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_gazetteer_tensor(self):
tensorizer = GazetteerTensorizer()
data = TSVDataSource(
train_file=SafeFileWrapper(
tests_module.test_file("train_dict_features.tsv")
),
test_file=None,
eval_file=None,
field_names=["text", "dict"],
schema={"text": str, "dict": Gazetteer},
)
init = tensorizer.initialize()
init.send(None) # kick
for row in data.train:
init.send(row)
init.close()
# UNK + PAD + 3 labels
self.assertEqual(5, len(tensorizer.vocab))
# only one row in test file:
# "Order coffee from Starbucks please"
for row in data.train:
idx, weights, lens = tensorizer.numberize(row)
self.assertEqual([1, 1, 2, 3, 1, 1, 4, 1, 1, 1], idx)
self.assertEqual(
[0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], weights
)
self.assertEqual([1, 2, 1, 1, 1], lens)
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_gazetteer_tensor(self):
tensorizer = GazetteerTensorizer()
data = TSVDataSource(
train_file=SafeFileWrapper(
tests_module.test_file("train_dict_features.tsv")),
test_file=None,
eval_file=None,
field_names=["text", "dict"],
schema={
"text": str,
"dict": Gazetteer
},
)
init = tensorizer.initialize()
init.send(None) # kick
for row in data.train:
init.send(row)
init.close()
# UNK + PAD + 5 labels
self.assertEqual(7, len(tensorizer.vocab))
# only two rows in test file:
# "Order coffee from Starbucks please"
# "Order some fries from McDonalds please"
for i, row in enumerate(data.train):
if i == 0:
idx, weights, lens = tensorizer.numberize(row)
self.assertEqual([1, 1, 2, 3, 1, 1, 4, 1, 1, 1], idx)
self.assertEqual(
[0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
weights)
self.assertEqual([1, 2, 1, 1, 1], lens)
if i == 1:
idx, weights, lens = tensorizer.numberize(row)
self.assertEqual([1, 1, 5, 1, 6, 1], idx)
self.assertEqual([0.0, 0.0, 1.0, 0.0, 1.0, 0.0], weights)
self.assertEqual([1, 1, 1, 1, 1, 1], lens)
feats, weights, lens = tensorizer.tensorize(
tensorizer.numberize(row) for row in data.train)
self.assertEqual(
[
[1, 1, 2, 3, 1, 1, 4, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 5, 1, 1, 1, 6, 1, 1, 1],
],
feats.numpy().tolist(),
)
self.assertEqual(
str([
[0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
]),
str([[round(w, 2) for w in utt_weights]
for utt_weights in weights.numpy()]),
)
self.assertEqual([[1, 2, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]],
lens.numpy().tolist())
def test_gazetteer_tensor_bad_json(self):
tensorizer = GazetteerTensorizer()
data = TSVDataSource(
train_file=SafeFileWrapper(
tests_module.test_file("train_dict_features_bad_json.tsv")
),
test_file=None,
eval_file=None,
field_names=["text", "dict"],
schema={"text": str, "dict": Gazetteer},
)
init = tensorizer.initialize()
init.send(None) # kick
with self.assertRaises(Exception):
for row in data.train:
init.send(row)
init.close()
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
