def BatchedOrientedNMSIndices(self, bboxes, scores, nms_iou_threshold,
score_threshold, max_boxes_per_class):
"""Runs batched version of a Per-Class 3D (7-DOF) Non Max Suppression.
All outputs have shape [batch_size, num_classes, max_boxes_per_class].
Args:
bboxes: A [batch_size, num_boxes, 7] floating point Tensor of bounding
boxes in [x, y, z, dx, dy, dz, phi] format.
scores: A [batch_size, num_boxes, num_classes] floating point Tensor
containing box scores.
nms_iou_threshold: Either a float or a list of floats of len num_classes
with the IoU threshold to use when determining whether two boxes overlap
for purposes of suppression.
score_threshold: Either a float or a list of floats of len num_classes
with the score threshold that allows NMS to quickly ignore boxes.
max_boxes_per_class: An integer scalar with the maximum number of boxes
per example to emit per class.
Returns:
A tuple of 3 tensors:
- bbox_indices: An int32 Tensor with the indices of the chosen boxes.
Values are in sort order until the class_idx switches.
- bbox_scores: A float32 Tensor with the score for each box.
- valid_mask: A float32 Tensor with 1/0 values indicating the validity of
each box. 1 indicates valid, and 0 invalid.
"""
bboxes = py_utils.HasShape(bboxes, [-1, -1, 7])
batch_size, num_boxes = py_utils.GetShape(bboxes, 2)
scores = py_utils.HasShape(scores, [batch_size, num_boxes, -1])
_, _, num_classes = py_utils.GetShape(scores)
# Force the thresholds to be tensors of len num_classes
nms_iou_threshold = tf.broadcast_to(
tf.convert_to_tensor(nms_iou_threshold), [num_classes])
score_threshold = tf.broadcast_to(
tf.convert_to_tensor(score_threshold), [num_classes])
def NMSBody(args):
per_sample_bboxes, per_sample_scores = args
indices, scores, mask = ops.non_max_suppression_3d(
per_sample_bboxes,
per_sample_scores,
nms_iou_threshold=nms_iou_threshold,
score_threshold=score_threshold,
max_boxes_per_class=max_boxes_per_class)
return indices, scores, mask
bbox_indices, bbox_scores, valid_mask = tf.map_fn(
fn=NMSBody,
elems=(bboxes, scores),
dtype=(tf.int32, tf.float32, tf.float32),
back_prop=False)
output_shape = [batch_size, num_classes, max_boxes_per_class]
bbox_indices = py_utils.PadOrTrimTo(bbox_indices, output_shape)
bbox_scores = py_utils.PadOrTrimTo(bbox_scores, output_shape)
valid_mask = py_utils.PadOrTrimTo(valid_mask, output_shape)
return bbox_indices, bbox_scores, valid_mask
def testSpectrumAugmenterWarpMatrixConstructor(self):
with self.session(use_gpu=False, graph=tf.Graph()) as sess:
inputs = tf.broadcast_to(tf.cast(tf.range(10), dtype=tf.float32),
(4, 10))
origin = tf.cast([2, 4, 4, 5], dtype=tf.float32)
destination = tf.cast([3, 2, 6, 8], dtype=tf.float32)
choose_range = tf.cast([4, 8, 8, 10], dtype=tf.float32)
outputs = []
for p in [
spectrum_augmenter.SpectrumAugmenter.Params(),
spectrum_augmenter_on_device.SpectrumAugmenterOnDevice.
Params()
]:
p.name = 'specAug_layers'
specaug_layer = p.Instantiate()
warp_matrix = specaug_layer._ConstructWarpMatrix(
batch_size=4,
matrix_size=10,
origin=origin,
destination=destination,
choose_range=choose_range,
dtype=tf.float32)
output = tf.einsum('bij,bj->bi', warp_matrix, inputs)
outputs.append(output)
layer_output, layer_output_on_device = sess.run(outputs)
self.assertAllClose(layer_output, layer_output_on_device)
def testSpectrumAugmenterWithDynamicTimeWarping(self):
with self.session(use_gpu=False, graph=tf.Graph()) as sess:
tf.random.set_seed(1234)
inputs = tf.broadcast_to(tf.cast(tf.range(10), dtype=tf.float32),
(3, 10))
inputs = tf.expand_dims(tf.expand_dims(inputs, -1), -1)
paddings = []
for i in range(3):
paddings.append(
tf.concat(
[tf.zeros([1, 2 * i + 5]),
tf.ones([1, 5 - 2 * i])],
axis=1))
paddings = tf.concat(paddings, axis=0)
hs = []
for p in [
spectrum_augmenter.SpectrumAugmenter.Params(),
spectrum_augmenter_on_device.SpectrumAugmenterOnDevice.
Params()
]:
p.name = 'specAug_layers'
p.freq_mask_max_bins = 0
p.time_mask_max_frames = 0
p.time_warp_max_ratio = 0.5
p.time_warp_bound = 'dynamic'
p.random_seed = 34567
specaug_layer = p.Instantiate()
h, _ = specaug_layer.FPropDefaultTheta(inputs, paddings)
hs.append(h)
layer_output, layer_output_on_device = sess.run(hs)
self.assertAllClose(layer_output, layer_output_on_device)
def testSpectrumAugmenterWithFreqWarping(self):
with self.session(use_gpu=False, graph=tf.Graph()):
tf.random.set_seed(1234)
inputs = tf.broadcast_to(
tf.cast(tf.range(8), dtype=tf.float32), (5, 1, 8))
inputs = tf.expand_dims(inputs, -1)
paddings = tf.zeros([3, 2])
p = spectrum_augmenter.SpectrumAugmenter.Params()
p.name = 'specAug_layers'
p.freq_mask_max_bins = 0
p.time_mask_max_frames = 0
p.freq_warp_max_bins = 4
p.time_warp_max_frames = 0
p.random_seed = 345678
specaug_layer = p.Instantiate()
# pyformat: disable
# pylint: disable=bad-whitespace,bad-continuation
expected_output = np.array(
[[[0.0, 4.0, 4.5714283, 5.142857, 5.714286, 6.285714, 6.8571434,
3.999998]],
[[0.0, 0.8, 1.6, 2.4, 3.2, 4.0, 5.3333335, 6.6666665]],
[[0.0, 0.6666667, 1.3333334, 2.0, 3.2, 4.4, 5.6000004, 6.8]],
[[0.0, 1.3333334, 2.6666667, 4.0, 4.8, 5.6000004, 6.3999996,
5.5999947]],
[[0.0, 2.0, 2.857143, 3.7142859, 4.571429, 5.4285717, 6.2857146,
5.999997]]])
# pylint: enable=bad-whitespace,bad-continuation
# pyformat: enable
h, _ = specaug_layer.FPropDefaultTheta(inputs, paddings)
actual_layer_output = self.evaluate(tf.squeeze(h, -1))
print(np.array_repr(actual_layer_output))
self.assertAllClose(actual_layer_output, expected_output)
def testSpectrumAugmenterWarpMatrixConstructor(self):
with self.session(use_gpu=False, graph=tf.Graph()):
inputs = tf.broadcast_to(tf.cast(tf.range(10), dtype=tf.float32), (4, 10))
origin = tf.cast([2, 4, 4, 5], dtype=tf.float32)
destination = tf.cast([3, 2, 6, 8], dtype=tf.float32)
choose_range = tf.cast([4, 8, 8, 10], dtype=tf.float32)
p = spectrum_augmenter.SpectrumAugmenter.Params()
p.name = 'specAug_layers'
specaug_layer = p.Instantiate()
# pyformat: disable
# pylint: disable=bad-whitespace,bad-continuation
expected_output = np.array(
[[0.0000000, 0.6666667, 1.3333333, 2.0000000, 4.0000000,
5.0000000, 6.0000000, 7.0000000, 8.0000000, 9.0000000],
[0.0000000, 2.0000000, 4.0000000, 4.6666667, 5.3333333,
6.0000000, 6.6666667, 7.3333333, 8.0000000, 9.0000000],
[0.0000000, 0.6666667, 1.3333333, 2.0000000, 2.6666667,
3.3333333, 4.0000000, 6.0000000, 8.0000000, 9.0000000],
[0.0000000, 0.6250000, 1.2500000, 1.8750000, 2.5000000,
3.1250000, 3.7500000, 4.3750000, 5.0000000, 7.5000000]])
# pylint: enable=bad-whitespace,bad-continuation
# pyformat: enable
warp_matrix = specaug_layer._ConstructWarpMatrix(
batch_size=4,
matrix_size=10,
origin=origin,
destination=destination,
choose_range=choose_range,
dtype=tf.float32)
outputs = tf.einsum('bij,bj->bi', warp_matrix, inputs)
actual_layer_output = self.evaluate(outputs)
print(np.array_repr(actual_layer_output))
self.assertAllClose(actual_layer_output, expected_output)
def _BroadcastExamplePairLabelsToAllItemPairs(example_pair_labels: tf.Tensor,
queries_shape: tf.TensorShape,
results_shape: tf.TensorShape):
"""Propagates each example-pair label to all pairs of their items.
Args:
example_pair_labels: Labels tensor for example pairs, shape
[query_batch_size, result_batch_size].
queries_shape: Batch shape of the query examples. Must start with
`query_batch_size`.
results_shape: Batch shape of the query examples. Must start with
`result_batch_size`.
Returns:
A labels tensor with shape `queries_shape + results_shape`.
"""
example_pair_labels.shape.assert_has_rank(2)
queries_shape.assert_is_fully_defined()
results_shape.assert_is_fully_defined()
# Expand [q, r] to [q, 1, ..., r, 1, ...]
all_slice = slice(None, None, None)
reshape_slice = ((all_slice,) + (None,) * (queries_shape.rank - 1) +
(all_slice,) + (None,) * (results_shape.rank - 1))
return tf.broadcast_to(example_pair_labels[reshape_slice],
queries_shape + results_shape)
def MultiLabelContrastiveLoss(labels, logits, axis: int = -1):
"""Computes a multi-label generalization of softmax cross entropy loss.
This loss generalizes softmax cross entropy in the following sense.
- If `labels` are one-hot (over `axis`), this loss is equivalent to softmax
cross entropy. Note in this case the per-example loss can be interpreted as
-log(p(positive_class)). Here p() is a distribution of over C classes,
namely 1 positive class and C-1 negative classes.
- In general, if `labels` are N-hot, this function computes the loss
`sum_i{ -log(p_i(positive_class_i)) } / N`
where p_i() is a distribution over the i'th positive class and the C-N
negative classes.
Note unlike `tf.nn.softmax_cross_entropy_with_logits()`, this function does
not support "soft" labels. Positive and negative labels must be represented as
1 and 0, respectively. Setting a label to any other value causes the example-
class pair to be ignored in the loss calculation. This is intended as a
feature, to give callers fine-grained control over which pairs are used in
the loss.
Args:
labels: Tensor of labels. Must have the same shape as `logits`.
logits: Tensor of logits (scores). Must have the same shape as `labels`.
axis: The class dimension, i.e. the one over which probability distributions
are normalized.
Returns:
A Tensor of per-example losses. Has the same type as `logits`, and the same
shape, except without `axis`.
Typically `labels` and `logits` are both [batch_size, num_classes], in
which case the result is [batch_size].
"""
labels.shape.assert_is_compatible_with(logits.shape)
# Set logits for non-negative pairs to -inf so they're effectively ignored.
is_negative_pair = tf.equal(labels, 0)
negative_pair_logits = tf.where(is_negative_pair, logits,
tf.broadcast_to(float('-inf'), logits.shape))
# Compute binary logits, log(p / (1-p)). Shift inputs by the max negative-pair
# score to improve numerical precision. The reason is that
# tf.reduce_logsumexp(x) == max(x) + log(sum_i(exp(x[i] - max(x))))
# and if the max is sufficiently large the second term disappears as round-off
# error.
adjustment = tf.reduce_max(negative_pair_logits, axis=axis, keepdims=True)
binary_logits = (logits - adjustment) - tf.reduce_logsumexp(
negative_pair_logits - adjustment, axis=axis, keepdims=True)
# Accumulate the losses of each positive sample vs. all negative ones. Note
# -log_sigmoid == sigmoid_cross_entropy_with_logits in the special case that
# all labels are 1.
is_positive_pair = tf.cast(tf.equal(labels, 1), binary_logits.dtype)
losses = is_positive_pair * -tf.math.log_sigmoid(binary_logits)
num_positives = tf.reduce_sum(is_positive_pair, axis=axis)
return tf.reduce_sum(losses, axis=axis) / num_positives
def _BBox2DImage(self, bbox_corners_image, input_images):
"""Compute [xmin, ymin, xmax, ymax] 2D bounding boxes from corners."""
# Clip the boundaries of the bounding box to the image width/height.
bci_x = bbox_corners_image[..., 0:1]
image_width = tf.broadcast_to(
input_images.width[..., tf.newaxis, tf.newaxis], tf.shape(bci_x))
bci_x = tf.clip_by_value(bci_x, 0.0, tf.cast(image_width, tf.float32))
bci_y = bbox_corners_image[..., 1:2]
image_height = tf.broadcast_to(
input_images.height[..., tf.newaxis, tf.newaxis], tf.shape(bci_y))
bci_y = tf.clip_by_value(bci_y, 0.0, tf.cast(image_height, tf.float32))
bbox_corners_image_clipped = tf.concat([bci_x, bci_y], axis=-1)
# Compute the [xmin, ymin, xmax, ymax] bounding boxes from [batch,
# num_boxes, 8, 2] extrema.
min_vals = tf.math.reduce_min(bbox_corners_image_clipped, axis=2)
max_vals = tf.math.reduce_max(bbox_corners_image_clipped, axis=2)
bbox2d_corners_image = tf.concat([min_vals, max_vals], axis=2)
return bbox2d_corners_image
def _Merge(*xs):
"""Broadcast all dimensions except the last, and concat on last dim."""
# Stack all shapes and take max on each dimension to get leading shape.
leading_shape = tf.stack([tf.shape(x)[:-1] for x in xs])
leading_shape = tf.reduce_max(leading_shape, axis=0)
# Broadcast each x.
broadcast_xs = []
for x in xs:
broadcast_shape = tf.concat([leading_shape, tf.shape(x)[-1:]], axis=0)
broadcast_xs.append(tf.broadcast_to(x, broadcast_shape))
# Concat on last dimension.
concat_xs = tf.concat(broadcast_xs, axis=-1)
return concat_xs
def _PaddedMaxFn(inp):
"""Apply padded max using reduce_max with paddings replaced by neginf."""
# Replace all padded features with -inf.
neginf_padding = tf.where(inp.padding > 0, -np.inf * inp.padding,
inp.padding)
features = inp.features + neginf_padding[..., tf.newaxis]
features = tf.reduce_max(features, axis=-2)
# Replace features of all padded points by zeros. If a batch of points are
# all padded, then reduce_min over the padding will be 1. We set the
# features to be zero, so that we don't get any downstream issue with
# NaNs. Note that inf * 0 = NaN.
all_padded = tf.cast(tf.reduce_min(inp.padding, axis=-1), tf.bool)
all_padded = tf.broadcast_to(all_padded[..., tf.newaxis],
py_utils.GetShape(features))
features = tf.where(all_padded, tf.zeros_like(features), features)
return py_utils.CheckNumerics(features)
def _FrequencyMask(self,
inputs,
global_seed,
dtype=tf.float32,
domain_id_index=0):
"""Applies frequency masking with given degree to inputs.
Args:
inputs: Batch of input features of shape (batch_size, time_length,
num_freq, channels).
global_seed: an integer seed tensor for stateless random ops.
dtype: Data type.
domain_id_index: domain id index.
Returns:
Inputs with random frequency masking applied.
"""
p = self.params
# Mask parameters.
freq_mask_max_bins = p.freq_mask_max_bins[domain_id_index]
multiplicity = p.freq_mask_count[domain_id_index]
# If masking length or count is zero, do nothing.
if freq_mask_max_bins == 0 or multiplicity == 0:
return inputs
# Arguments to pass to mask generator.
batch_size, _, num_freq, _ = py_utils.GetShape(inputs)
choose_range = tf.cast(tf.broadcast_to(num_freq, (batch_size, )),
dtype=tf.int32)
# Create masks in frequency direction and apply.
block_arrays = self._GetMask(tf.shape(inputs)[0],
choose_range=choose_range,
mask_size=num_freq,
global_seed=global_seed,
max_length=freq_mask_max_bins,
masks_per_frame=0.0,
multiplicity=multiplicity,
dtype=dtype,
max_ratio=1.0)
outputs = tf.einsum('bxyc,by->bxyc', inputs, block_arrays)
return outputs
def IsSpecialExample(task_ids, special_task_ids):
"""A utility function indicates whether inputs belong to specific tasks.
Args:
task_ids: Task ids for the input batch. Tensor of shape [batch].
special_task_ids: A list of specified task ids.
Returns:
A tensor indicating whether each sample in the batch belong to the
specified task. Return a tensor of size [batch].
"""
batch_size = py_utils.GetShape(task_ids)[0]
return tf.reduce_any(
tf.equal(
tf.expand_dims(task_ids, -1),
tf.cast(
tf.broadcast_to(
special_task_ids,
[batch_size, len(special_task_ids)]), tf.int32)), -1)
def _PaddedMeanFn(inp):
"""Apply padded mean using reduce_sum and dividing by # real points."""
# Replace all padded features with 0 by masking the padded features out.
mask = 1 - inp.padding
features = inp.features * mask[..., tf.newaxis]
features = tf.reduce_sum(features, axis=-2)
num_real_points = tf.reduce_sum(mask, axis=-1, keep_dims=True)
# Prevent the divisor of our padded mean from ever being 0, so that
# the gradient flowing back through this op doesn't give us NaNs.
num_real_points = tf.maximum(num_real_points, 1)
features = features / num_real_points
# Replace features of all padded points by zeros. If a batch of points are
# all padded, then num_real_points will be zero. We set the features to be
# zero, so that we don't get any downstream issue with NaNs.
# Note that inf * 0 = NaN.
all_padded = tf.equal(num_real_points, 0.)
all_padded = tf.broadcast_to(all_padded, py_utils.GetShape(features))
features = tf.where(all_padded, tf.zeros_like(features), features)
return py_utils.CheckNumerics(features)
def testSpectrumAugmenterWithDynamicTimeWarping(self):
with self.session(use_gpu=False, graph=tf.Graph()):
tf.random.set_seed(1234)
inputs = tf.broadcast_to(tf.cast(tf.range(10), dtype=tf.float32), (3, 10))
inputs = tf.expand_dims(tf.expand_dims(inputs, -1), -1)
paddings = []
for i in range(3):
paddings.append(
tf.concat([tf.zeros([1, 2 * i + 5]),
tf.ones([1, 5 - 2 * i])],
axis=1))
paddings = tf.concat(paddings, axis=0)
p = spectrum_augmenter.SpectrumAugmenter.Params()
p.name = 'specAug_layers'
p.freq_mask_max_bins = 0
p.time_mask_max_frames = 0
p.time_warp_max_ratio = 0.5
p.time_warp_bound = 'dynamic'
p.random_seed = 34567
specaug_layer = p.Instantiate()
# pyformat: disable
# pylint: disable=bad-whitespace,bad-continuation
expected_output = np.array(
[[[[0.0000000]], [[1.0000000]], [[2.0000000]], [[3.0000000]],
[[4.0000000]], [[5.0000000]], [[6.0000000]], [[7.0000000]],
[[8.0000000]], [[9.0000000]]],
[[[0.0000000]], [[0.8333333]], [[1.6666666]], [[2.5000000]],
[[3.3333333]], [[4.1666665]], [[5.0000000]], [[7.0000000]],
[[8.0000000]], [[9.0000000]]],
[[[0.0000000]], [[2.0000000]], [[2.8750000]], [[3.7500000]],
[[4.6250000]], [[5.5000000]], [[6.3750000]], [[7.2500000]],
[[8.1250000]], [[9.0000000]]]])
# pylint: enable=bad-whitespace,bad-continuation
# pyformat: enable
h, _ = specaug_layer.FPropDefaultTheta(inputs, paddings)
actual_layer_output = self.evaluate(h)
print(np.array_repr(actual_layer_output))
self.assertAllClose(actual_layer_output, expected_output)
def _BatchScatter(default_tensor, indices, values):
"""Performs tf.tensor_scatter_nd_update for each batch item.
Args:
default_tensor: A float tensor of shape [batch, vocab] that contains the
default values.
indices: An int tensor of shape [batch, k] that represents the k indices of
`default_tensor` to update.
values: A float tensor of shape [batch, k] that represents the value to
replace with for each corresponding element of `indices`.
Returns:
A tensor like `default_tensor` where the (i, indices[i][j]) element has been
replaced with values[i][j].
"""
batch_size = tf.shape(default_tensor)[0]
# Prepend batch indices to `indices`.
batch_indices = tf.range(batch_size, dtype=indices.dtype)
batch_indices = tf.expand_dims(batch_indices, 1)
batch_indices = tf.broadcast_to(batch_indices, tf.shape(indices))
batch_indices = tf.stack([batch_indices, indices], axis=2)
return tf.tensor_scatter_nd_update(default_tensor, batch_indices, values)
def _IgnorePairsWhere(condition, labels):
return tf.where(condition, tf.broadcast_to(IGNORE_PAIR_LABEL, labels.shape),
labels)
def _EncodeToIds(self, word):
# Below:
# * a token is a wordpiece ID.
# * the tokens array will be merged in-place.
# * the candidates array is an array of size len(tokens) - 1.
# It contains the token for the merged wordpiece, if it exists,
# -1 otherwise. For instance, candidate[3] = id(token[3] + token[4]).
# First, split into basic UTF-8 characters (letters).
chars = tf.strings.unicode_split(word, 'UTF-8')
tokens = self._StringToToken(chars)
tokens = tf.where(
tf.equal(tokens, NO_TOKEN),
# Unseen character.
tf.broadcast_to(self.unk_id, tf.shape(tokens)),
tokens)
# Create initial candidate list.
candidates = tf.map_fn(self._MergeTokens, (tokens[:-1], tokens[1:]),
dtype=tokens.dtype)
def _ShouldMerge(unused_tokens, candidates):
"""Merge until not possible, or we abort early according to merge_prob."""
return tf.logical_and(
tf.reduce_any(tf.not_equal(candidates, NO_TOKEN)),
tf.random.uniform([]) < self._merge_prob)
def _MergeOneToken(tokens, i):
return tf.expand_dims(self._MergeTokens(
(tokens[i], tokens[i + 1])),
axis=-1)
def _MergeCandidates(tokens, candidates):
"""Merge in the reverse binary tree."""
best_id = tf.argmin(candidates, output_type=tf.int32)
# Perform the merge at position best_id.
tokens = tf.concat([
tokens[:best_id], [candidates[best_id]], tokens[best_id + 2:]
],
axis=0)
# Recompute the merge candidates.
# Only the neighbors of best_id need to be recomputed.
empty = tf.zeros([0], dtype=candidates.dtype)
def _MergeLeft():
return tf.concat([
candidates[:best_id - 1],
_MergeOneToken(tokens, best_id - 1)
],
axis=0)
left_candidates = tf.cond(tf.equal(best_id, 0), lambda: empty,
_MergeLeft)
def _MergeRight():
return tf.concat([
_MergeOneToken(tokens, best_id), candidates[best_id + 2:]
],
axis=0)
right_candidates = tf.cond(
tf.greater_equal(best_id,
tf.size(tokens) - 1), lambda: empty,
_MergeRight)
candidates = tf.concat([left_candidates, right_candidates], axis=0)
return tokens, candidates
return tf.while_loop(_ShouldMerge,
_MergeCandidates, (tokens, candidates),
parallel_iterations=1,
back_prop=False)[0]
def _StringToToken(self, tokstr):
return tf.where(py_x_ops.token_in_vocab(tokstr, vocab=self._pieces),
py_x_ops.vocab_token_to_id(tokstr, vocab=self._pieces),
tf.broadcast_to(NO_TOKEN, tf.shape(tokstr)))
def GatherK(selected_pos, values, k, num_devices=1):
"""Gather up to k elements from given tensors at selected pos under SPMD.
Example::
# Input
k = 3
selected_pos = [
[0, 0, 1, 1],
[0, 1, 1, 0],
[0, 0, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 1], # topk(k=3) largest indices are selected in this row.
]
value_2d = [
[1, 3, 5, 7],
[9, 11, 13, 15],
[17, 19, 21, 23],
[25, 27, 29, 31],
[33, 35, 37, 39],
]
# Output:
output = [
[0, 5, 7],
[0, 11, 13],
[0, 0, 0],
[25, 27, 29],
[35, 37, 39],
]
# Output padding:
output_padding = [
[1, 0, 0],
[1, 0, 0],
[1, 1, 1],
[0, 0, 0],
[0, 0, 0],
]
Args:
selected_pos: a 0/1 2D tf.int32 tensor of shape [batch, time].
values: a list of tensors, the rank of each is at least rank=2. [batch,
time, ...].
k: a scalar tf.int32 tensor or a Python int. On TPU, k must be a
compile-time constant.
num_devices: number of TPU devices used in xla_sharding SPMD.
Returns:
A tuple (output, padding).
- output: a list of tensors of shape [batch, k, ...].
- padding: a 2D 0/1 tensor of shape [batch, k], '1's are padded locations.
"""
global_batch, seq_len = py_utils.GetShape(selected_pos, 2)
if num_devices:
device_batch = global_batch // num_devices
else:
device_batch = global_batch
for i in range(len(values)):
# Assert the first 2 dim of values[i] is [global_batch, seq_len]
values[i] = py_utils.HasShape(values[i], [global_batch, seq_len], 2)
# indices are 1-based for now, to distinguish between padding and selected
# locations.
indices = 1 + tf.range(tf.shape(values[0])[1], dtype=tf.int32)
# [1, seq_len]
indices = tf.expand_dims(indices, axis=0)
# if 0, the position is not selected.
# [1, seq_len] * [global_batch, seq_len] => [global_batch, t]
# -- topk --> [global_batch, k]
topk_indices, _ = tf.math.top_k(
indices * tf.cast(selected_pos, indices.dtype), k)
# [global_batch, k], sorted in ascending order.
indices = tf.reverse(topk_indices, [-1])
# [global_batch, k], padded positions are '1's.
padding = tf.cast(tf.equal(indices, 0), values[0].dtype)
padding = Split(padding, 0, num_devices)
# [global_batch, k], zero_based_indices
mp_idx = tf.maximum(0, indices - 1)
mp_idx = Split(mp_idx, 0, num_devices)
# [device_batch, k]
if num_devices > 1 and py_utils.use_tpu():
mp_idx = xla_sharding.auto_to_manual_spmd_partition(
mp_idx, xla_sharding.get_op_sharding(mp_idx.op))
# [device_batch, k, 1]
mp_idx = tf.expand_dims(mp_idx, -1)
# [device_batch]
batch_ids = tf.range(device_batch, dtype=tf.int32)
# [device_batch, 1, 1]
batch_ids = tf.reshape(batch_ids, [device_batch, 1, 1])
# [device_batch, k, 1]
batch_ids = tf.broadcast_to(batch_ids, [device_batch, k, 1])
# [device_batch, k, 2]
final_indices = tf.concat([batch_ids, mp_idx], axis=-1)
output = []
for v in values:
# Begin manually partition gather.
v = Split(v, 0, num_devices)
v_shape = v.shape.as_list()
if num_devices > 1 and py_utils.use_tpu():
op_sharding = xla_sharding.get_op_sharding(v.op)
v = xla_sharding.auto_to_manual_spmd_partition(v, op_sharding)
# Returns [global_batch, k, ...]
v_out = tf.gather_nd(v, final_indices)
if num_devices > 1 and py_utils.use_tpu():
v_shape[1] = k
v_out = xla_sharding.manual_to_auto_spmd_partition(
v_out, op_sharding, full_shape=tf.TensorShape(v_shape))
output.append(v_out)
return output, padding
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 _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 _AddNoise(self, batch):
"""Adding noise the src (see https://arxiv.org/pdf/1711.00043).
This function implement 3 types of noise (hyparams defined in
self.params.denoise):
1) slightly shuffle the sentence following p.shuffle_tok_range
2) randomly drop tokens with probability p.drop_tok_prob
3) randomly mask tokens with probability p.blank_tok_prob
The noises are added to the input with probability p.noise_sent_prob.
Args:
batch: a `.NestedMap` of the input batch.
"""
def IsSpecialExample(task_ids, special_task_ids):
"""A utility function indicates whether inputs belong to specific tasks.
Args:
task_ids: Task ids for the input batch. Tensor of shape [batch].
special_task_ids: A list of specified task ids.
Returns:
A tensor indicating whether each sample in the batch belong to the
specified task. Return a tensor of size [batch].
"""
batch_size = py_utils.GetShape(task_ids)[0]
return tf.reduce_any(
tf.equal(
tf.expand_dims(task_ids, -1),
tf.cast(
tf.broadcast_to(
special_task_ids,
[batch_size, len(special_task_ids)]), tf.int32)),
-1)
p = self.params.denoise
batch_size = tf.shape(batch.src.ids)[0]
source_max_len = tf.shape(batch.src.ids)[1]
# Shuffle tokens according to p.shuffle_tok_range
noise = tf.random.uniform([batch_size, source_max_len], 0,
p.shuffle_tok_range + 1)
# Don't shuffle eos or padding
shuffle_tok_range = tf.fill([batch_size, source_max_len],
float(p.shuffle_tok_range))
shifted_paddings = tf.pad(batch.src.paddings[:, 1:], [[0, 0], [0, 1]],
constant_values=1)
noise = tf.where(tf.equal(shifted_paddings, 0), noise,
shuffle_tok_range)
indices = tf.broadcast_to(tf.range(source_max_len, dtype=tf.int32),
[batch_size, source_max_len])
noisy_indices = tf.cast(indices, dtype=tf.float32) + noise
permutations = tf.argsort(noisy_indices)
stacked = tf.stack([batch.src.ids, permutations], axis=1)
denoise_src_ids = tf.stack(tf.map_fn(lambda x: tf.gather(x[0], x[1]),
stacked),
axis=0)
# Select tokens to drop with probability=p.drop_tok_prob
random_drop_tok = tf.random.uniform([batch_size, source_max_len])
# Don't drop eos token
is_keep_tok = tf.math.logical_or(
tf.greater(random_drop_tok, p.drop_tok_prob),
tf.equal(denoise_src_ids, self._src_tokenizer.eos_id))
denoise_src_ids = tf.ragged.boolean_mask(
denoise_src_ids,
is_keep_tok).to_tensor(default_value=0,
shape=tf.shape(batch.src.ids))
denoise_src_paddings = tf.ragged.boolean_mask(
batch.src.paddings,
is_keep_tok).to_tensor(default_value=1,
shape=tf.shape(batch.src.ids))
# Select tokens to blank with probability=p.blank_tok_prob
# Don't blank eos token
random_blank_tok = tf.random.uniform([batch_size, source_max_len])
shifted_paddings = tf.pad(denoise_src_paddings[:, 1:],
[[0, 0], [0, 1]],
constant_values=1)
is_blank_tok = tf.math.logical_and(
tf.less(random_blank_tok, p.blank_tok_prob),
tf.equal(shifted_paddings, 0))
blank_id = tf.fill([batch_size, source_max_len], p.blank_id)
denoise_src_ids = tf.where(is_blank_tok, blank_id, denoise_src_ids)
# Select denoising task examples with probability=p.denoise_sent_prob
random_uniform_sent = tf.random.uniform([batch_size])
is_denoise_sent = tf.math.logical_and(
tf.less(random_uniform_sent, p.noise_sent_prob),
IsSpecialExample(self._GetTaskIds(batch.src.source_ids[:, 0]),
p.task_ids))
batch.src.ids = tf.where(is_denoise_sent, denoise_src_ids,
batch.src.ids)
batch.src.paddings = tf.where(is_denoise_sent, denoise_src_paddings,
batch.src.paddings)
batch.src.ids_indicator = 1 - batch.src.paddings
batch.src.weights = batch.src.ids_indicator
