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jblitzar
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github-python

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help="If set, outputs sample from the dataset and quits.")
parser.add_argument("--sacred_id", type=str, default="nosacred", help="Sacred run id.")
parser.add_argument("--entmax_sampling", action="store_true", help="(experimental) use entmax sampling")
parser.add_argument("--export", action="store_true", help="If set, will export the model.")
args = parser.parse_args()
assert args.model is not None, "Model must be set"
return args
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 = params.get("input_fn", "sequential_input")
if input_fn == "sequential_input":
input_fn = sequential_input
elif input_fn == "generic_text":
input_fn = generic_text
pred_input_fn = pred_input
handle_pred_output_fn = handle_pred_output
# get current step
current_step = int(estimator_lib._load_global_step_from_checkpoint_dir(params["model_path"]))
logger.info(f"Current step {current_step}")
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)
if args.check_dataset:
check_dataset(input_fn, params)
# 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, params, global_step=current_step)
# 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
params["export"] = args.export
# Set sampling parameters
params["sampling_use_entmax"] = args.entmax_sampling
# 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"],