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 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 DecoderLayer(positions, d_feature, d_feedforward, n_heads, dropout, mode): """Transformer decoder layer. Args: positions: random vectors for positions 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. """ return [ tl.Residual( # Self-attention block. PreservePosition(tl.LayerNorm()), tl.Dup(), tl.Parallel( [], # activation for (q, k, v) tl.CausalMask(axis=-2)), # attention mask MultiHeadedAttentionPosition(positions, d_feature, n_heads=n_heads, dropout=dropout, mode=mode), PreservePosition(tl.Dropout(rate=dropout, mode=mode))), ResidualFeedForward(d_feature, d_feedforward, dropout, mode=mode) ]
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 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 ChunkedCausalMultiHeadedAttention(d_feature, n_heads=8, dropout=0.0, chunk_selector=None, mode='train'): """Transformer-style causal multi-headed attention operating on chunks. Accepts inputs that are a list of chunks and applies causal attention. Args: d_feature: int: depth of embedding n_heads: int: number of attention heads dropout: float: dropout rate chunk_selector: a function from chunk number to list of chunks to attend. mode: str: 'train' or 'eval' Returns: Multi-headed self-attention layer. """ prepare_attention_input = tl.Serial( tl.Branch( tl.Branch( # q = k = v = first input tl.NoOp(), tl.NoOp(), tl.NoOp()), tl.CausalMask(axis=-2), ), tl.Parallel( tl.Parallel( tl.Dense(d_feature), tl.Dense(d_feature), tl.Dense(d_feature), ), tl.NoOp())) return tl.Serial( tl.Map(prepare_attention_input), ChunkedAttentionSelector(selector=chunk_selector), # pylint: disable=no-value-for-parameter tl.Map(tl.PureMultiHeadedAttention(d_feature=d_feature, n_heads=n_heads, dropout=dropout, mode=mode), check_shapes=False), tl.Map(tl.Select(0), check_shapes=False), # drop masks tl.Map(tl.Dense(d_feature)))
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 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.Dup(), tl.Parallel([], tl.CausalMask(axis=-2)), # Create mask. # TODO(kitaev): add dropout tl.Attention(d_feature, n_heads=n_heads, dropout=None, mode=mode), tl.Parallel([], tl.Drop()), # Drop mask. ] # TODO(kitaev): Memory-efficient attention. This chunking is temporary. self_attention = [ Chunk(sections=n_attention_chunks), # pylint: disable=no-value-for-parameter self_attention, Unchunk(sections=n_attention_chunks), # pylint: disable=no-value-for-parameter ] feed_forward = [ FeedForward(d_feature, d_feedforward, dropout, mode=mode), ] return [ ReversibleResidual([self_attention], [feed_forward]), ]