def test_transformer_decoder():
reset_session()
# Construct the layer
from rinokeras.core.v1x.models.transformer import TransformerDecoder, TransformerInputEmbedding
tie = TransformerInputEmbedding(128, False)
layer = TransformerDecoder(embedding_layer=tie,
output_layer=tf.keras.layers.Dense(128),
n_layers=2,
n_heads=4,
d_model=128,
d_filter=32)
# Make sure that the layer is not None
assert layer is not None
# Encoded values
source_tensor, _ = random_tensor((2,32,128))
target_tensor, _ = random_tensor((2,32,64))
# Get random masking values
source_mask, _ = random_mask_tensor(2, 32)
target_mask, _ = random_mask_tensor(2, 32)
source_mask = convert_to_attention_mask(source_tensor, source_mask)
target_mask = convert_to_attention_mask(target_tensor, target_mask)
# Get the output of the layer
value = layer((source_tensor, target_tensor), mask=(source_mask, target_mask))
# Create a named temporary file for save/restore testing
with tempfile.TemporaryFile() as weights_file:
# Construct the session
output = run_simple_session_save_weights(inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
assert_not_none(output)
assert_expected_shapes(output, [(2,32,128)])
# Check loading and restoring
load_restore_test(output=output,
inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
# Do regression testing
check_regression('transformer_decoder_output',
output, __file__, 'regression_outputs/test_transformer_decoder_outputs.json', debug=_RK_REBUILD_REGRESSION)
# Do a config test
from_config_test(TransformerDecoder, layer)
def test_transformer_encoder_masking_with_conv():
reset_session()
# Construct the layer
from rinokeras.core.v1x.models.transformer import TransformerEncoder
layer = TransformerEncoder(embedding_layer=tf.keras.layers.Dense(64),
n_layers=2,
n_heads=4,
d_model=64,
d_filter=128)
# Make sure that the layer is not None
assert layer is not None
# Encoded values
input_tensor, _ = random_tensor((16, 32, 128))
input_mask, _ = random_mask_tensor(16, 32)
input_mask = convert_to_attention_mask(input_tensor, input_mask)
conv_mask, _ = random_mask_tensor(16, 32)
conv_mask = convert_sequence_length_to_sequence_mask(
input_tensor, conv_mask)
# Get the output of the layer
value = layer(input_tensor, mask=(input_mask, conv_mask))
# Create a named temporary file for save/restore testing
with tempfile.TemporaryFile() as weights_file:
# Construct the session
output = run_simple_session_save_weights(inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
assert_not_none(output)
assert_expected_shapes(output, [(16, 32, 64)])
# Check loading and restoring
load_restore_test(output=output,
inputs=[value],
feed={},
weights=[layer],
weights_file=weights_file)
# Do regression testing
check_regression(
'transformer_encoder_output_masking_with_conv',
output,
__file__,
'regression_outputs/test_transformer_encoder_outputs.json',
debug=_RK_REBUILD_REGRESSION)
# Do a config test
from_config_test(TransformerEncoder, layer)
def test_convert_to_attention_mask_2():
reset_session()
# Construct the layer
from rinokeras.core.v1x.utils import convert_to_attention_mask
# Encoded values
input_tensor, _ = random_sequence_tensor(2, 32, 128)
input_mask, _ = random_mask_tensor(2, 32)
# Get the output of the layer
value = convert_to_attention_mask(input_tensor, input_mask)
# Construct the session
output = run_simple_session(inputs=[value], feed={})
assert_not_none(output)
assert_expected_shapes(output, [(2, 32, 32)])
# Do regression testing
check_regression('convert_to_attention_mask_2',
output,
__file__,
'regression_outputs/test_utils_outputs.json',
debug=_RK_REBUILD_REGRESSION)