init

training_scripts/finetune_t5.py

@@ -13,10 +13,10 @@ from typing import List, Set, Dict
 from shutil import copyfile
 from statistics import mean
 from itertools import product
+from distutils.dir_util import copy_tree
 
 import torch
 import transformers
-from numba import cuda
 from datasets import load_dataset
 from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, pipeline
 from huggingface_hub import Repository
@@ -85,7 +85,6 @@ def train(
         skip_train: bool = False,
         skip_test: bool = False,
         skip_upload: bool = False,
-        eval_steps: float = 0.25,
         eval_batch_size: int = None):
     """Fine-tune seq2seq model."""
     logging.info(f'[CONFIG]\n\t *LM: {model_name}, \n\t *Data: {dataset} ({dataset_name})')
@@ -144,7 +143,7 @@ def train(
         for n, (lr_tmp, batch_tmp, epoch_tmp) in enumerate(product(lr, batch, epoch)):
             logging.info(f"[TRAIN {n}/{len(lr) * len(batch) * len(epoch)}] lr: {lr_tmp}, batch: {batch_tmp}")
             output_dir_tmp = f"{output_dir}/model_lr_{lr_tmp}_batch_{batch_tmp}_epoch_{epoch_tmp}"
-            if os.path.exists(output_dir_tmp):
+            if os.path.exists(f"{output_dir_tmp}/eval_results.json"):
                 continue
             model = load_model(
                 model_name=model_name, use_auth_token=use_auth_token, low_cpu_mem_usage=model_low_cpu_mem_usage
@@ -183,29 +182,29 @@ def train(
             # cuda.get_current_device().reset()
 
         model_score = {}
-        for eval_file in glob(f"{output_dir}/model_*/eval_result.json"):
+        for eval_file in glob(f"{output_dir}/model_*/eval_results.json"):
             with open(eval_file) as f:
                 model_score[os.path.dirname(eval_file)] = json.load(f)['eval_f1']
         best_model = max(model_score, key=model_score.get)
-
+        copy_tree(best_model, f'{output_dir}/best_model')
     else:
         logging.info('skip hyperparameter search & model training (already done)')
 
     # get metric on the test set
     if not skip_test:
         logging.info('run evaluation on test set')
-        if not os.path.exists(f'{output_dir}/model/prediction_test.txt'):
+        if not os.path.exists(f'{output_dir}/best_model/prediction_test.txt'):
             pipe = pipeline(
                 'text2text-generation',
-                model=f'{output_dir}/model',
+                model=f'{output_dir}/best_model',
                 device='cuda:0' if torch.cuda.is_available() else 'cpu',
             )
             input_data = [i[dataset_column_text] for i in dataset_instance[dataset_split_test]]
             output = pipe(input_data, batch_size=eval_batch_size)
             output = [i['generated_text'] for i in output]
-            with open(f'{output_dir}/model/prediction_test.txt', 'w') as f:
+            with open(f'{output_dir}/best_model/prediction_test.txt', 'w') as f:
                 f.write('\n'.join(output))
-        with open(f'{output_dir}/model/prediction_test.txt') as f:
+        with open(f'{output_dir}/best_model/prediction_test.txt') as f:
             output = [set(i.split(',')) for i in f.read().split('\n')]
         dataset_tmp = dataset_instance[dataset_split_test]
         label_list = dataset_tmp[dataset_column_label]
@@ -214,7 +213,7 @@ def train(
         ]
         eval_metric = get_f1_score(_references, output)
         logging.info(json.dumps(eval_metric, indent=4))
-        with open(f'{output_dir}/model/evaluation_metrics.json', 'w') as f:
+        with open(f'{output_dir}/best_model/evaluation_metrics.json', 'w') as f:
             json.dump(eval_metric, f)
 
     if not skip_upload:
@@ -222,15 +221,15 @@ def train(
             'model_organization must be specified when model_alias is specified'
         logging.info('uploading to huggingface')
         args = {'use_auth_token': use_auth_token, 'organization': model_organization}
-        model = load_model(model_name=f'{output_dir}/model')
+        model = load_model(model_name=f'{output_dir}/best_model')
         model.push_to_hub(model_alias, **args)
         tokenizer.push_to_hub(model_alias, **args)
         repo = Repository(model_alias, f'{model_organization}/{model_alias}')
-        copyfile(f'{output_dir}/model/hyperparameters.json', f'{model_alias}/hyperparameters.json')
-        if os.path.exists(f'{output_dir}/model/prediction_test.txt'):
-            copyfile(f'{output_dir}/model/prediction_test.txt', f'{model_alias}/prediction_test.txt')
-        if os.path.exists(f'{output_dir}/model/evaluation_metrics.json'):
-            copyfile(f'{output_dir}/model/evaluation_metrics.json', f'{model_alias}/evaluation_metrics.json')
+        copyfile(f'{output_dir}/best_model/hyperparameters.json', f'{model_alias}/hyperparameters.json')
+        if os.path.exists(f'{output_dir}/best_model/prediction_test.txt'):
+            copyfile(f'{output_dir}/best_model/prediction_test.txt', f'{model_alias}/prediction_test.txt')
+        if os.path.exists(f'{output_dir}/best_model/evaluation_metrics.json'):
+            copyfile(f'{output_dir}/best_model/evaluation_metrics.json', f'{model_alias}/evaluation_metrics.json')
         sample = [i[dataset_column_text] for i in dataset_instance[dataset_split_train]]
         sample = [i for i in sample if ''' not in i and ''' not in i][:3]
         widget = '\n'.join([f"- text: '{t}'\n  example_title: example {_n + 1}" for _n, t in enumerate(sample)])
@@ -303,7 +302,6 @@ if __name__ == '__main__':
         down_sample_validation=opt.down_sample_validation,
         random_seed=opt.random_seed,
         use_auth_token=opt.use_auth_token,
-        eval_steps=opt.eval_steps,
         output_dir=opt.output_dir,
         model_alias=opt.model_alias,
         model_organization=opt.model_organization,

training_scripts/script.sh

@@ -1,7 +1,6 @@
-python finetune_t5.py --dataset-name ja --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp
-python finetune_t5.py --dataset-name gr --model-alias mt5-small-tweet-topic-gr --model-organization cardiffnlp
-python finetune_t5.py --dataset-name es --model-alias mt5-small-tweet-topic-es --model-organization cardiffnlp
-python finetune_t5.py --dataset-name en --model-alias mt5-small-tweet-topic-en --model-organization cardiffnlp
+python finetune_t5.py --dataset-name ja --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-ja --model-organization cardiffnlp
+python finetune_t5.py --dataset-name gr --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-gr --model-organization cardiffnlp
+python finetune_t5.py --dataset-name es --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-es --model-organization cardiffnlp
+python finetune_t5.py --dataset-name en --low-cpu-mem-usage --model-alias mt5-small-tweet-topic-en --model-organization cardiffnlp
 
-python finetune_t5.py --dataset-name ja --skip-test --skip-upload