def save_unreplicated_checkpoint_background(train_dir,
optimizer_state,
params,
batch_stats,
training_metrics_state,
global_step,
preemption_count,
sum_train_cost,
max_to_keep=1):
"""Saves pytree, step, preemption_count, and sum_train_cost to train_dir."""
logging.info('Saving checkpoint to ckpt_%d', global_step)
unreplicated_optimizer_state = jax.device_get(
jax_utils.unreplicate(optimizer_state))
unreplicated_params = jax.device_get(jax_utils.unreplicate(params))
unreplicated_batch_stats = jax.device_get(
jax_utils.unreplicate(batch_stats))
unreplicated_training_metrics_state = jax.device_get(
jax_utils.unreplicate(training_metrics_state))
state = dict(global_step=global_step,
preemption_count=preemption_count,
sum_train_cost=sum_train_cost,
optimizer_state=unreplicated_optimizer_state,
params=unreplicated_params,
batch_stats=unreplicated_batch_stats,
training_metrics_grabber=unreplicated_training_metrics_state)
save_checkpoint_background(train_dir,
global_step,
state,
max_to_keep=max_to_keep)
logging.info('Done saving checkpoint.')
def write_summaries(self, example, logits, summary_writer, info, step,
state):
example = jax_utils.unreplicate(example)
outputs = jnp.argmax(jax_utils.unreplicate(logits), axis=-1)
text = summary_utils.human_readable_texts(example, outputs, info)
summary_writer.text('predictions', '<pre>{}</pre>'.format(text), step)
self.generate_plots(state, summary_writer, step)
def maybe_copy_model_from_pretraining(optimizer, pretrain_optimizer, step,
adam_opt_def):
"""Copy model parameters from pretraining."""
if step < FLAGS.num_pretrain_steps:
optimizer = jax_utils.unreplicate(optimizer)
state_dict = adam_opt_def.state_dict(
target=jax_utils.unreplicate(pretrain_optimizer).target,
state=optim.OptimizerState(jnp.asarray(step, dtype=jnp.int32),
optimizer.state.param_states))
optimizer = optimizer.restore_state(state_dict)
optimizer = jax_utils.replicate(optimizer)
return optimizer
def load_checkpoint(self, ckpt_dir):
"""Loads optimizer from ckpt_dir."""
target = jax_utils.unreplicate(self._optimizer)
optimizer = checkpoints.restore_checkpoint(ckpt_dir, target=target)
if optimizer is target:
raise ValueError('Unable to load checkpoint from %s' % ckpt_dir)
self.set_weights(optimizer)
def test_train_one_step(self):
"""Tests training loop over one step."""
iterator = self._dataset.get_train()
batch = next(iterator)
state = jax_utils.replicate(self._state)
optimizer = jax_utils.replicate(self._optimizer.create(self._model))
self._rng, step_key = jax.random.split(self._rng)
batch = training._shard_batch(batch)
sharded_keys = common_utils.shard_prng_key(step_key)
p_train_step = jax.pmap(functools.partial(
training.train_step, learning_rate_fn=self._learning_rate_fn),
axis_name='batch')
_, _, loss, gradient_norm = p_train_step(optimizer, batch,
sharded_keys, state)
loss = jnp.mean(loss)
gradient_norm = jax_utils.unreplicate(gradient_norm)
with self.subTest(name='test_loss_range'):
self.assertBetween(loss, self._min_loss, self._max_loss)
with self.subTest(name='test_gradient_norm'):
self.assertGreaterEqual(gradient_norm, 0)
def maybe_log_training_metrics(metrics_state, metrics_summary_fn,
metrics_logger):
"""If appropriate, send a summary tree of training metrics to the logger."""
if metrics_state:
unreplicated_metrics_state = jax_utils.unreplicate(metrics_state)
summary_tree = metrics_summary_fn(unreplicated_metrics_state)
metrics_logger.append_pytree(summary_tree)
def write_images(H, ema_params, viz_batch_original, viz_batch_processed, fname,
logprint):
rng = random.PRNGKey(H.seed_sample)
ema_apply = partial(
VAE(H).apply, {'params': jax_utils.unreplicate(ema_params)})
forward_get_latents = partial(ema_apply, method=VAE(H).forward_get_latents)
forward_samples_set_latents = partial(
ema_apply, method=VAE(H).forward_samples_set_latents)
forward_uncond_samples = partial(ema_apply,
method=VAE(H).forward_uncond_samples)
zs = [s['z'] for s in forward_get_latents(viz_batch_processed, rng)]
batches = [viz_batch_original.numpy()]
mb = viz_batch_processed.shape[0]
lv_points = np.floor(
np.linspace(0, 1, H.num_variables_visualize + 2) *
len(zs)).astype(int)[1:-1]
for i in lv_points:
batches.append(forward_samples_set_latents(mb, zs[:i], rng, t=0.1))
for t in [1.0, 0.9, 0.8, 0.7][:H.num_temperatures_visualize]:
batches.append(forward_uncond_samples(mb, rng, t=t))
n_rows = len(batches)
im = np.concatenate(batches, axis=0).reshape(
(n_rows, mb,
*viz_batch_processed.shape[1:])).transpose([0, 2, 1, 3, 4]).reshape([
n_rows * viz_batch_processed.shape[1],
mb * viz_batch_processed.shape[2], 3
])
logprint(f'printing samples to {fname}')
Image.fromarray(im).save(fname)
def save_model(path, optimizer, ema, epoch, H):
optimizer, ema = jax_utils.unreplicate((optimizer, ema))
checkpoints.save_checkpoint(path, (optimizer, epoch), optimizer.state.step)
checkpoints.save_checkpoint(path + '_ema', ema, optimizer.state.step)
from_log = os.path.join(H.save_dir, 'log.jsonl')
to_log = f'{os.path.dirname(path)}/{os.path.basename(path)}-log.jsonl'
subprocess.check_output(['cp', from_log, to_log])
def run_eval(self, flax_module, batch_stats, optimizer_state, global_step):
"""Computes the loss hessian and returns the max eigenvalue.
Note, the full lanczos tridiagonal matrix is saved via the logger to
train_dir/checkpoints/config['name'].
Args:
flax_module: Replicated flax module.
batch_stats: Replicated batch_stats from the trainer.
optimizer_state: Replicated optimizer state from the trainer.
global_step: Current training step.
Returns:
Max eigenvalue of the loss (full tridiag is saved to disk).
"""
del batch_stats
if self.callback_config.get('precondition'):
precondition_config = self.callback_config.get(
'precondition_config', default=FrozenConfigDict())
diag_preconditioner = precondition.make_diag_preconditioner(
self.hps.optimizer, self.hps.opt_hparams,
jax_utils.unreplicate(optimizer_state), precondition_config)
else:
diag_preconditioner = None
hessian_metrics, _, _ = self.hessian_evaluator.evaluate_spectrum(
flax_module, global_step, diag_preconditioner=diag_preconditioner)
if jax.host_id() == 0:
self.logger.append_pytree(hessian_metrics)
max_eig_key = self.name + '/max_eig'
return {max_eig_key: hessian_metrics['max_eig_hess']}
def save_checkpoint(self, ckpt_dir):
"""Saves unreplicated optimizer to ckpt_dir."""
optimizer = jax_utils.unreplicate(self._optimizer)
checkpoints.save_checkpoint(
ckpt_dir,
target=optimizer,
step=self._train_step,
)
def evaluate(self, state, dataset):
dataloader = get_batched_dataset(dataset, self.args.batch_size)
total = len(dataset) // self.args.batch_size
running_loss = jnp.array(0, dtype=jnp.float32)
i = 0
for batch in tqdm(dataloader, total=total, desc="Evaluating ... "):
batch = self.data_collator(batch)
metrics = self.val_step_fn(state, **batch)
running_loss += jax_utils.unreplicate(metrics["loss"])
i += 1
return running_loss / i
def save_checkpoint(self, save_dir, state):
state = jax_utils.unreplicate(state)
print(f"SAVING CHECKPOINT IN {save_dir}", end=" ... ")
self.model_save_fn(save_dir, params=state.params)
with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args, os.path.join(save_dir, "args.joblib"))
joblib.dump(self.data_collator,
os.path.join(save_dir, "data_collator.joblib"))
with open(os.path.join(save_dir, "training_state.json"), "w") as f:
json.dump({"step": state.step.item()}, f)
print("DONE")
def update_params(
workload: spec.Workload,
current_param_container: spec.ParameterContainer,
current_params_types: spec.ParameterTypeTree,
model_state: spec.ModelAuxiliaryState,
hyperparameters: spec.Hyperparamters,
input_batch: spec.Tensor,
label_batch: spec.Tensor,
# This will define the output activation via `output_activation_fn`.
loss_type: spec.LossType,
optimizer_state: spec.OptimizerState,
eval_results: List[Tuple[int, float]],
global_step: int,
rng: spec.RandomState) -> spec.UpdateReturn:
"""Return (updated_optimizer_state, updated_params, updated_model_state)."""
del current_params_types
del loss_type
del eval_results
del global_step
num_devices = jax.local_device_count()
input_shape = input_batch.shape
reshaped_input_batch = jnp.reshape(
input_batch,
(num_devices, input_shape[0] // num_devices, *input_shape[1:]))
reshaped_label_batch = jnp.reshape(label_batch,
(num_devices, label_batch.shape[0] //
num_devices, *label_batch.shape[1:]))
# TODO(znado) we should be more efficient than replicating state each step.
new_optimizer_state, updated_params, new_model_state = pmapped_update_params(
workload, jax_utils.replicate(current_param_container),
jax_utils.replicate(model_state), hyperparameters,
reshaped_input_batch, reshaped_label_batch,
jax_utils.replicate(optimizer_state), rng, jnp.arange(num_devices))
return (jax_utils.unreplicate(new_optimizer_state),
jax_utils.unreplicate(updated_params),
jax_utils.unreplicate(new_model_state))
def maybe_restore_checkpoint(params, train_dir,
external_checkpoint_path):
"""Helper function to replicate_and_maybe_restore a checkpoint."""
(_, ret_params, _, _, ret_global_step, ret_sum_train_cost,
ret_preemption_count, ret_is_restored
) = checkpoint.replicate_and_maybe_restore_checkpoint(
{}, params, {}, {}, train_dir, external_checkpoint_path)
ret_params_unrep = jax.device_get(
jax_utils.unreplicate(ret_params))
return (ret_params_unrep, ret_global_step, ret_sum_train_cost,
ret_preemption_count, ret_is_restored)
def evaluate(model, state, eval_ds, num_eval_steps=-1):
"""Evaluate the model on the given dataset."""
logging.info("Starting evaluation.")
eval_metrics = None
with StepTraceContextHelper("eval", 0) as trace_context:
for step, batch in enumerate(eval_ds): # pytype: disable=wrong-arg-types
batch = jax.tree_map(np.asarray, batch)
metrics_update = flax_utils.unreplicate(
eval_step(model, state, batch))
eval_metrics = (metrics_update if eval_metrics is None else
eval_metrics.merge(metrics_update))
if num_eval_steps > 0 and step + 1 == num_eval_steps:
break
trace_context.next_step()
return eval_metrics
def train(self, state, tr_dataset, val_dataset):
args = self.args
total = len(tr_dataset) // args.batch_size
rng = jax.random.PRNGKey(0)
drp_rng = jax.random.split(rng, jax.device_count())
for epoch in range(args.max_epochs):
running_loss = jnp.array(0, dtype=jnp.float32)
tr_dataloader = get_batched_dataset(tr_dataset,
args.batch_size,
seed=epoch)
i = 0
for batch in tqdm(tr_dataloader,
total=total,
desc=f"Running EPOCH-{epoch}"):
batch = self.data_collator(batch)
state, metrics, drp_rng = self.train_step_fn(
state, drp_rng, **batch)
running_loss += jax_utils.unreplicate(metrics["loss"])
i += 1
if i % args.logging_steps == 0:
state_step = jax_utils.unreplicate(state.step)
tr_loss = running_loss.item() / i
lr = self.scheduler_fn(state_step - 1)
eval_loss = self.evaluate(state, val_dataset)
logging_dict = dict(step=state_step.item(),
eval_loss=eval_loss.item(),
tr_loss=tr_loss,
lr=lr.item())
tqdm.write(str(logging_dict))
self.logger.log(logging_dict, commit=True)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f"-e{epoch}-s{i}",
state=state)
def maybe_reset_train_state(self):
optimizer = jax_utils.unreplicate(self.train_state.optimizer)
if self.hparams.get('reinitilize_params_at_each_step', False):
del optimizer.target
(flax_model, _, _) = pipeline_utils.create_flax_module(
optimizer.target.module,
self.task.dataset.meta_data['input_shape'], self.hparams,
nn.make_rng(),
self.task.dataset.meta_data.get('input_dtype', jnp.float32))
else:
flax_model = optimizer.target
# Reset optimizer
if self.hparams.get('reinitialize_optimizer_at_each_step', False):
self.lr_start_step = jax_utils.unreplicate(
self.train_state.global_step)
optimizer = optimizers.get_optimizer(
self.hparams).create(flax_model)
else:
optimizer = optimizer.replace(target=flax_model)
optimizer = jax_utils.replicate(optimizer)
self.train_state = self.train_state.replace(optimizer=optimizer)
def evaluate(self, workdir, dir_name='eval', ckpt_name=None):
"""Perform one evaluation."""
checkpoint_dir = os.path.join(workdir, 'checkpoints-0')
ckpt = checkpoint.Checkpoint(checkpoint_dir)
state_dict = ckpt.restore_dict(os.path.join(checkpoint_dir, ckpt_name))
ema_params = flax.core.FrozenDict(state_dict['ema_params'])
step = int(state_dict['step'])
# Distribute training.
ema_params = flax_utils.replicate(ema_params)
eval_logdir = os.path.join(workdir, dir_name)
tf.io.gfile.makedirs(eval_logdir)
writer = metric_writers.create_default_writer(
eval_logdir, just_logging=jax.process_index() > 0)
outputs = self._eval_epoch(params=ema_params)
outputs = flax_utils.unreplicate(outputs)
scalars, images = outputs['scalars'], outputs['images']
writer.write_scalars(step, scalars)
writer.write_images(step, images)
def save_checkpoint(experiment_dir, train_state, keep=3):
"""Saves a checkpoint.
First syncs the model state across replicas, then it unreplicates it by taking
the train state of the first replica and saves it as a checkpoint.
Args:
experiment_dir: str; Experiment directory for saving the checkpoint.
train_state: Dataclass; An instance of TrainState that holds the state of
training.
keep: int; Number of checkpoints to keep.
"""
if jax.host_id() == 0:
# get train state from the first replica
checkpoint_state = jax.device_get(jax_utils.unreplicate(train_state))
ckpt_path = checkpoint_path(experiment_dir,
int(checkpoint_state.global_step))
if not tf.io.gfile.exists(ckpt_path):
checkpoints.save_checkpoint(experiment_dir,
checkpoint_state,
int(checkpoint_state.global_step),
keep=keep)
def save_checkpoint(
model,
save_dir,
state,
cur_step: int,
with_opt: bool = True,
push_to_hub: bool = False,
):
state = jax_utils.unreplicate(state)
if with_opt:
logger.info(f"Saving optimizer and training state in {save_dir}...")
with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
f.write(to_bytes(state.opt_state))
with open(os.path.join(save_dir, "training_state.json"), "w") as f:
json.dump({"step": state.step.item()}, f)
logger.info(
f'Saving model in {save_dir} {"and pushing it to HF Hub" if push_to_hub else ""}'
)
model.save_pretrained(
save_dir,
params=state.params,
push_to_hub=push_to_hub,
commit_message=f"Saving weights and logs of step {cur_step}",
)
def get_env_aligned_pairs_idx(self, env_reps, env_batches, env_ids):
"""Computes alignments between all environment pairs.
Args:
env_reps: jnp array; Reps for different environments (sharded).
env_batches: list of dict; Batches of different environments (sharded).
env_ids: jnp array; Environment ids.
Returns:
alignment between batches of environment pairs (sharded).
"""
# TODO(riannevdberg, samiraabnar): aligning is done on the total
# unsharded batch, but that requires access between local batches
# when computing the loss. Unsure why this works! To be compatible
# with random alignment and sinkhorn soft alignment we should do
# alignment only within local batches.
env_reps = shard_util.unshard_env_batch(env_reps)
env_batches = shard_util.unshard(env_batches)
with nn.stochastic(jax_utils.unreplicate(self.train_state.rng)):
alignments = self.task.get_env_aligned_pairs_idx(env_reps, env_batches,
env_ids)
alignments = dataset_utils.shard(alignments)
return alignments
def run_train(run_configuration):
"""Runs the training workflow."""
config = run_configuration.config
run_dir = run_configuration.run_dir
adapter = run_configuration.adapter
log_dir = os.path.join(run_dir, 'train')
checkpoint_path = run_configuration.original_checkpoint_path
dataset = run_configuration.dataset_info.dataset
info = run_configuration.dataset_info.info
random_seed = 0
rng = jax.random.PRNGKey(random_seed)
rng = jax.random.fold_in(rng, jax.host_id())
rng, init_rng = jax.random.split(rng)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
# Set up optimizer.
optimizer = adapter.create_optimizer(run_configuration, rng=init_rng)
# Set up train step.
train_step = adapter.make_train_step()
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(log_dir)
# Set up checkpointing.
# TODO(dbieber): Set up phoenix.
checkpoint_dir = checkpoint_utils.build_checkpoint_dir(run_dir)
if checkpoint_path is None:
checkpoint_path = checkpoint_utils.latest_checkpoint(checkpoint_dir)
optimizer = checkpoint_utils.handle_restart_behavior(
checkpoint_path, optimizer, config)
start_step = int(optimizer.state.step)
num_train_steps = config.train.total_steps
# Replicate optimizer.
optimizer = flax.jax_utils.replicate(optimizer)
# Begin training loop.
dataset_iter_raw = iter(dataset)
dataset_iter = adapter.preprocess(dataset_iter_raw)
summary_freq = config.logging.summary_freq
metrics_all = []
tick = time.time()
for step, example in zip(range(start_step, num_train_steps), dataset_iter):
train_inputs = adapter.get_train_inputs(example)
optimizer, metrics, dropout_rngs, logits, state = train_step(
optimizer, train_inputs, dropout_rngs)
metrics_all.append(metrics)
# Save a Checkpoint
if ((step % config.logging.save_freq == 0 and step > 0)
or step == num_train_steps - 1):
if jax.host_id() == 0 and config.logging.save_freq:
# Save unreplicated optimizer + model state.
checkpoint_utils.save_checkpoint(
checkpoint_dir, jax_utils.unreplicate(optimizer), step)
# Periodic metric handling.
if summary_freq and step % summary_freq == 0 and step > 0:
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, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
summary['perplexity'] = jnp.clip(jnp.exp(summary['loss']),
a_max=1.0e4)
logging.info('train step: %d, loss: %.4f', step, summary['loss'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = summary_freq / (tock - tick)
examples_per_sec = denominator / (tock - tick)
tick = tock
summary_writer.scalar('per-second/steps', steps_per_sec, step)
summary_writer.scalar('per-second/examples', examples_per_sec,
step)
for key, val in summary.items():
summary_writer.scalar(key, val, step)
adapter.write_summaries(example, logits, summary_writer, info,
step, state)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.jax_backend_target:
jax.config.FLAGS.jax_xla_backend = "tpu_driver"
jax.config.FLAGS.jax_backend_target = FLAGS.jax_backend_target
# This seems to be necessary even when importing TF2?
tf.enable_v2_behavior()
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.host_id() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'train'))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'eval'))
if FLAGS.batch_size % n_devices:
raise ValueError(
'Batch size must be divisible by the number of devices')
vocab_path = FLAGS.vocab_path
if vocab_path is None:
vocab_path = os.path.join(FLAGS.model_dir, 'sentencepiece_model')
# Load Dataset
logging.info('Initializing dataset.')
train_ds, eval_ds, predict_ds, encoder = input_pipeline.get_wmt_datasets(
n_devices=n_devices,
dataset_name=FLAGS.dataset_name,
eval_dataset_name=FLAGS.eval_dataset_name,
shard_idx=jax.host_id(),
shard_count=jax.host_count(),
data_dir=FLAGS.data_dir,
vocab_path=vocab_path,
batch_size=FLAGS.batch_size,
max_length=FLAGS.max_target_length,
max_eval_length=FLAGS.max_eval_target_length)
train_iter = iter(train_ds)
vocab_size = int(encoder.vocab_size())
eos_token = 2 # Default Sentencepiece EOS token.
def decode_tokens(toks):
valid_toks = toks[:np.argmax(toks == eos_token) + 1].astype(np.int32)
return encoder.detokenize(valid_toks).numpy().decode('utf-8')
logging.info('Initializing model, optimizer, and step functions.')
# Build Model and Optimizer
transformer_kwargs = {
'vocab_size': vocab_size,
'output_vocab_size': vocab_size,
'emb_dim': 1024,
'num_heads': 16,
'num_layers': 6,
'qkv_dim': 1024,
'mlp_dim': 4096,
'max_len': max(FLAGS.max_target_length, FLAGS.max_eval_target_length),
'share_embeddings': FLAGS.share_embeddings,
'logits_via_embedding': FLAGS.logits_via_embedding,
}
start_step = 0
rng = random.PRNGKey(FLAGS.random_seed)
rng, init_rng = random.split(rng)
input_shape = (FLAGS.batch_size, FLAGS.max_target_length)
target_shape = (FLAGS.batch_size, FLAGS.max_target_length)
model, cache_def = create_model(init_rng, input_shape, target_shape,
transformer_kwargs)
optimizer = create_optimizer(model, FLAGS.learning_rate,
FLAGS.weight_decay)
# We access model only from optimizer below via optimizer.target.
del model
if FLAGS.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(FLAGS.model_dir, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=FLAGS.learning_rate,
warmup_steps=FLAGS.warmup_steps)
p_train_step = jax.pmap(functools.partial(
train_step,
learning_rate_fn=learning_rate_fn,
label_smoothing=FLAGS.label_smoothing,
use_bfloat16=FLAGS.use_bfloat16),
axis_name='batch')
p_eval_step = jax.pmap(functools.partial(
eval_step,
label_smoothing=FLAGS.label_smoothing,
use_bfloat16=FLAGS.use_bfloat16),
axis_name='batch')
p_pred_step = jax.pmap(
functools.partial(predict_step, use_bfloat16=FLAGS.use_bfloat16),
axis_name='batch',
static_broadcasted_argnums=(3,
4)) # eos token, max_length are constant
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, n_devices)
logging.info('Starting training loop.')
metrics_all = []
t_loop_start = time.time()
for step, batch in zip(range(start_step, FLAGS.num_train_steps),
train_iter):
# Shard data to devices and do a training step.
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, metrics, dropout_rngs = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
# Save a checkpoint on one host after every checkpoint_freq steps.
if (FLAGS.save_checkpoints and step % FLAGS.checkpoint_freq == 0
and step > 0 and jax.host_id() == 0):
checkpoints.save_checkpoint(FLAGS.model_dir,
jax_utils.unreplicate(optimizer), step)
# Periodic metric handling.
if step % FLAGS.eval_frequency != 0:
continue
logging.info('Gathering training metrics.')
# 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, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
steps_per_eval = FLAGS.eval_frequency if step != 0 else 1
steps_per_sec = steps_per_eval / (time.time() - t_loop_start)
t_loop_start = time.time()
if jax.host_id() == 0:
train_summary_writer.scalar('steps per second', steps_per_sec,
step)
for key, val in summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
metrics_all = []
logging.info('train in step: %d, loss: %.4f', step, summary['loss'])
# Eval Metrics
logging.info('Gathering evaluation metrics.')
t_eval_start = time.time()
eval_metrics = []
eval_iter = iter(eval_ds)
for _, eval_batch in zip(range(FLAGS.num_eval_steps), eval_iter):
eval_batch = jax.tree_map(lambda x: x._numpy(), eval_batch) # pylint: disable=protected-access
eval_batch = common_utils.shard(eval_batch)
metrics = p_eval_step(optimizer.target, eval_batch)
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:
for key, val in eval_summary.items():
eval_summary_writer.scalar(key, val, step)
eval_summary_writer.flush()
logging.info('eval in step: %d, loss: %.4f', step,
eval_summary['loss'])
logging.info('eval time: %.4f s step %d',
time.time() - t_eval_start, step)
# Translation and BLEU Score.
logging.info('Translating evaluation dataset.')
t_inference_start = time.time()
predict_iter = iter(predict_ds)
sources, references, predictions = [], [], []
for _, pred_batch in enumerate(predict_iter):
pred_batch = jax.tree_map(lambda x: x._numpy(), pred_batch) # pylint: disable=protected-access
# 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)
pred_batch = jax.tree_map(
lambda x: pad_examples(x, padded_size), pred_batch) # pylint: disable=cell-var-from-loop
pred_batch = common_utils.shard(pred_batch)
per_device_batchsize = pred_batch['inputs'].shape[1]
cache_dtype = jnp.bfloat16 if FLAGS.use_bfloat16 else jnp.float32
cache = jax_utils.replicate(
cache_def.initialize_cache(
(per_device_batchsize, FLAGS.max_predict_length),
dtype=cache_dtype))
predicted = p_pred_step(pred_batch['inputs'], optimizer.target,
cache, eos_token, FLAGS.max_predict_length)
predicted = tohost(predicted)
inputs = tohost(pred_batch['inputs'])
targets = tohost(pred_batch['targets'])
# Iterate through non-padding examples of batch.
for i, s in enumerate(predicted[:cur_pred_batch_size]):
sources.append(decode_tokens(inputs[i]))
references.append(decode_tokens(targets[i]))
predictions.append(decode_tokens(s))
logging.info('Translation: %d predictions %d references %d sources.',
len(predictions), len(references), len(sources))
logging.info('Translation time: %.4f s step %d.',
time.time() - t_inference_start, step)
# Calculate BLEU score for translated eval corpus against reference.
bleu_matches = bleu.bleu_partial(references, predictions)
all_bleu_matches = per_host_sum_pmap(bleu_matches)
bleu_score = bleu.complete_bleu(*all_bleu_matches)
# Save translation samples for tensorboard.
exemplars = ''
for n in np.random.choice(np.arange(len(predictions)), 8):
exemplars += f'{sources[n]}\n\n{references[n]}\n\n{predictions[n]}\n\n'
if jax.host_id() == 0:
eval_summary_writer.scalar('bleu', bleu_score, step)
eval_summary_writer.text('samples', exemplars, step)
eval_summary_writer.flush()
logging.info('Translation BLEU Score %.4f', bleu_score)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_t5_mlm",
model_args,
data_args,
framework="flax")
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome.")
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
level=logging.INFO,
datefmt="[%X]",
)
# Log on each process the small summary:
logger = logging.getLogger(__name__)
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Handle the repository creation
if training_args.push_to_hub:
if training_args.hub_model_id is None:
repo_name = get_full_repo_name(Path(
training_args.output_dir).absolute().name,
token=training_args.hub_token)
else:
repo_name = training_args.hub_model_id
repo = Repository(training_args.output_dir, clone_from=repo_name)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
extension,
data_files=data_files,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
use_auth_token=True if model_args.use_auth_token else None,
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.config_name:
config = T5Config.from_pretrained(
model_args.config_name,
cache_dir=model_args.cache_dir,
vocab_size=len(tokenizer),
use_auth_token=True if model_args.use_auth_token else None,
)
elif model_args.model_name_or_path:
config = T5Config.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning(
"You are instantiating a new config instance from scratch.")
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
# Since we make sure that all sequences are of the same length, no attention_mask is needed.
def tokenize_function(examples):
return tokenizer(examples[text_column_name],
return_attention_mask=False)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
# T5-like span masked language modeling will fuse consecutively masked tokens to a single sentinel token.
# To ensure that the input length is `max_seq_length`, we need to increase the maximum length
# according to `mlm_probability` and `mean_noise_span_length`. We can also define the label length accordingly.
expanded_inputs_length, targets_length = compute_input_and_target_lengths(
inputs_length=max_seq_length,
noise_density=data_args.mlm_probability,
mean_noise_span_length=data_args.mean_noise_span_length,
)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: list(chain(*examples[k]))
for k in examples.keys()
}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= expanded_inputs_length:
total_length = (total_length //
expanded_inputs_length) * expanded_inputs_length
# Split by chunks of max_len.
result = {
k: [
t[i:i + expanded_inputs_length]
for i in range(0, total_length, expanded_inputs_length)
]
for k, t in concatenated_examples.items()
}
return result
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
# might be slower to preprocess.
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
# Enable tensorboard only on the master node
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(
log_dir=Path(training_args.output_dir))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable.")
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
if model_args.model_name_or_path:
model = FlaxT5ForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
use_auth_token=True if model_args.use_auth_token else None,
)
else:
config.vocab_size = len(tokenizer)
model = FlaxT5ForConditionalGeneration(
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
)
# Data collator
# This one will take care of randomly masking the tokens.
data_collator = FlaxDataCollatorForT5MLM(
tokenizer=tokenizer,
noise_density=data_args.mlm_probability,
mean_noise_span_length=data_args.mean_noise_span_length,
input_length=max_seq_length,
target_length=targets_length,
pad_token_id=model.config.pad_token_id,
decoder_start_token_id=model.config.decoder_start_token_id,
)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(
training_args.per_device_train_batch_size) * jax.device_count()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = per_device_eval_batch_size * jax.device_count()
num_train_steps = len(
tokenized_datasets["train"]) // train_batch_size * num_epochs
num_of_hosts = jax.process_count()
current_host_idx = jax.process_index()
# Create learning rate schedule
warmup_fn = optax.linear_schedule(
init_value=0.0,
end_value=training_args.learning_rate,
transition_steps=training_args.warmup_steps)
decay_fn = optax.linear_schedule(
init_value=training_args.learning_rate,
end_value=0,
transition_steps=num_train_steps - training_args.warmup_steps,
)
linear_decay_lr_schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn],
boundaries=[training_args.warmup_steps])
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
# find out all LayerNorm parameters
layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
layer_norm_named_params = set([
layer[-2:] for layer_norm_name in layer_norm_candidates
for layer in flat_params.keys()
if layer_norm_name in "".join(layer).lower()
])
flat_mask = {
path: (path[-1] != "bias"
and path[-2:] not in layer_norm_named_params)
for path in flat_params
}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
if training_args.adafactor:
# We use the default parameters here to initialize adafactor,
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
optimizer = optax.adafactor(
learning_rate=linear_decay_lr_schedule_fn, )
else:
optimizer = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# Setup train state
state = train_state.TrainState.create(apply_fn=model.__call__,
params=model.params,
tx=optimizer)
# Define gradient update step fn
def train_step(state, batch, dropout_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def loss_fn(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch,
params=params,
dropout_rng=dropout_rng,
train=True)[0]
# compute loss
loss = optax.softmax_cross_entropy(
logits, onehot(labels, logits.shape[-1])).mean()
return loss
grad_fn = jax.value_and_grad(loss_fn)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = jax.lax.pmean(
{
"loss": loss,
"learning_rate": linear_decay_lr_schedule_fn(state.step)
},
axis_name="batch")
return new_state, metrics, new_dropout_rng
# Create parallel version of the train step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0, ))
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
# compute loss
loss = optax.softmax_cross_entropy(logits,
onehot(labels, logits.shape[-1]))
# compute accuracy
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels)
# summarize metrics
metrics = {"loss": loss.mean(), "accuracy": accuracy.mean()}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0, ))
# Replicate the train state on each device
state = jax_utils.replicate(state)
train_time = 0
epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
train_metrics = []
# Create sampling rng
rng, input_rng = jax.random.split(rng)
# Generate an epoch by shuffling sampling indices from the train dataset
num_train_samples = len(tokenized_datasets["train"])
# Avoid using jax.numpy here in case of TPU training
train_samples_idx = np.random.permutation(np.arange(num_train_samples))
train_batch_idx = generate_batch_splits(train_samples_idx,
train_batch_size)
# Gather the indexes for creating the batch and do a training step
for step, batch_idx in enumerate(
tqdm(train_batch_idx, desc="Training...", position=1)):
samples = [
tokenized_datasets["train"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples)
local_host_model_inputs = {
key: np.split(model_inputs.data[key], num_of_hosts,
axis=0)[current_host_idx]
for key, value in model_inputs.data.items()
}
# Model forward
model_inputs = shard(local_host_model_inputs)
state, train_metric, dropout_rngs = p_train_step(
state, model_inputs, dropout_rngs)
train_metrics.append(train_metric)
cur_step = epoch * (num_train_samples // train_batch_size) + step
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
# Save metrics
train_metric = jax_utils.unreplicate(train_metric)
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics,
train_time, cur_step)
epochs.write(
f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
f" {train_metric['learning_rate'].mean()})")
train_metrics = []
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
# ======================== Evaluating ==============================
num_eval_samples = len(tokenized_datasets["validation"])
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size,
drop_last=False)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...",
position=2)):
samples = [
tokenized_datasets["validation"][int(idx)]
for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params,
model_inputs.data,
min_device_batch=per_device_eval_batch_size)
eval_metrics.append(metrics)
# get eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
# Update progress bar
epochs.write(
f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
)
# Save metrics
if has_tensorboard and jax.process_index() == 0:
write_eval_metric(summary_writer, eval_metrics, cur_step)
if cur_step % training_args.save_steps == 0 and cur_step > 0:
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(
jax.tree_map(lambda x: x[0], state.params))
model.save_pretrained(training_args.output_dir,
params=params)
tokenizer.save_pretrained(training_args.output_dir)
if training_args.push_to_hub:
repo.push_to_hub(
commit_message=
f"Saving weights and logs of step {cur_step}",
blocking=False)
# Eval after training
if training_args.do_eval:
num_eval_samples = len(tokenized_datasets["validation"])
# Avoid using jax.numpy here in case of TPU training
eval_samples_idx = np.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size,
drop_last=False)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [
tokenized_datasets["validation"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params,
model_inputs.data,
min_device_batch=per_device_eval_batch_size)
eval_metrics.append(metrics)
# get eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(),
eval_metrics)
if jax.process_index() == 0:
eval_metrics = {
f"eval_{metric_name}": value
for metric_name, value in eval_metrics.items()
}
path = os.path.join(training_args.output_dir, "eval_results.json")
with open(path, "w") as f:
json.dump(eval_metrics, f, indent=4, sort_keys=True)
def train(config, workdir):
"""Runs a training loop.
Args:
config: Configuration to use.
workdir: Working directory for checkpoints and TF summaries. If this
contains checkpoint training will be resumed from the latest checkpoint.
"""
# Create directories for experimental logs
tf.io.gfile.makedirs(workdir)
sample_dir = os.path.join(workdir, "samples")
tf.io.gfile.makedirs(sample_dir)
rng = jax.random.PRNGKey(config.seed)
tb_dir = os.path.join(workdir, "tensorboard")
tf.io.gfile.makedirs(tb_dir)
if jax.host_id() == 0:
writer = tensorboard.SummaryWriter(tb_dir)
# Initialize model.
rng, model_rng = jax.random.split(rng)
model_name = config.model.name
ncsn_def = mutils.get_model(model_name).partial(config=config)
rng, run_rng = jax.random.split(rng)
# Whether the generative model is conditioned on class labels
class_conditional = "conditional" in config.training.loss.lower()
with nn.stateful() as init_model_state:
with nn.stochastic(run_rng):
input_shape = (jax.local_device_count(), config.data.image_size,
config.data.image_size, 3)
input_list = [(input_shape, jnp.float32), (input_shape[:1], jnp.int32)]
if class_conditional:
input_list.append(input_list[-1])
_, initial_params = ncsn_def.init_by_shape(
model_rng, input_list, train=True)
ncsn = nn.Model(ncsn_def, initial_params)
optimizer = losses.get_optimizer(config).create(ncsn)
state = mutils.State(step=0, optimizer=optimizer, lr=config.optim.lr,
model_state=init_model_state,
ema_rate=config.model.ema_rate,
params_ema=initial_params,
rng=rng) # pytype: disable=wrong-keyword-args
del ncsn, init_model_state # Do not keep a copy of the initial model.
# Create checkpoints directory and the initial checkpoint
checkpoint_dir = os.path.join(workdir, "checkpoints")
ckpt = utils.Checkpoint(
checkpoint_dir,
max_to_keep=None)
ckpt.restore_or_initialize(state)
# Save intermediate checkpoints to resume training automatically
checkpoint_meta_dir = os.path.join(workdir, "checkpoints-meta")
ckpt_meta = utils.Checkpoint(
checkpoint_meta_dir,
max_to_keep=1)
state = ckpt_meta.restore_or_initialize(state)
initial_step = int(state.step)
rng = state.rng
# Build input pipeline.
rng, ds_rng = jax.random.split(rng)
train_ds, eval_ds, _ = datasets.get_dataset(ds_rng, config)
train_iter = iter(train_ds) # pytype: disable=wrong-arg-types
eval_iter = iter(eval_ds) # pytype: disable=wrong-arg-types
scaler = datasets.get_data_scaler(config) # data normalizer
inverse_scaler = datasets.get_data_inverse_scaler(config)
# Distribute training.
optimize_fn = losses.optimization_manager(config)
if config.training.loss.lower() == "ddpm":
# Use score matching loss with DDPM-type perturbation.
ddpm_params = mutils.get_ddpm_params()
train_step = functools.partial(losses.ddpm_loss, ddpm_params=ddpm_params,
train=True, optimize_fn=optimize_fn)
eval_step = functools.partial(losses.ddpm_loss, ddpm_params=ddpm_params,
train=False)
else:
# Use score matching loss with NCSN-type perturbation.
sigmas = mutils.get_sigmas(config)
# Whether to use a continuous distribution of noise levels
continuous = "continuous" in config.training.loss.lower()
train_step = functools.partial(
losses.ncsn_loss,
sigmas=sigmas,
class_conditional=class_conditional,
continuous=continuous,
train=True,
optimize_fn=optimize_fn,
anneal_power=config.training.anneal_power)
eval_step = functools.partial(
losses.ncsn_loss,
sigmas=sigmas,
class_conditional=class_conditional,
continuous=continuous,
train=False,
anneal_power=config.training.anneal_power)
p_train_step = jax.pmap(train_step, axis_name="batch")
p_eval_step = jax.pmap(eval_step, axis_name="batch")
state = flax_utils.replicate(state)
num_train_steps = config.training.n_iters
logging.info("Starting training loop at step %d.", initial_step)
rng = jax.random.fold_in(rng, jax.host_id())
for step in range(initial_step, num_train_steps + 1):
# `step` is a Python integer. `state.step` is JAX integer on the GPU/TPU
# devices.
# Convert data to JAX arrays. Use ._numpy() to avoid copy.
batch = jax.tree_map(lambda x: scaler(x._numpy()), next(train_iter)) # pylint: disable=protected-access
rng, *next_rng = jax.random.split(rng, num=jax.local_device_count() + 1)
next_rng = jnp.asarray(next_rng)
loss, state = p_train_step(next_rng, state, batch)
loss = flax.jax_utils.unreplicate(loss)
# Quick indication that training is happening.
logging.log_first_n(logging.INFO, "Finished training step %d.", 5, step)
if jax.host_id() == 0 and step % 50 == 0:
logging.info("step: %d, training_loss: %.5e", step, loss)
writer.scalar("training_loss", loss, step)
# Save a temporary checkpoint to resume training after pre-emption.
if step % config.training.snapshot_freq_for_preemption == 0 and jax.host_id(
) == 0:
saved_state = flax_utils.unreplicate(state)
saved_state = saved_state.replace(rng=rng)
ckpt_meta.save(saved_state)
# Report the loss on an evaluation dataset.
if step % 100 == 0:
rng, *next_rng = jax.random.split(rng, num=jax.local_device_count() + 1)
next_rng = jnp.asarray(next_rng)
eval_batch = jax.tree_map(lambda x: scaler(x._numpy()), next(eval_iter)) # pylint: disable=protected-access
eval_loss, _ = p_eval_step(next_rng, state, eval_batch)
eval_loss = flax.jax_utils.unreplicate(eval_loss)
if jax.host_id() == 0:
logging.info("step: %d, eval_loss: %.5e", step, eval_loss)
writer.scalar("eval_loss", eval_loss, step)
# Save a checkpoint periodically and generate samples.
if (step +
1) % config.training.snapshot_freq == 0 or step == num_train_steps:
# Save the checkpoint.
if jax.host_id() == 0:
saved_state = flax_utils.unreplicate(state)
saved_state = saved_state.replace(rng=rng)
ckpt.save(saved_state)
# Generate and save samples
if config.training.snapshot_sampling:
rng, sample_rng = jax.random.split(rng)
init_shape = tuple(train_ds.element_spec["image"].shape)
samples = sampling.get_samples(sample_rng,
config,
flax_utils.unreplicate(state),
init_shape,
scaler,
inverse_scaler,
class_conditional=class_conditional)
this_sample_dir = os.path.join(
sample_dir, "iter_{}_host_{}".format(step, jax.host_id()))
tf.io.gfile.makedirs(this_sample_dir)
if config.sampling.final_only: # Do not save intermediate samples
sample = samples[-1]
image_grid = sample.reshape((-1, *sample.shape[2:]))
nrow = int(np.sqrt(image_grid.shape[0]))
sample = np.clip(sample * 255, 0, 255).astype(np.uint8)
with tf.io.gfile.GFile(
os.path.join(this_sample_dir, "sample.np"), "wb") as fout:
np.save(fout, sample)
with tf.io.gfile.GFile(
os.path.join(this_sample_dir, "sample.png"), "wb") as fout:
utils.save_image(image_grid, fout, nrow=nrow, padding=2)
else: # Save all intermediate samples produced during sampling.
for i, sample in enumerate(samples):
image_grid = sample.reshape((-1, *sample.shape[2:]))
nrow = int(np.sqrt(image_grid.shape[0]))
sample = np.clip(sample * 255, 0, 255).astype(np.uint8)
with tf.io.gfile.GFile(
os.path.join(this_sample_dir, "sample_{}.np".format(i)),
"wb") as fout:
np.save(fout, sample)
with tf.io.gfile.GFile(
os.path.join(this_sample_dir, "sample_{}.png".format(i)),
"wb") as fout:
utils.save_image(image_grid, fout, nrow=nrow, padding=2)
# Gather the indexes for creating the batch and do a training step
for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
model_inputs = data_collator(samples, pad_to_multiple_of=16)
# Model forward
model_inputs = shard(model_inputs.data)
state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
train_metrics.append(train_metric)
cur_step = epoch * (num_train_samples // train_batch_size) + step
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
# Save metrics
train_metric = jax_utils.unreplicate(train_metric)
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
epochs.write(
f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
)
train_metrics = []
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
# ======================== Evaluating ==============================
num_eval_samples = len(tokenized_datasets["validation"])
eval_samples_idx = jnp.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
def main():
args = parse_args()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# Handle the repository creation
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).absolute().name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
# label if at least two columns are provided.
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if args.task_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset("glue", args.task_name)
else:
# Loading the dataset from local csv or json file.
data_files = {}
if args.train_file is not None:
data_files["train"] = args.train_file
if args.validation_file is not None:
data_files["validation"] = args.validation_file
extension = (args.train_file if args.train_file is not None else args.valid_file).split(".")[-1]
raw_datasets = load_dataset(extension, data_files=data_files)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
if args.task_name is not None:
is_regression = args.task_name == "stsb"
if not is_regression:
label_list = raw_datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
else:
# Trying to have good defaults here, don't hesitate to tweak to your needs.
is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
if is_regression:
num_labels = 1
else:
# A useful fast method:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
label_list = raw_datasets["train"].unique("label")
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
# Load pretrained model and tokenizer
config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
model = FlaxAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, config=config)
# Preprocessing the datasets
if args.task_name is not None:
sentence1_key, sentence2_key = task_to_keys[args.task_name]
else:
# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
sentence1_key, sentence2_key = "sentence1", "sentence2"
else:
if len(non_label_column_names) >= 2:
sentence1_key, sentence2_key = non_label_column_names[:2]
else:
sentence1_key, sentence2_key = non_label_column_names[0], None
# Some models have set the order of the labels to use, so let's make sure we do use it.
label_to_id = None
if (
model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
and args.task_name is not None
and not is_regression
):
# Some have all caps in their config, some don't.
label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
logger.info(
f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
"Using it!"
)
label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
else:
logger.warning(
"Your model seems to have been trained with labels, but they don't match the dataset: ",
f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
"\nIgnoring the model labels as a result.",
)
elif args.task_name is None:
label_to_id = {v: i for i, v in enumerate(label_list)}
def preprocess_function(examples):
# Tokenize the texts
texts = (
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(*texts, padding="max_length", max_length=args.max_length, truncation=True)
if "label" in examples:
if label_to_id is not None:
# Map labels to IDs (not necessary for GLUE tasks)
result["labels"] = [label_to_id[l] for l in examples["label"]]
else:
# In all cases, rename the column to labels because the model will expect that.
result["labels"] = examples["label"]
return result
processed_datasets = raw_datasets.map(
preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
)
train_dataset = processed_datasets["train"]
eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"]
# Log a few random samples from the training set:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
# Define a summary writer
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(args.output_dir)
summary_writer.hparams(vars(args))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable."
)
def write_metric(train_metrics, eval_metrics, train_time, step):
summary_writer.scalar("train_time", train_time, step)
train_metrics = get_metrics(train_metrics)
for key, vals in train_metrics.items():
tag = f"train_{key}"
for i, val in enumerate(vals):
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
for metric_name, value in eval_metrics.items():
summary_writer.scalar(f"eval_{metric_name}", value, step)
num_epochs = int(args.num_train_epochs)
rng = jax.random.PRNGKey(args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
train_batch_size = args.per_device_train_batch_size * jax.local_device_count()
eval_batch_size = args.per_device_eval_batch_size * jax.local_device_count()
learning_rate_fn = create_learning_rate_fn(
len(train_dataset), train_batch_size, args.num_train_epochs, args.num_warmup_steps, args.learning_rate
)
state = create_train_state(
model, learning_rate_fn, is_regression, num_labels=num_labels, weight_decay=args.weight_decay
)
# define step functions
def train_step(
state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
) -> Tuple[train_state.TrainState, float]:
"""Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
targets = batch.pop("labels")
def loss_fn(params):
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss = state.loss_fn(logits, targets)
return loss
grad_fn = jax.value_and_grad(loss_fn)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
return new_state, metrics, new_dropout_rng
p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
def eval_step(state, batch):
logits = state.apply_fn(**batch, params=state.params, train=False)[0]
return state.logits_fn(logits)
p_eval_step = jax.pmap(eval_step, axis_name="batch")
if args.task_name is not None:
metric = load_metric("glue", args.task_name)
else:
metric = load_metric("accuracy")
logger.info(f"===== Starting training ({num_epochs} epochs) =====")
train_time = 0
# make sure weights are replicated on each device
state = replicate(state)
for epoch in range(1, num_epochs + 1):
logger.info(f"Epoch {epoch}")
logger.info(" Training...")
train_start = time.time()
train_metrics = []
rng, input_rng = jax.random.split(rng)
# train
for batch in glue_train_data_collator(input_rng, train_dataset, train_batch_size):
state, metrics, dropout_rngs = p_train_step(state, batch, dropout_rngs)
train_metrics.append(metrics)
train_time += time.time() - train_start
logger.info(f" Done! Training metrics: {unreplicate(metrics)}")
logger.info(" Evaluating...")
# evaluate
for batch in glue_eval_data_collator(eval_dataset, eval_batch_size):
labels = batch.pop("labels")
predictions = p_eval_step(state, batch)
metric.add_batch(predictions=chain(*predictions), references=chain(*labels))
# evaluate also on leftover examples (not divisible by batch_size)
num_leftover_samples = len(eval_dataset) % eval_batch_size
# make sure leftover batch is evaluated on one device
if num_leftover_samples > 0 and jax.process_index() == 0:
# take leftover samples
batch = eval_dataset[-num_leftover_samples:]
batch = {k: jnp.array(v) for k, v in batch.items()}
labels = batch.pop("labels")
predictions = eval_step(unreplicate(state), batch)
metric.add_batch(predictions=predictions, references=labels)
eval_metric = metric.compute()
logger.info(f" Done! Eval metrics: {eval_metric}")
cur_step = epoch * (len(train_dataset) // train_batch_size)
# Save metrics
if has_tensorboard and jax.process_index() == 0:
write_metric(train_metrics, eval_metric, train_time, cur_step)
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
model.save_pretrained(args.output_dir, params=params)
tokenizer.save_pretrained(args.output_dir)
if args.push_to_hub:
repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
# save the eval metrics in json
if jax.process_index() == 0:
eval_metric = {f"eval_{metric_name}": value for metric_name, value in eval_metric.items()}
path = os.path.join(args.output_dir, "eval_results.json")
with open(path, "w") as f:
json.dump(eval_metric, f, indent=4, sort_keys=True)
def train_loop(config, dropout_rngs, eval_ds, eval_freq, num_eval_steps,
num_train_steps, optimizer, state, p_eval_step, p_train_step,
start_step, train_iter, summary_writer):
"""Training loop.
Args:
config: experiment config.
dropout_rngs: float array; Jax PRNG key.
eval_ds: tf.dataset; Evaluation dataset.
eval_freq: int; Evaluation frequency;
num_eval_steps: int; Number of evaluation steps.
num_train_steps: int; Number of training steps.
optimizer: flax optimizer.
state: model state, e.g. batch statistics.
p_eval_step: fn; Pmapped evaluation step function.
p_train_step: fn; Pmapped train step function.
start_step: int; global training step.
train_iter: iter(tf.dataset); Training data iterator.
summary_writer: tensorflow summary writer.
Returns:
optimizer, global training step
"""
metrics_all = []
tick = time.time()
logging.info('Starting training')
logging.info('====================')
step = 0
for step, batch in zip(range(start_step, num_train_steps), train_iter):
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, state, metrics, dropout_rngs = p_train_step(
optimizer, state, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
# Save a Checkpoint
if ((step % config.checkpoint_freq == 0 and step > 0)
or step == num_train_steps - 1):
if jax.host_id() == 0 and config.save_checkpoints:
# Save unreplicated optimizer + model state.
checkpoints.save_checkpoint(FLAGS.model_dir,
(jax_utils.unreplicate(optimizer),
jax_utils.unreplicate(state)),
step)
# Periodic metric handling.
if step % eval_freq == 0 and step > 0:
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, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
logging.info('train in step: %d, loss: %.4f, acc: %.4f', step,
summary['loss'], summary['accuracy'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = eval_freq / (tock - tick)
tick = tock
summary_writer.scalar('examples_per_second',
steps_per_sec * config.batch_size, step)
for key, val in summary.items():
summary_writer.scalar(f'train_{key}', val, step)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []
# Eval Metrics
eval_metrics = []
eval_iter = iter(eval_ds)
if num_eval_steps == -1:
num_iter = itertools.repeat(1)
else:
num_iter = range(num_eval_steps)
for _, eval_batch in zip(num_iter, eval_iter):
# pylint: disable=protected-access
eval_batch = common_utils.shard(
jax.tree_map(lambda x: x._numpy(), eval_batch))
# pylint: enable=protected-access
metrics = p_eval_step(optimizer.target, state, eval_batch)
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)
logging.info('eval in step: %d, loss: %.4f, acc: %.4f', step,
eval_summary['loss'], eval_summary['accuracy'])
if jax.host_id() == 0:
for key, val in eval_summary.items():
summary_writer.scalar(f'val_{key}', val, step)
summary_writer.flush()
return optimizer, state, step
def main(_):
tf.enable_v2_behavior()
tf.random.set_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
# BOS special attention only makes sense if we are using relative attention
# and it's not the baseline.
if FLAGS.bos_special_attention and (not FLAGS.use_relative_attention
or FLAGS.attention_mask_type
== 'baseline'):
raise ValueError(
"bos_special_attention doesn't work when use_relative_attention={} and "
'attention_mask_type={}'.format(FLAGS.use_relative_attention,
FLAGS.attention_mask_type))
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)
program_shape = (FLAGS.per_device_batch_size, FLAGS.max_program_length)
# Setup DSL
# ---------------------------------------------------------------------------
# Build token tables.
id_char_table = {i + 1: char for (i, char) in enumerate(dsl.CHARACTER)}
char_id_table = {char: id for id, char in id_char_table.items()}
id_token_table, token_id_table = dsl_tokens.build_token_tables()
io_vocab_size = len(char_id_table) + 1 # For padding.
program_vocab_size = len(token_id_table) + 1
bos_token = token_id_table[dsl.BOS]
eos_token = token_id_table[dsl.EOS]
# Parse io and program token sequences (for eval).
def decode_io(inputs, outputs):
"""Decode io examples tokens."""
def decode_str(s):
"""Decode string tokens."""
return ''.join([id_char_table[c_id] for c_id in s if c_id > 0])
inps, outs = [], []
for inp, out in zip(inputs, outputs):
inps.append(decode_str(inp))
outs.append(decode_str(out))
return inps, outs
def decode_program(program):
"""Decode program tokens."""
program = program[:np.argmax(program == eos_token) + 1].astype(
np.int32)
program = program[program != bos_token]
try:
return dsl.decode_program(program.tolist(), id_token_table)
except: # pylint: disable=bare-except
return None # Program does not compile.
# 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 = (io_shape[1:], io_shape[1:], program_shape[1:])
logging.info('padded_shapes: %s', padded_shapes)
dataset = input_pipeline.create_dataset_from_tf_record(
FLAGS.dataset_filepattern, token_id_table, char_id_table)
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 = (predict_io_shape[1:], predict_io_shape[1:],
program_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 = input_pipeline.create_dataset_from_tf_record(
FLAGS.test_dataset_filepattern, token_id_table, char_id_table)
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
# ---------------------------------------------------------------------------
default_config = base_models.TransformerConfig(
vocab_size=io_vocab_size, output_vocab_size=program_vocab_size)
base_config = base_models.TransformerConfig(
vocab_size=io_vocab_size,
output_vocab_size=program_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_program_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_token,
num_input_relative_position_buckets=FLAGS.num_position_buckets,
max_input_distance=min(FLAGS.max_distance,
default_config.max_input_distance),
num_output_relative_position_buckets=FLAGS.num_position_buckets,
max_output_distance=min(FLAGS.max_distance,
default_config.max_output_distance),
num_input_cross_output_relative_position_buckets=(
FLAGS.num_position_buckets),
max_input_cross_output_distance=min(
FLAGS.max_distance,
default_config.max_input_cross_output_distance),
num_program_relative_position_buckets=FLAGS.num_position_buckets,
max_program_distance=min(FLAGS.max_distance,
default_config.max_program_distance),
num_program_cross_embed_relative_position_buckets=(
FLAGS.num_position_buckets),
max_program_cross_embed_distance=min(
FLAGS.max_distance,
default_config.max_program_cross_embed_distance),
bidirectional_program_attention=FLAGS.bidirectional_program_attention)
train_config = models.DecomposeAttentionTransformerConfig(
base_config=base_config,
attention_mask_type=FLAGS.attention_mask_type,
bos_special_attention=FLAGS.bos_special_attention)
eval_config = models.DecomposeAttentionTransformerConfig(
base_config=base_config.replace(deterministic=True),
attention_mask_type=FLAGS.attention_mask_type,
bos_special_attention=FLAGS.bos_special_attention)
predict_config = models.DecomposeAttentionTransformerConfig(
base_config=base_config.replace(
shift=False,
deterministic=True,
decode=not FLAGS.slow_decode,
max_len=max(FLAGS.max_characters, FLAGS.max_program_length,
FLAGS.predict_max_characters)),
attention_mask_type=FLAGS.attention_mask_type,
bos_special_attention=FLAGS.bos_special_attention)
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(program_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_token,
config=eval_config),
axis_name='batch')
p_init_cache = jax.pmap(functools.partial(
initialize_cache,
max_decode_len=FLAGS.max_program_length,
config=predict_config),
axis_name='batch')
p_pred_step = jax.pmap(functools.partial(
predict_step,
eos_token=eos_token,
max_decode_len=FLAGS.max_program_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, programs = common_utils.shard(next(train_iter))
optimizer, metrics, dropout_rng = p_train_step(optimizer,
inputs,
outputs,
programs,
dropout_rng=dropout_rng)
metrics_all.append(metrics)
is_last_step = step == FLAGS.num_train_steps - 1
# Save a Checkpoint
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)
# 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, programs = common_utils.shard(batches)
metrics = p_eval_step(optimizer.target, inputs, outputs,
programs)
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
pred_successes = collections.defaultdict(int)
pred_denominators = collections.defaultdict(int)
ios, targets, predictions, top_of_beams = [], [], [], []
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[0].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, programs = common_utils.shard(
pred_batch)
cache = (p_init_cache(inputs, outputs, programs)
if not FLAGS.slow_decode else None)
predicted = p_pred_step(optimizer.target, inputs,
outputs, cache, beam_size)
predicted = tohost(predicted)
inputs, outputs, programs = map(
tohost, (inputs, outputs, programs))
for i, beams in enumerate(predicted):
inps, outs = decode_io(inputs[i], outputs[i])
p, p_score = eval_predicted(
beams,
inps,
outs,
parse_beam_fn=decode_program)
# Split by length of program.
program = programs[i]
num_expressions = len(
decode_program(program).expressions)
pred_denominators[num_expressions] += 1
total_denominator += 1
if p_score >= len(inps):
pred_successes[num_expressions] += 1
total_successes += 1
ios.append(' ; '.join(map(str, zip(inps, outs))))
targets.append(
decode_program(programs[i]).to_string())
try:
predictions.append(p.to_string())
except: # pylint: disable=bare-except
predictions.append('Did not compile')
logging.info('ios: %s', ios[-1])
logging.info('target: %s', targets[-1])
beams_log = []
for beam in beams:
try:
beams_log.append(
decode_program(beam).to_string())
except: # pylint: disable=bare-except
beams_log.append('Did not compile')
logging.info('predicted beam: %s',
'\n'.join(beams_log))
top_of_beam = []
for index, beam in enumerate(beams[:-5:-1]):
try:
decoded_program = decode_program(
beam).to_string()
except: # pylint: disable=bare-except
decoded_program = 'Did not compile'
top_of_beam.append(
'index: {}, decoded: {}, tokens: {}'.
format(index, decoded_program, 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)))
all_pred_successes, all_pred_denominators = per_host_sum_pmap(
jax.tree_map(np.array,
(pred_successes, pred_denominators)))
# 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[n]}\n\n'
f'predicted: {predictions[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)
for length in sorted(all_pred_successes.keys()):
this_length_accuracy = (
100 * all_pred_successes[length] /
all_pred_denominators[length])
logging.info(
' accuracy for length %s: %s / %s = %.2f%%',
length, all_pred_successes[length],
all_pred_denominators[length],
this_length_accuracy)
summary_writer.scalar(
'{}-by-length/beam-size-{}-length-{}'.format(
predict_or_test, beam_size, length),
this_length_accuracy, step)
summary_writer.text(
'{}-samples-beam-{}'.format(
predict_or_test, beam_size),
'\n------\n'.join(message), step)
summary_writer.flush()
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.mkdir(FLAGS.save_dir)
hparam_str_dict = dict(seed=FLAGS.seed, lr=FLAGS.lr)
# Get hyperparmaters
if FLAGS.xm_parameters:
for key, value in json.loads(FLAGS.xm_parameters).items():
if key not in hparam_str_dict:
hparam_str_dict[key] = value
hparam_str = ','.join([
'%s=%s' % (k, str(hparam_str_dict[k]))
for k in sorted(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)
program_shape = (FLAGS.per_device_batch_size, FLAGS.max_program_length)
# Setup DSL
# ---------------------------------------------------------------------------
# Build token tables.
id_char_table = {i + 1: char for (i, char) in enumerate(dsl.CHARACTER)}
char_id_table = {char: id for id, char in id_char_table.items()}
id_token_table, token_id_table = dsl_tokens.build_token_tables()
io_vocab_size = len(char_id_table) + 1 # For padding.
program_vocab_size = len(token_id_table) + 1
bos_token = token_id_table[dsl.BOS]
eos_token = token_id_table[dsl.EOS]
def decode_io(inputs, outputs):
"""Decode io examples tokens."""
def decode_str(s):
"""Decode string tokens."""
return ''.join([id_char_table[c_id] for c_id in s if c_id > 0])
io_string = ''
inps, outs = [], []
for inp, out in zip(inputs, outputs):
inps.append(decode_str(inp))
outs.append(decode_str(out))
io_string += inps[-1] + ' < ' + outs[-1] + ' > '
return inps, outs, io_string[:-3] # Remove last separator.
def decode_program(program):
"""Decode program tokens."""
program = program[:np.argmax(program == eos_token) + 1].astype(
np.int32)
try:
p = dsl.decode_program(program, id_token_table)
return p, p.to_string()
except: # pylint: disable=bare-except
return None, '' # Program does not compile.
# Load Dataset
# ---------------------------------------------------------------------------
logging.info('Initializing dataset.')
if not FLAGS.dataset_filepattern:
raise ValueError('Must specify filepattern to dataset.')
# Training dataset.
dataset = input_pipeline.create_dataset_from_tf_record(
FLAGS.dataset_filepattern, token_id_table, char_id_table)
dataset = dataset.padded_batch(batch_size,
padded_shapes=(io_shape[1:], io_shape[1:],
program_shape[1:]),
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_ds = eval_ds.unbatch().padded_batch(
int(np.ceil(batch_size / 10)),
padded_shapes=(io_shape[1:], io_shape[1:], program_shape[1:]))
train_ds = dataset.skip(FLAGS.num_eval_steps).repeat()
train_iter = train_ds.as_numpy_iterator()
# Build Model and Optimizer
# ---------------------------------------------------------------------------
train_config = models.TransformerConfig(
vocab_size=io_vocab_size,
output_vocab_size=program_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_program_length),
use_relative_attention=FLAGS.use_relative_attention,
deterministic=False,
decode=False,
bos_token=bos_token)
eval_config = train_config.replace(deterministic=True)
predict_config = train_config.replace(shift=False,
deterministic=True,
decode=True)
rng = jax.random.PRNGKey(FLAGS.seed)
rng = jax.random.fold_in(rng, jax.host_id())
rng, init_rng = jax.random.split(rng)
m = models.ProgramTransformer(eval_config)
initial_variables = jax.jit(m.init)(init_rng,
jnp.ones(io_shape, jnp.float32),
jnp.ones(io_shape, jnp.float32),
jnp.ones(program_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)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = train_lib.create_learning_rate_scheduler(
base_learning_rate=FLAGS.lr)
p_train_step = jax.pmap(functools.partial(
train_lib.train_step,
learning_rate_fn=learning_rate_fn,
config=train_config),
axis_name='batch')
p_eval_step = jax.pmap(functools.partial(train_lib.eval_step,
config=eval_config),
axis_name='batch')
p_init_cache = jax.pmap(functools.partial(
train_lib.initialize_cache,
max_decode_len=FLAGS.max_program_length,
config=predict_config),
axis_name='batch')
p_pred_step = jax.pmap(functools.partial(train_lib.predict_step,
config=predict_config),
axis_name='batch',
static_broadcasted_argnums=(4, 5, 6))
# Main Train Loop
# ---------------------------------------------------------------------------
train_rngs = jax.random.split(rng, jax.local_device_count())
del rng
metrics_all = []
tick = time.time()
for step in range(start_step, FLAGS.num_train_steps):
inputs, outputs, programs = common_utils.shard(next(train_iter))
optimizer, metrics, train_rngs = p_train_step(optimizer,
inputs,
outputs,
programs,
train_rng=train_rngs)
metrics_all.append(metrics)
# Save a Checkpoint
if ((step % FLAGS.checkpoint_freq == 0 and step > 0)
or step == FLAGS.num_train_steps - 1):
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)
# Periodic metric handling.
if not step or step % FLAGS.log_freq != 0:
continue
logging.info('Gathering training metrics.')
# 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
logging.info('Gathering evaluation metrics.')
t_evaluation_start = time.time()
eval_metrics = []
for batches in eval_ds.as_numpy_iterator():
inputs, outputs, programs = common_utils.shard(batches)
metrics = p_eval_step(optimizer.target, inputs, outputs, programs)
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.
logging.info('Gathering beam search metrics.')
for beam_size in [10, 100]:
t_inference_start = time.time()
pred_acc = 0
pred_denominator = 0
ios, targets, predictions = [], [], []
for batches in predict_ds.as_numpy_iterator():
pred_batch = batches
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch[0].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: train_lib.pad_examples(x, padded_size),
pred_batch)
inputs, outputs, programs = common_utils.shard(pred_batch)
cache = p_init_cache(inputs, outputs, programs)
predicted = p_pred_step(optimizer.target, inputs, outputs,
cache, eos_token, programs.shape[-1],
beam_size)
predicted = train_lib.tohost(predicted)
inputs, outputs, programs = map(train_lib.tohost,
(inputs, outputs, programs))
pred_denominator += programs.shape[0]
for i, beams in enumerate(predicted):
inps, outs, io_string = decode_io(inputs[i], outputs[i])
p, p_score = train_lib.eval_predicted(
beams,
inps,
outs,
parse_beam_fn=lambda x: decode_program(x)[0])
if p_score >= len(inps):
pred_acc += 1
ios.append(io_string)
targets.append(decode_program(programs[i])[1])
predictions.append(p.to_string() if p else '')
all_pred_acc, all_pred_denominator = train_lib.per_host_sum_pmap(
jax.tree_map(np.array, (pred_acc, pred_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[n]}\n\n'
f'predicted: {predictions[n]}\n\n')
message.append(text)
# Write to tensorboard.
if jax.host_id() == 0:
logging.info(
'Prediction time (beam %d): %.4f s step %d, score %.4f.',
beam_size,
time.time() - t_inference_start, step,
all_pred_acc / all_pred_denominator)
summary_writer.scalar('predict/score-{}'.format(beam_size),
all_pred_acc / all_pred_denominator,
step)
summary_writer.text('samples-{}'.format(beam_size),
'\n------\n'.join(message), step)
summary_writer.flush()
