def EncoderLayer(feature_depth, feedforward_depth, num_heads, dropout, mode):
"""Transformer encoder layer.
The input to the encoder is a pair (embedded source, mask) where
the mask is created from the original source to prevent attending
to the padding part of the input.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a pair (actiavtions, mask).
"""
return tl.Serial(
tl.Residual( # Attention block here.
tl.Parallel(tl.LayerNorm(), tl.Copy()),
tl.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
tl.Parallel(tl.Dropout(rate=dropout, mode=mode), tl.Copy())),
tl.Parallel(
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode),
tl.Div(
divisor=2.0) # Mask added to itself in the residual, divide.
))
def EncoderBlock(d_feature, d_feedforward, n_heads, dropout, mode):
"""Returns a layer sequence that implements a Transformer encoder block.
The input to the layer sequence is a pair, (activations, mask), where the
mask was created from the original source tokens to prevent attending to the
padding part of the input.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
A sequence of layers that maps an (activations, mask) pair to an
(activations, mask) pair.
"""
attention = [
tl.LayerNorm(),
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Dropout(rate=dropout, mode=mode),
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [
tl.Residual(attention),
tl.Residual(feed_forward),
]
def DecoderBlock(d_feature, d_feedforward, n_heads, dropout, mode):
"""Returns a layer sequence that implements a Transformer decoder block.
The input to the layer sequence is an activation tensor.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
A sequence of layers that maps an activation tensor to an activation tensor.
"""
self_attention = [
tl.LayerNorm(), # vec
tl.Dup(), # vec vec
tl.Parallel([], tl.CausalMask(axis=-2)), # vec mask
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Parallel([], tl.Drop()), # vec
tl.Dropout(rate=dropout, mode=mode), # vec
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [
tl.Residual(self_attention),
tl.Residual(feed_forward),
]
def EncoderBlock(d_feature, d_feedforward, n_heads, dropout, mode):
"""Transformer encoder block.
The input to the encoder is a pair (embedded source, mask) where
the mask is created from the original source to prevent attending
to the padding part of the input.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a pair (activations, mask).
"""
attention = [
tl.LayerNorm(),
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Dropout(rate=dropout, mode=mode),
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [
tl.Residual(attention),
tl.Residual(feed_forward),
]
def DecoderLayer(feature_depth, feedforward_depth, num_heads, dropout, mode):
"""Transformer decoder layer.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer.
"""
return tl.Serial(
tl.Residual( # Self-attention block.
tl.LayerNorm(),
tl.Branch(tl.Copy(), tl.CausalMask(axis=-2)), # Create mask.
tl.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
tl.Select(0), # Drop the mask.
tl.Dropout(rate=dropout, mode=mode)),
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode))
def DecoderBlock(d_feature, d_feedforward, n_heads, dropout, mode):
"""Transformer decoder layer.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer.
"""
self_attention = [
tl.LayerNorm(),
tl.Branch([], tl.CausalMask(axis=-2)), # Create mask.
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Select(0), # Drop mask.
tl.Dropout(rate=dropout, mode=mode),
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [
tl.Residual(self_attention),
tl.Residual(feed_forward),
]
def DecoderLayer(feature_depth, feedforward_depth, num_heads, dropout, mode):
"""Transformer decoder layer.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer.
"""
return layers.Serial(
layers.Residual( # Self-attention block.
layers.LayerNorm(),
layers.Branch(),
layers.Parallel(
layers.Identity(), # activation for (q, k, v)
layers.CausalMask(axis=-2)), # attention mask
layers.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
layers.Dropout(rate=dropout, mode=mode)),
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode))
def EncoderDecoderLayer(feature_depth, feedforward_depth, num_heads, dropout,
mode):
"""Transformer encoder-decoder layer.
The input is a triple pair (encoder, mask, decoder_input) where
the mask is created from the original source to prevent attending
to the padding part of the encoder.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a triple (encoder, mask, decoder_activations).
"""
# Decoder self-attending to decoder.
self_attention = layers.Residual(
layers.LayerNorm(),
layers.Branch(),
layers.Parallel(
layers.Identity(), # activation for (q, k, v)
layers.CausalMask(axis=-2)), # attention mask
layers.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
layers.Dropout(rate=dropout, mode=mode))
# Decoder attending to encoder.
encoder_decoder_attention = layers.Serial(
layers.Reorder(output=((2, 0, 0), 1)), # ((dec, enc, enc), mask)
layers.MultiHeadedAttentionQKV( # ((q, k, v), mask) --> new v
feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
layers.Dropout(rate=dropout, mode=mode),
)
return layers.Serial(
layers.Parallel(layers.Identity(), layers.Identity(), self_attention),
layers.Branch(),
layers.Parallel(layers.Identity(), encoder_decoder_attention),
layers.UnnestBranches(), # (encoder, mask, old_act, new_act)
layers.Reorder(output=(0, 1, (2, 3))),
layers.Parallel( # Residual after encoder-decoder attention.
layers.Identity(), layers.Identity(), layers.SumBranches()),
layers.Parallel( # Feed-forward on the third component (decoder).
layers.Identity(), layers.Identity(),
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode)))
def EncoderDecoderLayer(feature_depth, feedforward_depth, num_heads, dropout,
mode):
"""Transformer encoder-decoder layer.
The input is a triple pair (encoder, mask, decoder_input) where
the mask is created from the original source to prevent attending
to the padding part of the encoder.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a triple (encoder, mask, decoder_activations).
"""
# Decoder self-attending to decoder.
self_attention = tl.Residual(
tl.LayerNorm(),
tl.Branch(tl.NoOp(), tl.CausalMask(axis=-2)), # create mask
tl.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
tl.Select(0), # drop mask
tl.Dropout(rate=dropout, mode=mode))
# Decoder attending to encoder.
encoder_decoder_attention = tl.Serial(
tl.Select((2, 0, 0, 1)), # (dec, enc, enc, mask)
tl.MultiHeadedAttentionQKV( # (q, k, v, mask) --> new, mask
feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
tl.Select(0), # drop the mask
tl.Dropout(rate=dropout, mode=mode),
)
return tl.Serial(
tl.Parallel(tl.NoOp(), tl.NoOp(), self_attention),
tl.Branch(tl.NoOp(), encoder_decoder_attention),
tl.Select(inputs=(('encoder', 'mask', 'old_act'), 'new_act'),
output=('encoder', 'mask', ('old_act', 'new_act'))),
tl.Parallel( # Residual after encoder-decoder attention.
tl.NoOp(), tl.NoOp(), tl.Add()),
tl.Parallel( # Feed-forward on the third component (decoder).
tl.NoOp(), tl.NoOp(),
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode)))
def EncoderDecoder(d_feature, d_feedforward, n_heads, dropout, mode):
"""Transformer encoder-decoder layer.
The input is a triple pair (decoder_input, mask, encoder) where
the mask is created from the original source to prevent attending
to the padding part of the encoder.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a triple (decoder_activations, mask, encoder).
"""
decoder_self_attention = [
# TODO(jonni): Work on combinators so that this flow is cleaner/clearer.
tl.LayerNorm(),
tl.Dup(),
tl.CausalMask(axis=-2), # Create the self-attention mask.
tl.Swap(), # Put mask behind the activations.
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Swap(), # Put self-attention mask on top.
tl.Drop(), # Drop self-attention mask.
tl.Dropout(rate=dropout, mode=mode),
]
decoder_to_encoder_attention = [
tl.Select((0, 2, 2, 1, 2)), # (dec, enc, enc, mask, enc-copy)
tl.
MultiHeadedAttentionQKV( # (q, k, v, mask, ...) --> (new, mask, ...)
d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Dropout(rate=dropout, mode=mode),
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [
tl.Residual(decoder_self_attention),
tl.Residual(decoder_to_encoder_attention),
tl.Residual(feed_forward),
]
def EncoderDecoder(d_feature, d_feedforward, n_heads, dropout, mode):
"""Transformer encoder-decoder layer.
The input is a triple (decoder_input, mask, encoder) where the mask is
created from the original source to prevent attending to the padding part
of the encoder.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a triple (decoder_activations, mask, encoder).
"""
decoder_self_attention = [ # vecs_d pmask vecs_e
tl.LayerNorm(), # vecs_d ..... ......
tl.Dup(), # vecs_d vecs_d ..... ......
tl.Parallel([],
tl.CausalMask(axis=-2)), # ______ masks ..... ......
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Parallel([], tl.Drop()), # ______ 0 ..... ......
tl.Dropout(rate=dropout, mode=mode), # vecs_d ..... ......
]
decoder_to_encoder_attention = [ # vecs_d masks vecs_e
tl.Parallel([], [], tl.Dup()), # ______ _____ vecs_e vecs_e
tl.Parallel([], tl.Swap()), # ______ vecs_e masks ......
tl.Parallel([], tl.Dup()), # ______ vecs_e vecs_e ..... ......
tl.MultiHeadedAttentionQKV( # (q k v masks ... --> vecs_d masks ...)
d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Dropout(rate=dropout, mode=mode), # vecs_d mask vecs_e
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [ # vecs_d masks vecs_e
tl.Residual(decoder_self_attention), # vecs_d masks vecs_e
tl.Residual(decoder_to_encoder_attention), # vecs_d masks vecs_e
tl.Residual(feed_forward), # vecs_d masks vecs_e
]
def EncoderDecoderLayer(feature_depth, feedforward_depth, num_heads, dropout,
mode):
"""Transformer encoder-decoder layer.
The input is a triple pair (decoder_input, mask, encoder) where
the mask is created from the original source to prevent attending
to the padding part of the encoder.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a triple (decoder_activations, mask, encoder).
"""
# Decoder self-attending to decoder.
self_attention = tl.Residual(
tl.LayerNorm(),
tl.Dup(),
tl.CausalMask(axis=-2), # Create the self-attention mask.
tl.Swap(), # Put mask behind the activations.
tl.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
tl.Swap(), # Put self-attention mask on top.
tl.Drop(), # Drop self-attention mask.
tl.Dropout(rate=dropout, mode=mode))
# Decoder attending to encoder.
encoder_decoder_attention = tl.Serial(
tl.Select((0, 2, 2, 1, 2)), # (dec, enc, enc, mask, enc-copy)
tl.
MultiHeadedAttentionQKV( # (q, k, v, mask, ...) --> (new, mask, ...)
feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
tl.Dropout(rate=dropout, mode=mode),
)
return tl.Serial(
self_attention, tl.Residual(encoder_decoder_attention),
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode))
def DecoderBlock(d_feature, d_feedforward, n_heads, n_attention_chunks,
dropout, mode):
"""Reversible transformer decoder layer.
Args:
d_feature: int: depth of embedding
d_feedforward: int: depth of feed-forward layer
n_heads: int: number of attention heads
n_attention_chunks: int: number of chunks for memory-efficient attention
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer.
"""
self_attention = [
tl.LayerNorm(),
tl.Branch([], tl.CausalMask(axis=-2)), # Create mask.
tl.MultiHeadedAttention(d_feature,
n_heads=n_heads,
dropout=dropout,
mode=mode),
tl.Select(0), # Drop mask.
tl.Dropout(rate=dropout, mode=mode),
]
# TODO(kitaev): Memory-efficient attention. This chunking is temporary.
self_attention = [
Split(sections=n_attention_chunks, axis=-2), # pylint: disable=no-value-for-parameter
Map(self_attention),
tl.Concatenate(axis=-2),
]
feed_forward = [
FeedForward(d_feature, d_feedforward, dropout, mode=mode),
]
return [
ReversibleResidual([self_attention], [feed_forward]),
]
def EncoderLayer(feature_depth,
feedforward_depth,
num_heads,
dropout,
mode):
"""Transformer encoder layer.
The input to the encoder is a pair (embedded source, mask) where
the mask is created from the original source to prevent attending
to the padding part of the input.
Args:
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
mode: str: 'train' or 'eval'
Returns:
the layer, returning a pair (actiavtions, mask).
"""
# The encoder block expects (activation, mask) as input and returns
# the new activations only, we add the mask back to output next.
encoder_block = layers.Serial(
layers.Residual( # Attention block here.
layers.Parallel(layers.LayerNorm(), layers.Identity()),
layers.MultiHeadedAttention(feature_depth, num_heads=num_heads,
dropout=dropout, mode=mode),
layers.Dropout(rate=dropout, mode=mode),
shortcut=layers.FirstBranch()
),
ResidualFeedForward(feature_depth, feedforward_depth, dropout, mode=mode)
)
# Now we add the mask back.
return layers.Serial(
layers.Reorder(output=((0, 1), 1)), # (x, mask) --> ((x, mask), mask)
layers.Parallel(encoder_block, layers.Identity())
)
def Transformer(source_vocab_size,
target_vocab_size,
mode='train',
num_layers=6,
feature_depth=512,
feedforward_depth=2048,
num_heads=8,
dropout=0.1,
shared_embedding=True,
max_len=200,
return_evals=False):
"""Transformer model.
Args:
source_vocab_size: int: source vocab size
target_vocab_size: int: target vocab size
mode: str: 'train' or 'eval'
num_layers: int: number of encoder/decoder layers
feature_depth: int: depth of embedding
feedforward_depth: int: depth of feed-forward layer
num_heads: int: number of attention heads
dropout: float: dropout rate (how much to drop out)
shared_embedding: bool: specify whether source/target embeddings are tied.
max_len: int: maximum symbol length for positional encoding
return_evals: bool: whether to generate decode-time evaluation functions
Returns:
A namedtuple containing model 'init' and 'apply' functions for training and
the 'evals' functions that itself returns a namedtuple containing evaluation
functions for the trained encoder, decoder, and generator substax.
"""
# Input embedding and positional encoding
inject_position = layers.Serial(
layers.Dropout(dropout, mode=mode),
layers.PositionalEncoding(feature_depth, max_len=max_len))
if shared_embedding:
assert source_vocab_size == target_vocab_size
# Weight-shared Embedding
embedding = layers.Share(
layers.Embedding(feature_depth, source_vocab_size))
source_embedding_layer = layers.Serial(embedding, inject_position)
target_embedding_layer = source_embedding_layer
else:
source_embedding = layers.Embedding(feature_depth, source_vocab_size)
target_embedding = layers.Embedding(feature_depth, target_vocab_size)
source_embedding_layer = layers.Serial(source_embedding,
inject_position)
target_embedding_layer = layers.Serial(target_embedding,
inject_position)
# Multi-headed Attention and Feed-forward layers
multi_attention = layers.MultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode)
# Encoder
@layers.Lambda
def Encoder(source, source_mask):
"""Transformer encoder stack.
Args:
source: layer variable: raw source sequences
source_mask: layer variable: self-attention mask
Returns:
Layer variable that outputs encoded source.
"""
encoder_layer = layers.Serial(
# input attends to self
layers.Residual(
layers.LayerNorm(),
layers.Branch(size=4),
layers.Parallel(
layers.Identity(), # query
layers.Identity(), # key
layers.Identity(), # value
source_mask), # attention mask
multi_attention,
layers.Dropout(dropout, mode=mode)),
# feed-forward
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode),
)
return layers.Serial(
source,
source_embedding_layer,
layers.repeat(encoder_layer, num_layers),
layers.LayerNorm(),
)
# Decoder
@layers.Lambda
def Decoder(memory, target, target_mask, memory_mask):
"""Transformer decoder stack.
Args:
memory: layer variable: encoded source sequences
target: layer variable: raw target sequences
target_mask: layer variable: self-attention mask
memory_mask: layer variable: memory attention mask
Returns:
Layer variable that outputs encoded source.
"""
decoder_layer = layers.Serial(
# target attends to self
layers.Residual(
layers.LayerNorm(),
layers.Branch(size=4),
layers.Parallel(
layers.Identity(), # query
layers.Identity(), # key
layers.Identity(), # value
target_mask), # attention mask
multi_attention,
layers.Dropout(dropout, mode=mode)),
# target attends to encoded source
layers.Residual(
layers.LayerNorm(),
layers.Branch(size=4),
layers.Parallel(
layers.Identity(), # query
memory, # key
memory, # value
memory_mask), # attention mask
multi_attention,
layers.Dropout(dropout, mode=mode)),
# feed-forward
ResidualFeedForward(feature_depth,
feedforward_depth,
dropout,
mode=mode))
return layers.Serial(
target,
target_embedding_layer,
layers.repeat(decoder_layer, num_layers),
layers.LayerNorm(),
)
# The Transformer
@layers.Lambda
def transformer(source, target, source_mask, target_mask, memory_mask): # pylint: disable=invalid-name
encoded_source = Encoder(source, source_mask)
return Decoder(encoded_source, target, target_mask, memory_mask)
# Finally, bind the generator transform to use later for inference.
@layers.Lambda
def Generator(encoded_target):
return layers.Serial(encoded_target, layers.Dense(target_vocab_size),
layers.LogSoftmax)
# Model-Building and Evaluation Functions
# Get entire model's the layer pair
top_init, top_apply = Generator(transformer)
# By default act as a normal constructor and emit an (init, apply) pair.
if not return_evals:
return (top_init, top_apply)
else:
raise ValueError('inference in this model is still a work in progress')
