def test_tokens_dictfeat_contextual(self):
# TODO (T65593688): this should be removed after
# https://github.com/pytorch/pytorch/pull/33645 is merged.
with torch.no_grad():
model = Seq2SeqModel.from_config(
Seq2SeqModel.Config(
source_embedding=WordEmbedding.Config(embed_dim=512),
target_embedding=WordEmbedding.Config(embed_dim=512),
inputs=Seq2SeqModel.Config.ModelInput(
dict_feat=GazetteerTensorizer.Config(
text_column="source_sequence"
),
contextual_token_embedding=ByteTokenTensorizer.Config(),
),
encoder_decoder=RNNModel.Config(
encoder=LSTMSequenceEncoder.Config(embed_dim=619)
),
dict_embedding=DictEmbedding.Config(),
contextual_token_embedding=ContextualTokenEmbedding.Config(
embed_dim=7
),
),
get_tensorizers(add_dict_feat=True, add_contextual_feat=True),
)
model.eval()
ts_model = model.torchscriptify()
res = ts_model(
["call", "mom"],
(["call", "mom"], [0.42, 0.17], [4, 3]),
[0.42] * (7 * 2),
)
assert res is not None
def test_tokens_dictfeat(self):
model = Seq2SeqModel.from_config(
Seq2SeqModel.Config(
source_embedding=WordEmbedding.Config(embed_dim=512),
target_embedding=WordEmbedding.Config(embed_dim=512),
inputs=Seq2SeqModel.Config.ModelInput(
dict_feat=GazetteerTensorizer.Config(
text_column="source_sequence")),
encoder_decoder=RNNModel.Config(
encoder=LSTMSequenceEncoder.Config(embed_dim=612)),
dict_embedding=DictEmbedding.Config(),
),
get_tensorizers(add_dict_feat=True),
)
model.eval()
ts_model = model.torchscriptify()
res = ts_model(["call", "mom"],
(["call", "mom"], [0.42, 0.17], [4, 3]))
assert res is not None