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Change GPU efficient textcat to use CNN, not BOW in generated configs (#11900)

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* Change GPU efficient textcat to use CNN, not BOW

If you generate a config with a textcat component using GPU
(transformers), the defaut option (efficiency) uses a BOW architecture,
which does not use tok2vec features. While that can make sense as part
of a larger pipeline, in the case of just a transformer and a textcat,
that means the transformer is doing a lot of work for no purpose.

This changes it so that the CNN architecture is used instead. It could
also be changed to be the same as the accuracy config, which uses the
ensemble architecture.

* Add the transformer when using a textcat with GPU

* Switch ubuntu-latest to ubuntu-20.04 in main tests (#11928)

* Switch ubuntu-latest to ubuntu-20.04 in main tests

* Only use 20.04 for 3.6

* Require thinc v8.1.7

* Require thinc v8.1.8

* Break up longer expression

---------

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
This commit is contained in:
Paul O'Leary McCann 2023-03-08 01:47:45 +09:00 committed by GitHub
parent 3bf4539e31
commit e656189ec3
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4 changed files with 27 additions and 12 deletions
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2
pyproject.toml
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@ -5,7 +5,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.1.0,<8.2.0",
"thinc>=8.1.8,<8.2.0",
"numpy>=1.15.0",
]
build-backend = "setuptools.build_meta"

2
requirements.txt
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@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
spacy-loggers>=1.0.0,<2.0.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.0,<8.2.0
thinc>=8.1.8,<8.2.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.2.0

4
setup.cfg
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@ -39,7 +39,7 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.1.0,<8.2.0
thinc>=8.1.8,<8.2.0
install_requires =
# Our libraries
spacy-legacy>=3.0.11,<3.1.0
@ -47,7 +47,7 @@ install_requires =
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.0,<8.2.0
thinc>=8.1.8,<8.2.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0

31
spacy/cli/templates/quickstart_training.jinja
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@ -24,8 +24,11 @@ gpu_allocator = null
lang = "{{ lang }}"
{%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
{%- set with_accuracy = optimize == "accuracy" -%}
{%- set has_accurate_textcat = has_textcat and with_accuracy -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "trainable_lemmatizer" in components or "entity_linker" in components or has_accurate_textcat) -%}
{# The BOW textcat doesn't need a source of features, so it can omit the
tok2vec/transformer. #}
{%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
{%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
{%- else -%}
{%- set full_pipeline = components -%}
@ -221,10 +224,16 @@ no_output_layer = false
{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
nO = null
[components.textcat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.textcat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{%- endif %}
@ -252,10 +261,16 @@ no_output_layer = false
{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
nO = null
[components.textcat_multilabel.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.textcat_multilabel.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{%- endif %}