def testApplyPackingErrors(self):
test_cases = {
'out of bound':
ApplyPackingTestCase([[0, 1], [2, 3]], 0, [[1, 1, 1], [1, 0, 0]],
[[1, 1, 1], [0, 0, 0]]),
'out of bound ':
ApplyPackingTestCase([[0, 1], [2, 3]], 0, [[1, 1], [1, 0]],
[[0, 0], [2, 0]]),
'out of bound.':
ApplyPackingTestCase(['a', 'b'], ',', [[1, 2]], [[1, 2]]),
'segment_ids and indices_in_input must be matrices':
ApplyPackingTestCase([[0, 1], [2, 3]], 0, [1, 1], [0, 0]),
'segment_ids and indices_in_input must be matrices of the same shape':
ApplyPackingTestCase([[0, 1], [2, 3]], 0, [[1, 1], [1, 0]],
[[0, 0], [0, 0], [0, 0]]),
'input must be a matrix or vector':
ApplyPackingTestCase([[[0, 1]]], 0, [[1]], [[0]]),
'padding must be a scalar':
ApplyPackingTestCase([[0, 1], [2, 3]], [-1], [[1]], [[0]]),
}
for name, test in test_cases.items():
with self.assertRaisesRegex(tf.errors.InvalidArgumentError, name):
with self.session():
ops.apply_packing(test.input, test.padding,
test.segment_ids,
test.indices_in_input).eval()
def _Pack(self, batch_in):
"""Packs a given batch, which changes the batch size."""
actual_seq_len = tf.math.reduce_sum(tf.cast(batch_in.segment_ids,
tf.int32),
axis=1)
(segment_ids, segment_pos, indices_in_input, _, _,
_) = ops.pack_sequences(
actual_seq_len,
actual_seq_len,
packed_batch_size=0,
packed_src_seq_len=self.params.max_sequence_length,
packed_tgt_seq_len=self.params.max_sequence_length)
def ApplyPacking(x):
return ops.apply_packing(x, 0, segment_ids, indices_in_input)
batch_out = batch_in.DeepCopy()
batch_out = batch_out.Transform(ApplyPacking)
batch_out.paddings = ops.apply_packing(batch_in.paddings, 1,
segment_ids, indices_in_input)
batch_out.segment_ids = tf.cast(segment_ids, tf.float32)
batch_out.segment_pos = segment_pos
return batch_out
def testApplyPackingStrings(self):
test_cases = {
'Basic':
ApplyPackingTestCase(['a', 'b'], ',', [[1, 1]], [[1, 0]],
[b'b,a']),
'Repeated':
ApplyPackingTestCase(['a', 'b'], ',', [[1, 1, 1]], [[1, 1, 1]],
[b'b']),
'Separator':
ApplyPackingTestCase(['a', 'b', 'c', 'd'], '=', [[1, 1, 1, 0]],
[[1, 0, 3, 2]], [b'b=a=d']),
'MultiRows':
ApplyPackingTestCase(['a', 'b', 'c', 'd'], ';',
[[1, 1, 1, 0], [0, 1, 1, 1]],
[[2, 2, 1, 0], [2, 0, 1, 1]],
[b'c;b', b'a;b']),
'SingleString':
ApplyPackingTestCase(['a', 'b', 'c', 'd'], ',',
[[0, 0, 1], [0, 1, 0]],
[[0, 1, 2], [0, 1, 2]], [b'c', b'b']),
'EmptyRow':
ApplyPackingTestCase(['a', 'b', 'c', 'd'], ',',
[[0, 0, 0], [1, 1, 1]],
[[0, 1, 2], [0, 0, 2]], [b'', b'a,c']),
}
for name, test in test_cases.items():
with self.session():
output = ops.apply_packing(
tf.constant(test.input, tf.string),
tf.constant(test.padding, tf.string),
tf.constant(test.segment_ids, tf.int32),
tf.constant(test.indices_in_input, tf.int32)).eval()
self.assertAllEqual(output, test.output, name)
def testApplyPackingSum(self):
test_cases = {
'Basic':
ApplyPackingTestCase(np.arange(10), 0, [[1, 1], [1, 1]],
[[1, 1], [5, 5]], [1, 5]),
'Padding':
ApplyPackingTestCase(np.arange(10), 0, [[1, 1, 0], [1, 1, 0]],
[[1, 1, 0], [3, 3, 0]], [1, 3]),
'Tiny':
ApplyPackingTestCase(np.arange(10), 0, [[1], [1]], [[3], [1]],
[3, 1]),
'Larger':
ApplyPackingTestCase(np.arange(10), 0,
[[1, 1, 2, 2], [0, 1, 2, 3], [0, 1, 1, 1]],
[[2, 2, 3, 3], [9, 4, 5, 6], [9, 8, 8, 8]],
[5, 15, 8]),
}
for name, test in test_cases.items():
for dtype in [
tf.int32, tf.int64, tf.float32, tf.float64, tf.uint32,
tf.uint64
]:
with self.session():
output = ops.apply_packing(
tf.constant(test.input, dtype),
tf.constant(test.padding, dtype),
tf.constant(test.segment_ids, tf.int32),
tf.constant(test.indices_in_input, tf.int32)).eval()
expected = tf.constant(test.output, dtype).eval()
self.assertAllEqual(output, expected, f'{name} {dtype}')
def testApplyPackingUnknownShape(self):
x = tf.compat.v1.placeholder(tf.int32, shape=[None, None])
self.assertAllEqual(x.shape.as_list(), [None, None])
with self.session():
x_val = np.array([[0, 1], [2, 3]])
output = ops.apply_packing(
x, 0, tf.constant([[1, 1], [1, 1]], tf.int32),
tf.constant([[1, 1], [0, 0]], tf.int32)).eval(feed_dict={x: x_val})
self.assertAllEqual(output, [[2, 3], [0, 1]])
def testApplyPackingTypes(self):
test = ApplyPackingTestCase([[0, 1], [2, 3]], 99, [[1, 1, 0], [1, 1, 0]],
[[1, 1, 0], [0, 0, 0]],
[[2, 3, 99], [0, 1, 99]])
for dtype in [
tf.int32, tf.int64, tf.float32, tf.float64, tf.uint32, tf.uint64
]:
with self.session():
output = ops.apply_packing(
tf.constant(test.input, dtype), tf.constant(test.padding, dtype),
tf.constant(test.segment_ids, tf.int32),
tf.constant(test.indices_in_input, tf.int32)).eval()
expected = tf.constant(test.output, dtype).eval()
self.assertAllEqual(output, expected, dtype)
def testApplyPacking(self):
test_cases = {
'Basic':
ApplyPackingTestCase([[0, 1], [2, 3]], 0, [[1, 1], [1, 1]],
[[1, 1], [0, 0]], [[2, 3], [0, 1]]),
'Padding':
ApplyPackingTestCase([[0, 1], [2, 3]], -1, [[1, 1, 0], [1, 1, 0]],
[[1, 1, 0], [0, 0, 0]],
[[2, 3, -1], [0, 1, -1]]),
'Tiny':
ApplyPackingTestCase([[0, 1], [2, 3]], 0, [[1], [1]], [[0], [1]],
[[0], [2]]),
'5x2 input to 2x4':
ApplyPackingTestCase(np.reshape(np.arange(10), [5, 2]), -1,
[[1, 1, 2, 2], [0, 1, 0, 2]],
[[2, 2, 3, 3], [0, 4, 0, 0]],
[[4, 5, 6, 7], [-1, 8, -1, 0]]),
'6x4 input to 3x4':
ApplyPackingTestCase(
np.reshape(np.arange(24), [6, 4]), -1,
[[0, 0, 0, 1], [1, 1, 1, 1], [1, 2, 3, 4]],
[[0, 0, 0, 0], [1, 1, 1, 1], [2, 3, 4, 5]],
[[-1, -1, -1, 0], [4, 5, 6, 7], [8, 12, 16, 20]]),
'6x4 input to 3x5':
ApplyPackingTestCase(
np.reshape(np.arange(24), [6, 4]), -1,
[[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 1, 1, 0, 2]],
[[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 0, 0, 0, 3]],
[[-1, -1, -1, -1, -1], [-1, 4, 5, -1, -1], [-1, 0, 1, -1, 12]
]),
'100x4 input to 3x5':
ApplyPackingTestCase(
np.reshape(np.arange(400), [100, 4]), -1,
[[1, 1, 1, 2, 2], [0, 1, 1, 1, 1], [2, 2, 0, 0, 3]],
[[99, 99, 99, 1, 1], [0, 50, 50, 50, 50], [90, 90, 0, 0, 3]],
[[396, 397, 398, 4, 5], [-1, 200, 201, 202, 203],
[360, 361, -1, -1, 12]]),
}
for name, test in test_cases.items():
with self.session():
output = ops.apply_packing(
tf.constant(test.input, tf.int32),
tf.constant(test.padding, tf.int32),
tf.constant(test.segment_ids, tf.int32),
tf.constant(test.indices_in_input, tf.int32)).eval()
self.assertAllEqual(output, test.output, name)
def ApplyPacking(x):
return ops.apply_packing(x, 0, segment_ids, indices_in_input)
def ApplyPackingToTarget(x):
if x.dtype == tf.string:
return ops.apply_packing(x, '\t', tgt_segment_ids,
tgt_indices_in_input)
return ops.apply_packing(x, 0, tgt_segment_ids,
tgt_indices_in_input)
def ApplyPackingToSource(x):
if x.dtype == tf.string:
return ops.apply_packing(x, '\t', src_segment_ids,
src_indices_in_input)
return ops.apply_packing(x, 0, src_segment_ids,
src_indices_in_input)
def _ApplyPacking(self, batch):
"""Packs a given batch.
Note that this may change the batch size.
This function packs the input batch and adds .segment_ids and .segment_pos
fields to its `src` and `tgt` fields.
Args:
batch: a `.NestedMap` of input tensors to be packed. It is modified in
place.
"""
src_actual_seq_len = tf.math.reduce_sum(tf.cast(
batch.src.ids_indicator, tf.int32),
axis=1)
tgt_actual_seq_len = tf.math.reduce_sum(tf.cast(
batch.tgt.ids_indicator, tf.int32),
axis=1)
summary_utils.histogram('source_seq_lengths', src_actual_seq_len)
summary_utils.histogram('target_seq_lengths', tgt_actual_seq_len)
if not self.params.packing_factor:
# Supply segment_ids and segment_pos with no packing.
batch.src.segment_ids = batch.src.ids_indicator
batch.src.segment_pos = _GetSegmentPos(batch.src.ids_indicator)
batch.tgt.segment_ids = batch.tgt.ids_indicator
batch.tgt.segment_pos = _GetSegmentPos(batch.tgt.ids_indicator)
return
(src_segment_ids, src_segment_pos, src_indices_in_input,
tgt_segment_ids, tgt_segment_pos,
tgt_indices_in_input) = ops.pack_sequences(
src_actual_seq_len, tgt_actual_seq_len, self._ScaledBatchSize(),
self.params.source_max_length, self.params.target_max_length)
uniq_src_indices_in_input = tf.unique(
tf.reshape(src_indices_in_input, [-1])).y
uniq_tgt_indices_in_input = tf.unique(
tf.reshape(tgt_indices_in_input, [-1])).y
summary_utils.histogram(
'packed_source_seq_lengths',
tf.gather(src_actual_seq_len, uniq_src_indices_in_input, axis=0))
summary_utils.histogram(
'packed_target_seq_lengths',
tf.gather(tgt_actual_seq_len, uniq_tgt_indices_in_input, axis=0))
# Ratio of number of non-padded tokens. If < 1.0, we are dropping
# input data due to p.packing_factor too high.
src_orig_tokens_count = tf.cast(tf.reduce_sum(src_actual_seq_len),
tf.float32)
src_packed_tokens_count = tf.reduce_sum(
tf.cast(src_segment_ids > 0, tf.float32))
summary_utils.scalar('examples/src_packed_token_ratio',
src_packed_tokens_count / src_orig_tokens_count)
tgt_orig_tokens_count = tf.cast(tf.reduce_sum(tgt_actual_seq_len),
tf.float32)
tgt_packed_tokens_count = tf.reduce_sum(
tf.cast(tgt_segment_ids > 0, tf.float32))
summary_utils.scalar('examples/tgt_packed_token_ratio',
tgt_packed_tokens_count / tgt_orig_tokens_count)
# We deferred adding .paddings and use its complement .ids_indicator
# exclusively so that we can apply the packing with padding set to 0 for all
# fields.
def ApplyPackingToSource(x):
if x.dtype == tf.string:
return ops.apply_packing(x, '\t', src_segment_ids,
src_indices_in_input)
return ops.apply_packing(x, 0, src_segment_ids,
src_indices_in_input)
src_paddings = ops.apply_packing(batch.src.paddings, 1,
src_segment_ids, src_indices_in_input)
batch.src = batch.src.Transform(ApplyPackingToSource)
batch.src.paddings = src_paddings
batch.src.segment_ids = tf.cast(src_segment_ids, tf.float32)
batch.src.segment_pos = src_segment_pos
def ApplyPackingToTarget(x):
if x.dtype == tf.string:
return ops.apply_packing(x, '\t', tgt_segment_ids,
tgt_indices_in_input)
return ops.apply_packing(x, 0, tgt_segment_ids,
tgt_indices_in_input)
tgt_paddings = ops.apply_packing(batch.tgt.paddings, 1,
tgt_segment_ids, tgt_indices_in_input)
batch.tgt = batch.tgt.Transform(ApplyPackingToTarget)
batch.tgt.paddings = tgt_paddings
batch.tgt.segment_ids = tf.cast(tgt_segment_ids, tf.float32)
batch.tgt.segment_pos = tgt_segment_pos
# The number of examples is indicated by the segment_ids of the target.
num_segments = tf.math.reduce_max(batch.tgt.segment_ids, axis=1)
num_examples = tf.reduce_sum(num_segments)
# Note that this is per infeed value when p.use_per_host_infeed = True.
metric_name = 'examples/num_packed_examples'
summary_utils.scalar(metric_name, num_examples)
def _Pack(self, batch):
"""Packs a given batch.
Note that this may change the batch size.
This function packs the input batch and adds .segment_ids and .segment_pos
fields to its `src` and `tgt` fields.
Args:
batch: a `.NestedMap` of input tensors to be packed. It is modified in
place.
"""
src_actual_seq_len = tf.math.reduce_sum(tf.cast(
batch.src.ids_indicator, tf.int32),
axis=1)
tgt_actual_seq_len = tf.math.reduce_sum(tf.cast(
batch.tgt.ids_indicator, tf.int32),
axis=1)
summary_utils.histogram('source_seq_lengths', src_actual_seq_len)
summary_utils.histogram('target_seq_lengths', tgt_actual_seq_len)
if not self.params.packing_factor:
# Supply segment_ids and segment_pos with no packing.
batch.src.segment_ids = batch.src.ids_indicator
batch.src.segment_pos = _GetSegmentPos(batch.src.ids_indicator)
batch.tgt.segment_ids = batch.tgt.ids_indicator
batch.tgt.segment_pos = _GetSegmentPos(batch.tgt.ids_indicator)
return
(src_segment_ids, src_segment_pos, src_indices_in_input,
tgt_segment_ids, tgt_segment_pos,
tgt_indices_in_input) = ops.pack_sequences(
src_actual_seq_len, tgt_actual_seq_len, self._ScaledBatchSize(),
self.params.source_max_length, self.params.target_max_length)
uniq_src_indices_in_input = tf.unique(
tf.reshape(src_indices_in_input, [-1])).y
uniq_tgt_indices_in_input = tf.unique(
tf.reshape(tgt_indices_in_input, [-1])).y
summary_utils.histogram(
'packed_source_seq_lengths',
tf.gather(src_actual_seq_len, uniq_src_indices_in_input, axis=0))
summary_utils.histogram(
'packed_target_seq_lengths',
tf.gather(tgt_actual_seq_len, uniq_tgt_indices_in_input, axis=0))
# We deferred adding .paddings and use its complement .ids_indicator
# exclusively so that we can apply the packing with padding set to 0 for all
# fields.
def ApplyPackingToSource(x):
if x.dtype == tf.string:
return ops.apply_packing(x, '\t', src_segment_ids,
src_indices_in_input)
return ops.apply_packing(x, 0, src_segment_ids,
src_indices_in_input)
src_paddings = ops.apply_packing(batch.src.paddings, 1,
src_segment_ids, src_indices_in_input)
batch.src = batch.src.Transform(ApplyPackingToSource)
batch.src.paddings = src_paddings
batch.src.segment_ids = tf.cast(src_segment_ids, tf.float32)
batch.src.segment_pos = src_segment_pos
def ApplyPackingToTarget(x):
if x.dtype == tf.string:
return ops.apply_packing(x, '\t', tgt_segment_ids,
tgt_indices_in_input)
return ops.apply_packing(x, 0, tgt_segment_ids,
tgt_indices_in_input)
tgt_paddings = ops.apply_packing(batch.tgt.paddings, 1,
tgt_segment_ids, tgt_indices_in_input)
batch.tgt = batch.tgt.Transform(ApplyPackingToTarget)
batch.tgt.paddings = tgt_paddings
batch.tgt.segment_ids = tf.cast(tgt_segment_ids, tf.float32)
batch.tgt.segment_pos = tgt_segment_pos
def testApplyPackingErrors(self, expected_error_type, expected_error,
test):
with self.assertRaisesRegex(expected_error_type, expected_error):
with self.session():
ops.apply_packing(test.input, test.padding, test.segment_ids,
test.indices_in_input).eval()
def testPackSingleSequence(self, input_lengths, max_packed_length,
require_sequential_order, expected_packed_idxs):
with self.session() as sess:
np.random.seed(12345)
segment_ids, indices_in_input = sess.run(
ops.pack_single_sequence(
input_lengths=input_lengths,
max_packed_length=max_packed_length,
require_sequential_order=require_sequential_order))
self.assertLen(expected_packed_idxs, segment_ids.shape[0])
# Test the output is compatible with apply_packing.
inputs = []
for i, length in enumerate(input_lengths):
inputs.append(
np.random.randint(100000,
size=[length, 2, 2],
dtype=np.int32))
outputs = sess.run(
ops.apply_packing(input=tf.stack([
tf.pad(
x,
[[0, max_packed_length - x.shape[0]], [0, 0], [0, 0]])
for x in inputs
]),
padding=0,
segment_ids=segment_ids,
indices_in_input=indices_in_input))
for segment_id, idxs, output, expected_idxs in zip(
segment_ids, indices_in_input, outputs,
expected_packed_idxs):
# Build the expected results from the provided expected_packed_idxs.
expected_segment_ids = []
expected_idxs_vec = []
expected_outputs = []
for i, idx in enumerate(expected_idxs):
expected_segment_ids += [i + 1] * input_lengths[idx]
expected_idxs_vec += [idx] * input_lengths[idx]
expected_outputs.append(inputs[idx])
expected_outputs = np.concatenate(expected_outputs)
expected_packed_length = len(expected_outputs)
self.assertLessEqual(expected_packed_length, max_packed_length)
self.assertLen(expected_segment_ids, expected_packed_length)
self.assertLen(expected_idxs_vec, expected_packed_length)
# Check indices_in_input is non-decreasing.
if expected_packed_length > 1:
self.assertAllGreaterEqual(
idxs[1:expected_packed_length] -
idxs[:expected_packed_length - 1], 0)
# Pad to max_packed_length.
pad_len = max_packed_length - expected_packed_length
expected_segment_ids += [0] * pad_len
expected_idxs_vec += [-1] * pad_len
expected_outputs = np.pad(expected_outputs, [(0, pad_len),
(0, 0), (0, 0)],
mode='constant')
self.assertAllEqual(expected_idxs_vec, idxs)
self.assertAllEqual(expected_segment_ids, segment_id)
self.assertAllEqual(expected_outputs, output)
