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2dbb332cea
* TextCatParametricAttention.v1: set key transform dimensions This is necessary for tok2vec implementations that initialize lazily (e.g. curated transformers). * Add lazily-initialized tok2vec to simulate transformers Add a lazily-initialized tok2vec to the tests and test the current textcat models with it. Fix some additional issues found using this test. * isort * Add `test.` prefix to `LazyInitTok2Vec.v1`
37 lines
1001 B
Python
37 lines
1001 B
Python
from typing import List
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from thinc.api import Model
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from thinc.types import Floats2d
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from spacy.tokens import Doc
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from spacy.util import registry
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@registry.architectures("test.LazyInitTok2Vec.v1")
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def build_lazy_init_tok2vec(*, width: int) -> Model[List[Doc], List[Floats2d]]:
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"""tok2vec model of which the output size is only known after
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initialization. This implementation does not output meaningful
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embeddings, it is strictly for testing."""
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return Model(
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"lazy_init_tok2vec",
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lazy_init_tok2vec_forward,
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init=lazy_init_tok2vec_init,
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dims={"nO": None},
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attrs={"width": width},
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)
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def lazy_init_tok2vec_init(model: Model, X=None, Y=None):
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width = model.attrs["width"]
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model.set_dim("nO", width)
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def lazy_init_tok2vec_forward(model: Model, X: List[Doc], is_train: bool):
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width = model.get_dim("nO")
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Y = [model.ops.alloc2f(len(doc), width) for doc in X]
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def backprop(dY):
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return []
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return Y, backprop
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