mirror of
https://github.com/explosion/spaCy.git
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e5debc68e4
* Tagger: use unnormalized probabilities for inference Using unnormalized softmax avoids use of the relatively expensive exp function, which can significantly speed up non-transformer models (e.g. I got a speedup of 27% on a German tagging + parsing pipeline). * Add spacy.Tagger.v2 with configurable normalization Normalization of probabilities is disabled by default to improve performance. * Update documentation, models, and tests to spacy.Tagger.v2 * Move Tagger.v1 to spacy-legacy * docs/architectures: run prettier * Unnormalized softmax is now a Softmax_v2 option * Require thinc 8.0.14 and spacy-legacy 3.0.9
435 lines
15 KiB
Python
435 lines
15 KiB
Python
import pytest
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from spacy.ml.models.tok2vec import build_Tok2Vec_model
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from spacy.ml.models.tok2vec import MultiHashEmbed, CharacterEmbed
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from spacy.ml.models.tok2vec import MishWindowEncoder, MaxoutWindowEncoder
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from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
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from spacy.vocab import Vocab
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from spacy.tokens import Doc
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from spacy.training import Example
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from spacy import util
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from spacy.lang.en import English
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from thinc.api import Config, get_current_ops
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from numpy.testing import assert_array_equal
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from ..util import get_batch, make_tempdir, add_vecs_to_vocab
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def test_empty_doc():
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width = 128
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embed_size = 2000
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vocab = Vocab()
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doc = Doc(vocab, words=[])
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tok2vec = build_Tok2Vec_model(
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MultiHashEmbed(
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width=width,
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rows=[embed_size, embed_size, embed_size, embed_size],
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include_static_vectors=False,
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attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
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),
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MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
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)
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tok2vec.initialize()
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vectors, backprop = tok2vec.begin_update([doc])
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assert len(vectors) == 1
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assert vectors[0].shape == (0, width)
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@pytest.mark.parametrize(
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"batch_size,width,embed_size", [[1, 128, 2000], [2, 128, 2000], [3, 8, 63]]
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)
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def test_tok2vec_batch_sizes(batch_size, width, embed_size):
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batch = get_batch(batch_size)
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tok2vec = build_Tok2Vec_model(
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MultiHashEmbed(
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width=width,
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rows=[embed_size] * 4,
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include_static_vectors=False,
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attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
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),
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MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
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)
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tok2vec.initialize()
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vectors, backprop = tok2vec.begin_update(batch)
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assert len(vectors) == len(batch)
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for doc_vec, doc in zip(vectors, batch):
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assert doc_vec.shape == (len(doc), width)
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@pytest.mark.parametrize(
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"width,embed_arch,embed_config,encode_arch,encode_config",
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# fmt: off
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[
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(8, MultiHashEmbed, {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
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(8, MultiHashEmbed, {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
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(8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
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(8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 3}),
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],
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# fmt: on
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)
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def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_config):
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embed_config["width"] = width
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encode_config["width"] = width
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docs = get_batch(3)
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tok2vec = build_Tok2Vec_model(
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embed_arch(**embed_config), encode_arch(**encode_config)
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)
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tok2vec.initialize(docs)
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vectors, backprop = tok2vec.begin_update(docs)
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assert len(vectors) == len(docs)
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assert vectors[0].shape == (len(docs[0]), width)
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backprop(vectors)
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def test_init_tok2vec():
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# Simple test to initialize the default tok2vec
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nlp = English()
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tok2vec = nlp.add_pipe("tok2vec")
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assert tok2vec.listeners == []
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nlp.initialize()
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assert tok2vec.model.get_dim("nO")
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cfg_string = """
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[nlp]
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lang = "en"
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pipeline = ["tok2vec","tagger"]
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[components]
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tok2vec.model.encode.width}
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rows = [2000, 1000, 1000, 1000]
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attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
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include_static_vectors = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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"""
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TRAIN_DATA = [
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(
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"I like green eggs",
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{"tags": ["N", "V", "J", "N"], "cats": {"preference": 1.0, "imperative": 0.0}},
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),
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(
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"Eat blue ham",
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{"tags": ["V", "J", "N"], "cats": {"preference": 0.0, "imperative": 1.0}},
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),
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]
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@pytest.mark.parametrize("with_vectors", (False, True))
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def test_tok2vec_listener(with_vectors):
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orig_config = Config().from_str(cfg_string)
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orig_config["components"]["tok2vec"]["model"]["embed"][
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"include_static_vectors"
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] = with_vectors
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nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
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if with_vectors:
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ops = get_current_ops()
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vectors = [
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("apple", ops.asarray([1, 2, 3])),
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("orange", ops.asarray([-1, -2, -3])),
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("and", ops.asarray([-1, -1, -1])),
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("juice", ops.asarray([5, 5, 10])),
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("pie", ops.asarray([7, 6.3, 8.9])),
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]
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add_vecs_to_vocab(nlp.vocab, vectors)
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assert nlp.pipe_names == ["tok2vec", "tagger"]
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tagger = nlp.get_pipe("tagger")
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tok2vec = nlp.get_pipe("tok2vec")
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tagger_tok2vec = tagger.model.get_ref("tok2vec")
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assert isinstance(tok2vec, Tok2Vec)
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assert isinstance(tagger_tok2vec, Tok2VecListener)
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train_examples = []
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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for tag in t[1]["tags"]:
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tagger.add_label(tag)
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# Check that the Tok2Vec component finds it listeners
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assert tok2vec.listeners == []
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optimizer = nlp.initialize(lambda: train_examples)
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assert tok2vec.listeners == [tagger_tok2vec]
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for i in range(5):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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doc = nlp("Running the pipeline as a whole.")
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doc_tensor = tagger_tok2vec.predict([doc])[0]
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ops = get_current_ops()
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assert_array_equal(ops.to_numpy(doc.tensor), ops.to_numpy(doc_tensor))
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# test with empty doc
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doc = nlp("")
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# TODO: should this warn or error?
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nlp.select_pipes(disable="tok2vec")
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assert nlp.pipe_names == ["tagger"]
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nlp("Running the pipeline with the Tok2Vec component disabled.")
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def test_tok2vec_listener_callback():
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orig_config = Config().from_str(cfg_string)
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nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
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assert nlp.pipe_names == ["tok2vec", "tagger"]
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tagger = nlp.get_pipe("tagger")
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tok2vec = nlp.get_pipe("tok2vec")
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nlp._link_components()
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docs = [nlp.make_doc("A random sentence")]
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tok2vec.model.initialize(X=docs)
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gold_array = [[1.0 for tag in ["V", "Z"]] for word in docs]
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label_sample = [tagger.model.ops.asarray(gold_array, dtype="float32")]
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tagger.model.initialize(X=docs, Y=label_sample)
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docs = [nlp.make_doc("Another entirely random sentence")]
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tok2vec.update([Example.from_dict(x, {}) for x in docs])
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Y, get_dX = tagger.model.begin_update(docs)
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# assure that the backprop call works (and doesn't hit a 'None' callback)
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assert get_dX(Y) is not None
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def test_replace_listeners():
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orig_config = Config().from_str(cfg_string)
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nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
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examples = [Example.from_dict(nlp.make_doc("x y"), {"tags": ["V", "Z"]})]
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nlp.initialize(lambda: examples)
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tok2vec = nlp.get_pipe("tok2vec")
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tagger = nlp.get_pipe("tagger")
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assert isinstance(tagger.model.layers[0], Tok2VecListener)
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assert tok2vec.listener_map["tagger"][0] == tagger.model.layers[0]
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assert (
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nlp.config["components"]["tok2vec"]["model"]["@architectures"]
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== "spacy.Tok2Vec.v2"
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)
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assert (
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nlp.config["components"]["tagger"]["model"]["tok2vec"]["@architectures"]
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== "spacy.Tok2VecListener.v1"
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)
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nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
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assert not isinstance(tagger.model.layers[0], Tok2VecListener)
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t2v_cfg = nlp.config["components"]["tok2vec"]["model"]
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assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
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assert nlp.config["components"]["tagger"]["model"]["tok2vec"] == t2v_cfg
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with pytest.raises(ValueError):
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nlp.replace_listeners("invalid", "tagger", ["model.tok2vec"])
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with pytest.raises(ValueError):
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nlp.replace_listeners("tok2vec", "parser", ["model.tok2vec"])
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with pytest.raises(ValueError):
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nlp.replace_listeners("tok2vec", "tagger", ["model.yolo"])
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with pytest.raises(ValueError):
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nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec", "model.yolo"])
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# attempt training with the new pipeline
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optimizer = nlp.initialize(lambda: examples)
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for i in range(2):
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losses = {}
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nlp.update(examples, sgd=optimizer, losses=losses)
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assert losses["tok2vec"] == 0.0
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assert losses["tagger"] > 0.0
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cfg_string_multi = """
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[nlp]
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lang = "en"
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pipeline = ["tok2vec","tagger", "ner"]
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[components]
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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[components.ner]
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factory = "ner"
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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[components.ner.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tok2vec.model.encode.width}
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rows = [2000, 1000, 1000, 1000]
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attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
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include_static_vectors = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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"""
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def test_replace_listeners_from_config():
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orig_config = Config().from_str(cfg_string_multi)
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nlp = util.load_model_from_config(orig_config, auto_fill=True)
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annots = {"tags": ["V", "Z"], "entities": [(0, 1, "A"), (1, 2, "B")]}
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examples = [Example.from_dict(nlp.make_doc("x y"), annots)]
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nlp.initialize(lambda: examples)
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tok2vec = nlp.get_pipe("tok2vec")
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tagger = nlp.get_pipe("tagger")
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ner = nlp.get_pipe("ner")
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assert tok2vec.listening_components == ["tagger", "ner"]
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assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
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assert any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
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with make_tempdir() as dir_path:
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nlp.to_disk(dir_path)
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base_model = str(dir_path)
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new_config = {
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"nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]},
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"components": {
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"tok2vec": {"source": base_model},
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"tagger": {
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"source": base_model,
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"replace_listeners": ["model.tok2vec"],
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},
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"ner": {"source": base_model},
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},
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}
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new_nlp = util.load_model_from_config(new_config, auto_fill=True)
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new_nlp.initialize(lambda: examples)
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tok2vec = new_nlp.get_pipe("tok2vec")
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tagger = new_nlp.get_pipe("tagger")
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ner = new_nlp.get_pipe("ner")
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assert tok2vec.listening_components == ["ner"]
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assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
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assert not any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
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t2v_cfg = new_nlp.config["components"]["tok2vec"]["model"]
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assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
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assert new_nlp.config["components"]["tagger"]["model"]["tok2vec"] == t2v_cfg
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assert (
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new_nlp.config["components"]["ner"]["model"]["tok2vec"]["@architectures"]
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== "spacy.Tok2VecListener.v1"
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)
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cfg_string_multi_textcat = """
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[nlp]
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lang = "en"
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pipeline = ["tok2vec","textcat_multilabel","tagger"]
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[components]
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[components.textcat_multilabel]
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factory = "textcat_multilabel"
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[components.textcat_multilabel.model]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.textcat_multilabel.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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[components.textcat_multilabel.model.linear_model]
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode.width}
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tok2vec.model.encode.width}
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rows = [2000, 1000, 1000, 1000]
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attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
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include_static_vectors = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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"""
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def test_tok2vec_listeners_textcat():
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orig_config = Config().from_str(cfg_string_multi_textcat)
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nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
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assert nlp.pipe_names == ["tok2vec", "textcat_multilabel", "tagger"]
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tagger = nlp.get_pipe("tagger")
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textcat = nlp.get_pipe("textcat_multilabel")
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tok2vec = nlp.get_pipe("tok2vec")
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tagger_tok2vec = tagger.model.get_ref("tok2vec")
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textcat_tok2vec = textcat.model.get_ref("tok2vec")
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assert isinstance(tok2vec, Tok2Vec)
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assert isinstance(tagger_tok2vec, Tok2VecListener)
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assert isinstance(textcat_tok2vec, Tok2VecListener)
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train_examples = []
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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optimizer = nlp.initialize(lambda: train_examples)
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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docs = list(nlp.pipe(["Eat blue ham", "I like green eggs"]))
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cats0 = docs[0].cats
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assert cats0["preference"] < 0.1
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assert cats0["imperative"] > 0.9
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cats1 = docs[1].cats
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assert cats1["preference"] > 0.1
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assert cats1["imperative"] < 0.9
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assert [t.tag_ for t in docs[0]] == ["V", "J", "N"]
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assert [t.tag_ for t in docs[1]] == ["N", "V", "J", "N"]
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