Don't share CNN, to reduce complexities

spacy/_ml.py

@@ -226,8 +226,8 @@ def drop_layer(layer, factor=2.):
     return model
 
 
-def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
+def Tok2Vec(width, embed_size, **kwargs):
-    assert pretrained_dims is not None
+    pretrained_dims = kwargs.get('pretrained_dims', 0)
     cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
@@ -474,20 +474,18 @@ def getitem(i):
         return X[i], None
     return layerize(getitem_fwd)
 
+
 def build_tagger_model(nr_class, token_vector_width, pretrained_dims=0, **cfg):
     embed_size = util.env_opt('embed_size', 4000)
     with Model.define_operators({'>>': chain, '+': add}):
-        # Input: (doc, tensor) tuples
+        tok2vec = Tok2Vec(token_vector_width, embed_size,
-        private_tok2vec = Tok2Vec(token_vector_width, embed_size,
+                          pretrained_dims=pretrained_dims)
-                                  pretrained_dims=pretrained_dims)
+        model = with_flatten(
-        model = (
+            tok2vec
-            fine_tune(private_tok2vec)
+            >> Softmax(nr_class, token_vector_width)
-            >> with_flatten(
-                Maxout(token_vector_width, token_vector_width)
-                >> Softmax(nr_class, token_vector_width)
-            )
         )
     model.nI = None
+    model.tok2vec = tok2vec
     return model
 
 

spacy/about.py

@@ -3,12 +3,13 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy-nightly'
-__version__ = '2.0.0a14'
+__version__ = '2.0.0a15'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Explosion AI'
 __email__ = 'contact@explosion.ai'
 __license__ = 'MIT'
+__release__ = False
 
 __docs_models__ = 'https://spacy.io/docs/usage/models'
 __download_url__ = 'https://github.com/explosion/spacy-models/releases/download'

spacy/cli/train.py

@@ -55,7 +55,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
         prints(dev_path, title="Development data not found", exits=1)
 
 
-    pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
+    pipeline = ['tags', 'dependencies', 'entities']
     if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
     if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
     if no_entities and 'entities' in pipeline: pipeline.remove('entities')

spacy/language.py

@@ -303,31 +303,17 @@ class Language(object):
             if self._optimizer is None:
                 self._optimizer = Adam(Model.ops, 0.001)
             sgd = self._optimizer
-        tok2vec = self.pipeline[0]
         grads = {}
         def get_grads(W, dW, key=None):
             grads[key] = (W, dW)
-        pipes = list(self.pipeline[1:])
+        pipes = list(self.pipeline)
         random.shuffle(pipes)
-        tokvecses, bp_tokvecses = tok2vec.model.begin_update(docs, 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
-            d_tokvecses = proc.update((docs, tokvecses), golds,
+            proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
-                                      drop=drop, sgd=get_grads, losses=losses)
-            if update_shared and d_tokvecses is not None:
-                for i, d_tv in enumerate(d_tokvecses):
-                    all_d_tokvecses[i] += d_tv
-        if update_shared and bp_tokvecses is not None:
-            bp_tokvecses(all_d_tokvecses, sgd=sgd)
         for key, (W, dW) in grads.items():
             sgd(W, dW, key=key)
-        # Clear the tensor variable, to free GPU memory.
-        # If we don't do this, the memory leak gets pretty
-        # bad, because we may be holding part of a batch.
-        for doc in docs:
-            doc.tensor = None
 
     def preprocess_gold(self, docs_golds):
         """Can be called before training to pre-process gold data. By default,
@@ -371,8 +357,6 @@ class Language(object):
         **cfg: Config parameters.
         returns: An optimizer
         """
-        if self.parser:
-            self.pipeline.append(NeuralLabeller(self.vocab))
         # Populate vocab
         if get_gold_tuples is not None:
             for _, annots_brackets in get_gold_tuples():
@@ -418,7 +402,6 @@ class Language(object):
         assert len(docs) == len(golds)
         for doc, gold in zip(docs, golds):
             scorer.score(doc, gold)
-            doc.tensor = None
         return scorer
 
     @contextmanager

spacy/pipeline.pyx

@@ -299,27 +299,25 @@ class NeuralTagger(BaseThincComponent):
         self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     def __call__(self, doc):
-        tags = self.predict(([doc], [doc.tensor]))
+        tags = self.predict([doc])
         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)
-            tag_ids = self.predict((docs, tokvecs))
             self.set_annotations(docs, tag_ids)
             yield from docs
 
-    def predict(self, docs_tokvecs):
+    def predict(self, docs):
-        scores = self.model(docs_tokvecs)
+        scores = self.model(docs)
         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])
+                    [len(d) for d in docs])
         return guesses
 
     def set_annotations(self, docs, batch_tag_ids):
@@ -339,20 +337,15 @@ class NeuralTagger(BaseThincComponent):
                 idx += 1
         doc.is_tagged = True
 
-    def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
+    def update(self, docs, 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
 
-        if self.model.nI is None:
+        tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
-            self.model.nI = tokvecs[0].shape[1]
-        tag_scores, bp_tag_scores = self.model.begin_update(docs_tokvecs, drop=drop)
         loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
 
-        d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
         if losses is not None:
             losses[self.name] += loss
-        return d_tokvecs
 
     def get_loss(self, docs, golds, scores):
         scores = self.model.ops.flatten(scores)
@@ -399,9 +392,9 @@ class NeuralTagger(BaseThincComponent):
                                     pretrained_dims=self.vocab.vectors_length)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
+    def Model(cls, n_tags, token_vector_width, pretrained_dims=0, **cfg):
         return build_tagger_model(n_tags, token_vector_width,
-                                  pretrained_dims)
+                                  pretrained_dims, **cfg)
 
     def use_params(self, params):
         with self.model.use_params(params):
@@ -573,15 +566,10 @@ class SimilarityHook(BaseThincComponent):
             yield self(doc)
 
     def predict(self, doc1, doc2):
-        return self.model.predict([(doc1.tensor, doc2.tensor)])
+        return self.model.predict([(doc1, doc2)])
 
-    def update(self, doc1_tensor1_doc2_tensor2, golds, sgd=None, drop=0.):
+    def update(self, doc1_doc2, golds, sgd=None, drop=0.):
-        doc1s, tensor1s, doc2s, tensor2s = doc1_tensor1_doc2_tensor2
+        sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
-        sims, bp_sims = self.model.begin_update(zip(tensor1s, tensor2s),
-                                                drop=drop)
-        d_tensor1s, d_tensor2s = bp_sims(golds, sgd=sgd)
-
-        return d_tensor1s, d_tensor2s
 
     def begin_training(self, _=tuple(), pipeline=None):
         """
@@ -636,15 +624,13 @@ class TextCategorizer(BaseThincComponent):
             for j, label in enumerate(self.labels):
                 doc.cats[label] = float(scores[i, j])
 
-    def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
+    def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
-        docs, tensors = docs_tensors
         scores, bp_scores = self.model.begin_update(docs, drop=drop)
         loss, d_scores = self.get_loss(docs, golds, scores)
-        d_tensors = bp_scores(d_scores, sgd=sgd)
+        bp_scores(d_scores, sgd=sgd)
         if losses is not None:
             losses.setdefault(self.name, 0.0)
             losses[self.name] += loss
-        return d_tensors
 
     def get_loss(self, docs, golds, scores):
         truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')

spacy/syntax/_beam_utils.pyx

@@ -147,10 +147,10 @@ def get_token_ids(states, int n_tokens):
 
 nr_update = 0
 def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
-                states, tokvecs, golds,
+                states, golds,
                 state2vec, vec2scores, 
                 int width, float density,
-                sgd=None, losses=None, drop=0.):
+                losses=None, drop=0.):
     global nr_update
     cdef MaxViolation violn
     nr_update += 1

spacy/syntax/nn_parser.pyx

@@ -48,7 +48,7 @@ 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 Residual, drop_layer, flatten
 from ..compat import json_dumps
 
 from . import _parse_features
@@ -244,8 +244,9 @@ cdef class Parser:
         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,
+        tok2vec = Tok2Vec(token_vector_width, embed_size,
-                                    pretrained_dims=cfg.get('pretrained_dims')))
+                          pretrained_dims=cfg.get('pretrained_dims', 0))
+        tok2vec = chain(tok2vec, flatten)
         if parser_maxout_pieces == 1:
             lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
                         nF=cls.nr_feature,
@@ -277,7 +278,7 @@ cdef class Parser:
             'hidden_width': hidden_width,
             'maxout_pieces': parser_maxout_pieces
         }
-        return (tensors, lower, upper), cfg
+        return (tok2vec, lower, upper), cfg
 
     def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
         """
@@ -309,7 +310,6 @@ cdef class Parser:
             cfg['beam_density'] = util.env_opt('beam_density', 0.0)
         if 'pretrained_dims' not in cfg:
             cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
-        cfg.setdefault('cnn_maxout_pieces', 2)
         self.cfg = cfg
         if 'actions' in self.cfg:
             for action, labels in self.cfg.get('actions', {}).items():
@@ -335,11 +335,11 @@ cdef class Parser:
             beam_density = self.cfg.get('beam_density', 0.0)
         cdef Beam beam
         if beam_width == 1:
-            states = self.parse_batch([doc], [doc.tensor])
+            states = self.parse_batch([doc])
             self.set_annotations([doc], states)
             return doc
         else:
-            beam = self.beam_parse([doc], [doc.tensor],
+            beam = self.beam_parse([doc], 
                         beam_width=beam_width, beam_density=beam_density)[0]
             output = self.moves.get_beam_annot(beam)
             state = <StateClass>beam.at(0)
@@ -368,11 +368,10 @@ cdef class Parser:
         cdef Beam beam
         for docs in cytoolz.partition_all(batch_size, docs):
             docs = list(docs)
-            tokvecs = [doc.tensor for doc in docs]
             if beam_width == 1:
-                parse_states = self.parse_batch(docs, tokvecs)
+                parse_states = self.parse_batch(docs)
             else:
-                beams = self.beam_parse(docs, tokvecs,
+                beams = self.beam_parse(docs,
                             beam_width=beam_width, beam_density=beam_density)
                 parse_states = []
                 for beam in beams:
@@ -380,7 +379,7 @@ cdef class Parser:
             self.set_annotations(docs, parse_states)
             yield from docs
 
-    def parse_batch(self, docs, tokvecses):
+    def parse_batch(self, docs):
         cdef:
             precompute_hiddens state2vec
             StateClass state
@@ -391,21 +390,15 @@ 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]
 
-        if USE_FINE_TUNE:
+        cuda_stream = get_cuda_stream()
-            tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
-        else:
+                                                                            0.0)
-            tokvecs = self.model[0].ops.flatten(tokvecses)
+
         nr_state = len(docs)
         nr_class = self.moves.n_moves
         nr_dim = tokvecs.shape[1]
         nr_feat = self.nr_feature
-
-        cuda_stream = get_cuda_stream()
-        state2vec, vec2scores = self.get_batch_model(nr_state, tokvecs,
-                                                     cuda_stream, 0.0)
         nr_piece = state2vec.nP
 
         states = self.moves.init_batch(docs)
@@ -448,19 +441,15 @@ cdef class Parser:
                     next_step.push_back(st)
         return states
 
-    def beam_parse(self, docs, tokvecses, int beam_width=3, float beam_density=0.001):
+    def beam_parse(self, docs, int beam_width=3, float beam_density=0.001):
         cdef Beam beam
         cdef np.ndarray scores
         cdef Doc doc
         cdef int nr_class = self.moves.n_moves
         cdef StateClass stcls, output
-        if USE_FINE_TUNE:
-            tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
-        else:
-            tokvecs = self.model[0].ops.flatten(tokvecses)
         cuda_stream = get_cuda_stream()
-        state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
-                                                     cuda_stream, 0.0)
+                                                                            0.0)
         beams = []
         cdef int offset = 0
         cdef int j = 0
@@ -520,30 +509,24 @@ cdef class Parser:
         free(scores)
         free(token_ids)
 
-    def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
+    def update(self, docs, golds, drop=0., sgd=None, losses=None):
         if not any(self.moves.has_gold(gold) for gold in golds):
             return None
         if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.5:
-            return self.update_beam(docs_tokvecs, golds,
+            return self.update_beam(docs, golds,
                     self.cfg['beam_width'], self.cfg['beam_density'],
                     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
         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)
-            tokvecs = self.model[0].ops.flatten(my_tokvecs)
-        else:
-            tokvecs = self.model[0].ops.flatten(docs_tokvecs[1])
 
         cuda_stream = get_cuda_stream()
 
         states, golds, max_steps = self._init_gold_batch(docs, golds)
-        state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
-                                                      0.0)
+                                                                            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)
+            bp_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
 
-    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)
+        self._make_updates(d_tokvecs, bp_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:]
 
     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):
+    def get_batch_model(self, docs, stream, dropout):
-        _, lower, upper = self.model
+        tok2vec, lower, upper = self.model
-        state2vec = precompute_hiddens(batch_size, tokvecs,
+        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
 

spacy/tests/parser/test_neural_parser.py

@@ -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)
+    parser.update([doc], [gold], sgd=optimize)
-    assert d_tokvecs[0].sum() == 0.
 
 
-def test_predict_doc_beam(parser, tok2vec, model, doc):
+def test_predict_doc_beam(parser, model, doc):
-    doc.tensor = tok2vec([doc])[0]
     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)
+    parser.update_beam([doc], [gold], sgd=optimize)
-    assert d_tokvecs[0].sum() == 0.