def test_filter_by_length(self):
def seq_len(seq):
return tf.reduce_sum(tf.cast(self.vocab.padding_mask(seq), tf.int32))
n, len1, len2 = 10, 20, 30
max_len = 25
x1 = tf.pad(
self.sampler.sample([n, len1]), [[0, 0], [0, len2 - len1]],
constant_values=self.vocab.padding_code)
x2 = self.sampler.sample([n, len2])
x = tf.concat([x1, x2], 0)
filter_fn = transforms.FilterByLength(
on='seq', vocab=self.vocab, max_len=max_len, precomputed=False)
ds_in = tf.data.Dataset.from_tensor_slices({'seq': x})
ds_out = ds_in.apply(filter_fn.call)
for ex in ds_out:
self.assertLessEqual(seq_len(ex['seq']), max_len)
filter_fn = transforms.FilterByLength(
on='seq', vocab=self.vocab, max_len=max_len, precomputed=False)
ds_in = tf.data.Dataset.from_tensor_slices(
{'seq': [tf.convert_to_tensor(self.vocab.decode(x_i)) for x_i in x]})
ds_out = ds_in.apply(filter_fn.call)
for ex in ds_out:
self.assertLessEqual(tf.strings.length(ex['seq']), max_len)
seq_lens = tf.convert_to_tensor([seq_len(x_i) for x_i in x], tf.int32)
filter_fn = transforms.FilterByLength(
on='seq_len', vocab=self.vocab, max_len=max_len, precomputed=True)
ds_in = tf.data.Dataset.from_tensor_slices({'seq': x, 'seq_len': seq_lens})
ds_out = ds_in.apply(filter_fn.call)
for ex in ds_out:
self.assertLessEqual(seq_len(ex['seq']), max_len)
def main(_):
has_context = FLAGS.task.endswith('with_ctx')
logging.info('seed: %d', FLAGS.seed)
logging.info('task: %s', FLAGS.task)
logging.info('split: %s', FLAGS.split)
logging.info('max_len: %d', FLAGS.max_len)
logging.info('index_keys: %s', FLAGS.index_keys)
logging.info('smoothing: %s', FLAGS.smoothing)
logging.info('branch_key: %s', FLAGS.branch_key)
logging.info('n_pairs: %d', FLAGS.n_pairs)
logging.info('has_context: %s', has_context)
tf.random.set_seed(FLAGS.seed)
extra_keys = ['cla_key', 'seq_key', 'seq_len'] + FLAGS.index_keys
if has_context:
extra_keys.extend(['start', 'end'])
ds_loader = LOADERS[FLAGS.loader_cls](root_dir=FLAGS.data_dir,
sub_dir='',
extra_keys=extra_keys,
task=FLAGS.task)
ds = ds_loader.load(FLAGS.split)
filter_fn = transforms.FilterByLength(max_len=FLAGS.max_len - 1)
pair_fn = align_transforms.StratifiedSamplingPairing(
index_keys=FLAGS.index_keys,
branch_key=FLAGS.branch_key,
smoothing=FLAGS.smoothing)
ds = ds.apply(filter_fn).apply(pair_fn).take(FLAGS.n_pairs)
ds = ds.map(make_serialize_fn(), num_parallel_calls=tf.data.AUTOTUNE)
ds = ds.prefetch(tf.data.AUTOTUNE)
out_dir = os.path.join(FLAGS.out_dir, FLAGS.task, FLAGS.branch_key,
FLAGS.split)
filename = f'{FLAGS.seed}.tfrecords'
tf.io.gfile.makedirs(out_dir)
path = os.path.join(out_dir, filename)
logging.info('Writing TFRecords to %s...', path)
with tf.io.TFRecordWriter(path) as writer:
for i, serialized_example in enumerate(ds):
if (i % 1024) == 0:
logging.info('Seed: %d, progress: %d/%d.', FLAGS.seed, i,
FLAGS.n_pairs)
writer.write(serialized_example.numpy())
def make_tape_builder(root_dir,
task,
target,
weights=None,
metadata=(),
max_len=1024,
input_sequence_key='primary',
output_sequence_key='sequence'):
"""Creates a DatasetBuilder for TAPE's benchmark."""
supported_tasks = list(TAPE_NUM_OUTPUTS)
if task not in supported_tasks:
raise ValueError(f'Task {task} not recognized.'
f'Supported tasks: {", ".join(supported_tasks)}.')
num_outputs = TAPE_NUM_OUTPUTS[task].get(target, 1)
used_keys = [input_sequence_key, target]
if weights is not None:
used_keys.append(weights)
if metadata:
used_keys.extend(metadata)
unused_keys = [k for k in TAPE_SPECS[task] if k not in used_keys]
ds_transformations = []
if max_len is not None:
ds_transformations.append(
transforms.FilterByLength(on=output_sequence_key,
precomputed=False,
max_len=max_len - 1))
transformations = [
transforms.Pop(on=unused_keys),
transforms.Reshape(on=output_sequence_key, shape=[]),
transforms.Encode(on=output_sequence_key),
transforms.EOS(on=output_sequence_key),
transforms.CropOrPad(on=output_sequence_key, size=max_len),
]
if target in TAPE_MULTI_CL_TASKS:
transformations.append(transforms.OneHot(on=target, depth=num_outputs))
elif target in TAPE_BACKBONE_ANGLE_TASKS:
transformations.append(transforms.BackboneAngleTransform(on=target))
elif target in TAPE_PROT_ENGINEERING_TASKS:
transformations.append(transforms.Reshape(on=target, shape=[-1]))
if target in TAPE_SEQ2SEQ_TASKS:
transformations.extend([
transforms.Reshape(on=target, shape=[-1, num_outputs]),
transforms.CropOrPadND(on=target, size=max_len, axis=0),
])
if weights is not None: # Note: no seq-level TAPE task has weights.
transformations.extend([
transforms.Reshape(on=weights, shape=[-1]),
transforms.CropOrPadND(on=weights, size=max_len),
])
embeddings_labels = [target] if weights is None else [(target, weights)]
return builder.DatasetBuilder(
data_loader=make_tape_loader(root_dir=root_dir, task=task),
ds_transformations=ds_transformations,
transformations=transformations,
labels=multi_task.Backbone(embeddings=embeddings_labels),
metadata=metadata,
sequence_key=output_sequence_key)
def make_pair_builder(max_len=512,
index_keys=('fam_key', 'ci_100'),
process_negatives=True,
gap_token='-',
sequence_key='sequence',
context_sequence_key='full_sequence',
loader_cls=make_pfam_pairs_loader,
pairing_cls=None,
lm_cls=None,
has_context=False,
append_eos=True,
append_eos_context=True,
add_random_tails=False,
**kwargs):
"""Creates a dataset for pairs of sequences."""
# Convenience function to index key pairs.
paired_keys = lambda k: tuple(f'{k}_{i}' for i in (1, 2))
def stack_and_pop(on):
stack = transforms.Stack(on=paired_keys(on), out=on)
pop = transforms.Pop(on=paired_keys(on))
return [stack, pop]
# Defines fields to be read from the TFRecords.
metadata_keys = ['cla_key', 'seq_key'] + list(index_keys)
extra_keys = metadata_keys.copy()
# Pre-paired datasets already been filtered by length, seq_len only needed
# when pairing sequences on-the-fly.
if pairing_cls is not None:
extra_keys.append('seq_len')
# Optionally, adds fields needed by the `AddAlignmentContext` `Transform`.
if has_context:
extra_keys.extend(['start', 'end'])
add_alignment_context_extra_args = (paired_keys(context_sequence_key) +
paired_keys('start') +
paired_keys('end'))
# Accounts for EOS token if necessary.
max_len_eos = max_len - 1 if append_eos else max_len
### Sets up the `DatasetTransform`s.
ds_transformations = []
if pairing_cls is not None:
filter_by_length = transforms.FilterByLength(max_len=max_len_eos)
# NOTE(fllinares): pairing on-the-fly is memory intensive on TPU for some
# reason not yet understood...
pair_sequences = pairing_cls(index_keys=index_keys)
ds_transformations.extend([filter_by_length, pair_sequences])
### Sets up the `Transform`s applied *before* batching.
project_msa_rows = align_transforms.ProjectMSARows(
on=paired_keys(sequence_key), token=gap_token)
append_eos_to_context = transforms.EOS(
on=paired_keys(context_sequence_key))
add_alignment_context = align_transforms.AddAlignmentContext(
on=paired_keys(sequence_key) + add_alignment_context_extra_args,
out=paired_keys(sequence_key),
max_len=max_len_eos,
gap_token=gap_token)
trim_alignment = align_transforms.TrimAlignment(
on=paired_keys(sequence_key), gap_token=gap_token)
pop_add_alignment_context_extra_args = transforms.Pop(
on=add_alignment_context_extra_args)
add_random_prefix_and_suffix = align_transforms.AddRandomTails(
on=paired_keys(sequence_key), max_len=max_len_eos)
create_alignment_targets = align_transforms.CreateAlignmentTargets(
on=paired_keys(sequence_key),
out='alignment/targets',
gap_token=gap_token,
n_prepend_tokens=0)
pid1 = align_transforms.PID(on=paired_keys(sequence_key),
out='alignment/pid1',
definition=1,
token=gap_token)
pid3 = align_transforms.PID(on=paired_keys(sequence_key),
out='alignment/pid3',
definition=3,
token=gap_token)
remove_gaps = transforms.RemoveTokens(on=paired_keys(sequence_key),
tokens=gap_token)
append_eos_to_sequence = transforms.EOS(on=paired_keys(sequence_key))
pad_sequences = transforms.CropOrPad(on=paired_keys(sequence_key),
size=max_len)
pad_alignment_targets = transforms.CropOrPadND(on='alignment/targets',
size=2 * max_len)
transformations = [project_msa_rows]
if has_context:
if append_eos_context:
transformations.append(append_eos_to_context)
transformations.extend([
add_alignment_context, trim_alignment,
pop_add_alignment_context_extra_args
])
if add_random_tails:
transformations.append(add_random_prefix_and_suffix)
transformations.append(create_alignment_targets)
transformations.extend([pid1, pid3, remove_gaps])
if append_eos:
transformations.append(append_eos_to_sequence)
transformations.extend([pad_sequences, pad_alignment_targets])
for key in [sequence_key] + metadata_keys:
transformations.extend(stack_and_pop(key))
### Sets up the `Transform`s applied *after* batching.
flatten_sequence_pairs = transforms.Reshape(on=sequence_key,
shape=[-1, max_len])
flatten_metadata_pairs = transforms.Reshape(on=metadata_keys, shape=[-1])
create_homology_targets = align_transforms.CreateHomologyTargets(
on='fam_key',
out='homology/targets',
process_negatives=process_negatives)
create_alignment_weights = align_transforms.CreateBatchedWeights(
on='alignment/targets', out='alignment/weights')
add_neg_alignment_targets_and_weights = align_transforms.PadNegativePairs(
on=('alignment/targets', 'alignment/weights'))
pad_neg_pid = align_transforms.PadNegativePairs(on=('alignment/pid1',
'alignment/pid3'),
value=-1.0)
batched_transformations = [
flatten_sequence_pairs, flatten_metadata_pairs, create_homology_targets
]
if process_negatives:
batched_transformations.extend([
create_alignment_weights, add_neg_alignment_targets_and_weights,
pad_neg_pid
])
if lm_cls is not None:
create_lm_targets = lm_cls(on=sequence_key,
out=(sequence_key, 'masked_lm/targets',
'masked_lm/weights'))
batched_transformations.append(create_lm_targets)
### Sets up the remainder of the `DatasetBuilder` configuration.
masked_lm_labels = ('masked_lm/targets', 'masked_lm/weights')
alignment_labels = ('alignment/targets' if not process_negatives else
('alignment/targets', 'alignment/weights'))
homology_labels = 'homology/targets'
embeddings = () if lm_cls is None else (masked_lm_labels, )
alignments = (alignment_labels, homology_labels)
return builder.DatasetBuilder(
data_loader=loader_cls(extra_keys),
ds_transformations=ds_transformations,
transformations=transformations,
batched_transformations=batched_transformations,
labels=multi_task.Backbone(embeddings=embeddings,
alignments=alignments),
metadata=('seq_key', 'alignment/pid1', 'alignment/pid3'),
sequence_key=sequence_key,
**kwargs)