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_contextual(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(
contextual_token_embedding=ByteTokenTensorizer.Config()),
contextual_token_embedding=ContextualTokenEmbedding.Config(
embed_dim=7),
encoder_decoder=RNNModel.Config(
encoder=LSTMSequenceEncoder.Config(embed_dim=519)),
),
get_tensorizers(add_contextual_feat=True),
)
model.eval()
ts_model = model.torchscriptify()
res = ts_model(["call", "mom"],
contextual_token_embedding=[0.42] * (7 * 2))
assert res is not None
def setUp(self):
contextual_emb_dim = 1
emb_module = EmbeddingList(
embeddings=[
WordEmbedding(num_embeddings=103, embedding_dim=100),
DictEmbedding(
num_embeddings=59, embed_dim=10, pooling_type=PoolingType.MEAN
),
ContextualTokenEmbedding(contextual_emb_dim),
],
concat=True,
)
self.training_model = RNNGModel(
input_for_trace=RNNGModel.get_input_for_trace(contextual_emb_dim),
embedding=emb_module,
ablation=RNNGParser.Config.AblationParams(),
constraints=RNNGParser.Config.RNNGConstraints(),
lstm_num_layers=2,
lstm_dim=32,
max_open_NT=10,
dropout=0.4,
num_actions=20,
shift_idx=0,
reduce_idx=1,
ignore_subNTs_roots=[8, 15],
valid_NT_idxs=[2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+ [12, 13, 14, 15, 16, 17, 18, 19],
valid_IN_idxs=[2, 4, 7, 8, 10, 12, 13, 14, 15],
valid_SL_idxs=[3, 5, 6, 9, 11, 16, 17, 18, 19],
embedding_dim=emb_module.embedding_dim,
p_compositional=CompositionalNN(lstm_dim=32, device="cpu"),
device="cpu",
)
self.training_model.train()
self.inference_model = RNNGInference(
self.training_model.trace_embedding(),
self.training_model.jit_model,
MockVocab(["<unk>", "foo", "bar"]),
MockVocab(["<unk>", "a", "b"]),
MockVocab(["SHIFT", "REDUCE", "IN:END_CALL", "SL:METHOD_CALL"]),
)
self.inference_model.eval()
def test_tokens_dictfeat_contextual(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"),
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