import pytest from spacy.ml.models.tok2vec import build_Tok2Vec_model from spacy.ml.models.tok2vec import MultiHashEmbed, CharacterEmbed from spacy.ml.models.tok2vec import MishWindowEncoder, MaxoutWindowEncoder from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener from spacy.vocab import Vocab from spacy.tokens import Doc from spacy.gold import Example from spacy import util from spacy.lang.en import English from .util import get_batch from thinc.api import Config from numpy.testing import assert_equal def test_empty_doc(): width = 128 embed_size = 2000 vocab = Vocab() doc = Doc(vocab, words=[]) tok2vec = build_Tok2Vec_model( MultiHashEmbed( width=width, rows=embed_size, also_use_static_vectors=False, also_embed_subwords=True, ), MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3), ) tok2vec.initialize() vectors, backprop = tok2vec.begin_update([doc]) assert len(vectors) == 1 assert vectors[0].shape == (0, width) @pytest.mark.parametrize( "batch_size,width,embed_size", [[1, 128, 2000], [2, 128, 2000], [3, 8, 63]] ) def test_tok2vec_batch_sizes(batch_size, width, embed_size): batch = get_batch(batch_size) tok2vec = build_Tok2Vec_model( MultiHashEmbed( width=width, rows=embed_size, also_use_static_vectors=False, also_embed_subwords=True, ), MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3), ) tok2vec.initialize() vectors, backprop = tok2vec.begin_update(batch) assert len(vectors) == len(batch) for doc_vec, doc in zip(vectors, batch): assert doc_vec.shape == (len(doc), width) # fmt: off @pytest.mark.parametrize( "width,embed_arch,embed_config,encode_arch,encode_config", [ (8, MultiHashEmbed, {"rows": 100, "also_embed_subwords": True, "also_use_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}), (8, MultiHashEmbed, {"rows": 100, "also_embed_subwords": True, "also_use_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}), (8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}), (8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2}, MishWindowEncoder, {"window_size": 1, "depth": 3}), ], ) # fmt: on def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_config): embed_config["width"] = width encode_config["width"] = width docs = get_batch(3) tok2vec = build_Tok2Vec_model( embed_arch(**embed_config), encode_arch(**encode_config) ) tok2vec.initialize(docs) vectors, backprop = tok2vec.begin_update(docs) assert len(vectors) == len(docs) assert vectors[0].shape == (len(docs[0]), width) backprop(vectors) def test_init_tok2vec(): # Simple test to initialize the default tok2vec nlp = English() tok2vec = nlp.add_pipe("tok2vec") assert tok2vec.listeners == [] nlp.begin_training() cfg_string = """ [nlp] lang = "en" pipeline = ["tok2vec","tagger"] [components] [components.tagger] factory = "tagger" [components.tagger.model] @architectures = "spacy.Tagger.v1" nO = null [components.tagger.model.tok2vec] @architectures = "spacy.Tok2VecListener.v1" width = ${components.tok2vec.model.encode.width} [components.tok2vec] factory = "tok2vec" [components.tok2vec.model] @architectures = "spacy.Tok2Vec.v1" [components.tok2vec.model.embed] @architectures = "spacy.MultiHashEmbed.v1" width = ${components.tok2vec.model.encode.width} rows = 2000 also_embed_subwords = true also_use_static_vectors = false [components.tok2vec.model.encode] @architectures = "spacy.MaxoutWindowEncoder.v1" width = 96 depth = 4 window_size = 1 maxout_pieces = 3 """ TRAIN_DATA = [ ("I like green eggs", {"tags": ["N", "V", "J", "N"]}), ("Eat blue ham", {"tags": ["V", "J", "N"]}), ] def test_tok2vec_listener(): orig_config = Config().from_str(cfg_string) nlp, config = util.load_model_from_config(orig_config, auto_fill=True, validate=True) assert nlp.pipe_names == ["tok2vec", "tagger"] tagger = nlp.get_pipe("tagger") tok2vec = nlp.get_pipe("tok2vec") tagger_tok2vec = tagger.model.get_ref("tok2vec") assert isinstance(tok2vec, Tok2Vec) assert isinstance(tagger_tok2vec, Tok2VecListener) train_examples = [] for t in TRAIN_DATA: train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1])) for tag in t[1]["tags"]: tagger.add_label(tag) # Check that the Tok2Vec component finds it listeners assert tok2vec.listeners == [] optimizer = nlp.begin_training(lambda: train_examples) assert tok2vec.listeners == [tagger_tok2vec] for i in range(5): losses = {} nlp.update(train_examples, sgd=optimizer, losses=losses) doc = nlp("Running the pipeline as a whole.") doc_tensor = tagger_tok2vec.predict([doc])[0] assert_equal(doc.tensor, doc_tensor) # TODO: should this warn or error? nlp.select_pipes(disable="tok2vec") assert nlp.pipe_names == ["tagger"] nlp("Running the pipeline with the Tok2Vec component disabled.")