def transformer_prepare_encoder(inputs, target_space, hparams):
"""Prepare one shard of the model for the encoder.
Args:
inputs: a Tensor.
target_space: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a Tensor, containing large negative values
to implement masked attention and possibly baises for diagonal
alignments
encoder_padding: a Tensor
"""
# Flatten inputs.
ishape_static = inputs.shape.as_list()
encoder_input = inputs
encoder_padding = common_attention.embedding_to_padding(encoder_input)
encoder_self_attention_bias = common_attention.attention_bias_ignore_padding(
encoder_padding)
# Append target_space_id embedding to inputs.
emb_target_space = common_layers.embedding(target_space,
32,
ishape_static[-1],
name="target_space_embedding")
emb_target_space = tf.reshape(emb_target_space, [1, 1, -1])
encoder_input += emb_target_space
if hparams.pos == "timing":
encoder_input = common_attention.add_timing_signal_1d(encoder_input)
return (encoder_input, encoder_self_attention_bias, encoder_padding)
def transformer_prepare_encoder(inputs, target_space, hparams):
"""Prepare one shard of the model for the encoder.
Args:
inputs: a Tensor.
target_space: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a Tensor, containing large negative values
to implement masked attention and possibly baises for diagonal
alignments
encoder_padding: a Tensor
"""
# Flatten inputs.
ishape_static = inputs.shape.as_list()
encoder_input = inputs
encoder_padding = common_attention.embedding_to_padding(encoder_input)
encoder_self_attention_bias = common_attention.attention_bias_ignore_padding(
encoder_padding)
# Append target_space_id embedding to inputs.
emb_target_space = common_layers.embedding(
target_space, 32, ishape_static[-1], name="target_space_embedding")
emb_target_space = tf.reshape(emb_target_space, [1, 1, -1])
encoder_input += emb_target_space
if hparams.pos == "timing":
encoder_input = common_attention.add_timing_signal_1d(encoder_input)
return (encoder_input, encoder_self_attention_bias, encoder_padding)
