mirror of
https://github.com/explosion/spaCy.git
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Start centralising registry calls
This commit is contained in:
Matthew Honnibal
parent
911539e9a4
commit
9d7b22c52e
58
spacy/registrations.py
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58
spacy/registrations.py
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"""Centralized registry population for spaCy components.
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This module centralizes registry decorations to prevent circular import issues
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with Cython annotation changes from __future__ import annotations. Functions
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remain in their original locations, but decoration is moved here.
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"""
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from typing import Dict, Any
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# Global flag to track if registry has been populated
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REGISTRY_POPULATED = False
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def populate_registry() -> None:
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"""Populate the registry with all necessary components.
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This function should be called before accessing the registry, to ensure
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it's populated. The function uses a global flag to prevent repopulation.
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"""
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global REGISTRY_POPULATED
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if REGISTRY_POPULATED:
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return
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# Import all necessary modules
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from .util import registry, make_first_longest_spans_filter
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# Register miscellaneous components
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registry.misc("spacy.first_longest_spans_filter.v1")(make_first_longest_spans_filter)
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# Import all pipeline components that were using registry decorators
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from .pipeline.tagger import make_tagger_scorer
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from .pipeline.ner import make_ner_scorer
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# Need to get references to the existing functions in registry by importing the function that is there
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# For the registry that was previously decorated
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# Import functions for use in registry
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from .scorer import get_ner_prf # Used for entity_ruler_scorer
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# Import ML components that use registry
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from .ml.models.tok2vec import tok2vec_listener_v1, build_hash_embed_cnn_tok2vec
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# Register scorers
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registry.scorers("spacy.tagger_scorer.v1")(make_tagger_scorer)
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registry.scorers("spacy.ner_scorer.v1")(make_ner_scorer)
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# span_ruler_scorer removed as it's not in span_ruler.py
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registry.scorers("spacy.entity_ruler_scorer.v1")(make_entityruler_scorer)
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registry.scorers("spacy.sentencizer_scorer.v1")(make_sentencizer_scorer)
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registry.scorers("spacy.senter_scorer.v1")(make_senter_scorer)
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registry.scorers("spacy.textcat_scorer.v1")(make_textcat_scorer)
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registry.scorers("spacy.textcat_multilabel_scorer.v1")(make_textcat_multilabel_scorer)
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registry.scorers("spacy.span_finder_scorer.v1")(make_span_finder_scorer)
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registry.scorers("spacy.spancat_scorer.v1")(make_spancat_scorer)
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# Register tok2vec architectures we've modified
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registry.architectures("spacy.Tok2VecListener.v1")(tok2vec_listener_v1)
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registry.architectures("spacy.HashEmbedCNN.v2")(build_hash_embed_cnn_tok2vec)
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# Set the flag to indicate that the registry has been populated
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REGISTRY_POPULATED = True
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284
spacy/tests/registry_contents.json
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284
spacy/tests/registry_contents.json
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{
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"architectures": [
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"spacy-legacy.CharacterEmbed.v1",
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"spacy-legacy.EntityLinker.v1",
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"spacy-legacy.HashEmbedCNN.v1",
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"spacy-legacy.MaxoutWindowEncoder.v1",
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"spacy-legacy.MishWindowEncoder.v1",
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"spacy-legacy.MultiHashEmbed.v1",
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"spacy-legacy.Tagger.v1",
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"spacy-legacy.TextCatBOW.v1",
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"spacy-legacy.TextCatCNN.v1",
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"spacy-legacy.TextCatEnsemble.v1",
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"spacy-legacy.Tok2Vec.v1",
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"spacy-legacy.TransitionBasedParser.v1",
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"spacy.CharacterEmbed.v2",
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"spacy.EntityLinker.v2",
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"spacy.HashEmbedCNN.v2",
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"spacy.MaxoutWindowEncoder.v2",
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"spacy.MishWindowEncoder.v2",
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"spacy.MultiHashEmbed.v2",
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"spacy.PretrainCharacters.v1",
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"spacy.PretrainVectors.v1",
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"spacy.SpanCategorizer.v1",
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"spacy.SpanFinder.v1",
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"spacy.Tagger.v2",
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"spacy.TextCatBOW.v2",
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"spacy.TextCatBOW.v3",
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"spacy.TextCatCNN.v2",
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"spacy.TextCatEnsemble.v2",
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"spacy.TextCatLowData.v1",
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"spacy.TextCatParametricAttention.v1",
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"spacy.TextCatReduce.v1",
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"spacy.Tok2Vec.v2",
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"spacy.Tok2VecListener.v1",
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"spacy.TorchBiLSTMEncoder.v1",
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"spacy.TransitionBasedParser.v2"
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],
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"augmenters": [
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"spacy.combined_augmenter.v1",
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"spacy.lower_case.v1",
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"spacy.orth_variants.v1"
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],
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"batchers": [
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"spacy.batch_by_padded.v1",
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"spacy.batch_by_sequence.v1",
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"spacy.batch_by_words.v1"
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],
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"callbacks": [
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"spacy.copy_from_base_model.v1",
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"spacy.models_and_pipes_with_nvtx_range.v1",
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"spacy.models_with_nvtx_range.v1"
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],
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"cli": [],
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"datasets": [],
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"displacy_colors": [],
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"factories": [
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"attribute_ruler",
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"beam_ner",
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"beam_parser",
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"doc_cleaner",
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"entity_linker",
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"entity_ruler",
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"future_entity_ruler",
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"lemmatizer",
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"merge_entities",
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"merge_noun_chunks",
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"merge_subtokens",
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"morphologizer",
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"ner",
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"parser",
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"sentencizer",
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"senter",
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"span_finder",
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"span_ruler",
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"spancat",
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"spancat_singlelabel",
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"tagger",
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"textcat",
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"textcat_multilabel",
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"tok2vec",
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"token_splitter",
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"trainable_lemmatizer"
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],
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"initializers": [
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"glorot_normal_init.v1",
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"glorot_uniform_init.v1",
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"he_normal_init.v1",
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"he_uniform_init.v1",
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"lecun_normal_init.v1",
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"lecun_uniform_init.v1",
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"normal_init.v1",
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"uniform_init.v1",
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"zero_init.v1"
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],
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"languages": [],
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"layers": [
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"CauchySimilarity.v1",
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"ClippedLinear.v1",
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"Dish.v1",
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"Dropout.v1",
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"Embed.v1",
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"Gelu.v1",
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"HardSigmoid.v1",
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"HardSwish.v1",
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"HardSwishMobilenet.v1",
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"HardTanh.v1",
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"HashEmbed.v1",
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"LSTM.v1",
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"LayerNorm.v1",
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"Linear.v1",
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"Logistic.v1",
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"MXNetWrapper.v1",
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"Maxout.v1",
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"Mish.v1",
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"MultiSoftmax.v1",
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"ParametricAttention.v1",
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"ParametricAttention.v2",
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"PyTorchLSTM.v1",
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"PyTorchRNNWrapper.v1",
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"PyTorchWrapper.v1",
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"PyTorchWrapper.v2",
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"PyTorchWrapper.v3",
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"Relu.v1",
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"ReluK.v1",
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"Sigmoid.v1",
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"Softmax.v1",
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"Softmax.v2",
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"SparseLinear.v1",
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"SparseLinear.v2",
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"Swish.v1",
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"add.v1",
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"bidirectional.v1",
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"chain.v1",
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"clone.v1",
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"concatenate.v1",
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"expand_window.v1",
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"list2array.v1",
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"list2padded.v1",
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"list2ragged.v1",
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"noop.v1",
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"padded2list.v1",
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"premap_ids.v1",
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"ragged2list.v1",
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"reduce_first.v1",
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"reduce_last.v1",
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"reduce_max.v1",
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"reduce_mean.v1",
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"reduce_sum.v1",
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"remap_ids.v1",
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"remap_ids.v2",
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"residual.v1",
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"resizable.v1",
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"siamese.v1",
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"sigmoid_activation.v1",
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"softmax_activation.v1",
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"spacy-legacy.StaticVectors.v1",
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"spacy.CharEmbed.v1",
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"spacy.FeatureExtractor.v1",
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"spacy.LinearLogistic.v1",
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"spacy.PrecomputableAffine.v1",
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"spacy.StaticVectors.v2",
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"spacy.TransitionModel.v1",
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"spacy.extract_ngrams.v1",
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"spacy.extract_spans.v1",
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"spacy.mean_max_reducer.v1",
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"strings2arrays.v1",
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"tuplify.v1",
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"uniqued.v1",
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"with_array.v1",
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"with_array2d.v1",
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"with_cpu.v1",
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"with_flatten.v1",
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"with_flatten.v2",
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"with_getitem.v1",
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"with_list.v1",
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"with_padded.v1",
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"with_ragged.v1",
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"with_reshape.v1"
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],
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"lemmatizers": [],
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"loggers": [
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"spacy-legacy.ConsoleLogger.v1",
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"spacy-legacy.ConsoleLogger.v2",
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"spacy-legacy.WandbLogger.v1",
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"spacy.ChainLogger.v1",
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"spacy.ClearMLLogger.v1",
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"spacy.ClearMLLogger.v2",
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"spacy.ConsoleLogger.v2",
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"spacy.ConsoleLogger.v3",
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"spacy.CupyLogger.v1",
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"spacy.LookupLogger.v1",
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"spacy.MLflowLogger.v1",
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"spacy.MLflowLogger.v2",
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"spacy.PyTorchLogger.v1",
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"spacy.WandbLogger.v1",
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"spacy.WandbLogger.v2",
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"spacy.WandbLogger.v3",
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"spacy.WandbLogger.v4",
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"spacy.WandbLogger.v5"
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],
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"lookups": [],
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"losses": [
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"CategoricalCrossentropy.v1",
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"CategoricalCrossentropy.v2",
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"CategoricalCrossentropy.v3",
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"CosineDistance.v1",
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"L2Distance.v1",
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"SequenceCategoricalCrossentropy.v1",
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"SequenceCategoricalCrossentropy.v2",
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"SequenceCategoricalCrossentropy.v3"
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],
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"misc": [
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"spacy.CandidateBatchGenerator.v1",
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"spacy.CandidateGenerator.v1",
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"spacy.EmptyKB.v1",
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"spacy.EmptyKB.v2",
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"spacy.KBFromFile.v1",
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"spacy.LookupsDataLoader.v1",
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"spacy.first_longest_spans_filter.v1",
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"spacy.levenshtein_compare.v1",
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"spacy.ngram_range_suggester.v1",
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"spacy.ngram_suggester.v1",
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"spacy.preset_spans_suggester.v1",
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"spacy.prioritize_existing_ents_filter.v1",
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"spacy.prioritize_new_ents_filter.v1"
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],
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"models": [],
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"ops": [
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"CupyOps",
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"MPSOps",
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"NumpyOps"
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],
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"optimizers": [
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"Adam.v1",
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"RAdam.v1",
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"SGD.v1"
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],
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"readers": [
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"ml_datasets.cmu_movies.v1",
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"ml_datasets.dbpedia.v1",
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"ml_datasets.imdb_sentiment.v1",
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"spacy.Corpus.v1",
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"spacy.JsonlCorpus.v1",
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"spacy.PlainTextCorpus.v1",
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"spacy.read_labels.v1",
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"srsly.read_json.v1",
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"srsly.read_jsonl.v1",
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"srsly.read_msgpack.v1",
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"srsly.read_yaml.v1"
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],
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"schedules": [
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"compounding.v1",
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"constant.v1",
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"constant_then.v1",
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"cyclic_triangular.v1",
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"decaying.v1",
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"slanted_triangular.v1",
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"warmup_linear.v1"
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],
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"scorers": [
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"spacy-legacy.textcat_multilabel_scorer.v1",
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"spacy-legacy.textcat_scorer.v1",
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"spacy.attribute_ruler_scorer.v1",
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"spacy.entity_linker_scorer.v1",
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"spacy.entity_ruler_scorer.v1",
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"spacy.lemmatizer_scorer.v1",
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"spacy.morphologizer_scorer.v1",
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"spacy.ner_scorer.v1",
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"spacy.overlapping_labeled_spans_scorer.v1",
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"spacy.parser_scorer.v1",
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"spacy.senter_scorer.v1",
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"spacy.span_finder_scorer.v1",
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"spacy.spancat_scorer.v1",
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"spacy.tagger_scorer.v1",
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"spacy.textcat_multilabel_scorer.v2",
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"spacy.textcat_scorer.v2"
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],
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"tokenizers": [
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"spacy.Tokenizer.v1"
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],
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"vectors": [
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"spacy.Vectors.v1"
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]
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}
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48
spacy/tests/test_registry_population.py
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48
spacy/tests/test_registry_population.py
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@ -0,0 +1,48 @@
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import json
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import os
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import pytest
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from pathlib import Path
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from spacy.util import registry
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# Path to the reference registry contents, relative to this file
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REFERENCE_FILE = Path(__file__).parent / "registry_contents.json"
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@pytest.fixture
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def reference_registry():
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"""Load reference registry contents from JSON file"""
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if not REFERENCE_FILE.exists():
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pytest.fail(f"Reference file {REFERENCE_FILE} not found.")
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with REFERENCE_FILE.open("r") as f:
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return json.load(f)
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def test_registry_types(reference_registry):
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"""Test that all registry types match the reference"""
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# Get current registry types
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current_registry_types = set(registry.get_registry_names())
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expected_registry_types = set(reference_registry.keys())
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# Check for missing registry types
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missing_types = expected_registry_types - current_registry_types
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assert not missing_types, f"Missing registry types: {', '.join(missing_types)}"
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def test_registry_entries(reference_registry):
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"""Test that all registry entries are present"""
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# Check each registry's entries
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for registry_name, expected_entries in reference_registry.items():
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# Skip if this registry type doesn't exist
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if not hasattr(registry, registry_name):
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pytest.fail(f"Registry '{registry_name}' does not exist.")
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# Get current entries
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reg = getattr(registry, registry_name)
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current_entries = sorted(list(reg.get_all().keys()))
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# Compare entries
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expected_set = set(expected_entries)
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current_set = set(current_entries)
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# Check for missing entries - these would indicate our new registry population
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# mechanism is missing something
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missing_entries = expected_set - current_set
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assert not missing_entries, f"Registry '{registry_name}' missing entries: {', '.join(missing_entries)}"
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