def run(self):
""" Export savedmodel for sequence generator.
Step 1: Build model and restore checkpoints.
Step 2: Export.
"""
with training_utils.get_strategy_scope(self.strategy):
model = self._build_and_restore_model()
keras_model = self.build_generation_model(self.task, model, self._search_layer)
keras_model.summary()
summary_model_variables(keras_model)
export_path = os.path.join(self._export_path, str(self._version))
logging.info("Saving model to {}".format(export_path))
tf.keras.models.save_model(
keras_model,
export_path,
overwrite=True,
include_optimizer=False,
save_format=None,
signatures=None,
options=None)
loaded = tf.saved_model.load(export_path)
logging.info("========== signatures ==========")
for x in loaded.signatures.keys():
logging.info(f"structured outputs for {x}:")
logging.info(" {}".format(str(loaded.signatures["serving_default"].structured_outputs)))
def run(self):
""" Evaluation on a existing model.
Step 1: Build model.
Step 2: Builds evaluation dataset.
Step 3: Restore checkpoints.
Step 4: Evaluate and reduce metric.
"""
assert not isinstance(self.custom_dataset, MultipleDataset), (
"SequenceEvaluator only supports single dataset.")
with training_utils.get_strategy_scope(self.strategy):
tfds = training_utils.build_datasets(compat.ModeKeys.EVAL, self.strategy,
self.custom_dataset, self.task)
keras_model = self.build_evaluation_model(self.task, self.model, self._criterion)
keras_model.summary()
summary_model_variables(keras_model)
# Step 4: Restore checkpoints.
stat = restore_checkpoint_if_possible(self.model, self.model_dir)
if not stat:
logging.info(f"WARNING: Fail to restore checkpoint from {self.model_dir}. "
"We assume this was done on purpose. ")
# Step 5: Evaluate and reduce metric.
predict_fn = keras_model.make_predict_function()
iterator = iter(training_utils.maybe_distribution_dataset(
self.strategy, tfds.prefetch(tf.data.experimental.AUTOTUNE)))
with tf.io.gfile.GFile(self._output_file, "w") as fw:
while True:
try:
preds = predict_fn(iterator)
for pred in self._criterion.reduce_sample_metrics(preds):
fw.write(str(pred) + "\n")
except (StopIteration, tf.errors.OutOfRangeError):
break
def run(self):
""" Evaluation on a existing model.
Step 1: Build model.
Step 2: Builds evaluation dataset.
Step 3: Restore checkpoints.
Step 4: Evaluate and reduce metric.
"""
with training_utils.get_strategy_scope(self.strategy):
tfds = training_utils.build_datasets(compat.ModeKeys.EVAL,
self.strategy,
self.custom_dataset,
self.task,
cache=True)
keras_model = self.build_evaluation_model(self.task, self.model,
self._criterion)
keras_model.summary()
summary_model_variables(keras_model)
# Step 4: Restore checkpoints.
stat = restore_checkpoint_if_possible(self.model, self.model_dir)
if not stat:
logging.info(
f"WARNING: Fail to restore checkpoint from {self.model_dir}. "
"We assume this was done on purpose. ")
# Step 5: Evaluate and reduce metric.
start_time = time.time()
results, avg_res, whole_res = training_utils.reduce_eval_results(
self._criterion, self.custom_dataset,
training_utils.make_predictions(self.strategy, keras_model, tfds,
self.custom_dataset))
logging.info("Evaluation elapsed: %.2fs", time.time() - start_time)
def _display(res, name=None):
if name:
logging.info(f"Evaluation Results ({name}):")
for k, v in res.items():
logging.info(" %s: %.2f", k, v)
if not isinstance(self.custom_dataset, MultipleDataset):
_display(results)
else:
for name, res in results.items():
_display(res, name)
_display(
avg_res,
f"on average by weights {self.custom_dataset.sample_weights}")
_display(whole_res, "mixed")
def run(self):
""" Training a neural model.
Step 1: Create training model
Step 2: Restore checkpoint/pretrain model/global_step if exists.
Step 3: Fetch training data.
Step 5: Fetch training training.
Step 6: TRAIN!!!
"""
if self._hvd_backend == "horovod":
import horovod.tensorflow.keras as hvd
elif self._hvd_backend == "byteps":
import byteps.tensorflow.keras as hvd
tfds = training_utils.build_datasets(compat.ModeKeys.TRAIN,
self.strategy,
self.custom_dataset, self.task)
if isinstance(self.custom_dataset, MultipleDataset):
_tfds = None
for _, ds in tfds.items():
if _tfds is None:
_tfds = ds
else:
_tfds = _tfds.concatenate(ds)
tfds = _tfds
tfds = tfds.prefetch(tf.data.experimental.AUTOTUNE)
# Step 1: create a model
with training_utils.get_strategy_scope(self.strategy):
inps = self.task.create_inputs(compat.ModeKeys.TRAIN)
formatted_inps = self.task.example_to_input(
inps, compat.ModeKeys.TRAIN)
model_out = self.model(formatted_inps, is_training=True)
for metric_layer in self.task.build_metric_layer():
model_out = metric_layer([formatted_inps, model_out])
if (LooseVersion(tf.__version__) < LooseVersion("2.3")
or LooseVersion(tf.__version__) >= LooseVersion("2.5")):
logging.info(
f"Warning: Need further check on AccumgradKerasModel when TF version={tf.__version__}. "
f"Here we ignore update_cycle={self._update_cycle}, "
f"clip_value={self._clip_value}, clip_norm={self._clip_norm}."
)
keras_model = tf.keras.Model(inps, model_out)
elif compat.IS_PREV_TF_2_4_0:
from neurst.training.gradaccum_keras_model import TF23GradAccumKerasModel
keras_model = TF23GradAccumKerasModel(
inps,
model_out,
update_cycle=self._update_cycle,
clip_value=self._clip_value,
clip_norm=self._clip_norm,
freeze_variables=self._freeze_variables)
else:
keras_model = GradAccumKerasModel(
inps,
model_out,
update_cycle=self._update_cycle,
clip_value=self._clip_value,
clip_norm=self._clip_norm,
freeze_variables=self._freeze_variables)
loss = self._criterion.reduce_loss(formatted_inps, model_out)
if compat.is_tf_tensor(loss) or isinstance(loss, (list, tuple)):
keras_model.add_loss(loss)
elif isinstance(loss, dict):
for _name, _loss in loss.items():
keras_model.add_loss(_loss)
keras_model.add_metric(_loss,
name=_name + "_mean",
aggregation="mean")
else:
raise ValueError("criterion.reduce_loss returns "
"unsupported value of type: {}".format(
type(loss)))
self._restore_ckpt_or_pretrain()
self._lr_schedule = build_lr_schedule(self._lr_schedule_args)
if self._pruning_schedule is not None:
self._optimizer = create_pruning_optimizer(
self._optimizer,
self.model,
self._pruning_schedule,
pruning_variable_pattern=self._pruning_variable_pattern,
nopruning_variable_pattern=self.
_nopruning_variable_pattern,
keep_prune_property=True)
self._optimizer = training_utils.handle_fp16_and_distributed_optimizer(
self._optimizer, self._lr_schedule, self._hvd_backend)
if self._hvd_backend is None:
keras_model.compile(self._optimizer)
else:
# NOTE: we already add Horovod DistributedOptimizer in `_handle_fp16_and_distributed_optimizer`.
# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
# uses hvd.DistributedOptimizer() to compute gradients.
keras_model.compile(self._optimizer,
experimental_run_tf_function=False)
keras_model.summary()
summary_model_variables(self.model, self._freeze_variables)
# initialize the checkpoint manager
_ = compat.get_saver_or_default(
self.model,
self.model_dir,
max_to_keep=self._checkpoints_max_to_keep)
# build training training
if not self._tb_log_dir:
self._tb_log_dir = os.path.join(self.model_dir, "train")
training_callbacks = [
MetricReductionCallback(self.strategy,
self._summary_steps,
self._tb_log_dir,
device="GPU:0",
lr_schedule=self._lr_schedule)
]
if self._hvd_backend is None or hvd.rank() == 0:
training_callbacks.append(
CustomCheckpointCallback(
self.task.model_configs(self.model),
save_checkpoint_steps=self._save_checkpoint_steps))
if self._validator is not None:
training_callbacks.append(
self._validator.build(self.strategy, self.task,
self.model))
if self._hvd_backend is not None:
# Horovod: average metrics among workers at the end of every epoch.
#
# Note: This callback must be in the list before the ReduceLROnPlateau,
# TensorBoard or other metrics-based training.
# NOTE!!! HERE we already integrate the metric averaging behaviour into the MetricReductionCallback.
# training_callbacks.insert(0, hvd.callbacks.MetricAverageCallback(device="GPU:0"))
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
training_callbacks.insert(
0,
hvd.callbacks.BroadcastGlobalVariablesCallback(0,
device="GPU:0"))
if self._lr_schedule is not None:
training_callbacks.append(
LearningRateScheduler(self._lr_schedule))
if self._experimental_count_batch_num:
logging.info("Scanning the dataset......")
iterator = iter(
training_utils.maybe_distribution_dataset(self.strategy, tfds))
cnt = 0
for _ in iterator:
cnt += 1
logging.info(f"Total {cnt} batches per EPOCH.")
history = keras_model.fit(
map_data_for_keras(tfds.repeat()),
initial_epoch=0,
epochs=1,
steps_per_epoch=self._train_steps, # * args["update_cycle"],
verbose=2,
callbacks=training_callbacks)
logging.info(history.history)
def run(self):
""" Sequence generation from an existing model checkpoint.
Step 1: Build model and restore checkpoints.
Step 2: Build test dataset.
Step 3: Sequence generation.
Step 4: Evaluation using metric.
"""
# Step 3: Build model.
with training_utils.get_strategy_scope(self.strategy):
model = self._build_and_restore_model()
keras_model = self.build_generation_model(self.task, model,
self._search_layer)
tfds = training_utils.build_datasets(compat.ModeKeys.INFER,
self.strategy,
self.custom_dataset,
self.task)
keras_model.summary()
summary_model_variables(keras_model)
# Step 5: Sequence Generation.
start_time = time.time()
results = training_utils.make_predictions(
self.strategy,
keras_model,
tfds,
self.custom_dataset,
map_func=lambda y: SequenceGenerator.postprocess_generation(
self.task, y))
logging.info("Generation elapsed: %.2fs", time.time() - start_time)
if self._output_file:
if isinstance(self.custom_dataset, MultipleDataset):
if isinstance(self._output_file, dict):
for name in results:
if self._output_file.get(name, None):
with tf.io.gfile.GFile(self._output_file[name],
"w") as fw:
fw.write("\n".join(results[name]) + "\n")
logging.info(
"Saving generation of dataset {} results into {}"
.format(name, self._output_file[name]))
else:
logging.info(
"Unsupported type of `output_file`={}({}) for MultipleDataset."
.format(self._output_file, type(self._output_file)))
else:
if isinstance(self._output_file, str):
with tf.io.gfile.GFile(self._output_file, "w") as fw:
fw.write("\n".join(results) + "\n")
logging.info("Saving generation results into {}".format(
self._output_file))
else:
logging.info(
f"WARNING: No generation results are saved due to unsupported type "
f"of `output_file`: {self._output_file} ({type(self._output_file)})"
)
# Step 6: evaluation using metric
def _display(res, name=None):
if name:
logging.info(f"Evaluation Result ({name}):")
else:
logging.info("Evaluation Result:")
for k, v in res.items():
logging.info(" %s=%.2f", k, v)
if self._metric is not None:
saving_metrics = dict()
if isinstance(self.custom_dataset, MultipleDataset):
on_average = {}
mixed_dsnames = []
mixed_hypos = []
mixed_refs = []
for name in tfds:
assert isinstance(self.custom_dataset.datasets[name],
TextGenDataset)
if self.custom_dataset.datasets[name].targets:
metric_result = self._metric(
results[name],
self.custom_dataset.datasets[name].targets)
for k, v in metric_result.items():
if k not in on_average:
on_average[k] = 0.
on_average[
k] += self.custom_dataset.sample_weights[
name] * v
_display(metric_result, name)
mixed_dsnames.append(name)
mixed_hypos.extend(results[name])
mixed_refs.extend(
self.custom_dataset.datasets[name].targets)
saving_metrics[name] = metric_result
if len(mixed_dsnames) > 1:
_display(
on_average,
f"on average by weights {self._custom_dataset.sample_weights}"
)
mixed_metric_result = self._metric(mixed_refs, mixed_hypos)
_display(mixed_metric_result,
"mixed of {}".format(",".join(mixed_dsnames)))
saving_metrics["MIXED"] = mixed_metric_result
else:
assert isinstance(self.custom_dataset, TextGenDataset)
if self.custom_dataset.targets is not None:
metric_result = self._metric(results,
self.custom_dataset.targets)
_display(metric_result)
saving_metrics = metric_result
if self._save_metric is not None:
logging.info(f"Saving metric results into {self._save_metric}")
with tf.io.gfile.GFile(self._save_metric, "w") as fw:
json.dump(saving_metrics, fw)