def test_recovery_nan_error(self, distribution_strategy, flag_mode):
model_dir = self.get_temp_dir()
flags_dict = dict(experiment='mock',
mode=flag_mode,
model_dir=model_dir,
params_override=json.dumps(self._test_config))
with flagsaver.flagsaver(**flags_dict):
params = train_utils.parse_configuration(flags.FLAGS)
train_utils.serialize_config(params, model_dir)
with distribution_strategy.scope():
# task = task_factory.get_task(params.task, logging_dir=model_dir)
task = mock_task.MockTask(params.task, logging_dir=model_dir)
# Set the loss to NaN to trigger RunTimeError.
def build_losses(labels, model_outputs, aux_losses=None):
del labels, model_outputs
return tf.constant([np.nan], tf.float32) + aux_losses
task.build_losses = build_losses
with self.assertRaises(RuntimeError):
train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode=flag_mode,
params=params,
model_dir=model_dir)
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
params = train_utils.parse_configuration(FLAGS)
model_dir = FLAGS.model_dir
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(
params.runtime.mixed_precision_dtype)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu,
**params.runtime.model_parallelism())
with distribution_strategy.scope():
task = classification_example.ClassificationExampleTask(params.task)
train_lib.run_experiment(distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
train_utils.save_gin_config(FLAGS.mode, model_dir)
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
params = train_utils.parse_configuration(FLAGS)
model_dir = FLAGS.model_dir
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
if 'train_and_eval' in FLAGS.mode:
assert (
params.task.train_data.feature_shape ==
params.task.validation_data.feature_shape), (
f'train {params.task.train_data.feature_shape} != validate '
f'{params.task.validation_data.feature_shape}')
if 'assemblenet' in FLAGS.experiment:
if 'eval' in FLAGS.mode:
# Use the feature shape in validation_data for all jobs. The number of
# frames in train_data will be used to construct the Assemblenet model.
params.task.model.backbone.assemblenet.num_frames = params.task.validation_data.feature_shape[
0]
shape = params.task.validation_data.feature_shape
else:
params.task.model.backbone.assemblenet.num_frames = params.task.train_data.feature_shape[
0]
shape = params.task.train_data.feature_shape
logging.info('mode %r num_frames %r feature shape %r', FLAGS.mode,
params.task.model.backbone.assemblenet.num_frames, shape)
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(
params.runtime.mixed_precision_dtype)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
with distribution_strategy.scope():
task = task_factory.get_task(params.task, logging_dir=model_dir)
train_lib.run_experiment(distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
train_utils.save_gin_config(FLAGS.mode, model_dir)
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
params = train_utils.parse_configuration(FLAGS)
model_dir = FLAGS.model_dir
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(
params.runtime.mixed_precision_dtype,
params.runtime.loss_scale,
use_experimental_api=True)
input_partition_dims = None
if FLAGS.mode == 'train_and_eval':
if np.prod(params.task.train_input_partition_dims) != np.prod(
params.task.eval_input_partition_dims):
raise ValueError('Train and eval input partition dims can not be'
'partitioned on the same node')
else:
input_partition_dims = get_computation_shape_for_model_parallelism(
params.task.train_input_partition_dims)
elif FLAGS.mode == 'train':
if params.task.train_input_partition_dims:
input_partition_dims = get_computation_shape_for_model_parallelism(
params.task.train_input_partition_dims)
elif FLAGS.mode == 'eval' or FLAGS.mode == 'continuous_eval':
if params.task.eval_input_partition_dims:
input_partition_dims = get_computation_shape_for_model_parallelism(
params.task.eval_input_partition_dims)
distribution_strategy = create_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
num_gpus=params.runtime.num_gpus,
input_partition_dims=input_partition_dims,
tpu_address=params.runtime.tpu)
with distribution_strategy.scope():
task = task_factory.get_task(params.task, logging_dir=model_dir)
train_lib.run_experiment(distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
params = train_utils.parse_configuration(FLAGS)
model_dir = FLAGS.model_dir
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
if isinstance(params, cfg.ExperimentConfig):
with distribution_strategy.scope():
task = task_factory.get_task(params.task, logging_dir=model_dir)
train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
elif isinstance(params, multi_cfg.MultiTaskExperimentConfig):
with distribution_strategy.scope():
task = multitask.MultiTask.from_config(params.task, model_dir)
model = multihead_model.build_model(params.task)
train_lib_multitask.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
model=model,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
else:
raise ValueError("Expected config to be either type cfg.ExperimentConfig" + \
"or multi_cfg.MultiTaskExperimentConfig, got %s" %type(params))
train_utils.save_gin_config(FLAGS.mode, model_dir)
def test_end_to_end(self, distribution_strategy, flag_mode, run_post_eval):
model_dir = self.get_temp_dir()
flags_dict = dict(
experiment='mock',
mode=flag_mode,
model_dir=model_dir,
params_override=json.dumps(self._test_config))
with flagsaver.flagsaver(**flags_dict):
params = train_utils.parse_configuration(flags.FLAGS)
train_utils.serialize_config(params, model_dir)
with distribution_strategy.scope():
task = task_factory.get_task(params.task, logging_dir=model_dir)
logs = train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode=flag_mode,
params=params,
model_dir=model_dir,
run_post_eval=run_post_eval)
if run_post_eval:
self.assertNotEmpty(logs)
else:
self.assertEmpty(logs)
self.assertNotEmpty(
tf.io.gfile.glob(os.path.join(model_dir, 'params.yaml')))
if flag_mode != 'eval':
self.assertNotEmpty(
tf.io.gfile.glob(os.path.join(model_dir, 'checkpoint')))
def main():
run_experiment_kwargs = load_params(PARAMS_FILE_NAME)
strategy_str = run_experiment_kwargs['distribution_strategy']
strategy = _DISTRIBUTION_STRATEGIES[strategy_str]()
run_experiment_kwargs.update(dict(distribution_strategy=strategy))
model, _ = train_lib.run_experiment(**run_experiment_kwargs)
model.save(run_experiment_kwargs['model_dir'])
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
params = train_utils.parse_configuration(FLAGS)
model_dir = FLAGS.model_dir
if "train" in FLAGS.mode:
train_utils.serialize_config(params, model_dir)
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu,
**params.runtime.model_parallelism())
with distribution_strategy.scope():
if params.task.use_crf:
task = ap_parsing_task.APParsingTaskCRF(params.task)
else:
task = ap_parsing_task.APParsingTaskBase(params.task)
ckpt_exporter = train_utils.maybe_create_best_ckpt_exporter(
params, model_dir)
trainer = train_utils.create_trainer(
params,
task,
train="train" in FLAGS.mode,
evaluate=("eval" in FLAGS.mode),
checkpoint_exporter=ckpt_exporter)
model, _ = train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
trainer=trainer,
model_dir=model_dir)
train_utils.save_gin_config(FLAGS.mode, model_dir)
# Export saved model.
if "train" in FLAGS.mode:
saved_model_path = os.path.join(model_dir, "saved_models/latest")
logging.info("Exporting SavedModel to %s", saved_model_path)
tf.saved_model.save(model, saved_model_path)
if ckpt_exporter:
logging.info("Loading best checkpoint for export")
trainer.checkpoint.restore(ckpt_exporter.best_ckpt_path)
saved_model_path = os.path.join(model_dir, "saved_models/best")
# Make sure restored and not re-initialized.
if trainer.global_step > 0:
logging.info(
"Exporting best saved model by %s (from global step: %d) to %s",
params.trainer.best_checkpoint_eval_metric,
trainer.global_step.numpy(), saved_model_path)
tf.saved_model.save(trainer.model, saved_model_path)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
superglue_flags.validate_flags(FLAGS, file_exists_fn=tf.io.gfile.exists)
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
with distribution_strategy.scope():
task = None
if 'train_eval' in FLAGS.mode:
logging.info('Starting training and eval...')
logging.info('Model dir: %s', FLAGS.model_dir)
exp_config = _get_exp_config(input_meta_data=input_meta_data,
exp_config_files=FLAGS.config_file)
train_utils.serialize_config(exp_config, FLAGS.model_dir)
task = task_factory.get_task(exp_config.task,
logging_dir=FLAGS.model_dir)
train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode='train_and_eval',
params=exp_config,
model_dir=FLAGS.model_dir)
if 'predict' in FLAGS.mode:
logging.info('Starting predict...')
# When mode is `predict`, `task` will be None.
if task is None:
exp_config = _get_exp_config(input_meta_data=input_meta_data,
exp_config_files=[
os.path.join(
FLAGS.model_dir,
'params.yaml')
])
task = task_factory.get_task(exp_config.task,
logging_dir=FLAGS.model_dir)
_write_submission_file(task, input_meta_data['max_seq_length'])
def testContinuousFinetune(self):
pretrain_steps = 1
src_model_dir = self.get_temp_dir()
flags_dict = dict(experiment='mock',
mode='continuous_train_and_eval',
model_dir=self._model_dir,
params_override={
'task': {
'init_checkpoint': src_model_dir,
},
'trainer': {
'continuous_eval_timeout': 1,
'steps_per_loop': 1,
'train_steps': 1,
'validation_steps': 1,
'best_checkpoint_export_subdir': 'best_ckpt',
'best_checkpoint_eval_metric': 'acc',
'optimizer_config': {
'optimizer': {
'type': 'sgd'
},
'learning_rate': {
'type': 'constant'
}
}
}
})
with flagsaver.flagsaver(**flags_dict):
# Train and save some checkpoints.
params = train_utils.parse_configuration(flags.FLAGS)
distribution_strategy = tf.distribute.get_strategy()
with distribution_strategy.scope():
task = task_factory.get_task(params.task,
logging_dir=src_model_dir)
_ = train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode='train',
params=params,
model_dir=src_model_dir)
params = train_utils.parse_configuration(FLAGS)
eval_metrics = continuous_finetune_lib.run_continuous_finetune(
FLAGS.mode,
params,
FLAGS.model_dir,
run_post_eval=True,
pretrain_steps=pretrain_steps)
self.assertIn('best_acc', eval_metrics)
self.assertFalse(
tf.io.gfile.exists(os.path.join(FLAGS.model_dir,
'checkpoint')))
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
print(FLAGS.experiment)
params = train_utils.parse_configuration(FLAGS)
model_dir = FLAGS.model_dir
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(
params.runtime.mixed_precision_dtype, params.runtime.loss_scale)
if params.runtime.worker_hosts != '' and params.runtime.worker_hosts is not None:
num_workers = distribute_utils.configure_cluster(
worker_hosts=params.runtime.worker_hosts,
task_index=params.runtime.task_index)
print(num_workers)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
with distribution_strategy.scope():
task = task_factory.get_task(params.task, logging_dir=model_dir)
train_lib.run_experiment(distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
def test_recovery(self, distribution_strategy, flag_mode):
loss_threshold = 1.0
model_dir = self.get_temp_dir()
flags_dict = dict(experiment='mock',
mode=flag_mode,
model_dir=model_dir,
params_override=json.dumps(self._test_config))
with flagsaver.flagsaver(**flags_dict):
params = train_utils.parse_configuration(flags.FLAGS)
params.trainer.loss_upper_bound = loss_threshold
params.trainer.recovery_max_trials = 1
train_utils.serialize_config(params, model_dir)
with distribution_strategy.scope():
task = task_factory.get_task(params.task,
logging_dir=model_dir)
# Saves a checkpoint for reference.
model = task.build_model()
checkpoint = tf.train.Checkpoint(model=model)
checkpoint_manager = tf.train.CheckpointManager(
checkpoint, self.get_temp_dir(), max_to_keep=2)
checkpoint_manager.save()
before_weights = model.get_weights()
def build_losses(labels, model_outputs, aux_losses=None):
del labels, model_outputs
return tf.constant([loss_threshold], tf.float32) + aux_losses
task.build_losses = build_losses
model, _ = train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode=flag_mode,
params=params,
model_dir=model_dir)
after_weights = model.get_weights()
for left, right in zip(before_weights, after_weights):
self.assertAllEqual(left, right)
def run_continuous_finetune(
mode: str,
params: config_definitions.ExperimentConfig,
model_dir: str,
run_post_eval: bool = False,
) -> Mapping[str, Any]:
"""Run modes with continuous training.
Currently only supports continuous_train_and_eval.
Args:
mode: A 'str', specifying the mode.
continuous_train_and_eval - monitors a checkpoint directory. Once a new
checkpoint is discovered, loads the checkpoint, finetune the model by
training it (probably on another dataset or with another task), then
evaluate the finetuned model.
params: ExperimentConfig instance.
model_dir: A 'str', a path to store model checkpoints and summaries.
run_post_eval: Whether to run post eval once after training, metrics logs
are returned.
Returns:
eval logs: returns eval metrics logs when run_post_eval is set to True,
othewise, returns {}.
"""
assert mode == 'continuous_train_and_eval', (
'Only continuous_train_and_eval is supported by continuous_finetune. '
'Got mode: {}'.format(mode))
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype,
params.runtime.loss_scale)
distribution_strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
retry_times = 0
while not tf.io.gfile.isdir(params.task.init_checkpoint):
# Wait for the init_checkpoint directory to be created.
if retry_times >= 60:
raise ValueError(
'ExperimentConfig.task.init_checkpoint must be a directory for '
'continuous_train_and_eval mode.')
retry_times += 1
time.sleep(60)
summary_writer = tf.summary.create_file_writer(
os.path.join(model_dir, 'eval'))
for pretrain_ckpt in tf.train.checkpoints_iterator(
checkpoint_dir=params.task.init_checkpoint,
min_interval_secs=10,
timeout=params.trainer.continuous_eval_timeout):
with distribution_strategy.scope():
global_step = train_utils.read_global_step_from_checkpoint(pretrain_ckpt)
if params.trainer.best_checkpoint_export_subdir:
best_ckpt_subdir = '{}_{}'.format(
params.trainer.best_checkpoint_export_subdir, global_step)
params_replaced = params.replace(
task={'init_checkpoint': pretrain_ckpt},
trainer={'best_checkpoint_export_subdir': best_ckpt_subdir})
else:
params_replaced = params.replace(task={'init_checkpoint': pretrain_ckpt})
params_replaced.lock()
logging.info('Running finetuning with params: %s', params_replaced)
with distribution_strategy.scope():
task = task_factory.get_task(params_replaced.task, logging_dir=model_dir)
_, eval_metrics = train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode='train_and_eval',
# replace params.task.init_checkpoint to make sure that we load
# exactly this pretrain checkpoint.
params=params_replaced,
model_dir=model_dir,
run_post_eval=True,
save_summary=False)
logging.info('Evaluation finished. Pretrain global_step: %d', global_step)
train_utils.write_json_summary(model_dir, global_step, eval_metrics)
if not os.path.basename(model_dir): # if model_dir.endswith('/')
summary_grp = os.path.dirname(model_dir) + '_' + task.__class__.__name__
else:
summary_grp = os.path.basename(model_dir) + '_' + task.__class__.__name__
summaries = {}
for name, value in eval_metrics.items():
summaries[summary_grp + '/' + name] = value
train_utils.write_summary(summary_writer, global_step, summaries)
train_utils.remove_ckpts(model_dir)
if run_post_eval:
return eval_metrics
return {}
def run_continuous_finetune(
mode: str,
params: config_definitions.ExperimentConfig,
model_dir: str,
run_post_eval: bool = False,
pretrain_steps: Optional[int] = None,
) -> Mapping[str, Any]:
"""Run modes with continuous training.
Currently only supports continuous_train_and_eval.
Args:
mode: A 'str', specifying the mode. continuous_train_and_eval - monitors a
checkpoint directory. Once a new checkpoint is discovered, loads the
checkpoint, finetune the model by training it (probably on another dataset
or with another task), then evaluate the finetuned model.
params: ExperimentConfig instance.
model_dir: A 'str', a path to store model checkpoints and summaries.
run_post_eval: Whether to run post eval once after training, metrics logs
are returned.
pretrain_steps: Optional, the number of total training steps for the
pretraining job.
Returns:
eval logs: returns eval metrics logs when run_post_eval is set to True,
othewise, returns {}.
"""
assert mode == 'continuous_train_and_eval', (
'Only continuous_train_and_eval is supported by continuous_finetune. '
'Got mode: {}'.format(mode))
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(
params.runtime.mixed_precision_dtype, params.runtime.loss_scale)
distribution_strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
retry_times = 0
while not tf.io.gfile.isdir(params.task.init_checkpoint):
# Wait for the init_checkpoint directory to be created.
if retry_times >= 60:
raise ValueError(
'ExperimentConfig.task.init_checkpoint must be a directory for '
'continuous_train_and_eval mode.')
retry_times += 1
time.sleep(60)
summary_writer = tf.summary.create_file_writer(
os.path.join(model_dir, 'eval'))
global_step = 0
def timeout_fn():
if pretrain_steps and global_step < pretrain_steps:
# Keeps waiting for another timeout period.
logging.info(
'Continue waiting for new checkpoint as current pretrain '
'global_step=%d and target is %d.', global_step,
pretrain_steps)
return False
# Quits the loop.
return True
for pretrain_ckpt in tf.train.checkpoints_iterator(
checkpoint_dir=params.task.init_checkpoint,
min_interval_secs=10,
timeout=params.trainer.continuous_eval_timeout,
timeout_fn=timeout_fn):
with distribution_strategy.scope():
global_step = train_utils.read_global_step_from_checkpoint(
pretrain_ckpt)
# Replaces params.task.init_checkpoint to make sure that we load
# exactly this pretrain checkpoint.
if params.trainer.best_checkpoint_export_subdir:
best_ckpt_subdir = '{}_{}'.format(
params.trainer.best_checkpoint_export_subdir, global_step)
params_replaced = params.replace(
task={'init_checkpoint': pretrain_ckpt},
trainer={'best_checkpoint_export_subdir': best_ckpt_subdir})
else:
params_replaced = params.replace(
task={'init_checkpoint': pretrain_ckpt})
params_replaced.lock()
logging.info('Running finetuning with params: %s', params_replaced)
with distribution_strategy.scope():
if isinstance(params, configs.MultiEvalExperimentConfig):
task = task_factory.get_task(params_replaced.task)
eval_tasks = multitask.MultiTask.from_config(
params_replaced.eval_tasks)
(_, eval_metrics
) = multitask_train_lib.run_experiment_wtih_multitask_eval(
distribution_strategy=distribution_strategy,
train_task=task,
eval_tasks=eval_tasks,
mode='train_and_eval',
params=params_replaced,
model_dir=model_dir,
run_post_eval=True,
save_summary=False)
else:
task = task_factory.get_task(params_replaced.task,
logging_dir=model_dir)
_, eval_metrics = train_lib.run_experiment(
distribution_strategy=distribution_strategy,
task=task,
mode='train_and_eval',
params=params_replaced,
model_dir=model_dir,
run_post_eval=True,
save_summary=False)
logging.info('Evaluation finished. Pretrain global_step: %d',
global_step)
train_utils.write_json_summary(model_dir, global_step, eval_metrics)
if not os.path.basename(model_dir): # if model_dir.endswith('/')
summary_grp = os.path.dirname(model_dir) + '_' + task.name
else:
summary_grp = os.path.basename(model_dir) + '_' + task.name
summaries = {}
for name, value in _flatten_dict(eval_metrics).items():
summaries[summary_grp + '/' + '-'.join(name)] = value
train_utils.write_summary(summary_writer, global_step, summaries)
train_utils.remove_ckpts(model_dir)
# In TF2, the resource life cycle is bound with the python object life
# cycle. Force trigger python garbage collection here so those resources
# can be deallocated in time, so it doesn't cause OOM when allocating new
# objects.
# TODO(b/169178664): Fix cycle reference in Keras model and revisit to see
# if we need gc here.
gc.collect()
if run_post_eval:
return eval_metrics
return {}
def main(_) -> None:
"""Train and evaluate the Ranking model."""
params = train_utils.parse_configuration(FLAGS)
mode = FLAGS.mode
model_dir = FLAGS.model_dir
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
if FLAGS.seed is not None:
logging.info('Setting tf seed.')
tf.random.set_seed(FLAGS.seed)
task = RankingTask(
params=params.task,
optimizer_config=params.trainer.optimizer_config,
logging_dir=model_dir,
steps_per_execution=params.trainer.steps_per_loop,
name='RankingTask')
enable_tensorboard = params.trainer.callbacks.enable_tensorboard
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
with strategy.scope():
model = task.build_model()
def get_dataset_fn(params):
return lambda input_context: task.build_inputs(params, input_context)
train_dataset = None
if 'train' in mode:
train_dataset = strategy.distribute_datasets_from_function(
get_dataset_fn(params.task.train_data),
options=tf.distribute.InputOptions(experimental_fetch_to_device=False))
validation_dataset = None
if 'eval' in mode:
validation_dataset = strategy.distribute_datasets_from_function(
get_dataset_fn(params.task.validation_data),
options=tf.distribute.InputOptions(experimental_fetch_to_device=False))
if params.trainer.use_orbit:
with strategy.scope():
checkpoint_exporter = train_utils.maybe_create_best_ckpt_exporter(
params, model_dir)
trainer = RankingTrainer(
config=params,
task=task,
model=model,
optimizer=model.optimizer,
train='train' in mode,
evaluate='eval' in mode,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
checkpoint_exporter=checkpoint_exporter)
train_lib.run_experiment(
distribution_strategy=strategy,
task=task,
mode=mode,
params=params,
model_dir=model_dir,
trainer=trainer)
else: # Compile/fit
checkpoint = tf.train.Checkpoint(model=model, optimizer=model.optimizer)
latest_checkpoint = tf.train.latest_checkpoint(model_dir)
if latest_checkpoint:
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
checkpoint_manager = tf.train.CheckpointManager(
checkpoint,
directory=model_dir,
max_to_keep=params.trainer.max_to_keep,
step_counter=model.optimizer.iterations,
checkpoint_interval=params.trainer.checkpoint_interval)
checkpoint_callback = keras_utils.SimpleCheckpoint(checkpoint_manager)
time_callback = keras_utils.TimeHistory(
params.task.train_data.global_batch_size,
params.trainer.time_history.log_steps,
logdir=model_dir if enable_tensorboard else None)
callbacks = [checkpoint_callback, time_callback]
if enable_tensorboard:
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=model_dir,
update_freq=min(1000, params.trainer.validation_interval),
profile_batch=FLAGS.profile_steps)
callbacks.append(tensorboard_callback)
num_epochs = (params.trainer.train_steps //
params.trainer.validation_interval)
current_step = model.optimizer.iterations.numpy()
initial_epoch = current_step // params.trainer.validation_interval
eval_steps = params.trainer.validation_steps if 'eval' in mode else None
if mode in ['train', 'train_and_eval']:
logging.info('Training started')
history = model.fit(
train_dataset,
initial_epoch=initial_epoch,
epochs=num_epochs,
steps_per_epoch=params.trainer.validation_interval,
validation_data=validation_dataset,
validation_steps=eval_steps,
callbacks=callbacks,
)
model.summary()
logging.info('Train history: %s', history.history)
elif mode == 'eval':
logging.info('Evaluation started')
validation_output = model.evaluate(validation_dataset, steps=eval_steps)
logging.info('Evaluation output: %s', validation_output)
else:
raise NotImplementedError('The mode is not implemented: %s' % mode)
def main(_):
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
params = train_utils.parse_configuration(FLAGS)
if params.runtime.num_hpus > 0:
import os
#TODO: remove when SW-49334 is fixed [SW-49404]
os.environ["TF_DISABLE_EAGER_TO_FUNC_REWRITER"] = "1"
from habana_frameworks.tensorflow import load_habana_module
load_habana_module()
if params.task.train_data.deterministic or params.task.validation_data.deterministic:
import os
os.environ['PYTHONHASHSEED'] = '0'
os.environ['TF_DETERMINISTIC_OPS'] = '1'
import numpy
numpy.random.seed(0)
import tensorflow as tf
tf.random.set_seed(0)
tf.compat.v1.set_random_seed(0)
import random
random.seed(0)
if FLAGS.dtype == "bf16":
print("Using bf16 config list {}".format(FLAGS.bf16_config_path))
os.environ['TF_BF16_CONVERSION'] = FLAGS.bf16_config_path
hls_addresses = str(os.environ.get("MULTI_HLS_IPS",
"127.0.0.1")).split(",")
TF_BASE_PORT = 2410
mpi_rank = comm_rank()
mpi_size = comm_size()
if params.runtime.num_hpus > 1:
model_dir = os.path.join(FLAGS.model_dir, "worker_" + str(mpi_rank))
else:
model_dir = FLAGS.model_dir
#prepare a comma-seperated list of device addreses
worker_list = []
for address in hls_addresses:
for rank in range(mpi_size // len(hls_addresses)):
worker_list.append(address + ':' + str(TF_BASE_PORT + rank))
worker_hosts = ",".join(worker_list)
task_index = mpi_rank
# Configures cluster spec for distribution strategy.
distribution_utils.configure_cluster(worker_hosts, task_index)
if 'train' in FLAGS.mode:
# Pure eval modes do not output yaml files. Otherwise continuous eval job
# may race against the train job for writing the same file.
train_utils.serialize_config(params, model_dir)
# Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
# can have significant impact on model speeds by utilizing float16 in case of
# GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
# dtype is float16
if params.runtime.mixed_precision_dtype:
performance.set_mixed_precision_policy(
params.runtime.mixed_precision_dtype)
distribution_strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
num_hpus=params.runtime.num_hpus,
tpu_address=params.runtime.tpu)
with distribution_strategy.scope():
task = task_factory.get_task(params.task, logging_dir=model_dir)
train_lib.run_experiment(distribution_strategy=distribution_strategy,
task=task,
mode=FLAGS.mode,
params=params,
model_dir=model_dir)
train_utils.save_gin_config(FLAGS.mode, model_dir)
