init

training_scripts/finetune_t5.py

@@ -71,7 +71,7 @@ def train(
         dataset_split_validation: str,
         dataset_split_test: str,
         lr: List,
-        epoch: int,
+        epoch: List,
         batch: List,
         down_sample_train: int,
         down_sample_validation: int,
@@ -138,7 +138,8 @@ def train(
     if not skip_train:
         lr = [1e-6, 1e-4] if lr is None else lr
         batch = [64] if batch is None else batch
-        for n, (lr_tmp, batch_tmp) in enumerate(product(lr, batch)):
+        epoch = [1, 3, 5] if epoch is None else epoch
+        for n, (lr_tmp, batch_tmp, epoch_tmp) in enumerate(product(lr, batch, epoch)):
             logging.info(f"[TRAIN {n}/{len(lr) * len(batch)}] lr: {lr_tmp}, batch: {batch_tmp}")
             model = load_model(
                 model_name=model_name, use_auth_token=use_auth_token, low_cpu_mem_usage=model_low_cpu_mem_usage
@@ -146,7 +147,7 @@ def train(
             trainer = Seq2SeqTrainer(
                 model=model,
                 args=Seq2SeqTrainingArguments(
-                    num_train_epochs=epoch,
+                    num_train_epochs=epoch_tmp,
                     learning_rate=lr_tmp,
                     output_dir=f"{output_dir}/model_{n}",
                     evaluation_strategy='steps',
@@ -166,12 +167,6 @@ def train(
             trainer.log_metrics("train", metrics)
             trainer.save_metrics("train", metrics)
             trainer.save_state()
-
-            # trainer.save_model(f'{output_dir}/model_{n}')
-            # tokenizer.save_pretrained(f'{output_dir}/model_{n}')
-            # # grid search
-            # with open(f'{output_dir}/model_{n}/hyperparameters.json', 'w') as f:
-            #     json.dump({"learning_rate": lr_tmp, "batch_size": batch_tmp}, f)
             del trainer
             gc.collect()
             torch.cuda.empty_cache()