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Merge pull request #6127 from explosion/feature/literal-nr_feature_tokens

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Ines Montani 2020-09-23 16:56:08 +02:00 committed by GitHub
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6 changed files with 24 additions and 6 deletions
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1
requirements.txt
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@ -20,6 +20,7 @@ pytokenizations
setuptools
packaging
importlib_metadata>=0.20; python_version < "3.8"
typing_extensions>=3.7.4; python_version < "3.8"
# Development dependencies
cython>=0.25
pytest>=4.6.5

1
setup.cfg
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@ -57,6 +57,7 @@ install_requires =
setuptools
packaging
importlib_metadata>=0.20; python_version < "3.8"
typing_extensions>=3.7.4; python_version < "3.8"
[options.entry_points]
console_scripts =

5
spacy/compat.py
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@ -22,6 +22,11 @@ try:
except ImportError:
cupy = None
try: # Python 3.8+
from typing import Literal
except ImportError:
from typing_extensions import Literal # noqa: F401
from thinc.api import Optimizer # noqa: F401
pickle = pickle

7
spacy/ml/models/parser.py
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@ -2,6 +2,7 @@ from typing import Optional, List
from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops
from thinc.types import Floats2d
from ...compat import Literal
from ...util import registry
from .._precomputable_affine import PrecomputableAffine
from ..tb_framework import TransitionModel
@ -11,7 +12,7 @@ from ...tokens import Doc
@registry.architectures.register("spacy.TransitionBasedParser.v1")
def build_tb_parser_model(
tok2vec: Model[List[Doc], List[Floats2d]],
nr_feature_tokens: int,
nr_feature_tokens: Literal[3, 6, 8, 13],
hidden_width: int,
maxout_pieces: int,
use_upper: bool = True,
@ -41,8 +42,8 @@ def build_tb_parser_model(
tok2vec (Model[List[Doc], List[Floats2d]]):
Subnetwork to map tokens into vector representations.
nr_feature_tokens (int): The number of tokens in the context to use to
construct the state vector. Valid choices are 1, 2, 3, 6, 8 and 13. The
2, 8 and 13 feature sets are designed for the parser, while the 3 and 6
construct the state vector. Valid choices are 3, 6, 8 and 13. The
8 and 13 feature sets are designed for the parser, while the 3 and 6
feature sets are designed for the NER. The recommended feature sets are
3 for NER, and 8 for the dependency parser.

14
spacy/tests/serialize/test_serialize_config.py
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@ -67,7 +67,7 @@ width = ${components.tok2vec.model.width}
parser_config_string = """
[model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 99
nr_feature_tokens = 3
hidden_width = 66
maxout_pieces = 2
@ -95,7 +95,7 @@ def my_parser():
MaxoutWindowEncoder(width=321, window_size=3, maxout_pieces=4, depth=2),
)
parser = build_tb_parser_model(
tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
tok2vec=tok2vec, nr_feature_tokens=8, hidden_width=65, maxout_pieces=5
)
return parser
@ -340,3 +340,13 @@ def test_config_auto_fill_extra_fields():
assert "extra" not in nlp.config["training"]
# Make sure the config generated is valid
load_model_from_config(nlp.config)
def test_config_validate_literal():
nlp = English()
config = Config().from_str(parser_config_string)
config["model"]["nr_feature_tokens"] = 666
with pytest.raises(ConfigValidationError):
nlp.add_pipe("parser", config=config)
config["model"]["nr_feature_tokens"] = 13
nlp.add_pipe("parser", config=config)

2
website/docs/api/architectures.md
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@ -449,7 +449,7 @@ consists of either two or three subnetworks:
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
| `nr_feature_tokens` | The number of tokens in the context to use to construct the state vector. Valid choices are `1`, `2`, `3`, `6`, `8` and `13`. The `2`, `8` and `13` feature sets are designed for the parser, while the `3` and `6` feature sets are designed for the entity recognizer. The recommended feature sets are `3` for NER, and `8` for the dependency parser. ~~int~~ |
| `nr_feature_tokens` | The number of tokens in the context to use to construct the state vector. Valid choices are `3`, `6`, `8` and `13`. The `8` and `13` feature sets are designed for the parser, while the `3` and `6` feature sets are designed for the entity recognizer. The recommended feature sets are `3` for NER, and `8` for the dependency parser. ~~int~~ |
| `hidden_width` | The width of the hidden layer. ~~int~~ |
| `maxout_pieces` | How many pieces to use in the state prediction layer. Recommended values are `1`, `2` or `3`. If `1`, the maxout non-linearity is replaced with a [`Relu`](https://thinc.ai/docs/api-layers#relu) non-linearity if `use_upper` is `True`, and no non-linearity if `False`. ~~int~~ |
| `use_upper` | Whether to use an additional hidden layer after the state vector in order to predict the action scores. It is recommended to set this to `False` for large pretrained models such as transformers, and `True` for smaller networks. The upper layer is computed on CPU, which becomes a bottleneck on larger GPU-based models, where it's also less necessary. ~~bool~~ |