def initialize_cache(inputs, outputs, targets, max_decode_len, config):
"""Initialize a cache for a given input shape and max decode length."""
target_shape = (targets.shape[0], max_decode_len)
dtype = config.base_config.dtype
initial_variables = models.DecomposeAttentionTransformer(config).init(
jax.random.PRNGKey(0), jnp.ones(inputs.shape, dtype),
jnp.ones(outputs.shape, dtype), jnp.ones(target_shape, dtype))
return initial_variables['cache']
def tokens_ids_to_logits(flat_ids):
"""Token slice to logits from decoder model."""
# --> [batch * beam, 1, vocab]
flat_logits = models.DecomposeAttentionTransformer(config=config).apply(
{'params': params},
flat_ids,
flat_encoded,
flat_encoded_padding_mask,
method=models.DecomposeAttentionTransformer.decode)
return flat_logits
def eval_step(params, inputs, outputs, targets, eos_token, config):
"""Collect metrics for evaluation during training."""
weights = jnp.where(
jnp.logical_and(
targets > 0,
jnp.logical_and(targets != config.base_config.bos_token,
targets != eos_token)), 1, 0).astype(jnp.float32)
logits = models.DecomposeAttentionTransformer(config).apply(
{'params': params}, inputs, outputs, targets)
return compute_metrics(logits, targets, weights)
def loss_fn(params):
"""Loss function used for training."""
logits = models.DecomposeAttentionTransformer(config).apply(
{'params': params},
inputs,
outputs,
targets,
rngs={'dropout': dropout_rng})
loss, weight_sum = compute_weighted_cross_entropy(logits, targets, weights)
mean_loss = loss / weight_sum
return mean_loss, logits
def tokens_ids_to_logits(flat_ids, flat_cache):
"""Token slice to logits from decoder model."""
# --> [batch * beam, 1, vocab]
flat_logits, new_vars = models.DecomposeAttentionTransformer(
config=config).apply(
{'params': params, 'cache': flat_cache},
flat_ids,
flat_encoded,
flat_encoded_padding_mask,
mutable=['cache'],
method=models.DecomposeAttentionTransformer.decode)
new_flat_cache = new_vars['cache']
# Remove singleton sequence-length dimension:
# [batch * beam, 1, vocab] --> [batch * beam, vocab]
flat_logits = flat_logits.squeeze(axis=1)
return flat_logits, new_flat_cache
def main(_):
tf.enable_v2_behavior()
tf.random.set_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
if not gfile.isdir(FLAGS.save_dir):
gfile.makedirs(FLAGS.save_dir)
hparam_str_dict = json.loads(FLAGS.xm_parameters)
hparam_str = ','.join([
'%s=%s' % (shorten(k), str(hparam_str_dict[k]))
for k in hparam_str_dict.keys()
])
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.save_dir, 'tb', hparam_str))
batch_size = FLAGS.per_device_batch_size * n_devices
io_shape = (FLAGS.per_device_batch_size, FLAGS.num_strings_per_task,
FLAGS.max_characters)
predict_io_shape = (FLAGS.per_device_batch_size,
FLAGS.num_strings_per_task,
FLAGS.predict_max_characters)
target_shape = (FLAGS.per_device_batch_size, FLAGS.max_target_length)
# Setup DSL
# ---------------------------------------------------------------------------
# Build token tables.
if FLAGS.dataset_type in ['robust_fill', 'robust_fill_base']:
spec_vocab = robust_fill_dsl.CHARACTER + input_pipeline.SEPARATOR_TOKEN
spec_id_token_table = {
i + 3: token
for i, token in enumerate(spec_vocab)
}
bos_id = 1
eos_id = 2
spec_id_token_table[bos_id] = robust_fill_dsl.BOS
spec_id_token_table[eos_id] = robust_fill_dsl.EOS
spec_token_id_table = {
token: id
for id, token in spec_id_token_table.items()
}
spec_vocab_size = len(spec_token_id_table) + 1 # For padding.
program_id_token_table, _ = dsl_tokens.build_token_tables()
program_vocab_size = len(program_id_token_table) + 1
elif FLAGS.dataset_type == 'scan':
# TODO(jxihong): Scan is not handled yet.
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
else:
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
# Parse io and program token sequences (for eval).
def decode_io(inputs, outputs):
"""Convert from int tensors to strings."""
if FLAGS.dataset_type == 'robust_fill':
def decode_str(s):
"""Decode string tokens."""
return ''.join(
[spec_id_token_table[t_id] for t_id in s if t_id > 0])
inps, outs = [], []
for inp, out in zip(inputs, outputs):
inps.append(decode_str(inp))
outs.append(decode_str(out))
return inps, outs
elif FLAGS.dataset_type == 'scan':
def decode_str(s):
"""Decode string tokens."""
return ' '.join(
[spec_id_token_table[t_id] for t_id in s if t_id > 0])
inps = [decode_str(inp) for inp in inputs]
dummy_outs = [''] * len(inps)
return inps, dummy_outs
else:
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
def decode_spec(target):
"""Convert from int tensor to a string."""
target = target[:np.argmax(target == eos_id)].astype(np.int32)
if FLAGS.dataset_type == 'robust_fill':
target = target[target != bos_id].tolist()
return ''.join(
[spec_id_token_table[t_id] for t_id in target if t_id > 0])
elif FLAGS.dataset_type == 'scan':
# TODO(jxihong): Scan is not handled yet.
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
else:
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
def decode_program(program):
"""Decode program tokens into a program (program object or string)."""
program = program[:np.argmax(program == eos_id) + 1].astype(np.int32)
if FLAGS.dataset_type == 'robust_fill':
# Returns either a Concat program object, or None.
program = program[program != bos_id].tolist()
try:
return robust_fill_dsl.decode_program(program,
program_id_token_table)
except: # pylint: disable=bare-except
return None # Program does not compile.
elif FLAGS.dataset_type == 'scan':
# Returns a string.
program = program[jnp.logical_and(program != bos_id,
program != eos_id)].tolist()
return ' '.join(scan_vocab.decode(program, program_id_token_table))
else:
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
def decode_program_str(program): # pylint: disable=unused-variable
"""Decode program tokens into a string."""
decoded = decode_program(program)
if FLAGS.dataset_type == 'robust_fill':
try:
return decoded.to_string() # pytype: disable=attribute-error
except: # pylint: disable=bare-except
return 'did not compile'
else:
assert isinstance(decoded,
str), '{} should be string'.format(decoded)
return decoded
# Load Dataset
# ---------------------------------------------------------------------------
logging.info('Initializing dataset.')
if not FLAGS.dataset_filepattern:
raise ValueError('Must specify filepattern to dataset.')
# Training dataset.
logging.info('Loading dataset from %s', FLAGS.dataset_filepattern)
padded_shapes = {
'inputs': io_shape[1:],
'outputs': io_shape[1:],
'target': target_shape[1:],
}
logging.info('padded_shapes: %s', padded_shapes)
if FLAGS.dataset_type == 'robust_fill':
if FLAGS.model_type == 'spec_decomposer_model':
create_dataset_fn = input_pipeline.create_robust_fill_dataset_for_spec_decomposer_model
elif FLAGS.model_type == 'synthesizer_model':
create_dataset_fn = input_pipeline.create_robust_fill_dataset_for_synthesizer_model
else:
raise ValueError(f'Unhandled model_type: {FLAGS.model_type}')
elif FLAGS.dataset_type == 'scan':
raise NotImplementedError() # TODO(kshi): Implement.
# create_dataset_fn = input_pipeline.create_scan_dataset_from_tf_record
else:
raise ValueError('Unhandled dataset_type: {}'.format(
FLAGS.dataset_type))
dataset = create_dataset_fn(FLAGS.dataset_filepattern, spec_token_id_table,
FLAGS.num_strings_per_task)
dataset = dataset.padded_batch(batch_size,
padded_shapes=padded_shapes,
drop_remainder=True)
# Split evaluation and training.
eval_ds = dataset.take(FLAGS.num_eval_steps)
# Decrease batch of predict dataset to handle beam search.
predict_padded_shapes = padded_shapes.copy()
predict_padded_shapes['inputs'] = predict_io_shape[1:]
predict_padded_shapes['outputs'] = predict_io_shape[1:]
logging.info('predict_padded_shapes: %s', predict_padded_shapes)
predict_ds = eval_ds.unbatch().padded_batch(
int(np.ceil(batch_size / 10)), padded_shapes=predict_padded_shapes)
train_ds = dataset.skip(FLAGS.num_eval_steps)
if FLAGS.train_set_batches > 0:
train_ds = train_ds.take(FLAGS.train_set_batches)
train_ds = train_ds.repeat()
test_dataset = create_dataset_fn(FLAGS.test_dataset_filepattern,
spec_token_id_table,
FLAGS.num_strings_per_task)
test_dataset = test_dataset.padded_batch(
batch_size, padded_shapes=predict_padded_shapes, drop_remainder=False)
quick_test_dataset = (test_dataset.take(
FLAGS.num_quick_test_steps).unbatch().padded_batch(
int(np.ceil(batch_size / 10)),
padded_shapes=predict_padded_shapes))
final_test_dataset = (test_dataset.take(
FLAGS.num_final_test_steps).unbatch().padded_batch(
int(np.ceil(batch_size / 10)),
padded_shapes=predict_padded_shapes))
# Build Model and Optimizer
# ---------------------------------------------------------------------------
if FLAGS.model_type == 'spec_decomposer_model':
output_vocab_size = spec_vocab_size
elif FLAGS.model_type == 'synthesizer_model':
output_vocab_size = program_vocab_size
else:
raise ValueError(f'Unhandled model_type: {FLAGS.model_type}')
base_config = base_models.TransformerConfig(
vocab_size=spec_vocab_size,
output_vocab_size=output_vocab_size,
shift=True,
emb_dim=FLAGS.embedding_dim,
num_heads=FLAGS.num_heads,
num_layers=FLAGS.num_layers,
qkv_dim=FLAGS.embedding_dim,
mlp_dim=FLAGS.hidden_dim,
max_len=max(FLAGS.max_characters, FLAGS.max_target_length),
dropout_rate=FLAGS.dropout_rate,
attention_dropout_rate=FLAGS.attention_dropout_rate,
use_relative_attention=FLAGS.use_relative_attention,
deterministic=False,
decode=False,
bos_token=bos_id,
num_input_relative_position_buckets=FLAGS.num_position_buckets,
max_input_distance=FLAGS.max_distance,
num_output_relative_position_buckets=FLAGS.num_position_buckets,
max_output_distance=FLAGS.max_distance,
num_input_cross_output_relative_position_buckets=(
FLAGS.num_position_buckets),
max_input_cross_output_distance=FLAGS.max_distance,
num_program_relative_position_buckets=FLAGS.num_position_buckets,
max_program_distance=FLAGS.max_distance,
num_program_cross_embed_relative_position_buckets=(
FLAGS.num_position_buckets),
max_program_cross_embed_distance=FLAGS.
max_program_cross_embed_distance,
num_flat_encoding_relative_position_buckets=(
FLAGS.num_position_buckets),
max_flat_encoding_distance=FLAGS.max_distance)
train_config = models.DecomposeAttentionTransformerConfig(
base_config=base_config,
dataset_type=FLAGS.dataset_type,
flat_encoded_self_attention=FLAGS.flat_encoded_self_attention)
eval_config = train_config.replace(base_config=base_config.replace(
deterministic=True))
predict_config = train_config.replace(base_config=base_config.replace(
shift=False,
deterministic=True,
decode=not FLAGS.slow_decode,
max_len=max(FLAGS.predict_max_characters, FLAGS.max_target_length)))
rng = jax.random.PRNGKey(FLAGS.seed)
rng = jax.random.fold_in(rng, jax.host_id())
rng, init_rng = jax.random.split(rng)
dropout_rng = jax.random.split(rng, jax.local_device_count())
del rng
m = models.DecomposeAttentionTransformer(eval_config)
initial_variables = jax.jit(m.init)(init_rng,
jnp.ones(io_shape, jnp.float32),
jnp.ones(io_shape, jnp.float32),
jnp.ones(target_shape, jnp.float32))
optimizer_def = optim.Adam(FLAGS.lr,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=FLAGS.weight_decay)
optimizer = optimizer_def.create(initial_variables['params'])
del initial_variables # Don't keep a copy of the initial model.
start_step = 0
if FLAGS.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(
os.path.join(FLAGS.save_dir, 'checkpoints', hparam_str), optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
logging.info('Found model checkpointed at step %d.', start_step)
if FLAGS.finetune_start_step > 0:
logging.info(
'Checking that start_step (%s) == finetune_start_step (%s)',
start_step, FLAGS.finetune_start_step)
assert start_step >= FLAGS.finetune_start_step
steps_to_skip = start_step - FLAGS.finetune_start_step
else:
steps_to_skip = start_step
# TODO(kshi): It is likely that this code can lead to the job stalling for
# 10+ hours when restarting from a checkpoint that had been trained a long
# time, possibly because dataset skipping is slow.
logging.info('Skipping %s steps...', steps_to_skip)
train_ds = train_ds.skip(steps_to_skip)
dummy_p_train_step = jax.pmap(
lambda dropout_rng: jax.random.split(dropout_rng)[1])
for _ in range(steps_to_skip):
dropout_rng = dummy_p_train_step(dropout_rng)
logging.info('Finished skipping steps')
logging.info('Host %s has dropout_rng = %s', jax.host_id(),
dropout_rng)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
# TODO(jxihong): Implement fast decoding.
assert FLAGS.slow_decode, 'Fast decoding is not implemented yet.'
if FLAGS.finetune_start_step <= 0:
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=FLAGS.lr)
else:
# Constant LR for finetuning.
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=FLAGS.lr, factors='constant')
p_train_step = jax.pmap(functools.partial(
train_step, learning_rate_fn=learning_rate_fn, config=train_config),
axis_name='batch')
p_eval_step = jax.pmap(functools.partial(eval_step,
eos_token=eos_id,
config=eval_config),
axis_name='batch')
p_init_cache = jax.pmap(functools.partial(
initialize_cache,
max_decode_len=FLAGS.max_target_length,
config=predict_config),
axis_name='batch')
p_pred_step = jax.pmap(functools.partial(
predict_step,
eos_token=eos_id,
max_decode_len=FLAGS.max_target_length,
config=predict_config,
slow_decode=FLAGS.slow_decode),
axis_name='batch',
static_broadcasted_argnums=(4, ))
# Main Train Loop
# ---------------------------------------------------------------------------
logging.info('Starting training!')
metrics_all = []
tick = time.time()
train_iter = train_ds.as_numpy_iterator()
for step in range(start_step, FLAGS.num_train_steps):
inputs, outputs, targets = load_data(next(train_iter))
optimizer, metrics, dropout_rng = p_train_step(optimizer,
inputs,
outputs,
targets,
dropout_rng=dropout_rng)
metrics_all.append(metrics)
is_last_step = step == FLAGS.num_train_steps - 1
# Periodic metric handling.
# Training Metrics
if (step and step % FLAGS.log_freq == 0) or is_last_step:
logging.info('Gathering training metrics.')
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(
lambda x: x / denominator, # pylint: disable=cell-var-from-loop
metrics_sums)
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
summary['perplexity'] = jnp.clip(jnp.exp(summary['loss']),
a_max=1.0e4)
if jax.host_id() == 0:
logging.info('Train in step: %d, loss: %.4f', step,
summary['loss'])
tock = time.time()
steps_per_sec = FLAGS.log_freq / (tock - tick)
tick = tock
summary_writer.scalar('train/steps per second', steps_per_sec,
step)
for key, val in summary.items():
summary_writer.scalar('train/' + key, val, step)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []
# Evaluation Metrics
if (step and step % FLAGS.eval_freq == 0) or is_last_step:
logging.info('Gathering evaluation metrics.')
t_evaluation_start = time.time()
eval_metrics = []
for batches in eval_ds.as_numpy_iterator():
inputs, outputs, targets = load_data(batches)
metrics = p_eval_step(optimizer.target, inputs, outputs,
targets)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
if jax.host_id() == 0:
logging.info('Evaluation time: %.4f s step %d, loss: %.4f.',
time.time() - t_evaluation_start, step,
eval_summary['loss'])
for key, val in eval_summary.items():
summary_writer.scalar('eval/' + key, val, step)
summary_writer.flush()
# Beam search metrics.
if (step and step % FLAGS.predict_freq == 0) or is_last_step:
logging.info('Gathering beam search metrics.')
test_ds = final_test_dataset if is_last_step else quick_test_dataset
for dataset, predict_or_test in [(predict_ds, 'predict'),
(test_ds, 'test')]:
for beam_size in [1, 10]:
t_inference_start = time.time()
total_successes = 0
total_denominator = 0
ios, targets_list, predictions, top_of_beams, scores = ([],
[],
[],
[],
[])
for batches in dataset.as_numpy_iterator():
pred_batch = batches
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch['inputs'].shape[0]
if cur_pred_batch_size % n_devices:
padded_size = int(
np.ceil(cur_pred_batch_size / n_devices) *
n_devices)
# pylint: disable=cell-var-from-loop
pred_batch = jax.tree_map(
lambda x: pad_examples(x, padded_size),
pred_batch)
inputs, outputs, targets = load_data(pred_batch)
cache = (p_init_cache(inputs, outputs, targets)
if not FLAGS.slow_decode else None)
predicted = p_pred_step(optimizer.target, inputs,
outputs, cache, beam_size)
predicted = tohost(predicted)
inputs, outputs, targets = map(
tohost, (inputs, outputs, targets))
for i, beams in enumerate(predicted):
inps, outs = decode_io(inputs[i], outputs[i])
if FLAGS.model_type == 'spec_decomposer_model':
ground_truth = decode_spec(targets[i])
best_prediction, score = eval_predicted_spec_decomposer_model(
beams, ground_truth, decode_spec)
decode_to_str_fn = decode_spec
elif FLAGS.model_type == 'synthesizer_model':
ground_truth = decode_program_str(targets[i])
best_prediction, score = eval_predicted_synthesizer_model(
beams, inps, outs, decode_program)
decode_to_str_fn = decode_program_str
else:
raise ValueError(
f'Unknown model type {FLAGS.model_type}')
if score > 0:
total_successes += 1
total_denominator += 1
beams_target = [
decode_to_str_fn(beam) for beam in beams
]
ios.append(' ; '.join(map(str, zip(inps, outs))))
targets_list.append(ground_truth)
predictions.append(best_prediction)
scores.append(score)
logging.info('')
logging.info('ios: %s', ios[-1])
logging.info('targets[%s]: %s', i, targets[i])
logging.info('ground_truth: %s', ground_truth)
logging.info('predicted beam: %s',
'\n'.join(beams_target))
logging.info('best_prediction: %s',
best_prediction)
logging.info('score: %s', score)
logging.info('beams: %s', beams)
if not ground_truth:
logging.warn('ground_truth is empty!')
top_of_beam = []
for index, beam in enumerate(beams[:-5:-1]):
top_of_beam.append(
'index: {}, decoded: {}, tokens: {}'.
format(index, decode_to_str_fn(beam),
beam))
top_of_beams.append('\n\n'.join(top_of_beam))
all_total_successes, all_total_denominator = per_host_sum_pmap(
jax.tree_map(np.array,
(total_successes, total_denominator)))
# Record beam search results as text summaries.
message = []
for n in np.random.choice(np.arange(len(predictions)), 8):
text = (
f'ios: {ios[n]}\n\ntarget: {targets_list[n]}\n\n'
f'predicted: {predictions[n]}\n\n'
f'score: {scores[n]}\n\n'
f'top of beam:\n\n{top_of_beams[n]}\n\n')
message.append(text)
# Write to tensorboard.
if jax.host_id() == 0:
accuracy = 100 * all_total_successes / all_total_denominator
logging.info(
'%s results, step %d, beam size %d: %s / %s = %.2f%% (%.2f s)',
predict_or_test, step, beam_size,
all_total_successes, all_total_denominator,
accuracy,
time.time() - t_inference_start)
summary_writer.scalar(
'{}/beam-size-{}'.format(predict_or_test,
beam_size), accuracy,
step)
summary_writer.text(
'{}-samples-beam-{}'.format(
predict_or_test, beam_size),
'\n------\n'.join(message), step)
summary_writer.flush()
# Save a Checkpoint. Do this at the end of the training loop, so that if a
# worker is descheduled during a round of prediction (which takes a while),
# we will redo prediction upon restarting (to avoid losing data).
if (step % FLAGS.checkpoint_freq == 0 and step > 0) or is_last_step:
if jax.host_id() == 0:
# Save unreplicated optimizer + model state.
checkpoints.save_checkpoint(
os.path.join(FLAGS.save_dir, 'checkpoints', hparam_str),
jax_utils.unreplicate(optimizer), step)
def predict_step(params,
inputs,
outputs,
cache,
beam_size,
eos_token,
max_decode_len,
config,
slow_decode=True):
"""Predict translation with fast decoding beam search on a batch."""
# Prepare transformer fast-decoder call for beam search: for beam search, we
# need to set up our decoder model to handle a batch size equal to
# batch_size * beam_size, where each batch item's data is expanded in-place
# rather than tiled.
flat_encoded = decode.flat_batch_beam_expand(
models.DecomposeAttentionTransformer(config).apply(
{'params': params},
inputs,
outputs,
method=models.DecomposeAttentionTransformer.encode), beam_size)
encoded_padding_mask = jnp.where(outputs > 0, 1, 0).astype(jnp.float32)
flat_encoded_padding_mask = decode.flat_batch_beam_expand(
encoded_padding_mask, beam_size)
if slow_decode:
def tokens_ids_to_logits(flat_ids):
"""Token slice to logits from decoder model."""
# --> [batch * beam, 1, vocab]
flat_logits = models.DecomposeAttentionTransformer(
config=config).apply(
{'params': params},
flat_ids,
flat_encoded,
flat_encoded_padding_mask,
method=models.DecomposeAttentionTransformer.decode)
return flat_logits
else:
def tokens_ids_to_logits(flat_ids, flat_cache):
"""Token slice to logits from decoder model."""
# --> [batch * beam, 1, vocab]
flat_logits, new_vars = models.DecomposeAttentionTransformer(
config=config).apply(
{
'params': params,
'cache': flat_cache
},
flat_ids,
flat_encoded,
flat_encoded_padding_mask,
mutable=['cache'],
method=models.DecomposeAttentionTransformer.decode)
new_flat_cache = new_vars['cache']
# Remove singleton sequence-length dimension:
# [batch * beam, 1, vocab] --> [batch * beam, vocab]
flat_logits = flat_logits.squeeze(axis=1)
return flat_logits, new_flat_cache
# Using the above-defined single-step decoder function, run a
# beam search over possible sequences given input encoding.
beam_seqs, _ = decode.beam_search(inputs,
cache,
tokens_ids_to_logits,
beam_size=beam_size,
alpha=0.6,
bos_token=config.base_config.bos_token,
eos_token=eos_token,
max_decode_len=max_decode_len,
slow_decode=slow_decode)
# Beam search returns [n_batch, n_beam, n_length] with beam dimension
# sorted in increasing order of log-probability.
return beam_seqs