style(nyz): add comments for rlhf dataset

PaParaZz1 · PaParaZz1 · commit 428c1a6fbdc2 · 2025-02-05T19:24:57.000+08:00
diff --git a/ding/utils/data/rlhf_offline_dataset.py b/ding/utils/data/rlhf_offline_dataset.py
@@ -3,12 +3,23 @@
 from tqdm import tqdm
 from torch.utils.data import Dataset
 from torch.distributed import get_rank
+from transformers import AutoTokenizer
 import torch
 import torch.nn.functional as F
 
 
 def zero_pad_sequences(sequences: List[torch.Tensor], side: str = "left", value: int = 0) -> torch.Tensor:
-    assert side in ("left", "right")
+    """
+    Overview:
+        Pad sequences with zeros to create a batch tensor of uniform length.
+    Arguments:
+        - sequences (List[torch.Tensor]): A list of PyTorch tensors to be padded.
+        - side (str): The side to pad ('left' or 'right'), default is 'left'.
+        - value (int): The padding value to use, default is 0.
+    Returns:
+        - padded_sequences (torch.Tensor): A padded tensor of shape [batch_size, max_sequence_length].
+    """
+    assert side in ("left", "right"), side
     max_len = max(seq.size(-1) for seq in sequences)
     padded_sequences = []
     for seq in sequences:
@@ -22,12 +33,13 @@ class OfflineRLDataset(Dataset):
     """
     Overview:
         PyTorch Dataset for OfflineRL LLM training like KTO and DPO.
+        This dataset supports pure text input, as well as image, video, audio, etc.
     """
 
     def __init__(
             self,
             dataset: Iterable[Dict],
-            tokenizer,
+            tokenizer: AutoTokenizer,
             max_length: int,
             input_key: str = "input",
             extra_input_keys: List[str] = [],
@@ -39,6 +51,24 @@ def __init__(
             num_processors: int = 8,
             parallel_load: bool = True
     ) -> None:
+        """
+        Overview:
+            Initialize the OfflineRLDataset.
+        Arguments:
+            - dataset (Iterable[Dict]): The iterable dataset object to be used, such as list or huggingface dataset.
+            - tokenizer (AutoTokenizer): The tokenizer to be used.
+            - max_length (int): The maximum length of the input.
+            - input_key (str): The key of the input, default is "input".
+            - extra_input_keys (List[str]): The extra input keys, such as "image", "video", "audio", etc.
+            - output_key (str): The key of the output, default is "output".
+            - label_key (str): The key of the label, default is "label".
+            - apply_chat_template (bool): Whether to apply the chat template, default is False.
+            - tokenizer_chat_template (str): The chat template to be used.
+            - input_template (str): The input template to be used.
+            - num_processors (int): The number of processors to be used, default is 8.
+            - parallel_load (bool): Whether to parallel load the dataset in the `__init__` method, default is True.
+                Parallel loading is usually used for huggingface dataset.
+        """
         super().__init__()
         self.tokenizer = tokenizer
         self.max_length = max_length
@@ -98,7 +128,23 @@ def _preprocess_data(
             output_key: str = "output",
             label_key: str = "label",
             apply_chat_template: Union[bool, Callable] = False,
-    ) -> str:
+    ) -> Dict[str, Any]:
+        """
+        Overview:
+            Preprocess the data and return the processed data.
+        Arguments:
+            - data (Dict[str, Any]): The data to be processed.
+            - input_template (str): The input template to be used.
+            - input_key (str): The key of the input, default is "input".
+            - extra_input_keys (List[str]): The extra input keys, such as "image", "video", "audio", etc.
+            - output_key (str): The key of the output, default is "output".
+            - label_key (str): The key of the label, default is "label".
+            - apply_chat_template (Union[bool, Callable]): Controls chat template application. If True, uses the \
+                tokenizer's default template. If a Callable is provided, uses that function to apply the template \
+                (typically tokenizer.apply_chat_template).
+        Returns:
+            - processed_data (Dict[str, Any]): The processed data.
+        """
         label = data[label_key]
         if extra_input_keys:
             extra_inputs = {key: data[key] for key in extra_input_keys}
@@ -160,6 +206,7 @@ def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, int]]:
         Returns:
             - item (Dict[str, Union[torch.Tensor, int]]): The item at the given index.
         """
+        # extra inputs: usually image, video, audio, etc.
         if self.extra_input_keys:
             extra_inputs = {key: getattr(self, key)[idx] for key in self.extra_input_keys}
         else:
@@ -173,6 +220,14 @@ def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, int]]:
         }
 
     def collate_fn(self, item_list: List[Dict[str, Union[torch.Tensor, int]]]):
+        """
+        Overview:
+            Collate the items into a batch, which is used to create a batch for training.
+        Arguments:
+            - item_list (List[Dict[str, Union[torch.Tensor, int]]]): The list of items to be collated.
+        Returns:
+            - collated_items (Dict[str, Union[torch.Tensor, int]]): The collated items.
+        """
 
         def tokenizer(prompt: str, response: str):
             text = (prompt + response).rstrip("\n")
@@ -191,6 +246,7 @@ def tokenizer(prompt: str, response: str):
             inputs["attention_mask"][0][-1] = True
             return inputs["input_ids"], inputs["attention_mask"]
 
+        # tot_extra_inputs: Dict[str, List[torch.Tensor]]
         tot_ids, tot_masks, tot_labels, prompt_ids_lens, tot_extra_inputs = [], [], [], [], {}
         for item in item_list:
             input_ids, attention_mask = tokenizer(item["prompt"], item["response"])
diff --git a/ding/utils/data/rlhf_online_dataset.py b/ding/utils/data/rlhf_online_dataset.py
@@ -2,18 +2,20 @@
 from tqdm import tqdm
 from torch.utils.data import Dataset
 from torch.distributed import get_rank
+from transformers import AutoTokenizer
 
 
 class OnlineRLDataset(Dataset):
     """
     Overview:
         PyTorch Dataset for OnlineRL LLM training like PPO.
+        This dataset only supports pure text input now.
     """
 
     def __init__(
             self,
             dataset: Iterable[Dict],
-            tokenizer,
+            tokenizer: AutoTokenizer,
             input_key: str = "input",
             apply_chat_template: bool = False,
             input_template: str = None,
@@ -23,9 +25,9 @@ def __init__(
             Initialize the OnlineRLDataset.
         Arguments:
             - dataset (torch.utils.data.Dataset): The dataset to preprocess.
-            - tokenizer (): The tokenizer to preprocess the data.
-            - input_key (str): The key of the input data.
-            - apply_chat_template (bool): Whether to apply the chat template.
+            - tokenizer (AutoTokenizer): The tokenizer to preprocess the data.
+            - input_key (str): The key of the input data, default is "input".
+            - apply_chat_template (bool): Whether to apply the chat template, default is False.
             - input_template (str): The template to format the data.
         """
         super().__init__()
@@ -57,7 +59,7 @@ def __getitem__(self, idx: int) -> str:
         """
         Overview:
             Get the item at the given index.
-        Args:
+        Arguments:
             - idx (int): The index of the item to get.
         Returns:
             - item (str): The item at the given index.
@@ -78,8 +80,9 @@ def _preprocess_data(
             - data (Dict[str, Any]): The data to preprocess.
             - input_template (str): The template to format the data.
             - input_key (str): The key of the input data.
-            - apply_chat_template (Union[bool, Callable]): The function to apply the chat template, \
-                usually is the `tokenizer.apply_chat_template`.
+            - apply_chat_template (Union[bool, Callable]): Controls chat template application. If True, uses the \
+                tokenizer's default template. If a Callable is provided, uses that function to apply the template \
+                (typically tokenizer.apply_chat_template).
         Returns:
             - prompt (str): The formatted prompt.
         """
