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 get_tensorizers(add_dict_feat=False, add_contextual_feat=False):
schema = {"source_sequence": str, "dict_feat": Gazetteer, "target_sequence": str}
data_source = TSVDataSource.from_config(
TSVDataSource.Config(
train_filename=TEST_FILE_NAME,
field_names=["source_sequence", "dict_feat", "target_sequence"],
),
schema,
)
src_tensorizer = TokenTensorizer.from_config(
TokenTensorizer.Config(
column="source_sequence", add_eos_token=True, add_bos_token=True
)
)
tgt_tensorizer = TokenTensorizer.from_config(
TokenTensorizer.Config(
column="target_sequence", add_eos_token=True, add_bos_token=True
)
)
tensorizers = {"src_seq_tokens": src_tensorizer, "trg_seq_tokens": tgt_tensorizer}
initialize_tensorizers(tensorizers, data_source.train)
if add_dict_feat:
tensorizers["dict_feat"] = GazetteerTensorizer.from_config(
GazetteerTensorizer.Config(
text_column="source_sequence", dict_column="dict_feat"
)
)
initialize_tensorizers(
{"dict_feat": tensorizers["dict_feat"]}, data_source.train
)
return tensorizers
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
