official/nlp/modeling/layers/reuse_attention.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Keras-based attention layer."""
# pylint: disable=g-classes-have-attributes
import collections
import math
import string
import numpy as np
import tensorflow as tf, tf_keras
from official.modeling import tf_utils
_CHR_IDX = string.ascii_lowercase
def _build_attention_equation(rank, attn_axes):
"""Builds einsum equations for the attention computation.
Query, key, value inputs after projection are expected to have the shape as:
`(bs, <non-attention dims>, <attention dims>, num_heads, channels)`.
`bs` and `<non-attention dims>` are treated as `<batch dims>`.
The attention operations can be generalized:
(1) Query-key dot product:
`(<batch dims>, <query attention dims>, num_heads, channels), (<batch dims>,
<key attention dims>, num_heads, channels) -> (<batch dims>,
num_heads, <query attention dims>, <key attention dims>)`
(2) Combination:
`(<batch dims>, num_heads, <query attention dims>, <key attention dims>),
(<batch dims>, <value attention dims>, num_heads, channels) -> (<batch dims>,
<query attention dims>, num_heads, channels)`
Args:
rank: Rank of query, key, value tensors.
attn_axes: List/tuple of axes, `[-1, rank)`,
that attention will be applied to.
Returns:
Einsum equations.
"""
target_notation = _CHR_IDX[:rank]
# `batch_dims` includes the head dim.
batch_dims = tuple(np.delete(range(rank), attn_axes + (rank - 1,)))
letter_offset = rank
source_notation = ""
for i in range(rank):
if i in batch_dims or i == rank - 1:
source_notation += target_notation[i]
else:
source_notation += _CHR_IDX[letter_offset]
letter_offset += 1
product_notation = "".join([target_notation[i] for i in batch_dims] +
[target_notation[i] for i in attn_axes] +
[source_notation[i] for i in attn_axes])
dot_product_equation = "%s,%s->%s" % (source_notation, target_notation,
product_notation)
attn_scores_rank = len(product_notation)
combine_equation = "%s,%s->%s" % (product_notation, source_notation,
target_notation)
return dot_product_equation, combine_equation, attn_scores_rank
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)
class ReuseMultiHeadAttention(tf_keras.layers.Layer):
"""MultiHeadAttention layer.
This is an implementation of multi-headed attention as described in the paper
"Attention is all you Need" (Vaswani et al., 2017).
If `query`, `key,` `value` are the same, then
this is self-attention. Each timestep in `query` attends to the
corresponding sequence in `key`, and returns a fixed-width vector.
This layer first projects `query`, `key` and `value`. These are
(effectively) a list of tensors of length `num_attention_heads`, where the
corresponding shapes are `(batch_size, <query dimensions>, key_dim)`,
`(batch_size, <key/value dimensions>, key_dim)`,
`(batch_size, <key/value dimensions>, value_dim)`.
Then, the query and key tensors are dot-producted and scaled. These are
softmaxed to obtain attention probabilities. The value tensors are then
interpolated by these probabilities, then concatenated back to a single
tensor.
Finally, the result tensor with the last dimension as value_dim can take an
linear projection and return.
Examples:
Performs 1D cross-attention over two sequence inputs with an attention mask.
Returns the additional attention weights over heads.
>>> layer = MultiHeadAttention(num_heads=2, key_dim=2)
>>> target = tf_keras.Input(shape=[8, 16])
>>> source = tf_keras.Input(shape=[4, 16])
>>> output_tensor, weights = layer(target, source,
... return_attention_scores=True)
>>> print(output_tensor.shape)
(None, 8, 16)
>>> print(weights.shape)
(None, 2, 8, 4)
Performs 2D self-attention over a 5D input tensor on axes 2 and 3.
>>> layer = MultiHeadAttention(num_heads=2, key_dim=2, attention_axes=(2, 3))
>>> input_tensor = tf_keras.Input(shape=[5, 3, 4, 16])
>>> output_tensor = layer(input_tensor, input_tensor)
>>> print(output_tensor.shape)
(None, 5, 3, 4, 16)
Args:
num_heads: Number of attention heads.
key_dim: Size of each attention head for query and key.
value_dim: Size of each attention head for value.
dropout: Dropout probability.
reuse_attention: An integer specifying number of heads to reuse.
-1 for all heads.
use_relative_pe: Whether to use relative position bias.
max_sequence_length: Used to set the size of the relative positin encodings.
use_bias: Boolean, whether the dense layers use bias vectors/matrices.
output_shape: The expected shape of an output tensor, besides the batch and
sequence dims. If not specified, projects back to the key feature dim.
attention_axes: axes over which the attention is applied. `None` means
attention over all axes, but batch, heads, and features.
kernel_initializer: Initializer for dense layer kernels.
bias_initializer: Initializer for dense layer biases.
kernel_regularizer: Regularizer for dense layer kernels.
bias_regularizer: Regularizer for dense layer biases.
activity_regularizer: Regularizer for dense layer activity.
kernel_constraint: Constraint for dense layer kernels.
bias_constraint: Constraint for dense layer kernels.
Call arguments:
query: Query `Tensor` of shape `(B, T, dim)`.
value: Value `Tensor` of shape `(B, S, 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.
attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
attention to certain positions. The boolean mask specifies which query
elements can attend to which key elements, 1 indicates attention and 0
indicates no attention. Broadcasting can happen for the missing batch
dimensions and the head dimension.
return_attention_scores: A boolean to indicate whether the output should
be attention output if True, or (attention_output, attention_scores) if
False. Defaults to False.
training: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (no dropout).
Defaults to either using the training mode of the parent layer/model,
or False (inference) if there is no parent layer.
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 project to the shape specified by `output_shape`.
attention_scores: [Optional] multi-head attention coeffients over
attention axes.
"""
def __init__(self,
num_heads,
key_dim,
value_dim=None,
dropout=0.0,
reuse_attention=0,
use_relative_pe=False,
pe_max_seq_length=512,
use_bias=True,
output_shape=None,
attention_axes=None,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super().__init__(**kwargs)
self._num_heads = num_heads
self._key_dim = key_dim
self._value_dim = value_dim if value_dim else key_dim
self._dropout = dropout
if reuse_attention > self._num_heads or reuse_attention < -1:
raise ValueError("reuse_attention should be between -1 "
"and %d in call to %s." % (self.__class__,
self._num_heads))
if reuse_attention == -1:
reuse_attention = self._num_heads
self._reuse_heads = reuse_attention
self._use_relative_pe = use_relative_pe
self._pe_max_seq_length = pe_max_seq_length
self._use_bias = use_bias
self._output_shape = output_shape
self._kernel_initializer = tf_keras.initializers.get(kernel_initializer)
self._bias_initializer = tf_keras.initializers.get(bias_initializer)
self._kernel_regularizer = tf_keras.regularizers.get(kernel_regularizer)
self._bias_regularizer = tf_keras.regularizers.get(bias_regularizer)
self._kernel_constraint = tf_keras.constraints.get(kernel_constraint)
self._bias_constraint = tf_keras.constraints.get(bias_constraint)
if attention_axes is not None and not isinstance(attention_axes,
collections.abc.Sized):
self._attention_axes = (attention_axes,)
else:
self._attention_axes = attention_axes
self._built_from_signature = False
self._query_shape, self._key_shape, self._value_shape = None, None, None
# Use relative PE only if reuse_heads < num_heads.
if self._use_relative_pe and self._reuse_heads < self._num_heads:
# Determine the dtype from global policy.
policy = tf_keras.mixed_precision.global_policy()
if policy.name == "mixed_bfloat16":
policy = tf.bfloat16
elif policy.name == "mixed_float16":
policy = tf.float16
else:
policy = tf.float32
self._position_embeddings = tf.Variable(
name="relative_position_embeddings",
initial_value=lambda: tf.random.truncated_normal( # pylint: disable=g-long-lambda
[
1, self._num_heads - self._reuse_heads, 2 * self.
_pe_max_seq_length - 1
], mean=0.0, stddev=0.2, dtype=policy),
trainable=True, dtype=policy)
def get_config(self):
config = {
"num_heads": self._num_heads,
"key_dim": self._key_dim,
"value_dim": self._value_dim,
"dropout": self._dropout,
"use_bias": self._use_bias,
"output_shape": self._output_shape,
"attention_axes": self._attention_axes,
"reuse_attention": self._reuse_heads,
"use_relative_pe": self._use_relative_pe,
"pe_max_seq_length": self._pe_max_seq_length,
"kernel_initializer":
tf_keras.initializers.serialize(self._kernel_initializer),
"bias_initializer":
tf_keras.initializers.serialize(self._bias_initializer),
"kernel_regularizer":
tf_keras.regularizers.serialize(self._kernel_regularizer),
"bias_regularizer":
tf_keras.regularizers.serialize(self._bias_regularizer),
"activity_regularizer":
tf_keras.regularizers.serialize(self._activity_regularizer),
"kernel_constraint":
tf_keras.constraints.serialize(self._kernel_constraint),
"bias_constraint":
tf_keras.constraints.serialize(self._bias_constraint),
"query_shape": self._query_shape,
"key_shape": self._key_shape,
"value_shape": self._value_shape,
}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
# If the layer has a different build() function from the Keras default,
# we need to trigger the customized build to create weights.
query_shape = config.pop("query_shape")
key_shape = config.pop("key_shape")
value_shape = config.pop("value_shape")
layer = cls(**config)
if None in [query_shape, key_shape, value_shape]:
tf.get_logger().warning(
"One of dimensions of the input shape is missing. It should have been"
" memorized when the layer was serialized. "
"%s is created without weights.",
str(cls))
else:
layer._build_from_signature(query_shape, value_shape, key_shape) # pylint: disable=protected-access
return layer
def _build_from_signature(self, query, value, key=None):
"""Builds layers and variables.
Once the method is called, self._built_from_signature will be set to True.
Args:
query: Query tensor or TensorShape.
value: Value tensor or TensorShape.
key: Key tensor or TensorShape.
"""
self._built_from_signature = True
if hasattr(query, "shape"):
self._query_shape = tf.TensorShape(query.shape)
else:
self._query_shape = tf.TensorShape(query)
if hasattr(value, "shape"):
self._value_shape = tf.TensorShape(value.shape)
else:
self._value_shape = tf.TensorShape(value)
if key is None:
self._key_shape = self._value_shape
elif hasattr(key, "shape"):
self._key_shape = tf.TensorShape(key.shape)
else:
self._key_shape = tf.TensorShape(key)
common_kwargs = dict(
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)
# Any setup work performed only once should happen in an `init_scope`
# to avoid creating symbolic Tensors that will later pollute any eager
# operations.
with tf.init_scope():
free_dims = self._query_shape.rank - 1
if self._reuse_heads < self._num_heads:
einsum_equation, bias_axes, output_rank = _build_proj_equation(
free_dims, bound_dims=1, output_dims=2)
self._query_dense = tf_keras.layers.EinsumDense(
einsum_equation,
output_shape=_get_output_shape(
output_rank - 1,
[self._num_heads - self._reuse_heads, self._key_dim]),
bias_axes=bias_axes if self._use_bias else None,
name="query",
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
**common_kwargs)
einsum_equation, bias_axes, output_rank = _build_proj_equation(
self._key_shape.rank - 1, bound_dims=1, output_dims=2)
self._key_dense = tf_keras.layers.EinsumDense(
einsum_equation,
output_shape=_get_output_shape(
output_rank - 1,
[self._num_heads - self._reuse_heads, self._key_dim]),
bias_axes=bias_axes if self._use_bias else None,
name="key",
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
**common_kwargs)
einsum_equation, bias_axes, output_rank = _build_proj_equation(
self._value_shape.rank - 1, bound_dims=1, output_dims=2)
self._value_dense = []
if self._reuse_heads > 0:
self._value_dense.append(
tf_keras.layers.EinsumDense(
einsum_equation,
output_shape=_get_output_shape(
output_rank - 1, [self._reuse_heads, self._value_dim]),
bias_axes=bias_axes if self._use_bias else None,
name="value_reuse",
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(
self._bias_initializer),
**common_kwargs))
if self._reuse_heads < self._num_heads:
self._value_dense.append(
tf_keras.layers.EinsumDense(
einsum_equation,
output_shape=_get_output_shape(
output_rank - 1,
[self._num_heads - self._reuse_heads, self._value_dim]),
bias_axes=bias_axes if self._use_bias else None,
name="value_new",
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(
self._bias_initializer),
**common_kwargs))
# Builds the attention computations for multi-head dot product attention.
# These computations could be wrapped into the keras attention layer once
# it support mult-head einsum computations.
self._build_attention(output_rank)
self._output_dense = []
if self._reuse_heads > 0:
self._output_dense.append(self._make_output_dense(
free_dims, common_kwargs, "attention_output_reuse"))
if self._reuse_heads < self._num_heads:
self._output_dense.append(self._make_output_dense(
free_dims, common_kwargs, "attention_output_new",
self._reuse_heads == 0))
def _make_output_dense(self, free_dims, common_kwargs, name=None,
use_bias=True):
"""Builds the output projection matrix.
Args:
free_dims: Number of free dimensions for einsum equation building.
common_kwargs: Common keyword arguments for einsum layer.
name: Name for the projection layer.
use_bias: Use bias if self._use_bias is true
Returns:
Projection layer.
"""
if self._output_shape:
if not isinstance(self._output_shape, collections.abc.Sized):
output_shape = [self._output_shape]
else:
output_shape = self._output_shape
else:
output_shape = [self._query_shape[-1]]
einsum_equation, bias_axes, output_rank = _build_proj_equation(
free_dims, bound_dims=2, output_dims=len(output_shape))
return tf_keras.layers.EinsumDense(
einsum_equation,
output_shape=_get_output_shape(output_rank - 1, output_shape),
bias_axes=bias_axes if (use_bias and self._use_bias) else None,
name=name,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
bias_initializer=tf_utils.clone_initializer(self._bias_initializer),
**common_kwargs)
def _build_attention(self, rank):
"""Builds multi-head dot-product attention computations.
This function builds attributes necessary for `_compute_attention` to
customize attention computation to replace the default dot-product
attention.
Args:
rank: the rank of query, key, value tensors.
"""
if self._attention_axes is None:
self._attention_axes = tuple(range(1, rank - 2))
else:
self._attention_axes = tuple(self._attention_axes)
self._dot_product_equation, self._combine_equation, attn_scores_rank = (
_build_attention_equation(rank, attn_axes=self._attention_axes))
norm_axes = tuple(
range(attn_scores_rank - len(self._attention_axes), attn_scores_rank))
self._softmax = tf_keras.layers.Softmax(axis=norm_axes)
self._dropout_layer = tf_keras.layers.Dropout(rate=self._dropout)
def _masked_softmax(self, attention_scores, attention_mask=None):
# Normalize the attention scores to probabilities.
# `attention_scores` = [B, N, T, S]
if attention_mask is not None:
# The expand dim happens starting from the `num_heads` dimension,
# (<batch_dims>, num_heads, <query_attention_dims, key_attention_dims>)
mask_expansion_axes = [-len(self._attention_axes) * 2 - 1]
for _ in range(len(attention_scores.shape) - len(attention_mask.shape)):
attention_mask = tf.expand_dims(
attention_mask, axis=mask_expansion_axes)
return self._softmax(attention_scores, attention_mask)
def _compute_relative_position(self, query_seq_length, key_seq_length):
position_zero = self._pe_max_seq_length - 1
# We take the vector position variable and concatenate to form a matrix of
# relative position encodings. i=0 indicates reltaive position is 0.
indices = tf.expand_dims(tf.range(0, -query_seq_length, -1),
-1) + tf.range(key_seq_length) + position_zero
indices = tf.maximum(indices, 0)
indices = tf.minimum(indices, 2*self._pe_max_seq_length-2)
attention_biases = tf.gather(self._position_embeddings, indices, axis=2)
return attention_biases
def _compute_attention(self,
query,
key,
value,
reuse_scores=None,
attention_mask=None,
training=None):
"""Applies Dot-product attention with query, key, value tensors.
This function defines the computation inside `call` with projected
multi-head Q, K, V inputs. Users can override this function for customized
attention implementation.
Args:
query: Projected query `Tensor` of shape `(B, T, N, key_dim)`.
key: Projected key `Tensor` of shape `(B, T, N, key_dim)`.
value: Projected value `Tensor` of shape `(B, T, N, value_dim)`.
reuse_scores: Attention scores from a previous layer if needed.
attention_mask: a boolean mask of shape `(B, T, S)`, that prevents
attention to certain positions.
training: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (doing nothing).
Returns:
attention_output: Multi-headed outputs of attention computation.
attention_scores: Multi-headed attention weights.
"""
# Partial or no reuse
if self._reuse_heads < self._num_heads:
query = tf.multiply(query, 1.0 / math.sqrt(float(self._key_dim)))
new_scores = tf.einsum(self._dot_product_equation, key, query)
# Add relative position embeddings if required.
if self._use_relative_pe:
new_scores = new_scores + self._compute_relative_position(
tf.shape(query)[1], tf.shape(key)[1])
new_scores = self._masked_softmax(new_scores, attention_mask)
if self._reuse_heads > 0: # Partial reuse
reuse_scores = reuse_scores[:, :self._reuse_heads, :, :]
attention_scores = tf.concat([new_scores, reuse_scores], 1)
else: # No reuse
attention_scores = new_scores
else: # Full reuse
attention_scores = reuse_scores
new_scores = None
# `context_layer` = [B, T, N, H]
attention_output = []
# Partial or full reuse
if self._reuse_heads > 0:
attention_output.append(
tf.einsum(self._combine_equation, self._dropout_layer(
reuse_scores, training=training), value[0]))
# Partial or no reuse
if self._reuse_heads < self._num_heads:
attention_output.append(
tf.einsum(self._combine_equation, self._dropout_layer(
new_scores, training=training), value[-1]))
return attention_output, attention_scores
def call(self,
query,
value,
key=None,
attention_mask=None,
return_attention_scores=False,
training=None,
reuse_attention_scores=None):
if self._reuse_heads > 0 and reuse_attention_scores is None:
raise ValueError("reuse_attention_scores cannot be None when "
"reuse_attention is True or > 0.")
if not self._built_from_signature:
self._build_from_signature(query=query, value=value, key=key)
if key is None:
key = value
# N = `num_attention_heads`
# H = `size_per_head`
# `value` = [B, S, N, H]
value = [vd(value) for vd in self._value_dense]
if self._reuse_heads < self._num_heads:
# `query` = [B, T, N ,H]
query = self._query_dense(query)
# `key` = [B, S, N, H]
key = self._key_dense(key)
else:
query, key = None, None
attention_output, attention_scores = self._compute_attention(
query, key, value, reuse_attention_scores, attention_mask, training)
attention_output = [od(attention_output[i]) for i, od in enumerate(
self._output_dense)]
if len(attention_output) == 1:
attention_output = attention_output[0]
else:
attention_output = attention_output[0] + attention_output[1]
if return_attention_scores:
return attention_output, attention_scores
return attention_output