def validate(self):
n_elems = 0
cost = 0
for batch in self.valid_data.__iter__():
n_elems += 1
cost += 100*self.model.validate(*batch)
cost /= numpy.float32(n_elems)
print ('** validation cost %6.3f computed in %s'
', best cost is %6.3f, test %6.3f, whole time %6.3f min') % (
cost,
print_time(time.time() - self.batch_start_time),
self.state['bvalidcost'],
self.state['testcost'],
(time.time() - self.start_time)/60. )
self.batch_start_time = time.time()
pos = self.step // self.state['validFreq']
self.timings['valid'][pos] = float(cost)
self.timings['test'][pos] = float(self.state['testcost'])
self.state['validcost'] = float(cost)
self.state['validtime'] = float(time.time() - self.start_time)/60.
if self.state['bvalidcost'] > cost:
self.state['bvalidcost'] = float(cost)
self.state['btraincost'] = float(self.state['traincost'])
self.test()
print_mem('validate')
def validate(self):
cost = self.model.validate()
print(
'** validation cost %6.3f computed in %s'
', best cost is %6.3f, test %6.3f, whole time %6.3f min') % (
cost, print_time(time.time() - self.batch_start_time),
self.state['bvalidcost'], self.state['testcost'],
(time.time() - self.start_time) / 60.)
self.batch_start_time = time.time()
pos = self.step // self.state['validFreq']
self.timings['valid'][pos] = float(cost)
self.timings['test'][pos] = float(self.state['testcost'])
self.state['validcost'] = float(cost)
self.state['validtime'] = float(time.time() - self.start_time) / 60.
if self.state['bvalidcost'] > cost:
self.state['bvalidcost'] = float(cost)
self.state['btraincost'] = float(self.state['traincost'])
self.test()
print_mem('validate')
def validate(self):
cost = self.model.validate()
print ('** validation cost %6.3f computed in %s'
', best cost is %6.3f, test %6.3f, whole time %6.3f min') % (
cost,
print_time(time.time() - self.batch_start_time),
self.state['bvalidcost'],
self.state['testcost'],
(time.time() - self.start_time)/60. )
self.batch_start_time = time.time()
pos = self.step // self.state['validFreq']
self.timings['valid'][pos] = float(cost)
self.timings['test'][pos] = float(self.state['testcost'])
self.state['validcost'] = float(cost)
self.state['validtime'] = float(time.time() - self.start_time)/60.
if self.state['bvalidcost'] > cost:
self.state['bvalidcost'] = float(cost)
self.state['btraincost'] = float(self.state['traincost'])
self.test()
print_mem('validate')
def print(self):
# Print the current state of all state (memory) elements of the CPU
print_val(self.PC, "PC")
print_mem(self.imem, "IMEM", val_width=16)
print_mem(self.regfile, "Regfile", label_all=True)
print_mem(self.dmem, "DMEM")
print_input(self.buttons, "Input")
print_matrix(self.matrix, "Output")
def validate(self):
n_elems = 0
cost = 0
for batch in self.valid_data.__iter__():
n_elems += 1
cost += 100 * self.model.validate(*batch)
cost /= numpy.float32(n_elems)
print(
'** validation cost %6.3f computed in %s'
', best cost is %6.3f, test %6.3f, whole time %6.3f min') % (
cost, print_time(time.time() - self.batch_start_time),
self.state['bvalidcost'], self.state['testcost'],
(time.time() - self.start_time) / 60.)
self.batch_start_time = time.time()
pos = self.step // self.state['validFreq']
self.timings['valid'][pos] = float(cost)
self.timings['test'][pos] = float(self.state['testcost'])
self.state['validcost'] = float(cost)
self.state['validtime'] = float(time.time() - self.start_time) / 60.
if self.state['bvalidcost'] > cost:
self.state['bvalidcost'] = float(cost)
self.state['btraincost'] = float(self.state['traincost'])
self.test()
print_mem('validate')
def main(argv):
#######################################################################
# Initial Setup. Logging, Flags, Random seeds.
#######################################################################
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
absl_logging.use_python_logging()
flags_dict = {
flag.name: flag.value
for flag in FLAGS.flags_by_module_dict()[argv[0]]
}
if FLAGS.use_subset:
message = (f"{colorama.Back.RED}{colorama.Fore.WHITE}"
f"{colorama.Style.BRIGHT}USING A SUBSET OF THE DATASET"
f"{colorama.Style.RESET_ALL}")
LOGGER.warning(message)
utils.log_module_args(LOGGER, argv[0])
if not FLAGS.output_dir.startswith("gs://"):
utils.check_exists(FLAG_OUTPUT_DIR.value)
if not tf.io.gfile.isdir(FLAG_OUTPUT_DIR.value):
raise RuntimeError("Output dir needs to be a directory.")
tf.random.set_seed(FLAG_RANDOM_SEED.value)
np.random.seed(FLAG_RANDOM_SEED.value)
# Prepare the instance output directory path and save the config there
folder_name = time.strftime(
f"{FLAG_RUN_NAME.value}_{FLAG_APPROACH_TYPE.value}_%Y%m%d-%H%M%S")
instance_output_dir = os.path.join(FLAG_OUTPUT_DIR.value,
folder_name).strip()
if not instance_output_dir.endswith("/"):
instance_output_dir += "/"
json_target = os.path.join(instance_output_dir, "training_params.json")
if not json_target.strip().startswith("gs://"):
subprocess.check_call(["mkdir", "-p", instance_output_dir])
utils.to_json_file(json_target, instance_output_dir)
##############################################################################
# Initialization and Configuration of the Devices.
##############################################################################
tpu_setup = None
# current_acelerator_type is always "CPU" in the beginning with TPUs
if tf_utils.current_accelerator_type() == "CPU":
tpu_setup = tf_utils.init_tpus()
LOGGER.debug("Devices we are computing on:\n%s",
utils.wrap_iterable(map(str, tf_utils.devices_to_use())))
LOGGER.debug("All devices:")
LOGGER.debug(tf_utils.device_mapping())
if tf_utils.current_accelerator_type() == "GPU":
tf.config.set_soft_device_placement(True)
if tf_utils.current_accelerator_type() != "TPU":
tf.debugging.set_log_device_placement(True)
if FLAG_DISTRIBUTE_MODE.value in constants.PURE_DATA_PARALLEL_STRATEGIES:
actual_num_replicas = len(tf_utils.devices_to_use())
elif FLAG_DISTRIBUTE_MODE.value in constants.DATA_PARALLEL_DMC:
actual_num_replicas = FLAG_NUM_REPLICAS.value
else:
actual_num_replicas = 1
##############################################################################
# We load the retriever model if it is needed.
##############################################################################
# Not currently used.
retriever = None
# if (FLAG_APPROACH_TYPE.value ==
# constants.ApproachTypeChoices.lm_and_realm):
# raise NotImplementedError("This part needs to be tested anew.")
# config_path = FLAG_RETRIEVER_CONFIG_PATH.value
# realm_save = tf_utils.REALMSave(**utils.from_json_file(config_path))
#
# # Approx 15 min when not in dev mode, on CPU
# with utils.log_duration(LOGGER, "main",
# "whole of BERTScaNNRetriever.__init__",
# logging.INFO):
# scann_config = retrievers.ScannConfig(
# **utils.from_json_file(FLAG_SCANN_CONFIG_PATH.value))
# retriever = retrievers.BERTScaNNRetriever(
# retriever_module_path=realm_save.query_embedder_path,
# block_records_path=realm_save.text_records,
# num_block_records=realm_save.num_block_records,
# mode=tf.estimator.ModeKeys.EVAL,
# scann_config=scann_config)
# elif (FLAG_APPROACH_TYPE.value ==
# constants.ApproachTypeChoices.cached_realm):
# raise NotImplementedError("This part needs to be tested anew.")
# config_path = FLAG_RETRIEVER_CONFIG_PATH.value
# realm_save = tf_utils.REALMSave(**utils.from_json_file(config_path))
#
# # Approx 15 min when not in dev mode, on CPU
# with utils.log_duration(LOGGER, "main",
# "whole of FullyCachedRetriever.__init__",
# logging.INFO):
#
# retriever = retrievers.FullyCachedRetriever(
# db_path=FLAG_FULLYCACHED_H5_PATH.value,
# block_records_path=realm_save.text_records,
# num_block_records=realm_save.num_block_records,
# )
##############################################################################
# Distributed training task
##############################################################################
if FLAG_TASK.value == constants.TaskChoices.train:
with utils.log_duration(LOGGER, "main", "Load model"):
utils.print_mem("before loading model", LOGGER)
model_specific = task_specific.load_model(
FLAG_MODEL_LOAD_PATH.value, FLAG_MODEL_KEY.value,
FLAG_DISTRIBUTE_MODE.value, tpu_setup, FLAG_NUM_REPLICAS.value)
utils.print_mem("after loading model", LOGGER)
model_or_replicas = model_specific.model
if isinstance(model_or_replicas, list):
model_or_replicas: List[transformers.TFGPT2LMHeadModel]
else:
model_or_replicas: transformers.TFGPT2LMHeadModel
tokenizer = model_specific.tokenizer
def make_optimizer():
return tensor2tensor.utils.adafactor.AdafactorOptimizer(
learning_rate=FLAG_LEARNING_RATE.value)
if model_specific.strategy:
with model_specific.strategy.scope():
optimizer = make_optimizer()
else:
optimizer = make_optimizer()
############################################################################
# Prepare the dataset functions
############################################################################
rg = np.random.default_rng(FLAG_RANDOM_SEED.value)
def call_lm_preproc(repeat, split, random_seed):
"""Using functools.partial prevents the linter from doing its job."""
if FLAG_DATASET_NAME.value == constants.DatasetNameChoices.kilt_eli5:
return task_specific.create_lm_ds_kilt_eli5(
tokenizer=tokenizer,
context_window_size=(
model_or_replicas[0].config.n_positions if isinstance(
model_or_replicas,
list) else model_or_replicas.config.n_positions),
dataset_name=FLAG_DATASET_NAME.value,
# Batches are split over the replicas:
batch_size=FLAG_BATCH_SIZE.value * actual_num_replicas,
db_path=FLAG_DB_PATH.value,
random_seed=random_seed,
use_subset=FLAG_USE_SUBSET.value,
subset_size=FLAG_SUBSET_SIZE.value,
use_helper_words=FLAG_USE_HELPER_WORDS.value,
approach_type=FLAG_APPROACH_TYPE.value,
num_retrievals=FLAG_NUM_RETRIEVALS.value,
retrieval_temperature=FLAG_RETRIEVAL_TEMPERATURE.value,
retriever=retriever,
repeat=repeat,
split=split,
enable_debug_checks=FLAG_DATASET_DEBUG.value,
retrieval_bank_size=FLAG_RETRIEVAL_BANK_SIZE.value,
dataset_type=FLAG_DATASET_TYPE.value,
qty_shuffle=FLAG_QTY_SHUFFLE.value,
tfr_prefix=FLAG_TFR_PREFIX.value,
max_length_generation=FLAG_MAX_LENGTH_GENERATION.value,
)
else:
raise NotImplementedError(
f"FLAG_DATASET_NAME.value unsupported: `{FLAG_DATASET_NAME.value}`"
)
make_training_dataset: Callable[Ellipsis,
tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="train",
repeat=False,
)
make_eval_dataset: Callable[Ellipsis,
tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="eval",
repeat=True,
)
############################################################################
# Prepare the step functions
############################################################################
utils.check_contained(FLAG_DISTRIBUTE_MODE.value,
constants.DistributeModeChoices.choices())
tf_function_flags = dict(
experimental_compile=FLAG_EXPERIMENTAL_COMPILE.value,
experimental_relax_shapes=not FLAG_INPUT_FIXED_SIZE.value)
if (FLAG_DISTRIBUTE_MODE.value ==
constants.DistributeModeChoices.split_and_data_parallel):
if not isinstance(model_or_replicas, list):
raise RuntimeError(type(model_or_replicas))
training_step = build_manual_data_parallel_training_step(
model_or_replicas, optimizer, tf_function_flags)
else:
training_step = build_regular_training_step(
model_or_replicas,
optimizer,
strategy=model_specific.strategy,
tf_function_kwargs=tf_function_flags)
evaluation_step = build_evaluation_step(model_or_replicas,
tf_function_flags)
secs_since_last_ckpt = time.time()
# Model checkpoints are saved to the tmp_directory and then rsynced to GCS
##########################################################################
# Prepare the different logging facilities
##########################################################################
train_log_dir = os.path.join(instance_output_dir, "tensorboard",
"train")
eval_log_dir = os.path.join(instance_output_dir, "tensorboard", "eval")
flags_log_dir = os.path.join(instance_output_dir, "tensorboard",
"params")
writers = dict(train=tf.summary.create_file_writer(train_log_dir),
eval=tf.summary.create_file_writer(eval_log_dir),
flags=tf.summary.create_file_writer(flags_log_dir))
with writers["flags"].as_default():
tf.summary.text(
"Flags",
# Tensorboard takes Markdown:
json.dumps(flags_dict, indent=4).replace("\n", "\n\n"),
step=0)
ma_loss = dict(train=utils.MovingAverage(0.9),
eval=utils.MovingAverage(0.9))
step_counters = dict(train=0, eval=0)
batch_counters = dict(train=0, eval=0)
prev_batch_end = time.time()
# The eval ds has no real concept of epoch, repeats forever, shuffling
# each time it reaches its end
with utils.log_duration(LOGGER, "main", "All of make_eval_dataset"):
eval_ds_instance = make_eval_dataset(random_seed=rg.integers(
-2**63, 2**63 - 1), )
LOGGER.debug("Distributing the eval dataset to the replicas.")
if FLAG_DATASET_TYPE.value == "tfr":
eval_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
eval_ds_instance))
LOGGER.debug("Done distributing the eval dataset to the replcias.")
eval_ds_instance = iter(eval_ds_instance)
##########################################################################
# Training Loop
##########################################################################
for epoch in itertools.count():
####################################################################
# Epoch Setup
####################################################################
LOGGER.debug("EPOCH %d START", epoch)
# Shuffle differently every epoch
with utils.log_duration(LOGGER, "main",
"All of make_training_dataset"):
train_ds_instance = make_training_dataset(
random_seed=rg.integers(-2**63, 2**63 - 1), )
LOGGER.debug(
"Attempting to distribute the training dataset to the replicas."
)
if FLAG_DATASET_TYPE.value == "tfr":
train_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
train_ds_instance))
LOGGER.debug(
"Done distributing the training dataset to the replicas.")
train_ds_instance = iter(train_ds_instance)
# This allows us to see if we reached the end of the training iterator,
# in which case "did_at_least_one_training_batch == False".
# We could also test that it did all the batches, to similar results.
did_at_least_one_training_batch = True
split = "eval"
while did_at_least_one_training_batch:
# Invert split
if split == "train":
split = "eval"
else:
split = "train"
# Prepare to test if we did at least one training batch
if split == "train":
did_at_least_one_training_batch = False
if split == "train":
dataset_iterator = itertools.islice(
train_ds_instance, FLAG_BATCHES_BETWEEN_EVALS.value)
else:
# The evaluation DS is tiny, so we reshuffle and take a random
dataset_iterator = itertools.islice(
eval_ds_instance, FLAG_NUMBER_EVAL_BATCHES.value)
LOGGER.debug("Batching")
for batch in dataset_iterator:
# LOGGER.debug("Input sentence:\n\"%s\"",
# tokenizer.decode([x for x in batch["input_ids"][0]
# if x != tokenizer.eos_token_id]))
# LOGGER.debug("Label:\n\"%s\"",
# tokenizer.decode([(x if x != -100 else 0)
# for x in batch["label_ids"][0]]))
if FLAG_DATASET_TYPE.value != "tfr":
batch = (model_specific.strategy.
experimental_distribute_values_from_function(
tf_utils.make_dict_distribute_fn(batch)))
# We only care about training epochs as, obviously, we don't train
# over eval samples; the number of eval samples seen only
# contributes to lowering the variance in the evaluation of when to
# do early stopping.
if split == "train":
did_at_least_one_training_batch = True
input_ids = batch["input_ids"]
label_ids = batch["label_ids"]
####################################################################
# Training Step
####################################################################
step_counters[split] += (FLAG_BATCH_SIZE.value *
actual_num_replicas)
if split == "train":
batch_counters[split] += 1
training_kwargs = dict(
input_ids=input_ids,
label_ids=label_ids,
)
if model_specific.strategy:
utils.print_mem("before running", LOGGER)
LOGGER.debug("Training, Calling strategy.run")
loss = model_specific.strategy.run(
training_step, kwargs=training_kwargs)
LOGGER.debug("Training, Done with strategy.run")
utils.print_mem("after running", LOGGER)
else:
loss = training_step(**training_kwargs) # pytype: disable=wrong-arg-count
# If we are in the strategy-free data parallel mode, we need
# to change the weights of all replicas to those of the model at
# index 0
if (FLAG_DISTRIBUTE_MODE.value ==
constants.DistributeModeChoices.
split_and_data_parallel):
for replica in model_or_replicas[1:]:
replica.set_weights(
model_or_replicas[0].get_weights())
####################################################################
# Evaluation Step
####################################################################
elif split == "eval":
evaluation_kwargs = dict(
input_ids=input_ids,
label_ids=label_ids,
)
if model_specific.strategy:
loss = model_specific.strategy.run(
evaluation_step, kwargs=evaluation_kwargs)
else:
loss = evaluation_step(**evaluation_kwargs)
else:
raise ValueError(
f"Unexpected value for split: {split}")
####################################################################
# Logging
####################################################################
if (FLAG_DISTRIBUTE_MODE.value
in constants.PURE_DATA_PARALLEL_STRATEGIES):
utils.check_equal(len(loss.values),
actual_num_replicas)
LOGGER.debug("Split: %s", split)
LOGGER.debug("Real num replicas: %s",
actual_num_replicas)
LOGGER.debug("Loss: %s", loss)
LOGGER.debug("Loss values: %s", loss.values)
average_loss = float(
tf.math.reduce_mean(loss.values).numpy())
else:
average_loss = float(loss.numpy())
# tf.debugging.check_numerics(loss)
now = time.time()
batch_duration = now - prev_batch_end
prev_batch_end = now
ma_loss[split].update(average_loss)
# Actual logging
LOGGER.info("Epoch: # %d", epoch)
LOGGER.info("Tensorboard_dir: %s", instance_output_dir)
LOGGER.info("Batch: %s # %d", split, batch_counters[split])
LOGGER.info("Step: %s # %d", split, step_counters[split])
if FLAG_USE_SUBSET.value:
LOGGER.warning(">> USING A SUBSET OF THE DATASET <<")
LOGGER.info("%(split)s Batch loss: %(metric)f",
dict(split=split, metric=average_loss))
LOGGER.info(
"%(split)s Moving average loss: %(metric)f",
dict(split=split, metric=ma_loss[split].average))
LOGGER.info(
"%(split)s Moving average ppl: %(metric)f",
dict(split=split,
metric=np.exp(ma_loss[split].average)))
LOGGER.info(
"%(split)s Batch duration: %(duration)s",
dict(split=split,
duration=utils.TimeStamp.from_seconds(
batch_duration).format()))
if FLAG_DISTRIBUTE_MODE.value in constants.DATA_PARALLEL_DMC:
LOGGER.info(
"%(split)s Duration per sample: %(duration)s",
dict(split=split,
duration=utils.TimeStamp.from_seconds(
batch_duration / (FLAG_BATCH_SIZE.value *
actual_num_replicas))))
# Write to Tensorboard
with writers[split].as_default():
tf.summary.scalar(f"Loss/{split}", average_loss,
step_counters[split])
tf.summary.scalar(f"PPL/{split}", np.exp(average_loss),
step_counters[split])
writers[split].flush()
# Save every 5 min
if (time.time() - secs_since_last_ckpt) / (60 * 20) >= 1:
secs_since_last_ckpt = time.time()
save_model(train_steps=step_counters["train"],
model_or_replicas=model_or_replicas,
instance_output_dir=instance_output_dir)
secs_since_last_ckpt = time.time()
save_model(train_steps=step_counters["train"],
model_or_replicas=model_or_replicas,
instance_output_dir=instance_output_dir)
#############################################################
# Post Training Cleanup
#######################################################################
for writer in writers.values():
writer.close()
def main(argv):
##############################################################################
# Initial Setup. Logging, Flags, Random seeds.
##############################################################################
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
absl_logging.use_python_logging()
flags_dict = {
flag.name: flag.value
for flag in FLAGS.flags_by_module_dict()[argv[0]]
}
if FLAGS.use_subset:
message = (f"{colorama.Back.RED}{colorama.Fore.WHITE}"
f"{colorama.Style.BRIGHT}USING A SUBSET OF THE DATASET"
f"{colorama.Style.RESET_ALL}")
LOGGER.warning(message)
utils.log_module_args(LOGGER, argv[0])
if not FLAGS.output_dir.startswith("gs://"):
utils.check_exists(FLAG_OUTPUT_DIR.value)
if not tf.io.gfile.isdir(FLAG_OUTPUT_DIR.value):
raise RuntimeError("Output dir needs to be a directory.")
tf.random.set_seed(FLAG_RANDOM_SEED.value)
np.random.seed(FLAG_RANDOM_SEED.value)
# Prepare the instance output directory path and save the config there
# Prepare the path
folder_name = time.strftime(
f"{FLAG_RUN_NAME.value}_{FLAG_APPROACH_TYPE.value}_%Y%m%d-%H%M%S")
instance_output_dir = os.path.join(FLAG_OUTPUT_DIR.value,
folder_name).strip()
if not instance_output_dir.endswith("/"):
instance_output_dir += "/"
json_target = os.path.join(instance_output_dir, "training_params.json")
# Make the folder if we're not on gcloud
if not json_target.strip().startswith("gs://"):
subprocess.check_call(["mkdir", "-p", instance_output_dir])
# Safe the config file
utils.to_json_file(json_target, flags_dict)
##############################################################################
# Initialization and Configuration of the Devices.
##############################################################################
tpu_setup = None
accel = tf_utils.current_accelerator_type()
if FLAG_TPU_IS_LOCAL.value:
assert accel == "TPU", accel
if accel == "TPU":
assert FLAG_TPU_IS_LOCAL.value, FLAG_TPU_IS_LOCAL.value
if tf_utils.current_accelerator_type() in {"CPU", "TPU"}:
tpu_setup = tf_utils.init_tpus(tpu_name=FLAG_TPU_NAME.value,
local=FLAG_TPU_IS_LOCAL.value)
LOGGER.debug("Devices we are computing on:\n%s",
utils.wrap_iterable(map(str, tf_utils.devices_to_use())))
LOGGER.debug("All devices:")
LOGGER.debug(tf_utils.device_mapping())
if tf_utils.current_accelerator_type() == "GPU":
tf.config.set_soft_device_placement(True)
if tf_utils.current_accelerator_type() != "TPU":
tf.debugging.set_log_device_placement(True)
utils.check_operator(operator.ne, tf_utils.current_accelerator_type(),
"CPU")
assert FLAG_TPU_NAME.value == socket.gethostname(), (
"This is a configuration choice. You can remove this. "
"There will be no side effects.")
if FLAG_DISTRIBUTE_MODE.value in constants.PURE_DATA_PARALLEL_STRATEGIES:
actual_num_replicas = len(tf_utils.devices_to_use())
elif FLAG_DISTRIBUTE_MODE.value in constants.DATA_PARALLEL_DMC:
actual_num_replicas = FLAG_NUM_REPLICAS.value
else:
actual_num_replicas = 1
##############################################################################
# We load the retriever model if it is needed.
##############################################################################
# Not currently used. See old commits.
retriever = None
##############################################################################
# Distributed training task
##############################################################################
if FLAG_TASK.value == constants.TaskChoices.train:
with utils.log_duration(LOGGER, "main", "Load model"):
utils.print_mem("before loading model", LOGGER)
model_specific = task_specific.load_model(
FLAG_MODEL_KEY.value, FLAG_DISTRIBUTE_MODE.value, tpu_setup,
FLAG_NUM_REPLICAS.value)
utils.print_mem("after loading model", LOGGER)
model = model_specific.model
if isinstance(model, list):
model: List[transformers.TFGPT2LMHeadModel]
else:
model: transformers.TFGPT2LMHeadModel
tokenizer = model_specific.tokenizer
def make_optimizer():
if FLAG_OPTIMIZER_TYPE.value == constants.OptimizerTypes.adafactor:
return tensor2tensor.utils.adafactor.AdafactorOptimizer(
learning_rate=FLAG_LEARNING_RATE.value)
elif FLAG_OPTIMIZER_TYPE.value == constants.OptimizerTypes.adam:
return tf.keras.optimizers.Adam(
learning_rate=FLAG_LEARNING_RATE.value)
else:
raise ValueError(FLAG_OPTIMIZER_TYPE.value)
if model_specific.strategy:
with model_specific.strategy.scope():
optimizer = make_optimizer()
else:
optimizer = make_optimizer()
############################################################################
# Prepare the dataset functions
############################################################################
rg = np.random.default_rng(FLAG_RANDOM_SEED.value)
def call_lm_preproc(repeat, split, random_seed):
"""Using functools.partial prevents the linter from doing its job."""
if FLAG_DATASET_NAME.value == constants.DatasetNameChoices.kilt_eli5:
return task_specific.create_lm_ds_kilt_eli5(
tokenizer=tokenizer,
context_window_size=model.config.n_positions,
dataset_name=FLAG_DATASET_NAME.value,
# Batches are split over the replicas:
batch_size=FLAG_BATCH_SIZE.value * actual_num_replicas,
db_path=FLAG_DB_PATH.value,
random_seed=random_seed,
use_subset=FLAG_USE_SUBSET.value,
subset_size=FLAG_SUBSET_SIZE.value,
use_helper_words=FLAG_USE_HELPER_WORDS.value,
approach_type=FLAG_APPROACH_TYPE.value,
num_retrievals=FLAG_NUM_RETRIEVALS.value,
retrieval_temperature=FLAG_RETRIEVAL_TEMPERATURE.value,
retriever=retriever,
repeat=repeat,
split=split,
enable_debug_checks=FLAG_DATASET_DEBUG.value,
retrieval_bank_size=FLAG_RETRIEVAL_BANK_SIZE.value,
dataset_type=FLAG_DATASET_TYPE.value,
qty_shuffle=FLAG_QTY_SHUFFLE.value,
tfr_prefix=FLAG_TFR_PREFIX.value,
max_length_generation=FLAG_MAX_LENGTH_GENERATION.value,
)
else:
raise NotImplementedError(
f"FLAG_DATASET_NAME.value unsupported: `{FLAG_DATASET_NAME.value}`"
)
make_training_dataset: Callable[...,
tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="train",
repeat=False,
)
make_eval_dataset: Callable[..., tf.data.Dataset] = functools.partial(
call_lm_preproc,
split="eval",
repeat=True,
)
############################################################################
# Prepare the step functions
############################################################################
utils.check_contained(FLAG_DISTRIBUTE_MODE.value,
constants.DistributeModeChoices.choices())
tf_function_flags = dict(
experimental_compile=FLAG_EXPERIMENTAL_COMPILE.value,
experimental_relax_shapes=not FLAG_INPUT_FIXED_SIZE.value)
training_step = build_regular_training_step(
model,
optimizer,
strategy=model_specific.strategy,
tf_function_kwargs=tf_function_flags)
evaluation_step = build_evaluation_step(model, tf_function_flags)
timestamp_last_ckpt_secs = time.time()
# Model checkpoints are saved to the tmp_directory and then rsynced to GCS
############################################################################
# Prepare the statistics and the logging facilities.
############################################################################
# Tensorboard
with model_specific.strategy.scope():
checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
saver = Saver(instance_output_dir, checkpoint)
train_log_dir = os.path.join(instance_output_dir, "tensorboard",
"train")
eval_log_dir = os.path.join(instance_output_dir, "tensorboard", "eval")
flags_log_dir = os.path.join(instance_output_dir, "tensorboard",
"params")
writers = dict(train=tf.summary.create_file_writer(train_log_dir),
eval=tf.summary.create_file_writer(eval_log_dir),
flags=tf.summary.create_file_writer(flags_log_dir))
with writers["flags"].as_default():
tf.summary.text(
"Flags",
# Tensorboard takes Markdown:
json.dumps(flags_dict, indent=4).replace("\n", "\n\n"),
step=0)
# Different information to log.
ma_loss = dict(train=utils.MovingAverage(0.9),
eval=utils.MovingAverage(0.9))
step_counters = dict(train=0, eval=0)
batch_counters = dict(train=0, eval=0)
prev_batch_end = time.time()
############################################################################
# Create the Eval DS object.
# ==========================================================================
# The eval ds has no real concept of epoch, repeats forever, shuffling
# each time it reaches its end.
############################################################################
# Create
with utils.log_duration(LOGGER, "main", "All of make_eval_dataset"):
eval_ds_instance = make_eval_dataset(random_seed=rg.integers(
-2**63, 2**63 - 1), )
# Maybe distribute
LOGGER.debug("Distributing the eval dataset to the replicas.")
if FLAG_DATASET_TYPE.value == "tfr":
eval_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
eval_ds_instance))
# Start the iteration. We step by calling `next(...)`.
LOGGER.debug("Done distributing the eval dataset to the replicas.")
eval_ds_instance = iter(eval_ds_instance)
step_function = dict(train=training_step, eval=evaluation_step)
############################################################################
# Training Loop
# ==========================================================================
# Create a new training dataset object that lasts for one epoch.
# This is different from the eval training dataset object, which loops
# forever.
############################################################################
for epoch in itertools.count():
##########################################################################
# Epoch Setup
##########################################################################
LOGGER.debug("EPOCH %d START", epoch)
# Shuffle differently every epoch
with utils.log_duration(LOGGER, "main",
"All of make_training_dataset"):
train_ds_instance = make_training_dataset(
random_seed=rg.integers(-2**63, 2**63 - 1), )
LOGGER.debug(
"Attempting to distribute the training dataset to the replicas."
)
if FLAG_DATASET_TYPE.value == "tfr":
train_ds_instance = (
model_specific.strategy.experimental_distribute_dataset(
train_ds_instance))
LOGGER.debug(
"Done distributing the training dataset to the replicas.")
train_ds_instance = iter(train_ds_instance)
# To change splits, we use `itertools.islice` over the dataset generator.
# When the training dataset generator is done, a new loop of the following
# while loop occurs, but no training batch is done because we are taking
# an `islice` of a generator that is done.
did_at_least_one_training_batch = True
split = "eval"
while did_at_least_one_training_batch:
utils.check_operator(operator.ne,
tf_utils.current_accelerator_type(),
"CPU")
# Invert split
if split == "train":
split = "eval"
else:
split = "train"
# Prepare to test if we did at least one training batch
if split == "train":
did_at_least_one_training_batch = False
########################################################################
# Take slices from the dataset iterator
# ======================================================================
# We only want to do a certain number of batches before switching splits
# We do this by using an `itertools.islice` of the dataset iterators.
########################################################################
if split == "train":
dataset_iterator = toolz.take(
FLAG_BATCHES_BETWEEN_EVALS.value, train_ds_instance)
else:
# The evaluation dataset generator is infinite, reshuffles everytime
# it gets to its end.
# Still, we take a fixed size slice form that infinite generator.
dataset_iterator = toolz.take(
FLAG_NUMBER_EVAL_BATCHES.value, eval_ds_instance)
LOGGER.debug("Batching")
for batch in dataset_iterator:
if FLAG_LOG_SAMPLES.value:
####################################################################
# Print elements of the dataset
####################################################################
# Make ourselves resistant to values possibly being a PerReplica
# object
LOGGER.warning(
f"%(red)sLOGGING SAMPLES. THIS IS VERY SLOW.%(reset)s",
dict(
red=colorama.Fore.RED,
reset=colorama.Style.RESET_ALL,
))
is_distributed = isinstance(batch["input_ids"],
values.PerReplica)
for in_batch_idx in range(FLAG_BATCH_SIZE.value):
for replica_idx in (range(actual_num_replicas)
if is_distributed else [0]):
if is_distributed:
sample = {
k: batch[k].values[replica_idx]
for k in batch
}
else:
sample = batch
# input_sentence = tokenizer.decode(
# [x for x in sample["input_ids"][i] if x != tokenizer.eos_token_id]
# )
# LOGGER.debug(
# "%sInput [%d / %d]%s:\n\"%s\"",
# colorama.Fore.GREEN,
# replica_idx + 1,
# actual_num_replicas,
# colorama.Style.RESET_ALL,
# input_sentence,
# )
#
# answer = tokenizer.decode(
# [(x if x != -100 else 0) for x in sample["label_ids"][i]]
# )
# LOGGER.debug(
# "%sLabel [%d / %d]%s:\n\"%s\"",
# colorama.Fore.GREEN,
# replica_idx + 1,
# actual_num_replicas,
# colorama.Style.RESET_ALL,
# answer,
# )
cons = console.Console()
sentences = table.Table()
sentences.add_column("BPE Index",
justify="center")
sentences.add_column("Inputs",
justify="center")
sentences.add_column("Labels",
justify="center")
for bpe_idx, (x, y) in enumerate(
itertools.zip_longest(
sample["input_ids"]
[in_batch_idx].numpy(),
sample["label_ids"]
[in_batch_idx].numpy(),
fillvalue=None,
)):
x_w = tokenizer.decode(
[x]) if x >= 0 else f"[ {x} ]"
y_w = tokenizer.decode(
[y]) if y >= 0 else f"[ {y} ]"
sentences.add_row(str(bpe_idx), x_w, y_w)
cons.print(sentences)
# We only care about training epochs as, obviously, we don't train
# over eval samples; the number of eval samples seen only
# contributes to lowering the variance in the evaluation of when to
# do early stopping.
if split == "train":
did_at_least_one_training_batch = True
input_ids = batch["input_ids"]
label_ids = batch["label_ids"]
# Per split step counter
step_counters[
split] += FLAG_BATCH_SIZE.value * actual_num_replicas
batch_counters[split] += 1
######################################################################
# Model step function.
######################################################################
step_function_kwargs = dict(
input_ids=input_ids,
label_ids=label_ids,
)
utils.print_mem(f"[{split}] - Mem before `strategy.run`",
LOGGER)
LOGGER.debug("[%s] - Calling `strategy.run`", split)
loss = model_specific.strategy.run(
step_function[split], kwargs=step_function_kwargs)
LOGGER.debug("[%s] - Done `strategy.run`", split)
utils.print_mem(f"[{split}] - Mem after `strategy.run`",
LOGGER)
####################################################################
# End of logging step code / Logging and saving the model.
####################################################################
if (FLAG_DISTRIBUTE_MODE.value
in constants.PURE_DATA_PARALLEL_STRATEGIES):
utils.check_equal(len(loss.values),
actual_num_replicas)
LOGGER.debug("[%s] - Real num replicas: %s", split,
actual_num_replicas)
average_loss = float(
tf.math.reduce_mean(loss.values).numpy())
LOGGER.debug("[%s] - Loss: %s", str(split),
str(average_loss))
else:
average_loss = float(loss.numpy())
tf.debugging.check_numerics(
loss.values if isinstance(loss, values.PerReplica) else
loss, "Numerics failed.")
now = time.time()
batch_duration = now - prev_batch_end
prev_batch_end = now
ma_loss[split].update(average_loss)
LOGGER.info("[%s] - Epoch: # %d", split, epoch)
LOGGER.info("[%s] - Tensorboard_dir: %s", split,
instance_output_dir)
LOGGER.info("[%s] - Batch: # %d", split,
batch_counters[split])
LOGGER.info("[%s] - Step: # %d", split,
step_counters[split])
if FLAG_USE_SUBSET.value:
LOGGER.warning(">> USING A SUBSET OF THE DATASET <<")
LOGGER.info(
"[%(split)s] - Batch loss: %(metric)f",
dict(split=split, metric=average_loss))
LOGGER.info(
"[%(split)s] - Moving average loss: %(metric)f",
dict(split=split, metric=ma_loss[split].average))
LOGGER.info(
"[%(split)s] - Moving average ppl: %(metric)f",
dict(split=split,
metric=np.exp(ma_loss[split].average)))
LOGGER.info(
"[%(split)s] - Batch duration: %(duration)s",
dict(split=split,
duration=utils.TimeStamp.from_seconds(
batch_duration).format()))
# Write to Tensorboard
with writers[split].as_default():
tf.summary.scalar(f"Loss/{split}", average_loss,
step_counters[split])
tf.summary.scalar(f"PPL/{split}", np.exp(average_loss),
step_counters[split])
writers[split].flush()
######################################################################
# Save every `FLAG_SAVE_PERIOD_MIN.value` minutes.
######################################################################
delta_sec = time.time() - timestamp_last_ckpt_secs
utils.check_operator(operator.gt, delta_sec, 0)
period_sec = 60 * FLAG_SAVE_PERIOD_MIN.value
utils.check_operator(operator.gt, period_sec, 0)
ratio = delta_sec / period_sec
LOGGER.info(
"[%(split)s] - RATIO: %(ratio)s",
dict(split=split, ratio=str(ratio)))
LOGGER.info(
"[%(split)s] - Target: %(target)s, Present: %(present)s",
dict(
split=split,
target=str(period_sec),
present=str(delta_sec),
))
if ratio >= 1:
dur = delta_sec / 60
timestamp_last_ckpt_secs = time.time()
LOGGER.debug(
"SAVING MODEL - CAUSE: DURATION - %0.2f min", dur)
# checkpoint.save(ckpt_prefix)
saver.save_model(
train_steps=step_counters["train"],
model_or_replicas=model,
optimizer=optimizer,
)
############################################################################
# Post Training Cleanup
############################################################################
for writer in writers.values():
writer.close()
def main(self):
print_mem('start')
self.state['gotNaN'] = 0
self.start_time = time.time()
self.batch_start_time = time.time()
self.step = 0
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
start_time = time.time()
self.start_time = start_time
while self.step < self.state['loopIters'] and \
last_cost > .1*self.state['minerr'] and \
(time.time() - start_time)/60. < self.state['timeStop']:
if (time.time() - self.save_time) / 60. > self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
pos = self.step // self.state['trainFreq']
self.timings[name][pos] = rvals[name]
if numpy.isinf(rvals['cost']) or numpy.isnan(rvals['cost']):
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.step % self.state['validFreq'] == 0 and self.step > 0:
self.validate()
self.step += 1
except:
self.state['wholetime'] = float(time.time() - start_time)
self.save()
if self.channel:
self.channel.save()
last_cost = 0
print 'Error in running algo (lr issue)'
print 'BEST SCORE'
print 'Validation', self.state['validcost']
print 'Validation time', print_time(self.state['validtime'])
print 'Train cost', self.state['traincost']
print 'Best Train', self.state['btraincost']
print 'Best Valid', self.state['bvalidcost']
print 'TEST', self.state['testcost']
print 'Took', (time.time() - start_time) / 60., 'min'
raise
self.state['wholetime'] = float(time.time() - start_time)
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'BEST SCORE'
print 'Validation', self.state['validcost']
print 'Validation time', print_time(self.state['validtime'])
print 'Train cost', self.state['traincost']
print 'Best Train', self.state['btraincost']
print 'Best Valid', self.state['bvalidcost']
print 'TEST', self.state['testcost']
print 'Took', (time.time() - start_time) / 60., 'min'
def main(self):
print_mem('start')
self.state['gotNaN'] = 0
self.start_time = time.time()
self.batch_start_time = time.time()
self.step = 0
self.save_iter = 0
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
last_cost = 1.
start_time = time.time()
self.start_time = start_time
while self.step < self.state['loopIters'] and \
last_cost > .1*self.state['minerr'] and \
(time.time() - start_time)/60. < self.state['timeStop']:
if (time.time() - self.save_time)/60. > self.state['saveFreq']:
self.save()
if self.channel is not None:
self.channel.save()
self.save_time = time.time()
st = time.time()
try:
rvals = self.algo()
self.state['traincost'] = float(rvals['cost'])
self.state['step'] = self.step
last_cost = rvals['cost']
for name in rvals.keys():
pos = self.step // self.state['trainFreq']
self.timings[name][pos] = rvals[name]
if numpy.isinf(rvals['cost']) or numpy.isnan(rvals['cost']):
self.state['gotNaN'] = 1
self.save()
if self.channel:
self.channel.save()
print 'Got NaN while training'
last_cost = 0
if self.step % self.state['validFreq'] == 0:
self.validate()
self.step += 1
except:
self.state['wholetime'] = float(time.time() - start_time)
self.save()
if self.channel:
self.channel.save()
last_cost = 0
print 'Error in running natgrad (lr issue)'
print 'BEST SCORE'
print 'Validation', self.state['validcost']
print 'Validation time', print_time(self.state['validtime'])
print 'Train cost', self.state['traincost']
print 'Best Train', self.state['btraincost']
print 'Best Valid', self.state['bvalidcost']
print 'TEST', self.state['testcost']
print 'Took', (time.time() - start_time)/60.,'min'
raise
self.state['wholetime'] = float(time.time() - start_time)
self.validate()
self.save()
if self.channel:
self.channel.save()
print 'BEST SCORE'
print 'Validation', self.state['validcost']
print 'Validation time', print_time(self.state['validtime'])
print 'Train cost', self.state['traincost']
print 'Best Train', self.state['btraincost']
print 'Best Valid', self.state['bvalidcost']
print 'TEST', self.state['testcost']
print 'Took', (time.time() - start_time)/60.,'min'
