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Bugfix textcat reproducibility on GPU (#6411) 

* add seed argument to ParametricAttention layer

* bump thinc to 7.4.3

* set thinc version range

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
This commit is contained in:
Sofie Van Landeghem 2020-11-23 12:29:35 +01:00 committed by GitHub
parent cdca44ac11
commit 2af31a8c8d
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2 changed files with 8 additions and 6 deletions
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4
spacy/_ml.py
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@ -646,7 +646,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
SpacyVectors SpacyVectors
>> flatten_add_lengths >> flatten_add_lengths
>> with_getitem(0, Affine(width, pretrained_dims)) >> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width) >> ParametricAttention(width, seed=100)
>> Pooling(sum_pool) >> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2 >> Residual(ReLu(width, width)) ** 2
>> zero_init(Affine(nr_class, width, drop_factor=0.0)) >> zero_init(Affine(nr_class, width, drop_factor=0.0))
@ -688,7 +688,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
cnn_model = ( cnn_model = (
tok2vec tok2vec
>> flatten_add_lengths >> flatten_add_lengths
>> ParametricAttention(width) >> ParametricAttention(width, seed=99)
>> Pooling(sum_pool) >> Pooling(sum_pool)
>> Residual(zero_init(Maxout(width, width))) >> Residual(zero_init(Maxout(width, width)))
>> zero_init(Affine(nr_class, width, drop_factor=0.0)) >> zero_init(Affine(nr_class, width, drop_factor=0.0))

8
spacy/tests/regression/test_issue6177.py
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@ -11,7 +11,6 @@ def test_issue6177():
# NOTE: no need to transform this code to v3 when 'master' is merged into 'develop'. # NOTE: no need to transform this code to v3 when 'master' is merged into 'develop'.
# A similar test exists already for v3: test_issue5551 # A similar test exists already for v3: test_issue5551
# This is just a backport # This is just a backport
results = [] results = []
for i in range(3): for i in range(3):
fix_random_seed(0) fix_random_seed(0)
@ -24,9 +23,12 @@ def test_issue6177():
nlp.add_pipe(textcat) nlp.add_pipe(textcat)
for label in set(example[1]["cats"]): for label in set(example[1]["cats"]):
textcat.add_label(label) textcat.add_label(label)
nlp.begin_training() # Train
optimizer = nlp.begin_training()
text, annots = example
nlp.update([text], [annots], sgd=optimizer)
# Store the result of each iteration # Store the result of each iteration
result = textcat.model.predict([nlp.make_doc(example[0])]) result = textcat.model.predict([nlp.make_doc(text)])
results.append(list(result[0])) results.append(list(result[0]))
# All results should be the same because of the fixed seed # All results should be the same because of the fixed seed