[chatgpt] unnify datasets (#3218)

applications/ChatGPT/chatgpt/dataset/sft_dataset.py

@@ -54,7 +54,8 @@ class SFTDataset(Dataset):
 
     def __init__(self, dataset, tokenizer: Callable, max_length: int=512) -> None:
         super().__init__()
-        self.prompts = []
+        # self.prompts = []
+        self.input_ids = []
 
         for data in tqdm(dataset, disable=not is_rank_0()):
             prompt = data['prompt'] + data['completion'] + "<|endoftext|>"
@@ -64,14 +65,18 @@ class SFTDataset(Dataset):
                                      truncation=True,
                                      return_tensors="pt")
 
-            self.prompts.append(prompt_token)
+            # self.prompts.append(prompt_token)s
+            self.input_ids.append(prompt_token)
+            self.labels = copy.deepcopy(self.input_ids)
 
     def __len__(self):
         length = len(self.prompts)
         return length
 
     def __getitem__(self, idx):
-        return self.prompts[idx]
+        # dict(input_ids=self.input_ids[i], labels=self.labels[i])
+        return dict(input_ids=self.input_ids[i], labels=self.labels[i])
+        # return dict(self.prompts[idx], self.prompts[idx])
     
 
 def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:

applications/ChatGPT/chatgpt/trainer/sft.py

@@ -63,11 +63,13 @@ class SFTTrainer(ABC):
             for batch_id, batch in enumerate(self.train_dataloader):
                 prompt_ids = batch["input_ids"]
                 p_mask = batch["attention_mask"]
+                labels = batch["labels"]
                 prompt_ids = prompt_ids.squeeze(1).cuda()
                 p_mask = p_mask.squeeze(1).cuda()
-                prompt_logits = self.model(prompt_ids, attention_mask=p_mask)
+                # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels)
+                loss, prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels)
 
-                loss = self.loss_fn(prompt_logits, prompt_ids)
+                # loss = self.loss_fn(prompt_logits, labels)
                 self.strategy.backward(loss, self.model, self.optimizer)
                 self.strategy.optimizer_step(self.optimizer)
                 self.optimizer.zero_grad()