mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 20:28:20 +03:00
cd70c3cb79
* initialize NLP with train corpus * add more pretraining tests * more tests * function to fetch tok2vec layer for pretraining * clarify parameter name * test different objectives * formatting * fix check for static vectors when using vectors objective * clarify docs * logger statement * fix init_tok2vec and proc.initialize order * test training after pretraining * add init_config tests for pretraining * pop pretraining block to avoid config validation errors * custom errors
346 lines
11 KiB
Python
346 lines
11 KiB
Python
from pathlib import Path
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import numpy as np
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import pytest
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import srsly
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from spacy.vocab import Vocab
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from thinc.api import Config
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from ..util import make_tempdir
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from ... import util
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from ...lang.en import English
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from ...training.initialize import init_nlp
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from ...training.loop import train
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from ...training.pretrain import pretrain
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from ...tokens import Doc, DocBin
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from ...language import DEFAULT_CONFIG_PRETRAIN_PATH, DEFAULT_CONFIG_PATH
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pretrain_string_listener = """
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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.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 342
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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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.v1"
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.width}
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[pretraining]
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max_epochs = 5
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[training]
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max_epochs = 5
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"""
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pretrain_string_internal = """
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[nlp]
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lang = "en"
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pipeline = ["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.v1"
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[components.tagger.model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 342
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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[pretraining]
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max_epochs = 5
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[training]
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max_epochs = 5
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"""
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pretrain_string_vectors = """
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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.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 342
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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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.v1"
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.width}
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[pretraining]
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max_epochs = 5
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[pretraining.objective]
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@architectures = spacy.PretrainVectors.v1
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maxout_pieces = 3
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hidden_size = 300
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loss = cosine
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[training]
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max_epochs = 5
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"""
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CHAR_OBJECTIVES = [
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{},
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{"@architectures": "spacy.PretrainCharacters.v1"},
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{
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"@architectures": "spacy.PretrainCharacters.v1",
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"maxout_pieces": 5,
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"hidden_size": 42,
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"n_characters": 2,
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},
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]
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VECTOR_OBJECTIVES = [
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{
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"@architectures": "spacy.PretrainVectors.v1",
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"maxout_pieces": 3,
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"hidden_size": 300,
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"loss": "cosine",
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},
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{
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"@architectures": "spacy.PretrainVectors.v1",
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"maxout_pieces": 2,
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"hidden_size": 200,
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"loss": "L2",
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},
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]
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def test_pretraining_default():
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"""Test that pretraining defaults to a character objective"""
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config = Config().from_str(pretrain_string_internal)
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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assert "PretrainCharacters" in filled["pretraining"]["objective"]["@architectures"]
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@pytest.mark.parametrize("objective", CHAR_OBJECTIVES)
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def test_pretraining_tok2vec_characters(objective):
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"""Test that pretraining works with the character objective"""
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config = Config().from_str(pretrain_string_listener)
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config["pretraining"]["objective"] = objective
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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with make_tempdir() as tmp_dir:
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file_path = write_sample_jsonl(tmp_dir)
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filled["paths"]["raw_text"] = file_path
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filled = filled.interpolate()
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assert filled["pretraining"]["component"] == "tok2vec"
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pretrain(filled, tmp_dir)
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assert Path(tmp_dir / "model0.bin").exists()
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assert Path(tmp_dir / "model4.bin").exists()
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assert not Path(tmp_dir / "model5.bin").exists()
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@pytest.mark.parametrize("objective", VECTOR_OBJECTIVES)
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def test_pretraining_tok2vec_vectors_fail(objective):
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"""Test that pretraining doesn't works with the vectors objective if there are no static vectors"""
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config = Config().from_str(pretrain_string_listener)
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config["pretraining"]["objective"] = objective
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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with make_tempdir() as tmp_dir:
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file_path = write_sample_jsonl(tmp_dir)
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filled["paths"]["raw_text"] = file_path
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filled = filled.interpolate()
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assert filled["initialize"]["vectors"] is None
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with pytest.raises(ValueError):
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pretrain(filled, tmp_dir)
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@pytest.mark.parametrize("objective", VECTOR_OBJECTIVES)
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def test_pretraining_tok2vec_vectors(objective):
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"""Test that pretraining works with the vectors objective and static vectors defined"""
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config = Config().from_str(pretrain_string_listener)
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config["pretraining"]["objective"] = objective
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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with make_tempdir() as tmp_dir:
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file_path = write_sample_jsonl(tmp_dir)
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filled["paths"]["raw_text"] = file_path
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nlp_path = write_vectors_model(tmp_dir)
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filled["initialize"]["vectors"] = nlp_path
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filled = filled.interpolate()
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pretrain(filled, tmp_dir)
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@pytest.mark.parametrize("config", [pretrain_string_internal, pretrain_string_listener])
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def test_pretraining_tagger_tok2vec(config):
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"""Test pretraining of the tagger's tok2vec layer (via a listener)"""
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config = Config().from_str(pretrain_string_listener)
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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with make_tempdir() as tmp_dir:
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file_path = write_sample_jsonl(tmp_dir)
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filled["paths"]["raw_text"] = file_path
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filled["pretraining"]["component"] = "tagger"
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filled["pretraining"]["layer"] = "tok2vec"
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filled = filled.interpolate()
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pretrain(filled, tmp_dir)
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assert Path(tmp_dir / "model0.bin").exists()
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assert Path(tmp_dir / "model4.bin").exists()
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assert not Path(tmp_dir / "model5.bin").exists()
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def test_pretraining_tagger():
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"""Test pretraining of the tagger itself will throw an error (not an appropriate tok2vec layer)"""
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config = Config().from_str(pretrain_string_internal)
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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with make_tempdir() as tmp_dir:
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file_path = write_sample_jsonl(tmp_dir)
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filled["paths"]["raw_text"] = file_path
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filled["pretraining"]["component"] = "tagger"
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filled = filled.interpolate()
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with pytest.raises(ValueError):
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pretrain(filled, tmp_dir)
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def test_pretraining_training():
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"""Test that training can use a pretrained Tok2Vec model"""
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config = Config().from_str(pretrain_string_internal)
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nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
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filled = nlp.config
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pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
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filled = pretrain_config.merge(filled)
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train_config = util.load_config(DEFAULT_CONFIG_PATH)
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filled = train_config.merge(filled)
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with make_tempdir() as tmp_dir:
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pretrain_dir = tmp_dir / "pretrain"
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pretrain_dir.mkdir()
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file_path = write_sample_jsonl(pretrain_dir)
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filled["paths"]["raw_text"] = file_path
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filled["pretraining"]["component"] = "tagger"
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filled["pretraining"]["layer"] = "tok2vec"
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train_dir = tmp_dir / "train"
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train_dir.mkdir()
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train_path, dev_path = write_sample_training(train_dir)
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filled["paths"]["train"] = train_path
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filled["paths"]["dev"] = dev_path
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filled = filled.interpolate()
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P = filled["pretraining"]
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nlp_base = init_nlp(filled)
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model_base = nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
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embed_base = None
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for node in model_base.walk():
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if node.name == "hashembed":
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embed_base = node
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pretrain(filled, pretrain_dir)
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pretrained_model = Path(pretrain_dir / "model3.bin")
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assert pretrained_model.exists()
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filled["initialize"]["init_tok2vec"] = str(pretrained_model)
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nlp = init_nlp(filled)
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model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
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embed = None
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for node in model.walk():
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if node.name == "hashembed":
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embed = node
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# ensure that the tok2vec weights are actually changed by the pretraining
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assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
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train(nlp, train_dir)
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def write_sample_jsonl(tmp_dir):
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data = [
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{
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"meta": {"id": "1"},
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"text": "This is the best TV you'll ever buy!",
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"cats": {"pos": 1, "neg": 0},
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},
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{
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"meta": {"id": "2"},
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"text": "I wouldn't buy this again.",
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"cats": {"pos": 0, "neg": 1},
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},
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]
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file_path = f"{tmp_dir}/text.jsonl"
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srsly.write_jsonl(file_path, data)
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return file_path
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def write_sample_training(tmp_dir):
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words = ["The", "players", "start", "."]
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tags = ["DT", "NN", "VBZ", "."]
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doc = Doc(English().vocab, words=words, tags=tags)
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doc_bin = DocBin()
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doc_bin.add(doc)
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train_path = f"{tmp_dir}/train.spacy"
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dev_path = f"{tmp_dir}/dev.spacy"
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doc_bin.to_disk(train_path)
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doc_bin.to_disk(dev_path)
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return train_path, dev_path
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def write_vectors_model(tmp_dir):
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import numpy
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vocab = Vocab()
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vector_data = {
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"dog": numpy.random.uniform(-1, 1, (300,)),
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"cat": numpy.random.uniform(-1, 1, (300,)),
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"orange": numpy.random.uniform(-1, 1, (300,))
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}
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for word, vector in vector_data.items():
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vocab.set_vector(word, vector)
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nlp_path = tmp_dir / "vectors_model"
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nlp = English(vocab)
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nlp.to_disk(nlp_path)
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return str(nlp_path)
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