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https://github.com/explosion/spaCy.git
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Don't share CNN, to reduce complexities
This commit is contained in:
parent
1d73dec8b1
commit
20193371f5
20
spacy/_ml.py
20
spacy/_ml.py
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@ -226,8 +226,8 @@ def drop_layer(layer, factor=2.):
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return model
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def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
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assert pretrained_dims is not None
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def Tok2Vec(width, embed_size, **kwargs):
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pretrained_dims = kwargs.get('pretrained_dims', 0)
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cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
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cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
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with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
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@ -474,20 +474,18 @@ def getitem(i):
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return X[i], None
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return layerize(getitem_fwd)
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def build_tagger_model(nr_class, token_vector_width, pretrained_dims=0, **cfg):
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embed_size = util.env_opt('embed_size', 4000)
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with Model.define_operators({'>>': chain, '+': add}):
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# Input: (doc, tensor) tuples
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private_tok2vec = Tok2Vec(token_vector_width, embed_size,
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pretrained_dims=pretrained_dims)
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model = (
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fine_tune(private_tok2vec)
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>> with_flatten(
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Maxout(token_vector_width, token_vector_width)
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>> Softmax(nr_class, token_vector_width)
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)
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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pretrained_dims=pretrained_dims)
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model = with_flatten(
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tok2vec
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>> Softmax(nr_class, token_vector_width)
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)
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model.nI = None
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model.tok2vec = tok2vec
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return model
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@ -3,12 +3,13 @@
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# https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
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__title__ = 'spacy-nightly'
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__version__ = '2.0.0a14'
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__version__ = '2.0.0a15'
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__summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
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__uri__ = 'https://spacy.io'
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__author__ = 'Explosion AI'
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__email__ = 'contact@explosion.ai'
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__license__ = 'MIT'
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__release__ = False
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__docs_models__ = 'https://spacy.io/docs/usage/models'
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__download_url__ = 'https://github.com/explosion/spacy-models/releases/download'
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@ -55,7 +55,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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prints(dev_path, title="Development data not found", exits=1)
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pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
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pipeline = ['tags', 'dependencies', 'entities']
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if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
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if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
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if no_entities and 'entities' in pipeline: pipeline.remove('entities')
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@ -303,31 +303,17 @@ class Language(object):
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if self._optimizer is None:
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self._optimizer = Adam(Model.ops, 0.001)
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sgd = self._optimizer
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tok2vec = self.pipeline[0]
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grads = {}
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def get_grads(W, dW, key=None):
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grads[key] = (W, dW)
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pipes = list(self.pipeline[1:])
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pipes = list(self.pipeline)
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random.shuffle(pipes)
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tokvecses, bp_tokvecses = tok2vec.model.begin_update(docs, drop=drop)
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all_d_tokvecses = [tok2vec.model.ops.allocate(tv.shape) for tv in tokvecses]
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for proc in pipes:
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if not hasattr(proc, 'update'):
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continue
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d_tokvecses = proc.update((docs, tokvecses), golds,
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drop=drop, sgd=get_grads, losses=losses)
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if update_shared and d_tokvecses is not None:
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for i, d_tv in enumerate(d_tokvecses):
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all_d_tokvecses[i] += d_tv
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if update_shared and bp_tokvecses is not None:
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bp_tokvecses(all_d_tokvecses, sgd=sgd)
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proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
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for key, (W, dW) in grads.items():
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sgd(W, dW, key=key)
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# Clear the tensor variable, to free GPU memory.
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# If we don't do this, the memory leak gets pretty
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# bad, because we may be holding part of a batch.
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for doc in docs:
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doc.tensor = None
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def preprocess_gold(self, docs_golds):
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"""Can be called before training to pre-process gold data. By default,
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@ -371,8 +357,6 @@ class Language(object):
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**cfg: Config parameters.
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returns: An optimizer
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"""
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if self.parser:
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self.pipeline.append(NeuralLabeller(self.vocab))
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# Populate vocab
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if get_gold_tuples is not None:
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for _, annots_brackets in get_gold_tuples():
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@ -418,7 +402,6 @@ class Language(object):
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assert len(docs) == len(golds)
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for doc, gold in zip(docs, golds):
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scorer.score(doc, gold)
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doc.tensor = None
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return scorer
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@contextmanager
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@ -299,27 +299,25 @@ class NeuralTagger(BaseThincComponent):
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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def __call__(self, doc):
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tags = self.predict(([doc], [doc.tensor]))
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tags = self.predict([doc])
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self.set_annotations([doc], tags)
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return doc
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for docs in cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
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tokvecs = [d.tensor for d in docs]
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tag_ids = self.predict((docs, tokvecs))
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tag_ids = self.predict(docs)
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self.set_annotations(docs, tag_ids)
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yield from docs
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def predict(self, docs_tokvecs):
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scores = self.model(docs_tokvecs)
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def predict(self, docs):
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scores = self.model(docs)
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scores = self.model.ops.flatten(scores)
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guesses = scores.argmax(axis=1)
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if not isinstance(guesses, numpy.ndarray):
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guesses = guesses.get()
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tokvecs = docs_tokvecs[1]
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guesses = self.model.ops.unflatten(guesses,
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[tv.shape[0] for tv in tokvecs])
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[len(d) for d in docs])
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return guesses
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def set_annotations(self, docs, batch_tag_ids):
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@ -339,20 +337,15 @@ class NeuralTagger(BaseThincComponent):
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idx += 1
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doc.is_tagged = True
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def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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if losses is not None and self.name not in losses:
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losses[self.name] = 0.
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docs, tokvecs = docs_tokvecs
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if self.model.nI is None:
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self.model.nI = tokvecs[0].shape[1]
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tag_scores, bp_tag_scores = self.model.begin_update(docs_tokvecs, drop=drop)
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tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
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loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
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d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
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if losses is not None:
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losses[self.name] += loss
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return d_tokvecs
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def get_loss(self, docs, golds, scores):
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scores = self.model.ops.flatten(scores)
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@ -399,9 +392,9 @@ class NeuralTagger(BaseThincComponent):
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pretrained_dims=self.vocab.vectors_length)
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@classmethod
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def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
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def Model(cls, n_tags, token_vector_width, pretrained_dims=0, **cfg):
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return build_tagger_model(n_tags, token_vector_width,
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pretrained_dims)
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pretrained_dims, **cfg)
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def use_params(self, params):
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with self.model.use_params(params):
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@ -573,15 +566,10 @@ class SimilarityHook(BaseThincComponent):
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yield self(doc)
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def predict(self, doc1, doc2):
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return self.model.predict([(doc1.tensor, doc2.tensor)])
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return self.model.predict([(doc1, doc2)])
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def update(self, doc1_tensor1_doc2_tensor2, golds, sgd=None, drop=0.):
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doc1s, tensor1s, doc2s, tensor2s = doc1_tensor1_doc2_tensor2
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sims, bp_sims = self.model.begin_update(zip(tensor1s, tensor2s),
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drop=drop)
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d_tensor1s, d_tensor2s = bp_sims(golds, sgd=sgd)
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return d_tensor1s, d_tensor2s
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def update(self, doc1_doc2, golds, sgd=None, drop=0.):
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sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
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def begin_training(self, _=tuple(), pipeline=None):
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"""
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@ -636,15 +624,13 @@ class TextCategorizer(BaseThincComponent):
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for j, label in enumerate(self.labels):
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doc.cats[label] = float(scores[i, j])
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def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
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docs, tensors = docs_tensors
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def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
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scores, bp_scores = self.model.begin_update(docs, drop=drop)
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loss, d_scores = self.get_loss(docs, golds, scores)
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d_tensors = bp_scores(d_scores, sgd=sgd)
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bp_scores(d_scores, sgd=sgd)
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if losses is not None:
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losses.setdefault(self.name, 0.0)
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losses[self.name] += loss
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return d_tensors
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def get_loss(self, docs, golds, scores):
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truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
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@ -147,10 +147,10 @@ def get_token_ids(states, int n_tokens):
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nr_update = 0
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def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
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states, tokvecs, golds,
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states, golds,
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state2vec, vec2scores,
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int width, float density,
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sgd=None, losses=None, drop=0.):
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losses=None, drop=0.):
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global nr_update
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cdef MaxViolation violn
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nr_update += 1
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@ -48,7 +48,7 @@ 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, rebatch, fine_tune
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from .._ml import Residual, drop_layer
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from .._ml import Residual, drop_layer, flatten
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from ..compat import json_dumps
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from . import _parse_features
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@ -244,8 +244,9 @@ cdef class Parser:
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hidden_width = util.env_opt('hidden_width', hidden_width)
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parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
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embed_size = util.env_opt('embed_size', 4000)
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tensors = fine_tune(Tok2Vec(token_vector_width, embed_size,
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pretrained_dims=cfg.get('pretrained_dims')))
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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pretrained_dims=cfg.get('pretrained_dims', 0))
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tok2vec = chain(tok2vec, flatten)
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if parser_maxout_pieces == 1:
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lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
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nF=cls.nr_feature,
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@ -277,7 +278,7 @@ cdef class Parser:
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'hidden_width': hidden_width,
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'maxout_pieces': parser_maxout_pieces
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}
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return (tensors, lower, upper), cfg
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return (tok2vec, lower, upper), cfg
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def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
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"""
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@ -309,7 +310,6 @@ cdef class Parser:
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cfg['beam_density'] = util.env_opt('beam_density', 0.0)
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if 'pretrained_dims' not in cfg:
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cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
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cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg = cfg
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if 'actions' in self.cfg:
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for action, labels in self.cfg.get('actions', {}).items():
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@ -335,11 +335,11 @@ cdef class Parser:
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beam_density = self.cfg.get('beam_density', 0.0)
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cdef Beam beam
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if beam_width == 1:
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states = self.parse_batch([doc], [doc.tensor])
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states = self.parse_batch([doc])
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self.set_annotations([doc], states)
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return doc
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else:
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beam = self.beam_parse([doc], [doc.tensor],
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beam = self.beam_parse([doc],
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beam_width=beam_width, beam_density=beam_density)[0]
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output = self.moves.get_beam_annot(beam)
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state = <StateClass>beam.at(0)
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@ -368,11 +368,10 @@ cdef class Parser:
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cdef Beam beam
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for docs in cytoolz.partition_all(batch_size, docs):
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docs = list(docs)
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tokvecs = [doc.tensor for doc in docs]
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if beam_width == 1:
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parse_states = self.parse_batch(docs, tokvecs)
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parse_states = self.parse_batch(docs)
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else:
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beams = self.beam_parse(docs, tokvecs,
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beams = self.beam_parse(docs,
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beam_width=beam_width, beam_density=beam_density)
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parse_states = []
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for beam in beams:
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@ -380,7 +379,7 @@ cdef class Parser:
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self.set_annotations(docs, parse_states)
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yield from docs
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def parse_batch(self, docs, tokvecses):
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def parse_batch(self, docs):
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cdef:
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precompute_hiddens state2vec
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StateClass state
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@ -391,21 +390,15 @@ cdef class Parser:
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int nr_class, nr_feat, nr_piece, nr_dim, nr_state
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if isinstance(docs, Doc):
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docs = [docs]
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if isinstance(tokvecses, np.ndarray):
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tokvecses = [tokvecses]
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if USE_FINE_TUNE:
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tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
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else:
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tokvecs = self.model[0].ops.flatten(tokvecses)
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cuda_stream = get_cuda_stream()
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(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
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0.0)
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nr_state = len(docs)
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nr_class = self.moves.n_moves
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nr_dim = tokvecs.shape[1]
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nr_feat = self.nr_feature
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cuda_stream = get_cuda_stream()
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state2vec, vec2scores = self.get_batch_model(nr_state, tokvecs,
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cuda_stream, 0.0)
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nr_piece = state2vec.nP
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states = self.moves.init_batch(docs)
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@ -448,19 +441,15 @@ cdef class Parser:
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next_step.push_back(st)
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return states
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def beam_parse(self, docs, tokvecses, int beam_width=3, float beam_density=0.001):
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def beam_parse(self, docs, int beam_width=3, float beam_density=0.001):
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cdef Beam beam
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cdef np.ndarray scores
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cdef Doc doc
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cdef int nr_class = self.moves.n_moves
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cdef StateClass stcls, output
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if USE_FINE_TUNE:
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tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
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else:
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tokvecs = self.model[0].ops.flatten(tokvecses)
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cuda_stream = get_cuda_stream()
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state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
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cuda_stream, 0.0)
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(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
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0.0)
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beams = []
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cdef int offset = 0
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cdef int j = 0
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@ -520,30 +509,24 @@ cdef class Parser:
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free(scores)
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free(token_ids)
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def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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if not any(self.moves.has_gold(gold) for gold in golds):
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return None
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if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.5:
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return self.update_beam(docs_tokvecs, golds,
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return self.update_beam(docs, golds,
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self.cfg['beam_width'], self.cfg['beam_density'],
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drop=drop, sgd=sgd, losses=losses)
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if losses is not None and self.name not in losses:
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losses[self.name] = 0.
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docs, tokvec_lists = 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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if USE_FINE_TUNE:
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my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
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tokvecs = self.model[0].ops.flatten(my_tokvecs)
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else:
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tokvecs = self.model[0].ops.flatten(docs_tokvecs[1])
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cuda_stream = get_cuda_stream()
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states, golds, max_steps = self._init_gold_batch(docs, golds)
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state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
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0.0)
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(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
|
||||
0.0)
|
||||
todo = [(s, g) for (s, g) in zip(states, golds)
|
||||
if not s.is_final() and g is not None]
|
||||
if not todo:
|
||||
|
@ -587,13 +570,9 @@ cdef class Parser:
|
|||
if n_steps >= max_steps:
|
||||
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:
|
||||
d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
|
||||
return d_tokvecs
|
||||
bp_tokvecs, backprops, sgd, cuda_stream)
|
||||
|
||||
def update_beam(self, docs_tokvecs, golds, width=None, density=None,
|
||||
def update_beam(self, docs, golds, width=None, density=None,
|
||||
drop=0., sgd=None, losses=None):
|
||||
if not any(self.moves.has_gold(gold) for gold in golds):
|
||||
return None
|
||||
|
@ -605,26 +584,20 @@ cdef class Parser:
|
|||
density = self.cfg.get('beam_density', 0.0)
|
||||
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
|
||||
if USE_FINE_TUNE:
|
||||
my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
|
||||
tokvecs = self.model[0].ops.flatten(my_tokvecs)
|
||||
else:
|
||||
tokvecs = self.model[0].ops.flatten(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)
|
||||
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, 0.0)
|
||||
|
||||
states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, 500,
|
||||
states, tokvecs, golds,
|
||||
states, golds,
|
||||
state2vec, vec2scores,
|
||||
width, density,
|
||||
sgd=sgd, drop=drop, losses=losses)
|
||||
drop=drop, losses=losses)
|
||||
backprop_lower = []
|
||||
cdef float batch_size = len(docs)
|
||||
for i, d_scores in enumerate(states_d_scores):
|
||||
|
@ -642,20 +615,7 @@ cdef class Parser:
|
|||
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:
|
||||
d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
|
||||
return d_tokvecs
|
||||
|
||||
def _pad_tokvecs(self, tokvecs):
|
||||
# Add a vector for missing values at the start of tokvecs
|
||||
xp = get_array_module(tokvecs)
|
||||
pad = xp.zeros((1, tokvecs.shape[1]), dtype=tokvecs.dtype)
|
||||
return xp.vstack((pad, tokvecs))
|
||||
|
||||
def _unpad_tokvecs(self, d_tokvecs):
|
||||
return d_tokvecs[1:]
|
||||
self._make_updates(d_tokvecs, bp_tokvecs, backprop_lower, sgd, cuda_stream)
|
||||
|
||||
def _init_gold_batch(self, whole_docs, whole_golds):
|
||||
"""Make a square batch, of length equal to the shortest doc. A long
|
||||
|
@ -693,7 +653,7 @@ cdef class Parser:
|
|||
max_moves = max(max_moves, len(oracle_actions))
|
||||
return states, golds, max_moves
|
||||
|
||||
def _make_updates(self, d_tokvecs, backprops, sgd, cuda_stream=None):
|
||||
def _make_updates(self, d_tokvecs, bp_tokvecs, backprops, sgd, cuda_stream=None):
|
||||
# Tells CUDA to block, so our async copies complete.
|
||||
if cuda_stream is not None:
|
||||
cuda_stream.synchronize()
|
||||
|
@ -704,6 +664,7 @@ cdef class Parser:
|
|||
d_state_features *= mask.reshape(ids.shape + (1,))
|
||||
self.model[0].ops.scatter_add(d_tokvecs, ids * mask,
|
||||
d_state_features)
|
||||
bp_tokvecs(d_tokvecs, sgd=sgd)
|
||||
|
||||
@property
|
||||
def move_names(self):
|
||||
|
@ -713,11 +674,12 @@ cdef class Parser:
|
|||
names.append(name)
|
||||
return names
|
||||
|
||||
def get_batch_model(self, batch_size, tokvecs, stream, dropout):
|
||||
_, lower, upper = self.model
|
||||
state2vec = precompute_hiddens(batch_size, tokvecs,
|
||||
def get_batch_model(self, docs, stream, dropout):
|
||||
tok2vec, lower, upper = self.model
|
||||
tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout)
|
||||
state2vec = precompute_hiddens(len(docs), tokvecs,
|
||||
lower, stream, drop=dropout)
|
||||
return state2vec, upper
|
||||
return (tokvecs, bp_tokvecs), state2vec, upper
|
||||
|
||||
nr_feature = 8
|
||||
|
||||
|
|
|
@ -61,33 +61,22 @@ def test_predict_doc(parser, tok2vec, model, doc):
|
|||
parser(doc)
|
||||
|
||||
|
||||
def test_update_doc(parser, tok2vec, model, doc, gold):
|
||||
def test_update_doc(parser, model, doc, gold):
|
||||
parser.model = model
|
||||
tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
|
||||
d_tokvecs = parser.update(([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.
|
||||
parser.update([doc], [gold], sgd=optimize)
|
||||
|
||||
|
||||
def test_predict_doc_beam(parser, tok2vec, model, doc):
|
||||
doc.tensor = tok2vec([doc])[0]
|
||||
def test_predict_doc_beam(parser, model, doc):
|
||||
parser.model = model
|
||||
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):
|
||||
def test_update_doc_beam(parser, 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.
|
||||
parser.update_beam([doc], [gold], sgd=optimize)
|
||||
|
||||
|
||||
|
|
Loading…
Reference in New Issue
Block a user