Add Pipe.is_distillable method

spacy/pipeline/pipe.pyx

@@ -87,6 +87,10 @@ cdef class Pipe:
             return self.scorer(examples, **scorer_kwargs)
         return {}
 
+    @property
+    def is_distillable(self) -> bool:
+        return False
+
     @property
     def is_trainable(self) -> bool:
         return False

spacy/pipeline/trainable_pipe.pyx

@@ -264,6 +264,10 @@ cdef class TrainablePipe(Pipe):
         """
         raise NotImplementedError(Errors.E931.format(parent="Pipe", method="add_label", name=self.name))
 
+    @property
+    def is_distillable(self) -> bool:
+        return not (self.__class__.distill is TrainablePipe.distill and self.__class__.get_teacher_student_loss is TrainablePipe.get_teacher_student_loss)
+
     @property
     def is_trainable(self) -> bool:
         return True

spacy/tests/parser/test_ner.py

@@ -617,6 +617,12 @@ def test_overfitting_IO(use_upper):
     assert ents[1].kb_id == 0
 
 
+def test_is_distillable():
+    nlp = English()
+    ner = nlp.add_pipe("ner")
+    assert ner.is_distillable
+
+
 def test_distill():
     teacher = English()
     teacher_ner = teacher.add_pipe("ner")

spacy/tests/parser/test_parse.py

@@ -396,6 +396,12 @@ def test_overfitting_IO(pipe_name):
     assert_equal(batch_deps_1, no_batch_deps)
 
 
+def test_is_distillable():
+    nlp = English()
+    parser = nlp.add_pipe("parser")
+    assert parser.is_distillable
+
+
 def test_distill():
     teacher = English()
     teacher_parser = teacher.add_pipe("parser")

spacy/tests/pipeline/test_edit_tree_lemmatizer.py

@@ -195,6 +195,12 @@ def test_overfitting_IO():
     assert doc4[3].lemma_ == "egg"
 
 
+def test_is_distillable():
+    nlp = English()
+    lemmatizer = nlp.add_pipe("trainable_lemmatizer")
+    assert lemmatizer.is_distillable
+
+
 def test_distill():
     teacher = English()
     teacher_lemmatizer = teacher.add_pipe("trainable_lemmatizer")

spacy/tests/pipeline/test_morphologizer.py

@@ -50,6 +50,12 @@ def test_implicit_label():
     nlp.initialize(get_examples=lambda: train_examples)
 
 
+def test_is_distillable():
+    nlp = English()
+    morphologizer = nlp.add_pipe("morphologizer")
+    assert morphologizer.is_distillable
+
+
 def test_no_resize():
     nlp = Language()
     morphologizer = nlp.add_pipe("morphologizer")

spacy/tests/pipeline/test_senter.py

@@ -11,6 +11,12 @@ from spacy.pipeline import TrainablePipe
 from spacy.tests.util import make_tempdir
 
 
+def test_is_distillable():
+    nlp = English()
+    senter = nlp.add_pipe("senter")
+    assert senter.is_distillable
+
+
 def test_label_types():
     nlp = Language()
     senter = nlp.add_pipe("senter")

spacy/tests/pipeline/test_tagger.py

@@ -213,6 +213,12 @@ def test_overfitting_IO():
     assert doc3[0].tag_ != "N"
 
 
+def test_is_distillable():
+    nlp = English()
+    tagger = nlp.add_pipe("tagger")
+    assert tagger.is_distillable
+
+
 def test_distill():
     teacher = English()
     teacher_tagger = teacher.add_pipe("tagger")

spacy/tests/pipeline/test_textcat.py

@@ -565,6 +565,12 @@ def test_initialize_examples(name, get_examples, train_data):
         nlp.initialize(get_examples=get_examples())
 
 
+def test_is_distillable():
+    nlp = English()
+    textcat = nlp.add_pipe("textcat")
+    assert not textcat.is_distillable
+
+
 def test_overfitting_IO():
     # Simple test to try and quickly overfit the single-label textcat component - ensuring the ML models work correctly
     fix_random_seed(0)