def _Proc(record):
"""Parses a serialized tf.Example record."""
outputs = [
('inputs', tf.io.VarLenFeature(tf.int64)),
('targets', tf.io.VarLenFeature(tf.int64)),
# Default eval weight to 1.0
('eval_weight',
tf.io.FixedLenFeature([], tf.float32, default_value=1.0)),
]
features = tf.io.parse_single_example(record, dict(outputs))
for k, v in six.iteritems(features):
if k != 'eval_weight':
features[k] = v.values
else:
eval_weight = v
src_ids = features['inputs']
tgt_labels = features['targets']
# Derive trivial segmentation for unpacked input.
src_paddings, tgt_ids, tgt_paddings, tgt_weights, bucket_key = _DerivePaddingsAndIds(
src_ids, tgt_labels)
src_len = tf.shape(src_ids)[0]
tgt_len = tf.shape(tgt_ids)[0]
src_pos = tf.range(src_len, dtype=tf.int32)
src_seg = tf.zeros_like(src_paddings)
tgt_pos = tf.range(tgt_len, dtype=tf.int32)
tgt_seg = tf.zeros_like(tgt_paddings)
return [
src_ids, src_paddings, tgt_ids, tgt_paddings, tgt_labels, tgt_weights,
src_pos, src_seg, tgt_pos, tgt_seg, eval_weight
], bucket_key
def MakeCausalPadding(seq_len, block_size, left_context, right_context):
"""Makes the causal padding tensor for a full sequence.
Args:
seq_len: int or scalar int tensor. Sequence length.
block_size: int. Number of time frames in a block.
left_context: int. Left context size.
right_context: int. Right context size.
Returns:
A tensor of [num_blocks, block_size, context_size] taking values in {0, 1},
where context_size = block_size + (left_context - 1) + right_context.
Element b, i, j is zero if in the b-th block, the i-th frame can access
the j-th frame in the context.
"""
seq_len = py_utils.with_dependencies([
py_utils.assert_greater_equal(
seq_len, 1, message='seq_len must be at least 1')
], seq_len)
num_blocks = (seq_len + block_size - 1) // block_size
context_size = block_size + (left_context - 1) + right_context
# [num_blocks, block_size]: source positions in the original sequence.
src_positions = tf.reshape(tf.range(num_blocks * block_size),
[num_blocks, block_size])
# [num_blocks,]: source positions at the start of each block.
block_start_positions = tf.range(0, num_blocks * block_size, block_size)
# [context_size]: positions relative to the block start.
relative_context_positions = tf.range(context_size) - (left_context - 1)
# [num_blocks, context_size]: target positions in the original sequence.
tgt_positions = (block_start_positions[:, tf.newaxis] +
relative_context_positions[tf.newaxis, :])
# [num_blocks, block_size, context_size]: position differences between source-
# target pairs.
position_diff = src_positions[:, :,
tf.newaxis] - tgt_positions[:, tf.newaxis, :]
# [num_blocks, block_size, context_size]: if attention is allowed between
# source-target pairs.
valid_atten = tf.math.logical_and(-right_context <= position_diff,
position_diff < left_context)
# [num_blocks, block_size]: if the source position is valid, not padded.
valid_src = src_positions < seq_len
# [num_blocks, context_size]: if the target position is valid, not padded.
valid_tgt = tf.math.logical_and(0 <= tgt_positions,
tgt_positions < seq_len)
valid_atten &= tf.math.logical_and(valid_src[:, :, tf.newaxis],
valid_tgt[:, tf.newaxis, :])
padding = 1.0 - tf.cast(valid_atten, dtype=tf.float32)
return padding
def SequenceConcat(x, x_paddings, y, y_paddings, pad=0):
"""Concats sequence `x` with sequence `y`.
This function is length aware (based off the paddings).
Args:
x: A sequence of tokens of shape [batch_size, x_len_max].
x_paddings: The paddings of `x`.
y: A sequence of tokens of shape [batch_size, y_len_max].
y_paddings: The paddings of `y`.
pad: The <pad> token to fill the concatenated sequence (of type integer).
Returns:
A tuple.
- Concatenation of `x` and `y` of shape
[batch_size, x_len_max + y_len_max].
- Paddings of the concatenation of shape
[batch_size, x_len_max + y_len_max].
"""
# Get the length (w/ eos).
x_len = tf.cast(tf.round(tf.reduce_sum(1 - x_paddings, 1)), tf.int32)
y_len = tf.cast(tf.round(tf.reduce_sum(1 - y_paddings, 1)), tf.int32)
batch_size = py_utils.GetShape(x)[0]
y_len_max = py_utils.GetShape(y)[1]
# Pad `x` with necessary <pad>.
x = tf.concat([x, tf.fill(py_utils.GetShape(y), pad)], 1)
# Replace all <pad> with 0.
x = tf.where(tf.not_equal(x, pad), x, tf.fill(py_utils.GetShape(x), 0))
# Compute the write indices of `y` in `xy`.
indices = tf.stack([
tf.tile(tf.expand_dims(tf.range(batch_size), 1), [1, y_len_max]),
(tf.tile(tf.expand_dims(tf.range(y_len_max), 0), [batch_size, 1]) +
tf.expand_dims(x_len, 1)),
], 2)
xy = x + tf.scatter_nd(indices, y, py_utils.GetShape(x))
# We need to remap all <pad> to `pad`.
xy = tf.where(
tf.less(tf.expand_dims(tf.range(py_utils.GetShape(xy)[1]), 0),
tf.expand_dims(x_len + y_len, 1)), xy,
tf.fill(py_utils.GetShape(xy), pad))
xy_paddings = 1 - tf.sequence_mask(x_len + y_len,
py_utils.GetShape(xy)[1],
x_paddings.dtype)
return xy, xy_paddings
def SequenceAppendToken(x, x_paddings, token, extend=False):
"""Appends <token> to sequence `x`.
Args:
x: A sequence of tokens of shape [batch_size, x_len_max].
x_paddings: The paddings of `x`.
token: The token to append (of type integer).
extend: Whether to extend `x` along the length dimension, this must be true
for any sequence length in `x` that is `x_len_max` or else an invalid
sequence will be emitted.
Returns:
A tuple.
- The new sequence, Tensor of shape [batch_size, x_len_max].
- The new paddings, Tensor of shape [batch_size, x_len_max].
"""
batch_size = py_utils.GetShape(x)[0]
x_len = tf.cast(tf.round(tf.reduce_sum(1 - x_paddings, 1)), tf.int32)
if extend:
x = tf.pad(x, [[0, 0], [0, 1]])
# Mask all invalid entries of `x` to 0.
x *= tf.sequence_mask(x_len, py_utils.GetShape(x)[1], x.dtype)
# Append the <token> based on `x_len`.
x += tf.scatter_nd(tf.stack([tf.range(batch_size), x_len], axis=1),
tf.cast(tf.fill([batch_size], token), x.dtype),
py_utils.GetShape(x))
x_paddings = 1 - tf.sequence_mask(x_len + 1,
py_utils.GetShape(x)[1],
x_paddings.dtype)
return x, x_paddings
def _Moments(inputs, mask, enable_cross_replica_sum_on_tpu=False):
"""Computes mean and variance over the valid data points in inputs."""
inputs = py_utils.with_dependencies([
py_utils.assert_equal(tf.rank(inputs), tf.rank(mask)),
py_utils.assert_greater_equal(mask, tf.zeros_like(mask)),
], inputs)
rank = tf.rank(mask)
reduce_over_dims = tf.range(0, rank - 1)
sum_v = tf.reduce_sum(inputs * tf.cast(mask, inputs.dtype),
reduce_over_dims)
count_v = tf.reduce_sum(mask, reduce_over_dims)
# Input shape is guaranteed to be a multiple of mask shape because the
# inputs * mask op above was successfully broadcasted.
mask_multiplier = tf.shape(inputs)[:-1] // tf.shape(mask)[:-1]
count_v *= tf.cast(tf.reduce_prod(mask_multiplier), count_v.dtype)
if py_utils.use_tpu() and enable_cross_replica_sum_on_tpu:
sum_v = tf.tpu.cross_replica_sum(sum_v)
count_v = tf.tpu.cross_replica_sum(count_v)
count_v = tf.maximum(count_v, 1.0)
mean = sum_v / count_v
sum_vv = tf.reduce_sum((inputs - mean) * (inputs - mean) * mask,
reduce_over_dims)
if py_utils.use_tpu() and enable_cross_replica_sum_on_tpu:
sum_vv = tf.tpu.cross_replica_sum(sum_vv)
variance = py_utils.with_dependencies([
py_utils.assert_greater_equal(sum_vv, tf.zeros_like(sum_vv)),
], sum_vv / count_v)
return mean, variance
def _InputBatch(self):
length = tf.reduce_prod(self.shape)
counter = summary_utils.StatsCounter('CountingInputGenerator')
new_value = tf.cast(counter.IncBy(length), dtype=tf.int32) - length
new_value = tf.stop_gradient(new_value)
values = new_value + tf.range(length)
shaped_values = tf.reshape(tf.cast(values, dtype=tf.float32), self.shape)
targets = tf.reduce_sum(shaped_values, axis=0)
return py_utils.NestedMap(src_ids=shaped_values, tgt_ids=targets)
def __init__(self, params):
super(NmtInput, self).__init__(params)
p = self.params
self.natural_order_model = p.natural_order_model
(self._src_ids, self._src_paddings, self._tgt_ids, self._tgt_paddings,
self._tgt_labels,
self._tgt_weights), self._bucket_keys = self._BuildDataSource()
if p.pad_to_max_seq_length:
assert p.source_max_length
if min(self.infeed_bucket_batch_limit) == max(
self.infeed_bucket_batch_limit):
source_shape = [
min(self.infeed_bucket_batch_limit), p.source_max_length
]
target_shape = [
min(self.infeed_bucket_batch_limit), p.target_max_length
]
else:
source_shape = None
target_shape = None
self._src_ids = py_utils.PadSequenceDimension(self._src_ids,
p.source_max_length, 0,
source_shape)
self._src_paddings = py_utils.PadSequenceDimension(
self._src_paddings, p.source_max_length, 1, source_shape)
self._tgt_ids = py_utils.PadSequenceDimension(self._tgt_ids,
p.target_max_length, 0,
target_shape)
self._tgt_paddings = py_utils.PadSequenceDimension(
self._tgt_paddings, p.target_max_length, 1, target_shape)
self._tgt_labels = py_utils.PadSequenceDimension(self._tgt_labels,
p.target_max_length, 0,
target_shape)
self._tgt_weights = py_utils.PadSequenceDimension(self._tgt_weights,
p.target_max_length, 0,
target_shape)
# TODO(zhifengc): come up more meaningful training sample ids here.
self._sample_ids = tf.range(0, self.InfeedBatchSize(), 1)
def reorder_tensor(reorder_mode,
values,
num_shards,
shard_size,
max_value=None,
axis=0):
"""Reorder tensor based on the mode passed in.
This method reorders rows or cols (based on `axis`) of the tensor passed in
from one sharding mode to another sharding mode. This method uses matmul for
reordering to be efficient on TPUs.
Args:
reorder_mode: Either mod_to_div or div_to_mod
values: Tensor to reorder
num_shards: Number of shards.
shard_size: Size of each shard.
max_value: If dtype=tf.int32, and we know maximum of the values, we can
efficiently implement it as matmuls.
axis: axis to gather on. Defaults to 0 (rows).
Returns:
A tensor of same shape as values but rows (or first axis) reordered.
"""
values = tf.convert_to_tensor(values)
with tf.name_scope("reorder_tensor_" + reorder_mode):
num_ids = num_shards * shard_size
# Elements to gather.
seq_ids = tf.range(num_ids)
if reorder_mode == "mod_to_div":
local_ids = seq_ids // shard_size
shard_ids = seq_ids % shard_size
ids = local_ids + shard_ids * num_shards
elif reorder_mode == "div_to_mod":
shard_ids = seq_ids % num_shards
local_ids = seq_ids // num_shards
ids = local_ids + shard_ids * shard_size
else:
raise NotImplementedError(
"Reorder mode: {} not implemented.".format(reorder_mode))
return fast_gather(values, ids, num_ids, max_value, axis=axis)
def FProp(self,
theta,
x,
x_paddings=None,
eos_id=1,
force_sample_last_token=True):
"""Applies SymbolInsertionLayer.
We take in a `x`, which represents the groundtruth sequence (i.e., English
sequence). We return a sampled rollin (observed) canvas (i.e., random subset
of the English sequence), as well as the target (indices) for an
insertion-based model (i.e., the targets given the random observed subset).
Args:
theta: Ignored, this can be None.
x: The symbol ids of shape `[batch_size, time_dim]`.
x_paddings: The paddings (1 or 0) of shape `[batch_size, time_dim]` where
0 is valid and 1 is invalid.
eos_id: The <eos> token id to represent end-of-slot.
force_sample_last_token: Set True to force sample the last token of `x`.
Returns:
A `NestedMap`.
- canvas: The canvas (based off of the `rollin_policy`) of shape
[batch_size, c_dim]. Note that, `c_dim` <= `time_dim` but need not be
equal.
- canvas_indices: The canvas indices (into `x`).
- canvas_paddings: The paddings of `canvas_indices`.
- target_indices: The target indices of shape [num_targets, 3].
`num_targets` is the number of total targets in the entire batch.
[:, 0] captures the batch, [:, 1] captures the slot, and [:, 2]
captures the token. Each row [batch, slot, vocab] represents the
indices of the target -- i.e., the batch, slot and vocab combination
of the target. Typical usage of these indices is to tf.gather_nd
the log-probs (from the softmax layer).
- target_weights: The target weights.
Raises:
ValueError: If invalid params.
"""
p = self.params
batch_size = py_utils.GetShape(x)[0]
time_dim = py_utils.GetShape(x)[1]
if x_paddings is None:
x_paddings = tf.zeros([batch_size, time_dim], tf.float32)
oracle_policy = p.oracle_policy
rollin_policy = (oracle_policy
if p.rollin_policy == 'oracle' else p.rollin_policy)
if rollin_policy != 'uniform':
raise ValueError('Unknown or unsupported rollin policy: %s' %
rollin_policy)
if oracle_policy != 'uniform':
raise ValueError('Unknown or unsupported oracle policy: %s' %
oracle_policy)
x_len = tf.cast(tf.round(tf.reduce_sum(1 - x_paddings, 1)), tf.int32)
# Compute the desired length per example in the batch.
ratio = tf.random.uniform([batch_size], 0.0, 1.0, seed=p.random_seed)
if force_sample_last_token:
c_len = tf.minimum(
tf.cast(ratio * tf.cast(x_len, tf.float32), tf.int32),
x_len - 1) + 1
else:
c_len = tf.minimum(
tf.cast(ratio * tf.cast(x_len + 1, tf.float32), tf.int32),
x_len)
# Compute the maximum length across the batch.
c_len_max = tf.reduce_max(c_len)
# Grab subset of random valid indices per example.
z_logits = tf.cast(
tf.expand_dims(tf.range(time_dim), 0) >= tf.expand_dims(x_len, 1),
tf.float32) * -1e9
if force_sample_last_token:
# Force sample the last token -- i.e., as indexed by `x_len - 1`. We can
# accomplish this by add +LARGE_NUMBER to the logits.
z_logits += tf.cast(
tf.equal(tf.expand_dims(tf.range(time_dim), 0),
tf.expand_dims(x_len - 1, 1)), tf.float32) * 1e9
# Gumbel-max trick to sample (we only sample valid positions per sample in
# the batch).
z = -tf.math.log(-tf.math.log(
tf.random.uniform([batch_size, time_dim], seed=p.random_seed)))
unused_c_values, c_indices = tf.nn.top_k(z_logits + z, time_dim)
# Trim everything > c_len_max.
c_indices = c_indices[:, :c_len_max]
# Invalidate any indices >= c_len, we use the last index as the default
# invalid index.
c_indices = tf.where(
tf.expand_dims(tf.range(c_len_max), 0) < tf.expand_dims(c_len, 1),
c_indices, tf.fill(py_utils.GetShape(c_indices), time_dim - 1))
# Materialize the canvas.
c_indices = tf.sort(c_indices)
c = tf.gather_nd(
x,
tf.stack([
tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, c_len_max]), [-1]),
tf.reshape(c_indices, [-1])
], 1))
c = tf.reshape(c, [batch_size, c_len_max])
# Compute the paddings.
c_paddings = 1 - tf.sequence_mask(
c_len, c_len_max, dtype=x_paddings.dtype)
c *= tf.cast(1 - c_paddings, tf.int32)
indices = tf.concat([
tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, c_len_max]), [batch_size * c_len_max, 1]),
tf.reshape(c_indices, [batch_size * c_len_max, 1])
], 1)
x_token_is_observed = tf.scatter_nd(
indices, tf.ones([batch_size * c_len_max], tf.int32),
py_utils.GetShape(x))
# `x_segments` captures which slot each `x` belongs to (both observed and
# tokens that need to be observed).
x_segments = tf.cumsum(x_token_is_observed, 1, exclusive=True)
x_token_is_observed = tf.cast(x_token_is_observed, tf.bool)
prev_x_token_is_observed = tf.pad(x_token_is_observed[:, :-1],
[[0, 0], [1, 0]],
constant_values=True)
x_token_is_observed = tf.reshape(x_token_is_observed, [-1])
prev_x_token_is_observed = tf.reshape(prev_x_token_is_observed, [-1])
x_is_valid = tf.cast(1 - x_paddings, tf.bool)
x_is_valid = tf.reshape(x_is_valid, [-1])
# Remap all the observed to <eos>, note some of these need a zero weight
# (or else there would be <eos> and valid token in the same slot).
target_indices = tf.cast(tf.reshape(x, [-1, 1]), tf.int32)
target_indices = tf.where(
x_token_is_observed,
tf.fill(py_utils.GetShape(target_indices), eos_id), target_indices)
# TODO(williamchan): We give uniform 1.0 weight, however, math suggests
# we may want to weigh this term by the original sequence length.
target_weights = tf.ones_like(target_indices, tf.float32)
# We need to set all the weights for <eos> which actually have valid tokens
# in the slot to zero.
target_weights = tf.where(
x_token_is_observed & ~prev_x_token_is_observed,
tf.zeros_like(target_weights), target_weights)
# TODO(williamchan): Consider dropping the entries w/ weight zero.
# Add the batch and slot indices.
target_indices = tf.concat([
tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, time_dim]), [batch_size * time_dim, 1]),
tf.reshape(x_segments, [-1, 1]), target_indices
], 1)
# Select only the valid indices. The selected valid ones include slots w/
# <eos>.
target_indices = target_indices[x_is_valid]
target_weights = target_weights[x_is_valid]
return py_utils.NestedMap(canvas=c,
canvas_indices=c_indices,
canvas_paddings=c_paddings,
target_indices=target_indices,
target_weights=target_weights)
def _CreateCanvasAndTargets(self, batch):
# pyformat: disable
"""Create the canvas and targets.
Args:
batch: A `.NestedMap`.
- src: A `.NestedMap`.
- ids: The source ids, ends in <eos>.
- paddings: The source paddings.
- tgt: A `.NestedMap`.
- ids: The target ids, ends in <eos>.
- paddings: The target paddings.
Returns:
A `NestedMap`.
- canvas: The canvas (based off of the `rollin_policy`) of shape
[batch_size, c_dim].
- canvas_paddings: The paddings of `canvas_indices`.
- target_indices: The target indices (i.e., use these indices to
tf.gather_nd the log-probs). Optional, only during training.
- target_weights: The target weights. Optional, only during training.
"""
# pyformat: enable
p = self.params
if not self.do_eval:
# Sample our src and tgt canvas.
src_descriptor = self._SampleCanvasAndTargets(
batch.src.ids, batch.src.paddings)
tgt_descriptor = self._SampleCanvasAndTargets(
batch.tgt.ids, batch.tgt.paddings)
# Offset the src ids (to unshare embeddings between src/tgt). Note, we
# only offset the canvas ids, but we do not offset the vocab ids. This
# will result in unshared embeddings, but shared softmax. This is due to
# GPU/TPU memory limitations, empirically it is known that unsharing
# everything results in better performance.
vocab_size = p.decoder.softmax.num_classes
src_descriptor.canvas = tf.where(
tf.equal(src_descriptor.canvas_paddings, 0),
src_descriptor.canvas + vocab_size, src_descriptor.canvas)
# Offset the tgt indices (need shift according to src length).
batch_size = py_utils.GetShape(batch.src.ids)[0]
# `target_batch` is a [num_targets, batch_size] tensor where each row
# identifies which batch the target belongs to. Note the observation that,
# tf.reduce_sum(target_batch, 1) == 1 \forall rows.
target_batch = tf.cast(
tf.equal(
tf.expand_dims(tf.range(batch_size), 0),
tf.expand_dims(tgt_descriptor.target_indices[:, 0], 1)),
tf.int32)
src_lens = tf.cast(
tf.reduce_sum(1 - src_descriptor.canvas_paddings, 1), tf.int32)
# `tgt_offset` is shape [num_targets] where each entry corresponds to the
# offset needed for that target (due to the source length).
tgt_offset = tf.matmul(target_batch, tf.expand_dims(src_lens, 1))
# We shift the tgt slot without touching the batch or vocab.
tgt_descriptor.target_indices += tf.concat([
tf.zeros_like(tgt_offset), tgt_offset,
tf.zeros_like(tgt_offset)
], 1)
# The canvas is simply the sequence-level concat of the src and tgt.
canvas, canvas_paddings = insertion.SequenceConcat(
src_descriptor.canvas, src_descriptor.canvas_paddings,
tgt_descriptor.canvas, tgt_descriptor.canvas_paddings)
target_indices = tf.concat(
[src_descriptor.target_indices, tgt_descriptor.target_indices],
0)
target_weights = tf.concat(
[src_descriptor.target_weights, tgt_descriptor.target_weights],
0)
return py_utils.NestedMap(canvas=canvas,
canvas_paddings=canvas_paddings,
target_indices=target_indices,
target_weights=target_weights)
def beam_search_step(in_scores,
in_atten_probs,
in_best_scores,
in_cumulative_scores,
in_histories,
cur_step,
eos_id,
num_beams,
beam_size,
num_hyps_per_beam,
valid_eos_max_logit_delta=5.0,
local_eos_threshold=-100.0,
merge_paths=False,
is_last_chunk=None,
eoc_id=0):
"""A single step of beam search.
Let "b" be the number of beams, "k" be the number hyps in each beam. This
function supports values with dtypes tf.float32 or tf.bfloat16.
The following data structures are allocated before the first decoding step and
are passed along from cur step to the next step:
Args:
in_scores: A tensor of shape [b * k, vocab_size], where [i, ...] is the
token score of the j-th hyps of the n-th beam. j = (i / k), and n = i % k
in_atten_probs: A tensor of shape [b*k, s_len], where in_atten_probs[i, ...]
is the attention probabilities over the source words of the j-th hyps of
n-th beam (where j, and n are derived as above).
in_best_scores: A vector of size [b], best scores of terminated hyps so far
in each of the beams.
in_cumulative_scores: A vector of size [b * k]. The cumulative score of each
active hyp before the current step.
in_histories: An int32 vector of size [b * k] containing hashes of the
histories of each active hyp. If 'merge_paths' is enabled, the histories
are used to identify hypotheses that are identical modulo epsilons (e.g.
"a <eps> b" and "a b <eps>") and merge them. See 'update_histories'
docstring for details.
cur_step: Current step id.
eos_id: Token id of the special end of sequence token.
num_beams: Number of beams.
beam_size: Search terminates if the delta between the scores of the active
hyps.
num_hyps_per_beam: Number of hyps in a beam.
valid_eos_max_logit_delta: We allow </s> to terminate a hyp only if its
logit is no more than 'valid_eos_max_logit_delta' away from the logit of
the best candidate.
local_eos_threshold: We allow </s> to terminate a hyp if the local score for
</s> is greater than local_eos_threshold.
merge_paths: If true, hyps which are identical when epsilons are removed
will be combined into a single hyp. The probability for that combined hyp
will be the sum of the probabilities of the component hyps. This can only
be applied for epsilon-emitting models (RNN-T and NT).
is_last_chunk: A tensor of shape [b * k, 1]. Used by neural transducer,
determines whether the current hypothesis reaches the last chunk and
should treat the next end-of-chunk symbol as end-of-sentence.
eoc_id: int, the id of the end of chunk (a.k.a epsilon) token used by neural
transducer models. Only relevant if 'merge_paths' is True or
'is_last_chunk' is provided.
Returns:
out_best_scores: A tensor of shape [b] of updated best scores for each of
the beams.
out_cumulative_scores: A tensor of shape [b * k]. The cumulative score of
the new hyps after the current decoding step.
out_scores: A tensor of shape [b * k] with scores of the token selected.
out_eos_scores: A tensor of shape [b * k] with token scores for the EOS, in
case the hyp was terminated, otherwise 0.0.
out_hyps: A tensor of shape [b * k] with ids of the token selected.
out_prev_hyps: A tensor of shape [b * k] with index to the previous hyps
which was selected.
out_done_hyps: A boolean tensor of shape [b * k] where value indicates
if hyps was terminated.
out_atten_probs: A tensor of shape [b * k, seq_len] which contain the
attention probabilities over the source words against word in the previous
hyps.
out_eos_atten_probs: A tensor of shape [b * k, seq_len] which contains the
attention probabilities over the source against word in the current hyp
which was terminated.
out_all_done: A scalar, whether decoding should terminate for all beams.
out_histories: A tensor of shape [b * k] containing new history hashes for
the active hypotheses. See 'update_histories' docstring for details.
Raises:
ValueError: if inputs are invalid.
"""
num_hyps_per_beam = int(num_hyps_per_beam)
if num_hyps_per_beam <= 0:
raise ValueError("num_hyps_per_beam = {} and must be > 0.".format(
num_hyps_per_beam))
in_scores = tf.convert_to_tensor(in_scores)
in_scores.shape.assert_has_rank(2)
num_classes = in_scores.get_shape()[1]
in_atten_probs = tf.convert_to_tensor(in_atten_probs)
in_atten_probs.shape.assert_has_rank(2)
in_best_scores = tf.convert_to_tensor(in_best_scores)
in_best_scores.shape.assert_has_rank(1)
in_cumulative_scores = tf.convert_to_tensor(in_cumulative_scores)
in_cumulative_scores.shape.assert_has_rank(1)
in_histories = tf.convert_to_tensor(in_histories)
in_histories.shape.assert_has_rank(1)
with tf.name_scope("beam_search_step"):
# For k = num_hyps_per_beam
# First step of beam search is to find the top tokens based on its score.
# Normally we select k+1, where the extra +1 is to make sure we have k
# non-eos tokens to select if EOS token is in the top-k. If path merging is
# on, we actually need to select k+2; this ensures there are k+1 tokens left
# after the merge, at least k of which are not EOS.
# TODO(b/118644069): Avoid casts when there is a XLA op available that takes
# in bfloat16.
num_candidates_per_input_hyp = (num_hyps_per_beam + 2 if merge_paths
else num_hyps_per_beam + 1)
# [b * k, num_candidates_per_input_hyp]
local_score_values, local_indices = xla_ops.top_k_with_unique(
tf.cast(in_scores, tf.float32), k=num_candidates_per_input_hyp)
local_score_values = tf.cast(local_score_values, in_scores.dtype)
# Compute the global score which is sum of the local score, and the
# cumulative scores for each of the hyps.
# [b * k, num_candidates_per_input_hyp]
global_score_values = local_score_values + tf.expand_dims(
in_cumulative_scores, 1)
values_dtype = local_score_values.dtype
is_first_step = tf.cast(tf.equal(cur_step, 0), values_dtype)
# Preprocessing to reorder the tensor from `mod` sharding to `div` so that
# we can use matrix/vector operations to complete the beam search.
# [b * k, num_candidates_per_input_hyp]
global_score_values = reorder_tensor("mod_to_div", global_score_values,
num_beams, num_hyps_per_beam)
local_score_values = reorder_tensor("mod_to_div", local_score_values,
num_beams, num_hyps_per_beam)
local_indices = reorder_tensor("mod_to_div",
local_indices,
num_beams,
num_hyps_per_beam,
max_value=num_classes - 1)
# [b * k, 1]
histories = reorder_tensor("mod_to_div",
tf.expand_dims(in_histories, 1), num_beams,
num_hyps_per_beam)
if is_last_chunk is None:
is_last_chunk = tf.zeros([num_beams * num_hyps_per_beam, 1],
tf.bool)
else:
is_last_chunk = tf.cast(
reorder_tensor(
"mod_to_div",
tf.reshape(is_last_chunk,
[num_beams * num_hyps_per_beam, 1]), num_beams,
num_hyps_per_beam), tf.bool)
# For the first step mask everything but the first row.
# [num_hyps_per_beam]
per_example_mask = tf.concat([
tf.constant([1.0], dtype=values_dtype),
tf.zeros([num_hyps_per_beam - 1], dtype=values_dtype)
], 0)
# [num_hyps_per_beam, num_beams] => [b*k, 1]
mask = tf.reshape(
tf.tile(per_example_mask, tf.expand_dims(num_beams, 0)),
[-1, 1]) * is_first_step + (1.0 - is_first_step)
local_score_values *= mask
global_score_values *= mask
# We add a large negative value for the unmasked values.
per_example_additive_mask = tf.concat([
tf.constant([0.0], dtype=values_dtype),
tf.constant(BEST_SCORES_INIT,
shape=[num_hyps_per_beam - 1],
dtype=values_dtype)
], 0)
additive_mask = tf.reshape(
tf.tile(per_example_additive_mask, tf.expand_dims(num_beams, 0)),
[-1, 1]) * is_first_step
local_score_values += additive_mask
global_score_values += additive_mask
if merge_paths:
with tf.name_scope("merge_paths"):
# Compute new history hashes for each hypothesis + new token.
# [b * k, num_candidates_per_input_hyp]
histories = update_histories(histories,
local_indices,
mask,
epsilon_id=eoc_id)
global_score_values, histories = merge_hyps(
global_score_values, histories, mask, num_beams,
num_hyps_per_beam)
# As we keep num_candidates_per_input_hyp, we have a total of
# num_candidates_per_input_hyp * k hyps active per example.
num_candidate_hyps = num_candidates_per_input_hyp * num_hyps_per_beam
batch_shape = [-1, num_candidate_hyps]
# Reshape score values so that each row corresponds to a particular example.
# [num_beams, num_candidate_hyps]
global_score_values_batch = tf.reshape(global_score_values,
batch_shape)
# First for each beam: Find the top 2 * num_hyps_per_beam candidates.
# The factor of 2 is to be able to process non EOS token ids in the case
# where top scoring token for each hyps is EOS token.
# [k * b, 2 * num_hyps_per_beam]
_, candidates_indices_in_top_k = xla_ops.top_k_with_unique(
tf.cast(global_score_values_batch, tf.float32),
k=2 * num_hyps_per_beam)
# Find the previous hyps of the candidate. We divide here by (k+1) to
# identify which hyps this token came from.
hyps_id = candidates_indices_in_top_k // num_candidates_per_input_hyp
# Add in offset so that we can get the candidate index in the [b * k] space.
offset = tf.expand_dims(tf.range(num_beams) * num_candidate_hyps, 1)
flat_candidates_indices_in_top_k = tf.reshape(
candidates_indices_in_top_k + offset, [-1])
flat_local_indices = tf.reshape(local_indices, [1, -1])
flat_token_scores = tf.reshape(local_score_values, [-1, 1])
flat_global_scores = tf.reshape(global_score_values, [-1, 1])
# Gather the token scores for each of 2*k candidates. We use tf.one_hot()
# followed by a tf.matmul() to speedup gather on TPUs.
total_num_candidates = num_beams * num_candidate_hyps
token_scores_for_beam = tf.reshape(
fast_gather(flat_token_scores, flat_candidates_indices_in_top_k,
total_num_candidates),
[num_beams, 2 * num_hyps_per_beam])
token_scores_for_beam_shape = tf.shape(token_scores_for_beam)
global_scores_for_beam = tf.reshape(
fast_gather(flat_global_scores, flat_candidates_indices_in_top_k,
total_num_candidates), token_scores_for_beam_shape)
# Local indices value's are between [0, vocab_size-1], hence we use the
# slower version of gather.
token_ids_for_beam = tf.reshape(
fast_gather(flat_local_indices,
flat_candidates_indices_in_top_k,
total_num_candidates,
max_value=num_classes - 1,
axis=1), token_scores_for_beam_shape)
# We have access to 2*num_hyps_per_beam hyps per beam.
# We shrink back to num_hyps_per_beam that does not include EOS, and move
# EOS that occurs in top-num_hyps_per_beam to the EOS done matrix.
# To determine the threshold at which eos is allowed to terminate a hyp,
# we need to know the maximum global score for that hyp with any additional
# token. If path merging is *not* enabled, the global_score_values are
# by construction in sorted order, so we can just look at its 0th column. If
# path merging is enabled, the global scores of deleted (merged) hyps break
# the sorted order, which means we have to do a full reduce_max.
if merge_paths:
max_global_score_per_input_hyp = tf.reduce_max(global_score_values,
axis=1,
keepdims=True)
else:
max_global_score_per_input_hyp = global_score_values[:, 0:1]
# [num_beams * num_hyps_per_beam, 1]
global_eos_threshold = (max_global_score_per_input_hyp -
valid_eos_max_logit_delta)
local_eos_threshold_tensor = local_eos_threshold * tf.ones_like(
global_eos_threshold)
# Find EOS in top num_hyps_per_beam token ids. We also treat EOC as EOS if
# the model has indicated this is the last chunk.
local_index_is_eos = tf.equal(local_indices, eos_id)
local_index_is_last_chunk_eoc = tf.math.logical_and(
tf.equal(local_indices, eoc_id), is_last_chunk)
eos_mask = tf.math.logical_and(
tf.math.logical_and(
tf.math.logical_and(
tf.greater(
local_score_values,
tf.tile(local_eos_threshold_tensor,
[1, num_candidates_per_input_hyp])),
tf.greater(
global_score_values,
tf.tile(global_eos_threshold,
[1, num_candidates_per_input_hyp]))),
tf.math.logical_or(local_index_is_eos,
local_index_is_last_chunk_eoc)),
tf.cast(mask, tf.bool))
end_hyps_bool_mask = tf.reshape(tf.reduce_any(eos_mask, 1), [-1, 1])
end_hyps_bool_mask = reorder_tensor("div_to_mod", end_hyps_bool_mask,
num_beams, num_hyps_per_beam)
eos_atten_probs = in_atten_probs * tf.cast(end_hyps_bool_mask,
in_atten_probs.dtype)
eos_atten_probs = tf.reshape(eos_atten_probs,
[num_beams * num_hyps_per_beam, -1])
# A boolean tensor of shape [b * k] where value indicates if hyps was
# terminated.
out_done_hyps = tf.reshape(end_hyps_bool_mask, [-1])
# Scores for EOS token.
eos_float_mask = tf.cast(eos_mask, values_dtype)
eos_local_scores = eos_float_mask * local_score_values
eos_additive_float_mask = (1.0 - eos_float_mask) * BEST_SCORES_INIT
eos_local_scores += eos_additive_float_mask
out_eos_scores = tf.reshape(tf.reduce_max(eos_local_scores, 1),
[-1, 1])
out_eos_scores = tf.reshape(
reorder_tensor("div_to_mod", out_eos_scores, num_beams,
num_hyps_per_beam), [-1])
# A tensor of shape [b] of updated best scores for each of the beams.
eos_global_scores = eos_float_mask * global_score_values
eos_global_scores += eos_additive_float_mask
best_scores = tf.reduce_max(
tf.reshape(eos_global_scores, [num_beams, -1]), 1)
# Following operations are to finds the top num_hyps_per_beam that are
# active.
# Active ones are the ones that do not correspond to EOS termination.
# We keep num_hyps_per_beam * 2 in case every hyps is terminated by EOS id.
# Top K with eos removed.
non_eos_mask = tf.not_equal(token_ids_for_beam, eos_id)
num_candidate_hyps = num_hyps_per_beam * 2 * num_beams
index = tf.where(
non_eos_mask,
tf.reshape(tf.range(num_candidate_hyps, dtype=tf.int32),
token_scores_for_beam_shape),
num_candidate_hyps *
tf.ones(dtype=tf.int32, shape=token_scores_for_beam_shape))
# Unrolled TopK.
sorted_indices = []
# Finds the first num_hyps_per_beam unmasked indexes and stores them in
# concated_index (shape: [num_beams, num_candidate_hyps])
# This is done by iteratively record the min index in each row, and reset
# it to the max, so that next iteration reduce_min returns the 2nd minimum
# index.
for _ in range(num_hyps_per_beam):
min_index = tf.reshape(tf.reduce_min(index, [1]), [num_beams, 1])
sorted_indices.append(min_index)
# Replace position with num_candidate_hyps value.
index = tf.where(
tf.equal(index, min_index),
num_candidate_hyps *
tf.ones(dtype=tf.int32, shape=token_scores_for_beam_shape),
index)
# Post processing ops to output expected tensors.
concated_sorted_indices = tf.concat(sorted_indices, 1)
flat_sorted_indices = tf.reshape(concated_sorted_indices, [-1])
# A tensor of shape [b * k] with scores of the token selected.
out_scores = tf.reshape(
fast_gather(tf.reshape(token_scores_for_beam, [-1, 1]),
flat_sorted_indices, num_candidate_hyps), [-1, 1])
out_scores = tf.reshape(
reorder_tensor("div_to_mod", out_scores, num_beams,
num_hyps_per_beam), [-1])
# Gather the updated histories of selected hypotheses if path merging is
# enabled. Otherwise, the histories are unused, so just output in_histories.
if merge_paths:
flat_histories = tf.reshape(histories, [-1, 1])
# [num_beams, 2 * num_hyps_per_beam]
histories_for_beam = tf.reshape(
fast_gather(flat_histories, flat_candidates_indices_in_top_k,
total_num_candidates), token_scores_for_beam_shape)
out_histories = tf.reshape(
fast_gather(tf.reshape(histories_for_beam, [-1, 1]),
flat_sorted_indices, num_candidate_hyps), [-1, 1])
out_histories = tf.reshape(
reorder_tensor("div_to_mod", out_histories, num_beams,
num_hyps_per_beam), [-1])
else:
out_histories = in_histories
prev_hyps_ids = tf.reshape(
tf.reshape(
fast_gather(tf.reshape(hyps_id, [1, -1]),
flat_sorted_indices,
num_candidate_hyps,
max_value=num_hyps_per_beam,
axis=1), [num_beams, -1]) * num_beams +
tf.expand_dims(tf.range(num_beams), 1), [-1, 1])
prev_hyps_ids = reorder_tensor("div_to_mod",
prev_hyps_ids,
num_beams,
num_hyps_per_beam,
max_value=num_hyps_per_beam)
# A tensor of shape [b * k] with index to the previous hyps which was
# selected.
out_prev_hyps = tf.reshape(prev_hyps_ids, [-1])
# A tensor of shape [b * k, seq_len] which contain the attention
# probabilities over the source words against word in the previous hyps.
out_atten_probs = tf.reshape(
fast_gather(in_atten_probs, out_prev_hyps,
num_beams * num_hyps_per_beam),
[num_beams * num_hyps_per_beam, -1])
sorted_top_k_ids = fast_gather(tf.reshape(token_ids_for_beam, [1, -1]),
flat_sorted_indices,
num_candidate_hyps,
max_value=num_classes - 1,
axis=1)
sorted_top_k_ids = reorder_tensor("div_to_mod",
sorted_top_k_ids,
num_beams,
num_hyps_per_beam,
max_value=num_classes - 1,
axis=1)
# A tensor of shape [b * k] with ids of the token selected.
out_hyps = tf.reshape(sorted_top_k_ids, [-1])
# A tensor of shape [b * k]. The cumulative score of the selected hyps after
# the current decoding step.
out_cumulative_scores = tf.reshape(
fast_gather(tf.reshape(global_scores_for_beam, [-1, 1]),
flat_sorted_indices, num_candidate_hyps), [-1, 1])
out_cumulative_scores = tf.reshape(
reorder_tensor("div_to_mod", out_cumulative_scores, num_beams,
num_hyps_per_beam), [-1])
out_best_scores = tf.maximum(best_scores, in_best_scores)
# A scalar, whether decoding should terminate for all beams.
out_all_done = tf.reshape(
tf.math.logical_not(
tf.reduce_any(
tf.greater(
out_cumulative_scores,
tf.reshape(
tf.tile(
tf.reshape(out_best_scores - beam_size,
[-1, 1]), [1, num_hyps_per_beam]),
[-1])))), [])
return (out_best_scores, out_cumulative_scores, out_scores,
out_eos_scores, out_hyps, out_prev_hyps, out_done_hyps,
out_atten_probs, eos_atten_probs, out_all_done, out_histories)
def _WordsToIds(i, words, ids_ta):
encoded_ids = self._EncodeToIds(BOW_STR + words[i])
ids_ta = ids_ta.scatter(
tf.range(ids_ta.size(),
ids_ta.size() + tf.size(encoded_ids)), encoded_ids)
return i + 1, words, ids_ta
def _GetSegmentPos(weights): """Returns a segment_pos tensor from the given weights tensor.""" maxlen = tf.shape(weights)[1] ret = tf.cast(tf.range(maxlen), dtype=tf.float32) return tf.cast(weights * ret, dtype=tf.int32)
def _ConstructWarpMatrix(self, batch_size, matrix_size, origin,
destination, choose_range, dtype):
"""Returns warp matrices according to origin, destination and choose_range.
This function constructs a batch of warp matrices which maps the batch
of origin points to the batch of destination points with fixed boundary
coordinates at 0 and choose_range.
The warping function, defined by the origin anchor point `origin`,
the destination of the origin anchor point `destination` and the
length of the domain in the warping axis `choose_range` is a piecewise
linear map that fixes the points 0 and `choose_range` and maps
`origin` to `destination`.
For the warping matrix to be non-singular, destination must lie in the
range 1<= destination <= choose_range - 1, so a destination
out of this range is adjusted to be in this range before the warping
matrix is constructed.
The warping map can be explicitly written by first defining the slopes:
1) slope_0 = origin / destination.
2) slope_1 = (choose_range - origin) / (choose_range - destination).
3) slope_2 = 1.0.
Then the origin point orig_i of the mapped coordinate i is given by:
1) i < destination: orig_i = slope_0 * i.
2) destination <= i < choose_range:
orig_i = slope_1 * i - (slope_1 - slope_0) * destination.
3) i >= choose_range: orig_i = i.
Denoting n_i = ceil(orig_i), the warp matrix element warp[i][j] is given by:
1) j = n_i: 1 - n_i + orig_i.
2) j = n_i - 1: n_i - orig_i.
3) Otherwise: 0.
Applying the warp matrix to an array of pixels, i.e.,
warped_pixel[i] = sum_j warp[i][j] * pixel[j], one would get
warped_pixel[i] = (n_i-orig_i) pixel[n_i-1] + (1-n_i+orig_i) pixel[n_i].
Args:
batch_size: Batch size. Integer number.
matrix_size: Dimension of the vector space the warp matrix is applied to.
Integer number.
origin: Origin anchor point for warping. Tensor of shape (batch_size,) and
data type dtype.
destination: Destination of the origin anchor point upon warping. Tensor
of shape (batch_size,) and data type dtype.
choose_range: Range within which the warp reference points must lie.
Tensor of shape (batch_size,) data type dtype.
dtype: Data type of origin, destination, choose_range and the output warp
matrix.
Returns:
warp_matrix: An array of fixed size warp matrices with shape
(batch_size, matrix_size, matrix_size).
"""
p = self.params
# Entries of destination must be in the range
# 1 <= destination <= choose_range - 1
# for warp matrix to have non-singular values.
destination = tf.minimum(tf.maximum(destination, 1.0),
choose_range - 1.0)
# Construct piece-wise linear function fixing boundary points
# specified by zero, choose_range and matrix size and maps
# the origin anchor point to the destination.
destination_bc = tf.broadcast_to(destination,
(matrix_size, batch_size))
destination_bc = tf.transpose(destination_bc)
choose_range_bc = tf.broadcast_to(choose_range,
(matrix_size, batch_size))
choose_range_bc = tf.transpose(choose_range_bc)
# Slopes of piece-wise linear function.
slope_0 = origin / destination
slope_1 = (choose_range - origin) / (choose_range - destination)
slope_2 = 1.0
# x is a batch of origin matrices.
# The origin matrix is the matrix such that
# origin[i][j] = Origin coordinate of coordinate i for the warp map.
# Denoting the destination of the origin anchor point in the
# warp map as "dest," the origin coordinate of point i is given by:
# 1) i < dest: slope_0 * i.
# 2) dest <= i < choose_range: slope_1 * i - (slope_1 - slope_0) * dest.
# 3) i >= choose_range: i.
x = tf.broadcast_to(tf.cast(tf.range(matrix_size), dtype=dtype),
(batch_size, matrix_size))
x = (self.EinsumBBmBm(slope_0, x) + self.EinsumBBmBm(
slope_1 - slope_0, tf.nn.relu(x - destination_bc)) +
self.EinsumBBmBm(slope_2 - slope_1,
tf.nn.relu(x - choose_range_bc)))
x = tf.broadcast_to(x, (matrix_size, batch_size, matrix_size))
x = tf.transpose(x, perm=[1, 2, 0])
# y is a batch of coordinate matrices.
# A coordinate matrix is a matrix such that
# coordinate[i][j] = j.
y = tf.broadcast_to(tf.cast(tf.range(matrix_size), dtype=dtype),
(batch_size, matrix_size, matrix_size))
# Warp matrix is obtained by applying hat function element-wise to (x-y).
# Denoting the origin point of i under the warp map as orig_i,
# and n_i = ceil(orig_i), the warp matrix element warp[i][j] is given by:
# 1) j = n_i: 1 - n_i + orig_i.
# 2) j = n_i - 1: n_i - orig_i.
# 3) Otherwise: 0.
# Applying the warp matrix to pixels, i.e.,
# warped_pixel[i] = sum_j warp[i][j] * original_pixel[j], one would get
# warped_pixel[i] = (n_i - orig_i) * original_pixel[n_i-1]
# + (1 - n_i + orig_i) * original_pixel[n_i].
warp_matrix = x - y
warp_matrix = _hat(warp_matrix)
if p.fprop_dtype is not None and p.fprop_dtype != dtype:
warp_matrix = tf.cast(warp_matrix, p.fprop_dtype)
return warp_matrix
def _GetMask(self,
batch_size,
choose_range,
mask_size,
global_seed,
max_length=None,
masks_per_frame=0.0,
multiplicity=1,
dtype=tf.float32,
max_ratio=1.0):
"""Returns fixed size multi-masks starting from random positions.
A multi-mask is a mask obtained by applying multiple masks.
This function when max_length is given:
1) Sample random mask lengths less than max_length with shape
(batch_size, multiplicity).
2) Truncate lengths to a max of (choose_range * max_ratio),
so that each mask is fully contained within the corresponding sequence.
3) Random sample start points of shape (batch_size, multiplicity)
with in (choose_range - lengths).
4) For each batch, multiple masks (whose number is given by the
multiplicity) are constructed.
5) Return a mask of shape (batch_size, mask_size) where masks are
obtained by composing the masks constructed in step 4).
If masks_per_frame > 0, the number is given by
min(masks_per_frame * choose_range, multiplicity).
If not, all the masks are composed. The masked regions are set to zero.
This function when max_length is not given:
1) Sample random mask lengths less than (choose_range * max_ratio)
with shape (batch_size, multiplicity).
2) Proceed to steps 3), 4) and 5) of the above.
Args:
batch_size: Batch size. Integer number.
choose_range: Range within which the masked entries must lie. Tensor of
shape (batch_size,).
mask_size: Size of the mask. Integer number.
global_seed: an integer seed tensor for stateless random ops.
max_length: Maximum number of allowed consecutive masked entries. Integer
number or None.
masks_per_frame: Number of masks per frame. Float number. If > 0, the
multiplicity of the mask is set to be masks_per_frame * choose_range.
multiplicity: Maximum number of total masks. Integer number.
dtype: Data type.
max_ratio: Maximum portion of the entire range allowed to be masked. Float
number.
Returns:
mask: a fixed size multi-mask starting from a random position with shape
(batch_size, mask_size).
"""
p = self.params
# Non-empty random seed values are only used for testing or when using
# stateless random ops. seed_1 and seed_2 are set separately to avoid
# correlation of mask size and mask position.
if p.use_input_dependent_random_seed:
seed_1 = global_seed + 1
seed_2 = global_seed + 2
elif p.random_seed:
seed_1 = p.random_seed + 1
seed_2 = 2 * p.random_seed
else:
seed_1 = p.random_seed
seed_2 = p.random_seed
# Sample lengths for multiple masks.
if max_length and max_length > 0:
max_length = tf.broadcast_to(tf.cast(max_length, dtype),
(batch_size, ))
else:
max_length = tf.cast(choose_range, dtype=dtype) * max_ratio
random_uniform = _random_uniform_op(p.use_input_dependent_random_seed)
masked_portion = random_uniform(shape=(batch_size, multiplicity),
minval=0.0,
maxval=1.0,
dtype=dtype,
seed=seed_1)
masked_frame_size = self.EinsumBBmBm(max_length, masked_portion)
masked_frame_size = tf.cast(masked_frame_size, dtype=tf.int32)
# Make sure the sampled length was smaller than max_ratio * length_bound.
# Note that sampling in this way was biased
# (shorter sequence may over-masked.)
choose_range = tf.expand_dims(choose_range, -1)
choose_range = tf.tile(choose_range, [1, multiplicity])
length_bound = tf.cast(choose_range, dtype=dtype)
length_bound = tf.cast(max_ratio * length_bound, dtype=tf.int32)
length = tf.minimum(masked_frame_size, tf.maximum(length_bound, 1))
# Choose starting point.
random_start = random_uniform(shape=(batch_size, multiplicity),
maxval=1.0,
seed=seed_2)
start_with_in_valid_range = random_start * tf.cast(
(choose_range - length + 1), dtype=dtype)
start = tf.cast(start_with_in_valid_range, tf.int32)
end = start + length - 1
# Shift starting and end point by small value.
delta = tf.constant(0.1)
start = tf.expand_dims(tf.cast(start, dtype) - delta, -1)
start = tf.tile(start, [1, 1, mask_size])
end = tf.expand_dims(tf.cast(end, dtype) + delta, -1)
end = tf.tile(end, [1, 1, mask_size])
# Construct pre-mask of shape (batch_size, multiplicity, mask_size).
diagonal = tf.expand_dims(
tf.expand_dims(tf.cast(tf.range(mask_size), dtype=dtype), 0), 0)
diagonal = tf.tile(diagonal, [batch_size, multiplicity, 1])
pre_mask = tf.cast(tf.math.logical_and(diagonal < end,
diagonal > start),
dtype=dtype)
# Sum masks with appropriate multiplicity.
if masks_per_frame > 0:
multiplicity_weights = tf.tile(
tf.expand_dims(tf.range(multiplicity, dtype=dtype), 0),
[batch_size, 1])
multiplicity_tensor = masks_per_frame * tf.cast(choose_range,
dtype=dtype)
multiplicity_weights = tf.cast(
multiplicity_weights < multiplicity_tensor, dtype=dtype)
pre_mask = self.EinsumBmtBmBt(pre_mask, multiplicity_weights)
else:
pre_mask = tf.reduce_sum(pre_mask, 1)
mask = tf.cast(1.0 - tf.cast(pre_mask > 0, dtype=dtype), dtype=dtype)
if p.fprop_dtype is not None and p.fprop_dtype != p.dtype:
mask = tf.cast(mask, p.fprop_dtype)
return mask
def MergeBeamSearchOutputs(max_hyps_per_beam, beam_search_outputs):
"""Merges beam search hyps from multiple decoders.
Args:
max_hyps_per_beam: the number of top hyps in the merged results. Must be
less than or equal to total number of input hyps.
beam_search_outputs: a list of BeamSearchDecodeOutput objects. Must share
the same source_batch and max sequence length.
Returns:
A BeamSearchDecodeOutput object containing max_hyps_per_beam hypotheses per
beam.
"""
source_batch = tf.shape(beam_search_outputs[0].topk_hyps)[0]
value_dict = {}
for output in beam_search_outputs:
hyps_per_beam = py_utils.with_dependencies([
py_utils.assert_equal(source_batch,
tf.shape(output.topk_hyps)[0]),
],
tf.shape(
output.topk_hyps)[1])
for k, v in six.iteritems(output._asdict()):
if v is None:
continue
if k == 'done_hyps':
v = tf.transpose(v)
if k not in value_dict:
value_dict[k] = []
value_dict[k].append(
tf.reshape(v, [source_batch, hyps_per_beam, -1]))
# Concatenate the tensors along the 'num_hyps_per_beam' dimension.
concatenated = {}
for k, values in six.iteritems(value_dict):
if len(values) != len(beam_search_outputs):
raise ValueError('Incomplete values for %s: %s' %
(k, beam_search_outputs))
concatenated[k] = tf.concat(values, axis=1)
scores = concatenated['topk_scores']
scores = tf.where(tf.equal(concatenated['topk_lens'], 0),
tf.fill(tf.shape(scores), -1e6), scores)
scores = tf.squeeze(scores, -1)
# Select top max_hyps_per_beam indices per beam.
_, top_indices = tf.nn.top_k(scores, max_hyps_per_beam)
batch_ids = tf.tile(tf.expand_dims(tf.range(source_batch), -1),
[1, max_hyps_per_beam])
# [source_batch, max_hyps_per_beam, 2]
gather_indices = tf.stack([batch_ids, top_indices], axis=-1)
# Gather the merged top hyps according to 'gather_indices'.
top = beam_search_outputs[0]._asdict()
total_hyps = source_batch * max_hyps_per_beam
for k, v in six.iteritems(concatenated):
v = tf.gather_nd(v, gather_indices)
if k == 'done_hyps':
v = tf.transpose(tf.reshape(v, [total_hyps, -1]))
elif k == 'topk_hyps':
v = tf.reshape(v, [source_batch, max_hyps_per_beam])
elif k == 'topk_ids':
v = tf.reshape(v, [total_hyps, -1])
elif k in ('topk_lens', 'topk_scores', 'topk_decoded'):
v = tf.reshape(v, [total_hyps])
else:
raise ValueError('Unexpected field: %s' % k)
top[k] = v
return BeamSearchDecodeOutput(**top)
def to_flat_indices(column_indices_per_row):
column_indices_per_row.shape.assert_has_rank(2)
flat_indices = (column_indices_per_row +
num_hyps_per_beam * candidates_per_hyp *
tf.reshape(tf.range(num_beams), [num_beams, 1]))
return tf.reshape(flat_indices, [-1])
