parser.add_argument('--steps_per_checkpoint', type=int, default=5000)
parser.add_argument('--autostack', action="store_false")
parser.add_argument('--auto_layout', action="store_true")
parser.add_argument('--auto_layout_and_mesh_shape', action="store_true")
parser.add_argument('--new', action='store_true')
parser.add_argument('--test', action='store_true')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--predict', action='store_true')
parser.add_argument('--no_delete_tpu', action='store_true')
parser.add_argument('--initial_heartbeat_timeout', type=int, default=7200)
parser.add_argument(
'--heartbeat_timeout', type=int, default=1800
) # kill and restart if nothing logged to tensorboard in this many seconds
args = parser.parse_args()
params = fetch_model_params(args.model)
ex = sacred.Experiment(args.experiment_name)
ex.observers.append(
sacred.observers.QueuedMongoObserver(url='127.0.0.1:27017',
db_name='db',
username='******',
password='******'))
def get_open_port(lo=8000, hi=8100):
for i in range(lo, hi):
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
if s.connect_ex(('localhost', i)) != 0:
return i
def main(args):
# Setup logging
logger = setup_logging(args)
# Read params of model
params = fetch_model_params(args.model)
# Fetch appropriate input functions
input_fn = generic_text
pred_input_fn = pred_input
handle_pred_output_fn = handle_pred_output
if params["mlm_training"]:
mlm_sample_text_fn = partial(mlm_sample_text, params)
input_fn = partial(generic_text, sample_text_fn=mlm_sample_text_fn)
# Fetch encoder per params
encoder = fetch_encoder(params)
pred_input_fn = partial(pred_input_fn,
path_to_prompt=args.prompt,
logger=logger,
enc=encoder)
# Sample from Dataset if check dataset flag is on
if args.check_dataset:
check_dataset(input_fn)
# Confirm deletion of checkpoint files if --new flag is set
if args.new:
if yes_or_no(
f"Are you sure you want to remove '{params['model_path']}' to start afresh?"
):
remove_gs_or_filepath(params["model_path"])
else:
exit()
# Save config to logdir for experiment management
save_config(params, params["model_path"])
# Add to params: auto_layout, auto_layout_and_mesh_shape, use_tpu, num_cores
mesh_shape = mtf.convert_to_shape(params["mesh_shape"])
params["num_cores"] = mesh_shape.size
params["auto_layout"] = args.auto_layout
params["auto_layout_and_mesh_shape"] = args.auto_layout_and_mesh_shape
params["use_tpu"] = True if not args.tpu is None else False
params["gpu_ids"] = args.gpu_ids
params["steps_per_checkpoint"] = args.steps_per_checkpoint
# Expand attention types param
params["attention_types"] = expand_attention_types_params(
params["attention_types"])
assert len(params["attention_types"]) == params[
"n_layer"] # Assert that the length of expanded list = num layers
params["predict_batch_size"] = params.get("predict_batch_size",
1) # Default to 1
params["predict"] = args.predict
params['model'] = params.get(
"model", "GPT"
) # Default model selection to GPT since it's the only option for now
# Sample quality of MoE models suffers when using the faster sampling method, so default to slow_sampling if
# moe layers are present
params[
"slow_sampling"] = True if params["moe_layers"] is not None else False
logger.info(f"params = {params}")
# Get eval tasks from params
eval_tasks = params.get("eval_tasks", [])
has_predict_or_eval_steps_or_eval_tasks = params[
"predict_steps"] > 0 or params["eval_steps"] > 0 or len(eval_tasks) > 0
for t in eval_tasks:
assert t in task_descriptors, f"Eval task '{t}' is not known"
task_descriptors[t]["init_fn"](params)
# Set up TPUs and Estimator
if args.tpu == "colab":
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
) if params["use_tpu"] else None
else:
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu) if params["use_tpu"] else None
config = tpu_config.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=params["model_path"],
save_checkpoints_steps=None, # Disable the default saver
save_checkpoints_secs=None, # Disable the default saver
log_step_count_steps=params["iterations"],
save_summary_steps=params["iterations"],
tpu_config=tpu_config.TPUConfig(
num_shards=mesh_shape.size,
iterations_per_loop=params["iterations"],
num_cores_per_replica=1,
per_host_input_for_training=tpu_config.InputPipelineConfig.
BROADCAST))
estimator = tpu_estimator.TPUEstimator(
use_tpu=params["use_tpu"],
model_fn=model_fn,
config=config,
train_batch_size=params["train_batch_size"],
eval_batch_size=params["train_batch_size"],
predict_batch_size=params["predict_batch_size"],
params=params)
def _make_task_estimator(task):
task_params = params.copy()
task_params["eval_task"] = task
return tpu_estimator.TPUEstimator(
use_tpu=params["use_tpu"],
model_fn=model_fn,
config=config,
train_batch_size=params["train_batch_size"],
eval_batch_size=params["train_batch_size"],
predict_batch_size=params["predict_batch_size"],
params=task_params)
eval_task_estimators = {
task: _make_task_estimator(task)
for task in eval_tasks
}
current_step = int(
estimator_lib._load_global_step_from_checkpoint_dir(
params["model_path"]))
logger.info(f"Current step {current_step}")
if args.predict:
# Predict
predictions = estimator.predict(input_fn=pred_input_fn)
logger.info("Predictions generated")
enc = fetch_encoder(params)
handle_pred_output_fn(predictions,
logger,
enc,
params,
out_name=f"predictions_{current_step}")
return
elif has_predict_or_eval_steps_or_eval_tasks:
# Eval and train - stop and predict and/or eval every checkpoint
while current_step < params["train_steps"]:
next_checkpoint = min(current_step + args.steps_per_checkpoint,
params["train_steps"])
estimator.train(input_fn=partial(input_fn, eval=False),
max_steps=next_checkpoint)
current_step = next_checkpoint
if params["predict_steps"] > 0:
logger.info("Running prediction...")
predictions = estimator.predict(input_fn=pred_input_fn)
enc = fetch_encoder(params)
handle_pred_output_fn(predictions,
logger,
enc,
params,
out_name=f"predictions_{current_step}")
if params["eval_steps"] > 0:
logger.info("Running evaluation...")
eval_results = estimator.evaluate(input_fn=partial(input_fn,
eval=True),
steps=params["eval_steps"])
logger.info(f"Eval results: {eval_results}")
for task in eval_tasks:
logger.info(f"Starting evaluation task '{task}'")
task_info = task_descriptors[task]["get_task_info_fn"](params)
task_estimator = eval_task_estimators[task]
task_input_fn = task_descriptors[task]["input_fn"]
eval_results = task_estimator.evaluate(
input_fn=task_input_fn,
steps=task_info["n_steps"],
name=task)
logger.info(f"Eval task '{task}' results: {eval_results}")
return
else:
# Else, just train
while current_step < params["train_steps"]:
# Else, don't stop and restart
estimator.train(input_fn=partial(input_fn, eval=False),
max_steps=params["train_steps"])