerate(token_ids):
output[i] += cached[j][tok_i]
def backward(d_vector, sgd=None):
d_inputs = ops.allocate((batch_size, n_feat, vec_width))
for i, token_ids in enumerate(batch_token_ids):
for j in range(len(token_ids)):
d_inputs[i][j] = backprops[j](d_vector, sgd)
# Return the IDs, so caller can associate to correct token
return (batch_token_ids, d_inputs)
return vector, backward
return chain(layerize(forward), model)
return precompute
def print_shape(prefix):
def forward(X, drop=0.):
return X, lambda dX, **kwargs: dX
@ -114,3 +73,47 @@ def flatten(seqs, drop=0.):
return d_X
X = ops.xp.concatenate([ops.asarray(seq) for seq in seqs])
return X, finish_update
#def build_feature_precomputer(model, feat_maps):
# '''Allow a model to be "primed" by pre-computing input features in bulk.
#
# This is used for the parser, where we want to take a batch of documents,
# and compute vectors for each (token, position) pair. These vectors can then
# be reused, especially for beam-search.
#
# Let's say we're using 12 features for each state, e.g. word at start of
# buffer, three words on stack, their children, etc. In the normal arc-eager
# system, a document of length N is processed in 2*N states. This means we'll
# create 2*N*12 feature vectors --- but if we pre-compute, we only need
# N*12 vector computations. The saving for beam-search is much better:
# if we have a beam of k, we'll normally make 2*N*12*K computations --
# so we can save the factor k. This also gives a nice CPU/GPU division:
# we can do all our hard maths up front, packed into large multiplications,
# and do the hard-to-program parsing on the CPU.
# '''
# def precompute(input_vectors):
# cached, backprops = zip(*[lyr.begin_update(input_vectors)
# for lyr in feat_maps)
# def forward(batch_token_ids, drop=0.):
# output = ops.allocate((batch_size, output_width))
# # i: batch index
# # j: position index (i.e. N0, S0, etc
# # tok_i: Index of the token within its document
# for i, token_ids in enumerate(batch_token_ids):
# for j, tok_i in enumerate(token_ids):
# output[i] += cached[j][tok_i]
# def backward(d_vector, sgd=None):
# d_inputs = ops.allocate((batch_size, n_feat, vec_width))
# for i, token_ids in enumerate(batch_token_ids):
# for j in range(len(token_ids)):
# d_inputs[i][j] = backprops[j](d_vector, sgd)
# # Return the IDs, so caller can associate to correct token
# return (batch_token_ids, d_inputs)
# return vector, backward
# return chain(layerize(forward), model)
# return precompute
#
#

1
spacy/syntax/parser.pxd
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@ -13,5 +13,6 @@ cdef class Parser:
cdef readonly object model
cdef readonly TransitionSystem moves
cdef readonly object cfg
cdef public object feature_maps
#cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil

102
spacy/syntax/parser.pyx
View File

@ -28,10 +28,11 @@ from murmurhash.mrmr cimport hash64
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get
from thinc.api import layerize
from numpy import exp
from thinc.api import layerize, chain
from thinc.neural import Model, Maxout
from .._ml import get_col
from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
@ -46,8 +47,9 @@ from ..strings cimport StringStore
from ..gold cimport GoldParse
from ..attrs cimport TAG, DEP
from .._ml import build_state2vec, build_model, precompute_hiddens
def get_templates(*args, **kwargs):
return []
USE_FTRL = True
DEBUG = False
@ -56,30 +58,39 @@ def set_debug(val):
DEBUG = val
def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, feat_maps, upper_model):
def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, upper_model, feat_maps):
cdef int[:, :] is_valid_
cdef float[:, :] costs_
cdef int[:, :] token_ids
lengths = [len(t) for t in tokvecs]
tokvecs = upper_model.ops.flatten(tokvecs)
is_valid = upper_model.ops.allocate((len(tokvecs), moves.n_moves), dtype='i')
costs = upper_model.ops.allocate((len(tokvecs), moves.n_moves), dtype='f')
token_ids = upper_model.ops.allocate((len(tokvecs), StateClass.nr_context_tokens()),
dtype='uint64')
token_ids = upper_model.ops.allocate((len(tokvecs), len(feat_maps)), dtype='i')
cached, backprops = zip(*[lyr.begin_update(tokvecs) for lyr in feat_maps])
is_valid_ = is_valid
costs_ = costs
def forward(states, drop=0.):
def forward(states_offsets, drop=0.):
nonlocal is_valid, costs, token_ids, moves
states, offsets = states_offsets
is_valid = is_valid[:len(states)]
costs = costs[:len(states)]
token_ids = token_ids[:len(states)]
is_valid = is_valid[:len(states)]
cdef StateClass state
for i, state in enumerate(states):
cdef int i
for i, (offset, state) in enumerate(zip(offsets, states)):
state.set_context_tokens(token_ids[i])
moves.set_valid(&is_valid_[i, 0], state.c)
features = cached[token_ids].sum(axis=1)
adjusted_ids = token_ids.copy()
for i, offset in enumerate(offsets):
adjusted_ids[i] *= token_ids[i] >= 0
adjusted_ids[i] += offset
features = upper_model.ops.allocate((len(states), 64), dtype='f')
for i in range(len(states)):
for j, tok_i in enumerate(adjusted_ids[i]):
if tok_i >= 0:
features[i] += cached[j][tok_i]
scores, bp_scores = upper_model.begin_update(features, drop=drop)
softmaxed = upper_model.ops.softmax(scores)
@ -89,15 +100,16 @@ def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, feat_maps, upper
def backward(golds, sgd=None):
nonlocal costs_, is_valid_, moves
cdef int i
for i, (state, gold) in enumerate(zip(states, golds)):
moves.set_costs(&is_valid_[i, 0], &costs_[i, 0],
state, gold)
d_scores = scores.copy()
d_scores.fill(0)
set_log_loss(upper_model.ops, d_scores,
scores, is_valid_, costs_)
scores, is_valid, costs)
d_tokens = bp_scores(d_scores, sgd)
return d_tokens
return (token_ids, d_tokens)
return softmaxed, backward
@ -127,14 +139,18 @@ def transition_batch(TransitionSystem moves, states, scores):
def init_states(TransitionSystem moves, docs):
states = []
cdef Doc doc
cdef StateClass state
offsets = []
states = []
offset = 0
for i, doc in enumerate(docs):
state = StateClass.init(doc.c, doc.length)
moves.initialize_state(state.c)
states.append(state)
return states
offsets.append(offset)
offset += len(doc)
return states, offsets
cdef class Parser:
@ -184,18 +200,22 @@ cdef class Parser:
cfg['actions'] = TransitionSystem.get_actions(**cfg)
self.moves = TransitionSystem(vocab.strings, cfg['actions'])
if model is None:
model = self.build_model(**cfg)
self.model = model
self.model, self.feature_maps = self.build_model(**cfg)
else:
self.model, self.feature_maps = model
self.cfg = cfg
def __reduce__(self):
return (Parser, (self.vocab, self.moves, self.model), None, None)
def build_model(self, width=32, nr_vector=1000, nF=1, nB=1, nS=1, nL=1, nR=1, **_):
def build_model(self, width=64, nr_vector=1000, nF=1, nB=1, nS=1, nL=1, nR=1, **_):
nr_context_tokens = StateClass.nr_context_tokens(nF, nB, nS, nL, nR)
self.model = build_model(width*2, 2, self.moves.n_moves)
model = chain(Maxout(width, width), Maxout(self.moves.n_moves, width))
# TODO
self.feature_maps = [] #build_feature_maps(nr_context_tokens, width, nr_vector)
feature_maps = [Maxout(width, width)
for i in range(nr_context_tokens)]
return model, feature_maps
def __call__(self, Doc tokens):
"""
@ -245,19 +265,21 @@ cdef class Parser:
cdef Doc doc
cdef StateClass state
model = get_greedy_model_for_batch([d.tensor for d in docs],
self.moves, self.model, self.feat_maps)
states = [StateClass.init(doc.c, doc.length) for doc in docs]
todo = list(states)
self.moves, self.model, self.feature_maps)
states, offsets = init_states(self.moves, docs)
all_states = list(states)
todo = list(zip(states, offsets))
while todo:
scores = model(todo)
transition_batch(self.moves, todo, scores)
todo = [st for st in states if not st.is_final()]
for state, doc in zip(states, docs):
states, offsets = zip(*todo)
scores = model((states, offsets))
transition_batch(self.moves, states, scores)
todo = [st for st in todo if not st[0].py_is_final()]
for state, doc in zip(all_states, docs):
self.moves.finalize_state(state.c)
for i in range(doc.length):
doc.c[i] = state.c._sent[i]
for doc in docs:
self.moves.finalize_parse(doc)
self.moves.finalize_doc(doc)
def update(self, docs, golds, drop=0., sgd=None):
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
@ -266,33 +288,23 @@ cdef class Parser:
self.moves.preprocess_gold(gold)
model = get_greedy_model_for_batch([d.tensor for d in docs],
self.moves, self.model, self.feat_maps)
states = init_states(self.moves, docs)
self.moves, self.model, self.feature_maps)
states, offsets = init_states(self.moves, docs)
d_tokens = [self.model.ops.allocate(d.tensor.shape) for d in docs]
output = list(d_tokens)
todo = zip(states, golds, d_tokens)
todo = zip(states, offsets, golds, d_tokens)
while todo:
states, golds, d_tokens = zip(*todo)
scores, finish_update = model.begin_update(token_ids)
d_state_features = finish_update(golds, sgd=sgd)
states, offsets, golds, d_tokens = zip(*todo)
scores, finish_update = model.begin_update((states, offsets))
(token_ids, d_state_features) = finish_update(golds, sgd=sgd)
for i, token_ids in enumerate(token_ids):
d_tokens[i][token_ids] += d_state_features[i]
transition_batch(self.moves, states)
transition_batch(self.moves, states, scores)
# Get unfinished states (and their matching gold and token gradients)
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
return output
def begin_training(self, docs, golds):
for gold in golds:
self.moves.preprocess_gold(gold)
states = self._init_states(docs)
tokvecs = [d.tensor for d in docs]
features = self._get_features(states, tokvecs)
self.model.begin_training(features)
def step_through(self, Doc doc, GoldParse gold=None):
"""
Set up a stepwise state, to introspect and control the transition sequence.