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spaCy/spacy/cli/templates/quickstart_training.jinja
Paul O'Leary McCann cbd85c9608 Change GPU efficient textcat to use CNN, not BOW in generated configs (#11900) 
* 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>
2023-03-09 10:32:51 +01:00

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{# This is a template for training configs used for the quickstart widget in
the docs and the init config command. It encodes various best practices and
can help generate the best possible configuration, given a user's requirements. #}
{%- set use_transformer = hardware != "cpu" and transformer_data -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "trainable_lemmatizer"] -%}
[paths]
train = null
dev = null
{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
vectors = null
{% else -%}
vectors = "{{ word_vectors }}"
{% endif -%}
[system]
{% if use_transformer -%}
gpu_allocator = "pytorch"
{% else -%}
gpu_allocator = null
{% endif %}
[nlp]
lang = "{{ lang }}"
{%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
{%- set with_accuracy = optimize == "accuracy" -%}
{# 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 -%}
{%- endif %}
pipeline = {{ full_pipeline|pprint()|replace("'", '"')|safe }}
batch_size = {{ 128 if hardware == "gpu" else 1000 }}
[components]
{# TRANSFORMER PIPELINE #}
{%- if use_transformer -%}
[components.transformer]
factory = "transformer"
[components.transformer.model]
@architectures = "spacy-transformers.TransformerModel.v3"
name = "{{ transformer["name"] }}"
tokenizer_config = {"use_fast": true}
[components.transformer.model.get_spans]
@span_getters = "spacy-transformers.strided_spans.v1"
window = 128
stride = 96
{% if "morphologizer" in components %}
[components.morphologizer]
factory = "morphologizer"
[components.morphologizer.model]
@architectures = "spacy.Tagger.v2"
nO = null
[components.morphologizer.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.morphologizer.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "tagger" in components %}
[components.tagger]
factory = "tagger"
[components.tagger.model]
@architectures = "spacy.Tagger.v2"
nO = null
[components.tagger.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.tagger.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "parser" in components -%}
[components.parser]
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = false
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.parser.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{% if "ner" in components -%}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.ner.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
{% if "spancat" in components -%}
[components.spancat]
factory = "spancat"
max_positive = null
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.spancat.model]
@architectures = "spacy.SpanCategorizer.v1"
[components.spancat.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128
[components.spancat.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = null
nI = null
[components.spancat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.spancat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]
{% endif -%}
{% if "trainable_lemmatizer" in components -%}
[components.trainable_lemmatizer]
factory = "trainable_lemmatizer"
backoff = "orth"
min_tree_freq = 3
overwrite = false
scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
top_k = 1
[components.trainable_lemmatizer.model]
@architectures = "spacy.Tagger.v2"
nO = null
normalize = false
[components.trainable_lemmatizer.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.trainable_lemmatizer.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
{% if "entity_linker" in components -%}
[components.entity_linker]
factory = "entity_linker"
get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
incl_context = true
incl_prior = true
[components.entity_linker.model]
@architectures = "spacy.EntityLinker.v2"
nO = null
[components.entity_linker.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.entity_linker.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{% endif -%}
{% if "textcat" in components %}
[components.textcat]
factory = "textcat"
{% if optimize == "accuracy" %}
[components.textcat.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null
[components.textcat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.textcat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = true
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 %}
{% if "textcat_multilabel" in components %}
[components.textcat_multilabel]
factory = "textcat_multilabel"
{% if optimize == "accuracy" %}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatEnsemble.v2"
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"
[components.textcat_multilabel.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = 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 %}
{# NON-TRANSFORMER PIPELINE #}
{% else -%}
{% if "tok2vec" in full_pipeline -%}
[components.tok2vec]
factory = "tok2vec"
[components.tok2vec.model]
@architectures = "spacy.Tok2Vec.v2"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = ${components.tok2vec.model.encode.width}
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 1000, 2500, 2500]
include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = {{ 96 if optimize == "efficiency" else 256 }}
depth = {{ 4 if optimize == "efficiency" else 8 }}
window_size = 1
maxout_pieces = 3
{% endif -%}
{% if "morphologizer" in components %}
[components.morphologizer]
factory = "morphologizer"
[components.morphologizer.model]
@architectures = "spacy.Tagger.v2"
nO = null
[components.morphologizer.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{%- endif %}
{% if "tagger" in components %}
[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}
{%- endif %}
{% if "parser" in components -%}
[components.parser]
factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "parser"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = true
nO = null
[components.parser.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{%- endif %}
{% if "ner" in components %}
[components.ner]
factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v2"
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true
nO = null
[components.ner.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif %}
{% if "spancat" in components %}
[components.spancat]
factory = "spancat"
max_positive = null
scorer = {"@scorers":"spacy.spancat_scorer.v1"}
spans_key = "sc"
threshold = 0.5
[components.spancat.model]
@architectures = "spacy.SpanCategorizer.v1"
[components.spancat.model.reducer]
@layers = "spacy.mean_max_reducer.v1"
hidden_size = 128
[components.spancat.model.scorer]
@layers = "spacy.LinearLogistic.v1"
nO = null
nI = null
[components.spancat.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
[components.spancat.suggester]
@misc = "spacy.ngram_suggester.v1"
sizes = [1,2,3]
{% endif %}
{% if "trainable_lemmatizer" in components -%}
[components.trainable_lemmatizer]
factory = "trainable_lemmatizer"
backoff = "orth"
min_tree_freq = 3
overwrite = false
scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
top_k = 1
[components.trainable_lemmatizer.model]
@architectures = "spacy.Tagger.v2"
nO = null
normalize = false
[components.trainable_lemmatizer.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif -%}
{% if "entity_linker" in components -%}
[components.entity_linker]
factory = "entity_linker"
get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
incl_context = true
incl_prior = true
[components.entity_linker.model]
@architectures = "spacy.EntityLinker.v2"
nO = null
[components.entity_linker.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
{% endif %}
{% if "textcat" in components %}
[components.textcat]
factory = "textcat"
{% if optimize == "accuracy" %}
[components.textcat.model]
@architectures = "spacy.TextCatEnsemble.v2"
nO = null
[components.textcat.model.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = ${components.tok2vec.model.encode.width}
[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
{%- endif %}
{%- endif %}
{% if "textcat_multilabel" in components %}
[components.textcat_multilabel]
factory = "textcat_multilabel"
{% if optimize == "accuracy" %}
[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.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
{%- endif %}
{%- endif %}
{% endif %}
{% for pipe in components %}
{% if pipe not in listener_components %}
{# Other components defined by the user: we just assume they're factories #}
[components.{{ pipe }}]
factory = "{{ pipe }}"
{% endif %}
{% endfor %}
[corpora]
[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
[training]
{% if use_transformer -%}
accumulate_gradient = {{ transformer["size_factor"] }}
{% endif -%}
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
[training.optimizer]
@optimizers = "Adam.v1"
{% if use_transformer -%}
[training.optimizer.learn_rate]
@schedules = "warmup_linear.v1"
warmup_steps = 250
total_steps = 20000
initial_rate = 5e-5
{% endif %}
{% if use_transformer %}
[training.batcher]
@batchers = "spacy.batch_by_padded.v1"
discard_oversize = true
size = 2000
buffer = 256
{%- else %}
[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
{% endif %}
[initialize]
vectors = ${paths.vectors}
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