def test_mutual_exclusivity(self):
with self.assertRaises(ValueError):
schedule.Manager(train_steps=100,
steps_between_evals=100,
train_epochs=2,
epochs_between_evals=1,
default_train_epochs=None,
batch_size=2048,
max_length=256)
def test_step_basis_tpu(self):
manager = schedule.Manager(train_steps=1000,
steps_between_evals=100,
train_epochs=None,
epochs_between_evals=None,
default_train_epochs=None,
batch_size=2048,
max_length=256,
use_tpu=True)
self.assertEqual(manager.single_iteration_train_steps, 100)
# num_eval_examples / (batch_size / max_length) == 3000 / (2048 / 256)
self.assertEqual(manager.single_iteration_eval_steps, 375)
self.assertIsNone(manager.repeat_dataset)
def test_epoch_basis(self):
manager = schedule.Manager(train_steps=None,
steps_between_evals=None,
train_epochs=10,
epochs_between_evals=2,
default_train_epochs=None,
batch_size=2048,
max_length=256)
# For non-TPU, estimator relies on dataset exhausion
self.assertIsNone(manager.single_iteration_train_steps)
self.assertIsNone(manager.single_iteration_eval_steps)
self.assertEqual(manager.repeat_dataset, 2)
def test_step_basis(self):
manager = schedule.Manager(train_steps=1000,
steps_between_evals=100,
train_epochs=None,
epochs_between_evals=None,
default_train_epochs=None,
batch_size=2048,
max_length=256)
self.assertEqual(manager.single_iteration_train_steps, 100)
# Evaluation uses the full set
self.assertIsNone(manager.single_iteration_eval_steps)
self.assertIsNone(manager.repeat_dataset)
def test_epoch_basis_tpu(self):
manager = schedule.Manager(train_steps=None,
steps_between_evals=None,
train_epochs=10,
epochs_between_evals=2,
default_train_epochs=None,
batch_size=2048,
max_length=256,
use_tpu=True)
self.assertEqual(
manager.single_iteration_train_steps,
schedule.NUM_EXAMPLES[tf.estimator.ModeKeys.TRAIN] * 2 //
(2048 / 256))
# num_eval_examples / (batch_size / max_length) == 3000 / (2048 / 256)
self.assertEqual(manager.single_iteration_eval_steps, 375)
self.assertEqual(manager.repeat_dataset, 2)
def run_transformer(flags_obj):
"""Create tf.Estimator to train and evaluate transformer model.
Args:
flags_obj: Object containing parsed flag values.
Returns:
Dict of results of the run. Contains the keys `eval_results`,
`train_hooks`, `bleu_cased`, and `bleu_uncased`. `train_hooks` is a list the
instances of hooks used during training.
"""
num_gpus = flags_core.get_num_gpus(flags_obj)
# Add flag-defined parameters to params object
params = PARAMS_MAP[flags_obj.param_set]
if num_gpus > 1:
if flags_obj.param_set == "big":
params = model_params.BIG_MULTI_GPU_PARAMS
elif flags_obj.param_set == "base":
params = model_params.BASE_MULTI_GPU_PARAMS
params["data_dir"] = flags_obj.data_dir
params["model_dir"] = flags_obj.model_dir
params["num_parallel_calls"] = flags_obj.num_parallel_calls
params["tpu"] = flags_obj.tpu
params["use_tpu"] = bool(flags_obj.tpu) # was a tpu specified.
params["static_batch"] = flags_obj.static_batch or params["use_tpu"]
params["allow_ffn_pad"] = not params["use_tpu"]
params["max_length"] = flags_obj.max_length or params["max_length"]
params["use_synthetic_data"] = flags_obj.use_synthetic_data
# Set batch size parameter, which depends on the availability of
# TPU and GPU, and distribution settings.
params["batch_size"] = (
flags_obj.batch_size
or (params["default_batch_size_tpu"]
if params["use_tpu"] else params["default_batch_size"]))
total_batch_size = params["batch_size"]
if not params["use_tpu"]:
params["batch_size"] = distribution_utils.per_replica_batch_size(
params["batch_size"], num_gpus)
schedule_manager = schedule.Manager(
train_steps=flags_obj.train_steps,
steps_between_evals=flags_obj.steps_between_evals,
train_epochs=flags_obj.train_epochs,
epochs_between_evals=flags_obj.epochs_between_evals,
default_train_epochs=DEFAULT_TRAIN_EPOCHS,
batch_size=params["batch_size"],
max_length=params["max_length"],
use_tpu=params["use_tpu"],
num_tpu_shards=flags_obj.num_tpu_shards)
params["repeat_dataset"] = schedule_manager.repeat_dataset
model_helpers.apply_clean(flags.FLAGS)
# Create hooks that log information about the training and metric values
train_hooks = hooks_helper.get_train_hooks(
flags_obj.hooks,
model_dir=flags_obj.model_dir,
tensors_to_log=TENSORS_TO_LOG, # used for logging hooks
batch_size=total_batch_size, # for ExamplesPerSecondHook
use_tpu=params["use_tpu"] # Not all hooks can run with TPUs
)
benchmark_logger = logger.get_benchmark_logger()
benchmark_logger.log_run_info(model_name="transformer",
dataset_name="wmt_translate_ende",
run_params=params,
test_id=flags_obj.benchmark_test_id)
# Train and evaluate transformer model
estimator = construct_estimator(flags_obj, params, schedule_manager)
stats = run_loop(
estimator=estimator,
# Training arguments
schedule_manager=schedule_manager,
train_hooks=train_hooks,
benchmark_logger=benchmark_logger,
# BLEU calculation arguments
bleu_source=flags_obj.bleu_source,
bleu_ref=flags_obj.bleu_ref,
bleu_threshold=flags_obj.stop_threshold,
vocab_file=flags_obj.vocab_file)
if flags_obj.export_dir and not params["use_tpu"]:
serving_input_fn = export.build_tensor_serving_input_receiver_fn(
shape=[None], dtype=tf.int64, batch_size=None)
# Export saved model, and save the vocab file as an extra asset. The vocab
# file is saved to allow consistent input encoding and output decoding.
# (See the "Export trained model" section in the README for an example of
# how to use the vocab file.)
# Since the model itself does not use the vocab file, this file is saved as
# an extra asset rather than a core asset.
estimator.export_savedmodel(
flags_obj.export_dir,
serving_input_fn,
assets_extra={"vocab.txt": flags_obj.vocab_file},
strip_default_attrs=True)
return stats
