Add FeatureExtractor from Thinc (#6170)

* move featureextractor from Thinc

* Update website/docs/api/architectures.md

Co-authored-by: Ines Montani <ines@ines.io>

* Update website/docs/api/architectures.md

Co-authored-by: Ines Montani <ines@ines.io>

Co-authored-by: Ines Montani <ines@ines.io>

spacy/ml/featureextractor.py

@@ -0,0 +1,25 @@
+from typing import List, Union, Callable, Tuple
+from thinc.types import Ints2d, Doc
+from thinc.api import Model, registry
+
+
+
+@registry.layers("spacy.FeatureExtractor.v1")
+def FeatureExtractor(columns: List[Union[int, str]]) -> Model[List[Doc], List[Ints2d]]:
+    return Model("extract_features", forward, attrs={"columns": columns})
+
+
+def forward(model: Model[List[Doc], List[Ints2d]], docs, is_train: bool) -> Tuple[List[Ints2d], Callable]:
+    columns = model.attrs["columns"]
+    features: List[Ints2d] = []
+    for doc in docs:
+        if hasattr(doc, "to_array"):
+            attrs = doc.to_array(columns)
+        else:
+            attrs = doc.doc.to_array(columns)[doc.start : doc.end]
+        if attrs.ndim == 1:
+            attrs = attrs.reshape((attrs.shape[0], 1))
+        features.append(model.ops.asarray2i(attrs, dtype="uint64"))
+
+    backprop: Callable[[List[Ints2d]], List] = lambda d_features: []
+    return features, backprop

spacy/ml/models/textcat.py

@@ -3,12 +3,13 @@ from thinc.api import Model, reduce_mean, Linear, list2ragged, Logistic
 from thinc.api import chain, concatenate, clone, Dropout, ParametricAttention
 from thinc.api import SparseLinear, Softmax, softmax_activation, Maxout, reduce_sum
 from thinc.api import HashEmbed, with_array, with_cpu, uniqued
-from thinc.api import Relu, residual, expand_window, FeatureExtractor
+from thinc.api import Relu, residual, expand_window
 
 from ...attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
 from ...util import registry
 from ..extract_ngrams import extract_ngrams
 from ..staticvectors import StaticVectors
+from ..featureextractor import FeatureExtractor
 
 
 @registry.architectures.register("spacy.TextCatCNN.v1")

spacy/ml/models/tok2vec.py

@@ -1,14 +1,14 @@
 from typing import Optional, List
-from thinc.api import chain, clone, concatenate, with_array, with_padded
-from thinc.api import Model, noop, list2ragged, ragged2list
-from thinc.api import FeatureExtractor, HashEmbed
-from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
 from thinc.types import Floats2d
+from thinc.api import chain, clone, concatenate, with_array, with_padded
+from thinc.api import Model, noop, list2ragged, ragged2list, HashEmbed
+from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
 
 from ...tokens import Doc
 from ...util import registry
 from ...ml import _character_embed
 from ..staticvectors import StaticVectors
+from ..featureextractor import FeatureExtractor
 from ...pipeline.tok2vec import Tok2VecListener
 from ...attrs import ORTH, NORM, PREFIX, SUFFIX, SHAPE
 

website/docs/api/architectures.md

@@ -144,9 +144,9 @@ argument that connects to the shared `tok2vec` component in the pipeline.
 Construct an embedding layer that separately embeds a number of lexical
 attributes using hash embedding, concatenates the results, and passes it through
 a feed-forward subnetwork to build mixed representations. The features used are
-the `NORM`, `PREFIX`, `SUFFIX` and `SHAPE`, which can have varying definitions
+the `NORM`, `PREFIX`, `SUFFIX` and `SHAPE`, and they are extracted with a
-depending on the `Vocab` of the `Doc` object passed in. Vectors from pretrained
+[FeatureExtractor](/api/architectures#FeatureExtractor) layer. Vectors from pretrained static
-static vectors can also be incorporated into the concatenated representation.
+vectors can also be incorporated into the concatenated representation.
 
 | Name                      | Description                                                                                                                                                                                                       |
 | ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -291,6 +291,24 @@ on [static vectors](/usage/embeddings-transformers#static-vectors) for details.
 | `key_attr`  | Defaults to `"ORTH"`. ~~str~~                                                                                                                                                                                           |
 | **CREATES** | The model using the architecture. ~~Model[List[Doc], Ragged]~~                                                                                                                                                          |
 
+### spacy.FeatureExtractor.v1 {#FeatureExtractor}
+
+> #### Example config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.FeatureExtractor.v1"
+> columns = ["NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]
+> ```
+
+Extract arrays of input features from [`Doc`](/api/doc) objects. Expects a list
+of feature names to extract, which should refer to token attributes.
+
+| Name        |  Description                                                             |
+| ----------- | ------------------------------------------------------------------------ |
+| `columns`   | The token attributes to extract. ~~List[Union[int, str]]~~               |
+| **CREATES** | The created feature extraction layer. ~~Model[List[Doc], List[Ints2d]]~~ |
+
 ## Transformer architectures {#transformers source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/architectures.py"}
 
 The following architectures are provided by the package

website/docs/usage/embeddings-transformers.md

@@ -585,8 +585,9 @@ vectors, but combines them via summation with a smaller table of learned
 embeddings.
 
 ```python
-from thinc.api import add, chain, remap_ids, Embed, FeatureExtractor
+from thinc.api import add, chain, remap_ids, Embed
 from spacy.ml.staticvectors import StaticVectors
+from spacy.ml.featureextractor import FeatureExtractor
 from spacy.util import registry
 
 @registry.architectures("my_example.MyEmbedding.v1")