diff --git a/llm_tricks/DPO_example/dpo.ipynb b/llm_tricks/DPO_example/dpo.ipynb
index ee49521..6fc8cf5 100644
--- a/llm_tricks/DPO_example/dpo.ipynb
+++ b/llm_tricks/DPO_example/dpo.ipynb
@@ -160,7 +160,7 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "def compute_logprobs(logits, labels):\n",
+    "def compute_logprobs(logits, labels, mask=None):\n",
     "    \"\"\"\n",
     "    logits:  shape (batch_size, sequence_len, vocab_size)\n",
     "    labels:  shape (batch_size, sequence_len)\n",
@@ -178,16 +178,73 @@
     "        input=logps,\n",
     "        dim=1,\n",
     "        index=labels.unsqueeze(1)\n",
-    "    ).squeeze(1)"
+    "    ).squeeze(1)\n",
+    "    \n",
+    "    if mask is not None:\n",
+    "        mask = mask[:,1:].clone()\n",
+    "        # 进行掩码padding部分\n",
+    "        select_logprobs = select_logprobs * mask\n",
+    "        # 计算平均\n",
+    "        average_logprobs = select_logprobs.sum(-1) / mask.sum(-1)\n",
+    "        return average_logprobs\n",
+    "    else:\n",
+    "        return  select_logprobs.mean(-1)"
    ],
    "metadata": {
     "collapsed": false
    },
    "id": "ca63797467a68c1e"
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "clone 示例"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5636ff98ad0814b3"
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([2, 3, 4])\n"
+     ]
+    }
+   ],
+   "source": [
+    "mask = torch.tensor([1,2,3])\n",
+    "mask1 = mask\n",
+    "mask1 += 1\n",
+    "print(mask)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-08-12T16:24:01.170731600Z",
+     "start_time": "2024-08-12T16:24:01.158727300Z"
+    }
+   },
+   "id": "72b9adb35c2cf553"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "tensor shape示例"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "dbac1fe29cc58257"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
    "outputs": [
     {
      "name": "stdout",
@@ -230,11 +287,44 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-08-12T10:05:21.666926700Z",
-     "start_time": "2024-08-12T10:05:21.637344Z"
+     "end_time": "2024-08-12T16:01:46.797611600Z",
+     "start_time": "2024-08-12T16:01:44.279995600Z"
     }
    },
    "id": "4b1f1f33b9e7f613"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "进行batch的dpo loss计算"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "4043bc53c7bcd4d0"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def compute_batch_loss(batch, policy_model, reference_model, beta):\n",
+    "    \"\"\"Compute the DPO loss on an input batch\"\"\"\n",
+    "    policy_chosen_logps = compute_logprobs(\n",
+    "        logits=policy_model(batch[\"chosen\"]),\n",
+    "        labels=batch[\"chosen\"],\n",
+    "        mask=batch[\"chosen_mask\"]\n",
+    "    )\n",
+    "    policy_rejected_logps = compute_logprobs(\n",
+    "        logits=policy_model(batch[\"rejected\"]),\n",
+    "        labels=batch[\"rejected\"],\n",
+    "        mask=batch[\"rejected_mask\"]\n",
+    "    )"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "3211a04a645fb478"
   }
  ],
  "metadata": {
diff --git a/llm_tricks/DPO_example/dpo.py b/llm_tricks/DPO_example/dpo.py
index e69de29..22e9ee6 100644
--- a/llm_tricks/DPO_example/dpo.py
+++ b/llm_tricks/DPO_example/dpo.py
@@ -0,0 +1,85 @@
+import torch.nn.functional as F
+import torch.nn as nn
+import torch
+
+
+def compute_logprobs(logits, labels, mask=None):
+    """
+    logits:  shape (batch_size, sequence_len, vocab_size)
+    labels:  shape (batch_size, sequence_len)
+    """
+
+    # 需要先进行位移操作
+    # 去掉标签的第一个
+    labels = labels[:, 1:].clone()
+    # 去掉模型输出的最后一个
+    logits = logits[:, :-1, :]
+
+    logps = F.log_softmax(logits, dim=-1)
+
+    select_logprobs = torch.gather(
+        input=logps,
+        dim=1,
+        index=labels.unsqueeze(1)
+    ).squeeze(1)
+
+    if mask is not None:
+        mask = mask[:, 1:].clone()
+        # 进行掩码padding部分
+        select_logprobs = select_logprobs * mask
+        # 计算平均
+        average_logprobs = select_logprobs.sum(-1) / mask.sum(-1)
+        return average_logprobs
+    else:
+        return select_logprobs.mean(-1)
+
+
+def compute_batch_loss(batch, policy_model, reference_model, beta):
+    """Compute the DPO loss on an input batch"""
+    policy_chosen_logps = compute_logprobs(
+        logits=policy_model(batch["chosen"]),
+        labels=batch["chosen"],
+        mask=batch["chosen_mask"]
+    )
+    policy_rejected_logps = compute_logprobs(
+        logits=policy_model(batch["rejected"]),
+        labels=batch["rejected"],
+        mask=batch["rejected_mask"]
+    )
+
+
+class DPOLoss(nn.Module):
+    """
+    DPO Loss
+    """
+
+    def __init__(self, beta: float = 0.1) -> None:
+        super().__init__()
+        self.beta = beta
+
+    def forward(
+            self,
+            policy_chosen_logps: torch.Tensor,
+            policy_rejected_logps: torch.Tensor,
+            reference_chosen_logps: torch.Tensor,
+            reference_rejected_logps: torch.Tensor,
+    ):
+        """
+        policy_chosen_logps: 模型输出的对数概率。Shape: (batch_size,)
+        policy_rejected_logps:   Shape: (batch_size,)
+        reference_chosen_logps: Shape: (batch_size,)
+        reference_rejected_logps: Shape: (batch_size,)
+
+        """
+        policy_logps = policy_chosen_logps - policy_rejected_logps
+        reference_logps = reference_chosen_logps - reference_rejected_logps
+        logits = policy_logps - reference_logps
+
+        loss = -F.logsigmoid(self.beta * logits)
+
+        # 下面两个用于追踪训练的进度
+        chosen_rewards = (policy_chosen_logps - reference_chosen_logps).detach()
+        rejected_rewards = (policy_rejected_logps - reference_rejected_logps).detach()
+
+        # 对每个batch进行平均
+        return loss.mean(), chosen_rewards.mean(), rejected_rewards.mean()