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

Browse Source 
This reverts commit ea8de11ad5, reversing
changes made to 08e443e083.
This commit is contained in:
Matthew Honnibal 2017-08-14 13:00:23 +02:00
parent ea8de11ad5
commit 52c180ecf5
15 changed files with 143 additions and 660 deletions
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1
setup.py
View File

@ -36,7 +36,6 @@ MOD_NAMES = [
'spacy.syntax.transition_system',
'spacy.syntax.arc_eager',
'spacy.syntax._parse_features',
'spacy.syntax._beam_utils',
'spacy.gold',
'spacy.tokens.doc',
'spacy.tokens.span',

120
spacy/_ml.py
View File

@ -5,12 +5,10 @@ from thinc.neural._classes.hash_embed import HashEmbed
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
import random
import cytoolz
from thinc.neural._classes.convolution import ExtractWindow
from thinc.neural._classes.static_vectors import StaticVectors
from thinc.neural._classes.batchnorm import BatchNorm
from thinc.neural._classes.layernorm import LayerNorm as LN
from thinc.neural._classes.resnet import Residual
from thinc.neural import ReLu
from thinc.neural._classes.selu import SELU
@ -21,12 +19,10 @@ from thinc.api import FeatureExtracter, with_getitem
from thinc.neural.pooling import Pooling, max_pool, mean_pool, sum_pool
from thinc.neural._classes.attention import ParametricAttention
from thinc.linear.linear import LinearModel
from thinc.api import uniqued, wrap, flatten_add_lengths
from thinc.api import uniqued, wrap
from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP
from .tokens.doc import Doc
from . import util
import numpy
import io
@ -57,27 +53,6 @@ def _logistic(X, drop=0.):
return Y, logistic_bwd
@layerize
def add_tuples(X, drop=0.):
"""Give inputs of sequence pairs, where each sequence is (vals, length),
sum the values, returning a single sequence.
If input is:
((vals1, length), (vals2, length)
Output is:
(vals1+vals2, length)
vals are a single tensor for the whole batch.
"""
(vals1, length1), (vals2, length2) = X
assert length1 == length2
def add_tuples_bwd(dY, sgd=None):
return (dY, dY)
return (vals1+vals2, length), add_tuples_bwd
def _zero_init(model):
def _zero_init_impl(self, X, y):
self.W.fill(0)
@ -86,7 +61,6 @@ def _zero_init(model):
model.W.fill(0.)
return model
@layerize
def _preprocess_doc(docs, drop=0.):
keys = [doc.to_array([LOWER]) for doc in docs]
@ -98,6 +72,7 @@ def _preprocess_doc(docs, drop=0.):
return (keys, vals, lengths), None
def _init_for_precomputed(W, ops):
if (W**2).sum() != 0.:
return
@ -105,7 +80,6 @@ def _init_for_precomputed(W, ops):
ops.xavier_uniform_init(reshaped)
W[:] = reshaped.reshape(W.shape)
@describe.on_data(_set_dimensions_if_needed)
@describe.attributes(
nI=Dimension("Input size"),
@ -210,36 +184,25 @@ class PrecomputableMaxouts(Model):
return Yfp, backward
def drop_layer(layer, factor=2.):
def drop_layer_fwd(X, drop=0.):
drop *= factor
mask = layer.ops.get_dropout_mask((1,), drop)
if mask is None or mask > 0:
return layer.begin_update(X, drop=drop)
else:
return X, lambda dX, sgd=None: dX
return wrap(drop_layer_fwd, layer)
def Tok2Vec(width, embed_size, preprocess=None):
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
norm = get_col(cols.index(NORM)) >> HashEmbed(width, embed_size, name='embed_lower')
norm = get_col(cols.index(NORM)) >> HashEmbed(width, embed_size, name='embed_lower')
prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size//2, name='embed_prefix')
suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2, name='embed_suffix')
shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size//2, name='embed_shape')
embed = (norm | prefix | suffix | shape ) >> Maxout(width, width*4, pieces=3)
embed = (norm | prefix | suffix | shape )
tok2vec = (
with_flatten(
asarray(Model.ops, dtype='uint64')
>> uniqued(embed, column=5)
>> drop_layer(
Residual(
(ExtractWindow(nW=1) >> ReLu(width, width*3))
)
) ** 4, pad=4
)
>> embed
>> Maxout(width, width*4, pieces=3)
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)),
pad=4)
)
if preprocess not in (False, None):
tok2vec = preprocess >> tok2vec
@ -334,8 +297,7 @@ def zero_init(model):
def doc2feats(cols=None):
if cols is None:
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE]
def forward(docs, drop=0.):
feats = []
for doc in docs:
@ -361,37 +323,6 @@ def get_token_vectors(tokens_attrs_vectors, drop=0.):
return vectors, backward
def fine_tune(embedding, combine=None):
if combine is not None:
raise NotImplementedError(
"fine_tune currently only supports addition. Set combine=None")
def fine_tune_fwd(docs_tokvecs, drop=0.):
docs, tokvecs = docs_tokvecs
lengths = model.ops.asarray([len(doc) for doc in docs], dtype='i')
vecs, bp_vecs = embedding.begin_update(docs, drop=drop)
flat_tokvecs = embedding.ops.flatten(tokvecs)
flat_vecs = embedding.ops.flatten(vecs)
output = embedding.ops.unflatten(
(model.mix[0] * flat_vecs + model.mix[1] * flat_tokvecs),
lengths)
def fine_tune_bwd(d_output, sgd=None):
bp_vecs(d_output, sgd=sgd)
flat_grad = model.ops.flatten(d_output)
model.d_mix[1] += flat_tokvecs.dot(flat_grad.T).sum()
model.d_mix[0] += flat_vecs.dot(flat_grad.T).sum()
if sgd is not None:
sgd(model._mem.weights, model._mem.gradient, key=model.id)
return d_output
return output, fine_tune_bwd
model = wrap(fine_tune_fwd, embedding)
model.mix = model._mem.add((model.id, 'mix'), (2,))
model.mix.fill(1.)
model.d_mix = model._mem.add_gradient((model.id, 'd_mix'), (model.id, 'mix'))
return model
@layerize
def flatten(seqs, drop=0.):
if isinstance(seqs[0], numpy.ndarray):
@ -438,27 +369,6 @@ def preprocess_doc(docs, drop=0.):
vals = ops.allocate(keys.shape[0]) + 1
return (keys, vals, lengths), None
def getitem(i):
def getitem_fwd(X, drop=0.):
return X[i], None
return layerize(getitem_fwd)
def build_tagger_model(nr_class, token_vector_width, **cfg):
embed_size = util.env_opt('embed_size', 7500)
with Model.define_operators({'>>': chain, '+': add}):
# Input: (doc, tensor) tuples
private_tok2vec = Tok2Vec(token_vector_width, embed_size, preprocess=doc2feats())
model = (
fine_tune(private_tok2vec)
>> with_flatten(
Maxout(token_vector_width, token_vector_width)
>> Softmax(nr_class, token_vector_width)
)
)
model.nI = None
return model
def build_text_classifier(nr_class, width=64, **cfg):
nr_vector = cfg.get('nr_vector', 200)
@ -473,7 +383,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
>> _flatten_add_lengths
>> with_getitem(0,
uniqued(
(embed_lower | embed_prefix | embed_suffix | embed_shape)
(embed_lower | embed_prefix | embed_suffix | embed_shape)
>> Maxout(width, width+(width//2)*3))
>> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
>> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
@ -494,7 +404,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
>> zero_init(Affine(nr_class, nr_class*2, drop_factor=0.0))
>> logistic
)
model.lsuv = False
return model

4
spacy/cli/convert.py
View File

@ -21,10 +21,10 @@ CONVERTERS = {
@plac.annotations(
input_file=("input file", "positional", None, str),
output_dir=("output directory for converted file", "positional", None, str),
n_sents=("Number of sentences per doc", "option", "n", int),
n_sents=("Number of sentences per doc", "option", "n", float),
morphology=("Enable appending morphology to tags", "flag", "m", bool)
)
def convert(cmd, input_file, output_dir, n_sents=1, morphology=False):
def convert(cmd, input_file, output_dir, n_sents, morphology):
"""
Convert files into JSON format for use with train command and other
experiment management functions.

5
spacy/cli/train.py
View File

@ -91,14 +91,15 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
for batch in minibatch(train_docs, size=batch_sizes):
docs, golds = zip(*batch)
nlp.update(docs, golds, sgd=optimizer,
drop=next(dropout_rates), losses=losses,
update_tensors=True)
drop=next(dropout_rates), losses=losses)
pbar.update(sum(len(doc) for doc in docs))
with nlp.use_params(optimizer.averages):
util.set_env_log(False)
epoch_model_path = output_path / ('model%d' % i)
nlp.to_disk(epoch_model_path)
with (output_path / ('model%d.pickle' % i)).open('wb') as file_:
dill.dump(nlp, file_, -1)
nlp_loaded = lang_class(pipeline=pipeline)
nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
scorer = nlp_loaded.evaluate(

25
spacy/language.py
View File

@ -277,8 +277,7 @@ class Language(object):
def make_doc(self, text):
return self.tokenizer(text)
def update(self, docs, golds, drop=0., sgd=None, losses=None,
update_tensors=False):
def update(self, docs, golds, drop=0., sgd=None, losses=None):
"""Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects.
@ -305,17 +304,14 @@ class Language(object):
grads[key] = (W, dW)
pipes = list(self.pipeline[1:])
random.shuffle(pipes)
tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop)
all_d_tokvecses = [tok2vec.model.ops.allocate(tv.shape) for tv in tokvecses]
for proc in pipes:
if not hasattr(proc, 'update'):
continue
tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop)
d_tokvecses = proc.update((docs, tokvecses), golds,
drop=drop, sgd=get_grads, losses=losses)
if update_tensors and d_tokvecses is not None:
for i, d_tv in enumerate(d_tokvecses):
all_d_tokvecses[i] += d_tv
bp_tokvecses(all_d_tokvecses, sgd=sgd)
if d_tokvecses is not None:
bp_tokvecses(d_tokvecses, sgd=sgd)
for key, (W, dW) in grads.items():
sgd(W, dW, key=key)
# Clear the tensor variable, to free GPU memory.
@ -385,18 +381,9 @@ class Language(object):
return optimizer
def evaluate(self, docs_golds):
scorer = Scorer()
docs, golds = zip(*docs_golds)
docs = list(docs)
golds = list(golds)
for pipe in self.pipeline:
if not hasattr(pipe, 'pipe'):
for doc in docs:
pipe(doc)
else:
docs = list(pipe.pipe(docs))
assert len(docs) == len(golds)
for doc, gold in zip(docs, golds):
scorer = Scorer()
for doc, gold in zip(self.pipe(docs, batch_size=32), golds):
scorer.score(doc, gold)
doc.tensor = None
return scorer

33
spacy/pipeline.pyx
View File

@ -42,7 +42,7 @@ from .compat import json_dumps
from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
from ._ml import build_text_classifier, build_tagger_model
from ._ml import build_text_classifier
from .parts_of_speech import X
@ -138,7 +138,7 @@ class TokenVectorEncoder(BaseThincComponent):
name = 'tensorizer'
@classmethod
def Model(cls, width=128, embed_size=4000, **cfg):
def Model(cls, width=128, embed_size=7500, **cfg):
"""Create a new statistical model for the class.
width (int): Output size of the model.
@ -253,25 +253,23 @@ class NeuralTagger(BaseThincComponent):
self.cfg = dict(cfg)
def __call__(self, doc):
tags = self.predict(([doc], [doc.tensor]))
tags = self.predict([doc.tensor])
self.set_annotations([doc], tags)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
tokvecs = [d.tensor for d in docs]
tag_ids = self.predict((docs, tokvecs))
tag_ids = self.predict(tokvecs)
self.set_annotations(docs, tag_ids)
yield from docs
def predict(self, docs_tokvecs):
scores = self.model(docs_tokvecs)
def predict(self, tokvecs):
scores = self.model(tokvecs)
scores = self.model.ops.flatten(scores)
guesses = scores.argmax(axis=1)
if not isinstance(guesses, numpy.ndarray):
guesses = guesses.get()
tokvecs = docs_tokvecs[1]
guesses = self.model.ops.unflatten(guesses,
[tv.shape[0] for tv in tokvecs])
return guesses
@ -284,8 +282,6 @@ class NeuralTagger(BaseThincComponent):
cdef Vocab vocab = self.vocab
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
if hasattr(doc_tag_ids, 'get'):
doc_tag_ids = doc_tag_ids.get()
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber preset POS tags
if doc.c[j].tag == 0 and doc.c[j].pos == 0:
@ -298,7 +294,8 @@ class NeuralTagger(BaseThincComponent):
if self.model.nI is None:
self.model.nI = tokvecs[0].shape[1]
tag_scores, bp_tag_scores = self.model.begin_update(docs_tokvecs, drop=drop)
tag_scores, bp_tag_scores = self.model.begin_update(tokvecs, drop=drop)
loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
@ -349,8 +346,10 @@ class NeuralTagger(BaseThincComponent):
@classmethod
def Model(cls, n_tags, token_vector_width):
return build_tagger_model(n_tags, token_vector_width)
return with_flatten(
chain(Maxout(token_vector_width, token_vector_width),
Softmax(n_tags, token_vector_width)))
def use_params(self, params):
with self.model.use_params(params):
yield
@ -433,7 +432,7 @@ class NeuralLabeller(NeuralTagger):
@property
def labels(self):
return self.cfg.setdefault('labels', {})
return self.cfg.get('labels', {})
@labels.setter
def labels(self, value):
@ -456,8 +455,10 @@ class NeuralLabeller(NeuralTagger):
@classmethod
def Model(cls, n_tags, token_vector_width):
return build_tagger_model(n_tags, token_vector_width)
return with_flatten(
chain(Maxout(token_vector_width, token_vector_width),
Softmax(n_tags, token_vector_width)))
def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
cdef int idx = 0

273
spacy/syntax/_beam_utils.pyx
View File

@ -1,273 +0,0 @@
# cython: infer_types=True
# cython: profile=True
cimport numpy as np
import numpy
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from thinc.extra.search cimport Beam
from thinc.extra.search import MaxViolation
from thinc.typedefs cimport hash_t, class_t
from .transition_system cimport TransitionSystem, Transition
from .stateclass cimport StateClass
from ..gold cimport GoldParse
from ..tokens.doc cimport Doc
# These are passed as callbacks to thinc.search.Beam
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
dest = <StateClass>_dest
src = <StateClass>_src
moves = <const Transition*>_moves
dest.clone(src)
moves[clas].do(dest.c, moves[clas].label)
cdef int _check_final_state(void* _state, void* extra_args) except -1:
return (<StateClass>_state).is_final()
def _cleanup(Beam beam):
for i in range(beam.width):
Py_XDECREF(<PyObject*>beam._states[i].content)
Py_XDECREF(<PyObject*>beam._parents[i].content)
cdef hash_t _hash_state(void* _state, void* _) except 0:
state = <StateClass>_state
if state.c.is_final():
return 1
else:
return state.c.hash()
cdef class ParserBeam(object):
cdef public TransitionSystem moves
cdef public object states
cdef public object golds
cdef public object beams
def __init__(self, TransitionSystem moves, states, golds,
int width=4, float density=0.001):
self.moves = moves
self.states = states
self.golds = golds
self.beams = []
cdef Beam beam
cdef StateClass state, st
for state in states:
beam = Beam(self.moves.n_moves, width, density)
beam.initialize(self.moves.init_beam_state, state.c.length, state.c._sent)
for i in range(beam.width):
st = <StateClass>beam.at(i)
st.c.offset = state.c.offset
self.beams.append(beam)
def __dealloc__(self):
if self.beams is not None:
for beam in self.beams:
if beam is not None:
_cleanup(beam)
@property
def is_done(self):
return all(b.is_done for b in self.beams)
def __getitem__(self, i):
return self.beams[i]
def __len__(self):
return len(self.beams)
def advance(self, scores, follow_gold=False):
cdef Beam beam
for i, beam in enumerate(self.beams):
if beam.is_done or not scores[i].size:
continue
self._set_scores(beam, scores[i])
if self.golds is not None:
self._set_costs(beam, self.golds[i], follow_gold=follow_gold)
if follow_gold:
assert self.golds is not None
beam.advance(_transition_state, NULL, <void*>self.moves.c)
else:
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
beam.check_done(_check_final_state, NULL)
if beam.is_done:
for j in range(beam.size):
if is_gold(<StateClass>beam.at(j), self.golds[i], self.moves.strings):
beam._states[j].loss = 0.0
elif beam._states[j].loss == 0.0:
beam._states[j].loss = 1.0
def _set_scores(self, Beam beam, float[:, ::1] scores):
cdef float* c_scores = &scores[0, 0]
for i in range(beam.size):
state = <StateClass>beam.at(i)
if not state.is_final():
for j in range(beam.nr_class):
beam.scores[i][j] = c_scores[i * beam.nr_class + j]
self.moves.set_valid(beam.is_valid[i], state.c)
def _set_costs(self, Beam beam, GoldParse gold, int follow_gold=False):
for i in range(beam.size):
state = <StateClass>beam.at(i)
if not state.c.is_final():
self.moves.set_costs(beam.is_valid[i], beam.costs[i], state, gold)
if follow_gold:
for j in range(beam.nr_class):
if beam.costs[i][j] >= 1:
beam.is_valid[i][j] = 0
def is_gold(StateClass state, GoldParse gold, strings):
predicted = set()
truth = set()
for i in range(gold.length):
if gold.cand_to_gold[i] is None:
continue
if state.safe_get(i).dep:
predicted.add((i, state.H(i), strings[state.safe_get(i).dep]))
else:
predicted.add((i, state.H(i), 'ROOT'))
id_, word, tag, head, dep, ner = gold.orig_annot[gold.cand_to_gold[i]]
truth.add((id_, head, dep))
return truth == predicted
def get_token_ids(states, int n_tokens):
cdef StateClass state
cdef np.ndarray ids = numpy.zeros((len(states), n_tokens),
dtype='int32', order='C')
c_ids = <int*>ids.data
for i, state in enumerate(states):
if not state.is_final():
state.c.set_context_tokens(c_ids, n_tokens)
else:
ids[i] = -1
c_ids += ids.shape[1]
return ids
nr_update = 0
def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
states, tokvecs, golds,
state2vec, vec2scores, drop=0., sgd=None,
losses=None, int width=4, float density=0.001):
global nr_update
nr_update += 1
pbeam = ParserBeam(moves, states, golds,
width=width, density=density)
gbeam = ParserBeam(moves, states, golds,
width=width, density=0.0)
cdef StateClass state
beam_maps = []
backprops = []
violns = [MaxViolation() for _ in range(len(states))]
for t in range(max_steps):
# The beam maps let us find the right row in the flattened scores
# arrays for each state. States are identified by (example id, history).
# We keep a different beam map for each step (since we'll have a flat
# scores array for each step). The beam map will let us take the per-state
# losses, and compute the gradient for each (step, state, class).
beam_maps.append({})
# Gather all states from the two beams in a list. Some stats may occur
# in both beams. To figure out which beam each state belonged to,
# we keep two lists of indices, p_indices and g_indices
states, p_indices, g_indices = get_states(pbeam, gbeam, beam_maps[-1], nr_update)
if not states:
break
# Now that we have our flat list of states, feed them through the model
token_ids = get_token_ids(states, nr_feature)
vectors, bp_vectors = state2vec.begin_update(token_ids, drop=drop)
scores, bp_scores = vec2scores.begin_update(vectors, drop=drop)
# Store the callbacks for the backward pass
backprops.append((token_ids, bp_vectors, bp_scores))
# Unpack the flat scores into lists for the two beams. The indices arrays
# tell us which example and state the scores-row refers to.
p_scores = [numpy.ascontiguousarray(scores[indices], dtype='f') for indices in p_indices]
g_scores = [numpy.ascontiguousarray(scores[indices], dtype='f') for indices in g_indices]
# Now advance the states in the beams. The gold beam is contrained to
# to follow only gold analyses.
pbeam.advance(p_scores)
gbeam.advance(g_scores, follow_gold=True)
# Track the "maximum violation", to use in the update.
for i, violn in enumerate(violns):
violn.check_crf(pbeam[i], gbeam[i])
# Only make updates if we have non-gold states
histories = [((v.p_hist + v.g_hist) if v.p_hist else []) for v in violns]
losses = [((v.p_probs + v.g_probs) if v.p_probs else []) for v in violns]
states_d_scores = get_gradient(moves.n_moves, beam_maps,
histories, losses)
assert len(states_d_scores) == len(backprops), (len(states_d_scores), len(backprops))
return states_d_scores, backprops
def get_states(pbeams, gbeams, beam_map, nr_update):
seen = {}
states = []
p_indices = []
g_indices = []
cdef Beam pbeam, gbeam
assert len(pbeams) == len(gbeams)
for eg_id, (pbeam, gbeam) in enumerate(zip(pbeams, gbeams)):
p_indices.append([])
g_indices.append([])
if pbeam.loss > 0 and pbeam.min_score > gbeam.score:
continue
for i in range(pbeam.size):
state = <StateClass>pbeam.at(i)
if not state.is_final():
key = tuple([eg_id] + pbeam.histories[i])
seen[key] = len(states)
p_indices[-1].append(len(states))
states.append(state)
beam_map.update(seen)
for i in range(gbeam.size):
state = <StateClass>gbeam.at(i)
if not state.is_final():
key = tuple([eg_id] + gbeam.histories[i])
if key in seen:
g_indices[-1].append(seen[key])
else:
g_indices[-1].append(len(states))
beam_map[key] = len(states)
states.append(state)
p_idx = [numpy.asarray(idx, dtype='i') for idx in p_indices]
g_idx = [numpy.asarray(idx, dtype='i') for idx in g_indices]
return states, p_idx, g_idx
def get_gradient(nr_class, beam_maps, histories, losses):
"""
The global model assigns a loss to each parse. The beam scores
are additive, so the same gradient is applied to each action
in the history. This gives the gradient of a single *action*
for a beam state -- so we have "the gradient of loss for taking
action i given history H."
Histories: Each hitory is a list of actions
Each candidate has a history
Each beam has multiple candidates
Each batch has multiple beams
So history is list of lists of lists of ints
"""
nr_step = len(beam_maps)
grads = []
for beam_map in beam_maps:
if beam_map:
grads.append(numpy.zeros((max(beam_map.values())+1, nr_class), dtype='f'))
assert len(histories) == len(losses)
for eg_id, hists in enumerate(histories):
for loss, hist in zip(losses[eg_id], hists):
key = tuple([eg_id])
for j, clas in enumerate(hist):
i = beam_maps[j][key]
# In step j, at state i action clas
# resulted in loss
grads[j][i, clas] += loss / len(histories)
key = key + tuple([clas])
return grads

1
spacy/syntax/_state.pxd
View File

@ -37,7 +37,6 @@ cdef cppclass StateC:
this.shifted = <bint*>calloc(length + (PADDING * 2), sizeof(bint))
this._sent = <TokenC*>calloc(length + (PADDING * 2), sizeof(TokenC))
this._ents = <Entity*>calloc(length + (PADDING * 2), sizeof(Entity))
this.offset = 0
cdef int i
for i in range(length + (PADDING * 2)):
this._ents[i].end = -1

1
spacy/syntax/arc_eager.pyx
View File

@ -385,7 +385,6 @@ cdef class ArcEager(TransitionSystem):
for i in range(self.n_moves):
if self.c[i].move == move and self.c[i].label == label:
return self.c[i]
return Transition(clas=0, move=MISSING, label=0)
def move_name(self, int move, attr_t label):
label_str = self.strings[label]

14
spacy/syntax/beam_parser.pyx
View File

@ -34,7 +34,6 @@ from ._parse_features cimport CONTEXT_SIZE
from ._parse_features cimport fill_context
from .stateclass cimport StateClass
from .parser cimport Parser
from ._beam_utils import is_gold
DEBUG = False
@ -238,3 +237,16 @@ def _check_train_integrity(Beam pred, Beam gold, GoldParse gold_parse, Transitio
raise Exception("Gold parse is not gold-standard")
def is_gold(StateClass state, GoldParse gold, StringStore strings):
predicted = set()
truth = set()
for i in range(gold.length):
if gold.cand_to_gold[i] is None:
continue
if state.safe_get(i).dep:
predicted.add((i, state.H(i), strings[state.safe_get(i).dep]))
else:
predicted.add((i, state.H(i), 'ROOT'))
id_, word, tag, head, dep, ner = gold.orig_annot[gold.cand_to_gold[i]]
truth.add((id_, head, dep))
return truth == predicted

4
spacy/syntax/nn_parser.pxd
View File

@ -14,4 +14,8 @@ cdef class Parser:
cdef readonly TransitionSystem moves
cdef readonly object cfg
cdef void _parse_step(self, StateC* state,
const float* feat_weights,
int nr_class, int nr_feat, int nr_piece) nogil
#cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil

220
spacy/syntax/nn_parser.pyx
View File

@ -37,17 +37,14 @@ from preshed.maps cimport MapStruct
from preshed.maps cimport map_get
from thinc.api import layerize, chain, noop, clone
from thinc.neural import Model, Affine, ReLu, Maxout
from thinc.neural._classes.selu import SELU
from thinc.neural._classes.layernorm import LayerNorm
from thinc.neural import Model, Affine, ELU, ReLu, Maxout
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
from .. import util
from ..util import get_async, get_cuda_stream
from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
from .._ml import Tok2Vec, doc2feats, rebatch, fine_tune
from .._ml import Residual, drop_layer
from .._ml import Tok2Vec, doc2feats, rebatch
from ..compat import json_dumps
from . import _parse_features
@ -62,11 +59,8 @@ from ..structs cimport TokenC
from ..tokens.doc cimport Doc
from ..strings cimport StringStore
from ..gold cimport GoldParse
from ..attrs cimport ID, TAG, DEP, ORTH, NORM, PREFIX, SUFFIX, TAG
from . import _beam_utils
from ..attrs cimport TAG, DEP
USE_FINE_TUNE = True
BEAM_PARSE = True
def get_templates(*args, **kwargs):
return []
@ -238,14 +232,11 @@ cdef class Parser:
Base class of the DependencyParser and EntityRecognizer.
"""
@classmethod
def Model(cls, nr_class, token_vector_width=128, hidden_width=300, depth=1, **cfg):
def Model(cls, nr_class, token_vector_width=128, hidden_width=128, depth=1, **cfg):
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)
parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
embed_size = util.env_opt('embed_size', 4000)
tensors = fine_tune(Tok2Vec(token_vector_width, embed_size,
preprocess=doc2feats()))
if parser_maxout_pieces == 1:
lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature,
@ -257,10 +248,15 @@ cdef class Parser:
nI=token_vector_width)
with Model.use_device('cpu'):
upper = chain(
clone(Residual(ReLu(hidden_width)), (depth-1)),
zero_init(Affine(nr_class, drop_factor=0.0))
)
if depth == 0:
upper = chain()
upper.is_noop = True
else:
upper = chain(
clone(Maxout(hidden_width), (depth-1)),
zero_init(Affine(nr_class, drop_factor=0.0))
)
upper.is_noop = False
# TODO: This is an unfortunate hack atm!
# Used to set input dimensions in network.
lower.begin_training(lower.ops.allocate((500, token_vector_width)))
@ -272,7 +268,7 @@ cdef class Parser:
'hidden_width': hidden_width,
'maxout_pieces': parser_maxout_pieces
}
return (tensors, lower, upper), cfg
return (lower, upper), cfg
def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
"""
@ -348,21 +344,17 @@ cdef class Parser:
The number of threads with which to work on the buffer in parallel.
Yields (Doc): Documents, in order.
"""
if BEAM_PARSE:
beam_width = 8
cdef StateClass parse_state
cdef Doc doc
cdef Beam beam
queue = []
for docs in cytoolz.partition_all(batch_size, docs):
docs = list(docs)
tokvecs = [doc.tensor for doc in docs]
tokvecs = [d.tensor for d in docs]
if beam_width == 1:
parse_states = self.parse_batch(docs, tokvecs)
else:
beams = self.beam_parse(docs, tokvecs,
beam_width=beam_width, beam_density=beam_density)
parse_states = []
for beam in beams:
parse_states.append(<StateClass>beam.at(0))
parse_states = self.beam_parse(docs, tokvecs,
beam_width=beam_width, beam_density=beam_density)
self.set_annotations(docs, parse_states)
yield from docs
@ -377,12 +369,8 @@ cdef class Parser:
int nr_class, nr_feat, nr_piece, nr_dim, nr_state
if isinstance(docs, Doc):
docs = [docs]
if isinstance(tokvecses, np.ndarray):
tokvecses = [tokvecses]
tokvecs = self.model[0].ops.flatten(tokvecses)
if USE_FINE_TUNE:
tokvecs += self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
nr_state = len(docs)
nr_class = self.moves.n_moves
@ -406,20 +394,27 @@ cdef class Parser:
cdef np.ndarray scores
c_token_ids = <int*>token_ids.data
c_is_valid = <int*>is_valid.data
cdef int has_hidden = not getattr(vec2scores, 'is_noop', False)
while not next_step.empty():
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)
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)
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:
@ -434,15 +429,11 @@ cdef class Parser:
cdef int nr_class = self.moves.n_moves
cdef StateClass stcls, output
tokvecs = self.model[0].ops.flatten(tokvecses)
if USE_FINE_TUNE:
tokvecs += self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
cuda_stream = get_cuda_stream()
state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
cuda_stream, 0.0)
beams = []
cdef int offset = 0
cdef int j = 0
cdef int k
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)
@ -455,31 +446,44 @@ cdef class Parser:
states = []
for i in range(beam.size):
stcls = <StateClass>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)
states.append(stcls)
token_ids = self.get_token_ids(states)
vectors = state2vec(token_ids)
scores = vec2scores(vectors)
j = 0
c_scores = <float*>scores.data
for i in range(beam.size):
stcls = <StateClass>beam.at(i)
if not stcls.is_final():
self.moves.set_valid(beam.is_valid[i], stcls.c)
for k in range(nr_class):
beam.scores[i][k] = c_scores[j * scores.shape[1] + k]
j += 1
for j in range(nr_class):
beam.scores[i][j] = scores[i, j]
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
beam.check_done(_check_final_state, NULL)
beams.append(beam)
return beams
cdef void _parse_step(self, StateC* state,
const float* feat_weights,
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 * 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 * nr_piece)
self.moves.set_valid(is_valid, state)
guess = arg_maxout_if_valid(scores, is_valid, nr_class, nr_piece)
action = self.moves.c[guess]
action.do(state, action.label)
free(is_valid)
free(scores)
free(token_ids)
def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
if BEAM_PARSE:
return self.update_beam(docs_tokvecs, golds, drop=drop, sgd=sgd,
losses=losses)
if losses is not None and self.name not in losses:
losses[self.name] = 0.
docs, tokvec_lists = docs_tokvecs
@ -487,10 +491,6 @@ cdef class Parser:
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
docs = [docs]
golds = [golds]
if USE_FINE_TUNE:
my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
my_tokvecs = self.model[0].ops.flatten(my_tokvecs)
tokvecs += my_tokvecs
cuda_stream = get_cuda_stream()
@ -517,13 +517,13 @@ cdef class Parser:
scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
d_scores = self.get_batch_loss(states, golds, scores)
d_vector = bp_scores(d_scores, sgd=sgd)
d_vector = bp_scores(d_scores / d_scores.shape[0], sgd=sgd)
if drop != 0:
d_vector *= mask
if isinstance(self.model[0].ops, CupyOps) \
and not isinstance(token_ids, state2vec.ops.xp.ndarray):
# Move token_ids and d_vector to GPU, asynchronously
# Move token_ids and d_vector to CPU, asynchronously
backprops.append((
get_async(cuda_stream, token_ids),
get_async(cuda_stream, d_vector),
@ -540,55 +540,7 @@ cdef class Parser:
break
self._make_updates(d_tokvecs,
backprops, sgd, cuda_stream)
d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
if USE_FINE_TUNE:
bp_my_tokvecs(d_tokvecs, sgd=sgd)
return d_tokvecs
def update_beam(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
if losses is not None and self.name not in losses:
losses[self.name] = 0.
docs, tokvecs = docs_tokvecs
lengths = [len(d) for d in docs]
assert min(lengths) >= 1
tokvecs = self.model[0].ops.flatten(tokvecs)
if USE_FINE_TUNE:
my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
my_tokvecs = self.model[0].ops.flatten(my_tokvecs)
tokvecs += my_tokvecs
states = self.moves.init_batch(docs)
for gold in golds:
self.moves.preprocess_gold(gold)
cuda_stream = get_cuda_stream()
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream, 0.0)
states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, 500,
states, tokvecs, golds,
state2vec, vec2scores,
drop, sgd, losses,
width=8)
backprop_lower = []
for i, d_scores in enumerate(states_d_scores):
if losses is not None:
losses[self.name] += (d_scores**2).sum()
ids, bp_vectors, bp_scores = backprops[i]
d_vector = bp_scores(d_scores, sgd=sgd)
if isinstance(self.model[0].ops, CupyOps) \
and not isinstance(ids, state2vec.ops.xp.ndarray):
backprop_lower.append((
get_async(cuda_stream, ids),
get_async(cuda_stream, d_vector),
bp_vectors))
else:
backprop_lower.append((ids, d_vector, bp_vectors))
d_tokvecs = self.model[0].ops.allocate(tokvecs.shape)
self._make_updates(d_tokvecs, backprop_lower, sgd, cuda_stream)
d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, lengths)
if USE_FINE_TUNE:
bp_my_tokvecs(d_tokvecs, sgd=sgd)
return d_tokvecs
return self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
def _init_gold_batch(self, whole_docs, whole_golds):
"""Make a square batch, of length equal to the shortest doc. A long
@ -633,10 +585,14 @@ cdef class Parser:
xp = get_array_module(d_tokvecs)
for ids, d_vector, bp_vector in backprops:
d_state_features = bp_vector(d_vector, sgd=sgd)
mask = ids >= 0
indices = xp.nonzero(mask)
self.model[0].ops.scatter_add(d_tokvecs, ids[indices],
d_state_features[indices])
active_feats = ids * (ids >= 0)
active_feats = active_feats.reshape((ids.shape[0], ids.shape[1], 1))
if hasattr(xp, 'scatter_add'):
xp.scatter_add(d_tokvecs,
ids, d_state_features * active_feats)
else:
xp.add.at(d_tokvecs,
ids, d_state_features * active_feats)
@property
def move_names(self):
@ -647,7 +603,7 @@ cdef class Parser:
return names
def get_batch_model(self, batch_size, tokvecs, stream, dropout):
_, lower, upper = self.model
lower, upper = self.model
state2vec = precompute_hiddens(batch_size, tokvecs,
lower, stream, drop=dropout)
return state2vec, upper
@ -737,12 +693,10 @@ cdef class Parser:
def to_disk(self, path, **exclude):
serializers = {
'tok2vec_model': lambda p: p.open('wb').write(
self.model[0].to_bytes()),
'lower_model': lambda p: p.open('wb').write(
self.model[1].to_bytes()),
self.model[0].to_bytes()),
'upper_model': lambda p: p.open('wb').write(
self.model[2].to_bytes()),
self.model[1].to_bytes()),
'vocab': lambda p: self.vocab.to_disk(p),
'moves': lambda p: self.moves.to_disk(p, strings=False),
'cfg': lambda p: p.open('w').write(json_dumps(self.cfg))
@ -763,29 +717,24 @@ cdef class Parser:
self.model, cfg = self.Model(**self.cfg)
else:
cfg = {}
with (path / 'tok2vec_model').open('rb') as file_:
bytes_data = file_.read()
self.model[0].from_bytes(bytes_data)
with (path / 'lower_model').open('rb') as file_:
bytes_data = file_.read()
self.model[1].from_bytes(bytes_data)
self.model[0].from_bytes(bytes_data)
with (path / 'upper_model').open('rb') as file_:
bytes_data = file_.read()
self.model[2].from_bytes(bytes_data)
self.model[1].from_bytes(bytes_data)
self.cfg.update(cfg)
return self
def to_bytes(self, **exclude):
serializers = OrderedDict((
('tok2vec_model', lambda: self.model[0].to_bytes()),
('lower_model', lambda: self.model[1].to_bytes()),
('upper_model', lambda: self.model[2].to_bytes()),
('lower_model', lambda: self.model[0].to_bytes()),
('upper_model', lambda: self.model[1].to_bytes()),
('vocab', lambda: self.vocab.to_bytes()),
('moves', lambda: self.moves.to_bytes(strings=False)),
('cfg', lambda: ujson.dumps(self.cfg))
))
if 'model' in exclude:
exclude['tok2vec_model'] = True
exclude['lower_model'] = True
exclude['upper_model'] = True
exclude.pop('model')
@ -796,7 +745,6 @@ cdef class Parser:
('vocab', lambda b: self.vocab.from_bytes(b)),
('moves', lambda b: self.moves.from_bytes(b, strings=False)),
('cfg', lambda b: self.cfg.update(ujson.loads(b))),
('tok2vec_model', lambda b: None),
('lower_model', lambda b: None),
('upper_model', lambda b: None)
))
@ -806,12 +754,10 @@ cdef class Parser:
self.model, cfg = self.Model(self.moves.n_moves)
else:
cfg = {}
if 'tok2vec_model' in msg:
self.model[0].from_bytes(msg['tok2vec_model'])
if 'lower_model' in msg:
self.model[1].from_bytes(msg['lower_model'])
self.model[0].from_bytes(msg['lower_model'])
if 'upper_model' in msg:
self.model[2].from_bytes(msg['upper_model'])
self.model[1].from_bytes(msg['upper_model'])
self.cfg.update(cfg)
return self

2
spacy/syntax/transition_system.pyx
View File

@ -107,8 +107,6 @@ cdef class TransitionSystem:
def is_valid(self, StateClass stcls, move_name):
action = self.lookup_transition(move_name)
if action.move == 0:
return False
return action.is_valid(stcls.c, action.label)
cdef int set_valid(self, int* is_valid, const StateC* st) nogil:

13
spacy/tests/parser/test_neural_parser.py
View File

@ -78,16 +78,3 @@ def test_predict_doc_beam(parser, tok2vec, model, doc):
parser(doc, beam_width=32, beam_density=0.001)
for word in doc:
print(word.text, word.head, word.dep_)
def test_update_doc_beam(parser, tok2vec, model, doc, gold):
parser.model = model
tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
d_tokvecs = parser.update_beam(([doc], tokvecs), [gold])
assert d_tokvecs[0].shape == tokvecs[0].shape
def optimize(weights, gradient, key=None):
weights -= 0.001 * gradient
bp_tokvecs(d_tokvecs, sgd=optimize)
assert d_tokvecs[0].sum() == 0.

87
spacy/tests/parser/test_nn_beam.py
View File

@ -1,87 +0,0 @@
from __future__ import unicode_literals
import pytest
import numpy
from thinc.api import layerize
from ...vocab import Vocab
from ...syntax.arc_eager import ArcEager
from ...tokens import Doc
from ...gold import GoldParse
from ...syntax._beam_utils import ParserBeam, update_beam
from ...syntax.stateclass import StateClass
@pytest.fixture
def vocab():
return Vocab()
@pytest.fixture
def moves(vocab):
aeager = ArcEager(vocab.strings, {})
aeager.add_action(2, 'nsubj')
aeager.add_action(3, 'dobj')
aeager.add_action(2, 'aux')
return aeager
@pytest.fixture
def docs(vocab):
return [Doc(vocab, words=['Rats', 'bite', 'things'])]
@pytest.fixture
def states(docs):
return [StateClass(doc) for doc in docs]
@pytest.fixture
def tokvecs(docs, vector_size):
output = []
for doc in docs:
vec = numpy.random.uniform(-0.1, 0.1, (len(doc), vector_size))
output.append(numpy.asarray(vec))
return output
@pytest.fixture
def golds(docs):
return [GoldParse(doc) for doc in docs]
@pytest.fixture
def batch_size(docs):
return len(docs)
@pytest.fixture
def beam_width():
return 4
@pytest.fixture
def vector_size():
return 6
@pytest.fixture
def beam(moves, states, golds, beam_width):
return ParserBeam(moves, states, golds, width=beam_width)
@pytest.fixture
def scores(moves, batch_size, beam_width):
return [
numpy.asarray(
numpy.random.uniform(-0.1, 0.1, (batch_size, moves.n_moves)),
dtype='f')
for _ in range(batch_size)]
def test_create_beam(beam):
pass
def test_beam_advance(beam, scores):
beam.advance(scores)
def test_beam_advance_too_few_scores(beam, scores):
with pytest.raises(IndexError):
beam.advance(scores[:-1])