Rename Transformer listener (#6001)

* rename to spacy-transformers.TransformerListener

* add some more tok2vec tests

* use select_pipes

* fix docs - annotation setter was not changed in the end

spacy/cli/templates/quickstart_training.jinja

@@ -42,7 +42,7 @@ factory = "tagger"
 nO = null
 
 [components.tagger.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
 
 [components.tagger.model.tok2vec.pooling]
@@ -62,7 +62,7 @@ use_upper = false
 nO = null
 
 [components.parser.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
 
 [components.parser.model.tok2vec.pooling]
@@ -82,7 +82,7 @@ use_upper = false
 nO = null
 
 [components.ner.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
 
 [components.ner.model.tok2vec.pooling]

spacy/pipeline/pipe.pyx

@@ -37,7 +37,7 @@ cdef class Pipe:
         and returned. This usually happens under the hood when the nlp object
         is called on a text and all components are applied to the Doc.
 
-        docs (Doc): The Doc to preocess.
+        docs (Doc): The Doc to process.
         RETURNS (Doc): The processed Doc.
 
         DOCS: https://spacy.io/api/pipe#call

spacy/pipeline/tok2vec.py

@@ -88,7 +88,7 @@ class Tok2Vec(Pipe):
         """Add context-sensitive embeddings to the Doc.tensor attribute, allowing
         them to be used as features by downstream components.
 
-        docs (Doc): The Doc to preocess.
+        docs (Doc): The Doc to process.
         RETURNS (Doc): The processed Doc.
 
         DOCS: https://spacy.io/api/tok2vec#call

spacy/tests/test_tok2vec.py

@@ -3,11 +3,18 @@ import pytest
 from spacy.ml.models.tok2vec import build_Tok2Vec_model
 from spacy.ml.models.tok2vec import MultiHashEmbed, CharacterEmbed
 from spacy.ml.models.tok2vec import MishWindowEncoder, MaxoutWindowEncoder
+from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
 from spacy.vocab import Vocab
 from spacy.tokens import Doc
-
+from spacy.gold import Example
+from spacy import util
+from spacy.lang.en import English
 from .util import get_batch
 
+from thinc.api import Config
+
+from numpy.testing import assert_equal
+
 
 def test_empty_doc():
     width = 128
@@ -41,7 +48,7 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
             also_use_static_vectors=False,
             also_embed_subwords=True,
         ),
-        MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3,),
+        MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
     )
     tok2vec.initialize()
     vectors, backprop = tok2vec.begin_update(batch)
@@ -74,3 +81,89 @@ def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_co
     assert len(vectors) == len(docs)
     assert vectors[0].shape == (len(docs[0]), width)
     backprop(vectors)
+
+
+def test_init_tok2vec():
+    # Simple test to initialize the default tok2vec
+    nlp = English()
+    tok2vec = nlp.add_pipe("tok2vec")
+    assert tok2vec.listeners == []
+    nlp.begin_training()
+
+
+cfg_string = """
+    [nlp]
+    lang = "en"
+    pipeline = ["tok2vec","tagger"]
+
+    [components]
+
+    [components.tagger]
+    factory = "tagger"
+
+    [components.tagger.model]
+    @architectures = "spacy.Tagger.v1"
+    nO = null
+
+    [components.tagger.model.tok2vec]
+    @architectures = "spacy.Tok2VecListener.v1"
+    width = ${components.tok2vec.model.encode.width}
+
+    [components.tok2vec]
+    factory = "tok2vec"
+
+    [components.tok2vec.model]
+    @architectures = "spacy.Tok2Vec.v1"
+
+    [components.tok2vec.model.embed]
+    @architectures = "spacy.MultiHashEmbed.v1"
+    width = ${components.tok2vec.model.encode.width}
+    rows = 2000
+    also_embed_subwords = true
+    also_use_static_vectors = false
+
+    [components.tok2vec.model.encode]
+    @architectures = "spacy.MaxoutWindowEncoder.v1"
+    width = 96
+    depth = 4
+    window_size = 1
+    maxout_pieces = 3
+    """
+
+TRAIN_DATA = [
+    ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
+    ("Eat blue ham", {"tags": ["V", "J", "N"]}),
+]
+
+def test_tok2vec_listener():
+    orig_config = Config().from_str(cfg_string)
+    nlp, config = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
+    assert nlp.pipe_names == ["tok2vec", "tagger"]
+    tagger = nlp.get_pipe("tagger")
+    tok2vec = nlp.get_pipe("tok2vec")
+    tagger_tok2vec = tagger.model.get_ref("tok2vec")
+    assert isinstance(tok2vec, Tok2Vec)
+    assert isinstance(tagger_tok2vec, Tok2VecListener)
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+        for tag in t[1]["tags"]:
+            tagger.add_label(tag)
+
+    # Check that the Tok2Vec component finds it listeners
+    assert tok2vec.listeners == []
+    optimizer = nlp.begin_training(lambda: train_examples)
+    assert tok2vec.listeners == [tagger_tok2vec]
+
+    for i in range(5):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    doc = nlp("Running the pipeline as a whole.")
+    doc_tensor = tagger_tok2vec.predict([doc])[0]
+    assert_equal(doc.tensor, doc_tensor)
+
+    # TODO: should this warn or error?
+    nlp.select_pipes(disable="tok2vec")
+    assert nlp.pipe_names == ["tagger"]
+    nlp("Running the pipeline with the Tok2Vec component disabled.")

website/docs/api/architectures.md

@@ -346,13 +346,13 @@ in other components, see
 | `tokenizer_config` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). ~~Dict[str, Any]~~                                                                                   |
 | **CREATES**        | The model using the architecture. ~~Model[List[Doc], FullTransformerBatch]~~                                                                                                                                                                          |
 
-### spacy-transformers.Tok2VecListener.v1 {#transformers-Tok2VecListener}
+### spacy-transformers.TransformerListener.v1 {#TransformerListener}
 
 > #### Example Config
 >
 > ```ini
 > [model]
-> @architectures = "spacy-transformers.Tok2VecListener.v1"
+> @architectures = "spacy-transformers.TransformerListener.v1"
 > grad_factor = 1.0
 >
 > [model.pooling]

website/docs/usage/embeddings-transformers.md

@@ -225,7 +225,7 @@ transformers as subnetworks directly, you can also use them via the
 
 ![The processing pipeline with the transformer component](../images/pipeline_transformer.svg)
 
-By default, the `Transformer` component sets the
+The `Transformer` component sets the
 [`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
 which lets you access the transformers outputs at runtime.
 
@@ -303,7 +303,7 @@ component:
 >
 > ```python
 > from spacy_transformers import Transformer, TransformerModel
-> from spacy_transformers.annotation_setters import configure_trfdata_setter
+> from spacy_transformers.annotation_setters import null_annotation_setter
 > from spacy_transformers.span_getters import get_doc_spans
 >
 > trf = Transformer(
@@ -313,7 +313,7 @@ component:
 >         get_spans=get_doc_spans,
 >         tokenizer_config={"use_fast": True},
 >     ),
->     annotation_setter=configure_trfdata_setter(),
+>     annotation_setter=null_annotation_setter,
 >     max_batch_items=4096,
 > )
 > ```
@@ -333,7 +333,7 @@ tokenizer_config = {"use_fast": true}
 @span_getters = "doc_spans.v1"
 
 [components.transformer.annotation_setter]
-@annotation_setters = "spacy-transformers.trfdata_setter.v1"
+@annotation_setters = "spacy-transformers.null_annotation_setter.v1"
 
 ```
 

website/docs/usage/v3.md

@@ -64,7 +64,7 @@ menu:
   [`TransformerData`](/api/transformer#transformerdata),
   [`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
 - **Architectures: ** [TransformerModel](/api/architectures#TransformerModel),
-  [Tok2VecListener](/api/architectures#transformers-Tok2VecListener),
+  [TransformerListener](/api/architectures#TransformerListener),
   [Tok2VecTransformer](/api/architectures#Tok2VecTransformer)
 - **Models:** [`en_core_trf_lg_sm`](/models/en)
 - **Implementation:**