mirror of
https://github.com/explosion/spaCy.git
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c067b5264c
When sourcing a component, the object from the original pipeline is added to the new pipeline as the same object. This creates a situation where there are several attributes that cannot be in sync between the original pipeline and the new pipeline at the same time for this one object: * component.name * component.listener_map / component.listening_components for tok2vec and transformer When running replace_listeners on a component, the config is not updated correctly if the state of the component is incorrect for the current pipeline (in particular changes that should be applied from model.attrs["replace_listener_cfg"] as used in spacy-transformers) due to the fact that: * find_listeners relies on component.name to set the name in the listener_map * replace_listeners relies on listener_map to determine how to modify the configs In addition, there are several places where pipeline components are modified and the listener map and/or internal component names aren't currently updated. In cases where there is a component shared by two pipelines that cannot be in sync, this PR chooses to prioritize the most recently modified or initialized pipeline. There is no actual solution with the current source behavior that will make both pipelines usable, so the current pipeline is updated whenever components are added/renamed/removed or the pipeline is initialized for training.
616 lines
21 KiB
Python
616 lines
21 KiB
Python
import pytest
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from numpy.testing import assert_array_equal
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from thinc.api import Config, get_current_ops
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from spacy import util
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from spacy.lang.en import English
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from spacy.ml.models.tok2vec import (
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MaxoutWindowEncoder,
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MultiHashEmbed,
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build_Tok2Vec_model,
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)
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from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
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from spacy.tokens import Doc
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from spacy.training import Example
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from spacy.util import registry
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from spacy.vocab import Vocab
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from ..util import add_vecs_to_vocab, get_batch, make_tempdir
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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.slow
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@pytest.mark.parametrize("width", [8])
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@pytest.mark.parametrize(
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"embed_arch,embed_config",
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# fmt: off
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[
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("spacy.MultiHashEmbed.v1", {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}),
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("spacy.MultiHashEmbed.v1", {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}),
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("spacy.CharacterEmbed.v1", {"rows": 100, "nM": 64, "nC": 8, "include_static_vectors": False}),
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("spacy.CharacterEmbed.v1", {"rows": 100, "nM": 16, "nC": 2, "include_static_vectors": False}),
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],
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# fmt: on
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)
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@pytest.mark.parametrize(
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"tok2vec_arch,encode_arch,encode_config",
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# fmt: off
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[
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("spacy.Tok2Vec.v1", "spacy.MaxoutWindowEncoder.v1", {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
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("spacy.Tok2Vec.v2", "spacy.MaxoutWindowEncoder.v2", {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
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("spacy.Tok2Vec.v1", "spacy.MishWindowEncoder.v1", {"window_size": 1, "depth": 6}),
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("spacy.Tok2Vec.v2", "spacy.MishWindowEncoder.v2", {"window_size": 1, "depth": 6}),
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],
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# fmt: on
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)
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def test_tok2vec_configs(
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width, tok2vec_arch, embed_arch, embed_config, encode_arch, encode_config
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):
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embed = registry.get("architectures", embed_arch)
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encode = registry.get("architectures", encode_arch)
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tok2vec_model = registry.get("architectures", tok2vec_arch)
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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 = tok2vec_model(embed(**embed_config), encode(**encode_config))
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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 its 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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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_tok2vec_listener_overfitting():
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"""Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
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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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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(get_examples=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, annotates=["tok2vec"])
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assert losses["tagger"] < 0.00001
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# test the trained model
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test_text = "I like blue eggs"
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doc = nlp(test_text)
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assert doc[0].tag_ == "N"
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assert doc[1].tag_ == "V"
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assert doc[2].tag_ == "J"
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assert doc[3].tag_ == "N"
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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assert doc2[0].tag_ == "N"
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assert doc2[1].tag_ == "V"
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assert doc2[2].tag_ == "J"
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assert doc2[3].tag_ == "N"
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def test_tok2vec_frozen_not_annotating():
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"""Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
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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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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(get_examples=lambda: train_examples)
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for i in range(2):
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losses = {}
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with pytest.raises(
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ValueError, match=r"the tok2vec embedding layer is not updated"
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):
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nlp.update(
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train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
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)
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def test_tok2vec_frozen_overfitting():
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"""Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
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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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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(get_examples=lambda: train_examples)
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for i in range(100):
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losses = {}
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nlp.update(
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train_examples,
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sgd=optimizer,
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losses=losses,
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exclude=["tok2vec"],
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annotates=["tok2vec"],
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)
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assert losses["tagger"] < 0.0001
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# test the trained model
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test_text = "I like blue eggs"
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doc = nlp(test_text)
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assert doc[0].tag_ == "N"
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assert doc[1].tag_ == "V"
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assert doc[2].tag_ == "J"
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assert doc[3].tag_ == "N"
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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assert doc2[0].tag_ == "N"
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assert doc2[1].tag_ == "V"
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assert doc2[2].tag_ == "J"
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assert doc2[3].tag_ == "N"
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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": {
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"lang": "en",
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"pipeline": ["tok2vec", "tagger2", "ner3", "tagger4"],
|
|
},
|
|
"components": {
|
|
"tok2vec": {"source": base_model},
|
|
"tagger2": {
|
|
"source": base_model,
|
|
"component": "tagger",
|
|
"replace_listeners": ["model.tok2vec"],
|
|
},
|
|
"ner3": {
|
|
"source": base_model,
|
|
"component": "ner",
|
|
},
|
|
"tagger4": {
|
|
"source": base_model,
|
|
"component": "tagger",
|
|
},
|
|
},
|
|
}
|
|
new_nlp = util.load_model_from_config(new_config, auto_fill=True)
|
|
new_nlp.initialize(lambda: examples)
|
|
tok2vec = new_nlp.get_pipe("tok2vec")
|
|
tagger = new_nlp.get_pipe("tagger2")
|
|
ner = new_nlp.get_pipe("ner3")
|
|
assert "ner" not in new_nlp.pipe_names
|
|
assert "tagger" not in new_nlp.pipe_names
|
|
assert tok2vec.listening_components == ["ner3", "tagger4"]
|
|
assert any(isinstance(node, Tok2VecListener) for node in ner.model.walk())
|
|
assert not any(isinstance(node, Tok2VecListener) for node in tagger.model.walk())
|
|
t2v_cfg = new_nlp.config["components"]["tok2vec"]["model"]
|
|
assert t2v_cfg["@architectures"] == "spacy.Tok2Vec.v2"
|
|
assert new_nlp.config["components"]["tagger2"]["model"]["tok2vec"] == t2v_cfg
|
|
assert (
|
|
new_nlp.config["components"]["ner3"]["model"]["tok2vec"]["@architectures"]
|
|
== "spacy.Tok2VecListener.v1"
|
|
)
|
|
assert (
|
|
new_nlp.config["components"]["tagger4"]["model"]["tok2vec"]["@architectures"]
|
|
== "spacy.Tok2VecListener.v1"
|
|
)
|
|
|
|
|
|
cfg_string_multi_textcat = """
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["tok2vec","textcat_multilabel","tagger"]
|
|
|
|
[components]
|
|
|
|
[components.textcat_multilabel]
|
|
factory = "textcat_multilabel"
|
|
|
|
[components.textcat_multilabel.model]
|
|
@architectures = "spacy.TextCatEnsemble.v2"
|
|
nO = null
|
|
|
|
[components.textcat_multilabel.model.tok2vec]
|
|
@architectures = "spacy.Tok2VecListener.v1"
|
|
width = ${components.tok2vec.model.encode.width}
|
|
|
|
[components.textcat_multilabel.model.linear_model]
|
|
@architectures = "spacy.TextCatBOW.v1"
|
|
exclusive_classes = false
|
|
ngram_size = 1
|
|
no_output_layer = false
|
|
|
|
[components.tagger]
|
|
factory = "tagger"
|
|
|
|
[components.tagger.model]
|
|
@architectures = "spacy.Tagger.v2"
|
|
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.v2"
|
|
|
|
[components.tok2vec.model.embed]
|
|
@architectures = "spacy.MultiHashEmbed.v1"
|
|
width = ${components.tok2vec.model.encode.width}
|
|
rows = [2000, 1000, 1000, 1000]
|
|
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
|
|
include_static_vectors = false
|
|
|
|
[components.tok2vec.model.encode]
|
|
@architectures = "spacy.MaxoutWindowEncoder.v2"
|
|
width = 96
|
|
depth = 4
|
|
window_size = 1
|
|
maxout_pieces = 3
|
|
"""
|
|
|
|
|
|
def test_tok2vec_listeners_textcat():
|
|
orig_config = Config().from_str(cfg_string_multi_textcat)
|
|
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
|
|
assert nlp.pipe_names == ["tok2vec", "textcat_multilabel", "tagger"]
|
|
tagger = nlp.get_pipe("tagger")
|
|
textcat = nlp.get_pipe("textcat_multilabel")
|
|
tok2vec = nlp.get_pipe("tok2vec")
|
|
tagger_tok2vec = tagger.model.get_ref("tok2vec")
|
|
textcat_tok2vec = textcat.model.get_ref("tok2vec")
|
|
assert isinstance(tok2vec, Tok2Vec)
|
|
assert isinstance(tagger_tok2vec, Tok2VecListener)
|
|
assert isinstance(textcat_tok2vec, Tok2VecListener)
|
|
train_examples = []
|
|
for t in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
optimizer = nlp.initialize(lambda: train_examples)
|
|
for i in range(50):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
docs = list(nlp.pipe(["Eat blue ham", "I like green eggs"]))
|
|
cats0 = docs[0].cats
|
|
assert cats0["preference"] < 0.1
|
|
assert cats0["imperative"] > 0.9
|
|
cats1 = docs[1].cats
|
|
assert cats1["preference"] > 0.1
|
|
assert cats1["imperative"] < 0.9
|
|
assert [t.tag_ for t in docs[0]] == ["V", "J", "N"]
|
|
assert [t.tag_ for t in docs[1]] == ["N", "V", "J", "N"]
|
|
|
|
|
|
def test_tok2vec_listener_source_link_name():
|
|
"""The component's internal name and the tok2vec listener map correspond
|
|
to the most recently modified pipeline.
|
|
"""
|
|
orig_config = Config().from_str(cfg_string_multi)
|
|
nlp1 = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
|
|
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
|
|
|
|
nlp2 = English()
|
|
nlp2.add_pipe("tok2vec", source=nlp1)
|
|
nlp2.add_pipe("tagger", name="tagger2", source=nlp1)
|
|
|
|
# there is no way to have the component have the right name for both
|
|
# pipelines, right now the most recently modified pipeline is prioritized
|
|
assert nlp1.get_pipe("tagger").name == nlp2.get_pipe("tagger2").name == "tagger2"
|
|
|
|
# there is no way to have the tok2vec have the right listener map for both
|
|
# pipelines, right now the most recently modified pipeline is prioritized
|
|
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2"]
|
|
nlp2.add_pipe("ner", name="ner3", source=nlp1)
|
|
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2", "ner3"]
|
|
nlp2.remove_pipe("ner3")
|
|
assert nlp2.get_pipe("tok2vec").listening_components == ["tagger2"]
|
|
nlp2.remove_pipe("tagger2")
|
|
assert nlp2.get_pipe("tok2vec").listening_components == []
|
|
|
|
# at this point the tok2vec component corresponds to nlp2
|
|
assert nlp1.get_pipe("tok2vec").listening_components == []
|
|
|
|
# modifying the nlp1 pipeline syncs the tok2vec listener map back to nlp1
|
|
nlp1.add_pipe("sentencizer")
|
|
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
|
|
|
|
# modifying nlp2 syncs it back to nlp2
|
|
nlp2.add_pipe("sentencizer")
|
|
assert nlp1.get_pipe("tok2vec").listening_components == []
|
|
|
|
|
|
def test_tok2vec_listener_source_replace_listeners():
|
|
orig_config = Config().from_str(cfg_string_multi)
|
|
nlp1 = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
|
|
assert nlp1.get_pipe("tok2vec").listening_components == ["tagger", "ner"]
|
|
nlp1.replace_listeners("tok2vec", "tagger", ["model.tok2vec"])
|
|
assert nlp1.get_pipe("tok2vec").listening_components == ["ner"]
|
|
|
|
nlp2 = English()
|
|
nlp2.add_pipe("tok2vec", source=nlp1)
|
|
assert nlp2.get_pipe("tok2vec").listening_components == []
|
|
nlp2.add_pipe("tagger", source=nlp1)
|
|
assert nlp2.get_pipe("tok2vec").listening_components == []
|
|
nlp2.add_pipe("ner", name="ner2", source=nlp1)
|
|
assert nlp2.get_pipe("tok2vec").listening_components == ["ner2"]
|