component tests single or multiple prediction

spacy/tests/pipeline/test_models.py

@@ -0,0 +1,46 @@
+from typing import List
+import pytest
+from numpy.testing import assert_equal
+from thinc.api import get_current_ops, Model, data_validation
+from thinc.types import Array2d
+
+from spacy.lang.en import English
+from spacy.tokens import Doc
+
+OPS = get_current_ops()
+
+texts = ["These are 4 words", "These just three"]
+l0 = [[1, 2], [3, 4], [5, 6], [7, 8]]
+l1 = [[9, 8], [7, 6], [5, 4]]
+out_list = [OPS.xp.asarray(l0, dtype="f"), OPS.xp.asarray(l1, dtype="f")]
+a1 = OPS.xp.asarray(l1, dtype="f")
+
+# Test components with a model of type Model[List[Doc], List[Floats2d]]
+@pytest.mark.parametrize("name", ["tagger", "tok2vec", "morphologizer", "senter"])
+def test_layers_batching_all_list(name):
+    nlp = English()
+    in_data = [nlp(text) for text in texts]
+    proc = nlp.create_pipe(name)
+    util_batch_unbatch_List(proc.model, in_data, out_list)
+
+def util_batch_unbatch_List(model: Model[List[Doc], List[Array2d]], in_data: List[Doc], out_data: List[Array2d]):
+    with data_validation(True):
+        model.initialize(in_data, out_data)
+        Y_batched = model.predict(in_data)
+        Y_not_batched = [model.predict([u])[0] for u in in_data]
+        assert_equal(Y_batched, Y_not_batched)
+
+# Test components with a model of type Model[List[Doc], Floats2d]
+@pytest.mark.parametrize("name", ["textcat"])
+def test_layers_batching_all_array(name):
+    nlp = English()
+    in_data = [nlp(text) for text in texts]
+    proc = nlp.create_pipe(name)
+    util_batch_unbatch_Array(proc.model, in_data, a1)
+
+def util_batch_unbatch_Array(model: Model[List[Doc], Array2d], in_data: List[Doc], out_data: Array2d):
+    with data_validation(True):
+        model.initialize(in_data, out_data)
+        Y_batched = model.predict(in_data)
+        Y_not_batched = [model.predict([u])[0] for u in in_data]
+        assert_equal(Y_batched, Y_not_batched)