spaCy/spacy/tests/test_models.py

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from typing import List
import pytest
from thinc.api import fix_random_seed, Adam, set_dropout_rate
from numpy.testing import assert_array_equal
import numpy
from spacy.ml.models import build_Tok2Vec_model
from spacy.ml.models import build_text_classifier, build_simple_cnn_text_classifier
from spacy.lang.en import English
from spacy.lang.en.examples import sentences as EN_SENTENCES
def get_all_params(model):
params = []
for node in model.walk():
for name in node.param_names:
params.append(node.get_param(name).ravel())
return node.ops.xp.concatenate(params)
def get_docs():
nlp = English()
return list(nlp.pipe(EN_SENTENCES + [" ".join(EN_SENTENCES)]))
def get_gradient(model, Y):
if isinstance(Y, model.ops.xp.ndarray):
dY = model.ops.alloc(Y.shape, dtype=Y.dtype)
dY += model.ops.xp.random.uniform(-1.0, 1.0, Y.shape)
return dY
elif isinstance(Y, List):
return [get_gradient(model, y) for y in Y]
else:
raise ValueError(f"Could not get gradient for type {type(Y)}")
def default_tok2vec():
return build_Tok2Vec_model(**TOK2VEC_KWARGS)
TOK2VEC_KWARGS = {
"width": 96,
"embed_size": 2000,
"subword_features": True,
"char_embed": False,
"conv_depth": 4,
"bilstm_depth": 0,
"maxout_pieces": 4,
"window_size": 1,
"dropout": 0.1,
"nM": 0,
"nC": 0,
"pretrained_vectors": None,
}
TEXTCAT_KWARGS = {
"width": 64,
"embed_size": 2000,
"pretrained_vectors": None,
"exclusive_classes": False,
"ngram_size": 1,
"window_size": 1,
"conv_depth": 2,
"dropout": None,
"nO": 7
}
TEXTCAT_CNN_KWARGS = {
"tok2vec": default_tok2vec(),
"exclusive_classes": False,
"nO": 13,
}
@pytest.mark.parametrize(
"seed,model_func,kwargs",
[
(0, build_Tok2Vec_model, TOK2VEC_KWARGS),
(0, build_text_classifier, TEXTCAT_KWARGS),
(0, build_simple_cnn_text_classifier, TEXTCAT_CNN_KWARGS),
],
)
def test_models_initialize_consistently(seed, model_func, kwargs):
fix_random_seed(seed)
model1 = model_func(**kwargs)
model1.initialize()
fix_random_seed(seed)
model2 = model_func(**kwargs)
model2.initialize()
params1 = get_all_params(model1)
params2 = get_all_params(model2)
assert_array_equal(params1, params2)
@pytest.mark.parametrize(
"seed,model_func,kwargs,get_X",
[
(0, build_Tok2Vec_model, TOK2VEC_KWARGS, get_docs),
(0, build_text_classifier, TEXTCAT_KWARGS, get_docs),
(0, build_simple_cnn_text_classifier, TEXTCAT_CNN_KWARGS, get_docs),
],
)
def test_models_predict_consistently(seed, model_func, kwargs, get_X):
fix_random_seed(seed)
model1 = model_func(**kwargs).initialize()
Y1 = model1.predict(get_X())
fix_random_seed(seed)
model2 = model_func(**kwargs).initialize()
Y2 = model2.predict(get_X())
if model1.has_ref("tok2vec"):
tok2vec1 = model1.get_ref("tok2vec").predict(get_X())
tok2vec2 = model2.get_ref("tok2vec").predict(get_X())
for i in range(len(tok2vec1)):
for j in range(len(tok2vec1[i])):
assert_array_equal(numpy.asarray(tok2vec1[i][j]), numpy.asarray(tok2vec2[i][j]))
if isinstance(Y1, numpy.ndarray):
assert_array_equal(Y1, Y2)
elif isinstance(Y1, List):
assert len(Y1) == len(Y2)
for y1, y2 in zip(Y1, Y2):
assert_array_equal(y1, y2)
else:
raise ValueError(f"Could not compare type {type(Y1)}")
@pytest.mark.parametrize(
"seed,dropout,model_func,kwargs,get_X",
[
(0, 0.2, build_Tok2Vec_model, TOK2VEC_KWARGS, get_docs),
(0, 0.2, build_text_classifier, TEXTCAT_KWARGS, get_docs),
(0, 0.2, build_simple_cnn_text_classifier, TEXTCAT_CNN_KWARGS, get_docs),
],
)
def test_models_update_consistently(seed, dropout, model_func, kwargs, get_X):
def get_updated_model():
fix_random_seed(seed)
optimizer = Adam(0.001)
model = model_func(**kwargs).initialize()
initial_params = get_all_params(model)
set_dropout_rate(model, dropout)
for _ in range(5):
Y, get_dX = model.begin_update(get_X())
dY = get_gradient(model, Y)
_ = get_dX(dY)
model.finish_update(optimizer)
updated_params = get_all_params(model)
with pytest.raises(AssertionError):
assert_array_equal(initial_params, updated_params)
return model
model1 = get_updated_model()
model2 = get_updated_model()
assert_array_equal(get_all_params(model1), get_all_params(model2))