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spaCy
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Mergery

This commit is contained in:
Matthew Honnibal 2017-05-08 08:29:36 -05:00
parent 245372973d 66252f3e71
commit bef89ef23d
5 changed files with 383 additions and 313 deletions
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27
bin/parser/train_ud.py
View File

@ -18,6 +18,8 @@ import spacy.attrs
import io
from thinc.neural.ops import CupyOps
from thinc.neural import Model
from spacy.es import Spanish
from spacy.attrs import POS
try:
import cupy
@ -156,20 +158,15 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
for tag in tags:
vocab.morphology.tag_map[tag] = {POS: tag.split('__', 1)[0]}
tagger = Tagger(vocab)
encoder = TokenVectorEncoder(vocab)
encoder = TokenVectorEncoder(vocab, width=64)
parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
Xs, ys = organize_data(vocab, train_sents)
dev_Xs, dev_ys = organize_data(vocab, dev_sents)
#Xs = Xs[:1000]
#ys = ys[:1000]
#dev_Xs = dev_Xs[:1000]
#dev_ys = dev_ys[:1000]
with encoder.model.begin_training(Xs[:100], ys[:100]) as (trainer, optimizer):
docs = list(Xs)
for doc in docs:
encoder(doc)
parser.begin_training(docs, ys)
nn_loss = [0.]
def track_progress():
with encoder.tagger.use_params(optimizer.averages):
@ -191,11 +188,23 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
upd_tokvecs(d_tokvecs, sgd=optimizer)
encoder.update(docs, golds, sgd=optimizer)
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))
nlp = LangClass(vocab=vocab, parser=parser)
scorer = score_model(vocab, encoder, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
#nlp.end_training(model_dir)
#scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
#print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
if __name__ == '__main__':
import cProfile
import pstats
if 1:
plac.call(main)
else:
cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
s = pstats.Stats("Profile.prof")
s.strip_dirs().sort_stats("time").print_stats()
plac.call(main)

331
spacy/_ml.py
View File

@ -7,8 +7,125 @@ from thinc.neural._classes.static_vectors import StaticVectors
from thinc.neural._classes.batchnorm import BatchNorm
from thinc.neural._classes.resnet import Residual
from thinc import describe
from thinc.describe import Dimension, Synapses, Biases, Gradient
from thinc.neural._classes.affine import _set_dimensions_if_needed
from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP
import numpy
@describe.on_data(_set_dimensions_if_needed)
@describe.attributes(
nI=Dimension("Input size"),
nF=Dimension("Number of features"),
nO=Dimension("Output size"),
W=Synapses("Weights matrix",
lambda obj: (obj.nO, obj.nF, obj.nI),
lambda W, ops: ops.xavier_uniform_init(W)),
b=Biases("Bias vector",
lambda obj: (obj.nO,)),
d_W=Gradient("W"),
d_b=Gradient("b")
)
class PrecomputableAffine(Model):
def __init__(self, nO=None, nI=None, nF=None, **kwargs):
Model.__init__(self, **kwargs)
self.nO = nO
self.nI = nI
self.nF = nF
def begin_update(self, X, drop=0.):
# X: (b, i)
# Xf: (b, f, i)
# dY: (b, o)
# dYf: (b, f, o)
#Yf = numpy.einsum('bi,ofi->bfo', X, self.W)
Yf = self.ops.xp.tensordot(
X, self.W, axes=[[1], [2]]).transpose((0, 2, 1))
Yf += self.b
def backward(dY_ids, sgd=None):
dY, ids = dY_ids
Xf = X[ids]
#dW = numpy.einsum('bo,bfi->ofi', dY, Xf)
dW = self.ops.xp.tensordot(dY, Xf, axes=[[0], [0]])
db = dY.sum(axis=0)
#dXf = numpy.einsum('bo,ofi->bfi', dY, self.W)
dXf = self.ops.xp.tensordot(dY, self.W, axes=[[1], [0]])
self.d_W += dW
self.d_b += db
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return dXf
return Yf, backward
@describe.on_data(_set_dimensions_if_needed)
@describe.attributes(
nI=Dimension("Input size"),
nF=Dimension("Number of features"),
nP=Dimension("Number of pieces"),
nO=Dimension("Output size"),
W=Synapses("Weights matrix",
lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI),
lambda W, ops: ops.xavier_uniform_init(W)),
b=Biases("Bias vector",
lambda obj: (obj.nO, obj.nP)),
d_W=Gradient("W"),
d_b=Gradient("b")
)
class PrecomputableMaxouts(Model):
def __init__(self, nO=None, nI=None, nF=None, pieces=2, **kwargs):
Model.__init__(self, **kwargs)
self.nO = nO
self.nP = pieces
self.nI = nI
self.nF = nF
def begin_update(self, X, drop=0.):
# X: (b, i)
# Yfp: (f, b, o, p)
# Yf: (f, b, o)
# Xf: (b, f, i)
# dY: (b, o)
# dYp: (b, o, p)
# W: (f, o, p, i)
# b: (o, p)
# Yfp = numpy.einsum('bi,fopi->fbop', X, self.W)
Yfp = self.ops.xp.tensordot(X, self.W,
axes=[[1], [3]]).transpose((1, 0, 2, 3))
Yfp = self.ops.xp.ascontiguousarray(Yfp)
Yfp += self.b
Yf = self.ops.allocate((self.nF, X.shape[0], self.nO))
which = self.ops.allocate((self.nF, X.shape[0], self.nO), dtype='i')
for i in range(self.nF):
Yf[i], which[i] = self.ops.maxout(Yfp[i])
def backward(dY_ids, sgd=None):
dY, ids = dY_ids
Xf = X[ids]
dYp = self.ops.allocate((dY.shape[0], self.nO, self.nP))
for i in range(self.nF):
dYp += self.ops.backprop_maxout(dY, which[i], self.nP)
#dXf = numpy.einsum('bop,fopi->bfi', dYp, self.W)
dXf = self.ops.xp.tensordot(dYp, self.W, axes=[[1,2], [1,2]])
#dW = numpy.einsum('bfi,bop->fopi', Xf, dYp)
dW = self.ops.xp.tensordot(Xf, dYp, axes=[[0], [0]])
dW = dW.transpose((0, 2, 3, 1))
db = dYp.sum(axis=0)
self.d_W += dW
self.d_b += db
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return dXf
return Yf, backward
def get_col(idx):
def forward(X, drop=0.):
@ -22,55 +139,36 @@ def get_col(idx):
return layerize(forward)
def build_model(state2vec, width, depth, nr_class):
with Model.define_operators({'>>': chain, '**': clone}):
model = (
state2vec
>> Maxout(width, 1344)
>> Maxout(width, width)
>> Affine(nr_class, width)
def build_tok2vec(lang, width, depth=2, embed_size=1000):
cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone}):
#static = get_col(cols.index(ID)) >> StaticVectors(lang, width)
lower = get_col(cols.index(LOWER)) >> HashEmbed(width, embed_size)
prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width//4, embed_size)
suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width//4, embed_size)
shape = get_col(cols.index(SHAPE)) >> HashEmbed(width//4, embed_size)
tok2vec = (
doc2feats(cols)
>> with_flatten(
#(static | prefix | suffix | shape)
(lower | prefix | suffix | shape)
>> Maxout(width)
>> (ExtractWindow(nW=1) >> Maxout(width, width*3))
>> (ExtractWindow(nW=1) >> Maxout(width, width*3))
)
)
return model
return tok2vec
def build_debug_model(state2vec, width, depth, nr_class):
with Model.define_operators({'>>': chain, '**': clone}):
model = (
state2vec
#>> Maxout(width)
>> Maxout(nr_class)
)
return model
def build_debug_state2vec(width, nr_vector=1000, nF=1, nB=0, nS=1, nL=2, nR=2):
ops = Model.ops
def forward(tokens_attrs_vectors, drop=0.):
tokens, attr_vals, tokvecs = tokens_attrs_vectors
orig_tokvecs_shape = tokvecs.shape
tokvecs = tokvecs.reshape((tokvecs.shape[0], tokvecs.shape[1] *
tokvecs.shape[2]))
vector = tokvecs
def backward(d_vector, sgd=None):
d_tokvecs = vector.reshape(orig_tokvecs_shape)
return (tokens, d_tokvecs)
return vector, backward
def doc2feats(cols):
def forward(docs, drop=0.):
feats = [doc.to_array(cols) for doc in docs]
feats = [model.ops.asarray(f, dtype='uint64') for f in feats]
return feats, None
model = layerize(forward)
return model
def build_state2vec(nr_context_tokens, width, nr_vector=1000):
ops = Model.ops
with Model.define_operators({'|': concatenate, '+': add, '>>': chain}):
#hiddens = [get_col(i) >> Maxout(width) for i in range(nr_context_tokens)]
features = [get_col(i) for i in range(nr_context_tokens)]
model = get_token_vectors >> concatenate(*features) >> ReLu(width)
return model
def print_shape(prefix):
def forward(X, drop=0.):
return X, lambda dX, **kwargs: dX
@ -86,87 +184,6 @@ def get_token_vectors(tokens_attrs_vectors, drop=0.):
return vectors, backward
def build_parser_state2vec(width, nr_vector=1000, nF=1, nB=0, nS=1, nL=2, nR=2):
embed_tags = _reshape(chain(get_col(0), HashEmbed(16, nr_vector)))
embed_deps = _reshape(chain(get_col(1), HashEmbed(16, nr_vector)))
ops = embed_tags.ops
def forward(tokens_attrs_vectors, drop=0.):
tokens, attr_vals, tokvecs = tokens_attrs_vectors
tagvecs, bp_tagvecs = embed_deps.begin_update(attr_vals, drop=drop)
depvecs, bp_depvecs = embed_tags.begin_update(attr_vals, drop=drop)
orig_tokvecs_shape = tokvecs.shape
tokvecs = tokvecs.reshape((tokvecs.shape[0], tokvecs.shape[1] *
tokvecs.shape[2]))
shapes = (tagvecs.shape, depvecs.shape, tokvecs.shape)
assert tagvecs.shape[0] == depvecs.shape[0] == tokvecs.shape[0], shapes
vector = ops.xp.hstack((tagvecs, depvecs, tokvecs))
def backward(d_vector, sgd=None):
d_tagvecs, d_depvecs, d_tokvecs = backprop_concatenate(d_vector, shapes)
assert d_tagvecs.shape == shapes[0], (d_tagvecs.shape, shapes)
assert d_depvecs.shape == shapes[1], (d_depvecs.shape, shapes)
assert d_tokvecs.shape == shapes[2], (d_tokvecs.shape, shapes)
bp_tagvecs(d_tagvecs)
bp_depvecs(d_depvecs)
d_tokvecs = d_tokvecs.reshape(orig_tokvecs_shape)
return (tokens, d_tokvecs)
return vector, backward
model = layerize(forward)
model._layers = [embed_tags, embed_deps]
return model
def backprop_concatenate(gradient, shapes):
grads = []
start = 0
for shape in shapes:
end = start + shape[1]
grads.append(gradient[:, start : end])
start = end
return grads
def _reshape(layer):
'''Transforms input with shape
(states, tokens, features)
into input with shape:
(states * tokens, features)
So that it can be used with a token-wise feature extraction layer, e.g.
an embedding layer. The embedding layer outputs:
(states * tokens, ndim)
But we want to concatenate the vectors for the tokens, so we produce:
(states, tokens * ndim)
We then need to reverse the transforms to do the backward pass. Recall
the simple rule here: each layer is a map:
inputs -> (outputs, (d_outputs->d_inputs))
So the shapes must match like this:
shape of forward input == shape of backward output
shape of backward input == shape of forward output
'''
def forward(X__bfm, drop=0.):
b, f, m = X__bfm.shape
B = b*f
M = f*m
X__Bm = X__bfm.reshape((B, m))
y__Bn, bp_yBn = layer.begin_update(X__Bm, drop=drop)
n = y__Bn.shape[1]
N = f * n
y__bN = y__Bn.reshape((b, N))
def backward(dy__bN, sgd=None):
dy__Bn = dy__bN.reshape((B, n))
dX__Bm = bp_yBn(dy__Bn, sgd)
if dX__Bm is None:
return None
else:
return dX__Bm.reshape((b, f, m))
return y__bN, backward
model = layerize(forward)
model._layers.append(layer)
return model
@layerize
def flatten(seqs, drop=0.):
ops = Model.ops
@ -177,32 +194,44 @@ def flatten(seqs, drop=0.):
return X, finish_update
def build_tok2vec(lang, width, depth=2, embed_size=1000):
cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone}):
#static = get_col(cols.index(ID)) >> StaticVectors(lang, width)
lower = get_col(cols.index(LOWER)) >> HashEmbed(width, embed_size)
prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size)
suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size)
shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size)
tok2vec = (
doc2feats(cols)
>> with_flatten(
#(static | prefix | suffix | shape)
(lower | prefix | suffix | shape)
>> Maxout(width, width*4)
>> Residual((ExtractWindow(nW=1) >> Maxout(width, width*3)))
>> Residual((ExtractWindow(nW=1) >> Maxout(width, width*3)))
>> Residual((ExtractWindow(nW=1) >> Maxout(width, width*3)))
)
)
return tok2vec
#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 doc2feats(cols):
def forward(docs, drop=0.):
feats = [doc.to_array(cols) for doc in docs]
feats = [model.ops.asarray(f, dtype='uint64') for f in feats]
return feats, None
model = layerize(forward)
return model

2
spacy/pipeline.pyx
View File

@ -23,7 +23,7 @@ class TokenVectorEncoder(object):
'''Assign position-sensitive vectors to tokens, using a CNN or RNN.'''
def __init__(self, vocab, **cfg):
self.vocab = vocab
self.model = build_tok2vec(vocab.lang, 64, **cfg)
self.model = build_tok2vec(vocab.lang, **cfg)
self.tagger = chain(
self.model,
flatten,

1
spacy/syntax/parser.pxd
View File

@ -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

335
spacy/syntax/parser.pyx
View File

@ -28,8 +28,11 @@ from murmurhash.mrmr cimport hash64
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get
from numpy import exp
from thinc.api import layerize, chain
from thinc.neural import Model, Maxout
from .._ml import PrecomputableAffine, PrecomputableMaxouts
from . import _parse_features
from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
@ -44,10 +47,9 @@ from ..strings cimport StringStore
from ..gold cimport GoldParse
from ..attrs cimport TAG, DEP
from .._ml import build_parser_state2vec, build_model
from .._ml import build_state2vec, build_model
from .._ml import build_debug_state2vec, build_debug_model
def get_templates(*args, **kwargs):
return []
USE_FTRL = True
DEBUG = False
@ -56,8 +58,109 @@ def set_debug(val):
DEBUG = val
def get_templates(*args, **kwargs):
return []
def get_greedy_model_for_batch(tokvecs, TransitionSystem moves, upper_model, lower_model):
cdef int[:, :] is_valid_
cdef float[:, :] costs_
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), lower_model.nF), dtype='i')
cached, bp_features = lower_model.begin_update(tokvecs, drop=0.)
is_valid_ = is_valid
costs_ = costs
def forward(states_offsets, drop=0.):
nonlocal is_valid, costs, token_ids, moves
states, offsets = states_offsets
assert len(states) != 0
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
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)
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), lower_model.nO), 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)
scores = upper_model.ops.relu(scores)
softmaxed = upper_model.ops.softmax(scores)
# Renormalize for invalid actions
softmaxed *= is_valid
totals = softmaxed.sum(axis=1)
for total in totals:
assert total > 0, (totals, scores, softmaxed)
assert total <= 1.1, totals
softmaxed /= softmaxed.sum(axis=1).reshape((softmaxed.shape[0], 1))
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)
upper_model.ops.backprop_relu(d_scores, scores, inplace=True)
d_features = bp_scores(d_scores, sgd)
d_tokens = bp_features((d_features, adjusted_ids), sgd)
return (token_ids, d_tokens)
return softmaxed, backward
return layerize(forward)
def set_log_loss(ops, gradients, scores, is_valid, costs):
"""Do multi-label log loss"""
n = gradients.shape[0]
scores = scores * is_valid
g_scores = scores * is_valid * (costs <= 0.)
exps = ops.xp.exp(scores - scores.max(axis=1).reshape((n, 1)))
exps *= is_valid
g_exps = ops.xp.exp(g_scores - g_scores.max(axis=1).reshape((n, 1)))
g_exps *= costs <= 0.
g_exps *= is_valid
gradients[:] = exps / exps.sum(axis=1).reshape((n, 1))
gradients -= g_exps / g_exps.sum(axis=1).reshape((n, 1))
def transition_batch(TransitionSystem moves, states, scores):
cdef StateClass state
cdef int guess
for state, guess in zip(states, scores.argmax(axis=1)):
action = moves.c[guess]
action.do(state.c, action.label)
def init_states(TransitionSystem moves, docs):
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)
offsets.append(offset)
offset += len(doc)
return states, offsets
cdef class Parser:
@ -107,8 +210,9 @@ 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):
@ -116,10 +220,10 @@ cdef class Parser:
def build_model(self, width=128, 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)
state2vec = build_state2vec(nr_context_tokens, width, nr_vector)
#state2vec = build_debug_state2vec(width, nr_vector)
model = build_debug_model(state2vec, width*2, 2, self.moves.n_moves)
return model
upper = chain(Maxout(width, width), Maxout(self.moves.n_moves, width))
lower = PrecomputableMaxouts(width, nF=nr_context_tokens, nI=width*2)
return upper, lower
def __call__(self, Doc tokens):
"""
@ -131,7 +235,6 @@ cdef class Parser:
None
"""
self.parse_batch([tokens])
self.moves.finalize_doc(tokens)
def pipe(self, stream, int batch_size=1000, int n_threads=2):
"""
@ -167,169 +270,53 @@ cdef class Parser:
yield doc
def parse_batch(self, docs):
states = self._init_states(docs)
nr_class = self.moves.n_moves
cdef Doc doc
cdef StateClass state
cdef int guess
tokvecs = [d.tensor for d in docs]
model = get_greedy_model_for_batch([d.tensor for d in docs],
self.moves, self.model, self.feature_maps)
states, offsets = init_states(self.moves, docs)
all_states = list(states)
todo = zip(states, tokvecs)
todo = list(zip(states, offsets))
while todo:
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
if not todo:
break
states, tokvecs = zip(*todo)
scores, _ = self._begin_update(states, tokvecs)
self._transition_batch(states, docs, scores)
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]
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]
d_tokens = [self.model.ops.allocate(d.tensor.shape) for d in docs]
nr_class = self.moves.n_moves
costs = self.model.ops.allocate((len(docs), nr_class), dtype='f')
gradients = self.model.ops.allocate((len(docs), nr_class), dtype='f')
is_valid = self.model.ops.allocate((len(docs), nr_class), dtype='i')
attr_names = numpy.zeros((2,), dtype='i')
attr_names[0] = TAG
attr_names[1] = DEP
features = self._get_features(states, tokvecs, attr_names)
self.model.begin_training(features)
for doc in docs:
self.moves.finalize_doc(doc)
def update(self, docs, golds, drop=0., sgd=None):
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
return self.update([docs], [golds], drop=drop)
for gold in golds:
self.moves.preprocess_gold(gold)
states = self._init_states(docs)
tokvecs = [d.tensor for d in docs]
model = get_greedy_model_for_batch([d.tensor for d in 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]
nr_class = self.moves.n_moves
output = list(d_tokens)
todo = zip(states, tokvecs, golds, d_tokens)
assert len(states) == len(todo)
losses = []
todo = zip(states, offsets, golds, d_tokens)
while todo:
# Get unfinished states (and their matching gold and token gradients)
todo = filter(lambda sp: not sp[0].py_is_final(), todo)
if not todo:
break
states, tokvecs, golds, d_tokens = zip(*todo)
scores, finish_update = self._begin_update(states, tokvecs)
token_ids, batch_token_grads = finish_update(golds, sgd=sgd, losses=losses,
force_gold=False)
batch_token_grads *= (token_ids >= 0).reshape((token_ids.shape[0], token_ids.shape[1], 1))
token_ids *= token_ids >= 0
if hasattr(self.model.ops.xp, 'scatter_add'):
for i, tok_ids in enumerate(token_ids):
self.model.ops.xp.scatter_add(d_tokens[i],
tok_ids, batch_token_grads[i])
else:
for i, tok_ids in enumerate(token_ids):
self.model.ops.xp.add.at(d_tokens[i],
tok_ids, batch_token_grads[i])
self._transition_batch(states, docs, scores)
return output, sum(losses)
def _begin_update(self, states, tokvecs, drop=0.):
nr_class = self.moves.n_moves
attr_names = numpy.zeros((2,), dtype='i')
attr_names[0] = TAG
attr_names[1] = DEP
features = self._get_features(states, tokvecs, attr_names)
scores, finish_update = self.model.begin_update(features, drop=drop)
assert scores.shape[0] == len(states), (len(states), scores.shape)
assert len(scores.shape) == 2
is_valid = self.model.ops.allocate((len(states), nr_class), dtype='i')
self._validate_batch(is_valid, states)
softmaxed = self.model.ops.softmax(scores)
softmaxed *= is_valid
softmaxed /= softmaxed.sum(axis=1).reshape((softmaxed.shape[0], 1))
def backward(golds, sgd=None, losses=[], force_gold=False):
nonlocal softmaxed
costs = self.model.ops.allocate((len(states), nr_class), dtype='f')
d_scores = self.model.ops.allocate((len(states), nr_class), dtype='f')
self._cost_batch(costs, is_valid, states, golds)
self._set_gradient(d_scores, scores, is_valid, costs)
losses.append(self.model.ops.xp.abs(d_scores).sum())
if force_gold:
softmaxed *= costs <= 0
return finish_update(d_scores, sgd=sgd)
return softmaxed, backward
def _init_states(self, docs):
states = []
cdef Doc doc
cdef StateClass state
for i, doc in enumerate(docs):
state = StateClass.init(doc.c, doc.length)
self.moves.initialize_state(state.c)
states.append(state)
return states
def _get_features(self, states, all_tokvecs, attr_names,
nF=1, nB=0, nS=2, nL=2, nR=2):
n_tokens = states[0].nr_context_tokens(nF, nB, nS, nL, nR)
vector_length = all_tokvecs[0].shape[1]
cpu_tokens = numpy.zeros((len(states), n_tokens), dtype='int32')
features = numpy.zeros((len(states), n_tokens, attr_names.shape[0]), dtype='uint64')
tokvecs = self.model.ops.allocate((len(states), n_tokens, vector_length), dtype='f')
for i, state in enumerate(states):
state.set_context_tokens(cpu_tokens[i], nF, nB, nS, nL, nR)
for i in range(len(states)):
for j, tok_i in enumerate(cpu_tokens[i]):
if tok_i >= 0:
tokvecs[i, j] = all_tokvecs[i][tok_i]
return (cpu_tokens, self.model.ops.asarray(features), tokvecs)
def _validate_batch(self, int[:, ::1] is_valid, states):
cdef StateClass state
cdef int i
for i, state in enumerate(states):
self.moves.set_valid(&is_valid[i, 0], state.c)
def _cost_batch(self, float[:, ::1] costs, int[:, ::1] is_valid,
states, golds):
cdef int i
cdef StateClass state
cdef GoldParse gold
for i, (state, gold) in enumerate(zip(states, golds)):
self.moves.set_costs(&is_valid[i, 0], &costs[i, 0], state, gold)
def _transition_batch(self, states, docs, scores):
cdef StateClass state
cdef int guess
for state, doc, guess in zip(states, docs, scores.argmax(axis=1)):
action = self.moves.c[guess]
orths = [t.lex.orth for t in state.c._sent[:state.c.length]]
words = [doc.vocab.strings[w] for w in orths]
if not action.is_valid(state.c, action.label):
ValueError("Invalid action", scores)
action.do(state.c, action.label)
def _set_gradient(self, gradients, scores, is_valid, costs):
"""Do multi-label log loss"""
cdef double Z, gZ, max_, g_max
n = gradients.shape[0]
scores = scores * is_valid
g_scores = scores * is_valid * (costs <= 0.)
exps = self.model.ops.xp.exp(scores - scores.max(axis=1).reshape((n, 1)))
exps *= is_valid
g_exps = self.model.ops.xp.exp(g_scores - g_scores.max(axis=1).reshape((n, 1)))
g_exps *= costs <= 0.
g_exps *= is_valid
gradients[:] = exps / exps.sum(axis=1).reshape((n, 1))
gradients -= g_exps / g_exps.sum(axis=1).reshape((n, 1))
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, scores)
return output
def step_through(self, Doc doc, GoldParse gold=None):
"""
@ -366,6 +353,50 @@ cdef class Parser:
self.cfg.setdefault('extra_labels', []).append(label)
def _begin_update(self, model, states, tokvecs, drop=0.):
nr_class = self.moves.n_moves
attr_names = self.model.ops.allocate((2,), dtype='i')
attr_names[0] = TAG
attr_names[1] = DEP
features = self._get_features(states, tokvecs, attr_names)
scores, finish_update = self.model.begin_update(features, drop=drop)
assert scores.shape[0] == len(states), (len(states), scores.shape)
assert len(scores.shape) == 2
is_valid = self.model.ops.allocate((len(states), nr_class), dtype='i')
self._validate_batch(is_valid, states)
softmaxed = self.model.ops.softmax(scores)
softmaxed *= is_valid
softmaxed /= softmaxed.sum(axis=1).reshape((softmaxed.shape[0], 1))
def backward(golds, sgd=None, losses=[], force_gold=False):
nonlocal softmaxed
costs = self.model.ops.allocate((len(states), nr_class), dtype='f')
d_scores = self.model.ops.allocate((len(states), nr_class), dtype='f')
self._cost_batch(costs, is_valid, states, golds)
self._set_gradient(d_scores, scores, is_valid, costs)
losses.append(numpy.abs(d_scores).sum())
if force_gold:
softmaxed *= costs <= 0
return finish_update(d_scores, sgd=sgd)
return softmaxed, backward
def _get_features(self, states, all_tokvecs, attr_names,
nF=1, nB=0, nS=2, nL=2, nR=2):
n_tokens = states[0].nr_context_tokens(nF, nB, nS, nL, nR)
vector_length = all_tokvecs[0].shape[1]
tokens = self.model.ops.allocate((len(states), n_tokens), dtype='int32')
features = self.model.ops.allocate((len(states), n_tokens, attr_names.shape[0]), dtype='uint64')
tokvecs = self.model.ops.allocate((len(states), n_tokens, vector_length), dtype='f')
for i, state in enumerate(states):
state.set_context_tokens(tokens[i], nF, nB, nS, nL, nR)
state.set_attributes(features[i], tokens[i], attr_names)
state.set_token_vectors(tokvecs[i], all_tokvecs[i], tokens[i])
return (tokens, features, tokvecs)
cdef int dropout(FeatureC* feats, int nr_feat, float prob) except -1:
if prob <= 0 or prob >= 1.:
return 0