[Bug] Step1: Not mask source part

Desciption: In DeepSpeed-Chat step1 PromptDataset. According to my understanding, the SFT part needs to mask the source part, and the source part does not participate in the loss calculation.

class PromptDataset(Dataset):

    def __init__(self, prompt_dataset, chosen_dataset, reject_dataset,
                 pad_token_id, train_phase) -> None:
        super().__init__()
        self.prompt_dataset = prompt_dataset
        self.chosen_dataset = chosen_dataset
        self.reject_dataset = reject_dataset
        self.pad_token_id = pad_token_id
        self.train_phase = train_phase

    def __len__(self):
        length = len(self.chosen_dataset)
        if self.train_phase == 3:
            length = len(self.prompt_dataset)
        return length

    def __getitem__(self, idx):
        if self.train_phase == 1:
            return {
                "input_ids": self.chosen_dataset[idx]["input_ids"],
                "attention_mask": self.chosen_dataset[idx]["attention_mask"],
                "labels": self.chosen_dataset[idx]["input_ids"] # maybe: [*[-100] * source_len, *[target_ids]]
            }
        elif self.train_phase == 2:
            return self.chosen_dataset[idx]["input_ids"], self.chosen_dataset[idx]["attention_mask"], \
                self.reject_dataset[idx]["input_ids"], self.reject_dataset[idx]["attention_mask"]
        elif self.train_phase == 3:
            return self.prompt_dataset[idx]["input_ids"],self.prompt_dataset[idx]["attention_mask"], \
                self.pad_token_id

Locate the PromptDataset class in the DeepSpeed-Chat step1 code and inspect how train_phase 1 constructs labels from chosen_dataset input_ids. Verify the source portion is represented with ignored labels while target tokens remain active for loss calculation, then confirm the resulting dataset output matches the issue’s expected masking behavior.