def create_model_config(args, benchmark_config=None, model_specs=None):
"""Return a dict with the given model, dataset and optimizer."""
# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
device = torch.device("cpu")
if args.model_name == "lm":
if args.use_synthetic_data:
dataloader_fn = get_synthetic_dataloaders
else:
dataloader_fn = get_real_dataloaders
data = dataloader_fn(args, device, benchmark_config, model_specs)
model, optimizer = get_model_and_optimizer(args, device, benchmark_config, model_specs)
return {
"model": model,
"optimizer": optimizer,
"data": data,
}
elif args.model_name == "seq":
data = get_synthetic_dataloaders(
args, device, offload_seq.get_benchmark_config(), offload_seq.get_model_config()
)
model, optimizer = get_model_and_optimizer(args, device, benchmark_config, model_specs)
return {
"model": model,
"optimizer": optimizer,
"data": data,
}
else:
raise RuntimeError(f"Unrecognized args.model_mame {args.model_name}")
def get_model_specs(model_name):
"""Return a dict with configurations required for configuring `model_name` model."""
if model_name == "lm":
return lm_wikitext2.get_model_config()
elif model_name == "seq":
return offload_seq.get_model_config()
else:
raise RuntimeError("Unrecognized args.model_mame " % args.model_name)
def create_benchmark_config(model_name):
"""Return a dict with configurations required for benchmarking `model_name` model."""
if args.model_name == "lm":
return lm_wikitext2.get_benchmark_config()
elif args.model_name == "seq":
return offload_seq.get_benchmark_config()
else:
raise RuntimeError(f"Unrecognized args.model_name {args.model_name}")
