def eval(self):
"""Evaluates the model."""
if not self.predict_model:
self.predict_model = transformer.create_model(self.params, False)
self._load_weights_if_possible(
self.predict_model,
tf.train.latest_checkpoint(self.flags_obj.model_dir))
self.predict_model.summary()
def test_create_model_not_train(self):
model = transformer.create_model(self.params, False)
inputs, outputs = model.inputs, model.outputs
self.assertEqual(len(inputs), 1)
self.assertEqual(len(outputs), 2)
self.assertEqual(inputs[0].shape.as_list(), [None, None])
self.assertEqual(inputs[0].dtype, tf.int64)
self.assertEqual(outputs[0].shape.as_list(), [None, None])
self.assertEqual(outputs[0].dtype, tf.int32)
self.assertEqual(outputs[1].shape.as_list(), [None])
self.assertEqual(outputs[1].dtype, tf.float32)
def create_model(self, mode):
logging.info('use_reformer = {}'.format(self.flags_obj.use_reformer))
if self.flags_obj.use_reformer:
logging.info(
f'num_hashes, test_num_hashes = {self.params["num_hashes"]}, {self.params["test_num_hashes"]}'
)
logging.info(
f'allow_duplicated_attention = {self.flags_obj.allow_duplicated_attention}'
)
return reformer.create_model(self.params, mode=mode)
else:
return transformer.create_model(self.params, mode=mode)
def train(self):
"""Trains the model."""
params = self.params
flags_obj = self.flags_obj
_ensure_dir(flags_obj.model_dir)
model = transformer.create_model(params, is_train=True)
opt = self._create_optimizer()
current_step = 0
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)
latest_checkpoint = tf.train.latest_checkpoint(flags_obj.model_dir)
if latest_checkpoint:
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
current_step = opt.iterations.numpy()
model.compile(opt)
model.summary()
train_ds = data_pipeline.train_input_fn(params)
map_data_fn = data_pipeline.map_data_for_transformer_fn
train_ds = train_ds.map(
map_data_fn, num_parallel_calls=params['num_parallel_calls'])
callbacks = self._create_callbacks(flags_obj.model_dir, 0, params)
while current_step < flags_obj.train_steps:
remaining_steps = flags_obj.train_steps - current_step
train_steps_per_eval = (remaining_steps if remaining_steps <
flags_obj.steps_between_evals else
flags_obj.steps_between_evals)
current_iteration = current_step // flags_obj.steps_between_evals
logging.info(
"Start train iteration at global step:{}".format(current_step))
history = model.fit(
train_ds,
initial_epoch=current_iteration,
epochs=current_iteration + 1,
steps_per_epoch=train_steps_per_eval,
callbacks=callbacks,
# If TimeHistory is enabled, progress bar would be messy. Increase
# the verbose level to get rid of it.
verbose=1)
current_step += train_steps_per_eval
logging.info("Train history: {}".format(history.history))
logging.info(
"End train iteration at global step:{}".format(current_step))
stats = _build_stats(history, callbacks)
return stats
def eval(self):
"""Evaluates the model."""
self.params['train'] = False
with distribution_utils.get_strategy_scope(self.distribution_strategy):
if not self.predict_model:
self.predict_model = transformer.create_model(self.params, False)
self._load_weights_if_possible(
self.predict_model,
tf.train.latest_checkpoint(self.flags_obj.model_dir))
self.predict_model.summary()
return evaluate_and_log_bleu(
self.predict_model, self.params, self.flags_obj.bleu_source,
self.flags_obj.bleu_ref, self.flags_obj.vocab_file,
self.distribution_strategy if self.use_tpu else None)
def eval(self):
"""Evaluates the model."""
# We only want to create the model under DS scope for TPU case.
# When 'distribution_strategy' is None, a no-op DummyContextManager will
# be used.
if not self.predict_model:
self.predict_model = transformer.create_model(self.params, False)
self._load_weights_if_possible(
self.predict_model,
tf.train.latest_checkpoint(self.flags_obj.model_dir))
self.predict_model.summary()
return evaluate_and_log_bleu(self.predict_model, self.params,
self.flags_obj.bleu_source,
self.flags_obj.bleu_ref,
self.flags_obj.vocab_file)
def predict(self):
"""Predicts result from the model."""
params = self.params
flags_obj = self.flags_obj
with tf.name_scope("model"):
model = transformer.create_model(params, is_train=False)
self._load_weights_if_possible(
model, tf.train.latest_checkpoint(self.flags_obj.model_dir))
model.summary()
subtokenizer = tokenizer.Subtokenizer(flags_obj.vocab_file)
ds = data_pipeline.eval_input_fn(params)
ds = ds.map(lambda x, y: x).take(_SINGLE_SAMPLE)
ret = model.predict(ds)
val_outputs, _ = ret
length = len(val_outputs)
for i in range(length):
translate.translate_from_input(val_outputs[i], subtokenizer)
def train(self):
"""Trains the model."""
params = self.params
flags_obj = self.flags_obj
# Sets config options.
keras_utils.set_session_config(enable_xla=flags_obj.enable_xla)
_ensure_dir(flags_obj.model_dir)
with distribution_utils.get_strategy_scope(self.distribution_strategy):
model = transformer.create_model(params, is_train=True)
opt = self._create_optimizer()
current_step = 0
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)
latest_checkpoint = tf.train.latest_checkpoint(flags_obj.model_dir)
if latest_checkpoint:
# checkpoint.restore(latest_checkpoint)
model.load_weights(latest_checkpoint)
logging.info("Loaded checkpoint %s", latest_checkpoint)
current_step = opt.iterations.numpy()
if params["use_ctl"]:
train_loss_metric = tf.keras.metrics.Mean("training_loss",
dtype=tf.float32)
if params["enable_tensorboard"]:
summary_writer = tf.compat.v2.summary.create_file_writer(
flags_obj.model_dir)
else:
summary_writer = tf.compat.v2.summary.create_noop_writer()
train_metrics = [train_loss_metric]
if params["enable_metrics_in_training"]:
train_metrics = train_metrics + model.metrics
else:
model.compile(opt)
model.summary()
if self.use_tpu:
# Different from experimental_distribute_dataset,
# experimental_distribute_datasets_from_function requires
# per-replica/local batch size.
params[
"batch_size"] /= self.distribution_strategy.num_replicas_in_sync
train_ds = (
self.distribution_strategy.
experimental_distribute_datasets_from_function(
lambda ctx: data_pipeline.train_input_fn(params, ctx)))
else:
train_ds = data_pipeline.train_input_fn(params)
map_data_fn = data_pipeline.map_data_for_transformer_fn
train_ds = train_ds.map(
map_data_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
if params["use_ctl"]:
train_ds_iterator = iter(train_ds)
callbacks = self._create_callbacks(flags_obj.model_dir, 0, params)
# Only TimeHistory callback is supported for CTL
if params["use_ctl"]:
callbacks = [
cb for cb in callbacks
if isinstance(cb, keras_utils.TimeHistory)
]
# TODO(b/139418525): Refactor the custom training loop logic.
@tf.function
def train_steps(iterator, steps):
"""Training steps function for TPU runs.
Args:
iterator: The input iterator of the training dataset.
steps: An integer, the number of training steps.
Returns:
A float, the loss value.
"""
def _step_fn(inputs):
"""Per-replica step function."""
inputs, targets = inputs
with tf.GradientTape() as tape:
logits = model([inputs, targets], training=True)
loss = metrics.transformer_loss(logits, targets,
params["label_smoothing"],
params["vocab_size"])
# Scales the loss, which results in using the average loss across all
# of the replicas for backprop.
scaled_loss = loss / self.distribution_strategy.num_replicas_in_sync
# De-dupes variables due to keras tracking issues.
tvars = list({id(v): v
for v in model.trainable_variables}.values())
grads = tape.gradient(scaled_loss, tvars)
opt.apply_gradients(zip(grads, tvars))
# For reporting, the metric takes the mean of losses.
train_loss_metric.update_state(loss)
for _ in tf.range(steps):
train_loss_metric.reset_states()
self.distribution_strategy.run(_step_fn,
args=(next(iterator), ))
cased_score, uncased_score = None, None
cased_score_history, uncased_score_history = [], []
while current_step < flags_obj.train_steps:
remaining_steps = flags_obj.train_steps - current_step
train_steps_per_eval = (remaining_steps if remaining_steps <
flags_obj.steps_between_evals else
flags_obj.steps_between_evals)
current_iteration = current_step // flags_obj.steps_between_evals
logging.info(
"Start train iteration at global step:{}".format(current_step))
history = None
if params["use_ctl"]:
if not self.use_tpu:
raise NotImplementedError(
"Custom training loop on GPUs is not implemented.")
# Runs training steps.
with summary_writer.as_default():
for cb in callbacks:
cb.on_epoch_begin(current_iteration)
cb.on_batch_begin(0)
train_steps(
train_ds_iterator,
tf.convert_to_tensor(train_steps_per_eval,
dtype=tf.int32))
current_step += train_steps_per_eval
train_loss = train_loss_metric.result().numpy().astype(
float)
logging.info("Train Step: %d/%d / loss = %s", current_step,
flags_obj.train_steps, train_loss)
for cb in callbacks:
cb.on_batch_end(train_steps_per_eval - 1)
cb.on_epoch_end(current_iteration)
if params["enable_tensorboard"]:
for metric_obj in train_metrics:
tf.compat.v2.summary.scalar(
metric_obj.name, metric_obj.result(),
current_step)
summary_writer.flush()
for cb in callbacks:
cb.on_train_end()
if flags_obj.enable_checkpointing:
# avoid check-pointing when running for benchmarking.
checkpoint_name = checkpoint.save(
os.path.join(flags_obj.model_dir,
"ctl_step_{}.ckpt".format(current_step)))
logging.info("Saved checkpoint to %s", checkpoint_name)
else:
if self.use_tpu:
raise NotImplementedError(
"Keras model.fit on TPUs is not implemented.")
history = model.fit(
train_ds,
initial_epoch=current_iteration,
epochs=current_iteration + 1,
steps_per_epoch=train_steps_per_eval,
callbacks=callbacks,
# If TimeHistory is enabled, progress bar would be messy. Increase
# the verbose level to get rid of it.
verbose=(2 if flags_obj.enable_time_history else 1))
current_step += train_steps_per_eval
logging.info("Train history: {}".format(history.history))
logging.info(
"End train iteration at global step:{}".format(current_step))
if (flags_obj.bleu_source and flags_obj.bleu_ref):
uncased_score, cased_score = self.eval()
cased_score_history.append(
[current_iteration + 1, cased_score])
uncased_score_history.append(
[current_iteration + 1, uncased_score])
stats = ({"loss": train_loss} if history is None else {})
misc.update_stats(history, stats, callbacks)
if uncased_score and cased_score:
stats["bleu_uncased"] = uncased_score
stats["bleu_cased"] = cased_score
stats["bleu_uncased_history"] = uncased_score_history
stats["bleu_cased_history"] = cased_score_history
return stats
def train(self):
"""Trains the model."""
params = self.params
flags_obj = self.flags_obj
# Sets config options.
keras_utils.set_session_config(enable_xla=flags_obj.enable_xla)
_ensure_dir(flags_obj.model_dir)
model = transformer.create_model(params, is_train=True)
opt = self._create_optimizer()
current_step = 0
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)
latest_checkpoint = tf.train.latest_checkpoint(flags_obj.model_dir)
if latest_checkpoint:
checkpoint.restore(latest_checkpoint)
logging.info("Loaded checkpoint %s", latest_checkpoint)
current_step = opt.iterations.numpy()
if params["use_ctl"]:
train_loss_metric = tf.keras.metrics.Mean("training_loss",
dtype=tf.float32)
if params["enable_tensorboard"]:
summary_writer = tf.compat.v2.summary.create_file_writer(
flags_obj.model_dir)
else:
summary_writer = tf.compat.v2.summary.create_noop_writer()
train_metrics = [train_loss_metric]
if params["enable_metrics_in_training"]:
train_metrics = train_metrics + model.metrics
else:
model.compile(opt)
model.summary()
train_ds = data_pipeline.train_input_fn(params)
train_ds = train_ds.map(
lambda x, y: ((x, y), ),
num_parallel_calls=params["num_parallel_calls"])
if params["use_ctl"]:
train_ds_iterator = iter(train_ds)
callbacks = self._create_callbacks(flags_obj.model_dir, 0, params)
# TODO(b/139418525): Refactor the custom training loop logic.
@tf.function
def train_steps(iterator, steps):
"""Training steps function for TPU runs.
Args:
iterator: The input iterator of the training dataset.
steps: An integer, the number of training steps.
Returns:
A float, the loss value.
"""
def _step_fn(inputs):
"""Per-replica step function."""
inputs, targets = inputs
with tf.GradientTape() as tape:
logits = model([inputs, targets], training=True)
loss = metrics.transformer_loss(logits, targets,
params["label_smoothing"],
params["vocab_size"])
# De-dupes variables due to keras tracking issues.
tvars = list({id(v): v
for v in model.trainable_variables}.values())
grads = tape.gradient(loss, tvars)
opt.apply_gradients(zip(grads, tvars))
# For reporting, the metric takes the mean of losses.
train_loss_metric.update_state(loss)
for _ in tf.range(steps):
train_loss_metric.reset_states()
_step_fn(next(iterator))
cased_score, uncased_score = None, None
cased_score_history, uncased_score_history = [], []
while current_step < flags_obj.train_steps:
remaining_steps = flags_obj.train_steps - current_step
train_steps_per_eval = (remaining_steps if remaining_steps <
flags_obj.steps_between_evals else
flags_obj.steps_between_evals)
current_iteration = current_step // flags_obj.steps_between_evals
logging.info(
"Start train iteration at global step:{}".format(current_step))
history = None
if params["use_ctl"]:
# Runs training steps.
with summary_writer.as_default():
train_steps(
train_ds_iterator,
tf.convert_to_tensor(train_steps_per_eval,
dtype=tf.int32))
current_step += train_steps_per_eval
train_loss = train_loss_metric.result().numpy().astype(
float)
logging.info("Train Step: %d/%d / loss = %s", current_step,
flags_obj.train_steps, train_loss)
if params["enable_tensorboard"]:
for metric_obj in train_metrics:
tf.summary.scalar(metric_obj.name,
metric_obj.result(),
current_step)
checkpoint_name = checkpoint.save(
os.path.join(flags_obj.model_dir,
"ctl_step_{}.ckpt".format(current_step)))
logging.info("Saved checkpoint to %s", checkpoint_name)
else:
history = model.fit(
train_ds,
initial_epoch=current_iteration,
epochs=current_iteration + 1,
steps_per_epoch=train_steps_per_eval,
callbacks=callbacks,
# If TimeHistory is enabled, progress bar would be messy. Increase
# the verbose level to get rid of it.
verbose=(2 if flags_obj.enable_time_history else 1))
current_step += train_steps_per_eval
logging.info("Train history: {}".format(history.history))
logging.info(
"End train iteration at global step:{}".format(current_step))
if (flags_obj.bleu_source and flags_obj.bleu_ref):
uncased_score, cased_score = self.eval()
cased_score_history.append(
[current_iteration + 1, cased_score])
uncased_score_history.append(
[current_iteration + 1, uncased_score])
stats = ({
"loss": train_loss
} if history is None else misc.build_stats(history, callbacks))
if uncased_score and cased_score:
stats["bleu_uncased"] = uncased_score
stats["bleu_cased"] = cased_score
stats["bleu_uncased_history"] = uncased_score_history
stats["bleu_cased_history"] = cased_score_history
return stats
def train(self):
"""Trains the model."""
self.params['train'] = True
params = self.params
flags_obj = self.flags_obj
# Sets config options.
keras_utils.set_session_config(enable_xla=flags_obj.enable_xla)
_ensure_dir(flags_obj.model_dir)
with distribution_utils.get_strategy_scope(self.distribution_strategy):
model = transformer.create_model(params, is_train=True)
opt = self._create_optimizer()
current_step = 0
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)
latest_checkpoint = tf.train.latest_checkpoint(flags_obj.model_dir)
if latest_checkpoint:
checkpoint.restore(latest_checkpoint)
logging.info("###Loaded checkpoint %s", latest_checkpoint)
current_step = opt.iterations.numpy()
logging.info(current_step)
model.compile(opt)
model.summary()
if self.use_tpu:
# Different from experimental_distribute_dataset,
# experimental_distribute_datasets_from_function requires
# per-replica/local batch size.
params[
"batch_size"] /= self.distribution_strategy.num_replicas_in_sync
train_ds = (
self.distribution_strategy.
experimental_distribute_datasets_from_function(
lambda ctx: data_pipeline.train_input_fn(params, ctx)))
else:
train_ds = data_pipeline.train_input_fn(params)
map_data_fn = data_pipeline.map_data_for_transformer_fn
train_ds = train_ds.map(
map_data_fn, num_parallel_calls=params["num_parallel_calls"])
# if params["use_ctl"]:
# train_ds_iterator = iter(train_ds)
callbacks = self._create_callbacks(flags_obj.model_dir, 0, params)
# TODO(b/139418525): Refactor the custom training loop logic.
train_loss = 0
# cased_score, uncased_score = None, None
# cased_score_history, uncased_score_history = [], []
while current_step < flags_obj.train_steps:
remaining_steps = flags_obj.train_steps - current_step
train_steps_per_eval = (remaining_steps if remaining_steps <
flags_obj.steps_between_evals else
flags_obj.steps_between_evals)
current_iteration = current_step // flags_obj.steps_between_evals
logging.info(
"Start train iteration at global step:{}".format(current_step))
history = None
if params["use_ctl"]:
if not self.use_tpu:
raise NotImplementedError(
"Custom training loop on GPUs is not implemented.")
# Runs training steps.
# train_steps(train_ds_iterator,
# tf.convert_to_tensor(train_steps_per_eval, dtype=tf.int32))
# current_step += train_steps_per_eval
# train_loss = train_loss_metric.result().numpy().astype(float)
# logging.info("Train Step: %d/%d / loss = %s",
# current_step, flags_obj.train_steps, train_loss)
#
# checkpoint_name = checkpoint.save(
# os.path.join(
# flags_obj.model_dir,
# "ctl_step_{}.ckpt".format(current_step)))
# logging.info("Saved checkpoint to %s", checkpoint_name)
else:
if self.use_tpu:
raise NotImplementedError(
"Keras model.fit on TPUs is not implemented.")
history = model.fit(
train_ds,
initial_epoch=current_iteration,
epochs=current_iteration + 1,
steps_per_epoch=train_steps_per_eval,
callbacks=callbacks,
# If TimeHistory is enabled, progress bar would be messy. Increase
# the verbose level to get rid of it.
verbose=(2 if flags_obj.enable_time_history else 1))
current_step += train_steps_per_eval
logging.info("Train history: {}".format(history.history))
logging.info(
"End train iteration at global step:{}".format(current_step))
# if (flags_obj.bleu_source and flags_obj.bleu_ref):
# uncased_score, cased_score = self.eval()
# cased_score_history.append([current_iteration + 1, cased_score])
# uncased_score_history.append([current_iteration + 1, uncased_score])
stats = ({
"loss": train_loss
} if history is None else misc.build_stats(history, callbacks))
# if uncased_score and cased_score:
# stats["bleu_uncased"] = uncased_score
# stats["bleu_cased"] = cased_score
# stats["bleu_uncased_history"] = uncased_score_history
# stats["bleu_cased_history"] = cased_score_history
return stats
