official/nlp/modeling/layers/relative_attention.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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"""Keras-based relative attention layers."""
import math
import string
import tensorflow as tf, tf_keras
_CHR_IDX = string.ascii_lowercase
def _build_proj_equation(free_dims, bound_dims, output_dims):
"""Builds an einsum equation for projections inside multi-head attention."""
input_str = ""
kernel_str = ""
output_str = ""
bias_axes = ""
letter_offset = 0
for i in range(free_dims):
char = _CHR_IDX[i + letter_offset]
input_str += char
output_str += char
letter_offset += free_dims
for i in range(bound_dims):
char = _CHR_IDX[i + letter_offset]
input_str += char
kernel_str += char
letter_offset += bound_dims
for i in range(output_dims):
char = _CHR_IDX[i + letter_offset]
kernel_str += char
output_str += char
bias_axes += char
equation = "%s,%s->%s" % (input_str, kernel_str, output_str)
return equation, bias_axes, len(output_str)
def _get_output_shape(output_rank, known_last_dims):
return [None] * (output_rank - len(known_last_dims)) + list(known_last_dims)
def _rel_shift(x, klen=-1):
"""Performs relative shift to form the relative attention score."""
x = tf.transpose(x, perm=[2, 3, 0, 1])
x_size = tf.shape(x)
x = tf.reshape(x, [x_size[1], x_size[0], x_size[2], x_size[3]])
x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1])
x = tf.reshape(x, [x_size[0], x_size[1] - 1, x_size[2], x_size[3]])
x = tf.slice(x, [0, 0, 0, 0], [-1, klen, -1, -1])
x = tf.transpose(x, perm=[2, 3, 0, 1])
return x
@tf_keras.utils.register_keras_serializable(package="Text")
class MultiHeadRelativeAttention(tf_keras.layers.MultiHeadAttention):
"""A multi-head attention layer with relative attention + position encoding.
This layer shares the same input/output projections as the common
`tf_keras.layers.MultiHeadAttention` layer.
When it calculates attention logits, position encoding is projected to form
relative keys. The logits are composed by shifted relative logits and content
logits.
**Note: This layer is currently experimental.
Attributes:
kernel_initializer: The kernel initializer. Defaults to variance_scaling.
Call args:
query: Query `Tensor` of shape `[B, T, dim]`.
value: Value `Tensor` of shape `[B, S, dim]`.
content_attention_bias: Bias `Tensor` for content based attention of shape
`[num_heads, dim]`.
positional_attention_bias: Bias `Tensor` for position based attention of
shape `[num_heads, dim]`.
key: Optional key `Tensor` of shape `[B, S, dim]`. If not given, will use
`value` for both `key` and `value`, which is the most common case.
relative_position_encoding: Relative positional encoding `Tensor` of shape
`[B, L, dim]`.
segment_matrix: Optional `Tensor` representing segmentation IDs used in
XLNet of shape `[B, S, S + M]`.
segment_encoding: Optional `Tensor` representing the segmentation encoding
as used in XLNet of shape `[2, num_heads, dim]`.
segment_attention_bias: Optional trainable bias parameter added to the query
had when calculating the segment-based attention score used in XLNet of
shape `[num_heads, dim]`.
state: Optional `Tensor` of shape `[B, M, E]` where M is the length of the
state or memory. If passed, this is also attended over as in Transformer
XL.
attention_mask: A boolean mask of shape `[B, T, S]` that prevents attention
to certain positions.
"""
def __init__(self,
kernel_initializer="variance_scaling",
**kwargs):
super().__init__(kernel_initializer=kernel_initializer,
**kwargs)
def _build_from_signature(self, query, value, key=None):
super(MultiHeadRelativeAttention, self)._build_from_signature(
query=query,
value=value,
key=key)
if hasattr(value, "shape"):
value_shape = tf.TensorShape(value.shape)
else:
value_shape = value
if key is None:
key_shape = value_shape
elif hasattr(key, "shape"):
key_shape = tf.TensorShape(key.shape)
else:
key_shape = key
common_kwargs = dict(
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activity_regularizer=self._activity_regularizer,
kernel_constraint=self._kernel_constraint,
bias_constraint=self._bias_constraint)
with tf.init_scope():
einsum_equation, _, output_rank = _build_proj_equation(
key_shape.rank - 1, bound_dims=1, output_dims=2)
self._encoding_dense = tf_keras.layers.EinsumDense(
einsum_equation,
output_shape=_get_output_shape(output_rank - 1,
[self._num_heads, self._key_dim]),
bias_axes=None,
name="encoding",
**common_kwargs)
def compute_attention(self,
query,
key,
value,
position,
content_attention_bias,
positional_attention_bias,
segment_matrix=None,
segment_encoding=None,
segment_attention_bias=None,
attention_mask=None):
"""Computes the attention.
This function defines the computation inside `call` with projected
multihead Q, K, V, R inputs.
Args:
query: Projected query `Tensor` of shape `[B, T, N, key_dim]`.
key: Projected key `Tensor` of shape `[B, S + M, N, key_dim]`.
value: Projected value `Tensor` of shape `[B, S + M, N, key_dim]`.
position: Projected position `Tensor` of shape `[B, L, N, key_dim]`.
content_attention_bias: Trainable bias parameter added to the query head
when calculating the content-based attention score.
positional_attention_bias: Trainable bias parameter added to the query
head when calculating the position-based attention score.
segment_matrix: Optional `Tensor` representing segmentation IDs used in
XLNet.
segment_encoding: Optional trainable `Tensor` representing the
segmentation encoding as used in XLNet.
segment_attention_bias: Optional trainable bias parameter added to the
query had when calculating the segment-based attention score used in
XLNet.
attention_mask: (default None) Optional mask that is added to attention
logits. If state is not None, the mask source sequence dimension should
extend M.
Returns:
attention_output: Multi-headed output of attention computation of shape
`[B, S, N, key_dim]`.
"""
content_attention = tf.einsum(self._dot_product_equation,
key,
query + content_attention_bias)
positional_attention = tf.einsum(self._dot_product_equation,
position,
query + positional_attention_bias)
positional_attention = _rel_shift(
positional_attention, klen=tf.shape(content_attention)[3])
if segment_matrix is not None:
segment_attention = tf.einsum("bind,snd->bnis",
query + segment_attention_bias,
segment_encoding)
target_shape = tf.shape(positional_attention)
segment_attention = tf.where(
tf.broadcast_to(tf.expand_dims(segment_matrix, 1), target_shape),
tf.broadcast_to(segment_attention[:, :, :, 1:], target_shape),
tf.broadcast_to(segment_attention[:, :, :, :1], target_shape))
attention_sum = (
content_attention + positional_attention + segment_attention)
else:
attention_sum = content_attention + positional_attention
attention_scores = tf.multiply(
attention_sum, 1.0 / math.sqrt(float(self._key_dim)))
attention_scores = self._masked_softmax(attention_scores, attention_mask)
attention_output = self._dropout_layer(attention_scores)
attention_output = tf.einsum(self._combine_equation,
attention_output,
value)
return attention_output
def call(self, # pytype: disable=signature-mismatch # overriding-parameter-count-checks
query,
value,
content_attention_bias,
positional_attention_bias,
key=None,
relative_position_encoding=None,
segment_matrix=None,
segment_encoding=None,
segment_attention_bias=None,
state=None,
attention_mask=None):
"""Compute multi-head relative attention over inputs.
Size glossary:
* Number of heads (H): the number of attention heads.
* Value size (V): the size of each value embedding per head.
* Key size (K): the size of each key embedding per head. Equally, the size
of each query embedding per head. Typically K <= V.
* Batch dimensions (B).
* Query (target) attention axes shape (T).
* Value (source) attention axes shape (S), the rank must match the target.
* Encoding length (L): The relative positional encoding length.
Args:
query: attention input.
value: attention input.
content_attention_bias: A trainable bias parameter added to the query head
when calculating the content-based attention score.
positional_attention_bias: A trainable bias parameter added to the query
head when calculating the position-based attention score.
key: attention input.
relative_position_encoding: relative positional encoding for key and
value.
segment_matrix: Optional `Tensor` representing segmentation IDs used in
XLNet.
segment_encoding: Optional `Tensor` representing the segmentation encoding
as used in XLNet.
segment_attention_bias: Optional trainable bias parameter added to the
query had when calculating the segment-based attention score used in
XLNet.
state: (default None) optional state. If passed, this is also attended
over as in TransformerXL.
attention_mask: (default None) Optional mask that is added to attention
logits. If state is not None, the mask source sequence dimension should
extend M.
Returns:
attention_output: The result of the computation, of shape [B, T, E],
where `T` is for target sequence shapes and `E` is the query input last
dimension if `output_shape` is `None`. Otherwise, the multi-head outputs
are projected to the shape specified by `output_shape`.
"""
if not self._built_from_signature:
self._build_from_signature(query, value, key=key)
if key is None:
key = value
if state is not None and state.shape.ndims > 1:
value = tf.concat([state, value], 1)
key = tf.concat([state, key], 1)
# `query` = [B, T, N ,H]
query = self._query_dense(query)
# `key` = [B, S + M, N, H]
key = self._key_dense(key)
# `value` = [B, S + M, N, H]
value = self._value_dense(value)
# `position` = [B, L, N, H]
position = self._encoding_dense(relative_position_encoding)
attention_output = self.compute_attention(
query=query,
key=key,
value=value,
position=position,
content_attention_bias=content_attention_bias,
positional_attention_bias=positional_attention_bias,
segment_matrix=segment_matrix,
segment_encoding=segment_encoding,
segment_attention_bias=segment_attention_bias,
attention_mask=attention_mask)
# `attention_output` = [B, S, N, H]
attention_output = self._output_dense(attention_output)
return attention_output
@tf_keras.utils.register_keras_serializable(package="Text")
class TwoStreamRelativeAttention(MultiHeadRelativeAttention):
"""Two-stream relative self-attention for XLNet.
In XLNet, each token has two associated vectors at each self-attention layer,
the content stream (h) and the query stream (g).
The content stream is the self-attention stream as in Transformer XL and
represents the context and content (the token itself).
The query stream only has access to contextual information and the position,
but not the content.
This layer shares the same build signature as
`tf_keras.layers.MultiHeadAttention` but has different input/output
projections.
**Note: This layer is currently experimental.
Call args:
content_stream: `Tensor` of shape `[B, T, dim]`.
content_attention_bias: Bias `Tensor` for content based attention of shape
`[num_heads, dim]`.
positional_attention_bias: Bias `Tensor` for position based attention of
shape `[num_heads, dim]`.
query_stream: `Tensor` of shape `[B, P, dim]`.
target_mapping: `Tensor` of shape `[B, P, S]`.
relative_position_encoding: Relative positional encoding `Tensor` of shape
`[B, L, dim]`.
segment_matrix: Optional `Tensor` representing segmentation IDs used in
XLNet of shape `[B, S, S + M]`.
segment_encoding: Optional `Tensor` representing the segmentation
encoding as used in XLNet of shape `[2, num_heads, dim]`.
segment_attention_bias: Optional trainable bias parameter added to the
query had when calculating the segment-based attention score used in
XLNet of shape `[num_heads, dim]`.
state: Optional `Tensor` of shape [B, M, E] where M is the length of the
state or memory.
If passed, this is also attended over as in Transformer XL.
content_attention_mask: a boolean mask of shape `[B, T, S]` that
prevents attention to certain positions for content attention computation.
query_attention_mask: a boolean mask of shape `[B, T, S]` that
prevents attention to certain position for query attention computation.
"""
def call(self,
content_stream,
content_attention_bias,
positional_attention_bias,
query_stream,
relative_position_encoding,
target_mapping=None,
segment_matrix=None,
segment_encoding=None,
segment_attention_bias=None,
state=None,
content_attention_mask=None,
query_attention_mask=None):
"""Compute multi-head relative attention over inputs.
Size glossary:
* Number of heads (H): the number of attention heads.
* Value size (V): the size of each value embedding per head.
* Key size (K): the size of each key embedding per head. Equally, the size
of each query embedding per head. Typically K <= V.
* Number of predictions (P): the number of predictions.
* Batch dimensions (B).
* Query (target) attention axes shape (T).
* Value (source) attention axes shape (S), the rank must match the target.
* Encoding length (L): The relative positional encoding length.
Args:
content_stream: The content representation, commonly referred to as h.
This serves a similar role to the standard hidden states in
Transformer-XL.
content_attention_bias: A trainable bias parameter added to the query head
when calculating the content-based attention score.
positional_attention_bias: A trainable bias parameter added to the query
head when calculating the position-based attention score.
query_stream: The query representation, commonly referred to as g. This
only has access to contextual information and position, but not content.
If not provided, then this is MultiHeadRelativeAttention with
self-attention.
relative_position_encoding: relative positional encoding for key and
value.
target_mapping: Optional `Tensor` representing the target mapping used in
partial prediction.
segment_matrix: Optional `Tensor` representing segmentation IDs used in
XLNet.
segment_encoding: Optional `Tensor` representing the segmentation encoding
as used in XLNet.
segment_attention_bias: Optional trainable bias parameter added to the
query head when calculating the segment-based attention score.
state: (default None) optional state. If passed, this is also attended
over as in TransformerXL and XLNet.
content_attention_mask: (default None) Optional mask that is added to
content attention logits. If state is not None, the mask source sequence
dimension should extend M.
query_attention_mask: (default None) Optional mask that is added to query
attention logits. If state is not None, the mask source sequence
dimension should extend M.
Returns:
content_attention_output, query_attention_output: the results of the
computation, both of shape [B, T, E]. `T` is for target sequence shapes,
`E` is the query input last dimension if `output_shape` is `None`.
Otherwise, the multi-head outputs are projected to the shape specified
by `output_shape`.
"""
if not self._built_from_signature:
self._build_from_signature(content_stream, content_stream, content_stream)
if state is not None and state.shape.ndims > 1:
content_and_memory_stream = tf.concat([state, content_stream], 1)
else:
content_and_memory_stream = content_stream
# `query` = [B, T, N, H]
query = self._query_dense(content_stream)
# `key` = [B, S + M, N, H]
key = self._key_dense(content_and_memory_stream)
# `value` = [B, S + M, N, H]
value = self._value_dense(content_and_memory_stream)
# `position` = [B, L, N, H]
position = self._encoding_dense(relative_position_encoding)
content_attention_output = self.compute_attention(
query=query,
key=key,
value=value,
position=position,
content_attention_bias=content_attention_bias,
positional_attention_bias=positional_attention_bias,
segment_matrix=segment_matrix,
segment_encoding=segment_encoding,
segment_attention_bias=segment_attention_bias,
attention_mask=content_attention_mask)
# `content_attention_output` = [B, S, N, H]
content_attention_output = self._output_dense(content_attention_output)
query_attention_output = None
if query_stream is not None:
query = self._query_dense(query_stream)
if target_mapping is not None:
query = tf.einsum("bmnd,bml->blnd", query, target_mapping)
query_attention_output = self.compute_attention(
query=query,
key=key,
value=value,
position=position,
content_attention_bias=content_attention_bias,
positional_attention_bias=positional_attention_bias,
segment_matrix=segment_matrix,
segment_encoding=segment_encoding,
segment_attention_bias=segment_attention_bias,
attention_mask=query_attention_mask)
query_attention_output = tf.einsum("blnd,bml->bmnd",
query_attention_output,
target_mapping)
else:
query_attention_output = self.compute_attention(
query=query,
key=key,
value=value,
position=position,
content_attention_bias=content_attention_bias,
positional_attention_bias=positional_attention_bias,
segment_matrix=segment_matrix,
segment_encoding=segment_encoding,
segment_attention_bias=segment_attention_bias,
attention_mask=query_attention_mask)
query_attention_output = self._output_dense(query_attention_output)
return content_attention_output, query_attention_output