def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_py_lstm_encoder_len(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
layers=3),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
lstm_dim=layer_dim,
mlp_hidden_dim=layer_dim,
trg_embed_dim=layer_dim,
vocab_size=100),
)
self.set_train(True)
for sent_i in range(10):
dy.renew_cg()
src = self.src_data[sent_i].get_padded_sent(
Vocab.ES, 4 - (len(self.src_data[sent_i]) % 4))
self.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder(embeddings)
self.assertEqual(int(math.ceil(len(embeddings) / float(4))),
len(encodings))
def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context,
vocab_size=100,
bridge=CopyBridge(self.model_context,
dec_layers=1)),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.model.initialize_generator()
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def test_loss_model1(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn_layer=UniLSTMSeqTransducer(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn_layer"),
mlp_layer=MLP(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_rnn_dim=layer_dim,
vocab_size=100,
yaml_path="model.decoder.rnn_layer"),
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model)
def test_py_lstm_mask(self):
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(self.exp_global, layers=1),
attender=MlpAttender(self.exp_global),
trg_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
decoder=MlpSoftmaxDecoder(self.exp_global, vocab_size=100),
)
batcher = xnmt.batcher.TrgBatcher(batch_size=3)
train_src, _ = \
batcher.pack(self.src_data, self.trg_data)
self.set_train(True)
for sent_i in range(3):
dy.renew_cg()
src = train_src[sent_i]
self.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder(embeddings)
if train_src[sent_i].mask is None:
assert encodings.mask is None
else:
np.testing.assert_array_almost_equal(
train_src[sent_i].mask.np_arr, encodings.mask.np_arr)
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
lstm_dim=layer_dim,
mlp_hidden_dim=layer_dim,
trg_embed_dim=layer_dim,
vocab_size=100,
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator()
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def setUp(self):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator(beam=1)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
class TestForcedDecodingOutputs(unittest.TestCase):
def assertItemsEqual(self, l1, l2):
self.assertEqual(len(l1), len(l2))
for i in range(len(l1)):
self.assertEqual(l1[i], l2[i])
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn_layer=UniLSTMSeqTransducer(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn_layer"),
mlp_layer=MLP(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_rnn_dim=layer_dim,
vocab_size=100,
yaml_path="model.decoder.rnn_layer"),
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator()
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
self.search = GreedySearch()
def assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate_output(
self.src_data[sent_id],
sent_id,
self.search,
forced_trg_ids=self.trg_data[sent_id])
self.assertItemsEqual(self.trg_data[sent_id], outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
def test_loss_model1(self):
model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model)
def test_loss_model2(self):
model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(self.model_context, layers=3),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model, pad_src_to_multiple=4)
class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_greedy_vs_beam(self):
dy.renew_cg()
outputs = self.model.generate(
xnmt.batcher.mark_as_batch([self.src_data[0]]), [0],
BeamSearch(beam_size=1),
forced_trg_ids=xnmt.batcher.mark_as_batch([self.trg_data[0]]))
output_score1 = outputs[0].score
dy.renew_cg()
outputs = self.model.generate(
xnmt.batcher.mark_as_batch([self.src_data[0]]), [0],
GreedySearch(),
forced_trg_ids=xnmt.batcher.mark_as_batch([self.trg_data[0]]))
output_score2 = outputs[0].score
self.assertAlmostEqual(output_score1, output_score2)
class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context,
vocab_size=100,
bridge=CopyBridge(self.model_context,
dec_layers=1)),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def test_greedy_vs_beam(self):
dy.renew_cg()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score1 = outputs[0].score
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score2 = outputs[0].score
self.assertAlmostEqual(output_score1, output_score2)
def setUp(self):
# Seeding
numpy.random.seed(2)
random.seed(2)
layer_dim = 64
xnmt.events.clear()
ParamManager.init_param_col()
self.segment_composer = SumComposer()
self.src_reader = CharFromWordTextReader()
self.trg_reader = PlainTextReader()
self.loss_calculator = AutoRegressiveMLELoss()
self.segmenting_encoder = SegmentingSeqTransducer(
segment_composer=self.segment_composer,
final_transducer=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
)
self.model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=self.segmenting_encoder,
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="decoder"),
transform=AuxNonLinear(input_dim=layer_dim,
output_dim=layer_dim,
aux_input_dim=layer_dim),
scorer=Softmax(vocab_size=100, input_dim=layer_dim),
trg_embed_dim=layer_dim,
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(True)
self.layer_dim = layer_dim
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
my_batcher = xnmt.batcher.TrgBatcher(batch_size=3,
src_pad_token=1,
trg_pad_token=2)
self.src, self.trg = my_batcher.pack(self.src_data, self.trg_data)
dy.renew_cg(immediate_compute=True, check_validity=True)
class TestForcedDecodingOutputs(unittest.TestCase):
def assertItemsEqual(self, l1, l2):
self.assertEqual(len(l1), len(l2))
for i in range(len(l1)):
self.assertEqual(l1[i], l2[i])
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate(
xnmt.batcher.mark_as_batch([self.src_data[sent_id]]), [sent_id],
BeamSearch(),
forced_trg_ids=xnmt.batcher.mark_as_batch([self.trg_data[sent_id]
]))
self.assertItemsEqual(self.trg_data[sent_id].words, outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim, lstm_dim=layer_dim, mlp_hidden_dim=layer_dim,
trg_embed_dim=layer_dim, vocab_size=100,
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator(beam=1)
self.src_data = list(self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(self.model.trg_reader.read_sents("examples/data/head.en"))
def test_single(self):
dy.renew_cg()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(self.src_data[0], 0,
forced_trg_ids=self.trg_data[0])
dy.renew_cg()
train_loss = self.model.calc_loss(src=self.src_data[0],
trg=outputs[0].actions,
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(-outputs[0].score, train_loss, places=4)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_single(self):
dy.renew_cg()
outputs = self.model.generate(
xnmt.batcher.mark_as_batch([self.src_data[0]]), [0],
GreedySearch(),
forced_trg_ids=xnmt.batcher.mark_as_batch([self.trg_data[0]]))
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_loss(
src=self.src_data[0],
trg=outputs[0],
loss_calculator=AutoRegressiveMLELoss()).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)
def test_py_lstm_mask(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim, layers=1),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim, decoder_input_dim=layer_dim, yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim*2, output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
batcher = xnmt.batcher.TrgBatcher(batch_size=3)
train_src, _ = \
batcher.pack(self.src_data, self.trg_data)
self.set_train(True)
for sent_i in range(3):
dy.renew_cg()
src = train_src[sent_i]
self.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder.transduce(embeddings)
if train_src[sent_i].mask is None:
assert encodings.mask is None
else:
np.testing.assert_array_almost_equal(train_src[sent_i].mask.np_arr, encodings.mask.np_arr)
def test_train_dev_loss_equal(self):
layer_dim = 512
batcher = SrcBatcher(batch_size=5, break_ties_randomly=False)
train_args = {}
train_args['src_file'] = "examples/data/head.ja"
train_args['trg_file'] = "examples/data/head.en"
train_args['loss_calculator'] = AutoRegressiveMLELoss()
train_args['model'] = DefaultTranslator(src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim*2, output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
train_args['dev_tasks'] = [LossEvalTask(model=train_args['model'],
src_file="examples/data/head.ja",
ref_file="examples/data/head.en",
batcher=batcher)]
train_args['trainer'] = DummyTrainer()
train_args['batcher'] = batcher
train_args['run_for_epochs'] = 1
training_regimen = xnmt.training_regimen.SimpleTrainingRegimen(**train_args)
training_regimen.run_training(save_fct = lambda: None)
self.assertAlmostEqual(training_regimen.train_loss_tracker.epoch_loss.sum_factors() / training_regimen.train_loss_tracker.epoch_words,
training_regimen.dev_loss_tracker.dev_score.loss, places=5)
def test_loss_model2(self):
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(self.exp_global, layers=3),
attender=MlpAttender(self.exp_global),
trg_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
decoder=MlpSoftmaxDecoder(self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model, pad_src_to_multiple=4)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context,
vocab_size=100,
bridge=CopyBridge(self.model_context,
dec_layers=1)),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.model.initialize_generator()
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def test_single(self):
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_loss(
src=self.training_corpus.train_src_data[0],
trg=outputs[0].actions).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)
class TestForcedDecodingOutputs(unittest.TestCase):
def assertItemsEqual(self, l1, l2):
self.assertEqual(len(l1), len(l2))
for i in range(len(l1)):
self.assertEqual(l1[i], l2[i])
def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
self.model.set_train(False)
self.model.initialize_generator()
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[sent_id],
sent_id,
forced_trg_ids=self.training_corpus.train_trg_data[sent_id])
self.assertItemsEqual(self.training_corpus.train_trg_data[sent_id],
outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
class TestForcedDecodingOutputs(unittest.TestCase):
def assertItemsEqual(self, l1, l2):
self.assertEqual(len(l1), len(l2))
for i in range(len(l1)):
self.assertEqual(l1[i], l2[i])
def setUp(self):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator(beam=1)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate_output(
self.src_data[sent_id],
sent_id,
forced_trg_ids=self.trg_data[sent_id])
self.assertItemsEqual(self.trg_data[sent_id], outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
def test_res_lstm_encoder_len(self):
model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=ResidualLSTMSeqTransducer(self.model_context, layers=3),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
self.assert_in_out_len_equal(model)
def test_uni_lstm_encoder_len(self):
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
encoder=UniLSTMSeqTransducer(self.exp_global),
attender=MlpAttender(self.exp_global),
trg_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
decoder=MlpSoftmaxDecoder(self.exp_global, vocab_size=100),
)
self.assert_in_out_len_equal(model)
def test_loss_model4(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=DotAttender(),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
lstm_dim=layer_dim,
mlp_hidden_dim=layer_dim,
trg_embed_dim=layer_dim,
vocab_size=100,
bridge=CopyBridge(dec_layers=1,
dec_dim=layer_dim)),
)
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model)
def test_loss_model2(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim, layers=3),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim*2, output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
model.set_train(False)
self.assert_single_loss_equals_batch_loss(model, pad_src_to_multiple=4)
def test_overfitting(self):
layer_dim = 16
batcher = SrcBatcher(batch_size=10, break_ties_randomly=False)
train_args = {}
train_args['src_file'] = "examples/data/head.ja"
train_args['trg_file'] = "examples/data/head.en"
train_args['loss_calculator'] = MLELoss()
train_args['model'] = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(vocab_size=100, emb_dim=layer_dim),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(vocab_size=100, emb_dim=layer_dim),
decoder=MlpSoftmaxDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn_layer=UniLSTMSeqTransducer(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn_layer"),
mlp_layer=MLP(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_rnn_dim=layer_dim,
vocab_size=100,
yaml_path="model.decoder.rnn_layer"),
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
train_args['dev_tasks'] = [
LossEvalTask(model=train_args['model'],
src_file="examples/data/head.ja",
ref_file="examples/data/head.en",
batcher=batcher)
]
train_args['run_for_epochs'] = 1
train_args['trainer'] = AdamTrainer(alpha=0.1)
train_args['batcher'] = batcher
training_regimen = xnmt.training_regimen.SimpleTrainingRegimen(
**train_args)
for _ in range(50):
training_regimen.run_training(save_fct=lambda: None,
update_weights=True)
self.assertAlmostEqual(
0.0,
training_regimen.train_loss_tracker.epoch_loss.sum() /
training_regimen.train_loss_tracker.epoch_words,
places=2)
class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_greedy_vs_beam(self):
dy.renew_cg()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(self.src_data[0],
0,
forced_trg_ids=self.trg_data[0])
output_score1 = outputs[0].score
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(self.src_data[0],
0,
forced_trg_ids=self.trg_data[0])
output_score2 = outputs[0].score
self.assertAlmostEqual(output_score1, output_score2)
def test_uni_lstm_encoder_len(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim, decoder_input_dim=layer_dim, yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim*2, output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.assert_in_out_len_equal(model)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator()
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_single(self):
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(self.src_data[0],
0,
forced_trg_ids=self.trg_data[0])
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_loss(
src=self.src_data[0],
trg=outputs[0].actions,
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)
