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Add n-dimensional embedding as the layer (vertical) timing signal.
Adds embeddings to represent the position of the layer in the tower.
Args:
x: a tensor with shape [batch, length, depth]
layer: layer num
num_layers: total number of layers
Returns:
a Tensor the same shape as x. | def add_layer_timing_signal_learned_1d(x, layer, num_layers):
"""Add n-dimensional embedding as the layer (vertical) timing signal.
Adds embeddings to represent the position of the layer in the tower.
Args:
x: a tensor with shape [batch, length, depth]
layer: layer num
num_layers: total number of layers
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[-1]
signal = get_layer_timing_signal_learned_1d(channels, layer, num_layers)
x += signal
return x |
Add sinusoids of different frequencies as layer (vertical) timing signal.
Args:
channels: dimension of the timing signal
layer: layer num
num_layers: total number of layers
Returns:
a Tensor of timing signals [1, 1, channels]. | def get_layer_timing_signal_sinusoid_1d(channels, layer, num_layers):
"""Add sinusoids of different frequencies as layer (vertical) timing signal.
Args:
channels: dimension of the timing signal
layer: layer num
num_layers: total number of layers
Returns:
a Tensor of timing signals [1, 1, channels].
"""
signal = get_timing_signal_1d(num_layers, channels)
layer_signal = tf.expand_dims(signal[:, layer, :], axis=1)
return layer_signal |
Add sinusoids of different frequencies as layer (vertical) timing signal.
Args:
x: a Tensor with shape [batch, length, channels]
layer: layer num
num_layers: total number of layers
Returns:
a Tensor the same shape as x. | def add_layer_timing_signal_sinusoid_1d(x, layer, num_layers):
"""Add sinusoids of different frequencies as layer (vertical) timing signal.
Args:
x: a Tensor with shape [batch, length, channels]
layer: layer num
num_layers: total number of layers
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[-1]
signal = get_layer_timing_signal_sinusoid_1d(channels, layer, num_layers)
return x + signal |
Adds sinusoids of diff frequencies to a Tensor, with timing position given.
Args:
x: a Tensor with shape [batch, length, channels]
position: a Tensor with shape [batch, length]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x. | def add_timing_signal_1d_given_position(x,
position,
min_timescale=1.0,
max_timescale=1.0e4):
"""Adds sinusoids of diff frequencies to a Tensor, with timing position given.
Args:
x: a Tensor with shape [batch, length, channels]
position: a Tensor with shape [batch, length]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[2]
num_timescales = channels // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = (
tf.expand_dims(tf.to_float(position), 2) * tf.expand_dims(
tf.expand_dims(inv_timescales, 0), 0))
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=2)
signal = tf.pad(signal, [[0, 0], [0, 0], [0, tf.mod(channels, 2)]])
signal = common_layers.cast_like(signal, x)
return x + signal |
Adds a bunch of sinusoids of different frequencies to a Tensor.
Each channel of the input Tensor is incremented by a sinusoid of a different
frequency and phase in one of the positional dimensions.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(a+b) and cos(a+b) can be
experessed in terms of b, sin(a) and cos(a).
x is a Tensor with n "positional" dimensions, e.g. one dimension for a
sequence or two dimensions for an image
We use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels // (n * 2). For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, d1 ... dn, channels]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x. | def add_timing_signal_nd(x, min_timescale=1.0, max_timescale=1.0e4):
"""Adds a bunch of sinusoids of different frequencies to a Tensor.
Each channel of the input Tensor is incremented by a sinusoid of a different
frequency and phase in one of the positional dimensions.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(a+b) and cos(a+b) can be
experessed in terms of b, sin(a) and cos(a).
x is a Tensor with n "positional" dimensions, e.g. one dimension for a
sequence or two dimensions for an image
We use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels // (n * 2). For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, d1 ... dn, channels]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
num_dims = len(x.get_shape().as_list()) - 2
channels = common_layers.shape_list(x)[-1]
num_timescales = channels // (num_dims * 2)
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
for dim in range(num_dims):
length = common_layers.shape_list(x)[dim + 1]
position = tf.to_float(tf.range(length))
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(
inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
prepad = dim * 2 * num_timescales
postpad = channels - (dim + 1) * 2 * num_timescales
signal = tf.pad(signal, [[0, 0], [prepad, postpad]])
for _ in range(1 + dim):
signal = tf.expand_dims(signal, 0)
for _ in range(num_dims - 1 - dim):
signal = tf.expand_dims(signal, -2)
x += signal
return x |
Adds positional embedding.
Args:
x: Tensor with shape [batch, length, depth].
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
positions: Tensor with shape [batch, length].
Returns:
Tensor of same shape as x. | def add_positional_embedding(x, max_length, name=None, positions=None):
"""Adds positional embedding.
Args:
x: Tensor with shape [batch, length, depth].
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
positions: Tensor with shape [batch, length].
Returns:
Tensor of same shape as x.
"""
with tf.name_scope("add_positional_embedding"):
_, length, depth = common_layers.shape_list(x)
var = tf.cast(tf.get_variable(name, [max_length, depth]), x.dtype)
if positions is None:
pad_length = tf.maximum(0, length - max_length)
sliced = tf.cond(
tf.less(length, max_length),
lambda: tf.slice(var, [0, 0], [length, -1]),
lambda: tf.pad(var, [[0, pad_length], [0, 0]]))
return x + tf.expand_dims(sliced, 0)
else:
return x + tf.gather(var, tf.to_int32(positions)) |
Adds n-dimensional positional embedding.
The embeddings add to all positional dimensions of the tensor.
Args:
x: Tensor with shape [batch, p1 ... pn, depth]. It has n positional
dimensions, i.e., 1 for text, 2 for images, 3 for video, etc.
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
Returns:
Tensor of same shape as x. | def add_positional_embedding_nd(x, max_length, name=None):
"""Adds n-dimensional positional embedding.
The embeddings add to all positional dimensions of the tensor.
Args:
x: Tensor with shape [batch, p1 ... pn, depth]. It has n positional
dimensions, i.e., 1 for text, 2 for images, 3 for video, etc.
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
Returns:
Tensor of same shape as x.
"""
with tf.name_scope("add_positional_embedding_nd"):
x_shape = common_layers.shape_list(x)
num_dims = len(x_shape) - 2
depth = x_shape[-1]
base_shape = [1] * (num_dims + 1) + [depth]
base_start = [0] * (num_dims + 2)
base_size = [-1] + [1] * num_dims + [depth]
for i in range(num_dims):
shape = base_shape[:]
start = base_start[:]
size = base_size[:]
shape[i + 1] = max_length
size[i + 1] = x_shape[i + 1]
var = tf.get_variable(
name + "_%d" % i,
shape,
initializer=tf.random_normal_initializer(0, depth**-0.5))
var = var * depth**0.5
x += tf.slice(var, start, size)
return x |
Gets edge vectors for the edge types in the adjacency matrix.
Args:
adjacency_matrix: A [batch, num_nodes, num_nodes] tensor of ints.
num_edge_types: Number of different edge types
depth: Number of channels
name: a string
Returns:
A [batch, num_nodes, num_nodes, depth] vector of tensors | def make_edge_vectors(adjacency_matrix, num_edge_types, depth, name=None):
"""Gets edge vectors for the edge types in the adjacency matrix.
Args:
adjacency_matrix: A [batch, num_nodes, num_nodes] tensor of ints.
num_edge_types: Number of different edge types
depth: Number of channels
name: a string
Returns:
A [batch, num_nodes, num_nodes, depth] vector of tensors
"""
with tf.variable_scope(name, default_name="edge_vectors"):
att_adj_vectors_shape = [num_edge_types, depth]
adjacency_matrix_shape = common_layers.shape_list(adjacency_matrix)
adj_vectors = (
tf.get_variable(
"adj_vectors",
att_adj_vectors_shape,
initializer=tf.random_normal_initializer(0, depth**-0.5)) *
(depth**0.5))
# Avoiding gathers so that it works on TPUs
# adjacency_matrix_one_hot has shape
# [batch, num_nodes, num_nodes, num_edge_types]
adjacency_matrix_one_hot = tf.one_hot(adjacency_matrix, num_edge_types)
att_adj_vectors = tf.matmul(
tf.reshape(tf.to_float(adjacency_matrix_one_hot), [-1, num_edge_types]),
adj_vectors)
return tf.reshape(att_adj_vectors,
[adjacency_matrix_shape[0], adjacency_matrix_shape[1],
adjacency_matrix_shape[2], depth]) |
Calculate the length of mask based on padding.
Args:
padding: a Tensor with shape [..., length].
Returns:
a Tensor with shape [...]. | def padding_to_length(padding):
"""Calculate the length of mask based on padding.
Args:
padding: a Tensor with shape [..., length].
Returns:
a Tensor with shape [...].
"""
non_padding = 1.0 - padding
return tf.to_int32(tf.reduce_sum(non_padding, axis=-1)) |
Create an bias tensor to be added to attention logits.
A position may attend to positions at most max_distance from it,
forward and backwards.
This does not actually save any computation.
Args:
length: int
max_backward: int, maximum distance backward to attend. Negative values
indicate unlimited.
max_forward: int, maximum distance forward to attend. Negative values
indicate unlimited.
Returns:
a `Tensor` with shape [1, 1, length, length]. | def attention_bias_local(length, max_backward, max_forward):
"""Create an bias tensor to be added to attention logits.
A position may attend to positions at most max_distance from it,
forward and backwards.
This does not actually save any computation.
Args:
length: int
max_backward: int, maximum distance backward to attend. Negative values
indicate unlimited.
max_forward: int, maximum distance forward to attend. Negative values
indicate unlimited.
Returns:
a `Tensor` with shape [1, 1, length, length].
"""
band = common_layers.ones_matrix_band_part(
length,
length,
max_backward,
max_forward,
out_shape=[1, 1, length, length])
return -1e9 * (1.0 - band) |
Create an bias tensor to be added to attention logits.
Positions with the same segment_ids can see each other.
Args:
query_segment_id: a float `Tensor` with shape [batch, query_length].
memory_segment_id: a float `Tensor` with shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, query_length, memory_length]. | def attention_bias_same_segment(query_segment_id, memory_segment_id):
"""Create an bias tensor to be added to attention logits.
Positions with the same segment_ids can see each other.
Args:
query_segment_id: a float `Tensor` with shape [batch, query_length].
memory_segment_id: a float `Tensor` with shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, query_length, memory_length].
"""
ret = (tf.to_float(
tf.not_equal(
tf.expand_dims(query_segment_id, 2),
tf.expand_dims(memory_segment_id, 1))) *
large_compatible_negative(memory_segment_id.dtype))
return tf.expand_dims(ret, axis=1) |
Create an bias tensor to be added to attention logits.
Args:
memory_padding: a float `Tensor` with shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, 1, memory_length]. | def attention_bias_ignore_padding(memory_padding):
"""Create an bias tensor to be added to attention logits.
Args:
memory_padding: a float `Tensor` with shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, 1, memory_length].
"""
ret = memory_padding * large_compatible_negative(memory_padding.dtype)
return tf.expand_dims(tf.expand_dims(ret, axis=1), axis=1) |
Inverse of attention_bias_ignore_padding().
Args:
attention_bias: a `Tensor` with shape [batch, 1, 1, memory_length], as
returned by attention_bias_ignore_padding().
cast_fn: function used to cast to output type.
Returns:
a Tensor with shape [batch, memory_length] with 1.0 in padding positions
and 0.0 in non-padding positions. Type is determined by cast_fn. | def attention_bias_to_padding(attention_bias, cast_fn=tf.to_float):
"""Inverse of attention_bias_ignore_padding().
Args:
attention_bias: a `Tensor` with shape [batch, 1, 1, memory_length], as
returned by attention_bias_ignore_padding().
cast_fn: function used to cast to output type.
Returns:
a Tensor with shape [batch, memory_length] with 1.0 in padding positions
and 0.0 in non-padding positions. Type is determined by cast_fn.
"""
# `attention_bias` is a large negative number in padding positions and 0.0
# elsewhere.
return tf.squeeze(cast_fn(tf.less(attention_bias, -1)), axis=[1, 2]) |
Create a bias tensor for prepend_mode="prepend_inputs_full_attention".
See prepend_inputs in common_hparams.py.
Produces a bias tensor to be used in self-attention.
This bias tensor allows for full connectivity in the "inputs" part of
the sequence and masked connectivity in the targets part.
Args:
padding: a float `Tensor` with shape [batch, length] with
ones in positions corresponding to padding. In each row, a single
padding position separates the input part from the target part.
Returns:
a `Tensor` with shape [batch, 1, length, length]. | def attention_bias_prepend_inputs_full_attention(padding):
"""Create a bias tensor for prepend_mode="prepend_inputs_full_attention".
See prepend_inputs in common_hparams.py.
Produces a bias tensor to be used in self-attention.
This bias tensor allows for full connectivity in the "inputs" part of
the sequence and masked connectivity in the targets part.
Args:
padding: a float `Tensor` with shape [batch, length] with
ones in positions corresponding to padding. In each row, a single
padding position separates the input part from the target part.
Returns:
a `Tensor` with shape [batch, 1, length, length].
"""
# Everything past the first padding position is part of the target.
# This Tensor has zeros for the source portion and separator,
# and ones for the target portion.
in_target = tf.cumsum(padding, axis=1, exclusive=True)
# The position within the target, or 0 if part of the source.
target_pos = tf.cumsum(in_target, axis=1)
# A position with a lesser target_pos cannot see a position with greater
# target_pos.
illegal_connections = tf.greater(
tf.expand_dims(target_pos, 1), tf.expand_dims(target_pos, 2))
bias = tf.to_float(illegal_connections) * -1e9
bias = tf.expand_dims(bias, 1)
return bias |
Bias for self-attention to encourage attention to close positions.
Args:
length: an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length] | def attention_bias_proximal(length):
"""Bias for self-attention to encourage attention to close positions.
Args:
length: an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
r = tf.to_float(tf.range(length))
diff = tf.expand_dims(r, 0) - tf.expand_dims(r, 1)
return tf.expand_dims(tf.expand_dims(-tf.log1p(tf.abs(diff)), 0), 0) |
Generate a mask to prevent the batch to attend to each others.
Args:
batch_coordinates_q: Int-like Tensor of shape [length_q, 1] containing the
coordinates of the batches
batch_coordinates_k: Int-like Tensor of shape [length_k, 1] containing the
coordinates of the batches. If None, do self-attention.
condition_fn: Callable defining the attention mask.
Returns:
Float-like Tensor of shape [length_q, length_k] containing either 0 or
-infinity (-1e9). | def attention_bias_batch(batch_coordinates_q,
batch_coordinates_k=None,
condition_fn=None):
"""Generate a mask to prevent the batch to attend to each others.
Args:
batch_coordinates_q: Int-like Tensor of shape [length_q, 1] containing the
coordinates of the batches
batch_coordinates_k: Int-like Tensor of shape [length_k, 1] containing the
coordinates of the batches. If None, do self-attention.
condition_fn: Callable defining the attention mask.
Returns:
Float-like Tensor of shape [length_q, length_k] containing either 0 or
-infinity (-1e9).
"""
if batch_coordinates_k is None:
batch_coordinates_k = batch_coordinates_q
# Convert to float first because of b/25387198.
def to_float(bc):
bc = tf.squeeze(bc, 1)
bc = tf.to_float(bc)
return bc
# Broadcast to create [length_q, length_k] mask.
bc_v = tf.expand_dims(to_float(batch_coordinates_q), 1)
bc_h = tf.expand_dims(to_float(batch_coordinates_k), 0)
bias_batch = bc_h - bc_v
bias_batch = condition_fn(bias_batch)
bias_batch *= -1e9
return bias_batch |
Reshape x so that the last dimension becomes two dimensions.
The first of these two dimensions is n.
Args:
x: a Tensor with shape [..., m]
n: an integer.
Returns:
a Tensor with shape [..., n, m/n] | def split_last_dimension(x, n):
"""Reshape x so that the last dimension becomes two dimensions.
The first of these two dimensions is n.
Args:
x: a Tensor with shape [..., m]
n: an integer.
Returns:
a Tensor with shape [..., n, m/n]
"""
x_shape = common_layers.shape_list(x)
m = x_shape[-1]
if isinstance(m, int) and isinstance(n, int):
assert m % n == 0
return tf.reshape(x, x_shape[:-1] + [n, m // n]) |
Reshape x so that the last two dimension become one.
Args:
x: a Tensor with shape [..., a, b]
Returns:
a Tensor with shape [..., ab] | def combine_last_two_dimensions(x):
"""Reshape x so that the last two dimension become one.
Args:
x: a Tensor with shape [..., a, b]
Returns:
a Tensor with shape [..., ab]
"""
x_shape = common_layers.shape_list(x)
a, b = x_shape[-2:]
return tf.reshape(x, x_shape[:-2] + [a * b]) |
Reshape x so that the first two dimension become one.
Args:
x: a Tensor with shape [a, b, ...]
Returns:
a Tensor with shape [ab, ...] | def combine_first_two_dimensions(x):
"""Reshape x so that the first two dimension become one.
Args:
x: a Tensor with shape [a, b, ...]
Returns:
a Tensor with shape [ab, ...]
"""
ret = tf.reshape(x, tf.concat([[-1], common_layers.shape_list(x)[2:]], 0))
old_shape = x.get_shape().dims
a, b = old_shape[:2]
new_shape = [a * b if a and b else None] + old_shape[2:]
ret.set_shape(new_shape)
return ret |
Compute color image summary.
Args:
attn: a Tensor with shape [batch, num_heads, query_length, memory_length]
image_shapes: optional tuple of integer scalars.
If the query positions and memory positions represent the
pixels of flattened images, then pass in their dimensions:
(query_rows, query_cols, memory_rows, memory_cols).
If the query positions and memory positions represent the
pixels x channels of flattened images, then pass in their dimensions:
(query_rows, query_cols, query_channels,
memory_rows, memory_cols, memory_channels). | def attention_image_summary(attn, image_shapes=None):
"""Compute color image summary.
Args:
attn: a Tensor with shape [batch, num_heads, query_length, memory_length]
image_shapes: optional tuple of integer scalars.
If the query positions and memory positions represent the
pixels of flattened images, then pass in their dimensions:
(query_rows, query_cols, memory_rows, memory_cols).
If the query positions and memory positions represent the
pixels x channels of flattened images, then pass in their dimensions:
(query_rows, query_cols, query_channels,
memory_rows, memory_cols, memory_channels).
"""
attn = tf.cast(attn, tf.float32)
num_heads = common_layers.shape_list(attn)[1]
# [batch, query_length, memory_length, num_heads]
image = tf.transpose(attn, [0, 2, 3, 1])
image = tf.pow(image, 0.2) # for high-dynamic-range
# Each head will correspond to one of RGB.
# pad the heads to be a multiple of 3
image = tf.pad(image, [[0, 0], [0, 0], [0, 0], [0, tf.mod(-num_heads, 3)]])
image = split_last_dimension(image, 3)
image = tf.reduce_max(image, 4)
if image_shapes is not None:
if len(image_shapes) == 4:
q_rows, q_cols, m_rows, m_cols = list(image_shapes)
image = tf.reshape(image, [-1, q_rows, q_cols, m_rows, m_cols, 3])
image = tf.transpose(image, [0, 1, 3, 2, 4, 5])
image = tf.reshape(image, [-1, q_rows * m_rows, q_cols * m_cols, 3])
else:
assert len(image_shapes) == 6
q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels = list(
image_shapes)
image = tf.reshape(
image,
[-1, q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels, 3])
image = tf.transpose(image, [0, 1, 4, 3, 2, 5, 6, 7])
image = tf.reshape(
image,
[-1, q_rows * m_rows * q_channnels, q_cols * m_cols * m_channels, 3])
tf.summary.image("attention", image, max_outputs=1) |
Multi-head dot-product attention with sparsity.
For each attention head, the queries are partitioned into groups.
For each group, only a subset of the key-value pairs are considered.
The choices of groups are selected based on trained predictors of
the total attention given the group inclusion.
memory_target_density indicates the average how many groups in which
a key-value pair should participate.
We use auxiliary losses to ensure that each group contains roughly
the same number of queries and the same number of key-value pairs.
If for a given sequence, the actual number of queries/pairs sent to
an expert exceeds this target by a factor of more than
multiplicative_overhead, then the last ones are dropped. We use
this drop-last policy to avoid bleeding information backwards, which
is necessary when using this function with autoregressive
prediction.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
num_groups: an integer
memory_target_density: a floating point scalar
multiplicative_overhead: a floating point scalar
additive_overhead: a floating point scalar
mask_right: a boolean
make_image_summary: a boolean
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads. | def grouped_attention_multihead(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
num_groups,
memory_target_density=2.0,
multiplicative_overhead=1.25,
additive_overhead=8.0,
mask_right=False,
make_image_summary=True,
name=None):
"""Multi-head dot-product attention with sparsity.
For each attention head, the queries are partitioned into groups.
For each group, only a subset of the key-value pairs are considered.
The choices of groups are selected based on trained predictors of
the total attention given the group inclusion.
memory_target_density indicates the average how many groups in which
a key-value pair should participate.
We use auxiliary losses to ensure that each group contains roughly
the same number of queries and the same number of key-value pairs.
If for a given sequence, the actual number of queries/pairs sent to
an expert exceeds this target by a factor of more than
multiplicative_overhead, then the last ones are dropped. We use
this drop-last policy to avoid bleeding information backwards, which
is necessary when using this function with autoregressive
prediction.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
num_groups: an integer
memory_target_density: a floating point scalar
multiplicative_overhead: a floating point scalar
additive_overhead: a floating point scalar
mask_right: a boolean
make_image_summary: a boolean
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
batch = common_layers.shape_list(query_antecedent)[0]
length_q = common_layers.shape_list(query_antecedent)[1]
length_kv = common_layers.shape_list(memory_antecedent)[1]
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
depth_qk = total_key_depth // num_heads
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
depth_v = total_value_depth // num_heads
with tf.variable_scope(
name, default_name="multihead_attention_sparse",
values=[query_antecedent, memory_antecedent]):
q = common_layers.dense(
query_antecedent, total_key_depth, use_bias=False, name="q_transform")
kv = common_layers.dense(
memory_antecedent,
total_key_depth + total_value_depth,
use_bias=False,
name="kv_transform")
q = split_heads(q, num_heads)
kv = split_heads(kv, num_heads)
# Make predictions about q_total and m_total.
# These are used to determine group inclusion.
# We will train these by auxiliary losses. We use stop_gradient here
# to keep these losses from back-propagating to the rest of the model.
# We add biases that help balance the usage of the experts.
q_pred = common_layers.dense(
tf.stop_gradient(query_antecedent),
num_heads * num_groups,
use_bias=False,
name="q_pred")
q_pred = split_heads(q_pred, num_heads)
q_bias = tf.get_variable("q_bias", [1, num_heads, 1, num_groups])
q_pred_biased = q_pred + q_bias
m_pred = common_layers.dense(
tf.stop_gradient(memory_antecedent),
num_heads * num_groups,
use_bias=False,
name="m_pred")
m_pred = split_heads(m_pred, num_heads)
m_bias = tf.get_variable("m_bias", [1, num_heads, 1, num_groups])
m_pred_biased = m_pred + m_bias
q *= depth_qk**-0.5
# q, kv, q_pred, m_pred are all [batch, heads, length_[q/m], ?]
# now reshape them all to [batch * heads, length, ?]
q = combine_first_two_dimensions(q)
kv = combine_first_two_dimensions(kv)
q_pred = combine_first_two_dimensions(q_pred)
m_pred = combine_first_two_dimensions(m_pred)
q_pred_biased = combine_first_two_dimensions(q_pred_biased)
m_pred_biased = combine_first_two_dimensions(m_pred_biased)
q_group = tf.argmax(q_pred_biased, axis=2)
q_requests = tf.one_hot(q_group, num_groups, axis=-1)
m_requests = tf.to_float(tf.greater(m_pred_biased, 0.0))
# include first memory position in all groups, to avoid division by zero.
m_requests = tf.maximum(
m_requests, tf.reshape(tf.one_hot([0], length_kv), [1, length_kv, 1]))
q_group_size = tf.reduce_sum(q_requests, 1)
m_group_size = tf.reduce_sum(m_requests, 1)
q_group_target_size = tf.to_float(length_q) / tf.to_float(num_groups)
m_group_target_size = (
tf.to_float(length_kv) * memory_target_density /
tf.to_float(num_groups))
capacity_q = tf.minimum(
length_q,
tf.to_int32(q_group_target_size * multiplicative_overhead +
additive_overhead))
capacity_m = tf.minimum(
length_kv,
tf.to_int32(m_group_target_size * multiplicative_overhead +
additive_overhead))
q_dispatcher = expert_utils.TruncatingDispatcher(q_requests, capacity_q)
m_dispatcher = expert_utils.TruncatingDispatcher(m_requests, capacity_m)
q_gates = q_dispatcher.gates()
m_gates = m_dispatcher.gates()
dispatched_q = q_dispatcher.dispatch(q)
dispatched_kv = m_dispatcher.dispatch(kv)
# dispatched_q: [batch * num_heads, num_groups, capacity_q, depth_qk]
# dispatched_kv:
# [batch * num_heads, num_groups, capacity_m, depth_qk + depth_v]
k, v = tf.split(dispatched_kv, [depth_qk, depth_v], axis=3)
logits = tf.matmul(dispatched_q, k, transpose_b=True)
bias = tf.expand_dims((m_dispatcher.nonpadding() - 1.0) * 1e9, 2)
if mask_right:
q_coordinate = tf.to_float(
tf.expand_dims(q_dispatcher.length_coordinate(), 3))
m_coordinate = tf.to_float(
tf.expand_dims(m_dispatcher.length_coordinate(), 2))
bias += tf.to_float(tf.greater(m_coordinate, q_coordinate)) * -1e9
logits += bias
log_weights = tf.nn.log_softmax(logits)
weights = tf.exp(log_weights)
# For each query, this is the log of the sum of the unnormalized weights.
q_total = tf.stop_gradient(logits[:, :, :, :1] - log_weights[:, :, :, :1])
# For each key, this is the sum of the normalized weights.
m_total = tf.expand_dims(
tf.reduce_sum(tf.stop_gradient(weights), axis=2), -1)
o = tf.matmul(weights, v)
o = q_dispatcher.combine(o)
o = tf.reshape(o, [batch, num_heads, length_q, depth_v])
o = combine_heads(o)
o = common_layers.dense(
o, output_depth, use_bias=False, name="output_transform")
m_total = m_dispatcher.combine(m_total)
q_total = q_dispatcher.combine(q_total)
q_total = tf.squeeze(q_total, -1)
m_total = tf.squeeze(m_total, -1)
# Compute summed m predictions for all groups
m_pred_used = tf.reduce_sum(tf.exp(m_pred) * m_dispatcher.gates(), axis=2)
q_pred_used = tf.reduce_sum(q_pred * q_dispatcher.gates(), axis=2)
epsilon = 1e-3
m_pred_used = tf.log(m_pred_used + epsilon)
m_total = tf.log(m_total + epsilon)
m_loss = tf.nn.l2_loss(m_total - m_pred_used)
q_loss = tf.nn.l2_loss(
(q_total - q_pred_used) * tf.reduce_sum(q_gates, axis=2))
q_loss /= tf.to_float(batch * length_q)
m_loss /= tf.to_float(batch * length_kv)
# We would like the query groups to be equal sized. The group
# size is discrete, so we need some trick here. We add a loss
# proportional to the product of the group size and the
# predictions for that group. This encourages the predictions to
# decrease for groups that are too big.
q_group_deviation = (q_group_size / q_group_target_size) - 1.0
q_balance_loss = tf.reduce_sum(
tf.reduce_mean(q_pred_biased, axis=1) *
q_group_deviation) / tf.to_float(batch)
m_group_deviation = (m_group_size / m_group_target_size) - 1.0
m_balance_loss = tf.reduce_sum(
tf.reduce_mean(m_pred_biased, axis=1) *
m_group_deviation) / tf.to_float(batch)
# The losses in this function only propagate back to variables
# defined in this function, and the losses outside of this
# function only propagate back to variables outside of this
# function. Assuming some kind of adaptive learning algorithm,
# it should not matter how much we scale the losses in this function.
# Still we scale them down a lot so that they should not show up
# much in the overall loss for the model.
extra_loss_multiplier = 1e-3
extra_loss = q_loss + m_loss + q_balance_loss + m_balance_loss
extra_loss *= extra_loss_multiplier
# Show a bunch of summaries.
if common_layers.should_generate_summaries() and make_image_summary:
tf.summary.histogram("q_group_size", q_group_size)
tf.summary.histogram("m_group_size", m_group_size)
tf.summary.scalar("q_loss", q_loss)
tf.summary.scalar("m_loss", m_loss)
tf.summary.scalar("q_balance_loss", q_balance_loss)
tf.summary.scalar("m_balance_loss", m_balance_loss)
tf.summary.histogram("m_pred_used", m_pred_used)
tf.summary.histogram("m_total", m_total)
tf.summary.histogram("q_pred_used", q_pred_used)
tf.summary.histogram("q_total", q_total)
if make_image_summary:
# image summaries are expensive.
# So we restrict them to head_num<4, query_position<512, batch_index=0.
trunc_heads = min(4, num_heads)
trunc_length_q = tf.minimum(length_q, 512)
# We recompute the attention for the first example, in an inefficient
# way - masking. This lets us show pretty pictures.
# [trunc_heads, length_q, group]
q_gates_trunc = q_gates[:trunc_heads, :trunc_length_q, :]
# [trunc_heads, length_kv, group]
m_gates_trunc = m_gates[:trunc_heads, :, :]
grouping_mask = tf.matmul(
q_gates_trunc, m_gates_trunc, transpose_b=True)
q_trunc = q[:trunc_heads, :trunc_length_q, :]
k_trunc = kv[:trunc_heads, :, :depth_qk]
logits_trunc = tf.matmul(q_trunc, k_trunc, transpose_b=True)
if mask_right:
band = common_layers.ones_matrix_band_part(trunc_length_q, length_kv,
-1, 0)
trunc_bias = tf.expand_dims((1.0 - band) * -1e9, 0)
logits_trunc += trunc_bias
att_trunc = tf.nn.softmax(logits_trunc)
mask_coverage = tf.reduce_sum(grouping_mask * att_trunc) / (
tf.to_float(trunc_length_q) * trunc_heads)
tf.summary.scalar("coverage", mask_coverage)
att_trunc_hdr = tf.pow(att_trunc, 0.2) # for high-dynamic-range
mask_channel = grouping_mask * tf.maximum(att_trunc_hdr, 0.3)
image = tf.stack([att_trunc_hdr, mask_channel, mask_channel], axis=3)
tf.summary.image("att", image, max_outputs=trunc_heads)
# show one group for each head.
att_per_group = tf.expand_dims(weights[:trunc_heads, 0, :, :], -1)
tf.summary.image(
"att_per_group_%d",
tf.pow(att_per_group, 0.2),
max_outputs=trunc_heads)
return o, extra_loss |
Make attention weights non-0 only on the top-hard_attention_k ones. | def harden_attention_weights(weights, hard_attention_k):
"""Make attention weights non-0 only on the top-hard_attention_k ones."""
# Subtract the top-kth weight and zero-out all lower ones.
# Note that currently in case of numerical ties it will retain more
# than k elements. In the future, we may want to avoid this.
weights -= common_layers.top_kth_iterative(weights, hard_attention_k)
weights = tf.nn.relu(weights)
# Re-normalize the weights.
weights_sum = tf.reduce_sum(weights, axis=-1, keep_dims=True)
weights_sum = tf.maximum(weights_sum, 1e-6) # Avoid division by 0.
weights /= weights_sum
return weights |
Dot-product attention.
Args:
q: Tensor with shape [..., length_q, depth_k].
k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
match with q.
v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
match with q.
bias: bias Tensor (see attention_bias())
dropout_rate: a float.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
name: an optional string
make_image_summary: True if you want an image summary.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
dropout_broadcast_dims: an optional list of integers less than rank of q.
Specifies in which dimensions to broadcast the dropout decisions.
activation_dtype: Used to define function activation dtype when using
mixed precision.
weight_dtype: The dtype weights are stored in when using mixed precision
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
Tensor with shape [..., length_q, depth_v]. | def dot_product_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
make_image_summary=True,
save_weights_to=None,
dropout_broadcast_dims=None,
activation_dtype=None,
weight_dtype=None,
hard_attention_k=0):
"""Dot-product attention.
Args:
q: Tensor with shape [..., length_q, depth_k].
k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
match with q.
v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
match with q.
bias: bias Tensor (see attention_bias())
dropout_rate: a float.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
name: an optional string
make_image_summary: True if you want an image summary.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
dropout_broadcast_dims: an optional list of integers less than rank of q.
Specifies in which dimensions to broadcast the dropout decisions.
activation_dtype: Used to define function activation dtype when using
mixed precision.
weight_dtype: The dtype weights are stored in when using mixed precision
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
Tensor with shape [..., length_q, depth_v].
"""
with tf.variable_scope(
name, default_name="dot_product_attention", values=[q, k, v]) as scope:
logits = tf.matmul(q, k, transpose_b=True) # [..., length_q, length_kv]
if bias is not None:
bias = common_layers.cast_like(bias, logits)
logits += bias
# If logits are fp16, upcast before softmax
logits = maybe_upcast(logits, activation_dtype, weight_dtype)
weights = tf.nn.softmax(logits, name="attention_weights")
if hard_attention_k > 0:
weights = harden_attention_weights(weights, hard_attention_k)
weights = common_layers.cast_like(weights, q)
if save_weights_to is not None:
save_weights_to[scope.name] = weights
save_weights_to[scope.name + "/logits"] = logits
# Drop out attention links for each head.
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
return tf.matmul(weights, v) |
Generates matrix of relative positions between inputs. | def _generate_relative_positions_matrix(length_q, length_k,
max_relative_position,
cache=False):
"""Generates matrix of relative positions between inputs."""
if not cache:
if length_q == length_k:
range_vec_q = range_vec_k = tf.range(length_q)
else:
range_vec_k = tf.range(length_k)
range_vec_q = range_vec_k[-length_q:]
distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
else:
distance_mat = tf.expand_dims(tf.range(-length_k+1, 1, 1), 0)
distance_mat_clipped = tf.clip_by_value(distance_mat, -max_relative_position,
max_relative_position)
# Shift values to be >= 0. Each integer still uniquely identifies a relative
# position difference.
final_mat = distance_mat_clipped + max_relative_position
return final_mat |
Generates tensor of size [1 if cache else length_q, length_k, depth]. | def _generate_relative_positions_embeddings(length_q, length_k, depth,
max_relative_position, name,
cache=False):
"""Generates tensor of size [1 if cache else length_q, length_k, depth]."""
with tf.variable_scope(name):
relative_positions_matrix = _generate_relative_positions_matrix(
length_q, length_k, max_relative_position, cache=cache)
vocab_size = max_relative_position * 2 + 1
# Generates embedding for each relative position of dimension depth.
embeddings_table = tf.get_variable("embeddings", [vocab_size, depth])
embeddings = tf.gather(embeddings_table, relative_positions_matrix)
return embeddings |
Relative position-aware dot-product attention inner calculation.
This batches matrix multiply calculations to avoid unnecessary broadcasting.
Args:
x: Tensor with shape [batch_size, heads, length or 1, length or depth].
y: Tensor with shape [batch_size, heads, length or 1, depth].
z: Tensor with shape [length or 1, length, depth].
transpose: Whether to transpose inner matrices of y and z. Should be true if
last dimension of x is depth, not length.
Returns:
A Tensor with shape [batch_size, heads, length, length or depth]. | def _relative_attention_inner(x, y, z, transpose):
"""Relative position-aware dot-product attention inner calculation.
This batches matrix multiply calculations to avoid unnecessary broadcasting.
Args:
x: Tensor with shape [batch_size, heads, length or 1, length or depth].
y: Tensor with shape [batch_size, heads, length or 1, depth].
z: Tensor with shape [length or 1, length, depth].
transpose: Whether to transpose inner matrices of y and z. Should be true if
last dimension of x is depth, not length.
Returns:
A Tensor with shape [batch_size, heads, length, length or depth].
"""
batch_size = tf.shape(x)[0]
heads = x.get_shape().as_list()[1]
length = tf.shape(x)[2]
# xy_matmul is [batch_size, heads, length or 1, length or depth]
xy_matmul = tf.matmul(x, y, transpose_b=transpose)
# x_t is [length or 1, batch_size, heads, length or depth]
x_t = tf.transpose(x, [2, 0, 1, 3])
# x_t_r is [length or 1, batch_size * heads, length or depth]
x_t_r = tf.reshape(x_t, [length, heads * batch_size, -1])
# x_tz_matmul is [length or 1, batch_size * heads, length or depth]
x_tz_matmul = tf.matmul(x_t_r, z, transpose_b=transpose)
# x_tz_matmul_r is [length or 1, batch_size, heads, length or depth]
x_tz_matmul_r = tf.reshape(x_tz_matmul, [length, batch_size, heads, -1])
# x_tz_matmul_r_t is [batch_size, heads, length or 1, length or depth]
x_tz_matmul_r_t = tf.transpose(x_tz_matmul_r, [1, 2, 0, 3])
return xy_matmul + x_tz_matmul_r_t |
Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer specifying the maximum distance between
inputs that unique position embeddings should be learned for.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
name: an optional string.
make_image_summary: Whether to make an attention image summary.
cache: whether use cache mode
allow_memory: whether to assume that recurrent memory is in use. If True,
the length dimension of k/v/bias may be longer than the queries, and it is
assumed that the extra memory entries precede the non-memory entries.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0. | def dot_product_attention_relative(q,
k,
v,
bias,
max_relative_position,
dropout_rate=0.0,
image_shapes=None,
save_weights_to=None,
name=None,
make_image_summary=True,
cache=False,
allow_memory=False,
hard_attention_k=0):
"""Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer specifying the maximum distance between
inputs that unique position embeddings should be learned for.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
name: an optional string.
make_image_summary: Whether to make an attention image summary.
cache: whether use cache mode
allow_memory: whether to assume that recurrent memory is in use. If True,
the length dimension of k/v/bias may be longer than the queries, and it is
assumed that the extra memory entries precede the non-memory entries.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name, default_name="dot_product_attention_relative",
values=[q, k, v]) as scope:
# This calculation only works for self attention.
# q, k and v must therefore have the same shape, unless memory is enabled.
if not cache and not allow_memory:
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# Use separate embeddings suitable for keys and values.
depth = k.get_shape().as_list()[3]
length_k = common_layers.shape_list(k)[2]
length_q = common_layers.shape_list(q)[2] if allow_memory else length_k
relations_keys = _generate_relative_positions_embeddings(
length_q, length_k, depth, max_relative_position,
"relative_positions_keys", cache=cache)
relations_values = _generate_relative_positions_embeddings(
length_q, length_k, depth, max_relative_position,
"relative_positions_values", cache=cache)
# Compute self attention considering the relative position embeddings.
logits = _relative_attention_inner(q, k, relations_keys, True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
if hard_attention_k > 0:
weights = harden_attention_weights(weights, hard_attention_k)
if save_weights_to is not None:
save_weights_to[scope.name] = weights
save_weights_to[scope.name + "/logits"] = logits
weights = tf.nn.dropout(weights, 1.0 - dropout_rate)
if not tf.get_variable_scope().reuse and make_image_summary:
attention_image_summary(weights, image_shapes)
return _relative_attention_inner(weights, v, relations_values, False) |
Helper to dot_product_self_attention_relative_v2.
Rearrange an attention logits or weights Tensor.
The dimensions of the input represent:
[batch, heads, query_position, memory_position - query_position + length - 1]
The dimensions of the output represent:
[batch, heads, query_position, memory_position]
Only works with masked_attention. Undefined behavior for regions of the
input where memory_position > query_position.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, length] | def _relative_position_to_absolute_position_masked(x):
"""Helper to dot_product_self_attention_relative_v2.
Rearrange an attention logits or weights Tensor.
The dimensions of the input represent:
[batch, heads, query_position, memory_position - query_position + length - 1]
The dimensions of the output represent:
[batch, heads, query_position, memory_position]
Only works with masked_attention. Undefined behavior for regions of the
input where memory_position > query_position.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, length]
"""
batch, heads, length, _ = common_layers.shape_list(x)
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0]])
x = tf.reshape(x, [batch, heads, 1 + length, length])
x = tf.slice(x, [0, 0, 1, 0], [-1, -1, -1, -1])
return x |
Helper function for dot_product_unmasked_self_attention_relative_v2.
Rearrange an attention logits or weights Tensor.
The dimensions of the input represent:
[batch, heads, query_position, memory_position]
The dimensions of the output represent:
[batch, heads, query_position, memory_position - query_position + length - 1]
Only works with unmasked_attention.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, 2*length-1] | def _absolute_position_to_relative_position_unmasked(x):
"""Helper function for dot_product_unmasked_self_attention_relative_v2.
Rearrange an attention logits or weights Tensor.
The dimensions of the input represent:
[batch, heads, query_position, memory_position]
The dimensions of the output represent:
[batch, heads, query_position, memory_position - query_position + length - 1]
Only works with unmasked_attention.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, 2*length-1]
"""
batch, heads, length, _ = common_layers.shape_list(x)
# padd along column
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [0, length-1]])
x_flat = tf.reshape(x, [batch, heads, length**2 + length*(length -1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = tf.pad(x_flat, [[0, 0], [0, 0], [length, 0]])
x = tf.reshape(x_flat, [batch, heads, length, 2*length])
x = tf.slice(x, [0, 0, 0, 1], [batch, heads, length,
2*length -1])
return x |
Instantiate or retrieve relative embeddings, sliced according to length.
Use for unmasked case where the relative attention looks both left and right.
Args:
max_relative_position: an Integer for the number of entries in the relative
embedding, which corresponds to the max relative distance that is
considered.
length: an Integer, specifies the length of the input sequence for which
this relative embedding is retrieved for.
depth: an Integer, specifies the depth for relative embeddings.
num_heads: an Integer, specifies the number of heads.
heads_share_relative_embedding: a Boolean specifying if the relative
embedding is shared across heads.
name: a string giving the name of the embedding variables.
Returns:
a Tensor with shape [length, depth] | def get_relative_embeddings_left_right(max_relative_position, length, depth,
num_heads,
heads_share_relative_embedding,
name):
"""Instantiate or retrieve relative embeddings, sliced according to length.
Use for unmasked case where the relative attention looks both left and right.
Args:
max_relative_position: an Integer for the number of entries in the relative
embedding, which corresponds to the max relative distance that is
considered.
length: an Integer, specifies the length of the input sequence for which
this relative embedding is retrieved for.
depth: an Integer, specifies the depth for relative embeddings.
num_heads: an Integer, specifies the number of heads.
heads_share_relative_embedding: a Boolean specifying if the relative
embedding is shared across heads.
name: a string giving the name of the embedding variables.
Returns:
a Tensor with shape [length, depth]
"""
initializer_stddev = depth**-0.5
max_relative_position_unmasked = 2 * max_relative_position - 1
if heads_share_relative_embedding:
embedding_shape = (max_relative_position_unmasked, depth)
else:
embedding_shape = (num_heads, max_relative_position_unmasked, depth)
relative_embeddings = tf.get_variable(
name=name, shape=embedding_shape,
initializer=tf.random_normal_initializer(stddev=initializer_stddev))
# Pad first before slice to avoid using tf.cond.
pad_length = tf.maximum(length - max_relative_position, 0)
slice_start_position = tf.maximum(max_relative_position-length, 0)
if heads_share_relative_embedding:
padded_relative_embeddings = tf.pad(
relative_embeddings,
[[pad_length, pad_length], [0, 0]])
used_relative_embeddings = tf.slice(
padded_relative_embeddings,
[slice_start_position, 0], [2 * length - 1, -1])
else:
padded_relative_embeddings = tf.pad(
relative_embeddings,
[[0, 0], [pad_length, pad_length], [0, 0]])
used_relative_embeddings = tf.slice(
padded_relative_embeddings,
[0, slice_start_position, 0], [-1, 2 * length - 1, -1])
return used_relative_embeddings |
Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0. | def dot_product_unmasked_self_attention_relative_v2(
q, k, v, bias, max_relative_position=None, dropout_rate=0.0,
image_shapes=None, name=None, make_image_summary=True,
dropout_broadcast_dims=None, heads_share_relative_embedding=False,
add_relative_to_values=False):
"""Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name,
default_name="dot_product_unmasked_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(q, k, transpose_b=True)
length = common_layers.shape_list(q)[2]
k_shape = common_layers.shape_list(k)
num_heads = k_shape[1]
depth_k = k_shape[-1]
key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, length, depth_k, num_heads,
heads_share_relative_embedding,
"key_relative_embeddings")
unmasked_rel_logits = matmul_with_relative_keys(
q, key_relative_embeddings, heads_share_relative_embedding)
unmasked_rel_logits = _relative_position_to_absolute_position_unmasked(
unmasked_rel_logits)
logits += unmasked_rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
# relative_weights.set_shape([None, None, None, max_length])
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, v)
if add_relative_to_values:
# Adds the contribution of the weighted relative embeddings to the values.
# [batch, num_heads, query_length, 2*memory_length-1]
relative_weights = _absolute_position_to_relative_position_unmasked(
weights)
depth_v = common_layers.shape_list(v)[3]
value_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, length, depth_v, num_heads,
heads_share_relative_embedding, "value_relative_embeddings")
ret += matmul_with_relative_values(
relative_weights, value_relative_embeddings,
heads_share_relative_embedding)
return ret |
Helper function for dot_product_unmasked_self_attention_relative_2d. | def _matmul_with_relative_keys_2d(x, y, heads_share_relative_embedding):
"""Helper function for dot_product_unmasked_self_attention_relative_2d."""
if heads_share_relative_embedding:
ret = tf.einsum("bhxyd,md->bhxym", x, y)
else:
ret = tf.einsum("bhxyd,hmd->bhxym", x, y)
return ret |
Calculate relative position unmasked dot-product self-attention 2d.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v in
height and width dimensions. for query index (i,j) and key index (l, m),
the logit is q_i k_j^T + q_i rh_{l-i}^T + q_i rw_{m-j}^T, where rh and ry are
the set of relative embeddings in height and width spatial dimensions,
respectively.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for adding relative embeddings to values.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
height_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for height.
width_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for width.
Raises:
ValueError: if max_relative_position is not > 0. | def dot_product_unmasked_self_attention_relative_2d(
q, k, v, bias, max_relative_position=None, dropout_rate=0.0,
image_shapes=None, name=None, make_image_summary=True,
dropout_broadcast_dims=None, heads_share_relative_embedding=False,
add_relative_to_values=False):
"""Calculate relative position unmasked dot-product self-attention 2d.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v in
height and width dimensions. for query index (i,j) and key index (l, m),
the logit is q_i k_j^T + q_i rh_{l-i}^T + q_i rw_{m-j}^T, where rh and ry are
the set of relative embeddings in height and width spatial dimensions,
respectively.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for adding relative embeddings to values.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
height_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for height.
width_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for width.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
if add_relative_to_values:
raise ValueError("Adding relative embeddings to values is not implemented")
with tf.variable_scope(
name,
default_name="dot_product_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape()[:-1].assert_is_compatible_with(v.get_shape()[:-1])
(height, width) = (common_layers.shape_list(q)[2],
common_layers.shape_list(q)[3])
k_shape = common_layers.shape_list(k)
num_heads = k_shape[1]
depth_k = k_shape[-1]
depth_v = common_layers.shape_list(v)[-1]
# flatten height width
flatten_hw = lambda x, d: tf.reshape(x, [-1, num_heads, height*width, d])
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(flatten_hw(q, depth_k), flatten_hw(k, depth_k),
transpose_b=True)
def _compute_2d_relative_logits(
query, key_relative_embeddings, height, width,
heads_share_relative_embedding, transpose_mask):
"""compute relative logits."""
unmasked_rel_logits = _matmul_with_relative_keys_2d(
query, key_relative_embeddings, heads_share_relative_embedding)
# collapse height and heads
unmasked_rel_logits = tf.reshape(unmasked_rel_logits,
[-1, num_heads*height, width,
2*width-1])
unmasked_rel_logits = (
_relative_position_to_absolute_position_unmasked(
unmasked_rel_logits))
# shape it back for tiling
unmasked_rel_logits = tf.reshape(
unmasked_rel_logits, [-1, num_heads, height, width, width])
# tiling it height times
unmasked_rel_logits = tf.expand_dims(
unmasked_rel_logits, axis=3)
unmasked_rel_logits = tf.tile(unmasked_rel_logits,
[1, 1, 1, height, 1, 1])
# bringing it to the right shape for adding to the logits.
unmasked_rel_logits = tf.transpose(unmasked_rel_logits, transpose_mask)
unmasked_rel_logits = tf.reshape(unmasked_rel_logits,
[-1, num_heads, height*width,
height*width])
return unmasked_rel_logits
# Relative logits in width dimension first.
width_key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, width, depth_k, num_heads,
heads_share_relative_embedding,
"width_key_relative_embeddings")
# [batch, heads, height, 2*width-1, 2*width-1]
width_unmasked_rel_logits = _compute_2d_relative_logits(
q, width_key_relative_embeddings, height, width,
heads_share_relative_embedding, [0, 1, 2, 4, 3, 5])
logits += width_unmasked_rel_logits
# Relative logits in height dimension next. For ease, we transpose
# height and width and repeat the above steps, and transpose to eventually
# put the logits in their right positions.
# [batch, heads, height, 2*height-1, 2*width-1]
height_key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, height, depth_k, num_heads,
heads_share_relative_embedding,
"height_key_relative_embeddings")
height_unmasked_rel_logits = _compute_2d_relative_logits(
tf.transpose(q, [0, 1, 3, 2, 4]),
height_key_relative_embeddings,
width,
height,
heads_share_relative_embedding, [0, 1, 4, 2, 5, 3])
logits += height_unmasked_rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, flatten_hw(v, depth_v))
# reshape back the same spatial dimensions as q
return (
tf.reshape(ret, [-1, num_heads, height, width, depth_v]),
height_key_relative_embeddings,
width_key_relative_embeddings) |
Helper function for local 2d attention.
Takes a tensor of [batch, heads, num_h_blocks, num_w_blocks,
height, width, depth] and returns two tensors which contain every alternate
position along the width
Args:
x_left_right_blocks: A [batch, num_h_blocks, num_w_blocks,
height, width, depth] tensor
Returns:
x_left_blocks, x_right_blocks: two [batch, num_h_blocks,
(num_w_blocks-2)/2, height, width,
depth] tensors | def _split_along_width(x_left_right_blocks):
"""Helper function for local 2d attention.
Takes a tensor of [batch, heads, num_h_blocks, num_w_blocks,
height, width, depth] and returns two tensors which contain every alternate
position along the width
Args:
x_left_right_blocks: A [batch, num_h_blocks, num_w_blocks,
height, width, depth] tensor
Returns:
x_left_blocks, x_right_blocks: two [batch, num_h_blocks,
(num_w_blocks-2)/2, height, width,
depth] tensors
"""
(_, x_num_h_blocks, x_num_outer_w_blocks, x_memory_flange_h,
x_memory_flange_w, depth) = common_layers.shape_list(x_left_right_blocks)
x_num_w_blocks = (x_num_outer_w_blocks-1)//2
# get it ready for splitting the left and right memory blocks
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1,
x_num_h_blocks,
x_num_outer_w_blocks//2, 2,
x_memory_flange_h,
x_memory_flange_w, depth])
x_left_blocks, x_right_blocks = tf.split(x_left_right_blocks,
num_or_size_splits=2, axis=3)
x_left_blocks = tf.squeeze(x_left_blocks, axis=3)
x_right_blocks = tf.squeeze(x_right_blocks, axis=3)
x_left_blocks = tf.slice(x_left_blocks, [0, 0, 0, 0, 0, 0],
[-1, -1, x_num_w_blocks, -1, -1, -1])
x_right_blocks = tf.slice(x_right_blocks, [0, 0, 1, 0, 0, 0],
[-1, -1, x_num_w_blocks, -1, -1, -1])
return x_left_blocks, x_right_blocks |
Helper function. Assumes that memory_flange is half of query sizes.
This function splits the tensor of width 'n' into two halves, where the
first half gets the width indices 0, 2, 4.. and the second half gets the
width indices 3, 5, ... We also fuse two blocks along the h dimension.
Args:
x: a 6-d tensor.
Returns:
x_left_blocks, x_right_blocks: Two 6-d tensors | def _get_left_right_blocks(x):
"""Helper function. Assumes that memory_flange is half of query sizes.
This function splits the tensor of width 'n' into two halves, where the
first half gets the width indices 0, 2, 4.. and the second half gets the
width indices 3, 5, ... We also fuse two blocks along the h dimension.
Args:
x: a 6-d tensor.
Returns:
x_left_blocks, x_right_blocks: Two 6-d tensors
"""
(_, x_num_outer_h_blocks, x_num_outer_w_blocks, x_memory_flange_h,
x_memory_flange_w, depth) = common_layers.shape_list(x)
x_left_right_blocks = tf.slice(x,
[0, 1, 0, 0, 0, 0],
[-1, x_num_outer_h_blocks-2, -1, -1,
-1, -1])
num_blocks_h = (x_num_outer_h_blocks-2)//2
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1,
num_blocks_h,
2, x_num_outer_w_blocks,
x_memory_flange_h,
x_memory_flange_w, depth])
x_left_right_blocks = tf.transpose(x_left_right_blocks,
[0, 1, 3, 2, 4, 5, 6])
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1, num_blocks_h,
x_num_outer_w_blocks, 2*x_memory_flange_h,
x_memory_flange_w, depth])
# get it ready for splitting the left and right memory blocks
x_left_blocks, x_right_blocks = _split_along_width(x_left_right_blocks)
return x_left_blocks, x_right_blocks |
Stitches together the local 2d memory blocks.
Args:
x: a [batch, height, width, depth tensor]
query_shape: 2-d integer list of query shape
memory_flange: 2-d integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor. | def get_2d_local_memory(x, query_shape, memory_flange):
"""Stitches together the local 2d memory blocks.
Args:
x: a [batch, height, width, depth tensor]
query_shape: 2-d integer list of query shape
memory_flange: 2-d integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
"""
(_, height, width, depth_x) = common_layers.shape_list(x)
x_center_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
# add extra padding to x so that we can extract the memory region
# around the center
paddings = [[0, 0], [memory_flange[0], memory_flange[0]],
[memory_flange[1], memory_flange[1]], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_x.set_shape([None, height+2*memory_flange[0],
width+2*memory_flange[1], depth_x])
x_outer_memory_blocks = _extract_blocks(padded_x,
memory_flange[0], memory_flange[1])
# We'll extract left and right memory blocks, top and bottom memory blocks,
# and then the corner memory blocks
# Each of these after will have shape
# [batch, num_h_blocks, num_w_blocks, query_shape[0],
# memory_flange[1], depth]
x_left_blocks, x_right_blocks = _get_left_right_blocks(
x_outer_memory_blocks)
t_hw_block = lambda x: tf.transpose(x, [0, 2, 1, 4, 3, 5])
# now to get top and bottom blocks, we should just transpose the outer
# blocks, call the same function and transpose back to get shape
# [batch, num_h_blocks, num_w_blocks, memory_flange[0],
# query_shape[1], depth]
x_top_center_blocks, x_bottom_center_blocks = (
map(t_hw_block, _get_left_right_blocks(
t_hw_block(x_outer_memory_blocks))))
# now to get the corner blocks
x_left_corner_blocks, x_right_corner_blocks = _split_along_width(
x_outer_memory_blocks)
# now to extract top and bottom for both k and v
# we need to transpose because _split_along_width separates along
# the width
# each of these should have shape [batch, num_h_blocks,
# num_w_blocks, memory_flange[0], memory_flange[1], depth]
t_hw = lambda x: tf.transpose(x, [0, 2, 1, 3, 4, 5])
x_top_left_corner_blocks, x_bottom_left_corner_blocks = (
map(t_hw, _split_along_width(t_hw(x_left_corner_blocks))))
x_top_right_corner_blocks, x_bottom_right_corner_blocks = (
map(t_hw, _split_along_width(t_hw(x_right_corner_blocks))))
# The memory is top_left top_center top_right
# left_center middle right_center
# bottom_left bottom_center bottom_right
# Assembling the above row by row
# first [x_top_left, x_top, x_top_right]
# to get [batch, num_h_blocks, num_w_blocks, memory_flange[0],
# query_shape[1]+2*memory_flange[1], depth]
# then [x_left, x_center, x_right]
# then [x_bottom_left, x_bottom, x_bottom_right]
x_top_memory = tf.concat(
[x_top_left_corner_blocks,
x_top_center_blocks,
x_top_right_corner_blocks], axis=4)
x_middle_memory = tf.concat(
[x_left_blocks, x_center_blocks, x_right_blocks], axis=4)
x_bottom_memory = tf.concat(
[x_bottom_left_corner_blocks,
x_bottom_center_blocks,
x_bottom_right_corner_blocks], axis=4)
# concat along height
x = tf.concat([x_top_memory, x_middle_memory, x_bottom_memory], axis=3)
return x |
Gathering memory blocks around query blocks. flange is half of query .
Only works if memory flanges are half of query sizes.
Args:
x: a [batch, height, width, depth tensor]
query_shape: 2-d integer list of query shape
memory_flange: 2-d integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor. | def get_2d_local_memory_v2(x, query_shape, memory_flange):
"""Gathering memory blocks around query blocks. flange is half of query .
Only works if memory flanges are half of query sizes.
Args:
x: a [batch, height, width, depth tensor]
query_shape: 2-d integer list of query shape
memory_flange: 2-d integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
"""
(_, height, width, depth_x) = common_layers.shape_list(x)
# add extra padding to x so that we can extract the memory region
# around the center
paddings = [[0, 0], [memory_flange[0], memory_flange[0]],
[memory_flange[1], memory_flange[1]], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_x.set_shape([None, height+2*memory_flange[0],
width+2*memory_flange[1], depth_x])
num_h_memory_blocks = height//query_shape[0] + 1
num_w_memory_blocks = width//query_shape[1] + 1
x_memory_blocks = _extract_blocks(padded_x,
query_shape[0], query_shape[1])
x_width_blocks = tf.split(x_memory_blocks, num_w_memory_blocks,
2)
x_left_width = tf.concat(x_width_blocks[:num_w_memory_blocks - 1], axis=2)
x_right_width = tf.concat(x_width_blocks[1:], axis=2)
x_memory_blocks = tf.concat([x_left_width, x_right_width], axis=4)
x_height_blocks = tf.split(x_memory_blocks, num_h_memory_blocks, 1)
x_top_height = tf.concat(x_height_blocks[:num_h_memory_blocks - 1], axis=1)
x_bottom_height = tf.concat(x_height_blocks[1:], axis=1)
x = tf.concat([x_top_height, x_bottom_height], axis=3)
return x |
Calculate unmasked dot-product local self-attention 2d on tpu.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention. | def dot_product_unmasked_attention_local_2d_tpu(
q, k, v, bias, max_relative_position=None, query_shape=(8, 8),
dropout_rate=0.0, image_shapes=None, name=None, make_image_summary=False,
dropout_broadcast_dims=None):
"""Calculate unmasked dot-product local self-attention 2d on tpu.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
"""
if max_relative_position:
raise ValueError("Relative local 2d attention not implemented")
with tf.variable_scope(
name,
default_name="dot_product_unmasked_attention_local_2d_tpu",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
orig_q_shape = common_layers.shape_list(q)
# Pad query, key, value to ensure multiple of corresponding lengths.
memory_flange = [int(query_shape[0]//2), int(query_shape[1]//2)]
q = pad_to_multiple_2d(q, query_shape)
k = pad_to_multiple_2d(k, query_shape)
v = pad_to_multiple_2d(v, query_shape)
q_shape = common_layers.shape_list(q)
(height, width) = (q_shape[2],
q_shape[3])
_, num_heads, height, width, depth_k = common_layers.shape_list(k)
depth_v = common_layers.shape_list(v)[-1]
num_h_blocks = height//query_shape[0]
num_w_blocks = width//query_shape[1]
# Extract center queries, keys, and values
q = tf.reshape(q, [-1, height, width, depth_k])
queries = _extract_blocks(
q, query_shape[0], query_shape[1])
k = tf.reshape(k, [-1, height, width, depth_k])
keys = get_2d_local_memory_v2(
k, query_shape, memory_flange)
v = tf.reshape(v, [-1, height, width, depth_v])
values = get_2d_local_memory_v2(
v, query_shape, memory_flange)
memory_h = query_shape[0] + 2*memory_flange[0]
memory_w = query_shape[1] + 2*memory_flange[1]
queries = tf.reshape(queries, [-1, num_heads, num_h_blocks, num_w_blocks,
query_shape[0]*query_shape[1], depth_k])
keys = tf.reshape(keys, [-1, num_heads, num_h_blocks, num_w_blocks,
memory_h*memory_w, depth_k])
values = tf.reshape(values, [-1, num_heads, num_h_blocks, num_w_blocks,
memory_h*memory_w, depth_v])
logits = tf.matmul(queries, keys, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, values)
# we need to get it back to shape [batch, heads, height, width]
ret = tf.reshape(ret, [-1, num_heads, num_h_blocks, num_w_blocks,
query_shape[0], query_shape[1], depth_v])
ret = tf.transpose(ret, [0, 1, 2, 4, 3, 5, 6])
ret = tf.reshape(ret, [-1, num_heads, num_h_blocks*query_shape[0],
num_w_blocks*query_shape[1], depth_v])
# slice if padding was introduced
ret = tf.slice(ret, [0, 0, 0, 0, 0], [-1, -1, orig_q_shape[2],
orig_q_shape[3], -1])
return ret |
Calculate simple unmasked dot-product local self-attention 2d on tpu.
The query, key, and value blocks are the same. We do not do a second linear
transformation after computing the values
Args:
x: a Tensor with shape [batch, height, width, depth].
bias: bias Tensor.
total_key_depth: the dimensions of the keys
total_value_depth: the dimensions of the values
num_heads: number of heads
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
ret: [batch, height, width, total_value_depth] tensor,
the output of attention.
q: [batch, height, width, total_key_depth] query tensor
k: [batch, height, width, total_key_depth] key tensor
v: [batch, height, width, total_value_depth] value tensor | def dot_product_unmasked_attention_local_2d_tpu_simple(
x, bias, total_key_depth, total_value_depth, num_heads,
query_shape=(8, 8),
dropout_rate=0.0, image_shapes=None, make_image_summary=False,
dropout_broadcast_dims=None):
"""Calculate simple unmasked dot-product local self-attention 2d on tpu.
The query, key, and value blocks are the same. We do not do a second linear
transformation after computing the values
Args:
x: a Tensor with shape [batch, height, width, depth].
bias: bias Tensor.
total_key_depth: the dimensions of the keys
total_value_depth: the dimensions of the values
num_heads: number of heads
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
ret: [batch, height, width, total_value_depth] tensor,
the output of attention.
q: [batch, height, width, total_key_depth] query tensor
k: [batch, height, width, total_key_depth] key tensor
v: [batch, height, width, total_value_depth] value tensor
"""
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
orig_x_shape = common_layers.shape_list(x)
# Pad query, key, value to ensure multiple of corresponding lengths if
# necessary
is_padded = False
if (orig_x_shape[1]%query_shape[0]) != 0 or (
orig_x_shape[2]%query_shape[1]) != 0:
x = pad_to_multiple_2d(x, query_shape)
is_padded = True
_, height, width, depth = common_layers.shape_list(x)
assert depth%num_heads == 0
num_h_blocks = height//query_shape[0]
num_w_blocks = width//query_shape[1]
# Extract center queries, keys, and values
x_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
x_blocks = tf.reshape(x_blocks, [-1, query_shape[0]*query_shape[1], depth])
q, k, v = compute_qkv(x_blocks, None, total_key_depth, total_value_depth)
hsplit = lambda x: split_heads(x, num_heads)
q, k, v = map(hsplit, [q, k, v])
logits = tf.matmul(q, k, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
output = tf.matmul(weights, v)
output = combine_heads(output)
# we need to get it back to shape [batch, height, width]
ret = tf.reshape(output, [-1, num_h_blocks, num_w_blocks,
query_shape[0], query_shape[1], total_value_depth])
ret = tf.transpose(ret, [0, 1, 3, 2, 4, 5])
ret = tf.reshape(ret, [-1, num_h_blocks*query_shape[0],
num_w_blocks*query_shape[1], total_value_depth])
# slice if padding was introduced
if is_padded:
ret = tf.slice(ret, [0, 0, 0, 0], [-1, orig_x_shape[1],
orig_x_shape[2], -1])
return ret, q, k, v |
Converts tensor from relative to aboslute indexing for local attention.
Args:
x: a Tensor of shape [batch (or batch*num_blocks), heads,
length, 2 * length - 1]
Returns:
A Tensor of shape [batch (or batch*num_blocks), heads, length, length-1] | def _relative_position_to_absolute_position_unmasked(x):
"""Converts tensor from relative to aboslute indexing for local attention.
Args:
x: a Tensor of shape [batch (or batch*num_blocks), heads,
length, 2 * length - 1]
Returns:
A Tensor of shape [batch (or batch*num_blocks), heads, length, length-1]
"""
x_shape = common_layers.shape_list(x)
batch = x_shape[0]
heads = x_shape[1]
length = x_shape[2]
# Concat columns of pad to shift from relative to absolute indexing.
col_pad = tf.zeros((batch, heads, length, 1))
x = tf.concat([x, col_pad], axis=3)
# Concat extra elements so to add up to shape (len+1, 2*len-1).
flat_x = tf.reshape(x, [batch, heads, length * 2 * length])
flat_pad = tf.zeros((batch, heads, length-1))
flat_x_padded = tf.concat([flat_x, flat_pad], axis=2)
# Reshape and slice out the padded elements.
final_x = tf.reshape(flat_x_padded, [batch, heads, length+1, 2*length-1])
final_x = final_x[:, :, :, length-1:]
final_x = final_x[:, :, :length, :]
return final_x |
Attention to the source position and a neighborhood to the left of it.
The sequence is divided into blocks of length block_length. Attention for a
given query position can only see memory positions less than or equal to the
query position, in the corresponding block and the previous block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v] | def masked_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
name=None):
"""Attention to the source position and a neighborhood to the left of it.
The sequence is divided into blocks of length block_length. Attention for a
given query position can only see memory positions less than or equal to the
query position, in the corresponding block and the previous block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="local_attention_1d", values=[q, k, v]):
batch, heads, length, depth_k = common_layers.shape_list(q)
depth_v = common_layers.shape_list(v)[-1]
if isinstance(block_length, tf.Tensor):
const = tf.contrib.util.constant_value(block_length)
if const is not None:
block_length = int(const)
# If (length < 2 * block_length), then we use only one block.
if isinstance(length, int) and isinstance(block_length, int):
block_length = length if length < block_length * 2 else block_length
else:
block_length = tf.where(
tf.less(length, block_length * 2), length, block_length)
# Pad query, key, value to ensure multiple of block length.
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
if isinstance(length, int) and isinstance(block_length, int):
num_blocks = length // block_length
else:
num_blocks = tf.div(length, block_length)
# Compute attention for the first query block.
first_q = tf.slice(q, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_k = tf.slice(k, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_v = tf.slice(v, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_output = dot_product_attention(
first_q,
first_k,
first_v,
attention_bias_lower_triangle(block_length),
dropout_rate=dropout_rate,
make_image_summary=make_image_summary,
name="first_block")
# Compute attention for all subsequent query blocks.
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
local_k = _make_local_block(k, depth_k, batch, heads, num_blocks,
block_length)
local_v = _make_local_block(v, depth_v, batch, heads, num_blocks,
block_length)
tail_q = tf.slice(q, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
tail_q = tf.reshape(tail_q,
[batch, heads, num_blocks - 1, block_length, depth_k])
local_length = common_layers.shape_list(local_k)[3]
# make sure source_pos <= target_pos
good_part = common_layers.ones_matrix_band_part(
block_length,
local_length,
-1,
block_length,
out_shape=[1, 1, 1, block_length, local_length])
bias = (1.0 - good_part) * -1e9
# TODO(noam): figure out how to show a summary for the remaining blocks.
# The naive way currently causes errors due to empty tensors.
# output: [batch, heads, num_blocks-1, block_length, depth_v]
tail_output = dot_product_attention(
tail_q,
local_k,
local_v,
bias,
dropout_rate=dropout_rate,
make_image_summary=False,
name="tail_block")
tail_output = tf.reshape(
tail_output, [batch, heads, (num_blocks - 1) * block_length, depth_v])
output = tf.concat([first_output, tail_output], axis=2)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output = tf.reshape(output, [batch, heads, original_length, depth_v])
return output |
Helper function to create a local version of the keys or values for 1d. | def _make_local_block(x, depth, batch, heads, num_blocks, block_length):
"""Helper function to create a local version of the keys or values for 1d."""
prev_block = tf.slice(x, [0, 0, 0, 0, 0],
[-1, -1, num_blocks - 1, -1, -1])
cur_block = tf.slice(x, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
local_block = tf.concat([prev_block, cur_block], 3)
return tf.reshape(local_block,
[batch, heads, num_blocks - 1, block_length * 2, depth]) |
Masked local 1d attention with relative positions.
The sequence is divided into blocks of length block_size.
Attention for a given query position can only see memory positions
less than or equal to the query position, in the corresponding block
and the previous block.
If mask_right is True, then a target position cannot see greater source
positions.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
heads_share_relative_embedding: a boolean for sharing relative embeddings.
add_relative_to_values: a boolean for whether to add relative component to
values.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
Raises:
ValueError: wwhen the name for the variable scope is not passed. | def masked_relative_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
heads_share_relative_embedding=False,
add_relative_to_values=False,
name=None):
"""Masked local 1d attention with relative positions.
The sequence is divided into blocks of length block_size.
Attention for a given query position can only see memory positions
less than or equal to the query position, in the corresponding block
and the previous block.
If mask_right is True, then a target position cannot see greater source
positions.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
heads_share_relative_embedding: a boolean for sharing relative embeddings.
add_relative_to_values: a boolean for whether to add relative component to
values.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
Raises:
ValueError: wwhen the name for the variable scope is not passed.
"""
if not name:
raise ValueError("Name must be assigned since reuse for variable scope is "
"set to tf.AUTO_REUSE, in order to reuse relative "
"embeddings of keys and values.")
# Reuse flag is set to auto_reuse to reuse relative embeddings of keys and
# values across blocks (first and tail blocks).
with tf.variable_scope(
name, default_name="masked_relative_local_attention_1d",
values=[q, k, v], reuse=tf.AUTO_REUSE):
default_block_length = block_length
batch = common_layers.shape_list(q)[0]
heads = common_layers.shape_list(q)[1]
length = common_layers.shape_list(q)[2]
# If (length < 2 * block_length), then we use only one block.
if isinstance(length, int) and isinstance(block_length, int):
block_length = length if length < block_length * 2 else block_length
else:
block_length = tf.where(
tf.less(length, block_length * 2), length, block_length)
depth_k = common_layers.shape_list(k)[3]
depth_v = common_layers.shape_list(v)[3]
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
num_blocks = length // block_length
# compute attention for the first query block.
first_q = tf.slice(q, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_k = tf.slice(k, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_v = tf.slice(v, [0, 0, 0, 0], [-1, -1, block_length, -1])
# Relative embeddings will be used later as well.
# TODO(avaswani,annahuang): check why 2*bl was breaking for music
# Needs to be known at static shape inference time, hence cannot be
# 2 * block_length.
rel_embed_length = 4 * default_block_length
# We only multiply with the needed embeddings as we slice them out.
first_rel_embeddings = get_relative_embeddings_left(
rel_embed_length, block_length, depth_k, heads,
heads_share_relative_embedding, "relative_embeddings")
first_rel_logits = matmul_with_relative_keys(
first_q, first_rel_embeddings, heads_share_relative_embedding)
first_logits = tf.matmul(first_q, first_k, transpose_b=True)
first_logits += (
_relative_position_to_absolute_position_masked(first_rel_logits))
# adding a mask
first_logits += (
common_layers.cast_like(attention_bias_lower_triangle(block_length),
first_logits))
first_att = tf.nn.softmax(first_logits,
name="first_attention_weights")
# dropping out the attention links for each of the heads
first_att = common_layers.dropout_with_broadcast_dims(
first_att, 1.0 - dropout_rate,
broadcast_dims=None)
# only call image summary for the first block
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(first_att, None)
first_output = tf.matmul(first_att, first_v)
# compute attention for all subsequent query blocks.
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
local_k = _make_local_block(k, depth_k, batch, heads, num_blocks,
block_length)
local_v = _make_local_block(v, depth_v, batch, heads, num_blocks,
block_length)
tail_q = tf.slice(q, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
tail_q = tf.reshape(tail_q,
[batch, heads, num_blocks - 1, block_length, depth_k])
local_length = common_layers.shape_list(local_k)[3]
# collapsing num blocks and batch size so that we can reuse
# functions
def _reshape_for_relative(x):
x_shape = common_layers.shape_list(x)
# [batch, num_blocks, heads, length, depth]
x = tf.transpose(x, [0, 2, 1, 3, 4])
x = tf.reshape(x, [batch*x_shape[2], heads, x_shape[3],
x_shape[4]])
return x
rel_tail_q = _reshape_for_relative(tail_q)
rel_k = _reshape_for_relative(local_k)
rel_v = _reshape_for_relative(local_v)
rel_embeddings = get_relative_embeddings_left(
rel_embed_length, 2 * block_length, depth_k, heads,
heads_share_relative_embedding, "relative_embeddings")
rel_logits = matmul_with_relative_keys(
rel_tail_q, rel_embeddings, heads_share_relative_embedding)
# Computing relative logits separately for the masked and unmasked parts
# because the reshaping logic is different for both
masked_rel_logits = tf.slice(rel_logits, [0, 0, 0, block_length],
[-1, -1, -1, -1])
masked_rel_logits = _relative_position_to_absolute_position_masked(
masked_rel_logits)
unmasked_rel_logits = tf.slice(rel_logits, [0, 0, 0, 0],
[-1, -1, -1, 2*block_length-1])
unmasked_rel_logits = _relative_position_to_absolute_position_unmasked(
unmasked_rel_logits)
all_rel_logits = tf.concat([unmasked_rel_logits, masked_rel_logits],
axis=3)
all_logits = (
tf.matmul(rel_tail_q, rel_k, transpose_b=True) + all_rel_logits)
# make sure source_pos <= target_pos
good_part = common_layers.ones_matrix_band_part(block_length,
local_length,
-1, block_length)
mask = (1.0 - good_part) * -1e9
mask = common_layers.cast_like(mask, all_logits)
all_logits += tf.reshape(mask, [1, 1, block_length, local_length])
weights = tf.nn.softmax(all_logits, name="attention_weights")
# [batch (* num_blocks), heads, query_length (=block_length),
# key_length (=2*block_length)]
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate,
broadcast_dims=None)
output = tf.matmul(weights, rel_v)
if add_relative_to_values:
# Adds the contribution of the weighted relative embeddings to the values.
weights_for_unmasked, weights_for_masked = (
tf.split(weights, 2, axis=3))
rel_weights_unmasked = _absolute_position_to_relative_position_unmasked(
weights_for_unmasked)
rel_weights_masked = _absolute_position_to_relative_position_masked(
weights_for_masked)
value_rel_embeddings_unmasked = get_relative_embeddings_left(
rel_embed_length, 2 * block_length, depth_v,
heads, heads_share_relative_embedding,
"value_relative_embeddings")
# The unmasked part starts with index -1 as opposed 0 has take uptil last.
if heads_share_relative_embedding:
value_rel_embeddings_unmasked = value_rel_embeddings_unmasked[:-1, :]
else:
value_rel_embeddings_unmasked = value_rel_embeddings_unmasked[:, :-1, :]
value_rel_embeddings_masked = get_relative_embeddings_left(
rel_embed_length, block_length, depth_v,
heads, heads_share_relative_embedding,
"value_relative_embeddings")
# [batch (*num_blocks), heads, query length, key length]
rel_weights = tf.concat(
[rel_weights_unmasked, rel_weights_masked], axis=3)
if heads_share_relative_embedding:
value_rel_embeddings_concat_axis = 0
else:
value_rel_embeddings_concat_axis = 1
value_rel_embeddings = tf.concat(
[value_rel_embeddings_unmasked, value_rel_embeddings_masked],
axis=value_rel_embeddings_concat_axis)
output_rel = matmul_with_relative_values(
rel_weights, value_rel_embeddings, heads_share_relative_embedding)
output += output_rel
# bring to [batch, heads, num_blocks-1, block_length, depth]
output = tf.reshape(output,
[batch, num_blocks-1, heads, block_length, depth_v])
output = tf.transpose(output, [0, 2, 1, 3, 4])
output = tf.reshape(
output, [batch, heads, (num_blocks - 1) * block_length, depth_v])
output = tf.concat([first_output, output], axis=2)
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output = tf.reshape(output, [batch, heads, original_length, depth_v])
return output |
Strided block local self-attention.
The sequence is divided into blocks of length block_length. Attention for a
given query position can see all memory positions in the corresponding block
and filter_width many positions to the left and right of the block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
filter_width: an integer indicating how much to look left and right of the
block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v] | def local_attention_1d(q, k, v, block_length=128, filter_width=100, name=None):
"""Strided block local self-attention.
The sequence is divided into blocks of length block_length. Attention for a
given query position can see all memory positions in the corresponding block
and filter_width many positions to the left and right of the block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
filter_width: an integer indicating how much to look left and right of the
block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="local_self_attention_1d", values=[q, k, v]):
# Check that q, k, v have the same shape except in their depth dimension.
q.get_shape()[:-1].assert_is_compatible_with(k.get_shape()[:-1])
q.get_shape()[:-1].assert_is_compatible_with(v.get_shape()[:-1])
batch_size, num_heads, original_length, _ = common_layers.shape_list(q)
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l_and_r(x, pad_length):
return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
# Set up query blocks.
# [batch, heads, blocks_q, block_length, depth_k]
q = pad_to_multiple(q, block_length)
q = reshape_by_blocks(q, common_layers.shape_list(q), block_length)
total_query_blocks = common_layers.shape_list(q)[2]
# Set up key and value blocks.
# [batch, heads, blocks_k, block_length, depth_k]
blocks_per_filter_width = filter_width // block_length
remaining_items = filter_width % block_length
k = pad_to_multiple(k, block_length)
v = pad_to_multiple(v, block_length)
k = pad_l_and_r(k, filter_width + block_length - remaining_items)
v = pad_l_and_r(v, filter_width + block_length - remaining_items)
k = reshape_by_blocks(k, common_layers.shape_list(k), block_length)
v = reshape_by_blocks(v, common_layers.shape_list(v), block_length)
total_kv_blocks = common_layers.shape_list(k)[2]
slices = []
# prepare the left-most and right-most partial blocks if needed
if remaining_items:
first_partial_block_k = tf.slice(
k, [0, 0, 0, block_length - remaining_items, 0],
[-1, -1, total_query_blocks, -1, -1])
first_partial_block_v = tf.slice(
v, [0, 0, 0, block_length - remaining_items, 0],
[-1, -1, total_query_blocks, -1, -1])
last_partial_block_k = tf.slice(
k, [0, 0, total_kv_blocks - total_query_blocks, 0, 0],
[-1, -1, -1, remaining_items, -1])
last_partial_block_v = tf.slice(
v, [0, 0, total_kv_blocks - total_query_blocks, 0, 0],
[-1, -1, -1, remaining_items, -1])
slices.append((first_partial_block_k, first_partial_block_v))
slices.append((last_partial_block_k, last_partial_block_v))
# Prepare the rest of the blocks
first_block_index = 1 if remaining_items else 0
attention_blocks = 2 * blocks_per_filter_width + 1
for i in range(first_block_index, attention_blocks + first_block_index):
block_k = tf.slice(k, [0, 0, i, 0, 0],
[-1, -1, total_query_blocks, -1, -1])
block_v = tf.slice(v, [0, 0, i, 0, 0],
[-1, -1, total_query_blocks, -1, -1])
slices.append((block_k, block_v))
# [batch, heads, blocks_q, block_length + 2 * filter_width, depth_k]
k = tf.concat([s[0] for s in slices], axis=3)
v = tf.concat([s[1] for s in slices], axis=3)
attention_bias = tf.expand_dims(embedding_to_padding(k) * -1e9, axis=-2)
depth_v = common_layers.shape_list(v)[-1]
output = dot_product_attention(
q,
k,
v,
attention_bias,
dropout_rate=0.,
name="local_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape([None if isinstance(dim, tf.Tensor) else dim for dim in
(batch_size, num_heads, original_length, depth_v)])
return output |
Reshapes input by splitting its length over blocks of memory_block_size.
Args:
x: a Tensor with shape [batch, heads, length, depth]
x_shape: tf.TensorShape of x.
memory_block_size: Integer which divides length.
Returns:
Tensor with shape
[batch, heads, length // memory_block_size, memory_block_size, depth]. | def reshape_by_blocks(x, x_shape, memory_block_size):
"""Reshapes input by splitting its length over blocks of memory_block_size.
Args:
x: a Tensor with shape [batch, heads, length, depth]
x_shape: tf.TensorShape of x.
memory_block_size: Integer which divides length.
Returns:
Tensor with shape
[batch, heads, length // memory_block_size, memory_block_size, depth].
"""
x = tf.reshape(x, [
x_shape[0], x_shape[1], x_shape[2] // memory_block_size,
memory_block_size, x_shape[3]
])
return x |
Dilated self-attention.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left and right.
Each will be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth] | def dilated_self_attention_1d(q,
k,
v,
query_block_size=128,
memory_block_size=128,
gap_size=2,
num_memory_blocks=2,
name=None):
"""Dilated self-attention.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left and right.
Each will be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
"""
with tf.variable_scope(
name, default_name="dilated_self_attention_1d", values=[q, k, v]):
v_list_shape = v.get_shape().as_list()
assert v_list_shape == k.shape.as_list(), "K and V depths must be equal"
v_shape = common_layers.shape_list(v)
depth_v = v_shape[3]
batch_size = v_shape[0]
num_heads = v_shape[1]
original_length = common_layers.shape_list(q)[2]
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l_and_r(x, pad_length):
return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
q = pad_to_multiple(q, query_block_size)
v = pad_to_multiple(v, query_block_size)
k = pad_to_multiple(k, query_block_size)
# Set up query blocks.
new_q_shape = common_layers.shape_list(q)
q = reshape_by_blocks(q, new_q_shape, query_block_size)
self_k_part = reshape_by_blocks(k, new_q_shape, query_block_size)
self_v_part = reshape_by_blocks(v, new_q_shape, query_block_size)
# Set up key and value windows.
k_v_padding = (gap_size + memory_block_size) * num_memory_blocks
k = pad_l_and_r(k, k_v_padding)
v = pad_l_and_r(v, k_v_padding)
# Get gather indices.
index_length = (new_q_shape[2] - query_block_size + memory_block_size)
indices = tf.range(0, index_length, delta=1, name="index_range")
indices = tf.reshape(indices, [1, -1, 1]) # [1, length, 1] for convs
kernel = tf.expand_dims(tf.eye(memory_block_size), axis=1)
gather_indices = tf.nn.conv1d(
tf.cast(indices, tf.float32),
kernel,
query_block_size,
padding="VALID",
name="gather_conv")
gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
# Get left and right memory blocks for each query.
# [length, batch, heads, dim]
k_t = tf.transpose(k, [2, 0, 1, 3])
v_t = tf.transpose(v, [2, 0, 1, 3])
left_k = gather_dilated_memory_blocks(
k_t[:-k_v_padding, :, :, :], num_memory_blocks, gap_size,
query_block_size, memory_block_size, gather_indices)
left_v = gather_dilated_memory_blocks(
v_t[:-k_v_padding, :, :, :], num_memory_blocks, gap_size,
query_block_size, memory_block_size, gather_indices)
right_k = gather_dilated_memory_blocks(
k_t[k_v_padding:, :, :, :],
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="right")
right_v = gather_dilated_memory_blocks(
v_t[k_v_padding:, :, :, :],
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="right")
k_windows = tf.concat([left_k, self_k_part, right_k], axis=3)
v_windows = tf.concat([left_v, self_v_part, right_v], axis=3)
attention_bias = tf.expand_dims(
embedding_to_padding(k_windows) * -1e9, axis=-2)
output = dot_product_attention(
q,
k_windows,
v_windows,
attention_bias,
dropout_rate=0.,
name="dilated_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape(v_list_shape)
return output |
Gathers blocks with gaps in between.
Args:
x: Tensor of shape [length, batch, heads, depth]
num_memory_blocks: how many memory blocks to look in "direction". Each will
be separated by gap_size.
gap_size: an integer indicating the gap size
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gather_indices: The indices to gather from.
direction: left or right
Returns:
Tensor of shape [batch, heads, blocks, block_length, depth] | def gather_dilated_memory_blocks(x,
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="left"):
"""Gathers blocks with gaps in between.
Args:
x: Tensor of shape [length, batch, heads, depth]
num_memory_blocks: how many memory blocks to look in "direction". Each will
be separated by gap_size.
gap_size: an integer indicating the gap size
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gather_indices: The indices to gather from.
direction: left or right
Returns:
Tensor of shape [batch, heads, blocks, block_length, depth]
"""
gathered_blocks = []
# gathering memory blocks
for block_id in range(num_memory_blocks):
block_end_index = -(query_block_size + gap_size *
(block_id + 1) + memory_block_size * block_id)
block_start_index = (
(memory_block_size + gap_size) * (num_memory_blocks - (block_id + 1)))
if direction != "left":
[block_end_index,
block_start_index] = [-block_start_index, -block_end_index]
if block_end_index == 0:
x_block = x[block_start_index:]
else:
x_block = x[block_start_index:block_end_index]
def gather_dilated_1d_blocks(x, gather_indices):
x_new = tf.gather(x, gather_indices)
# [batch, heads, blocks, block_length, dim]
return tf.transpose(x_new, [2, 3, 0, 1, 4])
gathered_blocks.append(gather_dilated_1d_blocks(x_block, gather_indices))
return tf.concat(gathered_blocks, 3) |
Dilated self-attention. TODO(avaswani): Try it and write a paper on it.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer
memory_block_size: an integer indicating how much to look left.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left. Each will
be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth] | def masked_dilated_self_attention_1d(q,
k,
v,
query_block_size=64,
memory_block_size=64,
gap_size=2,
num_memory_blocks=2,
name=None):
"""Dilated self-attention. TODO(avaswani): Try it and write a paper on it.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer
memory_block_size: an integer indicating how much to look left.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left. Each will
be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
"""
with tf.variable_scope(
name, default_name="masked_dilated_self_attention_1d", values=[q, k, v]):
v_list_shape = v.get_shape().as_list()
assert v_list_shape == k.shape.as_list(), "K and V depths must be equal"
v_shape = common_layers.shape_list(v)
depth_v = v_shape[3]
batch_size = v_shape[0]
num_heads = v_shape[1]
original_length = common_layers.shape_list(q)[2]
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l(x, left_pad_length):
return tf.pad(x, [[0, 0], [0, 0], [left_pad_length, 0], [0, 0]])
q = pad_to_multiple(q, query_block_size)
v = pad_to_multiple(v, query_block_size)
k = pad_to_multiple(k, query_block_size)
# Set up query blocks.
new_q_shape = common_layers.shape_list(q)
q = reshape_by_blocks(q, new_q_shape, query_block_size)
# Set up key and value windows.
self_k_part = reshape_by_blocks(k, new_q_shape, query_block_size)
self_v_part = reshape_by_blocks(v, new_q_shape, query_block_size)
k_v_padding = (gap_size + memory_block_size) * num_memory_blocks
k = pad_l(k, k_v_padding)
v = pad_l(v, k_v_padding)
# Get gather indices.
index_length = (new_q_shape[2] - query_block_size + memory_block_size)
indices = tf.range(0, index_length, delta=1, name="index_range")
indices = tf.reshape(indices, [1, -1, 1]) # [1, length, 1] for convs
kernel = tf.expand_dims(tf.eye(memory_block_size), axis=1)
gather_indices = tf.nn.conv1d(
tf.cast(indices, tf.float32),
kernel,
query_block_size,
padding="VALID",
name="gather_conv")
gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
# Get left and right memory blocks for each query.
# [length, batch, heads, dim]
k_t = tf.transpose(k, [2, 0, 1, 3])
v_t = tf.transpose(v, [2, 0, 1, 3])
k_unmasked_windows = gather_dilated_memory_blocks(
k_t, num_memory_blocks, gap_size, query_block_size, memory_block_size,
gather_indices)
v_unmasked_windows = gather_dilated_memory_blocks(
v_t, num_memory_blocks, gap_size, query_block_size, memory_block_size,
gather_indices)
# Combine memory windows.
block_q_shape = common_layers.shape_list(q)
masked_attention_bias = tf.tile(
tf.expand_dims(attention_bias_lower_triangle(query_block_size), axis=0),
[block_q_shape[0], block_q_shape[1], block_q_shape[2], 1, 1])
padding_attention_bias = tf.expand_dims(
embedding_to_padding(k_unmasked_windows) * -1e9, axis=-2)
padding_attention_bias = tf.tile(padding_attention_bias,
[1, 1, 1, query_block_size, 1])
attention_bias = tf.concat(
[masked_attention_bias, padding_attention_bias], axis=-1)
# combine memory windows
k_windows = tf.concat([self_k_part, k_unmasked_windows], 3)
v_windows = tf.concat([self_v_part, v_unmasked_windows], 3)
output = dot_product_attention(
q,
k_windows,
v_windows,
attention_bias,
dropout_rate=0.,
name="dilated_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape(v_list_shape)
return output |
Strided block local self-attention.
The 2-D sequence is divided into 2-D blocks of shape query_shape. Attention
for a given query position can only see memory positions less than or equal to
the query position. The memory positions are the corresponding block with
memory_flange many positions to add to the height and width of the block
(namely, left, top, and right).
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
from each query block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v] | def local_attention_2d(q,
k,
v,
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""Strided block local self-attention.
The 2-D sequence is divided into 2-D blocks of shape query_shape. Attention
for a given query position can only see memory positions less than or equal to
the query position. The memory positions are the corresponding block with
memory_flange many positions to add to the height and width of the block
(namely, left, top, and right).
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
from each query block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
"""
with tf.variable_scope(
name, default_name="local_self_attention_2d", values=[q, k, v]):
v_shape = common_layers.shape_list(v)
# Pad query, key, value to ensure multiple of corresponding lengths.
q = pad_to_multiple_2d(q, query_shape)
k = pad_to_multiple_2d(k, query_shape)
v = pad_to_multiple_2d(v, query_shape)
paddings = [[0, 0], [0, 0], [memory_flange[0], memory_flange[1]],
[memory_flange[0], memory_flange[1]], [0, 0]]
k = tf.pad(k, paddings)
v = tf.pad(v, paddings)
# Set up query blocks.
q_indices = gather_indices_2d(q, query_shape, query_shape)
q_new = gather_blocks_2d(q, q_indices)
# Set up key and value blocks.
memory_shape = (query_shape[0] + 2 * memory_flange[0],
query_shape[1] + 2 * memory_flange[1])
k_and_v_indices = gather_indices_2d(k, memory_shape, query_shape)
k_new = gather_blocks_2d(k, k_and_v_indices)
v_new = gather_blocks_2d(v, k_and_v_indices)
attention_bias = tf.expand_dims(
tf.to_float(embedding_to_padding(k_new)) * -1e9, axis=-2)
output = dot_product_attention(
q_new,
k_new,
v_new,
attention_bias,
dropout_rate=0.,
name="local_2d",
make_image_summary=False)
# Put representations back into original shapes.
padded_q_shape = common_layers.shape_list(q)
output = scatter_blocks_2d(output, q_indices, padded_q_shape)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0, 0],
[-1, -1, v_shape[2], v_shape[3], -1])
return output |
Making sure x is a multiple of shape.
Args:
x: a [batch, heads, h, w, depth] or [batch, h, w, depth] tensor
block_shape: a 2-d list of integer shapes
Returns:
padded_x: a [batch, heads, h, w, depth] or [batch, h, w, depth] tensor | def pad_to_multiple_2d(x, block_shape):
"""Making sure x is a multiple of shape.
Args:
x: a [batch, heads, h, w, depth] or [batch, h, w, depth] tensor
block_shape: a 2-d list of integer shapes
Returns:
padded_x: a [batch, heads, h, w, depth] or [batch, h, w, depth] tensor
"""
old_shape = x.get_shape().dims
last = old_shape[-1]
if len(old_shape) == 4:
height_padding = -common_layers.shape_list(x)[1] % block_shape[0]
width_padding = -common_layers.shape_list(x)[2] % block_shape[1]
paddings = [[0, 0], [0, height_padding], [0, width_padding], [0, 0]]
elif len(old_shape) == 5:
height_padding = -common_layers.shape_list(x)[2] % block_shape[0]
width_padding = -common_layers.shape_list(x)[3] % block_shape[1]
paddings = [[0, 0], [0, 0], [0, height_padding], [0, width_padding], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_shape = padded_x.get_shape().as_list()
padded_shape = padded_shape[:-1] + [last]
padded_x.set_shape(padded_shape)
return padded_x |
Gathers flattened blocks from x. | def gather_blocks_2d(x, indices):
"""Gathers flattened blocks from x."""
x_shape = common_layers.shape_list(x)
x = reshape_range(x, 2, 4, [tf.reduce_prod(x_shape[2:4])])
# [length, batch, heads, dim]
x_t = tf.transpose(x, [2, 0, 1, 3])
x_new = tf.gather(x_t, indices)
# returns [batch, heads, num_blocks, block_length ** 2, dim]
return tf.transpose(x_new, [2, 3, 0, 1, 4]) |
Reshapes a tensor between dimensions i and j. | def reshape_range(tensor, i, j, shape):
"""Reshapes a tensor between dimensions i and j."""
t_shape = common_layers.shape_list(tensor)
target_shape = t_shape[:i] + shape + t_shape[j:]
return tf.reshape(tensor, target_shape) |
scatters blocks from x into shape with indices. | def scatter_blocks_2d(x, indices, shape):
"""scatters blocks from x into shape with indices."""
x_shape = common_layers.shape_list(x)
# [length, batch, heads, dim]
x_t = tf.transpose(
tf.reshape(x, [x_shape[0], x_shape[1], -1, x_shape[-1]]), [2, 0, 1, 3])
x_t_shape = common_layers.shape_list(x_t)
indices = tf.reshape(indices, [-1, 1])
scattered_x = tf.scatter_nd(indices, x_t, x_t_shape)
scattered_x = tf.transpose(scattered_x, [1, 2, 0, 3])
return tf.reshape(scattered_x, shape) |
Getting gather indices. | def gather_indices_2d(x, block_shape, block_stride):
"""Getting gather indices."""
# making an identity matrix kernel
kernel = tf.eye(block_shape[0] * block_shape[1])
kernel = reshape_range(kernel, 0, 1, [block_shape[0], block_shape[1], 1])
# making indices [1, h, w, 1] to appy convs
x_shape = common_layers.shape_list(x)
indices = tf.range(x_shape[2] * x_shape[3])
indices = tf.reshape(indices, [1, x_shape[2], x_shape[3], 1])
indices = tf.nn.conv2d(
tf.cast(indices, tf.float32),
kernel,
strides=[1, block_stride[0], block_stride[1], 1],
padding="VALID")
# making indices [num_blocks, dim] to gather
dims = common_layers.shape_list(indices)[:3]
if all([isinstance(dim, int) for dim in dims]):
num_blocks = functools.reduce(operator.mul, dims, 1)
else:
num_blocks = tf.reduce_prod(dims)
indices = tf.reshape(indices, [num_blocks, -1])
return tf.cast(indices, tf.int32) |
Creates a mask for 2d block raster scan.
The query mask can look to the left, top left, top, and top right, but
not to the right. Inside the query, we have the standard raster scan
masking.
Args:
query_shape: A tuple of ints (query_height, query_width)
memory_flange: A tuple of ints
(memory_flange_height, memory_flange_width)
Returns:
A tensor of shape query_size, memory_size | def make_2d_block_raster_mask(query_shape, memory_flange):
"""Creates a mask for 2d block raster scan.
The query mask can look to the left, top left, top, and top right, but
not to the right. Inside the query, we have the standard raster scan
masking.
Args:
query_shape: A tuple of ints (query_height, query_width)
memory_flange: A tuple of ints
(memory_flange_height, memory_flange_width)
Returns:
A tensor of shape query_size, memory_size
"""
# mask inside the query block
query_triangle = common_layers.ones_matrix_band_part(
np.prod(query_shape), np.prod(query_shape), -1, 0)
split_query_masks = tf.split(query_triangle, query_shape[0], axis=1)
# adding mask for left and right
mask_pieces = [
tf.concat( # pylint: disable=g-complex-comprehension
[tf.ones([np.prod(query_shape), memory_flange[1]]),
split_query_masks[i],
tf.zeros([np.prod(query_shape), memory_flange[1]])],
axis=1) for i in range(query_shape[0])
]
# adding mask for top
final_mask = tf.concat(
[
tf.ones([
np.prod(query_shape),
(query_shape[1] + 2 * memory_flange[1]) * memory_flange[0]
]),
tf.concat(mask_pieces, axis=1)
],
axis=1)
# 0.0 is visible location, 1.0 is masked.
return 1. - final_mask |
Get the memory regions that surround a 2d query.
The memory regions will be the left and top right.
Args:
x: A tensor with shape [batch, heads, height, width, depth]
query_block_shape: a 2-d tuple of integers
memory_flange: a 2-d tuple of integers
q_indices: a tensor of indices for each of the center blocks.
[num_blocks, block_length]
Returns:
x_flange: A tensor of shape [batch, heads, #blocks, block_length, depth] | def get_memory_region(x, query_block_shape, memory_flange, q_indices):
"""Get the memory regions that surround a 2d query.
The memory regions will be the left and top right.
Args:
x: A tensor with shape [batch, heads, height, width, depth]
query_block_shape: a 2-d tuple of integers
memory_flange: a 2-d tuple of integers
q_indices: a tensor of indices for each of the center blocks.
[num_blocks, block_length]
Returns:
x_flange: A tensor of shape [batch, heads, #blocks, block_length, depth]
"""
# Padding x to be multiple of query_shape and then
# extracting the memory blocks from the same regions as the query blocks
x_query_padded = pad_to_multiple_2d(x, query_block_shape)
x_center = gather_blocks_2d(x_query_padded, q_indices)
# Then padding the flange region
paddings = [[0, 0], [0, 0], [memory_flange[0], 0],
[memory_flange[1], memory_flange[1]], [0, 0]]
x_memory_padded = tf.pad(x_query_padded, paddings)
left_x = None
top_x = None
# Extracting the memory regions around the query block. left_x_region extends
# to the left and the top_x_region is the combination of top left, top, and
# top right of the query block
# if no left region
if memory_flange[1] > 0:
left_x_region = x_memory_padded[:, :, memory_flange[
0]:, :-(query_block_shape[1] + memory_flange[1]), :]
left_memory_shape = (query_block_shape[0], memory_flange[1])
left_indices = gather_indices_2d(left_x_region, left_memory_shape,
query_block_shape)
left_x = gather_blocks_2d(left_x_region, left_indices)
# if no top region
if memory_flange[0] > 0:
top_x_region = x_memory_padded[:, :, :-query_block_shape[0], :, :]
top_memory_shape = (memory_flange[0],
query_block_shape[1] + 2 * memory_flange[1])
top_indices = gather_indices_2d(top_x_region, top_memory_shape,
query_block_shape)
top_x = gather_blocks_2d(top_x_region, top_indices)
x_flange = None
if top_x is not None and left_x is not None:
x_flange = tf.concat([top_x, left_x], axis=3)
else:
x_flange = top_x if top_x is not None else left_x
return x_flange, x_center |
Get right shifted blocks for masked local attention 2d.
Args:
x: A tensor with shape [batch, heads, height, width, depth]
indices: The indices to gather blocks
Returns:
x_shifted: a tensor of extracted blocks, each block right shifted along
length. | def get_shifted_center_blocks(x, indices):
"""Get right shifted blocks for masked local attention 2d.
Args:
x: A tensor with shape [batch, heads, height, width, depth]
indices: The indices to gather blocks
Returns:
x_shifted: a tensor of extracted blocks, each block right shifted along
length.
"""
center_x = gather_blocks_2d(x, indices)
# Shift right along the length dimension
def shift_right_2d_blocks(x):
"""Shift the second to last dimension of x right by one."""
shifted_targets = (
tf.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0], [0, 0]])[:, :, :, :-1, :])
return shifted_targets
x_shifted = shift_right_2d_blocks(center_x)
return x_shifted |
Right shifts once in every block.
Args:
x: a tensor of shape [batch, height, width, depth]
query_shape: A 2d tuple of ints
name: a string
Returns:
output: a tensor of the same shape as x | def right_shift_blockwise(x, query_shape, name=None):
"""Right shifts once in every block.
Args:
x: a tensor of shape [batch, height, width, depth]
query_shape: A 2d tuple of ints
name: a string
Returns:
output: a tensor of the same shape as x
"""
with tf.variable_scope(
name, default_name="right_shift_blockwise", values=[x]):
x_list_shape = x.get_shape().as_list()
x_shape = common_layers.shape_list(x)
# Add a dummy dimension for heads.
x = tf.expand_dims(x, axis=1)
x = pad_to_multiple_2d(x, query_shape)
padded_x_shape = common_layers.shape_list(x)
# Set up q blocks.
x_indices = gather_indices_2d(x, query_shape, query_shape)
x_new = get_shifted_center_blocks(x, x_indices)
# Put representations back into original shapes.
output = scatter_blocks_2d(x_new, x_indices, padded_x_shape)
# Remove the dummy head dimension.
output = tf.squeeze(output, axis=1)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, x_shape[1], x_shape[2], -1])
output.set_shape(x_list_shape)
return output |
Strided block local self-attention.
Each position in a query block can attend to all the generated queries in
the query block, which are generated in raster scan, and positions that are
generated to the left and top. The shapes are specified by query shape and
memory flange. Note that if you're using this function, you do not need to
right shift. Right shifting happens inside this function separately for each
block.
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
query_shape = block_shape
memory_flange: an integer indicating how much to look in height and width
from each query block.
memory shape = query_shape + (block_flange[0], 2*block_flange[1])
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v] | def masked_local_attention_2d(q,
k,
v,
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""Strided block local self-attention.
Each position in a query block can attend to all the generated queries in
the query block, which are generated in raster scan, and positions that are
generated to the left and top. The shapes are specified by query shape and
memory flange. Note that if you're using this function, you do not need to
right shift. Right shifting happens inside this function separately for each
block.
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
query_shape = block_shape
memory_flange: an integer indicating how much to look in height and width
from each query block.
memory shape = query_shape + (block_flange[0], 2*block_flange[1])
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
"""
with tf.variable_scope(
name, default_name="local_masked_self_attention_2d", values=[q, k, v]):
v_shape = common_layers.shape_list(v)
# Pad query to ensure multiple of corresponding lengths.
q = pad_to_multiple_2d(q, query_shape)
# Set up query blocks.
q_indices = gather_indices_2d(q, query_shape, query_shape)
q_new = gather_blocks_2d(q, q_indices)
# Set up key and value blocks.
k_flange, k_center = get_memory_region(k, query_shape, memory_flange,
q_indices)
v_flange, v_center = get_memory_region(v, query_shape, memory_flange,
q_indices)
if k_flange is not None:
k_new = tf.concat([k_flange, k_center], axis=3)
v_new = tf.concat([v_flange, v_center], axis=3)
else:
k_new = k_center
v_new = v_center
# Set up the masks.
query_elements = np.prod(query_shape)
padding_mask = None
if k_flange is not None:
padding_mask = tf.expand_dims(
embedding_to_padding(k_flange) * -1e9, axis=-2)
padding_mask = tf.tile(padding_mask, [1, 1, 1, query_elements, 1])
center_attention_bias = attention_bias_lower_triangle(
np.prod(query_elements))
center_attention_bias = tf.reshape(
center_attention_bias, [1, 1, 1, query_elements, query_elements])
v_center_shape = common_layers.shape_list(v_center)
center_attention_bias = tf.tile(
center_attention_bias,
[v_center_shape[0], v_center_shape[1], v_center_shape[2], 1, 1])
if padding_mask is not None:
# Combine the mask for padding and visible region.
attention_bias = tf.concat([padding_mask, center_attention_bias], axis=4)
else:
attention_bias = center_attention_bias
output = dot_product_attention(
q_new,
k_new,
v_new,
attention_bias,
dropout_rate=0.,
name="masked_local_2d",
make_image_summary=False)
# Put representations back into original shapes.
padded_q_shape = common_layers.shape_list(q)
output = scatter_blocks_2d(output, q_indices, padded_q_shape)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0, 0],
[-1, -1, v_shape[2], v_shape[3], -1])
return output |
Computes attention compoenent (query, key or value).
Args:
antecedent: a Tensor with shape [batch, length, channels]
total_depth: an integer
filter_width: An integer specifying how wide you want the attention
component to be.
padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
name: a string specifying scope name.
vars_3d_num_heads: an optional integer (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
c : [batch, length, depth] tensor | def compute_attention_component(antecedent,
total_depth,
filter_width=1,
padding="VALID",
name="c",
vars_3d_num_heads=0,
layer_collection=None):
"""Computes attention compoenent (query, key or value).
Args:
antecedent: a Tensor with shape [batch, length, channels]
total_depth: an integer
filter_width: An integer specifying how wide you want the attention
component to be.
padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
name: a string specifying scope name.
vars_3d_num_heads: an optional integer (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
c : [batch, length, depth] tensor
"""
if layer_collection is not None:
if filter_width != 1 or vars_3d_num_heads != 0:
raise ValueError(
"KFAC implementation only supports filter_width=1 (actual: {}) and "
"vars_3d_num_heads=0 (actual: {}).".format(
filter_width, vars_3d_num_heads))
if vars_3d_num_heads > 0:
assert filter_width == 1
input_depth = antecedent.get_shape().as_list()[-1]
depth_per_head = total_depth // vars_3d_num_heads
initializer_stddev = input_depth ** -0.5
if "q" in name:
initializer_stddev *= depth_per_head ** -0.5
var = tf.get_variable(
name, [input_depth,
vars_3d_num_heads,
total_depth // vars_3d_num_heads],
initializer=tf.random_normal_initializer(stddev=initializer_stddev))
var = tf.cast(var, antecedent.dtype)
var = tf.reshape(var, [input_depth, total_depth])
return tf.tensordot(antecedent, var, axes=1)
if filter_width == 1:
return common_layers.dense(
antecedent, total_depth, use_bias=False, name=name,
layer_collection=layer_collection)
else:
return common_layers.conv1d(
antecedent, total_depth, filter_width, padding=padding, name=name) |
Computes query, key and value.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
vars_3d_num_heads: an optional (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
q, k, v : [batch, length, depth] tensors | def compute_qkv(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
q_filter_width=1,
kv_filter_width=1,
q_padding="VALID",
kv_padding="VALID",
vars_3d_num_heads=0,
layer_collection=None):
"""Computes query, key and value.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
vars_3d_num_heads: an optional (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
q, k, v : [batch, length, depth] tensors
"""
if memory_antecedent is None:
memory_antecedent = query_antecedent
q = compute_attention_component(
query_antecedent,
total_key_depth,
q_filter_width,
q_padding,
"q",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
k = compute_attention_component(
memory_antecedent,
total_key_depth,
kv_filter_width,
kv_padding,
"k",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
v = compute_attention_component(
memory_antecedent,
total_value_depth,
kv_filter_width,
kv_padding,
"v",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
return q, k, v |
Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
max_relative_position: Maximum distance between inputs to generate
unique relation embeddings for. Only relevant
when using "dot_product_relative" attention.
heads_share_relative_embedding: boolean to share relative embeddings
add_relative_to_values: a boolean for whether to add relative component to
values.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
block_length: an integer - relevant for "local_mask_right"
block_width: an integer - relevant for "local_unmasked"
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is "VALID":
no padding.
cache: dict containing Tensors which are the results of previous
attentions, used for fast decoding. Expects the dict to contrain two
keys ('k' and 'v'), for the initial call the values for these keys
should be empty Tensors of the appropriate shape.
'k' [batch_size, 0, key_channels]
'v' [batch_size, 0, value_channels]
gap_size: Integer option for dilated attention to indicate spacing between
memory blocks.
num_memory_blocks: Integer option to indicate how many memory blocks to look
at.
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
vars_3d: use 3-dimensional variables for input/output transformations
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
recurrent_memory: An optional transformer_memory.RecurrentMemory, which
retains state across chunks. Default is None.
chunk_number: an optional integer Tensor with shape [batch] used to operate
the recurrent_memory.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k).
max_area_width: the max width allowed for an area.
max_area_height: the max height allowed for an area.
memory_height: the height of the memory.
area_key_mode: the mode for computing area keys, which can be "mean",
"concat", "sum", "sample_concat", and "sample_sum".
area_value_mode: the mode for computing area values, which can be either
"mean", or "sum".
training: indicating if it is in the training mode.
**kwargs (dict): Parameters for the attention function.
Caching:
WARNING: For decoder self-attention, i.e. when memory_antecedent == None,
the caching assumes that the bias contains future masking.
The caching works by saving all the previous key and value values so that
you are able to send just the last query location to this attention
function. I.e. if the cache dict is provided it assumes the query is of the
shape [batch_size, 1, hidden_dim] rather than the full memory.
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, hidden_dim]
unless the cache dict is provided in which case only the last memory
position is calculated and the output shape is [batch_size, 1, hidden_dim]
Optionally returns an additional loss parameters (ex: load balance loss for
the experts) returned by the attention_type function.
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads. | def multihead_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
attention_type="dot_product",
max_relative_position=None,
heads_share_relative_embedding=False,
add_relative_to_values=False,
image_shapes=None,
block_length=128,
block_width=128,
q_filter_width=1,
kv_filter_width=1,
q_padding="VALID",
kv_padding="VALID",
cache=None,
gap_size=0,
num_memory_blocks=2,
name="multihead_attention",
save_weights_to=None,
make_image_summary=True,
dropout_broadcast_dims=None,
vars_3d=False,
layer_collection=None,
recurrent_memory=None,
chunk_number=None,
hard_attention_k=0,
max_area_width=1,
max_area_height=1,
memory_height=1,
area_key_mode="mean",
area_value_mode="sum",
training=True,
**kwargs):
"""Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
max_relative_position: Maximum distance between inputs to generate
unique relation embeddings for. Only relevant
when using "dot_product_relative" attention.
heads_share_relative_embedding: boolean to share relative embeddings
add_relative_to_values: a boolean for whether to add relative component to
values.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
block_length: an integer - relevant for "local_mask_right"
block_width: an integer - relevant for "local_unmasked"
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is "VALID":
no padding.
cache: dict containing Tensors which are the results of previous
attentions, used for fast decoding. Expects the dict to contrain two
keys ('k' and 'v'), for the initial call the values for these keys
should be empty Tensors of the appropriate shape.
'k' [batch_size, 0, key_channels]
'v' [batch_size, 0, value_channels]
gap_size: Integer option for dilated attention to indicate spacing between
memory blocks.
num_memory_blocks: Integer option to indicate how many memory blocks to look
at.
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
vars_3d: use 3-dimensional variables for input/output transformations
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
recurrent_memory: An optional transformer_memory.RecurrentMemory, which
retains state across chunks. Default is None.
chunk_number: an optional integer Tensor with shape [batch] used to operate
the recurrent_memory.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k).
max_area_width: the max width allowed for an area.
max_area_height: the max height allowed for an area.
memory_height: the height of the memory.
area_key_mode: the mode for computing area keys, which can be "mean",
"concat", "sum", "sample_concat", and "sample_sum".
area_value_mode: the mode for computing area values, which can be either
"mean", or "sum".
training: indicating if it is in the training mode.
**kwargs (dict): Parameters for the attention function.
Caching:
WARNING: For decoder self-attention, i.e. when memory_antecedent == None,
the caching assumes that the bias contains future masking.
The caching works by saving all the previous key and value values so that
you are able to send just the last query location to this attention
function. I.e. if the cache dict is provided it assumes the query is of the
shape [batch_size, 1, hidden_dim] rather than the full memory.
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, hidden_dim]
unless the cache dict is provided in which case only the last memory
position is calculated and the output shape is [batch_size, 1, hidden_dim]
Optionally returns an additional loss parameters (ex: load balance loss for
the experts) returned by the attention_type function.
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
vars_3d_num_heads = num_heads if vars_3d else 0
if layer_collection is not None:
if cache is not None:
raise ValueError("KFAC implementation only supports cache is None.")
if vars_3d:
raise ValueError("KFAC implementation does not support 3d vars.")
if recurrent_memory is not None:
if memory_antecedent is not None:
raise ValueError("Recurrent memory requires memory_antecedent is None.")
if cache is not None:
raise ValueError("Cache is not supported when using recurrent memory.")
if vars_3d:
raise ValueError("3d vars are not supported when using recurrent memory.")
if layer_collection is not None:
raise ValueError("KFAC is not supported when using recurrent memory.")
if chunk_number is None:
raise ValueError("chunk_number is required when using recurrent memory.")
with tf.variable_scope(name, default_name="multihead_attention",
values=[query_antecedent, memory_antecedent]):
if recurrent_memory is not None:
(
recurrent_memory_transaction,
query_antecedent, memory_antecedent, bias,
) = recurrent_memory.pre_attention(
chunk_number,
query_antecedent, memory_antecedent, bias,
)
if cache is None or memory_antecedent is None:
q, k, v = compute_qkv(query_antecedent, memory_antecedent,
total_key_depth, total_value_depth, q_filter_width,
kv_filter_width, q_padding, kv_padding,
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
if cache is not None:
if attention_type not in ["dot_product", "dot_product_relative"]:
# TODO(petershaw): Support caching when using relative position
# representations, i.e. "dot_product_relative" attention.
raise NotImplementedError(
"Caching is not guaranteed to work with attention types other than"
" dot_product.")
if bias is None:
raise ValueError("Bias required for caching. See function docstring "
"for details.")
if memory_antecedent is not None:
# Encoder-Decoder Attention Cache
q = compute_attention_component(query_antecedent, total_key_depth,
q_filter_width, q_padding, "q",
vars_3d_num_heads=vars_3d_num_heads)
k = cache["k_encdec"]
v = cache["v_encdec"]
else:
k = split_heads(k, num_heads)
v = split_heads(v, num_heads)
decode_loop_step = kwargs.get("decode_loop_step")
if decode_loop_step is None:
k = cache["k"] = tf.concat([cache["k"], k], axis=2)
v = cache["v"] = tf.concat([cache["v"], v], axis=2)
else:
# Inplace update is required for inference on TPU.
# Inplace_ops only supports inplace_update on the first dimension.
# The performance of current implementation is better than updating
# the tensor by adding the result of matmul(one_hot,
# update_in_current_step)
tmp_k = tf.transpose(cache["k"], perm=[2, 0, 1, 3])
tmp_k = inplace_ops.alias_inplace_update(
tmp_k, decode_loop_step, tf.squeeze(k, axis=2))
k = cache["k"] = tf.transpose(tmp_k, perm=[1, 2, 0, 3])
tmp_v = tf.transpose(cache["v"], perm=[2, 0, 1, 3])
tmp_v = inplace_ops.alias_inplace_update(
tmp_v, decode_loop_step, tf.squeeze(v, axis=2))
v = cache["v"] = tf.transpose(tmp_v, perm=[1, 2, 0, 3])
q = split_heads(q, num_heads)
if cache is None:
k = split_heads(k, num_heads)
v = split_heads(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
if not vars_3d:
q *= key_depth_per_head**-0.5
additional_returned_value = None
if callable(attention_type): # Generic way to extend multihead_attention
x = attention_type(q, k, v, **kwargs)
if isinstance(x, tuple):
x, additional_returned_value = x # Unpack
elif attention_type == "dot_product":
if max_area_width > 1 or max_area_height > 1:
x = area_attention.dot_product_area_attention(
q, k, v, bias, dropout_rate, image_shapes,
save_weights_to=save_weights_to,
dropout_broadcast_dims=dropout_broadcast_dims,
max_area_width=max_area_width,
max_area_height=max_area_height,
memory_height=memory_height,
area_key_mode=area_key_mode,
area_value_mode=area_value_mode,
training=training)
else:
x = dot_product_attention(q, k, v, bias, dropout_rate, image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
activation_dtype=kwargs.get(
"activation_dtype"),
hard_attention_k=hard_attention_k)
elif attention_type == "dot_product_relative":
x = dot_product_attention_relative(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
cache=cache is not None,
allow_memory=recurrent_memory is not None,
hard_attention_k=hard_attention_k)
elif attention_type == "dot_product_unmasked_relative_v2":
x = dot_product_unmasked_self_attention_relative_v2(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values)
elif attention_type == "dot_product_relative_v2":
x = dot_product_self_attention_relative_v2(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values)
elif attention_type == "local_within_block_mask_right":
x = masked_within_block_local_attention_1d(
q, k, v, block_length=block_length)
elif attention_type == "local_relative_mask_right":
x = masked_relative_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary,
dropout_rate=dropout_rate,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values,
name="masked_relative_local_attention_1d")
elif attention_type == "local_mask_right":
x = masked_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary)
elif attention_type == "local_unmasked":
x = local_attention_1d(
q, k, v, block_length=block_length, filter_width=block_width)
elif attention_type == "masked_dilated_1d":
x = masked_dilated_self_attention_1d(q, k, v, block_length, block_width,
gap_size, num_memory_blocks)
else:
assert attention_type == "unmasked_dilated_1d"
x = dilated_self_attention_1d(q, k, v, block_length, block_width,
gap_size, num_memory_blocks)
x = combine_heads(x)
# Set last dim specifically.
x.set_shape(x.shape.as_list()[:-1] + [total_value_depth])
if vars_3d:
o_var = tf.get_variable(
"o", [num_heads, total_value_depth // num_heads, output_depth])
o_var = tf.cast(o_var, x.dtype)
o_var = tf.reshape(o_var, [total_value_depth, output_depth])
x = tf.tensordot(x, o_var, axes=1)
else:
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform",
layer_collection=layer_collection)
if recurrent_memory is not None:
x = recurrent_memory.post_attention(recurrent_memory_transaction, x)
if additional_returned_value is not None:
return x, additional_returned_value
return x |
2d Multihead scaled-dot-product attention with inp/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, h, w, depth_k]
memory_antecedent: a Tensor with shape [batch, h, w, depth_k]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
attention_type: String, type of attention function to use.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
name: an optional string
Returns:
A Tensor of shape [batch, h, w, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads. | def multihead_attention_2d(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
attention_type="local_attention_2d",
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""2d Multihead scaled-dot-product attention with inp/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, h, w, depth_k]
memory_antecedent: a Tensor with shape [batch, h, w, depth_k]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
attention_type: String, type of attention function to use.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
name: an optional string
Returns:
A Tensor of shape [batch, h, w, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
with tf.variable_scope(
name,
default_name="multihead_attention_2d",
values=[query_antecedent, memory_antecedent]):
q, k, v = compute_qkv(query_antecedent, memory_antecedent, total_key_depth,
total_value_depth)
# after splitting, shape is [batch, heads, h, w, depth]
q = split_heads_2d(q, num_heads)
k = split_heads_2d(k, num_heads)
v = split_heads_2d(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
q *= key_depth_per_head**-0.5
if attention_type == "local_attention_2d":
x = local_attention_2d(
q, k, v, query_shape=query_shape, memory_flange=memory_flange)
elif attention_type == "masked_local_attention_2d":
assert attention_type == "masked_local_attention_2d"
x = masked_local_attention_2d(
q, k, v, query_shape=query_shape, memory_flange=memory_flange)
else:
assert attention_type == "unmasked_local_attention_2d_tpu"
x = dot_product_unmasked_attention_local_2d_tpu(
q, k, v, None, max_relative_position=None, query_shape=query_shape)
x = combine_heads_2d(x)
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform")
return x |
Self-attention feedforward layer.
We use self-attention to do feedforward computations. We apply this function
positionwise where for each position, we linearly transform the output to have
depth filter_depth, and break up the result depth-wise into num_parts
contiguous parts. The parts self-attend, we concatenate the results
depth-wise, and we linearly transform to a depth of output_depth. The goal is
to get multiplicative interactions between components of a representation.
Args:
x: a Tensor with shape [batch, length, channels]
filter_depth: an integer
output_depth: an integer
num_parts: an integer dividing filter depth
dropout_rate: a floating point number
share_kv: Share the key value transform
name: an optional string
Returns:
A Tensor with shape [batch, length, output_depth]. | def ffn_self_attention_layer(x,
filter_depth,
output_depth,
num_parts,
dropout_rate,
share_kv=False,
name=None):
"""Self-attention feedforward layer.
We use self-attention to do feedforward computations. We apply this function
positionwise where for each position, we linearly transform the output to have
depth filter_depth, and break up the result depth-wise into num_parts
contiguous parts. The parts self-attend, we concatenate the results
depth-wise, and we linearly transform to a depth of output_depth. The goal is
to get multiplicative interactions between components of a representation.
Args:
x: a Tensor with shape [batch, length, channels]
filter_depth: an integer
output_depth: an integer
num_parts: an integer dividing filter depth
dropout_rate: a floating point number
share_kv: Share the key value transform
name: an optional string
Returns:
A Tensor with shape [batch, length, output_depth].
"""
with tf.variable_scope(
name, default_name="feedforward_self_attention", values=[x]):
x_shape = common_layers.shape_list(x)
part_depth = filter_depth // num_parts
if not share_kv:
combined = common_layers.dense(
x, filter_depth * 3, use_bias=False, name="qkv_transform")
combined = tf.expand_dims(combined, axis=2)
q, k, v = tf.split(combined, 3, axis=3)
else:
q = tf.expand_dims(
common_layers.dense(
x, filter_depth, use_bias=False, name="q_transform"),
axis=2)
kv_combined = tf.expand_dims(
common_layers.dense(
tf.concat([x, x], axis=1),
filter_depth,
use_bias=False,
name="kv_transform"),
axis=2)
k, v = tf.split(kv_combined, [x_shape[1], x_shape[1]], axis=1)
batch_q = tf.reshape(q, [-1, 1, num_parts, part_depth])
batch_k = tf.reshape(k, [-1, 1, num_parts, part_depth])
batch_v = tf.reshape(v, [-1, 1, num_parts, part_depth])
batch_q *= part_depth**-0.5
# non-masked bias
bias = None
x = dot_product_attention(batch_q, batch_k, batch_v, bias, dropout_rate)
x = tf.reshape(x, [x_shape[0], x_shape[1], filter_depth])
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform")
return x |
Attention over parameters.
We use the same multi-headed attention as in the other layers, but the memory
keys and values are model parameters. There are no linear transformation on
the keys or values.
We are also a bit more careful about memory usage, since the number of
memory positions may be very large.
Args:
x: a Tensor with shape [batch, length_q, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
memory_rows: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth]. | def parameter_attention(x,
total_key_depth,
total_value_depth,
output_depth,
memory_rows,
num_heads,
dropout_rate,
name=None):
"""Attention over parameters.
We use the same multi-headed attention as in the other layers, but the memory
keys and values are model parameters. There are no linear transformation on
the keys or values.
We are also a bit more careful about memory usage, since the number of
memory positions may be very large.
Args:
x: a Tensor with shape [batch, length_q, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
memory_rows: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth].
"""
with tf.variable_scope(name, default_name="parameter_attention", values=[x]):
head_size_k = total_key_depth // num_heads
head_size_v = total_value_depth // num_heads
var_shape_k = [num_heads, memory_rows, head_size_k]
var_shape_v = [num_heads, memory_rows, head_size_v]
k = tf.get_variable(
"k",
var_shape_k,
initializer=tf.random_normal_initializer(
0, output_depth**-0.5 * (num_heads**0.5)))
v = tf.get_variable(
"v",
var_shape_v,
initializer=tf.random_normal_initializer(
0, output_depth**-0.5 * (output_depth**0.5)))
batch_size = common_layers.shape_list(x)[0]
length = common_layers.shape_list(x)[1]
q = common_layers.dense(
x, total_key_depth, use_bias=False, name="q_transform")
if dropout_rate:
# This is a cheaper form of attention dropout where we use to use
# the same dropout decisions across batch elements and query positions,
# but different decisions across heads and memory positions.
v = tf.nn.dropout(
v, 1.0 - dropout_rate, noise_shape=[num_heads, memory_rows, 1])
# query is [batch, length, hidden_size]
# reshape and transpose it to [heads, batch * length, head_size]
q = tf.reshape(q, [batch_size, length, num_heads, head_size_k])
q = tf.transpose(q, [2, 0, 1, 3])
q = tf.reshape(q, [num_heads, batch_size * length, head_size_k])
weights = tf.matmul(q, k, transpose_b=True)
weights = tf.nn.softmax(weights)
y = tf.matmul(weights, v)
y = tf.reshape(y, [num_heads, batch_size, length, head_size_v])
y = tf.transpose(y, [1, 2, 0, 3])
y = tf.reshape(y, [batch_size, length, total_value_depth])
y.set_shape([None, None, total_value_depth])
y = common_layers.dense(
y, output_depth, use_bias=False, name="output_transform")
return y |
Return a tensor with given shape containing coordinate along given axis.
Args:
shape: a Tensor representing the shape of the output Tensor
axis: an integer
Returns:
A tensor with shape shape and type tf.int32, where each elements its
coordinate along the given axis. | def coordinate_tensor(shape, axis):
"""Return a tensor with given shape containing coordinate along given axis.
Args:
shape: a Tensor representing the shape of the output Tensor
axis: an integer
Returns:
A tensor with shape shape and type tf.int32, where each elements its
coordinate along the given axis.
"""
if axis < 0:
axis = tf.size(shape) + axis # Convert to positive for the one_hot indice
r = tf.range(shape[axis])
r_shape = tf.one_hot(
axis, tf.size(shape), on_value=-1, off_value=1, dtype=tf.int32)
return tf.zeros(shape, dtype=tf.int32) + tf.reshape(r, r_shape) |
Implementing attention that runs inside each expert.
Args:
x: A tensor of shape[batch, depth]. Contains representations from
different positions, which are lexicographically ordered.
batch_coordinate: A tensor of shape [batch, 1] containing the batch
coordinate of each element in x. This is needed to make sure that
positions from different sequences don't attend to each other.
mask_right: A bool. If true, we will not attend to positions on the right,
just as decoder self attention.
split_batch (bool): If True, each sequence of the batch is processed
individually on a loop. If False, the sequences are processed all at
once and a mask is applied to isolate the sequences from each others
attention_num_head (int): number of attention heads
attention_kq_size (int): dimension used for the attention key, and query
attention_v_size (int): dimension used for the attention value
Returns:
out: A tensor of shape [batch, depth].
example use:
expert_utils.local_moe(
...
expert_fn=functools.partial(self_attention_expert, mask_right=)
) | def self_attention_expert(x,
batch_coordinate,
mask_right=True,
split_batch=False,
attention_num_head=1,
attention_kq_size=None,
attention_v_size=None):
"""Implementing attention that runs inside each expert.
Args:
x: A tensor of shape[batch, depth]. Contains representations from
different positions, which are lexicographically ordered.
batch_coordinate: A tensor of shape [batch, 1] containing the batch
coordinate of each element in x. This is needed to make sure that
positions from different sequences don't attend to each other.
mask_right: A bool. If true, we will not attend to positions on the right,
just as decoder self attention.
split_batch (bool): If True, each sequence of the batch is processed
individually on a loop. If False, the sequences are processed all at
once and a mask is applied to isolate the sequences from each others
attention_num_head (int): number of attention heads
attention_kq_size (int): dimension used for the attention key, and query
attention_v_size (int): dimension used for the attention value
Returns:
out: A tensor of shape [batch, depth].
example use:
expert_utils.local_moe(
...
expert_fn=functools.partial(self_attention_expert, mask_right=)
)
"""
depth = x.get_shape().as_list()[-1]
length = common_layers.shape_list(batch_coordinate)[0]
# Print a warning message if one of the expert isn't used (useful at
# inference where summaries aren't used and the gating function don't add
# noise)
global _expert_count # Hack to make each expert have a unique id
_expert_count += 1
length = tf.cond(
tf.equal(length, 0),
lambda: tf.Print( # pylint: disable=g-long-lambda
length, [length], "Expert {} empty: ".format(_expert_count)),
lambda: length,
)
tf.summary.scalar("batch_size", length, family="experts_stats_batch_size")
attention_kq_size = attention_kq_size or depth
attention_v_size = attention_v_size or depth
def length_not_null(x, batch_coordinate):
"""Branch of the graph only evaluated when length isn't null."""
# Mask between the sequences (not used if map_ids is used)
bias_batch = attention_bias_coordinates(batch_coordinate)
def add_or_set_if(prev_bias, new_bias, condition):
"""Add the bias together while considering the None case."""
if not condition:
return prev_bias
if prev_bias is None:
return new_bias
return prev_bias + new_bias
def mask_and_call_attention(x):
"""Function applied once for each sequence of the batch."""
# Mask to prevent sequences of attending to the future
length = common_layers.shape_list(x)[1] # x has shape [1, length,...]
bias_past = tf.reshape(
attention_bias_lower_triangle(length), [length, length])
# bias has shape [length, length]
bias = None
bias = add_or_set_if(bias, bias_past, mask_right)
bias = add_or_set_if(bias, bias_batch, not split_batch)
bias = tf.reshape(bias, [1, 1, length, length])
return multihead_attention(
x,
None,
bias,
total_key_depth=attention_kq_size,
total_value_depth=attention_v_size,
output_depth=depth,
num_heads=attention_num_head,
dropout_rate=0.0)
if split_batch:
out = expert_utils.map_ids(x, batch_coordinate, mask_and_call_attention)
else:
x = tf.reshape(x, [1, length, depth])
out = mask_and_call_attention(x)
out = tf.squeeze(out, 0)
return out
# If the length is empty, just forward an empty tensor (avoid having to
# evaluate multihead_attention with tensor having dim equal to zeros)
out = tf.cond(
tf.equal(length, 0),
lambda: tf.zeros(shape=[0, depth], dtype=tf.float32, name="empty_out"),
lambda: length_not_null(x, batch_coordinate),
)
return out |
Attention using a mixture of experts.
Positions sent to the same expert can attend to each other.
The mixture of experts is "local" in that it is replicated on each
datashard.
local_moe flatten all batches so to avoid problems with padding (ex: all
padding going to the same expert, self attention attending to non null
padding tokens,...), the padding should be removed before.
Args:
x: a Tensor with shape [batch, length, depth] or [1, batch*length, depth]
k: The number of experts to dispatch each example to
loss_coef: a scalar. A multiplier for the expert loss
attention_num_experts: The number of experts to use
train: a boolean for the current mode
batch_coordinate (tf.Tensor): int32 tensor of shape [1, batch*length, 1]
containing the batch ids. If None, deduced from first dim of x.
**kwargs: Arguments to forward to self_attention_expert
Returns:
y: a Tensor with shape [batch, length, depth]
loss: a Scalar | def local_expert_attention(x,
k,
loss_coef,
attention_num_experts,
train=True,
batch_coordinate=None,
**kwargs):
"""Attention using a mixture of experts.
Positions sent to the same expert can attend to each other.
The mixture of experts is "local" in that it is replicated on each
datashard.
local_moe flatten all batches so to avoid problems with padding (ex: all
padding going to the same expert, self attention attending to non null
padding tokens,...), the padding should be removed before.
Args:
x: a Tensor with shape [batch, length, depth] or [1, batch*length, depth]
k: The number of experts to dispatch each example to
loss_coef: a scalar. A multiplier for the expert loss
attention_num_experts: The number of experts to use
train: a boolean for the current mode
batch_coordinate (tf.Tensor): int32 tensor of shape [1, batch*length, 1]
containing the batch ids. If None, deduced from first dim of x.
**kwargs: Arguments to forward to self_attention_expert
Returns:
y: a Tensor with shape [batch, length, depth]
loss: a Scalar
"""
if batch_coordinate is None:
batch_coordinate = tf.expand_dims(
coordinate_tensor(common_layers.shape_list(x)[:-1], axis=0), axis=-1)
with tf.variable_scope("local_expert_attention"):
additional_dispatch_params = {"batch_coordinate": batch_coordinate}
return expert_utils.local_moe(
x,
train,
functools.partial(self_attention_expert, **kwargs),
attention_num_experts,
k=k,
loss_coef=loss_coef,
pass_x=True,
pass_gates=False,
additional_dispatch_params=additional_dispatch_params,
) |
Perform dot product on a subset of the sequence.
Can add a mask to the attention to prevent sequences to attend to each other
and to prevent attention to the future.
Args:
q (tf.Tensor): Queries of shape [length_expert_q, depth_k]
k (tf.Tensor): Keys of shape [length_expert_k, depth_k]
v (tf.Tensor): Values of shape [length_expert_k, depth_v]
info_q (BatchInfo): Batch info for queries. If None, no mask is added
info_k (BatchInfo): Batch info for keys
Returns:
tf.Tensor: dot product attention output ([length_expert_q, depth_v]) | def expert_dot_product(q, k, v, info_q, info_k):
"""Perform dot product on a subset of the sequence.
Can add a mask to the attention to prevent sequences to attend to each other
and to prevent attention to the future.
Args:
q (tf.Tensor): Queries of shape [length_expert_q, depth_k]
k (tf.Tensor): Keys of shape [length_expert_k, depth_k]
v (tf.Tensor): Values of shape [length_expert_k, depth_v]
info_q (BatchInfo): Batch info for queries. If None, no mask is added
info_k (BatchInfo): Batch info for keys
Returns:
tf.Tensor: dot product attention output ([length_expert_q, depth_v])
"""
length_q = common_layers.shape_list(q)[0]
length_k = common_layers.shape_list(k)[0]
depth_v = v.get_shape().as_list()[-1]
# Create the mask
bias = attention_bias_coordinates(info_q.coordinates, info_k.coordinates)
if info_k.order is not None:
bias += attention_bias_future(info_q.order, info_k.order)
# Restore batch and head dimension
q, k, v = [tf.expand_dims(tf.expand_dims(t, 0), 0) for t in (q, k, v)]
def is_zero():
zeros = tf.zeros(shape=[1, 1, length_q, depth_v], dtype=tf.float32)
zeros = tf.Print(zeros, [length_k, length_q], "length_k/length_q: ")
return zeros
def is_not_zero():
return dot_product_attention(
q,
k,
v,
bias=bias,
# No image summary to avoid "Retval[0] does not have value" (because
# inside a condition)
make_image_summary=False,
)
# TODO(epot): Should make sure a query gets at least one key. Because the
# different sequences of a batch are merged, it's possible that a
# query from a sequence only receive memory from another sequence, so
# with the mask, the query will perform a softmax on -infinity values.
# A hack could be to add at least one sequence of each batch on each group so
# the query can attend to at least one element.
# Softmax(Q.K)*V
v_out = tf.cond(
tf.logical_or(tf.equal(length_q, 0), tf.equal(length_k, 0)),
is_zero,
is_not_zero,
)
# Remove batch and head dimension
v_out = tf.squeeze(v_out, axis=0)
v_out = tf.squeeze(v_out, axis=0)
return v_out |
Perform a dot product attention on a single sequence on a single head.
This function dispatch the q, k, v and loop over the buckets to compute the
attention dot product on each subsequences.
Args:
q (tf.Tensor): [length_q, depth_q]
k (tf.Tensor): [length_k, depth_q]
v (tf.Tensor): [length_k, depth_v]
gates_q (tf.Tensor): One-hot vector of shape [length_q, nb_buckets]
gates_k (tf.Tensor): One-hot vector of shape [length_k, nb_buckets]
bi (BatchInfo): Contains the batch coordinates and sequence order
Returns:
tf.Tensor: [length_q, depth_v] | def dot_product_single_head(q, k, v, gates_q, gates_k, bi):
"""Perform a dot product attention on a single sequence on a single head.
This function dispatch the q, k, v and loop over the buckets to compute the
attention dot product on each subsequences.
Args:
q (tf.Tensor): [length_q, depth_q]
k (tf.Tensor): [length_k, depth_q]
v (tf.Tensor): [length_k, depth_v]
gates_q (tf.Tensor): One-hot vector of shape [length_q, nb_buckets]
gates_k (tf.Tensor): One-hot vector of shape [length_k, nb_buckets]
bi (BatchInfo): Contains the batch coordinates and sequence order
Returns:
tf.Tensor: [length_q, depth_v]
"""
nb_buckets = gates_q.get_shape().as_list()[-1]
q_dispatcher = expert_utils.SparseDispatcher(nb_buckets, gates_q)
k_dispatcher = expert_utils.SparseDispatcher(nb_buckets, gates_k)
def eventually_dispatch(dispatcher, value):
if value is not None:
return dispatcher.dispatch(value)
return [None] * nb_buckets
# Iterate over every dispatched group
list_v_out = []
for (
q_i,
k_i,
v_i,
qbc,
qbo,
kbc,
kbo,
) in zip(
# Dispatch queries, keys and values
q_dispatcher.dispatch(q),
k_dispatcher.dispatch(k),
k_dispatcher.dispatch(v),
# Also dispatch the sequence positions and batch coordinates
eventually_dispatch(q_dispatcher, bi.coordinates),
eventually_dispatch(q_dispatcher, bi.order),
eventually_dispatch(k_dispatcher, bi.coordinates),
eventually_dispatch(k_dispatcher, bi.order),
):
list_v_out.append(
expert_dot_product(
q_i,
k_i,
v_i,
info_q=BatchInfo(coordinates=qbc, order=qbo),
info_k=BatchInfo(coordinates=kbc, order=kbo)))
# Combine all buckets together to restore the original length
return q_dispatcher.combine(list_v_out) |
Construct the graph with either tf.map_fn or a python for loop.
This function is mainly for for benchmarking purpose.
tf.map_fn is dynamic but is much slower than creating a static graph with
for loop. However, having a for loop make the graph much longer to build
and can consume too much RAM on distributed setting.
Args:
fn (fct): same that tf.map_fn but for now can only return a single tensor
value (instead of a tuple of tensor for the general case)
elems (tuple): same that tf.map_fn
use_map_fn (bool): If True, tf.map_fn is used, if False, for _ in _: is used
instead
**kwargs: Additional tf.map_fn arguments (ignored if use_map_fn is False)
Returns:
tf.Tensor: the output of tf.map_fn | def map_fn_switch(fn, elems, use_map_fn=True, **kwargs):
"""Construct the graph with either tf.map_fn or a python for loop.
This function is mainly for for benchmarking purpose.
tf.map_fn is dynamic but is much slower than creating a static graph with
for loop. However, having a for loop make the graph much longer to build
and can consume too much RAM on distributed setting.
Args:
fn (fct): same that tf.map_fn but for now can only return a single tensor
value (instead of a tuple of tensor for the general case)
elems (tuple): same that tf.map_fn
use_map_fn (bool): If True, tf.map_fn is used, if False, for _ in _: is used
instead
**kwargs: Additional tf.map_fn arguments (ignored if use_map_fn is False)
Returns:
tf.Tensor: the output of tf.map_fn
"""
if use_map_fn:
return tf.map_fn(fn, elems, **kwargs)
elems_unpacked = (tf.unstack(e) for e in elems)
out_unpacked = [fn(e) for e in zip(*elems_unpacked)]
out = tf.stack(out_unpacked)
return out |
Sparse multihead self attention.
Perform an approximation of the full multihead attention by dispatching
the tokens using their keys/values. Thus the attention matrix are only
computed each times on a subset of the tokens.
Notes:
* The function don't perform scaling here (multihead_attention does
the /sqrt(depth)).
* The padding should have been removed (so batch size should be 1 but length
contains the elements from all different batches)
* Right now, only self attention is supported so length_q and length_kv
should be identical and the function will add triangular mask.
* If bi.order is not None, The bias is added inside this function to
prevent attention to the future.
Args:
q (tf.Tensor): Queries of shape [batch, heads, length_q, depth_k]
k (tf.Tensor): Keys of shape [batch, heads, length_q, depth_k]
v (tf.Tensor): Values of shape [batch, heads, length_kv, depth_v]
bi (BatchInfo): Contains the batch coordinates and sequence order
use_map_fn (bool): Use either tf.map_fn of python for loop to compute the
heads separately
experts_params (dict): Additional params for the local expert
Returns:
tf.Tensor: Approximation of Softmax(Q.K) * V, of shape
[batch, heads, length_q, depth_v] | def sparse_dot_product_attention(q, k, v, bi, use_map_fn, experts_params):
"""Sparse multihead self attention.
Perform an approximation of the full multihead attention by dispatching
the tokens using their keys/values. Thus the attention matrix are only
computed each times on a subset of the tokens.
Notes:
* The function don't perform scaling here (multihead_attention does
the /sqrt(depth)).
* The padding should have been removed (so batch size should be 1 but length
contains the elements from all different batches)
* Right now, only self attention is supported so length_q and length_kv
should be identical and the function will add triangular mask.
* If bi.order is not None, The bias is added inside this function to
prevent attention to the future.
Args:
q (tf.Tensor): Queries of shape [batch, heads, length_q, depth_k]
k (tf.Tensor): Keys of shape [batch, heads, length_q, depth_k]
v (tf.Tensor): Values of shape [batch, heads, length_kv, depth_v]
bi (BatchInfo): Contains the batch coordinates and sequence order
use_map_fn (bool): Use either tf.map_fn of python for loop to compute the
heads separately
experts_params (dict): Additional params for the local expert
Returns:
tf.Tensor: Approximation of Softmax(Q.K) * V, of shape
[batch, heads, length_q, depth_v]
"""
batch_size, nb_heads, _, depth = common_layers.shape_list(q)
@expert_utils.add_name_scope()
def flatten_first_dims(x):
"""Reshape such that x is [num_heads, -1, depth]."""
# Case 1: Either constant batch size of size 1 or batch already flattened
if x.get_shape().as_list()[0] == 1:
return tf.squeeze(x, axis=0)
# Case 2: Flatten batch dimension
x = tf.transpose(x, perm=[1, 0, 2, 3])
x = tf.reshape(x, [nb_heads, -1, depth])
return x
def flatten_batch(x):
if x is None:
return x
return expert_utils.flatten_all_but_last(x)
q = flatten_first_dims(q)
k = flatten_first_dims(k)
v = flatten_first_dims(v)
bi = BatchInfo(
coordinates=flatten_batch(bi.coordinates),
order=flatten_batch(bi.order),
)
# Unstack heads
list_q = tf.unstack(q) # list[tf.Tensor(shape=[batch * length, depth])]
list_k = tf.unstack(k)
list_v = tf.unstack(v)
list_gates_q = []
list_gates_k = []
total_loss = 0.0
# There might be a more optimized way to compute all heads at once
for single_q, single_k, _ in zip(list_q, list_k, list_v):
# Each head get its own dispatcher
lhs_gating = LshGating(
depth=single_q.get_shape().as_list()[-1], **experts_params)
list_gates_q.append(lhs_gating.get_gates(single_q))
list_gates_k.append(lhs_gating.get_gates(single_k))
gates_q = tf.stack(list_gates_q)
gates_k = tf.stack(list_gates_k)
# Process each head separately.
v_out = map_fn_switch(
lambda args: dot_product_single_head(bi=bi, *args),
elems=(q, k, v, gates_q, gates_k),
dtype=(tf.float32),
parallel_iterations=2,
use_map_fn=use_map_fn,
)
# Restore original shape as expected by multihead_attention
if isinstance(batch_size, int) and batch_size == 1:
v_out = tf.expand_dims(v_out, axis=0) # Restore batch_size = 1
else:
v_out = tf.reshape(v_out, [nb_heads, batch_size, -1, depth])
v_out = tf.transpose(v_out, [1, 0, 2, 3])
return v_out, total_loss / nb_heads |
Perform a dot product attention on a single sequence on a single head.
This function dispatch the q, k, v and loop over the buckets to compute the
attention dot product on each subsequences.
Args:
q (tf.Tensor): [batch*heads, length_q, depth_q]
k (tf.Tensor): [batch*heads, length_k, depth_q]
v (tf.Tensor): [batch*heads, length_k, depth_v]
gates_q (tf.Tensor): One-hot of shape [batch*heads, length_q, nb_buckets]
gates_k (tf.Tensor): One-hot of shape [batch*heads, length_k, nb_buckets]
mask_right (bool): Add a bias to prevent attention to the future
Returns:
tf.Tensor: [length_q, depth_v] | def dot_product_batched_head(q, k, v, gates_q, gates_k, mask_right=False):
"""Perform a dot product attention on a single sequence on a single head.
This function dispatch the q, k, v and loop over the buckets to compute the
attention dot product on each subsequences.
Args:
q (tf.Tensor): [batch*heads, length_q, depth_q]
k (tf.Tensor): [batch*heads, length_k, depth_q]
v (tf.Tensor): [batch*heads, length_k, depth_v]
gates_q (tf.Tensor): One-hot of shape [batch*heads, length_q, nb_buckets]
gates_k (tf.Tensor): One-hot of shape [batch*heads, length_k, nb_buckets]
mask_right (bool): Add a bias to prevent attention to the future
Returns:
tf.Tensor: [length_q, depth_v]
"""
nb_buckets = common_layers.shape_list(gates_q)[-1]
@expert_utils.add_name_scope()
def get_dispatcher(gates):
"""Construct dispatcher for gates."""
length = common_layers.shape_list(gates)[1]
# Count the number of ones per batch (and keep the max value)
nb_elems_to_dispatch = tf.reduce_sum(gates, axis=[1, 2])
nb_elems_to_dispatch = tf.reduce_max(nb_elems_to_dispatch)
nb_elems_to_dispatch = tf.to_int32(nb_elems_to_dispatch)
capacity = nb_elems_to_dispatch // nb_buckets * 2 # Capacity is hardcoded
capacity = tf.minimum(length, capacity)
tf.summary.scalar("dispatch_capacity", capacity, family="lsh")
return expert_utils.TruncatingDispatcher(gates, capacity)
def add_summary_capacity(x, prefix):
# Monitor if capacity overflow
x = x[0, ...] # Take first batch/head
x = tf.reduce_sum(x, axis=0)
tf.summary.scalar(prefix + "_min", tf.reduce_min(x), family="lsh")
tf.summary.scalar(prefix + "_max", tf.reduce_max(x), family="lsh")
tf.summary.histogram(prefix + "capacity_distribution", x, family="lsh")
for i in range(3): # Show the first 3 buckets
tf.summary.scalar("{}_{}".format(prefix, i), x[i], family="lsh")
add_summary_capacity(gates_q, "q")
add_summary_capacity(gates_k, "k")
q_dispatcher = get_dispatcher(gates_q)
k_dispatcher = get_dispatcher(gates_k)
q = q_dispatcher.dispatch(q)
k = k_dispatcher.dispatch(k)
v = k_dispatcher.dispatch(v)
# Bias of shape [batch*heads, nb_buckets, 1, capacity] broadcasted to every
# queries
bias = tf.expand_dims((k_dispatcher.nonpadding() - 1.0) * 1e9, 2)
if mask_right:
q_coordinate = tf.to_float(
tf.expand_dims(q_dispatcher.length_coordinate(), 3))
k_coordinate = tf.to_float(
tf.expand_dims(k_dispatcher.length_coordinate(), 2))
bias += tf.to_float(tf.greater(k_coordinate, q_coordinate)) * -1e9
# The sequence padding is not masked but is ignored on the next layers
# q, k, v now have shape [batch*heads, nb_bucket, capacity, depth]
# The buckets can be seen as different heads
v_out = dot_product_attention(q, k, v, bias=bias)
# Combine all buckets together to restore the original length
return q_dispatcher.combine(v_out) |
Sparse multihead self attention.
Perform an approximation of the full multihead attention by dispatching
the tokens using their keys/values. Thus the attention matrix are only
computed each times on a subset of the tokens.
Notes:
* The function don't perform scaling here (multihead_attention does
the /sqrt(depth)).
* The padding should have been removed (so batch size should be 1 but length
contains the elements from all different batches)
* Right now, only self attention is supported so length_q and length_kv
should be identical and the function will add triangular mask.
* If bi.order is not None, The bias is added inside this function to
prevent attention to the future.
Args:
q (tf.Tensor): Queries of shape [batch, heads, length_q, depth_k]
k (tf.Tensor): Keys of shape [batch, heads, length_q, depth_k]
v (tf.Tensor): Values of shape [batch, heads, length_kv, depth_v]
bi (BatchInfo): Contains the batch coordinates and sequence order
experts_params (dict): Additional params for the local expert
use_map_fn (bool): Use either tf.map_fn of python for loop to compute the
heads separately
mask_right (bool):
Returns:
tf.Tensor: Approximation of Softmax(Q.K) * V, of shape
[batch, heads, length_q, depth_v] | def sparse_dot_product_attention_truncated(
q,
k,
v,
bi, # Unused
experts_params,
use_map_fn=False, # Unused
mask_right=False,
): # pylint: disable=unused-argument
"""Sparse multihead self attention.
Perform an approximation of the full multihead attention by dispatching
the tokens using their keys/values. Thus the attention matrix are only
computed each times on a subset of the tokens.
Notes:
* The function don't perform scaling here (multihead_attention does
the /sqrt(depth)).
* The padding should have been removed (so batch size should be 1 but length
contains the elements from all different batches)
* Right now, only self attention is supported so length_q and length_kv
should be identical and the function will add triangular mask.
* If bi.order is not None, The bias is added inside this function to
prevent attention to the future.
Args:
q (tf.Tensor): Queries of shape [batch, heads, length_q, depth_k]
k (tf.Tensor): Keys of shape [batch, heads, length_q, depth_k]
v (tf.Tensor): Values of shape [batch, heads, length_kv, depth_v]
bi (BatchInfo): Contains the batch coordinates and sequence order
experts_params (dict): Additional params for the local expert
use_map_fn (bool): Use either tf.map_fn of python for loop to compute the
heads separately
mask_right (bool):
Returns:
tf.Tensor: Approximation of Softmax(Q.K) * V, of shape
[batch, heads, length_q, depth_v]
"""
# Currently depth is the same for for q and v
batch_size, nb_heads, _, depth = common_layers.shape_list(q)
total_loss = 0.0
# Each head get its own dispatcher
list_lsh = [LshGating(depth=depth, **experts_params) for _ in range(nb_heads)]
@expert_utils.add_name_scope()
def get_gates_head(x, add_first=False):
"""Return the gates for each heads of the current x.
Args:
x (tf.Tensor): of shape [batch, heads, length, depth]
add_first (bool): if True, add the first element on each bucket
Returns:
tf.Tensor: gates of shape [batch, heads, length, num_buckets]
"""
length = common_layers.shape_list(x)[2]
# Invert heads/batch
x = tf.transpose(x, perm=[1, 0, 2, 3])
x = tf.reshape(x, [nb_heads, batch_size * length, depth])
list_x = tf.unstack(x) # list[tf.Tensor(shape=[batch * length, depth])]
# Unstack heads
list_gates = []
# There might be a more optimized way to compute all heads at once
for lsh, single_x in zip(list_lsh, list_x):
# Each head get its own dispatcher
gates = lsh.get_gates(single_x)
nb_buckets = gates.get_shape().as_list()[-1]
# Reshape to [batch, length, depth] but should consider sequence
# padding in that case (also dispatch the padding)
gates = tf.reshape(gates, [batch_size, length, nb_buckets])
list_gates.append(gates)
gates = tf.stack(list_gates)
# Restore original shape
gates = tf.reshape(gates, [nb_heads, batch_size, length, nb_buckets])
gates = tf.transpose(gates, [1, 0, 2, 3])
# Dispatch the first element to every gates to avoid empty buckets
if add_first:
gates = tf.maximum(gates,
tf.reshape(tf.one_hot([0], length), [1, 1, length, 1]))
return gates
gates_q = get_gates_head(q)
gates_k = get_gates_head(k, add_first=True)
# [batch, heads, length, depth] => [batch*heads, length, depth]
q, k, v, gates_q, gates_k = [
combine_first_two_dimensions(t) for t in (q, k, v, gates_q, gates_k)
]
v_out = dot_product_batched_head(q, k, v, gates_q, gates_k, mask_right)
# Restore original dimension
v_out = tf.reshape(v_out, [batch_size, nb_heads, -1, depth])
return v_out, total_loss / nb_heads |
Increase the length and change the dimensionality.
Expand/project each positions of dim depth of the input into
factor*tokens of dim out_depth
Args:
x (tf.Tensor): shape [batch_size, length, depth]
factor (int): Multiplicative factor of each tokens.
out_depth (int): Output depth (if None, keep depth constant)
Returns:
tf.Tensor: shape [batch_size, length*factor, out_depth] | def deconv_elems_1d(x, factor, out_depth=None):
"""Increase the length and change the dimensionality.
Expand/project each positions of dim depth of the input into
factor*tokens of dim out_depth
Args:
x (tf.Tensor): shape [batch_size, length, depth]
factor (int): Multiplicative factor of each tokens.
out_depth (int): Output depth (if None, keep depth constant)
Returns:
tf.Tensor: shape [batch_size, length*factor, out_depth]
"""
out_depth = out_depth or x.get_shape().as_list()[-1]
x = tf.expand_dims(x, 1) # [batch_size, 1, length, depth]
x = layers().Conv2DTranspose(
filters=out_depth,
kernel_size=(1, factor),
strides=(1, factor),
padding="valid",
data_format="channels_last",
)(x) # [batch_size, 1, length*factor, out_depth]
x = tf.squeeze(x, 1) # [batch_size, length*factor, depth]
return x |
Decrease the length and change the dimensionality.
Merge/restore/compress factors positions of dim depth of the input into
a single position of dim out_depth.
This is basically just a strided convolution without overlap
between each strides. The original length has to be divided by factor.
Args:
x (tf.Tensor): shape [batch_size, length, depth]
factor (int): Length compression factor.
out_depth (int): Output depth
Returns:
tf.Tensor: shape [batch_size, length//factor, out_depth] | def conv_elems_1d(x, factor, out_depth=None):
"""Decrease the length and change the dimensionality.
Merge/restore/compress factors positions of dim depth of the input into
a single position of dim out_depth.
This is basically just a strided convolution without overlap
between each strides. The original length has to be divided by factor.
Args:
x (tf.Tensor): shape [batch_size, length, depth]
factor (int): Length compression factor.
out_depth (int): Output depth
Returns:
tf.Tensor: shape [batch_size, length//factor, out_depth]
"""
out_depth = out_depth or x.get_shape().as_list()[-1]
# with tf.control_dependencies( # Dynamic assertion
# [tf.assert_equal(tf.shape(x)[1] % factor, 0)]):
x = tf.expand_dims(x, 1) # [batch_size, 1, length, depth]
x = layers().Conv2D(
filters=out_depth,
kernel_size=(1, factor),
strides=(1, factor),
padding="valid",
data_format="channels_last",
)(x) # [batch_size, 1, length//factor, out_depth]
x = tf.squeeze(x, 1) # [batch_size, length//factor, depth]
return x |
Reduce the length dimension using self attention.
Args:
x (tf.Tensor): float32 of shape [batch, length, depth]
block_length (int): Block length for local attention (Compression factor)
multihead_params (dict): parameters for multihead attention
Returns:
tf.Tensor: Compressed tensor of shape [batch, length // factor, depth] | def local_reduction_attention(x, block_length, multihead_params):
"""Reduce the length dimension using self attention.
Args:
x (tf.Tensor): float32 of shape [batch, length, depth]
block_length (int): Block length for local attention (Compression factor)
multihead_params (dict): parameters for multihead attention
Returns:
tf.Tensor: Compressed tensor of shape [batch, length // factor, depth]
"""
@expert_utils.add_name_scope()
def dot_product_self_local_attention_flattened(q, k, v):
"""Strided block local self-attention.
No overlap between the blocks.
Args:
q (tf.Tensor): shape [batch, heads, length, depth_k]
k (tf.Tensor): shape [batch, heads, length, depth_k]
v (tf.Tensor): shape [batch, heads, length, depth_v]
Returns:
tf.Tensor: shape [batch, heads, length, depth_v]
"""
_, num_head, _, depth = q.get_shape().as_list()
# Extract the blocks
def pad_and_reshape(x):
"""Split the length dim into [num_block, block_length]."""
length_x = common_layers.shape_list(x)[2]
# Add some padding, but won't matter as the last block will never be
# attended by the query (after compression)
x = tf.pad(x, [[0, 0], [0, 0], [0, -length_x % block_length], [0, 0]])
x = tf.reshape(
x,
[
common_layers.shape_list(x)[0], # Batch
num_head, # Head
common_layers.shape_list(x)[2] // block_length, # Num blocks
block_length, # Block length
depth, # Depth
])
return x
q, k, v = [pad_and_reshape(t) for t in (q, k, v)]
# Perform attention on the flattened dot product
logits = tf.matmul(q, k, transpose_b=True)
logits = tf.reshape(
logits,
[
common_layers.shape_list(logits)[0], # Batch
num_head, # Head
common_layers.shape_list(logits)[2], # Num blocks
block_length**2, # Flatten last dimension
])
weights = tf.nn.softmax(logits)
weights = tf.reshape(
weights,
[
common_layers.shape_list(weights)[0], # Batch
num_head, # Head
common_layers.shape_list(weights)[2], # Num blocks
block_length,
block_length, # Restore the block length dimension
])
weights = tf.reduce_sum(weights, axis=3, keep_dims=True) # Compress block
v_out = tf.matmul(weights, v) # [1, block_length] @ [block_length, depth]
v_out = tf.squeeze(v_out, axis=3)
return v_out
return multihead_attention(
x,
None,
bias=None,
output_depth=x.get_shape().as_list()[-1],
attention_type=dot_product_self_local_attention_flattened,
**multihead_params) |
Reduce the length dimension by compressing with conv.
Args:
x (tf.Tensor): float32 of shape [batch, length, depth]
memory_antecedent (tf.Tensor): Unsupported for now
bias (tf.Tensor): Ignored
factor (int): compression factor for the memory sequence
multihead_params (dict): parameters for multihead attention
nonlinearity (str): Add some non-linearity after the memory block
reduction_type (str): type of compression
add_mask (bool): If True, add the bias to prevent attention to the future
Returns:
(tf.Tensor): float32 of shape [batch, length, depth]
Raises:
ValueError: If reduction_type or nonlinearity is invalid | def multihead_self_attention_reduced(
x,
memory_antecedent=None,
bias=None,
factor=None,
multihead_params=None,
nonlinearity="none",
reduction_type="conv",
add_mask=True,
):
"""Reduce the length dimension by compressing with conv.
Args:
x (tf.Tensor): float32 of shape [batch, length, depth]
memory_antecedent (tf.Tensor): Unsupported for now
bias (tf.Tensor): Ignored
factor (int): compression factor for the memory sequence
multihead_params (dict): parameters for multihead attention
nonlinearity (str): Add some non-linearity after the memory block
reduction_type (str): type of compression
add_mask (bool): If True, add the bias to prevent attention to the future
Returns:
(tf.Tensor): float32 of shape [batch, length, depth]
Raises:
ValueError: If reduction_type or nonlinearity is invalid
"""
if not factor or not multihead_params:
raise ValueError("factor and multihead_params should be set")
if memory_antecedent is not None:
raise NotImplementedError(
"multihead_self_attention_reduced only works with self-attention")
depth = x.get_shape().as_list()[-1]
# Could try to have some overlap between the blocks but that would
# create conv artifacts, would make it difficult to not attend to the future
# within one group and the padding should be handled specially.
# Reduce the memory dimension
if reduction_type == "attention":
memory_x = local_reduction_attention(x, factor, multihead_params)
elif reduction_type == "conv":
# With valid padding, the last block won't be computed (not attended anyway)
memory_x = conv_elems_1d(x, factor)
else:
raise ValueError("Unknown reduction type {}".format(reduction_type))
if nonlinearity == "silu":
memory_x *= tf.nn.sigmoid(memory_x)
elif nonlinearity != "none":
raise ValueError("Unknown non linearity {}".format(nonlinearity))
memory_x = tf.concat(
# Add the first elem to make it attendable by everyone (otherwise the
# first block cannot attend to anything)
[x[:, :1, :], memory_x],
axis=1,
)
# Construct the bias
@expert_utils.add_name_scope()
def construct_bias_vectors(t, axis):
length = tf.to_float(common_layers.shape_list(t)[1])
length_coordinates = tf.range(length, dtype=tf.float32)
length_coordinates = tf.expand_dims(length_coordinates, axis=axis)
# [1, length_k] or [length_q, 1]
return length_coordinates
if add_mask: # Create mask to prevent attention to the future
bias = tf.to_float(
tf.greater(
# Because we add the first elem to the memory block and it can be
# attended by anyone,we don't need to add +1 anymore to prevent self
# attention Use * factor to make sure the last tokens of a block
# cannot attend the block
construct_bias_vectors(memory_x, 0) * factor,
# +epsilon to avoid float equality
construct_bias_vectors(x, 1) + 1e-3,
)) * -1e9
bias = tf.expand_dims(bias, axis=0)
bias = tf.expand_dims(bias, axis=0) # [1, 1, length_k, length_q]
else:
bias = None
return multihead_attention(
query_antecedent=x,
memory_antecedent=memory_x,
bias=bias,
output_depth=depth,
**multihead_params) |
Scaled dot-product attention. One head. One spatial dimension.
Args:
q: a Tensor with shape [batch, length_q, depth_k]
k: a Tensor with shape [batch, length_kv, depth_k]
v: a Tensor with shape [batch, length_kv, depth_v]
bias: optional Tensor broadcastable to [batch, length_q, length_kv]
name: an optional string
Returns:
A Tensor. | def scaled_dot_product_attention_simple(q, k, v, bias, name=None):
"""Scaled dot-product attention. One head. One spatial dimension.
Args:
q: a Tensor with shape [batch, length_q, depth_k]
k: a Tensor with shape [batch, length_kv, depth_k]
v: a Tensor with shape [batch, length_kv, depth_v]
bias: optional Tensor broadcastable to [batch, length_q, length_kv]
name: an optional string
Returns:
A Tensor.
"""
with tf.variable_scope(
name, default_name="scaled_dot_product_attention_simple"):
scalar = tf.rsqrt(tf.to_float(common_layers.shape_list(q)[2]))
logits = tf.matmul(q * scalar, k, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
if common_layers.should_generate_summaries():
tf.summary.image(
"attention", tf.expand_dims(tf.pow(weights, 0.2), 3), max_outputs=1)
return tf.matmul(weights, v) |
Multihead scaled-dot-product self-attention.
Includes layer norm.
Returns multihead-self-attention(layer_norm(x))
Computes one attention head at a time to avoid exhausting memory.
If forget=True, then forget all forwards activations and recompute on
the backwards pass.
Args:
x: a Tensor with shape [batch, length, input_size]
bias: an attention bias tensor broadcastable to [batch, 1, length, length]
num_heads: an integer
head_size: an optional integer - defaults to input_size/num_heads
epsilon: a float, for layer norm
forget: a boolean - forget forwards activations and recompute on backprop
test_vars: optional tuple of variables for testing purposes
name: an optional string
Returns:
A Tensor. | def multihead_self_attention_memory_efficient(x,
bias,
num_heads,
head_size=None,
epsilon=1e-6,
forget=True,
test_vars=None,
name=None):
"""Multihead scaled-dot-product self-attention.
Includes layer norm.
Returns multihead-self-attention(layer_norm(x))
Computes one attention head at a time to avoid exhausting memory.
If forget=True, then forget all forwards activations and recompute on
the backwards pass.
Args:
x: a Tensor with shape [batch, length, input_size]
bias: an attention bias tensor broadcastable to [batch, 1, length, length]
num_heads: an integer
head_size: an optional integer - defaults to input_size/num_heads
epsilon: a float, for layer norm
forget: a boolean - forget forwards activations and recompute on backprop
test_vars: optional tuple of variables for testing purposes
name: an optional string
Returns:
A Tensor.
"""
io_size = x.get_shape().as_list()[-1]
if head_size is None:
assert io_size % num_heads == 0
head_size = io_size / num_heads
def forward_internal(x, wqkv, wo, attention_bias, norm_scale, norm_bias):
"""Forward function."""
n = common_layers.layer_norm_compute(x, epsilon, norm_scale, norm_bias)
wqkv_split = tf.unstack(wqkv, num=num_heads)
wo_split = tf.unstack(wo, num=num_heads)
y = 0
for h in range(num_heads):
with tf.control_dependencies([y] if h > 0 else []):
combined = tf.nn.conv1d(n, wqkv_split[h], 1, "SAME")
q, k, v = tf.split(combined, 3, axis=2)
o = scaled_dot_product_attention_simple(q, k, v, attention_bias)
y += tf.nn.conv1d(o, wo_split[h], 1, "SAME")
return y
key = (
"multihead_self_attention_memory_efficient %s %s" % (num_heads, epsilon))
if not forget:
forward_fn = forward_internal
elif key in _function_cache:
forward_fn = _function_cache[key]
else:
@function.Defun(compiled=True)
def grad_fn(x, wqkv, wo, attention_bias, norm_scale, norm_bias, dy):
"""Custom gradient function."""
with tf.control_dependencies([dy]):
n = common_layers.layer_norm_compute(x, epsilon, norm_scale, norm_bias)
wqkv_split = tf.unstack(wqkv, num=num_heads)
wo_split = tf.unstack(wo, num=num_heads)
deps = []
dwqkvs = []
dwos = []
dn = 0
for h in range(num_heads):
with tf.control_dependencies(deps):
combined = tf.nn.conv1d(n, wqkv_split[h], 1, "SAME")
q, k, v = tf.split(combined, 3, axis=2)
o = scaled_dot_product_attention_simple(q, k, v, attention_bias)
partial_y = tf.nn.conv1d(o, wo_split[h], 1, "SAME")
pdn, dwqkvh, dwoh = tf.gradients(
ys=[partial_y],
xs=[n, wqkv_split[h], wo_split[h]],
grad_ys=[dy])
dn += pdn
dwqkvs.append(dwqkvh)
dwos.append(dwoh)
deps = [dn, dwqkvh, dwoh]
dwqkv = tf.stack(dwqkvs)
dwo = tf.stack(dwos)
with tf.control_dependencies(deps):
dx, dnorm_scale, dnorm_bias = tf.gradients(
ys=[n], xs=[x, norm_scale, norm_bias], grad_ys=[dn])
return (dx, dwqkv, dwo, tf.zeros_like(attention_bias), dnorm_scale,
dnorm_bias)
@function.Defun(
grad_func=grad_fn, compiled=True, separate_compiled_gradients=True)
def forward_fn(x, wqkv, wo, attention_bias, norm_scale, norm_bias):
return forward_internal(x, wqkv, wo, attention_bias, norm_scale,
norm_bias)
_function_cache[key] = forward_fn
if bias is not None:
bias = tf.squeeze(bias, 1)
with tf.variable_scope(name, default_name="multihead_attention", values=[x]):
# TODO(noam): it would be nice to save memory by casting x to float16
# here, but this causes problems with the gradients. Figure out if there
# is a way to leave the gradients as float32.
if test_vars is not None:
wqkv, wo, norm_scale, norm_bias = list(test_vars)
else:
wqkv = tf.get_variable(
"wqkv", [num_heads, 1, io_size, 3 * head_size],
initializer=tf.random_normal_initializer(stddev=io_size**-0.5))
wo = tf.get_variable(
"wo", [num_heads, 1, head_size, io_size],
initializer=tf.random_normal_initializer(
stddev=(head_size * num_heads)**-0.5))
norm_scale, norm_bias = common_layers.layer_norm_vars(io_size)
y = forward_fn(x, wqkv, wo, bias, norm_scale, norm_bias)
y.set_shape(x.get_shape())
return y |
Convert an group index to its bit representation. | def _idx_to_bits(self, i):
"""Convert an group index to its bit representation."""
bits = bin(i)[2:].zfill(self.nb_hyperplanes) # Pad the bits str with 0
return [-1.0 if b == "0" else 1.0 for b in bits] |
Return the bucket id of the given tensor.
Args:
x (tf.Tensor): float32 of shape [length, depth]
Returns:
tf.Tensor: One-hot vector int64 of shape [heads, length, nb_buckets]
containing the id of the bucket | def get_gates(self, x):
"""Return the bucket id of the given tensor.
Args:
x (tf.Tensor): float32 of shape [length, depth]
Returns:
tf.Tensor: One-hot vector int64 of shape [heads, length, nb_buckets]
containing the id of the bucket
"""
# The balance loss don't propagate to the rest of the network
x = tf.stop_gradient(x)
# [length, depth] * [depth, nb_vectors * replicat]
x = tf.matmul(x, self.t_vectors)
# [length, nb_vector * replicat]
x = tf.sign(x) # Get on which side of the hyperplane the keys are.
# x = tf.reshape(x, [-1, nb_replicat, nb_vector])
# [length, replicat, nb_vector] * [nb_vector, 2^nb_vector - 1]
x = tf.matmul(x, self.t_group, transpose_b=True) / self.nb_hyperplanes
# We get a similarity score for each of the group between [-1, 1]
# [length, (replicat,) 2^nb_vector - 1]
# Do an argmax to get the most likely group for each replicat
x = tf.argmax(x, axis=-1)
# [length(, replicat)]
# One-hot for compatibility with the sparse dispatcher
x = tf.one_hot(x, self.nb_buckets)
# TODO(epot): Use a loss to force an even distribution
return x |
The image encoder for the VAN.
Similar architecture as Ruben's paper
(http://proceedings.mlr.press/v70/villegas17a/villegas17a.pdf).
Args:
x: The image to encode.
first_depth: The depth of the first layer. Depth is increased in subsequent
layers.
reuse: To reuse in variable scope or not.
hparams: The python hparams.
Returns:
The encoded image. | def van_image_enc_2d(x, first_depth, reuse=False, hparams=None):
"""The image encoder for the VAN.
Similar architecture as Ruben's paper
(http://proceedings.mlr.press/v70/villegas17a/villegas17a.pdf).
Args:
x: The image to encode.
first_depth: The depth of the first layer. Depth is increased in subsequent
layers.
reuse: To reuse in variable scope or not.
hparams: The python hparams.
Returns:
The encoded image.
"""
with tf.variable_scope('van_image_enc', reuse=reuse):
enc_history = [x]
enc = tf.layers.conv2d(
x, first_depth, 3, padding='same', activation=tf.nn.relu, strides=1)
enc = tf.contrib.layers.layer_norm(enc)
enc = tf.layers.conv2d(
enc, first_depth, 3, padding='same', activation=tf.nn.relu, strides=1)
enc = tf.nn.max_pool(enc, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
enc = tf.nn.dropout(enc, hparams.van_keep_prob)
enc = tf.contrib.layers.layer_norm(enc)
enc_history.append(enc)
enc = tf.layers.conv2d(
enc,
first_depth * 2,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
enc = tf.layers.conv2d(
enc,
first_depth * 2,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
enc = tf.nn.max_pool(enc, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
enc = tf.nn.dropout(enc, hparams.van_keep_prob)
enc = tf.contrib.layers.layer_norm(enc)
enc_history.append(enc)
enc = tf.layers.conv2d(
enc,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
enc = tf.layers.conv2d(
enc,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
enc = tf.layers.conv2d(
enc,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
enc = tf.nn.max_pool(enc, [1, 2, 2, 1], [1, 2, 2, 1], 'SAME')
return enc, enc_history |
The higher level structure encoder for the VAN.
The high level structure is a vector instead of an image.
Args:
x: The higher level structure to encode.
first_depth: The depth of the first layer. Depth is increased in subsequent
layers.
reuse: To reuse in variable scope or not.
Returns:
The encoded image. | def van_enc_2d(x, first_depth, reuse=False):
"""The higher level structure encoder for the VAN.
The high level structure is a vector instead of an image.
Args:
x: The higher level structure to encode.
first_depth: The depth of the first layer. Depth is increased in subsequent
layers.
reuse: To reuse in variable scope or not.
Returns:
The encoded image.
"""
with tf.variable_scope('van_enc', reuse=reuse):
a = 4 # depends on the inputs size
b = 4
# a, b = 4,4
enc = tf.nn.relu(x)
enc = tf.layers.dense(enc, first_depth * a * b, tf.nn.relu)
enc = tf.contrib.layers.layer_norm(enc)
enc = tf.reshape(enc, [-1, a, b, first_depth])
enc = tf.layers.conv2d_transpose(
enc, first_depth, 3, padding='same', activation=tf.nn.relu, strides=1)
enc = tf.contrib.layers.layer_norm(enc)
enc = tf.layers.conv2d_transpose(
enc,
first_depth * 2,
3,
padding='same',
activation=tf.nn.relu,
strides=2)
van_higher_level_2 = tf.reshape(enc, [-1, a * 2 * b * 2 * first_depth * 2])
enc = tf.layers.conv2d_transpose(
enc,
first_depth * 2,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
enc = tf.contrib.layers.layer_norm(enc)
enc = tf.layers.conv2d_transpose(
enc,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
van_higher_level_4 = tf.reshape(enc, [-1, a * 2 * b * 2 * first_depth * 4])
van_higher_level = tf.concat([x, van_higher_level_2, van_higher_level_4], 1)
return enc, van_higher_level |
The VAN decoder.
Args:
x: The analogy information to decode.
skip_connections: The encoder layers which can be used as skip connections.
output_shape: The shape of the desired output image.
first_depth: The depth of the first layer of the van image encoder.
hparams: The python hparams.
Returns:
The decoded image prediction. | def van_dec_2d(x, skip_connections, output_shape, first_depth, hparams=None):
"""The VAN decoder.
Args:
x: The analogy information to decode.
skip_connections: The encoder layers which can be used as skip connections.
output_shape: The shape of the desired output image.
first_depth: The depth of the first layer of the van image encoder.
hparams: The python hparams.
Returns:
The decoded image prediction.
"""
with tf.variable_scope('van_dec'):
dec = tf.layers.conv2d_transpose(
x, first_depth * 4, 3, padding='same', activation=tf.nn.relu, strides=2)
dec = tf.nn.dropout(dec, hparams.van_keep_prob)
dec = tf.contrib.layers.layer_norm(dec)
dec = tf.layers.conv2d_transpose(
dec,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
dec = tf.nn.dropout(dec, hparams.van_keep_prob)
dec = tf.layers.conv2d_transpose(
dec,
first_depth * 2,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
dec = tf.nn.dropout(dec, hparams.van_keep_prob)
dec = tf.contrib.layers.layer_norm(dec)
dec = tf.layers.conv2d_transpose(
dec,
first_depth * 2,
3,
padding='same',
activation=tf.nn.relu,
strides=2)
dec = tf.nn.dropout(dec, hparams.van_keep_prob)
dec = tf.layers.conv2d_transpose(
dec, first_depth, 3, padding='same', activation=tf.nn.relu, strides=1)
dec = tf.nn.dropout(dec, hparams.van_keep_prob)
dec = tf.contrib.layers.layer_norm(dec)
dec = tf.layers.conv2d_transpose(
dec,
output_shape[3] + 1,
3,
padding='same',
activation=tf.nn.relu,
strides=2)
dec = tf.nn.dropout(dec, hparams.van_keep_prob)
out_mask = tf.layers.conv2d_transpose(
dec, output_shape[3] + 1, 3, strides=1, padding='same', activation=None)
mask = tf.nn.sigmoid(out_mask[:, :, :, 3:4])
out = out_mask[:, :, :, :3]
return out * mask + skip_connections[0] * (1 - mask) |
Implements the deep analogy computation. | def analogy_computation_2d(f_first_enc,
f_first_frame,
f_current_enc,
first_depth):
"""Implements the deep analogy computation."""
with tf.variable_scope('analogy_computation'):
frame_enc_diff = f_first_frame - f_first_enc
frame_enc_diff_enc = tf.layers.conv2d(
frame_enc_diff,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
f_current_enc_enc = tf.layers.conv2d(
f_current_enc,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
analogy = tf.concat([frame_enc_diff_enc, f_current_enc_enc], 3)
analogy = tf.layers.conv2d(
analogy,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
analogy = tf.contrib.layers.layer_norm(analogy)
analogy = tf.layers.conv2d(
analogy,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1)
return tf.layers.conv2d(
analogy,
first_depth * 4,
3,
padding='same',
activation=tf.nn.relu,
strides=1) |
Implements a VAN.
Args:
first_enc: The first encoding.
first_frame: The first ground truth frame.
current_enc: The encoding of the frame to generate.
gt_image: The ground truth image, only used for regularization.
reuse: To reuse in variable scope or not.
scope_prefix: The prefix before the scope name.
hparams: The python hparams.
Returns:
The generated image. | def van(first_enc,
first_frame,
current_enc,
gt_image,
reuse=False,
scope_prefix='',
hparams=None):
"""Implements a VAN.
Args:
first_enc: The first encoding.
first_frame: The first ground truth frame.
current_enc: The encoding of the frame to generate.
gt_image: The ground truth image, only used for regularization.
reuse: To reuse in variable scope or not.
scope_prefix: The prefix before the scope name.
hparams: The python hparams.
Returns:
The generated image.
"""
with tf.variable_scope(scope_prefix + 'van', reuse=reuse):
output_shape = first_frame.get_shape().as_list()
output_shape[0] = -1
first_depth = 64
f_first_enc, _ = van_enc_2d(first_enc, first_depth)
f_first_frame, image_enc_history = van_image_enc_2d(
first_frame, first_depth, hparams=hparams)
f_current_enc, van_higher_level = van_enc_2d(
current_enc, first_depth, reuse=True)
f_gt_image, _ = van_image_enc_2d(gt_image, first_depth, True,
hparams=hparams)
analogy_t = analogy_computation_2d(
f_first_enc, f_first_frame, f_current_enc, first_depth)
enc_img = f_current_enc + analogy_t
img = van_dec_2d(
enc_img, image_enc_history, output_shape, first_depth, hparams=hparams)
batch_size = tf.to_float(tf.shape(first_enc)[0])
r_loss = tf.nn.l2_loss(f_gt_image - f_current_enc - analogy_t) / batch_size
return img, r_loss, van_higher_level |
VGG network to use as encoder without the top few layers.
Can be pretrained.
Args:
x: The image to encode. In the range 0 to 1.
enc_final_size: The desired size of the encoding.
reuse: To reuse in variable scope or not.
scope_prefix: The prefix before the scope name.
hparams: The python hparams.
is_training: boolean value indicating if training is happening.
Returns:
The generated image. | def encoder_vgg(x, enc_final_size, reuse=False, scope_prefix='', hparams=None,
is_training=True):
"""VGG network to use as encoder without the top few layers.
Can be pretrained.
Args:
x: The image to encode. In the range 0 to 1.
enc_final_size: The desired size of the encoding.
reuse: To reuse in variable scope or not.
scope_prefix: The prefix before the scope name.
hparams: The python hparams.
is_training: boolean value indicating if training is happening.
Returns:
The generated image.
"""
with tf.variable_scope(scope_prefix + 'encoder', reuse=reuse):
# Preprocess input
x *= 256
x = x - COLOR_NORMALIZATION_VECTOR
with arg_scope(vgg.vgg_arg_scope()):
# Padding because vgg_16 accepts images of size at least VGG_IMAGE_SIZE.
x = tf.pad(x, [[0, 0], [0, VGG_IMAGE_SIZE - IMG_WIDTH],
[0, VGG_IMAGE_SIZE - IMG_HEIGHT], [0, 0]])
_, end_points = vgg.vgg_16(
x,
num_classes=enc_final_size,
is_training=is_training)
pool5_key = [key for key in end_points.keys() if 'pool5' in key]
assert len(pool5_key) == 1
enc = end_points[pool5_key[0]]
# Undoing padding.
enc = tf.slice(enc, [0, 0, 0, 0], [-1, 2, 2, -1])
enc_shape = enc.get_shape().as_list()
enc_shape[0] = -1
enc_size = enc_shape[1] * enc_shape[2] * enc_shape[3]
enc_flat = tf.reshape(enc, (-1, enc_size))
enc_flat = tf.nn.dropout(enc_flat, hparams.enc_keep_prob)
enc_flat = tf.layers.dense(
enc_flat,
enc_final_size,
kernel_initializer=tf.truncated_normal_initializer(stddev=1e-4,))
if hparams.enc_pred_use_l2norm:
enc_flat = tf.nn.l2_normalize(enc_flat, 1)
return enc_flat |
LSTM predictor network. | def predictor(enc_flat,
action,
lstm_states,
pred_depth,
reuse=False,
scope_prefix='',
hparams=None):
"""LSTM predictor network."""
with tf.variable_scope(scope_prefix + 'predict', reuse=reuse):
enc_final_size = enc_flat.get_shape().as_list()[1]
action_size = action.get_shape().as_list()[1]
initial_size = (enc_final_size + action_size)
batch_size = tf.shape(enc_flat)[0]
init_stddev = 1e-2
pre_pred = tf.concat([enc_flat, action], 1)
pre_pred = tf.layers.dense(
pre_pred,
initial_size,
kernel_initializer=tf.truncated_normal_initializer(stddev=init_stddev))
# This is only needed or the GAN version.
if hparams.pred_noise_std > 0:
# Add the noise like this so a pretrained model can be used.
pred_noise = tf.random_normal(
shape=[batch_size, 100], stddev=hparams.pred_noise_std)
pre_pred += tf.layers.dense(
pred_noise,
initial_size,
kernel_initializer=tf.truncated_normal_initializer(
stddev=init_stddev),
name='noise_dense')
pre_pred = tf.nn.relu(pre_pred)
if lstm_states[pred_depth - 2] is None:
back_connect = tf.tile(
tf.get_variable(
'back_connect_init',
shape=[1, initial_size * 2],
initializer=tf.truncated_normal_initializer(stddev=init_stddev))
, (batch_size, 1))
else:
back_connect = lstm_states[pred_depth - 2]
lstm_init_stddev = 1e-4
part_pred, lstm_states[0] = common_video.lstm_cell(
tf.concat([pre_pred, back_connect], 1),
lstm_states[0],
initial_size,
use_peepholes=True,
initializer=tf.truncated_normal_initializer(stddev=lstm_init_stddev),
num_proj=initial_size)
part_pred = tf.contrib.layers.layer_norm(part_pred)
pred = part_pred
for pred_layer_num in range(1, pred_depth, 2):
part_pred, lstm_states[pred_layer_num] = common_video.lstm_cell(
pred,
lstm_states[pred_layer_num],
initial_size,
use_peepholes=True,
initializer=tf.truncated_normal_initializer(stddev=lstm_init_stddev),
num_proj=initial_size)
pred += part_pred
part_pred, lstm_states[pred_layer_num + 1] = common_video.lstm_cell(
tf.concat([pred, pre_pred], 1),
lstm_states[pred_layer_num + 1],
initial_size,
use_peepholes=True,
initializer=tf.truncated_normal_initializer(stddev=lstm_init_stddev),
num_proj=initial_size)
part_pred = tf.contrib.layers.layer_norm(part_pred)
pred += part_pred
pred = tf.layers.dense(
pred,
enc_final_size,
kernel_initializer=tf.truncated_normal_initializer(stddev=init_stddev))
if hparams.enc_pred_use_l2norm:
pred = tf.nn.l2_normalize(pred, 1)
return pred |
Constructs the tensorflow graph of the hierarchical model. | def construct_model(images,
actions=None,
context_frames=2,
hparams=None,
is_training=True):
"""Constructs the tensorflow graph of the hierarchical model."""
pred_depth = 20
enc_out_all, pred_out_all, van_out_all, van_on_enc_all = [], [], [], []
lstm_states = [None] * (pred_depth + 2)
enc_out = encoder_vgg(
images[0], hparams.enc_size, False, scope_prefix='timestep/',
hparams=hparams, is_training=is_training)
enc_out = tf.identity(enc_out, 'enc_out')
enc_out_all.append(enc_out)
num_timesteps = len(actions) - 1
sum_freq = int(num_timesteps / 4 + 1)
reuse = False
for timestep, action in zip(range(len(actions) - 1), actions[:-1]):
done_warm_start = timestep > context_frames - 1
with tf.variable_scope('timestep', reuse=reuse):
if done_warm_start:
pred_input = pred_out_all[-1]
else:
pred_input = enc_out_all[-1]
pred_out = predictor(
pred_input, action, lstm_states, pred_depth, False, hparams=hparams)
pred_out = tf.identity(pred_out, 'pred_out')
if timestep % sum_freq == 0: # and not hparams.use_tpu:
tf.summary.histogram('pred_out', pred_out)
pred_out_all.append(pred_out)
if timestep % sum_freq == 0: # and not hparams.use_tpu:
tf.summary.histogram('lstm_state', lstm_states[0])
van_out, _, _ = van(
enc_out_all[0],
images[0],
pred_out,
images[timestep + 1],
tf.AUTO_REUSE,
hparams=hparams)
van_out = tf.identity(van_out, 'van_out')
van_out_all.append(van_out)
enc_out = encoder_vgg(
images[timestep + 1], hparams.enc_size, True, hparams=hparams,
is_training=is_training)
enc_out = tf.identity(enc_out, 'enc_out')
if timestep % sum_freq == 0: # and not hparams.use_tpu:
tf.summary.histogram('enc_out', enc_out)
enc_out_all.append(enc_out)
van_input = images[0]
enc_noise = tf.zeros_like(enc_out)
if timestep % sum_freq == 0: # and not hparams.use_tpu:
tf.summary.histogram('enc_noise', enc_noise)
van_on_enc, _, _ = van(
enc_out_all[0],
van_input,
enc_out + enc_noise,
images[timestep + 1],
tf.AUTO_REUSE,
hparams=hparams)
van_on_enc = tf.identity(van_on_enc, 'van_on_enc')
van_on_enc_all.append(van_on_enc)
reuse = True
return enc_out_all, pred_out_all, van_out_all, van_on_enc_all |
Image quality metric based on maximal signal power vs. power of the noise.
Args:
true: the ground truth image.
pred: the predicted image.
Returns:
peak signal to noise ratio (PSNR) | def peak_signal_to_noise_ratio(true, pred):
"""Image quality metric based on maximal signal power vs. power of the noise.
Args:
true: the ground truth image.
pred: the predicted image.
Returns:
peak signal to noise ratio (PSNR)
"""
return 10.0 * tf.log(1.0 / mean_squared_error(true, pred)) / tf.log(10.0) |
L1 distance between tensors true and pred. | def l1_error(true, pred):
"""L1 distance between tensors true and pred."""
return tf.reduce_sum(tf.abs(true - pred)) / tf.to_float(tf.size(pred)) |
L2 distance between tensors true and pred.
Args:
true: the ground truth image.
pred: the predicted image.
Returns:
mean squared error between ground truth and predicted image. | def mean_squared_error(true, pred):
"""L2 distance between tensors true and pred.
Args:
true: the ground truth image.
pred: the predicted image.
Returns:
mean squared error between ground truth and predicted image.
"""
result = tf.reduce_sum(
tf.squared_difference(true, pred)) / tf.to_float(tf.size(pred))
return result |
Calculates loss and psnr for predictions over multiple timesteps. | def calc_loss_psnr(gen_images, images, name, hparams=None, use_l1_loss=False):
"""Calculates loss and psnr for predictions over multiple timesteps."""
del hparams
with tf.name_scope(name):
loss, error, psnr_all = 0.0, 0.0, 0.0
for _, x, gx in zip(range(len(gen_images)), images, gen_images):
recon_cost = mean_squared_error(x, gx)
if use_l1_loss:
recon_cost = l1_error(x, gx)
error_i = l1_error(x, gx)
psnr_i = peak_signal_to_noise_ratio(x, gx)
psnr_all += psnr_i
error += error_i
loss += recon_cost
psnr_all /= tf.to_float(len(gen_images))
loss /= tf.to_float(len(gen_images))
error /= tf.to_float(len(gen_images))
# if not hparams.use_tpu:
tf.summary.scalar('psnr_all', psnr_all)
tf.summary.scalar('loss', loss)
return loss, psnr_all |
SV2P model hparams. | def next_frame_sv2p():
"""SV2P model hparams."""
hparams = basic_stochastic.next_frame_basic_stochastic()
hparams.optimizer = "true_adam"
hparams.learning_rate_schedule = "constant"
hparams.learning_rate_constant = 1e-3
hparams.video_num_input_frames = 1
hparams.video_num_target_frames = 3
hparams.batch_size = 16
hparams.bottom = {
"inputs": modalities.video_raw_bottom,
"targets": modalities.video_raw_targets_bottom,
}
hparams.loss = {
"targets": modalities.video_l2_raw_loss,
}
hparams.top = {
"targets": modalities.video_raw_top,
}
hparams.video_modality_loss_cutoff = 0.0
hparams.scheduled_sampling_mode = "count"
hparams.scheduled_sampling_k = 900.0
hparams.add_hparam("reward_prediction", True)
hparams.add_hparam("reward_prediction_stop_gradient", False)
hparams.add_hparam("reward_prediction_buffer_size", 0)
hparams.add_hparam("model_options", "CDNA")
hparams.add_hparam("num_masks", 10)
hparams.add_hparam("multi_latent", False)
hparams.add_hparam("relu_shift", 1e-12)
hparams.add_hparam("dna_kernel_size", 5)
hparams.add_hparam("upsample_method", "conv2d_transpose")
hparams.add_hparam("reward_model", "basic")
hparams.add_hparam("visualize_logits_histogram", True)
return hparams |
SV2P discrete model hparams. | def next_frame_sv2p_discrete():
"""SV2P discrete model hparams."""
hparams = next_frame_sv2p()
hparams.action_injection = "multiplicative"
hparams.small_mode = True
hparams.add_hparam("bottleneck_bits", 128)
hparams.add_hparam("bottleneck_noise", 0.02)
hparams.add_hparam("discrete_warmup_steps", 40000)
hparams.add_hparam("full_latent_tower", False)
hparams.add_hparam("latent_predictor_state_size", 128)
hparams.add_hparam("latent_predictor_temperature", 0.5)
hparams.add_hparam("discretize_warmup_steps", 40000)
return hparams |
SV2P model for atari. | def next_frame_sv2p_atari():
"""SV2P model for atari."""
hparams = next_frame_sv2p()
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
hparams.action_injection = "multiplicative"
hparams.num_iterations_1st_stage = 12000
hparams.num_iterations_2nd_stage = 12000
hparams.anneal_end = 40000
hparams.latent_loss_multiplier_schedule = "noisy_linear_cosine_decay"
hparams.latent_loss_multiplier = 1e-3
hparams.information_capacity = 0.0
hparams.small_mode = True
return hparams |
SV2P model for atari with softmax. | def next_frame_sv2p_atari_softmax():
"""SV2P model for atari with softmax."""
hparams = next_frame_sv2p_atari()
hparams.bottom = {}
hparams.loss = {}
hparams.top = {}
hparams.internal_loss = True
return hparams |
Tiny SV2P model. | def next_frame_sv2p_tiny():
"""Tiny SV2P model."""
hparams = next_frame_sv2p_atari_softmax()
hparams.batch_size = 2
hparams.tiny_mode = True
hparams.num_masks = 1
hparams.video_modality_loss_cutoff = 0.4
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 4
return hparams |
SV2P model with additional cutoff in L2 loss for environments like pong. | def next_frame_sv2p_cutoff():
"""SV2P model with additional cutoff in L2 loss for environments like pong."""
hparams = next_frame_sv2p()
hparams.video_modality_loss_cutoff = 0.4
hparams.video_num_input_frames = 4
hparams.video_num_target_frames = 1
return hparams |
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