Add test

spacy/tests/conftest.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 import pytest
 from spacy.util import get_lang_class
 from hypothesis import settings
@@ -47,6 +48,12 @@ def pytest_runtest_setup(item):
             pytest.skip("not referencing any issues")
 
 
+@pytest.fixture
+def test_dir(request):
+    print(request.fspath)
+    return Path(request.fspath).parent
+
+
 # Fixtures for language tokenizers (languages sorted alphabetically)
 
 

spacy/tests/training/test_training.py

@@ -1,4 +1,5 @@
 import random
+from confection import Config
 
 import numpy
 import pytest
@@ -11,8 +12,10 @@ from spacy.training import offsets_to_biluo_tags
 from spacy.training.alignment_array import AlignmentArray
 from spacy.training.align import get_alignments
 from spacy.training.converters import json_to_docs
+from spacy.training.initialize import init_nlp
+from spacy.training.loop import train
 from spacy.util import get_words_and_spaces, load_model_from_path, minibatch
-from spacy.util import load_config_from_str
+from spacy.util import load_config_from_str, registry, load_model_from_config
 from thinc.api import compounding
 
 from ..util import make_tempdir
@@ -1112,3 +1115,90 @@ def test_retokenized_docs(doc):
         retokenizer.merge(doc1[0:2])
         retokenizer.merge(doc1[5:7])
     assert example.get_aligned("ORTH", as_string=True) == expected2
+
+
+training_config_string = """
+[nlp]
+lang = "en"
+pipeline = ["tok2vec", "tagger"]
+
+[components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.HashEmbedCNN.v1"
+pretrained_vectors = null
+width = 342
+depth = 4
+window_size = 1
+embed_size = 2000
+maxout_pieces = 3
+subword_features = true
+
+[components.tagger]
+factory = "tagger"
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v2"
+
+[components.tagger.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.width}
+
+[corpora]
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = null
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = null
+
+[training]
+train_corpus = "corpora.train"
+dev_corpus = "corpora.dev"
+seed = 1
+gpu_allocator = "pytorch"
+dropout = 0.1
+accumulate_gradient = 3
+patience = 5000
+max_epochs = 1
+max_steps = 6
+eval_frequency = 10
+
+[training.batcher]
+@batchers = "spacy.batch_by_padded.v1"
+discard_oversize = False
+get_length = null
+size = 1
+buffer = 256
+"""
+
+
+def test_training_before_update(test_dir):
+    ran_before_update = False
+
+    @registry.callbacks(f"test_training_before_update_callback")
+    def make_before_creation():
+        def before_update(nlp, args):
+            nonlocal ran_before_update
+            ran_before_update = True
+            assert "step" in args
+            assert "epoch" in args
+
+        return before_update
+
+    config = Config().from_str(training_config_string, interpolate=False)
+    config["corpora"]["train"]["path"] = str(test_dir / "toy-en-corpus.spacy")
+    config["corpora"]["dev"]["path"] = str(test_dir / "toy-en-corpus.spacy")
+    config["training"]["before_update"] = {
+        "@callbacks": "test_training_before_update_callback"
+    }
+    nlp = load_model_from_config(config, auto_fill=True, validate=True)
+
+    nlp = init_nlp(config)
+    train(nlp)
+    assert ran_before_update == True