def attention_lm_moe_prepare_decoder(targets, hparams):
"""Prepare one shard of the model for the decoder.
Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
decoder_self_attention_bias: a Tensor, containing large negative values
to implement masked attention and possibly baises for diagonal alignments
pad_remover (expert_utils.PadRemover): an util object to remove padding
"""
targets_pad_mask = common_attention.embedding_to_padding(targets)
with tf.name_scope("pad_remover"):
pad_remover = expert_utils.PadRemover(targets_pad_mask)
if hparams.prepend_mode == "prepend_inputs_full_attention":
decoder_self_attention_bias = (
common_attention.attention_bias_prepended(targets_pad_mask))
else:
decoder_self_attention_bias = (
common_attention.attention_bias_lower_triangle(
tf.shape(targets)[1]))
decoder_input = common_layers.shift_left_3d(targets)
if hparams.pos == "timing":
decoder_input = common_attention.add_timing_signal_1d(decoder_input)
return (decoder_input, decoder_self_attention_bias, pad_remover)
def prepare_decoder(targets, target_space_emb):
"""Prepare decoder."""
decoder_self_attention_bias = (
common_attention.attention_bias_lower_triangle(tf.shape(targets)[1]))
target_space_emb = tf.reshape(target_space_emb, [1, 1, -1])
target_space_emb = tf.tile(target_space_emb, [tf.shape(targets)[0], 1, 1])
decoder_input = common_layers.shift_left_3d(targets,
pad_value=target_space_emb)
decoder_input = common_attention.add_timing_signal_1d(decoder_input)
return (decoder_input, decoder_self_attention_bias)
def attention_lm_prepare_decoder(targets, hparams):
"""Prepare one shard of the model for the decoder.
Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
decoder_self_attention_bias: a Tensor, containing large negative values
to implement masked attention and possibly baises for diagonal alignments
"""
decoder_self_attention_bias = (
common_attention.attention_bias_lower_triangle(tf.shape(targets)[1]))
decoder_input = common_layers.shift_left_3d(targets)
if hparams.pos == "timing":
decoder_input = common_attention.add_timing_signal_1d(decoder_input)
return (decoder_input, decoder_self_attention_bias)
def long_answer_prepare_decoder(inputs, targets, hparams):
"""Prepare one shard of the model for the decoder.
Args:
inputs: a Tensor.
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
"""
decoder_input = tf.concat([
length_embedding(targets, hparams), inputs,
common_layers.shift_left_3d(targets)
], 1)
if hparams.pos == "timing":
decoder_input = common_attention.add_timing_signal_1d(decoder_input)
return decoder_input
def transformer_prepare_decoder(targets, hparams):
"""Prepare one shard of the model for the decoder.
Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
decoder_self_attention_bias: a bias tensor for use in encoder self-attention
"""
decoder_self_attention_bias = (
common_attention.attention_bias_lower_triangle(tf.shape(targets)[1]))
if hparams.proximity_bias:
decoder_self_attention_bias += common_attention.attention_bias_proximal(
tf.shape(targets)[1])
decoder_input = common_layers.shift_left_3d(targets)
if hparams.pos == "timing":
decoder_input = common_attention.add_timing_signal_1d(decoder_input)
return (decoder_input, decoder_self_attention_bias)
def transformer_prepare_decoder(targets, hparams):
"""Prepare one shard of the model for the decoder.
Args:
targets: a Tensor.
hparams: run hyperparameters
Returns:
decoder_input: a Tensor, bottom of decoder stack
decoder_self_attention_bias: a bias tensor for use in encoder self-attention
"""
decoder_self_attention_bias = (comm_attn.attention_bias_lower_triangle(
tf.shape(targets)[1]))
if hparams.proximity_bias:
decoder_self_attention_bias += comm_attn.attention_bias_proximal(
tf.shape(targets)[1])
decoder_input = common_layers.shift_left_3d(targets)
if hparams.pos == 'timing':
decoder_input = comm_attn.add_timing_signal_1d(decoder_input)
# Putting this here since always called immediately after...
decoder_input = with_dropout(decoder_input, hparams)
return DecoderState(input=decoder_input,
self_attn_bias=decoder_self_attention_bias)
