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Merge pull request #6205 from explosion/feature/embed-features

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Ines Montani 2020-10-05 21:49:10 +02:00 committed by GitHub
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6 changed files with 138 additions and 66 deletions
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11
spacy/cli/templates/quickstart_training.jinja
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@ -171,9 +171,14 @@ factory = "tok2vec"
[components.tok2vec.model.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode.width}
rows = {{ 2000 if optimize == "efficiency" else 7000 }}
also_embed_subwords = {{ "true" if has_letters else "false" }}
also_use_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
{% if has_letters -%}
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
rows = [5000, 2500, 2500, 2500]
{% else -%}
attrs = ["ORTH", "SHAPE"]
rows = [5000, 2500]
{% endif -%}
include_static_vectors = {{ "true" if optimize == "accuracy" else "false" }}
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"

83
spacy/ml/models/tok2vec.py
View File

@ -1,4 +1,4 @@
from typing import Optional, List, Union
from typing import Optional, List, Union, Dict
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
@ -11,7 +11,7 @@ from ...ml import _character_embed
from ..staticvectors import StaticVectors
from ..featureextractor import FeatureExtractor
from ...pipeline.tok2vec import Tok2VecListener
from ...attrs import ORTH, LOWER, PREFIX, SUFFIX, SHAPE, intify_attr
from ...attrs import ORTH, NORM, LOWER, PREFIX, SUFFIX, SHAPE, intify_attr
@registry.architectures.register("spacy.Tok2VecListener.v1")
@ -54,12 +54,18 @@ def build_hash_embed_cnn_tok2vec(
a language such as Chinese.
pretrained_vectors (bool): Whether to also use static vectors.
"""
if subword_features:
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
row_sizes = [embed_size, embed_size//2, embed_size//2, embed_size//2]
else:
attrs = ["NORM"]
row_sizes = [embed_size]
return build_Tok2Vec_model(
embed=MultiHashEmbed(
width=width,
rows=embed_size,
also_embed_subwords=subword_features,
also_use_static_vectors=bool(pretrained_vectors),
rows=row_sizes,
attrs=attrs,
include_static_vectors=bool(pretrained_vectors),
),
encode=MaxoutWindowEncoder(
width=width,
@ -93,58 +99,65 @@ def build_Tok2Vec_model(
@registry.architectures.register("spacy.MultiHashEmbed.v1")
def MultiHashEmbed(
width: int, rows: int, also_embed_subwords: bool, also_use_static_vectors: bool
width: int,
attrs: List[Union[str, int]],
rows: List[int],
include_static_vectors: bool,
) -> Model[List[Doc], List[Floats2d]]:
"""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 a mixed representations.
The features used are the LOWER, PREFIX, SUFFIX and SHAPE, which can have
varying definitions depending on the Vocab of the Doc object passed in.
Vectors from pretrained static vectors can also be incorporated into the
concatenated representation.
The features used can 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 contruction a fully character-based
representation. If pretrained vectors are available, they can be included in
the representation as well, with the vectors table will be kept static
(i.e. it's not updated).
The `width` parameter specifices the output width of the layer and the widths
of all embedding tables. If static vectors are included, a learned linear
layer is used to map the vectors to the specified width before concatenating
it with the other embedding outputs. A single Maxout layer is then used to
reduce the concatenated vectors to the final width.
The `rows` parameter controls the number of rows used by the `HashEmbed`
tables. The HashEmbed layer needs surprisingly few rows, due to its use of
the hashing trick. Generally between 2000 and 10000 rows is sufficient,
even for very large vocabularies. A number of rows must be specified for each
table, so the `rows` list must be of the same length as the `attrs` parameter.
width (int): The output width. Also used as the width of the embedding tables.
Recommended values are between 64 and 300.
rows (int): The number of rows for the embedding tables. Can be low, due
to the hashing trick. Embeddings for prefix, suffix and word shape
use half as many rows. Recommended values are between 2000 and 10000.
also_embed_subwords (bool): Whether to use the PREFIX, SUFFIX and SHAPE
features in the embeddings. If not using these, you may need more
rows in your hash embeddings, as there will be increased chance of
collisions.
also_use_static_vectors (bool): Whether to also use static word vectors.
attrs (list of attr IDs): The token attributes to embed. A separate
embedding table will be constructed for each attribute.
rows (List[int]): The number of rows in the embedding tables. Must have the
same length as attrs.
include_static_vectors (bool): Whether to also use static word vectors.
Requires a vectors table to be loaded in the Doc objects' vocab.
"""
cols = [LOWER, PREFIX, SUFFIX, SHAPE, ORTH]
if len(rows) != len(attrs):
raise ValueError(f"Mismatched lengths: {len(rows)} vs {len(attrs)}")
seed = 7
def make_hash_embed(feature):
def make_hash_embed(index):
nonlocal seed
seed += 1
return HashEmbed(
width,
rows if feature == LOWER else rows // 2,
column=cols.index(feature),
rows[index],
column=index,
seed=seed,
dropout=0.0,
)
if also_embed_subwords:
embeddings = [
make_hash_embed(LOWER),
make_hash_embed(PREFIX),
make_hash_embed(SUFFIX),
make_hash_embed(SHAPE),
]
else:
embeddings = [make_hash_embed(LOWER)]
concat_size = width * (len(embeddings) + also_use_static_vectors)
if also_use_static_vectors:
embeddings = [make_hash_embed(i) for i in range(len(attrs))]
concat_size = width * (len(embeddings) + include_static_vectors)
if include_static_vectors:
model = chain(
concatenate(
chain(
FeatureExtractor(cols),
FeatureExtractor(attrs),
list2ragged(),
with_array(concatenate(*embeddings)),
),
@ -155,7 +168,7 @@ def MultiHashEmbed(
)
else:
model = chain(
FeatureExtractor(cols),
FeatureExtractor(list(attrs)),
list2ragged(),
with_array(concatenate(*embeddings)),
with_array(Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)),

22
spacy/tests/pipeline/test_tok2vec.py
View File

@ -24,9 +24,9 @@ def test_empty_doc():
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=width,
rows=embed_size,
also_use_static_vectors=False,
also_embed_subwords=True,
rows=[embed_size, embed_size, embed_size, embed_size],
include_static_vectors=False,
attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
),
MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
)
@ -44,9 +44,9 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=width,
rows=embed_size,
also_use_static_vectors=False,
also_embed_subwords=True,
rows=[embed_size] * 4,
include_static_vectors=False,
attrs=["NORM", "PREFIX", "SUFFIX", "SHAPE"],
),
MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
)
@ -61,8 +61,8 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
@pytest.mark.parametrize(
"width,embed_arch,embed_config,encode_arch,encode_config",
[
(8, MultiHashEmbed, {"rows": 100, "also_embed_subwords": True, "also_use_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
(8, MultiHashEmbed, {"rows": 100, "also_embed_subwords": True, "also_use_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
(8, MultiHashEmbed, {"rows": [100, 100], "attrs": ["SHAPE", "LOWER"], "include_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 2}),
(8, MultiHashEmbed, {"rows": [100, 20], "attrs": ["ORTH", "PREFIX"], "include_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 6}),
(8, CharacterEmbed, {"rows": 100, "nM": 64, "nC": 8, "also_use_static_vectors": False}, MaxoutWindowEncoder, {"window_size": 1, "maxout_pieces": 3, "depth": 3}),
(8, CharacterEmbed, {"rows": 100, "nM": 16, "nC": 2, "also_use_static_vectors": False}, MishWindowEncoder, {"window_size": 1, "depth": 3}),
],
@ -118,9 +118,9 @@ cfg_string = """
[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
rows = [2000, 1000, 1000, 1000]
attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[components.tok2vec.model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"

6
spacy/tests/serialize/test_serialize_config.py
View File

@ -89,9 +89,9 @@ def my_parser():
tok2vec = build_Tok2Vec_model(
MultiHashEmbed(
width=321,
rows=5432,
also_embed_subwords=True,
also_use_static_vectors=False,
attrs=["LOWER", "SHAPE"],
rows=[5432, 5432],
include_static_vectors=False,
),
MaxoutWindowEncoder(width=321, window_size=3, maxout_pieces=4, depth=2),
)

31
spacy/tests/test_models.py
View File

@ -61,7 +61,10 @@ def get_tok2vec_kwargs():
# This actually creates models, so seems best to put it in a function.
return {
"embed": MultiHashEmbed(
width=32, rows=500, also_embed_subwords=True, also_use_static_vectors=False
width=32,
rows=[500, 500, 500],
attrs=["NORM", "PREFIX", "SHAPE"],
include_static_vectors=False
),
"encode": MaxoutWindowEncoder(
width=32, depth=2, maxout_pieces=2, window_size=1
@ -73,6 +76,32 @@ def test_tok2vec():
return build_Tok2Vec_model(**get_tok2vec_kwargs())
def test_multi_hash_embed():
embed = MultiHashEmbed(
width=32,
rows=[500, 500, 500],
attrs=["NORM", "PREFIX", "SHAPE"],
include_static_vectors=False
)
hash_embeds = [node for node in embed.walk() if node.name == "hashembed"]
assert len(hash_embeds) == 3
# Check they look at different columns.
assert list(sorted(he.attrs["column"] for he in hash_embeds)) == [0, 1, 2]
# Check they use different seeds
assert len(set(he.attrs["seed"] for he in hash_embeds)) == 3
# Check they all have the same number of rows
assert [he.get_dim("nV") for he in hash_embeds] == [500, 500, 500]
# Now try with different row factors
embed = MultiHashEmbed(
width=32,
rows=[1000, 50, 250],
attrs=["NORM", "PREFIX", "SHAPE"],
include_static_vectors=False
)
hash_embeds = [node for node in embed.walk() if node.name == "hashembed"]
assert [he.get_dim("nV") for he in hash_embeds] == [1000, 50, 250]
@pytest.mark.parametrize(
"seed,model_func,kwargs",
[

47
website/docs/api/architectures.md
View File

@ -136,24 +136,49 @@ argument that connects to the shared `tok2vec` component in the pipeline.
> [model]
> @architectures = "spacy.MultiHashEmbed.v1"
> width = 64
> rows = 2000
> also_embed_subwords = false
> also_use_static_vectors = false
> attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
> rows = [2000, 1000, 1000, 1000]
> include_static_vectors = true
> ```
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`, and they are extracted with a
[FeatureExtractor](/api/architectures#FeatureExtractor) layer. Vectors from pretrained static
vectors can also be incorporated into the concatenated representation.
attributes using hash embedding, concatenates the results, and passes it
through a feed-forward subnetwork to build a mixed representations.
The features used can 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 contruction a fully character-based
representation. If pretrained vectors are available, they can be included in
the representation as well, with the vectors table will be kept static
(i.e. it's not updated).
The `width` parameter specifices the output width of the layer and the widths
of all embedding tables. If static vectors are included, a learned linear
layer is used to map the vectors to the specified width before concatenating
it with the other embedding outputs. A single Maxout layer is then used to
reduce the concatenated vectors to the final width.
The `rows` parameter controls the number of rows used by the `HashEmbed`
tables. The HashEmbed layer needs surprisingly few rows, due to its use of
the hashing trick. Generally between 2000 and 10000 rows is sufficient,
even for very large vocabularies. A number of rows must be specified for each
table, so the `rows` list must be of the same length as the `attrs` parameter.
attrs (list of attr IDs): The token attributes to embed. A separate
embedding table will be constructed for each attribute.
rows (List[int]): The number of rows in the embedding tables. Must have the
same length as attrs.
include_static_vectors (bool): Whether to also use static word vectors.
Requires a vectors table to be loaded in the Doc objects' vocab.
| Name | Description |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The output width. Also used as the width of the embedding tables. Recommended values are between `64` and `300`. ~~int~~ |
| `rows` | The number of rows for the embedding tables. Can be low, due to the hashing trick. Embeddings for prefix, suffix and word shape use half as many rows. Recommended values are between `2000` and `10000`. ~~int~~ |
| `also_embed_subwords` | Whether to use the `PREFIX`, `SUFFIX` and `SHAPE` features in the embeddings. If not using these, you may need more rows in your hash embeddings, as there will be increased chance of collisions. ~~bool~~ |
| `also_use_static_vectors` | Whether to also use static word vectors. Requires a vectors table to be loaded in the [Doc](/api/doc) objects' vocab. ~~bool~~ |
| `attrs` | The token attributes to embed. A separate |
embedding table will be constructed for each attribute. ~~List[Union[int, str]]~~ |
| `rows` | The number of rows for each embedding tables. Can be low, due to the hashing trick. Recommended values are between `1000` and `10000`. ~~List[int]~~ |
| `include_static_vectors` | Whether to also use static word vectors. Requires a vectors table to be loaded in the [Doc](/api/doc) objects' vocab. ~~bool~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.CharacterEmbed.v1 {#CharacterEmbed}