def attention_bias_local_2d_block(mesh,
h_dim,
w_dim,
memory_h_dim,
memory_w_dim,
dtype=tf.int32):
"""Bias for attention for local blocks where attention to right is disallowed.
Create the bias matrix by using two separate masks, one for the memory part
which doesn't overlap with the query and second which interacts with the query
and should be disallowed to look to the right of the current query position.
Args:
mesh: a MeshTensorflow object
h_dim: a mtf.Dimension
w_dim: a mtf.Dimension
memory_h_dim: a mtf.Dimension
memory_w_dim: a mtf.Dimension
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [block_length, memory_length]
"""
memory_height = mtf.Dimension(memory_h_dim.name, h_dim.size)
memory_width = mtf.Dimension(memory_w_dim.name, w_dim.size)
mask_top_visible = mtf.zeros(mesh, [h_dim, memory_height], dtype=dtype)
mask_left_visible = mtf.zeros(mesh, [w_dim, memory_width], dtype=dtype)
mask_query = mtf.greater(
mtf.range(mesh, memory_height, dtype=tf.int32),
mtf.range(mesh, memory_width, dtype=dtype))
width_mask = mtf.concat([mask_left_visible, mask_query], memory_width.name)
mask = mtf.cast(
mtf.concat([mask_top_visible, width_mask], memory_height.name),
dtype=tf.float32) * -1e9
return mask
def attention_bias_local_block(mesh, block_length, memory_length,
dtype=tf.int32):
"""Bias for attention for local blocks where attention to right is disallowed.
Create the bias matrix by using two separate masks, one for the memory part
which doesn't overlap with the query and second which interacts with the query
and should be disallowed to look to the right of the current query position.
Args:
mesh: a MeshTensorflow object
block_length: a mtf.Dimension
memory_length: a mtf.Dimension
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [block_length, memory_length]
"""
memory_length = mtf.Dimension(memory_length.name, block_length.size)
memory_mask = mtf.zeros(mesh, [block_length, memory_length], dtype=dtype)
mask = mtf.cast(mtf.less(mtf.range(mesh, block_length, dtype=dtype),
mtf.range(mesh, memory_length, dtype=dtype)),
dtype=dtype)
mask = mtf.cast(
mtf.concat([memory_mask, mask], memory_length.name),
dtype=tf.float32) * -1e9
return mask
def masked_local_attention_1d_incremental(x,
prev_k,
prev_v,
step_num,
master_dtype=None,
slice_dtype=None,
params=None,
name=None):
"""Incremental local self-attention (one decode step).
Incremental version of masked_local_attention_1d()
Args:
x: a mtf.Tensor with shape [batch..., io_channels]
prev_k: mtf.Tensor with shape
[batch..., heads, window_length, kv_channels]
prev_v: mtf.Tensor with shape
[batch..., heads, window_length, kv_channels]
step_num: mtf Scalar with dtype tf.int32
master_dtype: a tf.dtype (deprecated)
slice_dtype: a tf.dtype (deprecated)
params: a quadruple of Tensors (see multihead_attention_params())
name: an optional string.
Returns:
y: A mtf.Tensor with shape [batch..., io_channels]
new_k: mtf.Tensor with shape
[batch..., heads, window_length, kv_channels]
new_v: mtf.Tensor with shape
[batch..., heads, window_length, kv_channels]
Raises:
ValueError: if the dimensions do not match.
"""
batch_dims = x.shape.dims[:-1]
io_channels = x.shape.dims[-1]
heads, window_length, kv_channels = prev_k.shape.dims[-3:]
with tf.variable_scope(name, default_name="masked_local_attention_1d"):
if params is None:
wq, wk, wv, wo = multihead_attention_vars(
x.mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, x.dtype)
else:
wq, wk, wv, wo = params
q = mtf.einsum([x, wq], mtf.Shape(batch_dims + [heads, kv_channels]))
k = mtf.einsum([x, wk], mtf.Shape(batch_dims + [heads, kv_channels]))
v = mtf.einsum([x, wv], mtf.Shape(batch_dims + [heads, kv_channels]))
current_position = mtf.equal(
mtf.range(x.mesh, window_length, dtype=tf.int32),
mtf.mod(step_num, window_length.size))
k = mtf.where(current_position, k, prev_k, output_shape=prev_k.shape)
v = mtf.where(current_position, v, prev_v, output_shape=prev_v.shape)
o = dot_product_attention(q, k, v, mask=None)
y = mtf.einsum([o, wo], x.shape)
return y, k, v
def inner_loop(i, alive_seq, alive_log_probs, finished_seq,
finished_scores, finished_flags, *states):
"""Inner beam search loop.
There are three groups of tensors, alive, finished, and topk.
The alive group contains information about the current alive sequences
The topk group contains information about alive + topk current decoded words
the finished group contains information about finished sentences, that is,
the ones that have decoded to <EOS>. These are what we return.
The general beam search algorithm is as follows:
While we haven't terminated (pls look at termination condition)
1. Grow the current alive to get beam*2 topk sequences
2. Among the topk, keep the top beam_size ones that haven't reached EOS
into alive
3. Among the topk, keep the top beam_size ones have reached EOS into
finished
Repeat
To make things simple with using fixed size tensors, we will end
up inserting unfinished sequences into finished in the beginning. To stop
that we add -ve INF to the score of the unfinished sequence so that when a
true finished sequence does appear, it will have a higher score than all the
unfinished ones.
Args:
i: loop index
alive_seq: Topk sequences decoded so far [batch_size, beam_size, i+1]
alive_log_probs: probabilities of the beams. [batch_size, beam_size]
finished_seq: Current finished sequences.
[batch_size, beam_size, i+1]
finished_scores: scores for each of these sequences.
[batch_size, beam_size]
finished_flags: finished bools for each of these sequences.
[batch_size, beam_size]
*states: mtf Tensors
Returns:
Tuple of
(Incremented loop index
New alive sequences,
Log probs of the alive sequences,
New finished sequences,
Scores of the new finished sequences,
Flags indicating which sequence in finished as reached EOS,
dict of final decoding states)
"""
states = [
mtf.replace_dimensions(state, batch_and_beam_dim,
[batch_dim, beam_dim]) for state in states
]
# Each inner loop, we carry out three steps:
# 1. Get the current topk items.
# 2. Extract the ones that have finished and haven't finished
# 3. Recompute the contents of finished based on scores.
(top2k_seq, top2k_log_probs, top2k_scores, top2k_finished, new_states,
first_selector) = grow_topk(i, alive_seq, alive_log_probs, states)
with tf.variable_scope("grow_alive"):
alive_seq, alive_log_probs, _, second_selector = grow_alive(
top2k_seq, top2k_scores, top2k_log_probs, top2k_finished)
with tf.variable_scope("grow_finished"):
finished_seq, finished_scores, finished_flags, _ = grow_finished(
finished_seq, finished_scores, finished_flags, top2k_seq,
top2k_scores, top2k_finished)
old_beam_dim = mtf.Dimension("old_beam", beam_dim.size)
selector = mtf.einsum([
mtf.rename_dimension(first_selector, beam_dim.name,
old_beam_dim.name), second_selector
],
output_shape=[batch_dim, old_beam_dim, beam_dim])
gathered_states = []
if use_tpu and layout is not None and mesh_shape is not None:
# This hack combines the beam dimension with some of the batch dimension.
# It makes gathering faster on TPU.
#
# Instead of multiplying by a [beam, beam] selector matrix, we instead
# multiply by a [minor_batch*beam, minor_batch*beam] selector matrix.
# This is theoretically more FLOPs, but it brings the matrix size closer
# to the magic optimal value of 128.
#
# TODO(noam): file a bug with the XLA team to do this automatically
major_batch_size = mtf.tensor_dim_to_mesh_dim_size(
layout, mesh_shape, batch_dim)
major_batch = mtf.Dimension(batch_dim.name, major_batch_size)
minor_batch = mtf.Dimension("minor_batch",
batch_dim.size // major_batch.size)
old_minor_batch = mtf.Dimension("old_minor_batch",
minor_batch.size)
old_combined = mtf.Dimension("old_combined",
minor_batch.size * beam_dim.size)
combined = mtf.Dimension("new_combined", old_combined.size)
same_minor_batch = mtf.to_float(
mtf.equal(mtf.range(mesh, old_minor_batch, tf.float32),
mtf.range(mesh, minor_batch, tf.float32)))
selector = mtf.reshape(
selector, [major_batch, minor_batch, old_beam_dim, beam_dim])
selector = mtf.einsum([selector, same_minor_batch],
output_shape=[
major_batch, old_minor_batch,
old_beam_dim, minor_batch, beam_dim
],
reduced_dims=[])
selector = mtf.reshape(selector,
[major_batch, old_combined, combined])
for state in new_states:
s = mtf.replace_dimensions(state, [batch_dim, beam_dim],
[major_batch, old_combined])
s = mtf.einsum([s, mtf.cast(selector, state.dtype)],
reduced_dims=[old_combined],
output_shape=mtf.replace_dimensions(
state.shape, [batch_dim, beam_dim],
[major_batch, combined]))
gathered_states.append(
mtf.replace_dimensions(s, [major_batch, combined],
batch_and_beam_dim))
else:
for state in new_states:
state = mtf.einsum([
mtf.rename_dimension(state, beam_dim.name,
old_beam_dim.name),
mtf.cast(selector, state.dtype)
],
reduced_dims=[old_beam_dim],
output_shape=state.shape)
state = mtf.replace_dimensions(state, [batch_dim, beam_dim],
batch_and_beam_dim)
gathered_states.append(state)
return (i + 1, alive_seq, alive_log_probs, finished_seq,
finished_scores, finished_flags) + tuple(gathered_states)
def multihead_self_attention_incremental(query_antecedent,
prev_k,
prev_v,
step_num,
master_dtype,
slice_dtype,
name="multihead_attention"):
"""Incremental self-attention (one decode step).
In order to use only one variable containing the four weight matrices
packed together, we insist that the query and memory antecedents have the
same dimensionality (io_channels) and that the keys and values have the
same dimensionality (kv_channels).
Args:
query_antecedent: a mtf.Tensor with shape [batch..., io_channels]
prev_k: mtf.Tensor with shape [batch..., heads, memory_length, kv_channels]
prev_v: mtf.Tensor with shape [batch..., heads, memory_length, kv_channels]
step_num: mtf Scalar with dtype tf.int32
master_dtype: a tf.dtype
slice_dtype: a tf.dtype
name: an optional string.
Returns:
y: A mtf.Tensor with shape [batch..., io_channels]
new_k: mtf.Tensor with shape [batch..., heads, memory_length, kv_channels]
new_v: mtf.Tensor with shape [batch..., heads, memory_length, kv_channels]
Raises:
ValueError: if the dimensions do not match.
"""
batch_dims = query_antecedent.shape.dims[:-1]
io_channels = query_antecedent.shape.dims[-1]
heads, memory_length, kv_channels = prev_k.shape.dims[-3:]
with tf.variable_scope(name, default_name="multihead_attention"):
wq, wk, wv, wo = multihead_attention_vars(
query_antecedent.mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, query_antecedent.dtype)
memory_antecedent = query_antecedent
q = mtf.einsum(
[query_antecedent, wq],
mtf.Shape(batch_dims + [heads, kv_channels]))
k = mtf.einsum(
[memory_antecedent, wk],
mtf.Shape(batch_dims + [heads, kv_channels]))
v = mtf.einsum(
[memory_antecedent, wv],
mtf.Shape(batch_dims + [heads, kv_channels]))
k = prev_k + mtf.multiply(
k, mtf.one_hot(step_num, memory_length, dtype=prev_k.dtype),
output_shape=prev_k.shape)
v = prev_v + mtf.multiply(
v, mtf.one_hot(step_num, memory_length, dtype=prev_v.dtype),
output_shape=prev_v.shape)
mask = mtf.cast(
mtf.greater(mtf.range(
query_antecedent.mesh, memory_length, dtype=tf.int32), step_num),
q.dtype) * -1e9
o = dot_product_attention(q, k, v, mask)
y = mtf.einsum([o, wo], query_antecedent.shape)
return y, k, v
def masked_local_attention_1d(x,
kv_channels,
heads,
window_size=128,
master_dtype=tf.float32,
slice_dtype=tf.float32,
length_per_split=None,
return_kv=None,
params=None,
name=None):
"""Attention to the source position and a neighborhood to the left of it.
Attention for a given query position p can only see memory positions
in the range (p - window_size, p].
Args:
x: a mtf.Tensor with shape batch_dims + [length, io_channels]
kv_channels: a mtf.Dimension (the size of the key and value vectors)
heads: a mtf.Dimension (the number of heads)
window_size: an integer
master_dtype: a tf.dtype (deprecated - use params arg)
slice_dtype: a tf.dtype (deprecated - use params arg)
length_per_split: an optional integer indicating the part of the length
dimension per processor. You can omit if the length dimension is not
split.
return_kv: an optional list onto which to append the computed k and v.
params: an optional quadruple of Tensors (see multihead_attention_params())
name: an optional string.
Returns:
a Tensor with the same shape as x
Raises:
ValueError: if channels or depth don't match.
"""
with tf.variable_scope(
name, default_name="masked_local_attention_1d", values=[x]):
batch_dims = x.shape.dims[:-2]
length, io_channels = x.shape.dims[-2:]
if params is None:
wq, wk, wv, wo = multihead_attention_vars(
x.mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, x.dtype)
else:
wq, wk, wv, wo = params
# Get query q, keys k and values v.
qkv_shape = mtf.Shape(batch_dims + [heads, length, kv_channels])
q = mtf.einsum([x, wq], qkv_shape)
k = mtf.einsum([x, wk], qkv_shape)
v = mtf.einsum([x, wv], qkv_shape)
if return_kv is not None:
return_kv.extend([k, v])
# Choose a suitable block size.
# We choose the greatest divisor of length_per_split less than or equal
# to max(window_size, 128)
if length_per_split is None:
length_per_split = length.size
block_length = max(window_size, 128)
while length_per_split % block_length != 0:
block_length -= 1
query_block_length = mtf.Dimension("query_block_length", block_length)
memory_block_length = mtf.Dimension("memory_block_length", block_length)
# The num_blocks dimension gets the same name as the length dimension,
# so it will be split in the same way.
num_blocks = mtf.Dimension(length.name, length.size // block_length)
q_shape = batch_dims + [heads, num_blocks, query_block_length, kv_channels]
kv_shape = batch_dims + [
heads, num_blocks, memory_block_length, kv_channels]
q = mtf.reshape(q, q_shape)
k = mtf.reshape(k, kv_shape)
v = mtf.reshape(v, kv_shape)
# augment the keys and values for each block with keys and values for
# the previous window_size timesteps.
k = mtf.left_halo_exchange(k, num_blocks, memory_block_length, window_size)
v = mtf.left_halo_exchange(v, num_blocks, memory_block_length, window_size)
padded_memory_block_length = mtf.Dimension(
"memory_block_length", window_size + block_length)
mpos = mtf.range(x.mesh, padded_memory_block_length, tf.float32)
qpos = mtf.range(x.mesh, query_block_length, tf.float32) + window_size
# prevent looking forward
mask = mtf.cast(mtf.greater(mpos, qpos), x.dtype) * -1e9
# prevent looking >=block_length timesteps backward
mask += mtf.cast(mtf.less_equal(mpos, qpos - block_length), x.dtype) * -1e9
# Note: The first window_size-1 positions can see back into pre-time
# where all the keys and values are zero. We could mask this out, but we
# don't.
o = dot_product_attention(q, k, v, mask=mask)
o = mtf.reshape(o, batch_dims + [heads, length, kv_channels])
return mtf.einsum([o, wo], mtf.Shape(batch_dims + [length, io_channels]))
def multihead_self_attention_memory_compressed(x,
mask_right,
compression_factor,
kv_channels,
heads,
dropout=0.0,
dropout_broadcast_dims=None,
master_dtype=tf.float32,
slice_dtype=tf.float32,
name="multihead_attention"):
"""Memory-compressed self-attention.
The memory is first average-pooled (strided) to make it shorter by
a factor of compression_factor.
Args:
x: a mtf.Tensor with shape
[<batch_dims>, query_length, io_channels]
mask_right: a boolean
compression_factor: an integer
kv_channels: a mtf.Dimension (the size of the key and value vectors)
heads: a mtf.Dimension (the number of heads)
dropout: a floating point value
dropout_broadcast_dims: an optional list of mtf.Dimension
master_dtype: a tf.dtype
slice_dtype: a tf.dtype
name: an optional string.
Returns:
A mtf.Tensor with shape [batch, query_length, io_channels]
Raises:
ValueError: if the dimensions do not match.
"""
batch_dims = x.shape.dims[:-2]
length, io_channels = x.shape.dims[-2:]
with tf.variable_scope(name,
default_name="compressed_attention",
values=[x]):
wq, wk, wv, wo = multihead_attention_vars(
x.mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, x.dtype)
memory_antecedent = compress_mean(x, length, compression_factor)
memory_antecedent = rename_length_to_memory_length(memory_antecedent)
memory_length = memory_antecedent.shape.dims[-2]
q = mtf.einsum(
[x, wq],
mtf.Shape(batch_dims + [heads, length, kv_channels]))
k = mtf.einsum(
[memory_antecedent, wk],
mtf.Shape(batch_dims + [heads, memory_length, kv_channels]))
v = mtf.einsum(
[memory_antecedent, wv],
mtf.Shape(batch_dims + [heads, memory_length, kv_channels]))
if mask_right:
query_pos = mtf.range(x.mesh, length, dtype=tf.int32)
memory_pos = (
mtf.range(x.mesh, memory_length, dtype=tf.int32) * compression_factor
+ (compression_factor - 1))
mask = mtf.cast(mtf.greater(memory_pos, query_pos), x.dtype) * -1e9
else:
mask = None
o = dot_product_attention(
q, k, v, mask, dropout, dropout_broadcast_dims, extra_logit=0.0)
return mtf.einsum(
[o, wo], mtf.Shape(batch_dims + [length, io_channels]))