mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 09:26:27 +03:00
Remove state argument in pipeline. Other changes
This commit is contained in:
Matthew Honnibal
parent
66ea9aebe7
commit
c12ab47a56
|
|
@ -33,7 +33,7 @@ from .morphology cimport Morphology
|
|||
from .vocab cimport Vocab
|
||||
|
||||
from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
|
||||
from ._ml import Tok2Vec, flatten, get_col, doc2feats
|
||||
from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
|
||||
from .parts_of_speech import X
|
||||
|
||||
|
||||
|
|
@ -57,18 +57,12 @@ class TokenVectorEncoder(object):
|
|||
docs = [docs]
|
||||
tokvecs = self.predict(docs)
|
||||
self.set_annotations(docs, tokvecs)
|
||||
state = {} if state is None else state
|
||||
state['tokvecs'] = tokvecs
|
||||
return state
|
||||
|
||||
def pipe(self, stream, batch_size=128, n_threads=-1):
|
||||
for batch in cytoolz.partition_all(batch_size, stream):
|
||||
docs, states = zip(*batch)
|
||||
for docs in cytoolz.partition_all(batch_size, stream):
|
||||
tokvecs = self.predict(docs)
|
||||
self.set_annotations(docs, tokvecs)
|
||||
for state in states:
|
||||
state['tokvecs'] = tokvecs
|
||||
yield from zip(docs, states)
|
||||
yield from docs
|
||||
|
||||
def predict(self, docs):
|
||||
feats = self.doc2feats(docs)
|
||||
|
|
@ -81,18 +75,12 @@ class TokenVectorEncoder(object):
|
|||
doc.tensor = tokvecs[start : start + len(doc)]
|
||||
start += len(doc)
|
||||
|
||||
def update(self, docs, golds, state=None,
|
||||
drop=0., sgd=None):
|
||||
def begin_update(self, docs, drop=0.):
|
||||
if isinstance(docs, Doc):
|
||||
docs = [docs]
|
||||
golds = [golds]
|
||||
state = {} if state is None else state
|
||||
feats = self.doc2feats(docs)
|
||||
tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
|
||||
state['feats'] = feats
|
||||
state['tokvecs'] = tokvecs
|
||||
state['bp_tokvecs'] = bp_tokvecs
|
||||
return state
|
||||
return tokvecs, bp_tokvecs
|
||||
|
||||
def get_loss(self, docs, golds, scores):
|
||||
raise NotImplementedError
|
||||
|
|
@ -113,22 +101,16 @@ class NeuralTagger(object):
|
|||
self.vocab = vocab
|
||||
self.model = model
|
||||
|
||||
def __call__(self, doc, state=None):
|
||||
assert state is not None
|
||||
assert 'tokvecs' in state
|
||||
tokvecs = state['tokvecs']
|
||||
tags = self.predict(tokvecs)
|
||||
def __call__(self, doc):
|
||||
tags = self.predict(doc.tensor)
|
||||
self.set_annotations([doc], tags)
|
||||
return state
|
||||
|
||||
def pipe(self, stream, batch_size=128, n_threads=-1):
|
||||
for batch in cytoolz.partition_all(batch_size, stream):
|
||||
docs, states = zip(*batch)
|
||||
tag_ids = self.predict(states[0]['tokvecs'])
|
||||
for docs in cytoolz.partition_all(batch_size, stream):
|
||||
tokvecs = self.model.ops.flatten([d.tensor for d in docs])
|
||||
tag_ids = self.predict(tokvecs)
|
||||
self.set_annotations(docs, tag_ids)
|
||||
for state in states:
|
||||
state['tag_ids'] = tag_ids
|
||||
yield from zip(docs, states)
|
||||
yield from docs
|
||||
|
||||
def predict(self, tokvecs):
|
||||
scores = self.model(tokvecs)
|
||||
|
|
@ -150,11 +132,9 @@ class NeuralTagger(object):
|
|||
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
|
||||
idx += 1
|
||||
|
||||
def update(self, docs, golds, state=None, drop=0., sgd=None):
|
||||
state = {} if state is None else state
|
||||
def update(self, docs_tokvecs, golds, drop=0., sgd=None):
|
||||
docs, tokvecs = docs_tokvecs
|
||||
|
||||
tokvecs = state['tokvecs']
|
||||
bp_tokvecs = state['bp_tokvecs']
|
||||
if self.model.nI is None:
|
||||
self.model.nI = tokvecs.shape[1]
|
||||
|
||||
|
|
@ -163,19 +143,19 @@ class NeuralTagger(object):
|
|||
|
||||
d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
|
||||
|
||||
bp_tokvecs(d_tokvecs, sgd=sgd)
|
||||
|
||||
state['tag_scores'] = tag_scores
|
||||
state['tag_loss'] = loss
|
||||
return state
|
||||
return d_tokvecs
|
||||
|
||||
def get_loss(self, docs, golds, scores):
|
||||
tag_index = {tag: i for i, tag in enumerate(self.vocab.morphology.tag_names)}
|
||||
|
||||
cdef int idx = 0
|
||||
correct = numpy.zeros((scores.shape[0],), dtype='i')
|
||||
guesses = scores.argmax(axis=1)
|
||||
for gold in golds:
|
||||
for tag in gold.tags:
|
||||
if tag is None:
|
||||
correct[idx] = guesses[idx]
|
||||
else:
|
||||
correct[idx] = tag_index[tag]
|
||||
idx += 1
|
||||
correct = self.model.ops.xp.array(correct, dtype='i')
|
||||
|
|
@ -198,15 +178,16 @@ class NeuralTagger(object):
|
|||
cdef Vocab vocab = self.vocab
|
||||
vocab.morphology = Morphology(vocab.strings, new_tag_map,
|
||||
vocab.morphology.lemmatizer)
|
||||
self.model = Softmax(self.vocab.morphology.n_tags)
|
||||
print("Tagging", self.model.nO, "tags")
|
||||
token_vector_width = pipeline[0].model.nO
|
||||
self.model = rebatch(1024, Softmax(self.vocab.morphology.n_tags,
|
||||
token_vector_width))
|
||||
#self.model = Softmax(self.vocab.morphology.n_tags)
|
||||
|
||||
def use_params(self, params):
|
||||
with self.model.use_params(params):
|
||||
yield
|
||||
|
||||
|
||||
|
||||
cdef class EntityRecognizer(LinearParser):
|
||||
"""
|
||||
Annotate named entities on Doc objects.
|
||||
|
|
@ -275,8 +256,6 @@ cdef class NeuralEntityRecognizer(NeuralParser):
|
|||
return ids
|
||||
|
||||
|
||||
|
||||
|
||||
cdef class BeamDependencyParser(BeamParser):
|
||||
TransitionSystem = ArcEager
|
||||
|
||||
|
|
|
|||
|
|
@ -35,12 +35,12 @@ from preshed.maps cimport map_get
|
|||
|
||||
from thinc.api import layerize, chain
|
||||
from thinc.neural import Model, Affine, ELU, ReLu, Maxout
|
||||
from thinc.neural.ops import NumpyOps
|
||||
from thinc.neural.ops import NumpyOps, CupyOps
|
||||
|
||||
from .. import util
|
||||
from ..util import get_async, get_cuda_stream
|
||||
from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
|
||||
from .._ml import Tok2Vec, doc2feats
|
||||
from .._ml import Tok2Vec, doc2feats, rebatch
|
||||
|
||||
from . import _parse_features
|
||||
from ._parse_features cimport CONTEXT_SIZE
|
||||
|
|
@ -229,6 +229,8 @@ cdef class Parser:
|
|||
nI=token_vector_width,
|
||||
pieces=maxout_pieces)
|
||||
|
||||
lower = rebatch(1024, lower)
|
||||
|
||||
with Model.use_device('cpu'):
|
||||
upper = chain(
|
||||
Maxout(hidden_width),
|
||||
|
|
@ -274,7 +276,7 @@ cdef class Parser:
|
|||
def __reduce__(self):
|
||||
return (Parser, (self.vocab, self.moves, self.model), None, None)
|
||||
|
||||
def __call__(self, Doc tokens, state=None):
|
||||
def __call__(self, Doc doc):
|
||||
"""
|
||||
Apply the parser or entity recognizer, setting the annotations onto the Doc object.
|
||||
|
||||
|
|
@ -283,10 +285,9 @@ cdef class Parser:
|
|||
Returns:
|
||||
None
|
||||
"""
|
||||
self.parse_batch([tokens], state['tokvecs'])
|
||||
return state
|
||||
self.parse_batch([doc], doc.tensor)
|
||||
|
||||
def pipe(self, stream, int batch_size=1000, int n_threads=2):
|
||||
def pipe(self, docs, int batch_size=1000, int n_threads=2):
|
||||
"""
|
||||
Process a stream of documents.
|
||||
|
||||
|
|
@ -301,12 +302,11 @@ cdef class Parser:
|
|||
cdef StateClass parse_state
|
||||
cdef Doc doc
|
||||
queue = []
|
||||
for batch in cytoolz.partition_all(batch_size, stream):
|
||||
batch = list(batch)
|
||||
docs, states = zip(*batch)
|
||||
parse_states = self.parse_batch(docs, states[0]['tokvecs'])
|
||||
for docs in cytoolz.partition_all(batch_size, docs):
|
||||
tokvecs = self.model[0].ops.flatten([d.tensor for d in docs])
|
||||
parse_states = self.parse_batch(docs, tokvecs)
|
||||
self.set_annotations(docs, parse_states)
|
||||
yield from zip(docs, states)
|
||||
yield from docs
|
||||
|
||||
def parse_batch(self, docs, tokvecs):
|
||||
cuda_stream = get_cuda_stream()
|
||||
|
|
@ -324,10 +324,8 @@ cdef class Parser:
|
|||
todo = [st for st in states if not st.is_final()]
|
||||
return states
|
||||
|
||||
def update(self, docs, golds, state=None, drop=0., sgd=None):
|
||||
assert state is not None
|
||||
assert 'tokvecs' in state
|
||||
assert 'bp_tokvecs' in state
|
||||
def update(self, docs_tokvecs, golds, drop=0., sgd=None):
|
||||
docs, tokvecs = docs_tokvecs
|
||||
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
|
||||
docs = [docs]
|
||||
golds = [golds]
|
||||
|
|
@ -336,9 +334,6 @@ cdef class Parser:
|
|||
for gold in golds:
|
||||
self.moves.preprocess_gold(gold)
|
||||
|
||||
tokvecs = state['tokvecs']
|
||||
bp_tokvecs = state['bp_tokvecs']
|
||||
|
||||
states = self.moves.init_batch(docs)
|
||||
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
|
||||
drop)
|
||||
|
|
@ -357,17 +352,17 @@ cdef class Parser:
|
|||
|
||||
d_scores = self.get_batch_loss(states, golds, scores)
|
||||
d_vector = bp_scores(d_scores, sgd=sgd)
|
||||
loss += (d_scores**2).sum()
|
||||
|
||||
if not isinstance(tokvecs, state2vec.ops.xp.ndarray):
|
||||
backprops.append((token_ids, d_vector, bp_vector))
|
||||
else:
|
||||
if isinstance(self.model[0].ops, CupyOps) \
|
||||
and not isinstance(token_ids, state2vec.ops.xp.ndarray):
|
||||
# Move token_ids and d_vector to CPU, asynchronously
|
||||
backprops.append((
|
||||
get_async(cuda_stream, token_ids),
|
||||
get_async(cuda_stream, d_vector),
|
||||
bp_vector
|
||||
))
|
||||
else:
|
||||
backprops.append((token_ids, d_vector, bp_vector))
|
||||
self.transition_batch(states, scores)
|
||||
todo = [st for st in todo if not st[0].is_final()]
|
||||
# Tells CUDA to block, so our async copies complete.
|
||||
|
|
@ -385,9 +380,7 @@ cdef class Parser:
|
|||
else:
|
||||
xp.add.at(d_tokvecs,
|
||||
token_ids, d_state_features * active_feats)
|
||||
bp_tokvecs(d_tokvecs, sgd)
|
||||
state['parser_loss'] = loss
|
||||
return state
|
||||
return d_tokvecs
|
||||
|
||||
def get_batch_model(self, batch_size, tokvecs, stream, dropout):
|
||||
lower, upper = self.model
|
||||
|
|
@ -445,7 +438,6 @@ cdef class Parser:
|
|||
self.moves.finalize_doc(doc)
|
||||
|
||||
def add_label(self, label):
|
||||
# Doesn't set label into serializer -- subclasses override it to do that.
|
||||
for action in self.moves.action_types:
|
||||
added = self.moves.add_action(action, label)
|
||||
if added:
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user