def __init__(
self,
model: TFPreTrainedModel,
args: TFTrainingArguments,
train_dataset: Optional[tf.data.Dataset] = None,
eval_dataset: Optional[tf.data.Dataset] = None,
test_dataset: Optional[tf.data.Dataset] = None,
dataset_info: Optional[DatasetInfo] = None,
):
self.model = model
self.args = args
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
self.test_dataset = test_dataset
self.dataset_info = dataset_info
self.gradient_accumulator = GradientAccumulator()
self.accum_steps = 1
if self.args.strategy_name == "mirrored":
self.strategy = tf.distribute.MirroredStrategy()
elif self.args.strategy_name == "onedevice":
if len(tf.config.list_physical_devices('GPU')) >= 1:
self.strategy = tf.distribute.OneDeviceStrategy(
device="/gpu:0")
else:
self.strategy = tf.distribute.OneDeviceStrategy(
device="/cpu:0")
else:
raise ValueError("The strategy {} does not exists.".format(
self.args.strategy_name))
# To conform with Trainer's API we call this from here.
# All args should be in the `args` already.
self._setup_training()
def __init__(self,
config_path: str = None,
config: TrainerConfig = None,
**kwargs):
"""
The list of keys in kwargs here should be generic to all the possible models/architectures
and not specific to such or such dataset/task.
"""
if config and not config_path:
if not isinstance(config, TrainerConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
.format(self.__class__.__name__))
self.config = config
elif config_path and not config:
self.config, unused_kwargs = TrainerConfig.from_trainer(
config_path,
return_unused_kwargs=True,
return_unused_config=True,
**kwargs)
else:
raise ValueError(
"the config_path and config parameters cannot be both filled or None."
)
self.strategy_name: str = unused_kwargs.pop("strategy_name",
"onedevice")
self.data_processor_config: Dict = unused_kwargs.pop(
"data_processor", None)
assert len(
unused_kwargs) == 0, "unrecognized params passed: %s" % ",".join(
unused_kwargs.keys())
self.datasets: Dict[str, tf.data.Dataset] = {}
self.dataset_info: DatasetInfo
self.gradient_accumulator = GradientAccumulator()
self.accum_steps = 1
if self.config.mode == "classification":
self.processor = DataProcessorForSequenceClassification(
**self.data_processor_config)
self.model_class = TFAutoModelForSequenceClassification
elif self.config.mode == "labelling":
self.processor = DataProcessorForTokenClassification(
**self.data_processor_config)
self.model_class = TFAutoModelForTokenClassification
if self.strategy_name == "mirrored":
self.strategy = tf.distribute.MirroredStrategy()
elif self.strategy_name == "onedevice":
if len(tf.config.list_physical_devices('GPU')) >= 1:
self.strategy = tf.distribute.OneDeviceStrategy(
device="/gpu:0")
else:
self.strategy = tf.distribute.OneDeviceStrategy(
device="/cpu:0")
else:
raise ValueError("The strategy {} does not exists.".format(
self.strategy_name))
def testGradientAccumulatorDistributionStrategy(self):
context._context = None
ops.enable_eager_execution_internal()
physical_devices = tf.config.list_physical_devices("CPU")
if len(physical_devices) == 1:
tf.config.set_logical_device_configuration(physical_devices[0], [
tf.config.LogicalDeviceConfiguration(),
tf.config.LogicalDeviceConfiguration()
])
devices = tf.config.list_logical_devices(device_type="CPU")
strategy = tf.distribute.MirroredStrategy(devices=devices[:2])
with strategy.scope():
accumulator = GradientAccumulator()
variable = tf.Variable([4.0, 3.0])
optimizer, _ = create_optimizer(5e-5, 10, 5)
gradient_placeholder = tf.Variable([0.0, 0.0], trainable=False)
def accumulate_on_replica(gradient):
accumulator([gradient])
def apply_on_replica():
optimizer.apply_gradients(
list(zip(accumulator.gradients, [variable])))
@tf.function
def accumulate(grad1, grad2):
with strategy.scope():
local_variables = strategy.experimental_local_results(
gradient_placeholder)
local_variables[0].assign(grad1)
local_variables[1].assign(grad2)
strategy.experimental_run_v2(accumulate_on_replica,
args=(gradient_placeholder, ))
@tf.function
def apply_grad():
with strategy.scope():
strategy.experimental_run_v2(apply_on_replica)
def _check_local_values(grad1, grad2):
values = strategy.experimental_local_results(
accumulator._gradients[0])
self.assertListAlmostEqual(values[0].value(), grad1, tol=1e-2)
self.assertListAlmostEqual(values[1].value(), grad2, tol=1e-2)
accumulate([1.0, 2.0], [-1.0, 1.0])
accumulate([3.0, -1.0], [-1.0, -1.0])
accumulate([-2.0, 2.0], [3.0, -2.0])
self.assertEqual(accumulator.step, 3)
_check_local_values([2.0, 3.0], [1.0, -2.0])
apply_grad()
self.assertListAlmostEqual(variable.value(), [4.0, 3.0], tol=1e-2)
accumulator.reset()
self.assertEqual(accumulator.step, 0)
_check_local_values([0.0, 0.0], [0.0, 0.0])
def testGradientAccumulator(self):
accumulator = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0])])
accumulator([tf.constant([-2.0, 1.0])])
accumulator([tf.constant([-1.0, 2.0])])
with self.assertRaises(ValueError):
accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
self.assertEqual(accumulator.step, 3)
self.assertEqual(len(accumulator.gradients), 1)
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [-2.0, 5.0], tol=1e-2)
accumulator.reset()
self.assertEqual(accumulator.step, 0)
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [0.0, 0.0], tol=1e-2)
def train(args, strategy, train_dataset, tokenizer, model, num_train_examples,
labels, train_batch_size, pad_token_label_id):
if args["max_steps"] > 0:
num_train_steps = args["max_steps"] * args[
"gradient_accumulation_steps"]
args["num_train_epochs"] = 1
else:
num_train_steps = (math.ceil(num_train_examples / train_batch_size) //
args["gradient_accumulation_steps"] *
args["num_train_epochs"])
writer = tf.summary.create_file_writer("/tmp/mylogs")
with strategy.scope():
loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE)
optimizer = create_optimizer(args["learning_rate"], num_train_steps,
args["warmup_steps"])
if args["fp16"]:
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, "dynamic")
loss_metric = tf.keras.metrics.Mean(name="loss", dtype=tf.float32)
gradient_accumulator = GradientAccumulator()
logging.info("***** Running training *****")
logging.info(" Num examples = %d", num_train_examples)
logging.info(" Num Epochs = %d", args["num_train_epochs"])
logging.info(" Instantaneous batch size per device = %d",
args["per_device_train_batch_size"])
logging.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
train_batch_size * args["gradient_accumulation_steps"],
)
logging.info(" Gradient Accumulation steps = %d",
args["gradient_accumulation_steps"])
logging.info(" Total training steps = %d", num_train_steps)
model.summary()
@tf.function
def apply_gradients():
grads_and_vars = []
for gradient, variable in zip(gradient_accumulator.gradients,
model.trainable_variables):
if gradient is not None:
scaled_gradient = gradient / (
args["n_device"] * args["gradient_accumulation_steps"])
grads_and_vars.append((scaled_gradient, variable))
else:
grads_and_vars.append((gradient, variable))
optimizer.apply_gradients(grads_and_vars, args["max_grad_norm"])
gradient_accumulator.reset()
@tf.function
def train_step(train_features, train_labels):
def step_fn(train_features, train_labels):
inputs = {
"attention_mask": train_features["attention_mask"],
"training": True
}
if "token_type_ids" in train_features:
inputs["token_type_ids"] = train_features["token_type_ids"]
with tf.GradientTape() as tape:
logits = model(train_features["input_ids"], **inputs)[0]
active_loss = tf.reshape(train_labels,
(-1, )) != pad_token_label_id
active_logits = tf.boolean_mask(
tf.reshape(logits, (-1, len(labels))), active_loss)
active_labels = tf.boolean_mask(
tf.reshape(train_labels, (-1, )), active_loss)
cross_entropy = loss_fct(active_labels, active_logits)
loss = tf.reduce_sum(cross_entropy) * (1.0 / train_batch_size)
grads = tape.gradient(loss, model.trainable_variables)
gradient_accumulator(grads)
return cross_entropy
per_example_losses = strategy.experimental_run_v2(step_fn,
args=(train_features,
train_labels))
mean_loss = strategy.reduce(tf.distribute.ReduceOp.MEAN,
per_example_losses,
axis=0)
return mean_loss
current_time = datetime.datetime.now()
train_iterator = master_bar(range(args["num_train_epochs"]))
global_step = 0
logging_loss = 0.0
for epoch in train_iterator:
epoch_iterator = progress_bar(train_dataset,
total=num_train_steps,
parent=train_iterator,
display=args["n_device"] > 1)
step = 1
with strategy.scope():
for train_features, train_labels in epoch_iterator:
loss = train_step(train_features, train_labels)
if step % args["gradient_accumulation_steps"] == 0:
strategy.experimental_run_v2(apply_gradients)
loss_metric(loss)
global_step += 1
if args["logging_steps"] > 0 and global_step % args[
"logging_steps"] == 0:
# Log metrics
if (
args["n_device"] == 1
and args["evaluate_during_training"]
): # Only evaluate when single GPU otherwise metrics may not average well
y_true, y_pred, eval_loss = evaluate(
args,
strategy,
model,
tokenizer,
labels,
pad_token_label_id,
mode="dev")
report = metrics.classification_report(y_true,
y_pred,
digits=4)
logging.info("Eval at step " + str(global_step) +
"\n" + report)
logging.info("eval_loss: " + str(eval_loss))
precision = metrics.precision_score(y_true, y_pred)
recall = metrics.recall_score(y_true, y_pred)
f1 = metrics.f1_score(y_true, y_pred)
with writer.as_default():
tf.summary.scalar("eval_loss", eval_loss,
global_step)
tf.summary.scalar("precision", precision,
global_step)
tf.summary.scalar("recall", recall,
global_step)
tf.summary.scalar("f1", f1, global_step)
lr = optimizer.learning_rate
learning_rate = lr(step)
with writer.as_default():
tf.summary.scalar("lr", learning_rate, global_step)
tf.summary.scalar(
"loss", (loss_metric.result() - logging_loss) /
args["logging_steps"], global_step)
logging_loss = loss_metric.result()
with writer.as_default():
tf.summary.scalar("loss",
loss_metric.result(),
step=step)
if args["save_steps"] > 0 and global_step % args[
"save_steps"] == 0:
# Save model checkpoint
output_dir = os.path.join(
args["output_dir"],
"checkpoint-{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model.save_pretrained(output_dir)
logging.info("Saving model checkpoint to %s",
output_dir)
train_iterator.child.comment = f"loss : {loss_metric.result()}"
step += 1
train_iterator.write(f"loss epoch {epoch + 1}: {loss_metric.result()}")
loss_metric.reset_states()
logging.info(" Training took time = {}".format(datetime.datetime.now() -
current_time))
def _custom_train(
self,
train_dataset,
tokenizer,
model,
num_train_examples,
train_batch_size,
):
config = self.parent.config._asdict()
config["strategy"] = self.parent.config.strategy
config["n_device"] = self.parent.config.n_device
labels = config["ner_tags"]
if config["max_steps"] > 0:
num_train_steps = (
config["max_steps"] * config["gradient_accumulation_steps"]
)
config["epochs"] = 1
else:
num_train_steps = (
math.ceil(num_train_examples / train_batch_size)
// config["gradient_accumulation_steps"]
* config["epochs"]
)
with config["strategy"].scope():
loss_fct = self.tf.keras.losses.SparseCategoricalCrossentropy(
reduction=self.tf.keras.losses.Reduction.NONE
)
optimizer = create_optimizer(
config["learning_rate"],
num_train_steps,
config["warmup_steps"],
)
if config["use_fp16"]:
optimizer = self.tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, "dynamic"
)
loss_metric = self.tf.keras.metrics.Mean(
name="loss", dtype=self.tf.float32
)
gradient_accumulator = GradientAccumulator()
self.logger.info("***** Running training *****")
self.logger.info(" Num examples = %d", num_train_examples)
self.logger.info(" Num Epochs = %d", config["epochs"])
self.logger.info(
" Instantaneous batch size per device = %d",
config["per_device_train_batch_size"],
)
self.logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
train_batch_size * config["gradient_accumulation_steps"],
)
self.logger.info(
" Gradient Accumulation steps = %d",
config["gradient_accumulation_steps"],
)
self.logger.info(" Total training steps = %d", num_train_steps)
self.logger.debug(model.summary())
@self.tf.function
def apply_gradients():
grads_and_vars = []
for gradient, variable in zip(
gradient_accumulator.gradients, model.trainable_variables
):
if gradient is not None:
scaled_gradient = gradient / (
config["n_device"]
* config["gradient_accumulation_steps"]
)
grads_and_vars.append((scaled_gradient, variable))
else:
grads_and_vars.append((gradient, variable))
optimizer.apply_gradients(grads_and_vars, config["max_grad_norm"])
gradient_accumulator.reset()
@self.tf.function
def train_step(train_features, train_labels):
def step_fn(train_features, train_labels):
inputs = {
"attention_mask": train_features["input_mask"],
"training": True,
}
if config["model_architecture_type"] != "distilbert":
inputs["token_type_ids"] = (
train_features["segment_ids"]
if config["model_architecture_type"]
in ["bert", "xlnet"]
else None
)
with self.tf.GradientTape() as tape:
logits = model(train_features["input_ids"], **inputs)[0]
logits = self.tf.reshape(logits, (-1, len(labels) + 1))
active_loss = self.tf.reshape(
train_features["input_mask"], (-1,)
)
active_logits = self.tf.boolean_mask(logits, active_loss)
train_labels = self.tf.reshape(train_labels, (-1,))
active_labels = self.tf.boolean_mask(
train_labels, active_loss
)
cross_entropy = loss_fct(active_labels, active_logits)
loss = self.tf.reduce_sum(cross_entropy) * (
1.0 / train_batch_size
)
grads = tape.gradient(loss, model.trainable_variables)
print(grads)
gradient_accumulator(grads)
return cross_entropy
per_example_losses = config["strategy"].run(
step_fn, args=(train_features, train_labels)
)
mean_loss = config["strategy"].reduce(
self.tf.distribute.ReduceOp.MEAN, per_example_losses, axis=0
)
return mean_loss
current_time = datetime.datetime.now()
train_iterator = master_bar(range(config["epochs"]))
global_step = 0
self.logger_loss = 0.0
for epoch in train_iterator:
epoch_iterator = progress_bar(
train_dataset,
total=num_train_steps,
parent=train_iterator,
display=config["n_device"] > 1,
)
step = 1
with config["strategy"].scope():
for train_features, train_labels in epoch_iterator:
loss = train_step(train_features, train_labels)
if step % config["gradient_accumulation_steps"] == 0:
config["strategy"].run(apply_gradients)
loss_metric(loss)
global_step += 1
if (
config["save_steps"] > 0
and global_step % config["save_steps"] == 0
):
# Save model checkpoint
output_dir = os.path.join(
config["output_dir"],
"checkpoint-{}".format(global_step),
)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model.save_pretrained(output_dir)
self.logger.info(
"Saving model checkpoint to %s", output_dir
)
train_iterator.child.comment = (
f"loss : {loss_metric.result()}"
)
step += 1
train_iterator.write(
f"loss epoch {epoch + 1}: {loss_metric.result()}"
)
loss_metric.reset_states()
self.logger.debug(
" Training took time = {}".format(
datetime.datetime.now() - current_time
)
)
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
TrainingArguments, LoggingArguments))
model_args, data_args, train_args, log_args = parser.parse_args_into_dataclasses(
)
tf.random.set_seed(train_args.seed)
tf.autograph.set_verbosity(0)
# Settings init
parse_bool = lambda arg: arg == "true"
do_gradient_accumulation = train_args.gradient_accumulation_steps > 1
do_xla = not parse_bool(train_args.skip_xla)
do_eager = parse_bool(train_args.eager)
skip_sop = parse_bool(train_args.skip_sop)
skip_mlm = parse_bool(train_args.skip_mlm)
pre_layer_norm = parse_bool(model_args.pre_layer_norm)
fast_squad = parse_bool(log_args.fast_squad)
dummy_eval = parse_bool(log_args.dummy_eval)
squad_steps = get_squad_steps(log_args.extra_squad_steps)
is_sagemaker = data_args.fsx_prefix.startswith("/opt/ml")
disable_tqdm = is_sagemaker
global max_grad_norm
max_grad_norm = train_args.max_grad_norm
# Horovod init
hvd.init()
gpus = tf.config.list_physical_devices("GPU")
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.set_visible_devices(gpus[hvd.local_rank()], "GPU")
# XLA, AutoGraph
tf.config.optimizer.set_jit(do_xla)
tf.config.experimental_run_functions_eagerly(do_eager)
if hvd.rank() == 0:
# Run name should only be used on one process to avoid race conditions
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
platform = "sm" if is_sagemaker else "eks"
if skip_sop:
loss_str = "-skipsop"
elif skip_mlm:
loss_str = "-skipmlm"
else:
loss_str = ""
metadata = (f"{model_args.model_type}"
f"-{model_args.model_size}"
f"-{model_args.load_from}"
f"-{hvd.size()}gpus"
f"-{train_args.batch_size}batch"
f"-{train_args.gradient_accumulation_steps}accum"
f"-{train_args.learning_rate}maxlr"
f"-{train_args.end_learning_rate}endlr"
f"-{train_args.learning_rate_decay_power}power"
f"-{train_args.max_grad_norm}maxgrad"
f"-{train_args.optimizer}opt"
f"-{train_args.total_steps}steps"
f"-{data_args.max_seq_length}seq"
f"-{data_args.max_predictions_per_seq}preds"
f"-{'preln' if pre_layer_norm else 'postln'}"
f"{loss_str}"
f"-{model_args.hidden_dropout_prob}dropout"
f"-{train_args.seed}seed")
run_name = f"{current_time}-{platform}-{metadata}-{train_args.name if train_args.name else 'unnamed'}"
# Logging should only happen on a single process
# https://stackoverflow.com/questions/9321741/printing-to-screen-and-writing-to-a-file-at-the-same-time
level = logging.INFO
format = "%(asctime)-15s %(name)-12s: %(levelname)-8s %(message)s"
handlers = [
logging.FileHandler(
f"{data_args.fsx_prefix}/logs/albert/{run_name}.log"),
TqdmLoggingHandler(),
]
logging.basicConfig(level=level, format=format, handlers=handlers)
# Check that arguments passed in properly, only after registering the alert_func and logging
assert not (skip_sop
and skip_mlm), "Cannot use --skip_sop and --skip_mlm"
wrap_global_functions(do_gradient_accumulation)
if model_args.model_type == "albert":
model_desc = f"albert-{model_args.model_size}-v2"
elif model_args.model_type == "bert":
model_desc = f"bert-{model_args.model_size}-uncased"
config = AutoConfig.from_pretrained(model_desc)
config.pre_layer_norm = pre_layer_norm
config.hidden_dropout_prob = model_args.hidden_dropout_prob
model = TFAutoModelForPreTraining.from_config(config)
# Create optimizer and enable AMP loss scaling.
schedule = LinearWarmupPolyDecaySchedule(
max_learning_rate=train_args.learning_rate,
end_learning_rate=train_args.end_learning_rate,
warmup_steps=train_args.warmup_steps,
total_steps=train_args.total_steps,
power=train_args.learning_rate_decay_power,
)
if train_args.optimizer == "lamb":
opt = LAMB(
learning_rate=schedule,
weight_decay_rate=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
)
elif train_args.optimizer == "adam":
opt = AdamW(weight_decay=0.0, learning_rate=schedule)
opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(
opt, loss_scale="dynamic")
gradient_accumulator = GradientAccumulator()
loaded_opt_weights = None
if model_args.load_from == "scratch":
pass
elif model_args.load_from.startswith("huggingface"):
assert (model_args.model_type == "albert"
), "Only loading pretrained albert models is supported"
huggingface_name = f"albert-{model_args.model_size}-v2"
if model_args.load_from == "huggingface":
albert = TFAlbertModel.from_pretrained(huggingface_name,
config=config)
model.albert = albert
else:
model_ckpt, opt_ckpt = get_checkpoint_paths_from_prefix(
model_args.checkpoint_path)
model = TFAutoModelForPreTraining.from_config(config)
if hvd.rank() == 0:
model.load_weights(model_ckpt)
loaded_opt_weights = np.load(opt_ckpt, allow_pickle=True)
# We do not set the weights yet, we have to do a first step to initialize the optimizer.
# Train filenames are [1, 2047], Val filenames are [0]. Note the different subdirectories
# Move to same folder structure and remove if/else
if model_args.model_type == "albert":
train_glob = f"{data_args.fsx_prefix}/albert_pretraining/tfrecords/train/max_seq_len_{data_args.max_seq_length}_max_predictions_per_seq_{data_args.max_predictions_per_seq}_masked_lm_prob_15/albert_*.tfrecord"
validation_glob = f"{data_args.fsx_prefix}/albert_pretraining/tfrecords/validation/max_seq_len_{data_args.max_seq_length}_max_predictions_per_seq_{data_args.max_predictions_per_seq}_masked_lm_prob_15/albert_*.tfrecord"
if model_args.model_type == "bert":
train_glob = f"{data_args.fsx_prefix}/bert_pretraining/max_seq_len_{data_args.max_seq_length}_max_predictions_per_seq_{data_args.max_predictions_per_seq}_masked_lm_prob_15/training/*.tfrecord"
validation_glob = f"{data_args.fsx_prefix}/bert_pretraining/max_seq_len_{data_args.max_seq_length}_max_predictions_per_seq_{data_args.max_predictions_per_seq}_masked_lm_prob_15/validation/*.tfrecord"
train_filenames = glob.glob(train_glob)
validation_filenames = glob.glob(validation_glob)
train_dataset = get_mlm_dataset(
filenames=train_filenames,
max_seq_length=data_args.max_seq_length,
max_predictions_per_seq=data_args.max_predictions_per_seq,
batch_size=train_args.batch_size,
) # Of shape [batch_size, ...]
# Batch of batches, helpful for gradient accumulation. Shape [grad_steps, batch_size, ...]
train_dataset = train_dataset.batch(train_args.gradient_accumulation_steps)
# One iteration with 10 dupes, 8 nodes seems to be 60-70k steps.
train_dataset = train_dataset.prefetch(buffer_size=8)
# Validation should only be done on one node, since Horovod doesn't allow allreduce on a subset of ranks
if hvd.rank() == 0:
validation_dataset = get_mlm_dataset(
filenames=validation_filenames,
max_seq_length=data_args.max_seq_length,
max_predictions_per_seq=data_args.max_predictions_per_seq,
batch_size=train_args.batch_size,
)
# validation_dataset = validation_dataset.batch(1)
validation_dataset = validation_dataset.prefetch(buffer_size=8)
pbar = tqdm.tqdm(train_args.total_steps, disable=disable_tqdm)
summary_writer = None # Only create a writer if we make it through a successful step
logger.info(f"Starting training, job name {run_name}")
i = 0
start_time = time.perf_counter()
for batch in train_dataset:
learning_rate = schedule(step=tf.constant(i, dtype=tf.float32))
loss_scale = opt.loss_scale()
loss, mlm_loss, mlm_acc, sop_loss, sop_acc, grad_norm, weight_norm = train_step(
model=model,
opt=opt,
gradient_accumulator=gradient_accumulator,
batch=batch,
gradient_accumulation_steps=train_args.gradient_accumulation_steps,
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
# Don't want to wrap broadcast_variables() in a tf.function, can lead to asynchronous errors
if i == 0:
if hvd.rank() == 0 and loaded_opt_weights is not None:
opt.set_weights(loaded_opt_weights)
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
i = opt.get_weights()[0] - 1
is_final_step = i >= train_args.total_steps - 1
do_squad = i in squad_steps or is_final_step
# Squad requires all the ranks to train, but results are only returned on rank 0
if do_squad:
squad_results = get_squad_results_while_pretraining(
model=model,
model_size=model_args.model_size,
fsx_prefix=data_args.fsx_prefix,
step=i,
fast=log_args.fast_squad,
dummy_eval=log_args.dummy_eval,
)
if hvd.rank() == 0:
squad_exact, squad_f1 = squad_results["exact"], squad_results[
"f1"]
logger.info(
f"SQuAD step {i} -- F1: {squad_f1:.3f}, Exact: {squad_exact:.3f}"
)
# Re-wrap autograph so it doesn't get arg mismatches
wrap_global_functions(do_gradient_accumulation)
if hvd.rank() == 0:
do_log = i % log_args.log_frequency == 0
do_checkpoint = (
(i > 0) and
(i % log_args.checkpoint_frequency == 0)) or is_final_step
do_validation = (
(i > 0) and
(i % log_args.validation_frequency == 0)) or is_final_step
pbar.update(1)
description = f"Loss: {loss:.3f}, MLM: {mlm_loss:.3f}, SOP: {sop_loss:.3f}, MLM_acc: {mlm_acc:.3f}, SOP_acc: {sop_acc:.3f}"
pbar.set_description(description)
if do_log:
elapsed_time = time.perf_counter() - start_time
if i == 0:
logger.info(f"First step: {elapsed_time:.3f} secs")
else:
it_per_sec = log_args.log_frequency / elapsed_time
logger.info(
f"Train step {i} -- {description} -- It/s: {it_per_sec:.2f}"
)
start_time = time.perf_counter()
if do_checkpoint:
checkpoint_prefix = f"{data_args.fsx_prefix}/checkpoints/albert/{run_name}-step{i}"
model_ckpt = f"{checkpoint_prefix}.ckpt"
opt_ckpt = f"{checkpoint_prefix}-opt.npy"
logger.info(
f"Saving model at {model_ckpt}, optimizer at {opt_ckpt}")
model.save_weights(model_ckpt)
# model.load_weights(model_ckpt)
opt_weights = opt.get_weights()
np.save(opt_ckpt, opt_weights)
# opt.set_weights(opt_weights)
if do_validation:
val_loss, val_mlm_loss, val_mlm_acc, val_sop_loss, val_sop_acc = run_validation(
model=model,
validation_dataset=validation_dataset,
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
description = f"Loss: {val_loss:.3f}, MLM: {val_mlm_loss:.3f}, SOP: {val_sop_loss:.3f}, MLM_acc: {val_mlm_acc:.3f}, SOP_acc: {val_sop_acc:.3f}"
logger.info(f"Validation step {i} -- {description}")
# Create summary_writer after the first step
if summary_writer is None:
summary_writer = tf.summary.create_file_writer(
f"{data_args.fsx_prefix}/logs/albert/{run_name}")
with summary_writer.as_default():
HP_MODEL_TYPE = hp.HParam("model_type",
hp.Discrete(["albert", "bert"]))
HP_MODEL_SIZE = hp.HParam("model_size",
hp.Discrete(["base", "large"]))
HP_LEARNING_RATE = hp.HParam("learning_rate",
hp.RealInterval(1e-5, 1e-1))
HP_BATCH_SIZE = hp.HParam("global_batch_size",
hp.IntInterval(1, 64))
HP_PRE_LAYER_NORM = hp.HParam("pre_layer_norm",
hp.Discrete([True, False]))
HP_HIDDEN_DROPOUT = hp.HParam("hidden_dropout")
hparams = [
HP_MODEL_TYPE,
HP_MODEL_SIZE,
HP_BATCH_SIZE,
HP_LEARNING_RATE,
HP_PRE_LAYER_NORM,
HP_HIDDEN_DROPOUT,
]
HP_F1 = hp.Metric("squad_f1")
HP_EXACT = hp.Metric("squad_exact")
HP_MLM = hp.Metric("val_mlm_acc")
HP_SOP = hp.Metric("val_sop_acc")
HP_TRAIN_LOSS = hp.Metric("train_loss")
HP_VAL_LOSS = hp.Metric("val_loss")
metrics = [
HP_TRAIN_LOSS, HP_VAL_LOSS, HP_F1, HP_EXACT, HP_MLM,
HP_SOP
]
hp.hparams_config(
hparams=hparams,
metrics=metrics,
)
hp.hparams(
{
HP_MODEL_TYPE: model_args.model_type,
HP_MODEL_SIZE: model_args.model_size,
HP_LEARNING_RATE: train_args.learning_rate,
HP_BATCH_SIZE: train_args.batch_size * hvd.size(),
HP_PRE_LAYER_NORM: model_args.pre_layer_norm
== "true",
HP_HIDDEN_DROPOUT: model_args.hidden_dropout_prob,
},
trial_id=run_name,
)
# Log to TensorBoard
with summary_writer.as_default():
tf.summary.scalar("weight_norm", weight_norm, step=i)
tf.summary.scalar("loss_scale", loss_scale, step=i)
tf.summary.scalar("learning_rate", learning_rate, step=i)
tf.summary.scalar("train_loss", loss, step=i)
tf.summary.scalar("train_mlm_loss", mlm_loss, step=i)
tf.summary.scalar("train_mlm_acc", mlm_acc, step=i)
tf.summary.scalar("train_sop_loss", sop_loss, step=i)
tf.summary.scalar("train_sop_acc", sop_acc, step=i)
tf.summary.scalar("grad_norm", grad_norm, step=i)
if do_validation:
tf.summary.scalar("val_loss", val_loss, step=i)
tf.summary.scalar("val_mlm_loss", val_mlm_loss, step=i)
tf.summary.scalar("val_mlm_acc", val_mlm_acc, step=i)
tf.summary.scalar("val_sop_loss", val_sop_loss, step=i)
tf.summary.scalar("val_sop_acc", val_sop_acc, step=i)
if do_squad:
tf.summary.scalar("squad_f1", squad_f1, step=i)
tf.summary.scalar("squad_exact", squad_exact, step=i)
i += 1
if is_final_step:
break
if hvd.rank() == 0:
pbar.close()
logger.info(f"Finished pretraining, job name {run_name}")
def main():
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments,
LoggingArguments, PathArguments))
(
model_args,
data_args,
train_args,
log_args,
path_args,
remaining_strings,
) = parser.parse_args_into_dataclasses(return_remaining_strings=True)
# SageMaker may have some extra strings. TODO: Test this on SM.
assert len(remaining_strings
) == 0, f"The args {remaining_strings} could not be parsed."
tf.random.set_seed(train_args.seed)
tf.autograph.set_verbosity(0)
# Settings init
parse_bool = lambda arg: arg == "true"
do_gradient_accumulation = train_args.gradient_accumulation_steps > 1
do_xla = not parse_bool(train_args.skip_xla)
do_eager = parse_bool(train_args.eager)
skip_sop = parse_bool(train_args.skip_sop)
skip_mlm = parse_bool(train_args.skip_mlm)
pre_layer_norm = parse_bool(model_args.pre_layer_norm)
fast_squad = parse_bool(log_args.fast_squad)
dummy_eval = parse_bool(log_args.dummy_eval)
is_sagemaker = path_args.filesystem_prefix.startswith("/opt/ml")
disable_tqdm = is_sagemaker
global max_grad_norm
max_grad_norm = train_args.max_grad_norm
# Horovod init
hvd.init()
gpus = tf.config.list_physical_devices("GPU")
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.set_visible_devices(gpus[hvd.local_rank()], "GPU")
# XLA, AutoGraph
tf.config.optimizer.set_jit(do_xla)
tf.config.experimental_run_functions_eagerly(do_eager)
if hvd.rank() == 0:
# Run name should only be used on one process to avoid race conditions
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
platform = "sm" if is_sagemaker else "eks"
if skip_sop:
loss_str = "-skipsop"
elif skip_mlm:
loss_str = "-skipmlm"
else:
loss_str = ""
if log_args.run_name is None:
metadata = (
f"{model_args.model_type}"
f"-{model_args.model_size}"
f"-{model_args.load_from}"
f"-{hvd.size()}gpus"
f"-{train_args.per_gpu_batch_size * hvd.size() * train_args.gradient_accumulation_steps}globalbatch"
f"-{train_args.learning_rate}maxlr"
f"-{train_args.learning_rate_decay_power}power"
f"-{train_args.optimizer}opt"
f"-{train_args.total_steps}steps"
f"-{'preln' if pre_layer_norm else 'postln'}"
f"{loss_str}"
f"-{model_args.hidden_dropout_prob}dropout")
run_name = f"{current_time}-{platform}-{metadata}-{train_args.name if train_args.name else 'unnamed'}"
else:
run_name = log_args.run_name
# Logging should only happen on a single process
# https://stackoverflow.com/questions/9321741/printing-to-screen-and-writing-to-a-file-at-the-same-time
level = logging.INFO
format = "%(asctime)-15s %(name)-12s: %(levelname)-8s %(message)s"
handlers = [
logging.FileHandler(
os.path.join(path_args.filesystem_prefix, path_args.log_dir,
f"{run_name}.log")),
TqdmLoggingHandler(),
]
logging.basicConfig(level=level, format=format, handlers=handlers)
# Check that arguments passed in properly, only after registering the alert_func and logging
assert not (skip_sop
and skip_mlm), "Cannot use --skip_sop and --skip_mlm"
wrap_global_functions(do_gradient_accumulation)
# Create optimizer and enable AMP loss scaling.
if train_args.optimizer == "lamb":
optimizer = get_lamb_optimizer(train_args)
elif train_args.optimizer == "adamw":
optimizer = get_adamw_optimizer(train_args)
optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimizer, loss_scale="dynamic")
gradient_accumulator = GradientAccumulator()
loaded_optimizer_weights = None
model = create_model(model_class=TFAutoModelForPreTraining,
model_args=model_args)
tokenizer = create_tokenizer(model_args.model_type)
if model_args.load_from == "checkpoint":
checkpoint_path = os.path.join(path_args.filesystem_prefix,
model_args.checkpoint_path)
model_ckpt, optimizer_ckpt = get_checkpoint_paths_from_prefix(
checkpoint_path)
if hvd.rank() == 0:
model.load_weights(model_ckpt)
if model_args.load_optimizer_state == "true":
loaded_optimizer_weights = np.load(optimizer_ckpt,
allow_pickle=True)
# We do not set the weights yet, we have to do a first step to initialize the optimizer.
# Train filenames are [1, 2047], Val filenames are [0]. Note the different subdirectories
# Move to same folder structure and remove if/else
train_glob = os.path.join(path_args.filesystem_prefix, path_args.train_dir,
"*.tfrecord")
validation_glob = os.path.join(path_args.filesystem_prefix,
path_args.val_dir, "*.tfrecord")
train_filenames = glob.glob(train_glob)
validation_filenames = glob.glob(validation_glob)
train_dataset = get_dataset_from_tfrecords(
model_type=model_args.model_type,
filenames=train_filenames,
max_seq_length=data_args.max_seq_length,
max_predictions_per_seq=data_args.max_predictions_per_seq,
per_gpu_batch_size=train_args.per_gpu_batch_size,
) # Of shape [per_gpu_batch_size, ...]
# Batch of batches, helpful for gradient accumulation. Shape [grad_steps, per_gpu_batch_size, ...]
train_dataset = train_dataset.batch(train_args.gradient_accumulation_steps)
# One iteration with 10 dupes, 8 nodes seems to be 60-70k steps.
train_dataset = train_dataset.prefetch(buffer_size=8)
# Validation should only be done on one node, since Horovod doesn't allow allreduce on a subset of ranks
if hvd.rank() == 0:
validation_dataset = get_dataset_from_tfrecords(
model_type=model_args.model_type,
filenames=validation_filenames,
max_seq_length=data_args.max_seq_length,
max_predictions_per_seq=data_args.max_predictions_per_seq,
per_gpu_batch_size=train_args.per_gpu_batch_size,
)
# validation_dataset = validation_dataset.batch(1)
validation_dataset = validation_dataset.prefetch(buffer_size=8)
pbar = tqdm.tqdm(total=train_args.total_steps, disable=disable_tqdm)
summary_writer = None # Only create a writer if we make it through a successful step
logger.info(f"Starting training, job name {run_name}")
i = 1
start_time = time.perf_counter()
for batch in train_dataset:
learning_rate = optimizer.learning_rate(
step=tf.constant(i, dtype=tf.float32))
# weight_decay = wd_schedule(step=tf.constant(i, dtype=tf.float32))
loss_scale = optimizer.loss_scale()
loss, mlm_loss, mlm_acc, sop_loss, sop_acc, grad_norm, weight_norm = train_step(
model=model,
optimizer=optimizer,
gradient_accumulator=gradient_accumulator,
batch=batch,
gradient_accumulation_steps=train_args.gradient_accumulation_steps,
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
# Don't want to wrap broadcast_variables() in a tf.function, can lead to asynchronous errors
if i == 1:
if hvd.rank() == 0 and loaded_optimizer_weights is not None:
optimizer.set_weights(loaded_optimizer_weights)
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
i = optimizer.get_weights()[0]
is_final_step = i >= train_args.total_steps
do_squad = (log_args.squad_frequency != 0) and (
(i % log_args.squad_frequency == 0) or is_final_step)
# Squad requires all the ranks to train, but results are only returned on rank 0
if do_squad:
squad_results = get_squad_results_while_pretraining(
model=model,
tokenizer=tokenizer,
model_size=model_args.model_size,
filesystem_prefix=path_args.filesystem_prefix,
step=i,
dataset=data_args.squad_version,
fast=log_args.fast_squad,
dummy_eval=log_args.dummy_eval,
)
if hvd.rank() == 0:
squad_exact, squad_f1 = squad_results["exact"], squad_results[
"f1"]
logger.info(
f"SQuAD step {i} -- F1: {squad_f1:.3f}, Exact: {squad_exact:.3f}"
)
# Re-wrap autograph so it doesn't get arg mismatches
wrap_global_functions(do_gradient_accumulation)
gc.collect()
if hvd.rank() == 0:
do_log = i % log_args.log_frequency == 0
do_checkpoint = (log_args.checkpoint_frequency != 0) and (
(i % log_args.checkpoint_frequency == 0) or is_final_step)
do_validation = (log_args.validation_frequency != 0) and (
(i % log_args.validation_frequency == 0) or is_final_step)
pbar.update(1)
description = f"Loss: {loss:.3f}, MLM: {mlm_loss:.3f}, SOP: {sop_loss:.3f}, MLM_acc: {mlm_acc:.3f}, SOP_acc: {sop_acc:.3f}"
pbar.set_description(description)
if do_log:
elapsed_time = time.perf_counter() - start_time
if i == 1:
logger.info(f"First step: {elapsed_time:.3f} secs")
else:
it_per_sec = log_args.log_frequency / elapsed_time
logger.info(
f"Train step {i} -- {description} -- It/s: {it_per_sec:.2f}"
)
start_time = time.perf_counter()
if do_checkpoint:
checkpoint_prefix = os.path.join(path_args.filesystem_prefix,
path_args.checkpoint_dir,
f"{run_name}-step{i}")
model_ckpt = f"{checkpoint_prefix}.ckpt"
optimizer_ckpt = f"{checkpoint_prefix}-optimizer.npy"
logger.info(
f"Saving model at {model_ckpt}, optimizer at {optimizer_ckpt}"
)
model.save_weights(model_ckpt)
# model.load_weights(model_ckpt)
optimizer_weights = optimizer.get_weights()
np.save(optimizer_ckpt, optimizer_weights)
# optimizer.set_weights(optimizer_weights)
if do_validation:
val_loss, val_mlm_loss, val_mlm_acc, val_sop_loss, val_sop_acc = run_validation(
model=model,
validation_dataset=validation_dataset,
skip_sop=skip_sop,
skip_mlm=skip_mlm,
)
description = f"Loss: {val_loss:.3f}, MLM: {val_mlm_loss:.3f}, SOP: {val_sop_loss:.3f}, MLM_acc: {val_mlm_acc:.3f}, SOP_acc: {val_sop_acc:.3f}"
logger.info(f"Validation step {i} -- {description}")
# Create summary_writer after the first step
if summary_writer is None:
summary_writer = tf.summary.create_file_writer(
os.path.join(path_args.filesystem_prefix,
path_args.log_dir, run_name))
config = {
**asdict(model_args),
**asdict(data_args),
**asdict(train_args),
**asdict(log_args),
"global_batch_size":
train_args.per_gpu_batch_size * hvd.size(),
}
if is_wandb_available():
wandb.init(config=config, project=model_args.model_type)
wandb.run.save()
wandb_run_name = wandb.run.name
train_metrics = {
"weight_norm": weight_norm,
"grad_norm": grad_norm,
"loss_scale": loss_scale,
"learning_rate": learning_rate,
"train/loss": loss,
"train/mlm_loss": mlm_loss,
"train/mlm_acc": mlm_acc,
"train/sop_loss": sop_loss,
"train/sop_acc": sop_acc,
}
all_metrics = {**train_metrics}
if do_validation:
val_metrics = {
"val/loss": val_loss,
"val/mlm_loss": val_mlm_loss,
"val/mlm_acc": val_mlm_acc,
"val/sop_loss": val_sop_loss,
"val/sop_acc": val_sop_acc,
}
all_metrics = {**all_metrics, **val_metrics}
if do_squad:
squad_metrics = {
"squad/f1": squad_f1,
"squad/exact": squad_exact,
}
all_metrics = {**all_metrics, **squad_metrics}
# Log to TensorBoard
with summary_writer.as_default():
for name, val in all_metrics.items():
tf.summary.scalar(name, val, step=i)
# Log to Weights & Biases
if is_wandb_available():
wandb.log({"step": i, **all_metrics})
i += 1
if is_final_step:
break
if hvd.rank() == 0:
pbar.close()
logger.info(f"Finished pretraining, job name {run_name}")
class TFTrainer:
model: TFPreTrainedModel
args: TFTrainingArguments
train_dataset: Optional[tf.data.Dataset]
eval_dataset: Optional[tf.data.Dataset]
test_dataset: Optional[tf.data.Dataset]
dataset_info: DatasetInfo
strategy: Strategy
def __init__(
self,
model: TFPreTrainedModel,
args: TFTrainingArguments,
train_dataset: Optional[tf.data.Dataset] = None,
eval_dataset: Optional[tf.data.Dataset] = None,
test_dataset: Optional[tf.data.Dataset] = None,
dataset_info: Optional[DatasetInfo] = None,
):
self.model = model
self.args = args
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
self.test_dataset = test_dataset
self.dataset_info = dataset_info
self.gradient_accumulator = GradientAccumulator()
self.accum_steps = 1
if self.args.strategy_name == "mirrored":
self.strategy = tf.distribute.MirroredStrategy()
elif self.args.strategy_name == "onedevice":
if len(tf.config.list_physical_devices('GPU')) >= 1:
self.strategy = tf.distribute.OneDeviceStrategy(
device="/gpu:0")
else:
self.strategy = tf.distribute.OneDeviceStrategy(
device="/cpu:0")
else:
raise ValueError("The strategy {} does not exists.".format(
self.args.strategy_name))
# To conform with Trainer's API we call this from here.
# All args should be in the `args` already.
self._setup_training()
def _setup_training(self,
checkpoint_path: str = "checkpoints",
log_path: str = "logs") -> None:
"""
Setup the different steps to train a model:
- check if all the data are given
- create the proper strategy
- create the features
- prepare the model settings
Args:
checkpoint_path: the directory path where the model checkpoints will be saved, "./checkpoints" folder by default.
log_path: the directory path where the Tensorboard logs will be saved, "./logs" folder by default.
data_cache_dir: the directory path where the data will be cached, "./cache" folder by default.
model_cache_dir (optional): the directory path where the pretrained model will be cached.
"""
self._prepare_dataset()
with self.strategy.scope():
self._create_optimizer()
_ = self.optimizer.iterations
self._set_loss_and_metric()
self._create_checkpoint_manager(checkpoint_path)
self._create_summary_writer(log_path)
def _set_loss_and_metric(self) -> None:
"""
Create the training loss and metric with their name. Allowed names are those listed
in the Tensorflow documentation and those contained in the transformers library.
"""
try:
self.loss = tf.keras.losses.get({
"class_name": self.args.loss_name,
"config": {
"from_logits": True,
"reduction": tf.keras.losses.Reduction.NONE
}
})
except TypeError:
self.loss = tf.keras.losses.get({
"class_name": self.args.loss_name,
"config": {
"reduction": tf.keras.losses.Reduction.NONE
}
})
self.train_acc_metric = tf.keras.metrics.get({
"class_name": self.args.metric_name,
"config": {
"name": "train_accuracy"
}
})
self.test_acc_metric = tf.keras.metrics.get({
"class_name": self.args.metric_name,
"config": {
"name": "test_accuracy"
}
})
def _create_summary_writer(self, log_path: str) -> None:
"""
Create a summary writer to be able to read the logs in Tensorboard.
Args:
log_path: the directory path where the Tensorboard logs will be saved.
"""
self.log_path = log_path
self.train_writer = tf.summary.create_file_writer(log_path + "/train")
self.test_writer = tf.summary.create_file_writer(log_path + "/test")
def _prepare_dataset(self) -> None:
"""
Prepare the training, validation and test data.
Args:
data_cache_dir: the directory path where the cached data are / should be saved.
"""
train_batch = self.args.per_gpu_train_batch_size * self.strategy.num_replicas_in_sync
eval_batch = self.args.per_gpu_eval_batch_size * self.strategy.num_replicas_in_sync
test_batch = self.args.per_gpu_eval_batch_size
self.train_steps = math.ceil(self.dataset_info.sizes["train"] /
train_batch)
self.train_dataset = self.train_dataset.shuffle(128).batch(
train_batch).repeat(-1)
self.train_dataset = self.strategy.experimental_distribute_dataset(
self.train_dataset)
self.validation_steps = math.ceil(
self.dataset_info.sizes["validation"] / eval_batch)
self.eval_dataset = self.eval_dataset.batch(eval_batch)
self.eval_dataset = self.strategy.experimental_distribute_dataset(
self.eval_dataset)
self.test_steps = math.ceil(self.dataset_info.sizes["test"] /
test_batch)
self.test_dataset = self.test_dataset.batch(test_batch)
def _create_optimizer(self) -> None:
"""
Create the training optimizer with its name. Allowed names are those listed
in the Tensorflow documentation and those contained in the transformers library.
"""
if self.args.optimizer_name == "adamw":
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=self.args.learning_rate,
decay_steps=self.train_steps,
end_learning_rate=0.0)
if self.args.warmup_steps:
learning_rate_fn = WarmUp(
initial_learning_rate=self.args.learning_rate,
decay_schedule_fn=learning_rate_fn,
warmup_steps=self.args.warmup_steps)
self.optimizer = AdamWeightDecay(
learning_rate=learning_rate_fn,
weight_decay_rate=0.01,
epsilon=self.args.adam_epsilon,
exclude_from_weight_decay=["layer_norm", "bias"])
else:
try:
self.optimizer = tf.keras.optimizers.get({
"class_name":
self.args.optimizer_name,
"config": {
"learning_rate": self.args.learning_rate,
"epsilon": self.args.adam_epsilon
}
})
except TypeError:
# This is for the case where the optimizer is not Adam-like such as SGD
self.optimizer = tf.keras.optimizers.get({
"class_name":
self.args.optimizer_name,
"config": {
"learning_rate": self.args.learning_rate
}
})
def _create_checkpoint_manager(self,
checkpoint_path: str,
max_to_keep: int = 5,
load_model: bool = True) -> None:
"""
Create a checkpoint manager in order to be able to make the training
fault-tolerant.
Args:
checkpoint_path: the directory path where the model checkpoints will be saved.
max_to_keep: the maximum number of checkpoints to keep in the checkpoint path.
load_model: if we want to start the training from the latest checkpoint.
"""
ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self.model)
self.model.ckpt_manager = tf.train.CheckpointManager(
ckpt, checkpoint_path, max_to_keep=max_to_keep)
if load_model:
ckpt.restore(self.model.ckpt_manager.latest_checkpoint)
def _evaluate_steps(self, per_replica_features, per_replica_labels):
"""
One step evaluation across replica.
Args:
features: the batched features.
labels: the batched labels.
Returns:
The loss corresponding to the given batch.
"""
per_replica_loss = self.strategy.experimental_run_v2(
self._run_model,
args=(per_replica_features, per_replica_labels, False))
return self.strategy.reduce(tf.distribute.ReduceOp.MEAN,
per_replica_loss, None)
def _evaluate(self) -> None:
"""
Evaluate the model during the training at the end of each epoch.
"""
step = 1
loss = 0.0
for features, labels in self.eval_dataset:
step = tf.convert_to_tensor(step, dtype=tf.int64)
loss = self._evaluate_steps(features, labels)
loss = tf.reduce_mean(loss)
with self.test_writer.as_default():
tf.summary.scalar("loss", loss, step=step)
if step % self.validation_steps == 0:
break
step += 1
return loss
def train(self) -> None:
"""
Train method to train the model.
"""
tf.summary.trace_on(graph=True, profiler=True)
self.gradient_accumulator.reset()
iterations = self.optimizer.iterations
tf.summary.experimental.set_step(iterations)
for epoch in range(int(self.args.num_train_epochs)):
for training_loss in self._training_steps():
step = iterations.numpy()
training_loss = tf.reduce_mean(training_loss)
with self.train_writer.as_default():
tf.summary.scalar("loss", training_loss, step=step)
if step == 1:
with self.train_writer.as_default():
tf.summary.trace_export(name="training",
step=step,
profiler_outdir=self.log_path)
if step % 10 == 0:
logger.info(
"Epoch {} Step {} Loss {:.4f} Train Accuracy {:.4f}".
format(epoch, step, training_loss.numpy(),
self.train_acc_metric.result()))
if step % 100 == 0:
ckpt_save_path = self.model.ckpt_manager.save()
logger.info("Saving checkpoint for step {} at {}".format(
step, ckpt_save_path))
if step % self.train_steps == 0:
break
test_loss = self._evaluate()
logger.info(
"Epoch {} Step {} Train Loss {:.4f} Train Accuracy {:.4f}".
format(epoch, step, training_loss.numpy(),
self.train_acc_metric.result()))
logger.info(
"Epoch {} Validation Loss {:.4f} Validation Accuracy {:.4f}".
format(epoch, test_loss.numpy(),
self.test_acc_metric.result()))
self.train_acc_metric.reset_states()
self.test_acc_metric.reset_states()
def _training_steps(self):
"""
Returns a generator over training steps (i.e. parameters update).
Args:
dataset: The training dataset.
Returns:
A generator that yields a loss value to report for this step.
"""
for i, loss in enumerate(self._accumulate_next_gradients()):
if i % self.accum_steps == 0:
self._apply_gradients()
yield loss
@tf.function
def _apply_gradients(self):
"""Applies the gradients (cross-replica)."""
self.strategy.experimental_run_v2(self._step)
def _step(self):
"""Applies gradients and resets accumulation."""
gradient_scale = self.gradient_accumulator.step * self.strategy.num_replicas_in_sync
gradients = [
gradient / tf.cast(gradient_scale, gradient.dtype)
for gradient in self.gradient_accumulator.gradients
]
gradients = [(tf.clip_by_value(grad, -self.args.max_grad_norm,
self.args.max_grad_norm))
for grad in gradients]
vars = self.model.trainable_variables
if self.args.mode == "labelling":
vars = [
var for var in self.model.trainable_variables
if "pooler" not in var.name
]
self.optimizer.apply_gradients(list(zip(gradients, vars)))
self.gradient_accumulator.reset()
def _accumulate_next_gradients(self):
"""Accumulates the gradients from the next element in dataset."""
iterator = iter(self.train_dataset)
@tf.function
def _accumulate_next():
per_replica_features, per_replica_labels = next(iterator)
return self._accumulate_gradients(per_replica_features,
per_replica_labels)
while True:
try:
yield _accumulate_next()
except tf.errors.OutOfRangeError:
break
def _accumulate_gradients(self, per_replica_features, per_replica_labels):
"""Accumulates the gradients across all the replica."""
per_replica_loss = self.strategy.experimental_run_v2(
self._forward, args=(per_replica_features, per_replica_labels))
return self.strategy.reduce(tf.distribute.ReduceOp.MEAN,
per_replica_loss, None)
def _forward(self, features, labels):
"""Forwards a training example and accumulates the gradients."""
per_example_loss = self._run_model(features, labels, True)
loss = tf.nn.compute_average_loss(
per_example_loss,
global_batch_size=self.args.per_gpu_train_batch_size)
vars = self.model.trainable_variables
if self.args.mode == "labelling":
vars = [
var for var in self.model.trainable_variables
if "pooler" not in var.name
]
gradients = self.optimizer.get_gradients(loss, vars)
self.gradient_accumulator(gradients)
return per_example_loss
def _run_model(self, features, labels, training):
"""
Computes the loss of the given features and labels pair.
Args:
features: the batched features.
labels: the batched labels.
"""
if self.args.mode == "classification" or self.args.mode == "labelling":
logits = self.model(features, training=training)[0]
else:
logits = self.model(features, training=training)
if self.args.mode == "labelling":
active_loss = tf.reshape(labels, (-1, )) != -1
logits = tf.boolean_mask(
tf.reshape(logits, (-1, len(self.dataset_info.labels))),
active_loss)
labels = tf.boolean_mask(tf.reshape(labels, (-1, )), active_loss)
loss = self.loss(labels, logits)
if training:
self.train_acc_metric(labels, logits)
else:
self.test_acc_metric(labels, logits)
return loss
def test(self) -> None:
"""
Test the model over the test dataset and print a report.
"""
y_true = []
results = self.model.predict(self.test_dataset, steps=self.test_steps)
if self.args.mode == "classification":
for batch in self.test_dataset:
y_true.extend(batch[1].numpy().tolist())
y_pred = np.reshape(np.argmax(results, axis=-1), (-1, 1)).tolist()
y_true = list(itertools.chain.from_iterable(y_true))
y_pred = list(itertools.chain.from_iterable(y_pred))
logger.info(
classification_report(y_true,
y_pred,
target_names=self.dataset_info.labels))
def save_model(self, save_path: str) -> None:
"""
Save the pretrained model and create a Tensorflow saved model.
Args:
save_path: directory path where the pretrained model and
Tensorflow saved model will be saved
"""
logger.info("Saving model in {}".format(save_path))
path = os.path.join(save_path, "saved_model")
os.makedirs(path, exist_ok=True)
self.model.save_pretrained(save_path)
tf.saved_model.save(self.model, path)