mirror of https://github.com/explosion/spaCy.git
Remove state argument in pipeline. Other changes
This commit is contained in:
Matthew Honnibal
parent
66ea9aebe7
commit
c12ab47a56
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@ -33,7 +33,7 @@ from .morphology cimport Morphology
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from .vocab cimport Vocab
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from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
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from ._ml import Tok2Vec, flatten, get_col, doc2feats
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from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
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from .parts_of_speech import X
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@ -57,18 +57,12 @@ class TokenVectorEncoder(object):
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docs = [docs]
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tokvecs = self.predict(docs)
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self.set_annotations(docs, tokvecs)
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state = {} if state is None else state
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state['tokvecs'] = tokvecs
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return state
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for batch in cytoolz.partition_all(batch_size, stream):
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docs, states = zip(*batch)
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for docs in cytoolz.partition_all(batch_size, stream):
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tokvecs = self.predict(docs)
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self.set_annotations(docs, tokvecs)
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for state in states:
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state['tokvecs'] = tokvecs
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yield from zip(docs, states)
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yield from docs
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def predict(self, docs):
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feats = self.doc2feats(docs)
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@ -81,18 +75,12 @@ class TokenVectorEncoder(object):
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doc.tensor = tokvecs[start : start + len(doc)]
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start += len(doc)
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def update(self, docs, golds, state=None,
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drop=0., sgd=None):
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def begin_update(self, docs, drop=0.):
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if isinstance(docs, Doc):
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docs = [docs]
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golds = [golds]
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state = {} if state is None else state
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feats = self.doc2feats(docs)
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tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
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state['feats'] = feats
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state['tokvecs'] = tokvecs
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state['bp_tokvecs'] = bp_tokvecs
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return state
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return tokvecs, bp_tokvecs
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def get_loss(self, docs, golds, scores):
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raise NotImplementedError
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@ -113,22 +101,16 @@ class NeuralTagger(object):
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self.vocab = vocab
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self.model = model
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def __call__(self, doc, state=None):
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assert state is not None
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assert 'tokvecs' in state
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tokvecs = state['tokvecs']
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tags = self.predict(tokvecs)
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def __call__(self, doc):
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tags = self.predict(doc.tensor)
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self.set_annotations([doc], tags)
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return state
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for batch in cytoolz.partition_all(batch_size, stream):
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docs, states = zip(*batch)
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tag_ids = self.predict(states[0]['tokvecs'])
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for docs in cytoolz.partition_all(batch_size, stream):
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tokvecs = self.model.ops.flatten([d.tensor for d in docs])
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tag_ids = self.predict(tokvecs)
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self.set_annotations(docs, tag_ids)
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for state in states:
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state['tag_ids'] = tag_ids
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yield from zip(docs, states)
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yield from docs
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def predict(self, tokvecs):
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scores = self.model(tokvecs)
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@ -150,11 +132,9 @@ class NeuralTagger(object):
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vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
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idx += 1
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def update(self, docs, golds, state=None, drop=0., sgd=None):
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state = {} if state is None else state
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def update(self, docs_tokvecs, golds, drop=0., sgd=None):
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docs, tokvecs = docs_tokvecs
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tokvecs = state['tokvecs']
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bp_tokvecs = state['bp_tokvecs']
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if self.model.nI is None:
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self.model.nI = tokvecs.shape[1]
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@ -163,20 +143,20 @@ class NeuralTagger(object):
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d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
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bp_tokvecs(d_tokvecs, sgd=sgd)
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state['tag_scores'] = tag_scores
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state['tag_loss'] = loss
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return state
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return d_tokvecs
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def get_loss(self, docs, golds, scores):
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tag_index = {tag: i for i, tag in enumerate(self.vocab.morphology.tag_names)}
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cdef int idx = 0
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correct = numpy.zeros((scores.shape[0],), dtype='i')
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guesses = scores.argmax(axis=1)
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for gold in golds:
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for tag in gold.tags:
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correct[idx] = tag_index[tag]
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if tag is None:
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correct[idx] = guesses[idx]
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else:
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correct[idx] = tag_index[tag]
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idx += 1
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correct = self.model.ops.xp.array(correct, dtype='i')
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d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
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@ -198,15 +178,16 @@ class NeuralTagger(object):
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cdef Vocab vocab = self.vocab
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vocab.morphology = Morphology(vocab.strings, new_tag_map,
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vocab.morphology.lemmatizer)
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self.model = Softmax(self.vocab.morphology.n_tags)
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print("Tagging", self.model.nO, "tags")
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token_vector_width = pipeline[0].model.nO
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self.model = rebatch(1024, Softmax(self.vocab.morphology.n_tags,
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token_vector_width))
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#self.model = Softmax(self.vocab.morphology.n_tags)
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def use_params(self, params):
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with self.model.use_params(params):
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yield
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cdef class EntityRecognizer(LinearParser):
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"""
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Annotate named entities on Doc objects.
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@ -275,8 +256,6 @@ cdef class NeuralEntityRecognizer(NeuralParser):
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return ids
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cdef class BeamDependencyParser(BeamParser):
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TransitionSystem = ArcEager
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@ -35,12 +35,12 @@ from preshed.maps cimport map_get
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from thinc.api import layerize, chain
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from thinc.neural import Model, Affine, ELU, ReLu, Maxout
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from thinc.neural.ops import NumpyOps
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from thinc.neural.ops import NumpyOps, CupyOps
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from .. import util
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from ..util import get_async, get_cuda_stream
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from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
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from .._ml import Tok2Vec, doc2feats
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from .._ml import Tok2Vec, doc2feats, rebatch
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from . import _parse_features
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from ._parse_features cimport CONTEXT_SIZE
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@ -229,6 +229,8 @@ cdef class Parser:
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nI=token_vector_width,
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pieces=maxout_pieces)
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lower = rebatch(1024, lower)
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with Model.use_device('cpu'):
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upper = chain(
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Maxout(hidden_width),
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@ -274,7 +276,7 @@ cdef class Parser:
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def __reduce__(self):
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return (Parser, (self.vocab, self.moves, self.model), None, None)
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def __call__(self, Doc tokens, state=None):
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def __call__(self, Doc doc):
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"""
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Apply the parser or entity recognizer, setting the annotations onto the Doc object.
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@ -283,10 +285,9 @@ cdef class Parser:
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Returns:
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None
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"""
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self.parse_batch([tokens], state['tokvecs'])
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return state
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self.parse_batch([doc], doc.tensor)
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def pipe(self, stream, int batch_size=1000, int n_threads=2):
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def pipe(self, docs, int batch_size=1000, int n_threads=2):
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"""
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Process a stream of documents.
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@ -301,12 +302,11 @@ cdef class Parser:
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cdef StateClass parse_state
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cdef Doc doc
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queue = []
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for batch in cytoolz.partition_all(batch_size, stream):
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batch = list(batch)
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docs, states = zip(*batch)
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parse_states = self.parse_batch(docs, states[0]['tokvecs'])
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for docs in cytoolz.partition_all(batch_size, docs):
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tokvecs = self.model[0].ops.flatten([d.tensor for d in docs])
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parse_states = self.parse_batch(docs, tokvecs)
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self.set_annotations(docs, parse_states)
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yield from zip(docs, states)
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yield from docs
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def parse_batch(self, docs, tokvecs):
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cuda_stream = get_cuda_stream()
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@ -324,10 +324,8 @@ cdef class Parser:
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todo = [st for st in states if not st.is_final()]
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return states
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def update(self, docs, golds, state=None, drop=0., sgd=None):
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assert state is not None
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assert 'tokvecs' in state
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assert 'bp_tokvecs' in state
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def update(self, docs_tokvecs, golds, drop=0., sgd=None):
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docs, tokvecs = docs_tokvecs
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if isinstance(docs, Doc) and isinstance(golds, GoldParse):
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docs = [docs]
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golds = [golds]
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@ -336,9 +334,6 @@ cdef class Parser:
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for gold in golds:
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self.moves.preprocess_gold(gold)
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tokvecs = state['tokvecs']
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bp_tokvecs = state['bp_tokvecs']
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states = self.moves.init_batch(docs)
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state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
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drop)
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@ -357,17 +352,17 @@ cdef class Parser:
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d_scores = self.get_batch_loss(states, golds, scores)
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d_vector = bp_scores(d_scores, sgd=sgd)
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loss += (d_scores**2).sum()
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if not isinstance(tokvecs, state2vec.ops.xp.ndarray):
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backprops.append((token_ids, d_vector, bp_vector))
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else:
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if isinstance(self.model[0].ops, CupyOps) \
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and not isinstance(token_ids, state2vec.ops.xp.ndarray):
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# Move token_ids and d_vector to CPU, asynchronously
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backprops.append((
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get_async(cuda_stream, token_ids),
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get_async(cuda_stream, d_vector),
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bp_vector
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))
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else:
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backprops.append((token_ids, d_vector, bp_vector))
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self.transition_batch(states, scores)
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todo = [st for st in todo if not st[0].is_final()]
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# Tells CUDA to block, so our async copies complete.
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@ -385,9 +380,7 @@ cdef class Parser:
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else:
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xp.add.at(d_tokvecs,
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token_ids, d_state_features * active_feats)
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bp_tokvecs(d_tokvecs, sgd)
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state['parser_loss'] = loss
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return state
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return d_tokvecs
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def get_batch_model(self, batch_size, tokvecs, stream, dropout):
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lower, upper = self.model
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@ -445,7 +438,6 @@ cdef class Parser:
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self.moves.finalize_doc(doc)
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def add_label(self, label):
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# Doesn't set label into serializer -- subclasses override it to do that.
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for action in self.moves.action_types:
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added = self.moves.add_action(action, label)
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if added:
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