init

training_scripts/finetune_t5.py

@@ -1,6 +1,6 @@
 """ Fine-tune T5 on topic classification (multi-label multi-class classification)
 ```
-python finetune_t5.py --dataset-name ja --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp
+python finetune_t5.py --dataset-name ja --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp --low-cpu-mem-usage
 ```
 """
 import json
@@ -8,7 +8,7 @@ import logging
 import os
 import argparse
 import gc
-from typing import List, Set
+from typing import List, Set, Dict
 from shutil import copyfile
 from statistics import mean
 from itertools import product
@@ -46,7 +46,7 @@ def load_model(
     return model
 
 
-def get_f1_score(references: List[Set[str]], predictions: List[Set[str]]):
+def get_f1_score(references: List[Set[str]], predictions: List[Set[str]]) -> Dict[str, float]:
     scores = []
     for g, r in zip(references, predictions):
         tp = len(set(g).intersection(set(r)))
@@ -84,11 +84,10 @@ def train(
         skip_test: bool = False,
         skip_upload: bool = False,
         eval_steps: float = 0.25,
-        eval_batch_size: int = 16):
+        eval_batch_size: int = None):
     """Fine-tune seq2seq model."""
     logging.info(f'[CONFIG]\n\t *LM: {model_name}, \n\t *Data: {dataset} ({dataset_name})')
-    # set up the output directory
-    if output_dir is None:
+    if not output_dir:
         output_dir = f'ckpt/{os.path.basename(model_name)}.{os.path.basename(dataset)}.{dataset_name}'
     # dataset process
     tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, use_auth_token=use_auth_token)
@@ -117,7 +116,7 @@ def train(
         else:
             tokenized_dataset[f'{s}_ds'] = tokenized_dataset[s]
 
-    def compute_metric(eval_pred):  # for parameter search
+    def compute_metric(eval_pred) -> Dict[str, float]:  # for parameter search
 
         def decode_tokens(token_ids) -> List[Set[str]]:
             return [
@@ -137,10 +136,11 @@ 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
-        epoch = [1, 3, 5] if epoch is None else epoch
+        batch = [64] if not batch else batch
+        epoch = [1, 3, 5] if not epoch else epoch
+        eval_batch_size = min(batch) if not eval_batch_size else eval_batch_size
         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}")
+            logging.info(f"[TRAIN {n}/{len(lr) * len(batch) * len(epoch)}] 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
             )
@@ -161,7 +161,6 @@ def train(
                 eval_dataset=tokenized_dataset['validation_ds'],
                 compute_metrics=compute_metric,
             )
-
             # train
             result = trainer.train()
             trainer.save_model()  # Saves the tokenizer too for easy upload
@@ -169,13 +168,13 @@ def train(
             trainer.log_metrics("train", metrics)
             trainer.save_metrics("train", metrics)
             trainer.save_state()
-
             # evaluate
             metrics = trainer.evaluate()
             trainer.log_metrics("eval", metrics)
             trainer.save_metrics("eval", metrics)
-
+            # clean up memory
             del trainer
+            del model
             gc.collect()
             torch.cuda.empty_cache()
     else: