Merge pull request #16 from boostcampaitech5/feat/#2-compute_metrics

Feat/#2 compute metrics

Author: jihye-moon

Diff for: finetuning/config.yaml

@@ -21,6 +21,7 @@ weight_decay: 0.
 warmup_ratio: 0.1
 lr_scheduler_type: linear
 resume_from_checkpoint: None
+use_compute_metrics: False
 
 # lora hyperparams
 lora_r: 8

Diff for: finetuning/train.py

@@ -15,6 +15,7 @@
 from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast, BitsAndBytesConfig
 from utils.data_loader import *
 from utils.arguments import TrainArguments
+from utils.compute_metrics import *
 
 
 def train(train_args: TrainArguments):
@@ -122,6 +123,7 @@ def train(train_args: TrainArguments):
         data_collator=transformers.DataCollatorForSeq2Seq(
             tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
         ),
+        compute_metrics=train_compute_metrics if train_args.use_compute_metrics else None,
     )
     model.config.use_cache = False
 
@@ -135,6 +137,9 @@ def train(train_args: TrainArguments):
 
     with torch.autocast("cuda"):
         trainer.train(resume_from_checkpoint=train_args.resume_from_checkpoint)
+
+    evaluation_result = trainer.evaluate(eval_dataset=val_data)
+    print(evaluation_result)
 
     model.save_pretrained(train_args.finetune_dir)
 

Diff for: finetuning/utils/arguments.py

@@ -24,6 +24,7 @@ class TrainArguments(BaseModel):
     warmup_ratio: float = 0.1
     lr_scheduler_type: str = 'linear'
     resume_from_checkpoint: str = None
+    use_compute_metrics: bool = False
     # lora hyperparams
     lora_r: int = 32
     lora_alpha: int = 64

Diff for: finetuning/utils/compute_metrics.py

@@ -0,0 +1,86 @@
+from datasets import load_metric
+from transformers import GPTNeoXTokenizerFast, GPTNeoXForCausalLM
+import numpy as np
+from rouge import Rouge
+from statistics import geometric_mean
+import torch
+from tqdm import tqdm
+
+
+def train_compute_metrics(pred):
+
+    model = GPTNeoXForCausalLM.from_pretrained('nlpai-lab/kullm-polyglot-12.8b-v2')
+
+    logits = torch.tensor(pred.predictions.argmax(-1).flatten(), dtype=torch.int64)
+    logits = logits.unsqueeze(0)  # torch.Size([1, 35200])
+
+    max_length = model.config.max_position_embeddings # 2048
+    stride = 1024
+    seq_len = logits.size(1)
+
+    nlls = []
+    prev_end_loc = 0
+    for begin_loc in tqdm(range(0, seq_len, stride)):
+        end_loc = min(begin_loc + max_length, seq_len)
+        trg_len = end_loc - prev_end_loc  # 마지막 루프의 스트라이드 값과 다를 수 있음
+        input_ids = logits[:, begin_loc:end_loc]
+        target_ids = input_ids.clone()
+        target_ids[:, :-trg_len] = -100
+
+        with torch.no_grad():
+            outputs = model(input_ids, labels=target_ids)
+
+            # 손실은 모든 유효한 레이블에 대한 평균값을 구하는 교차 엔트로피(cross entropy)로 계산됩니다.
+            # 나이브 베이지안 모델은 내부적으로 레이블을 왼쪽으로 1개씩 밀기 때문에, (타켓 - 1)개 만큼의 레이블에 대해 손실을 계산합니다.
+            neg_log_likelihood = outputs.loss
+
+        nlls.append(neg_log_likelihood)
+        prev_end_loc = end_loc
+        if end_loc == seq_len:
+            break
+
+    ppl = torch.exp(torch.stack(nlls).mean())
+
+    return {'perplexity':ppl}
+
+
+def test_compute_metrics(pred):
+    tokenizer = GPTNeoXTokenizerFast.from_pretrained('nlpai-lab/kullm-polyglot-12.8b-v2')
+
+    # 사용할 metric을 불러옵니다.
+    metric_bleu = load_metric("sacrebleu")
+    metric_meteor = load_metric("meteor")
+    metric_rouge = Rouge(metrics=["rouge-1", "rouge-2", "rouge-3", "rouge-4", "rouge-5", "rouge-l"])
+    metric_bertscore = load_metric("bertscore")
+
+    # 학습에서 산출된 pred를 preds(모델이 생성)와 label(정답 데이터)로 분리합니다.
+    preds = pred.predictions.argmax(-1)
+    labels = pred.label_ids
+    labels = np.where(pred.label_ids != -100, labels, tokenizer.pad_token_id)
+
+    scores = {
+        'sacre_bleu': [],
+        'meteor': [],
+        'rouge_l_f1': [],
+        'bert_score_f1': [],
+    }
+
+    for i in range(len(preds)):
+        decoded_preds = tokenizer.decode(preds[i], skip_special_tokens=True)
+        decoded_labels = tokenizer.decode(labels[i], skip_special_tokens=True)
+        if "### 응답:" in decoded_preds:
+            decoded_preds = decoded_preds.split('### 응답:\n')[1][:-1]
+
+        bleu_score = metric_bleu.compute(predictions=[decoded_preds], references=[[decoded_labels]])["score"]
+        meteor_score = metric_meteor.compute(predictions=[decoded_preds], references=[decoded_labels])["meteor"]
+        rouge_scores = metric_rouge.get_scores(decoded_preds, decoded_labels, avg=True)["rouge-l"]['f']
+        bert_score = metric_bertscore.compute(predictions=[decoded_preds], references=[decoded_labels], lang='ko')["f1"][0]
+
+        scores['sacre_bleu'].append(bleu_score / 100)
+        scores['meteor'].append(meteor_score)
+        scores['rouge_l_f1'].append(rouge_scores)
+        scores['bert_score_f1'].append(bert_score)
+
+    scores = {k: geometric_mean(v) for k, v in scores.items()}
+
+    return {k: round(v, 5) for k, v in scores.items()}

Diff for: finetuning/utils/data_loader.py

@@ -11,6 +11,8 @@ def load_and_preprocess_data(train_args: TrainArguments, tokenizer: GPTNeoXToken
     else:
         data = load_dataset(train_args.data_path)
 
+    print(data)
+
     prompter = Prompter(template_name = train_args.prompt_template_name,
                         verbose = False)