Dev -> main

prize_qualification_baselines/self_tuning/pytorch_nadamw_full_budget.py

@@ -1,5 +1,6 @@
 """Submission file for an NAdamW optimizer with warmup+cosine LR in PyTorch."""
 
+import collections
 import math
 from typing import Any, Dict, Iterator, List, Optional, Tuple
 
@@ -24,6 +25,7 @@
     "weight_decay": 0.08121616522670176,
     "warmup_factor": 0.02
 }
+HPARAMS = collections.namedtuple('Hyperparameters', HPARAMS.keys())(**HPARAMS)
 
 
 # Modified from github.com/pytorch/pytorch/blob/v1.12.1/torch/optim/adamw.py.

prize_qualification_baselines/self_tuning/pytorch_nadamw_target_setting.py

@@ -1,5 +1,6 @@
 """Submission file for an NAdamW optimizer with warmup+cosine LR in PyTorch."""
 
+import collections
 import math
 from typing import Any, Dict, Iterator, List, Optional, Tuple
 
@@ -24,6 +25,7 @@
     "weight_decay": 0.08121616522670176,
     "warmup_factor": 0.02
 }
+HPARAMS = collections.namedtuple('Hyperparameters', HPARAMS.keys())(**HPARAMS)
 
 
 # Modified from github.com/pytorch/pytorch/blob/v1.12.1/torch/optim/adamw.py.

submission_runner.py

@@ -668,7 +668,7 @@ def score_submission_on_workload(workload: spec.Workload,
       score, _ = train_once(
           workload, workload_name, global_batch_size, global_eval_batch_size,
           data_dir, imagenet_v2_data_dir,
-          init_optimizer_state, update_params, data_selection,
+          init_optimizer_state, update_params, data_selection, prepare_for_eval,
           None, rng_seed, rng, profiler, max_global_steps, log_dir,
           save_checkpoints=save_checkpoints)
   return score