diff --git a/paddleformers/data/causal_dataset.py b/paddleformers/data/causal_dataset.py
index d3789287649..fc5169877ad 100644
--- a/paddleformers/data/causal_dataset.py
+++ b/paddleformers/data/causal_dataset.py
@@ -13,6 +13,8 @@
 from .indexed_dataset import make_dataset as make_indexed_dataset
 
 local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
+INFER_SERVER_IP = os.getenv("INFER_SERVER_IP", "127.0.0.1")
+INFER_SERVER_PORT = os.getenv("INFER_SERVER_PORT", "8008")
 
 
 # class FakeHCG:
@@ -25,6 +27,57 @@
 #     def get_model_parallel_group(self):
 #         return None
 
+import pickle
+
+import requests
+
+
+def get_logits(batch_ids, max_retries=1, timeout=1200, retry_delay=1, prob_nums=10):
+    """
+    Retrieve logits with retry mechanism if no response is received within the specified time
+
+    Parameters:
+        batch_ids: Input token ids
+        max_retries: Maximum number of retry attempts (default: 1)
+        timeout: Request timeout in seconds (default: 1200 seconds)
+        retry_delay: Delay between retries in seconds (default: 1 second)
+        prob_nums: Number of probabilities to return
+
+    Returns:
+        tuple: (logits, ids)
+    Raises:
+        Exception: Thrown when all retry attempts fail
+    """
+
+    headers = {"Content_Type": "application/json"}
+    url = f"http://{INFER_SERVER_IP}:{INFER_SERVER_PORT}/generate"
+    payload = {
+        "prompt_token_ids": batch_ids,
+        "max_tokens": 1,
+        "top_p": 1,
+        "top_k": -1,
+        "temperature": 1,
+        "prompt_logprobs": prob_nums,
+        "logprobs": prob_nums,
+    }
+
+    for attempt in range(max_retries):
+        try:
+            response = requests.post(url=url, json=payload, headers=headers, timeout=timeout)
+            response.raise_for_status()  # 检查HTTP错误
+
+            data = pickle.loads(response.content)
+            all_token = data.get("logits", [])
+            all_ids = data.get("ids", [])
+            all_token = paddle.to_tensor(all_token, dtype="bfloat16")
+            all_ids = paddle.to_tensor(all_ids, dtype="int64")
+            return all_token, all_ids
+
+        except (requests.exceptions.RequestException, IOError) as e:
+            if attempt == max_retries - 1:
+                raise Exception(f"Failed after {max_retries} attempts. Last error: {str(e)}")
+            time.sleep(retry_delay)
+
 
 def check_data_split(splits_string, do_train, do_eval, do_predict):
     splits = []
@@ -125,6 +178,8 @@ def build_train_valid_test_datasets(
     *,
     data_cache_path=None,
     need_data=True,
+    self_constraint_cpt=False,
+    prob_nums=10,
 ):
     """Build train, valid, and test datasets."""
 
@@ -141,6 +196,8 @@ def build_train_valid_test_datasets(
             share_folder=share_folder,
             data_cache_path=data_cache_path,
             need_data=need_data,
+            self_constraint_cpt=self_constraint_cpt,
+            prob_nums=prob_nums,
         )
 
     # Blending dataset.
@@ -168,6 +225,8 @@ def build_train_valid_test_datasets(
             share_folder=share_folder,
             data_cache_path=data_cache_path,
             need_data=need_data,
+            self_constraint_cpt=self_constraint_cpt,
+            prob_nums=prob_nums,
         )
         if train_ds:
             train_datasets.append(train_ds)
@@ -212,8 +271,14 @@ def _build_train_valid_test_datasets(
     *,
     data_cache_path=None,
     need_data=True,
+    self_constraint_cpt=False,
+    prob_nums=10,
 ):
     """Build train, valid, and test datasets."""
+    CPT = False
+    if data_prefix.endswith("::CPT"):
+        data_prefix = data_prefix[: -len("::CPT")]
+        CPT = True
 
     # Indexed dataset.
     if need_data:
@@ -254,6 +319,9 @@ def build_dataset(index, name):
             share_folder,
             data_cache_path=data_cache_path,
             need_data=need_data,
+            CPT=CPT,
+            self_constraint_cpt=self_constraint_cpt,
+            prob_nums=prob_nums,
         )
         if need_data:
             return dataset if splits[index + 1] > splits[index] else None
@@ -295,11 +363,17 @@ def __init__(
         *,
         data_cache_path=None,
         need_data=True,
+        CPT=False,
+        self_constraint_cpt=False,
+        prob_nums=10,
     ):
 
         self.name = name
         self.indexed_dataset = indexed_dataset
         self.return_doc_ids = return_doc_ids
+        self.CPT = CPT
+        self.self_constraint_cpt = self_constraint_cpt
+        self.prob_nums = prob_nums
 
         # Build index mappings.
         if need_data and len(documents) > 0:
@@ -397,21 +471,41 @@ def __getitem__(self, idx):
             sample = np.concatenate(sample_list)
             if append_mask:
                 mask = np.concatenate(mask_list)
-        # print(sample)
+
+        kl_logits, kl_ids = None, None
+        if self.self_constraint_cpt:
+            kl_logits, kl_ids = get_logits([sample.tolist()], prob_nums=self.prob_nums)
+
+        res = None
         if self.return_doc_ids:  # for retro preprocessing
             if mask is None:
-                return {"text": np.array(sample, dtype=np.int64), "doc_ids": np.array(doc_ids, dtype=np.int64)}
+                res = {
+                    "text": np.array(sample, dtype=np.int64),
+                    "doc_ids": np.array(doc_ids, dtype=np.int64),
+                }
             else:
-                return {
+                res = {
                     "text": np.array(sample, dtype=np.int64),
                     "doc_ids": np.array(doc_ids, dtype=np.int64),
                     "mask": np.array(mask, dtype=np.int64),
                 }
         else:
             if mask is None:
-                return {"text": np.array(sample, dtype=np.int64)}
+                res = {"text": np.array(sample, dtype=np.int64)}
             else:
-                return {"text": np.array(sample, dtype=np.int64), "mask": np.array(mask, dtype=np.int64)}
+                res = {
+                    "text": np.array(sample, dtype=np.int64),
+                    "mask": np.array(mask, dtype=np.int64),
+                }
+
+        if self.self_constraint_cpt:
+            res.update = {
+                "logits": kl_logits,
+                "ids": kl_ids,
+                "CPT": self.CPT,
+            }
+
+        return res
 
 
 def _build_index_mappings(