Tagger: use unnormalized probabilities for inference (#10197)

* Tagger: use unnormalized probabilities for inference

Using unnormalized softmax avoids use of the relatively expensive exp function,
which can significantly speed up non-transformer models (e.g. I got a speedup
of 27% on a German tagging + parsing pipeline).

* Add spacy.Tagger.v2 with configurable normalization

Normalization of probabilities is disabled by default to improve
performance.

* Update documentation, models, and tests to spacy.Tagger.v2

* Move Tagger.v1 to spacy-legacy

* docs/architectures: run prettier

* Unnormalized softmax is now a Softmax_v2 option

* Require thinc 8.0.14 and spacy-legacy 3.0.9

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.0.12,<8.1.0",
+    "thinc>=8.0.14,<8.1.0",
     "blis>=0.4.0,<0.8.0",
     "pathy",
     "numpy>=1.15.0",

requirements.txt

@@ -3,7 +3,7 @@ spacy-legacy>=3.0.9,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.0.12,<8.1.0
+thinc>=8.0.14,<8.1.0
 blis>=0.4.0,<0.8.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0

setup.cfg

@@ -38,7 +38,7 @@ setup_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.0.12,<8.1.0
+    thinc>=8.0.14,<8.1.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.9,<3.1.0
@@ -46,7 +46,7 @@ install_requires =
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.0.12,<8.1.0
+    thinc>=8.0.14,<8.1.0
     blis>=0.4.0,<0.8.0
     wasabi>=0.8.1,<1.1.0
     srsly>=2.4.1,<3.0.0

spacy/cli/templates/quickstart_training.jinja

@@ -54,7 +54,7 @@ stride = 96
 factory = "morphologizer"
 
 [components.morphologizer.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 nO = null
 
 [components.morphologizer.model.tok2vec]
@@ -70,7 +70,7 @@ grad_factor = 1.0
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 nO = null
 
 [components.tagger.model.tok2vec]
@@ -238,7 +238,7 @@ maxout_pieces = 3
 factory = "morphologizer"
 
 [components.morphologizer.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 nO = null
 
 [components.morphologizer.model.tok2vec]
@@ -251,7 +251,7 @@ width = ${components.tok2vec.model.encode.width}
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 nO = null
 
 [components.tagger.model.tok2vec]

spacy/ml/models/tagger.py

@@ -1,14 +1,14 @@
 from typing import Optional, List
-from thinc.api import zero_init, with_array, Softmax, chain, Model
+from thinc.api import zero_init, with_array, Softmax_v2, chain, Model
 from thinc.types import Floats2d
 
 from ...util import registry
 from ...tokens import Doc
 
 
-@registry.architectures("spacy.Tagger.v1")
+@registry.architectures("spacy.Tagger.v2")
 def build_tagger_model(
-    tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None
+    tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None, normalize=False
 ) -> Model[List[Doc], List[Floats2d]]:
     """Build a tagger model, using a provided token-to-vector component. The tagger
     model simply adds a linear layer with softmax activation to predict scores
@@ -19,7 +19,9 @@ def build_tagger_model(
     """
     # TODO: glorot_uniform_init seems to work a bit better than zero_init here?!
     t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
-    output_layer = Softmax(nO, t2v_width, init_W=zero_init)
+    output_layer = Softmax_v2(
+        nO, t2v_width, init_W=zero_init, normalize_outputs=normalize
+    )
     softmax = with_array(output_layer)  # type: ignore
     model = chain(tok2vec, softmax)
     model.set_ref("tok2vec", tok2vec)

spacy/pipeline/morphologizer.pyx

@@ -25,7 +25,7 @@ BACKWARD_EXTEND = False
 
 default_model_config = """
 [model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [model.tok2vec]
 @architectures = "spacy.Tok2Vec.v2"

spacy/pipeline/senter.pyx

@@ -20,7 +20,7 @@ BACKWARD_OVERWRITE = False
 
 default_model_config = """
 [model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"

spacy/pipeline/tagger.pyx

@@ -27,7 +27,7 @@ BACKWARD_OVERWRITE = False
 
 default_model_config = """
 [model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"

spacy/tests/pipeline/test_tok2vec.py

@@ -100,7 +100,7 @@ cfg_string = """
     factory = "tagger"
 
     [components.tagger.model]
-    @architectures = "spacy.Tagger.v1"
+    @architectures = "spacy.Tagger.v2"
     nO = null
 
     [components.tagger.model.tok2vec]
@@ -263,7 +263,7 @@ cfg_string_multi = """
     factory = "tagger"
 
     [components.tagger.model]
-    @architectures = "spacy.Tagger.v1"
+    @architectures = "spacy.Tagger.v2"
     nO = null
 
     [components.tagger.model.tok2vec]
@@ -373,7 +373,7 @@ cfg_string_multi_textcat = """
     factory = "tagger"
 
     [components.tagger.model]
-    @architectures = "spacy.Tagger.v1"
+    @architectures = "spacy.Tagger.v2"
     nO = null
 
     [components.tagger.model.tok2vec]

spacy/tests/serialize/test_serialize_config.py

@@ -59,7 +59,7 @@ subword_features = true
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [components.tagger.model.tok2vec]
 @architectures = "spacy.Tok2VecListener.v1"
@@ -110,7 +110,7 @@ subword_features = true
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [components.tagger.model.tok2vec]
 @architectures = "spacy.Tok2VecListener.v1"

spacy/tests/serialize/test_serialize_language.py

@@ -70,7 +70,7 @@ factory = "ner"
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 nO = null
 
 [components.tagger.model.tok2vec]

spacy/tests/training/test_pretraining.py

@@ -38,7 +38,7 @@ subword_features = true
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [components.tagger.model.tok2vec]
 @architectures = "spacy.Tok2VecListener.v1"
@@ -62,7 +62,7 @@ pipeline = ["tagger"]
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [components.tagger.model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v1"
@@ -106,7 +106,7 @@ subword_features = true
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 
 [components.tagger.model.tok2vec]
 @architectures = "spacy.Tok2VecListener.v1"

spacy/tests/training/test_training.py

@@ -241,7 +241,7 @@ maxout_pieces = 3
 factory = "tagger"
 
 [components.tagger.model]
-@architectures = "spacy.Tagger.v1"
+@architectures = "spacy.Tagger.v2"
 nO = null
 
 [components.tagger.model.tok2vec]

website/docs/api/architectures.md

@@ -104,7 +104,7 @@ consisting of a CNN and a layer-normalized maxout activation function.
 > factory = "tagger"
 >
 > [components.tagger.model]
-> @architectures = "spacy.Tagger.v1"
+> @architectures = "spacy.Tagger.v2"
 >
 > [components.tagger.model.tok2vec]
 > @architectures = "spacy.Tok2VecListener.v1"
@@ -158,8 +158,8 @@ be configured with the `attrs` argument. The suggested attributes are `NORM`,
 `PREFIX`, `SUFFIX` and `SHAPE`. This lets the model take into account some
 subword information, without construction a fully character-based
 representation. If pretrained vectors are available, they can be included in the
-representation as well, with the vectors table kept static (i.e. it's
-not updated).
+representation as well, with the vectors table kept static (i.e. it's not
+updated).
 
 | Name                     | Description                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 | ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -613,14 +613,15 @@ same signature, but the `use_upper` argument was `True` by default.
 
 ## Tagging architectures {#tagger source="spacy/ml/models/tagger.py"}
 
-### spacy.Tagger.v1 {#Tagger}
+### spacy.Tagger.v2 {#Tagger}
 
 > #### Example Config
 >
 > ```ini
 > [model]
-> @architectures = "spacy.Tagger.v1"
+> @architectures = "spacy.Tagger.v2"
 > nO = null
+> normalize = false
 >
 > [model.tok2vec]
 > # ...
@@ -634,8 +635,18 @@ the token vectors.
 | ----------- | ------------------------------------------------------------------------------------------ |
 | `tok2vec`   | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
 | `nO`        | The number of tags to output. Inferred from the data if `None`. ~~Optional[int]~~          |
+| `normalize` | Normalize probabilities during inference. Defaults to `False`. ~~bool~~                    |
 | **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                     |
 
+<Accordion title="Previous versions of spacy.Tagger" spaced>
+
+- The `normalize` argument was added in `spacy.Tagger.v2`. `spacy.Tagger.v1`
+  always normalizes probabilities during inference.
+
+The other arguments are shared between all versions.
+
+</Accordion>
+
 ## Text classification architectures {#textcat source="spacy/ml/models/textcat.py"}
 
 A text classification architecture needs to take a [`Doc`](/api/doc) as input,