Non-resizable textcats to legacy (#10)

* non-resizable textcat architectures

* TextCatEnsemble_v2 is in core spacy

* consult registry instead of using imports

* bump to 3.0.6

* add the two new architectures to the test suite

setup.cfg

@@ -1,5 +1,5 @@
 [metadata]
-version = 3.0.5
+version = 3.0.6
 description = Legacy registered functions for spaCy backwards compatibility
 url = https://spacy.io
 author = Explosion
@@ -37,6 +37,8 @@ spacy_architectures =
     spacy-legacy.Tok2Vec.v1 = spacy_legacy.architectures.tok2vec:Tok2Vec_v1
     spacy-legacy.MaxoutWindowEncoder.v1 = spacy_legacy.architectures.tok2vec:MaxoutWindowEncoder_v1
     spacy-legacy.MishWindowEncoder.v1 = spacy_legacy.architectures.tok2vec:MishWindowEncoder_v1
+    spacy-legacy.TextCatCNN.v1 = spacy_legacy.architectures.textcat:TextCatCNN_v1
+    spacy-legacy.TextCatBOW.v1 = spacy_legacy.architectures.textcat:TextCatBOW_v1
     spacy-legacy.TextCatEnsemble.v1 = spacy_legacy.architectures.textcat:TextCatEnsemble_v1
     spacy-legacy.HashEmbedCNN.v1 = spacy_legacy.architectures.tok2vec:HashEmbedCNN_v1
     spacy-legacy.MultiHashEmbed.v1 = spacy_legacy.architectures.tok2vec:MultiHashEmbed_v1

spacy_legacy/architectures/textcat.py

@@ -1,7 +1,69 @@
-from typing import Optional
-from thinc.api import Model
+from typing import Optional, List
+from thinc.types import Floats2d
+from thinc.api import Model, with_cpu
 from spacy.attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
 from spacy.util import registry
+from spacy.tokens import Doc
+
+# TODO: replace with registered layer after spacy v3.0.7
+from spacy.ml import extract_ngrams
+
+
+def TextCatCNN_v1(
+    tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
+) -> Model[List[Doc], Floats2d]:
+    """
+    Build a simple CNN text classifier, given a token-to-vector model as inputs.
+    If exclusive_classes=True, a softmax non-linearity is applied, so that the
+    outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
+    is applied instead, so that outputs are in the range [0, 1].
+    """
+    chain = registry.get("layers", "chain.v1")
+    reduce_mean = registry.get("layers", "reduce_mean.v1")
+    Logistic = registry.get("layers", "Logistic.v1")
+    Softmax = registry.get("layers", "Softmax.v1")
+    Linear = registry.get("layers", "Linear.v1")
+    list2ragged = registry.get("layers", "list2ragged.v1")
+
+    # extract_ngrams = registry.get("layers", "spacy.extract_ngrams.v1")
+
+    with Model.define_operators({">>": chain}):
+        cnn = tok2vec >> list2ragged() >> reduce_mean()
+        if exclusive_classes:
+            output_layer = Softmax(nO=nO, nI=tok2vec.maybe_get_dim("nO"))
+            model = cnn >> output_layer
+            model.set_ref("output_layer", output_layer)
+        else:
+            linear_layer = Linear(nO=nO, nI=tok2vec.maybe_get_dim("nO"))
+            model = cnn >> linear_layer >> Logistic()
+            model.set_ref("output_layer", linear_layer)
+    model.set_ref("tok2vec", tok2vec)
+    model.set_dim("nO", nO)
+    model.attrs["multi_label"] = not exclusive_classes
+    return model
+
+
+def TextCatBOW_v1(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    nO: Optional[int] = None,
+) -> Model[List[Doc], Floats2d]:
+    chain = registry.get("layers", "chain.v1")
+    Logistic = registry.get("layers", "Logistic.v1")
+    SparseLinear = registry.get("layers", "SparseLinear.v1")
+    softmax_activation = registry.get("layers", "softmax_activation.v1")
+
+    with Model.define_operators({">>": chain}):
+        sparse_linear = SparseLinear(nO)
+        model = extract_ngrams(ngram_size, attr=ORTH) >> sparse_linear
+        model = with_cpu(model, model.ops)
+        if not no_output_layer:
+            output_layer = softmax_activation() if exclusive_classes else Logistic()
+            model = model >> with_cpu(output_layer, output_layer.ops)
+    model.set_ref("output_layer", sparse_linear)
+    model.attrs["multi_label"] = not exclusive_classes
+    return model
 
 
 def TextCatEnsemble_v1(

spacy_legacy/tests/test_legacy.py

@@ -6,14 +6,17 @@
     ("architectures", "Tok2Vec.v1"),
     ("architectures", "MaxoutWindowEncoder.v1"),
     ("architectures", "MishWindowEncoder.v1"),
+    ("architectures", "TextCatBOW.v1"),
+    ("architectures", "TextCatCNN.v1"),
     ("architectures", "TextCatEnsemble.v1"),
     ("architectures", "HashEmbedCNN.v1"),
     ("architectures", "MultiHashEmbed.v1"),
     ("architectures", "CharacterEmbed.v1"),
     ("loggers", "WandbLogger.v1"),
-    ("layers", "StaticVectors.v1")
+    ("layers", "StaticVectors.v1"),
 ]
 
+
 @pytest.mark.parametrize("package", PACKAGES)
 @pytest.mark.parametrize("reg_name,name", FUNCTIONS)
 def test_registry(package, reg_name, name):