removing unnecessary code

Author: init-22

reference_algorithms/schedule_free/jax/submission.py

@@ -36,12 +36,6 @@ def init_optimizer_state(workload: spec.Workload,
   model_params
   del model_state
   del rng
-  lr=HPARAMS['learning_rate']
-  betas=(1.0 - HPARAMS['one_minus_beta1'], HPARAMS['beta2'])
-  warmup_steps=int(HPARAMS['warmup_factor'] * workload.step_hint * 0.75)
-  weight_decay=HPARAMS['weight_decay']
-  weight_lr_power=HPARAMS['weight_lr_power']
-  r=HPARAMS['r']
 
   opt_init_fn, opt_update_fn = schedule_free_adamw(
       learning_rate=HPARAMS['learning_rate'],

reference_algorithms/schedule_free/pytorch/submission.py

@@ -266,14 +266,6 @@ def closure():
 
   loss = optimizer_state['optimizer'].step(closure)
 
-  # # current_state  = current_model.module.state_dict() is a workaround for DDP
-  # if global_step==1:
-  #   torch.save(current_model.module.state_dict(), "/results/pytorch_base_model_criteo1tb_11may_global_step2.pth")
-  #   import torch.distributed as dist
-  #   import sys
-  #   dist.destroy_process_group()
-  #   sys.exit(0)
-
   return (optimizer_state, current_param_container, new_model_state)