mirror of
https://github.com/explosion/spaCy.git
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287 lines
8.9 KiB
Python
287 lines
8.9 KiB
Python
from typing import List
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import numpy
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import pytest
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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from thinc.api import (
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Adam,
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Logistic,
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Ragged,
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Relu,
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chain,
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fix_random_seed,
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reduce_mean,
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set_dropout_rate,
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)
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from spacy.lang.en import English
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from spacy.lang.en.examples import sentences as EN_SENTENCES
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from spacy.ml.extract_spans import _get_span_indices, extract_spans
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from spacy.ml.models import (
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MaxoutWindowEncoder,
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MultiHashEmbed,
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build_bow_text_classifier,
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build_simple_cnn_text_classifier,
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build_spancat_model,
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build_Tok2Vec_model,
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)
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from spacy.ml.staticvectors import StaticVectors
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def get_textcat_bow_kwargs():
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return {
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"exclusive_classes": True,
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"ngram_size": 1,
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"no_output_layer": False,
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"nO": 34,
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}
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def get_textcat_cnn_kwargs():
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return {"tok2vec": make_test_tok2vec(), "exclusive_classes": False, "nO": 13}
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def get_all_params(model):
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params = []
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for node in model.walk():
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for name in node.param_names:
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params.append(node.get_param(name).ravel())
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return node.ops.xp.concatenate(params)
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def get_docs():
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nlp = English()
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return list(nlp.pipe(EN_SENTENCES + [" ".join(EN_SENTENCES)]))
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def get_gradient(model, Y):
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if isinstance(Y, model.ops.xp.ndarray):
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dY = model.ops.alloc(Y.shape, dtype=Y.dtype)
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dY += model.ops.xp.random.uniform(-1.0, 1.0, Y.shape)
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return dY
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elif isinstance(Y, List):
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return [get_gradient(model, y) for y in Y]
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else:
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raise ValueError(f"Could not get gradient for type {type(Y)}")
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def get_tok2vec_kwargs():
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# This actually creates models, so seems best to put it in a function.
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return {
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"embed": MultiHashEmbed(
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width=32,
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rows=[500, 500, 500],
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attrs=["NORM", "PREFIX", "SHAPE"],
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include_static_vectors=False,
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),
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"encode": MaxoutWindowEncoder(
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width=32, depth=2, maxout_pieces=2, window_size=1
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),
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}
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def make_test_tok2vec():
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return build_Tok2Vec_model(**get_tok2vec_kwargs())
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def test_multi_hash_embed():
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embed = MultiHashEmbed(
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width=32,
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rows=[500, 500, 500],
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attrs=["NORM", "PREFIX", "SHAPE"],
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include_static_vectors=False,
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)
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hash_embeds = [node for node in embed.walk() if node.name == "hashembed"]
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assert len(hash_embeds) == 3
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# Check they look at different columns.
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assert list(sorted(he.attrs["column"] for he in hash_embeds)) == [0, 1, 2]
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# Check they use different seeds
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assert len(set(he.attrs["seed"] for he in hash_embeds)) == 3
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# Check they all have the same number of rows
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assert [he.get_dim("nV") for he in hash_embeds] == [500, 500, 500]
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# Now try with different row factors
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embed = MultiHashEmbed(
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width=32,
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rows=[1000, 50, 250],
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attrs=["NORM", "PREFIX", "SHAPE"],
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include_static_vectors=False,
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)
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hash_embeds = [node for node in embed.walk() if node.name == "hashembed"]
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assert [he.get_dim("nV") for he in hash_embeds] == [1000, 50, 250]
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@pytest.mark.parametrize(
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"seed,model_func,kwargs",
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[
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(0, build_Tok2Vec_model, get_tok2vec_kwargs()),
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(0, build_bow_text_classifier, get_textcat_bow_kwargs()),
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(0, build_simple_cnn_text_classifier, get_textcat_cnn_kwargs()),
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],
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)
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def test_models_initialize_consistently(seed, model_func, kwargs):
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fix_random_seed(seed)
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model1 = model_func(**kwargs)
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model1.initialize()
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fix_random_seed(seed)
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model2 = model_func(**kwargs)
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model2.initialize()
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params1 = get_all_params(model1)
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params2 = get_all_params(model2)
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assert_array_equal(model1.ops.to_numpy(params1), model2.ops.to_numpy(params2))
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@pytest.mark.parametrize(
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"seed,model_func,kwargs,get_X",
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[
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(0, build_Tok2Vec_model, get_tok2vec_kwargs(), get_docs),
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(0, build_bow_text_classifier, get_textcat_bow_kwargs(), get_docs),
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(0, build_simple_cnn_text_classifier, get_textcat_cnn_kwargs(), get_docs),
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],
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)
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def test_models_predict_consistently(seed, model_func, kwargs, get_X):
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fix_random_seed(seed)
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model1 = model_func(**kwargs).initialize()
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Y1 = model1.predict(get_X())
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fix_random_seed(seed)
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model2 = model_func(**kwargs).initialize()
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Y2 = model2.predict(get_X())
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if model1.has_ref("tok2vec"):
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tok2vec1 = model1.get_ref("tok2vec").predict(get_X())
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tok2vec2 = model2.get_ref("tok2vec").predict(get_X())
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for i in range(len(tok2vec1)):
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for j in range(len(tok2vec1[i])):
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assert_array_equal(
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numpy.asarray(model1.ops.to_numpy(tok2vec1[i][j])),
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numpy.asarray(model2.ops.to_numpy(tok2vec2[i][j])),
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)
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try:
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Y1 = model1.ops.to_numpy(Y1)
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Y2 = model2.ops.to_numpy(Y2)
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except Exception:
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pass
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if isinstance(Y1, numpy.ndarray):
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assert_array_equal(Y1, Y2)
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elif isinstance(Y1, List):
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assert len(Y1) == len(Y2)
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for y1, y2 in zip(Y1, Y2):
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try:
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y1 = model1.ops.to_numpy(y1)
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y2 = model2.ops.to_numpy(y2)
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except Exception:
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pass
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assert_array_equal(y1, y2)
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else:
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raise ValueError(f"Could not compare type {type(Y1)}")
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@pytest.mark.parametrize(
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"seed,dropout,model_func,kwargs,get_X",
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[
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(0, 0.2, build_Tok2Vec_model, get_tok2vec_kwargs(), get_docs),
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(0, 0.2, build_bow_text_classifier, get_textcat_bow_kwargs(), get_docs),
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(0, 0.2, build_simple_cnn_text_classifier, get_textcat_cnn_kwargs(), get_docs),
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],
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)
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def test_models_update_consistently(seed, dropout, model_func, kwargs, get_X):
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def get_updated_model():
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fix_random_seed(seed)
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optimizer = Adam(0.001)
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model = model_func(**kwargs).initialize()
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initial_params = get_all_params(model)
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set_dropout_rate(model, dropout)
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for _ in range(5):
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Y, get_dX = model.begin_update(get_X())
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dY = get_gradient(model, Y)
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get_dX(dY)
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model.finish_update(optimizer)
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updated_params = get_all_params(model)
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with pytest.raises(AssertionError):
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assert_array_equal(
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model.ops.to_numpy(initial_params), model.ops.to_numpy(updated_params)
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)
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return model
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model1 = get_updated_model()
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model2 = get_updated_model()
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assert_array_almost_equal(
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model1.ops.to_numpy(get_all_params(model1)),
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model2.ops.to_numpy(get_all_params(model2)),
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decimal=5,
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)
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@pytest.mark.parametrize("model_func,kwargs", [(StaticVectors, {"nO": 128, "nM": 300})])
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def test_empty_docs(model_func, kwargs):
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nlp = English()
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model = model_func(**kwargs).initialize()
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# Test the layer can be called successfully with 0, 1 and 2 empty docs.
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for n_docs in range(3):
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docs = [nlp("") for _ in range(n_docs)]
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# Test predict
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model.predict(docs)
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# Test backprop
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output, backprop = model.begin_update(docs)
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backprop(output)
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def test_init_extract_spans():
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extract_spans().initialize()
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def test_extract_spans_span_indices():
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model = extract_spans().initialize()
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spans = Ragged(
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model.ops.asarray([[0, 3], [2, 3], [5, 7]], dtype="i"),
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model.ops.asarray([2, 1], dtype="i"),
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)
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x_lengths = model.ops.asarray([5, 10], dtype="i")
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indices = _get_span_indices(model.ops, spans, x_lengths)
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assert list(indices) == [0, 1, 2, 2, 10, 11]
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def test_extract_spans_forward_backward():
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model = extract_spans().initialize()
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X = Ragged(model.ops.alloc2f(15, 4), model.ops.asarray([5, 10], dtype="i"))
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spans = Ragged(
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model.ops.asarray([[0, 3], [2, 3], [5, 7]], dtype="i"),
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model.ops.asarray([2, 1], dtype="i"),
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)
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Y, backprop = model.begin_update((X, spans))
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assert list(Y.lengths) == [3, 1, 2]
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assert Y.dataXd.shape == (6, 4)
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dX, spans2 = backprop(Y)
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assert spans2 is spans
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assert dX.dataXd.shape == X.dataXd.shape
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assert list(dX.lengths) == list(X.lengths)
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def test_spancat_model_init():
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model = build_spancat_model(
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build_Tok2Vec_model(**get_tok2vec_kwargs()), reduce_mean(), Logistic()
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)
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model.initialize()
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def test_spancat_model_forward_backward(nO=5):
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tok2vec = build_Tok2Vec_model(**get_tok2vec_kwargs())
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docs = get_docs()
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spans_list = []
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lengths = []
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for doc in docs:
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spans_list.append(doc[:2])
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spans_list.append(doc[1:4])
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lengths.append(2)
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spans = Ragged(
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tok2vec.ops.asarray([[s.start, s.end] for s in spans_list], dtype="i"),
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tok2vec.ops.asarray(lengths, dtype="i"),
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)
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model = build_spancat_model(
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tok2vec, reduce_mean(), chain(Relu(nO=nO), Logistic())
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).initialize(X=(docs, spans))
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Y, backprop = model((docs, spans), is_train=True)
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assert Y.shape == (spans.dataXd.shape[0], nO)
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backprop(Y)
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