如题，具体错误为:
```
Unable to connect to the server: net/http: TLS handshake timeout
```
但是命令行ssh能正常连接
发现只要把k8s相关的域名都加入`NO_PROXY`中即可，如我在clash中加入了以下域名:
```
192.168.0.0/16,10.0.0.0/8,172.16.0.0/12,127.0.0.1,localhost,*.local,<k8s服务器api>,*.*.iiis.co,172.16.0.0/12,.cluster.local,.svc

```

k8s vscode插件连接不上解决办法

最近在做多模态大模型相关的实验，采取的codebase是使用`LLaVA-1.5`作为backbone的，但是这个模型显然太老了，我希望希望能把它改编成`Qwen3.5`的模型。

# 修改思路
`llava`风格的模型各个部分的划分还是比较清晰的，只要在`llava/language_model/`下添加一个继承自`LlavaMetaModel`的模型，使用`Qwen3.5`作为实际的backbone。在`llava/multimodal_encoder/`中添加`Qwen`对应的`Visual Model`作为ViT.

于是我就把`Qwen3.5/Qwen3`的源码也拉下来了，让Copilot根据源码以及我的意图进行修改，没想到Copilot给我带来了无穷的烦恼..

# 出现的问题

### 数值NaN
首当其冲的是梯度变成NaN的问题:
```bash
After: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
Before: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
After: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
[step 1] loss=4.1013
  GOLD : Medium.
  PRED : Based<|im_end|>
[step 1] loss=3.9050
  GOLD : Cylindrical.
  PRED : Basedylindrical<|im_end|>
[step 1] loss=2.8436
  GOLD : Closed.
  PRED : The<|im_end|>
/root/miniconda3/envs/qwen-3d/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argume
nt. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Before: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
After: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
[step 1] loss=4.7032
  GOLD : Cabinet.
  PRED : Basedabinet<|im_end|>
Before: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
After: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
Before: Before: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
After: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
Before: tensor(0, device='cuda:2') tensor(0, device='cuda:3') tensor(0, device='cuda:2')
tensor(0, device='cuda:3')
After: After: tensor(0, device='cuda:2') tensor(0, device='cuda:3') tensor(0, device='cuda:2')
tensor(0, device='cuda:3')
[step 2] loss=2.7932
  GOLD : The corridor is lined with a series of doors along its length, indicating the way to adjacent rooms. there are no visible windows, which is common in corridor design. the walls are decorated in light colors, the floor is dark hardwood, and the ceiling is a flat plane.
  PRED : Based corridor region a with ** ** of doors, its length, leading it room to other rooms. The are no windows windows in as is typical for interior design to The walls are typically with a colors, and floor is made,, and the ceiling is white light white with
[step 2] loss=4.6449
  GOLD : Standing upright.
  PRED : Based on on
[step 2] loss=2.2299
  GOLD : No.
  PRED : No,
Before: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
After: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
Before: Before: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
After: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
tensor(0, device='cuda:0') tensor(0, device='cuda:0')
After: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
Before: tensor(0, device='cuda:3') tensor(0, device='cuda:3')
After: tensor(0, device='cuda:3') tensor(0, device='cuda:3')
[step 3] loss=5.1326
  GOLD : <chair 14> used in conjunction with table to meet additional seating needs, highlighting its functional role.
  PRED : Basedanswer>1>><|im_end|> as the with <  provide<|im_end|> needs needs<|im_end|> typically its role role in
[step 3] loss=2.4497
  GOLD : White.
  PRED : Based<|im_end|>
[step 3] loss=5.7281
  GOLD : Yes.
  PRED : No,
[step 3] loss=2.8701
  GOLD : No, there isn't.
  PRED : No, there is't a
Before: Before: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
After: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
tensor(0, device='cuda:0') tensor(0, device='cuda:0')
Before: After: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
Before: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
After: tensor(0, device='cuda:1') tensor(0, device='cuda:3') tensor(0, device='cuda:1')
tensor(0, device='cuda:3')
After: tensor(0, device='cuda:3') tensor(0, device='cuda:3')
[step 4] loss=5.2262
  GOLD : In good condition.
  PRED : Based the condition<|im_end|>
[step 4] loss=3.8528
  GOLD : No, the window is furniture and the window is decorations.
  PRED : Based, the two does not, the chair is not.
[step 4] loss=2.3563
  GOLD : No, there isn't.
  PRED : No, there is't a
[step 4] loss=3.6326
  GOLD : Table.
  PRED : Based<|im_end|>
{'loss': '3.64', 'grad_norm': '1.414', 'learning_rate': '0', 'epoch': '0.1481'}
 14%|����������������������                                                                                                                               | 1/7 [00:18<01:46, 17.75s/it]Before: tensor(11368960, device='cuda:1') tensor(0, device='cuda:1')
After: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
Before: tensor(11048960, device='cuda:2') tensor(0, device='cuda:2')
After: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
Before: tensor(10127360, device='cuda:0') tensor(0, device='cuda:0')
After: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
Before: tensor(9446400, device='cuda:3') tensor(0, device='cuda:3')
After: tensor(0, device='cuda:3') tensor(0, device='cuda:3')
[step 5] loss=nan
  GOLD : Closed.
  PRED : !!
[step 5] loss=nan
  GOLD : This staircase has a classic design with a dark wood finish complemented by white steps. It takes on a rectangular shape, with each step having rounded edges. The dark handrail, crafted from polished wood, follows a curved design, providing both safety and aesthetic appeal. The staircase is of standard size, running vertically and positioned inside a room. It is in good condition, showing no obvious signs of wear or damage. Leading upwards, the staircase indicates access to the floor above. Functionally, it serves as a pathway for moving between floors. A design highlight is the contrast between the dark wood steps and the white walls, adding depth and dimension to the space.
  PRED : !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
[step 5] loss=nan
  GOLD : Turned on.
  PRED : !!!!
[step 5] loss=nan
  GOLD : No.
  PRED : !!
Before: tensor(11530240, device='cuda:1') tensor(0, device='cuda:1')
After: tensor(0, device='cuda:1') tensor(0, device='cuda:1')
Before: tensor(13811200, device='cuda:2') tensor(0, device='cuda:2')
After: tensor(0, device='cuda:2') tensor(0, device='cuda:2')
Before: tensor(11425280, device='cuda:3') tensor(0, device='cuda:3')
After: tensor(0, device='cuda:3') tensor(0, device='cuda:3')
Before: tensor(11440640, device='cuda:0') tensor(0, device='cuda:0')
After: tensor(0, device='cuda:0') tensor(0, device='cuda:0')
[step 6] loss=nan
  GOLD : Provides hot and cold water for kitchen sink use.
  PRED : !!!!!!!!!!
  ```
可以发现，在训练几个step之后loss就爆炸了，模型也开始输出无意义内容.

首先值得怀疑的是输入输出中是否还有NaN, 但是理论上我的数据预处理并没有动，但为了保险起见还是检查了一下他的代码，确实会对nan进行处理，在检查模型的输入的时候也确实没有问题。

我又继续在一众大模型的联合指导下在forward和backward上添加了hook检查，检查每一层的输入输出从什么时候出现了NaN，梯度什么时候出现了NaN. 结果如下:
```
NaN gradient in model.layers.28.linear_attn.out_proj.weight
NaN gradient in model.layers.28.linear_attn.dt_bias
NaN gradient in model.layers.28.linear_attn.A_log
NaN gradient in model.layers.28.linear_attn.conv1d.weight
NaN gradient in model.layers.28.linear_attn.in_proj_a.weight
NaN gradient in model.layers.28.linear_attn.in_proj_b.weight
NaN gradient in model.layers.28.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.28.input_layernorm.weight
NaN gradient in model.layers.28.input_layernorm.weight
NaN gradient in model.layers.27.mlp.down_proj.weight
NaN gradient in model.layers.27.mlp.down_proj.weight
NaN gradient in model.layers.27.mlp.up_proj.weight
NaN gradient in model.layers.27.mlp.up_proj.weight
NaN gradient in model.layers.27.mlp.gate_proj.weight
NaN gradient in model.layers.27.post_attention_layernorm.weight
NaN gradient in model.layers.27.mlp.gate_proj.weight
NaN gradient in model.layers.27.post_attention_layernorm.weight
NaN gradient in model.layers.27.self_attn.o_proj.weight
NaN gradient in model.layers.27.self_attn.o_proj.weight
NaN gradient in model.layers.27.self_attn.v_proj.weight
NaN gradient in model.layers.27.self_attn.k_norm.weight
NaN gradient in model.layers.27.self_attn.v_proj.weight
NaN gradient in model.layers.27.self_attn.k_norm.weight
NaN gradient in model.layers.27.self_attn.k_proj.weight
NaN gradient in model.layers.27.self_attn.k_proj.weight
NaN gradient in model.layers.27.self_attn.q_norm.weight
NaN gradient in model.layers.27.input_layernorm.weight
NaN gradient in model.layers.27.self_attn.q_norm.weight
NaN gradient in model.layers.27.input_layernorm.weight
NaN gradient in model.layers.27.self_attn.q_proj.weight
NaN gradient in model.layers.27.self_attn.q_proj.weight
NaN gradient in model.layers.27.input_layernorm.weight
NaN gradient in model.layers.27.input_layernorm.weight
NaN gradient in model.layers.26.mlp.down_proj.weight
NaN gradient in model.layers.26.mlp.down_proj.weight
NaN gradient in model.layers.26.mlp.down_proj.weight
NaN gradient in model.layers.26.mlp.up_proj.weight
NaN gradient in model.layers.26.mlp.down_proj.weight
NaN gradient in model.layers.26.mlp.up_proj.weight
NaN gradient in model.layers.26.mlp.up_proj.weight
NaN gradient in model.layers.26.mlp.gate_proj.weight
NaN gradient in model.layers.26.mlp.up_proj.weight
NaN gradient in model.layers.26.post_attention_layernorm.weight
NaN gradient in model.layers.26.mlp.gate_proj.weight
NaN gradient in model.layers.26.mlp.gate_proj.weight
NaN gradient in model.layers.26.post_attention_layernorm.weight
NaN gradient in model.layers.26.post_attention_layernorm.weight
NaN gradient in model.layers.26.mlp.gate_proj.weight
NaN gradient in model.layers.26.post_attention_layernorm.weight
NaN gradient in model.layers.26.linear_attn.out_proj.weight
NaN gradient in model.layers.26.linear_attn.out_proj.weight
NaN gradient in model.layers.26.linear_attn.out_proj.weight
NaN gradient in model.layers.26.linear_attn.out_proj.weight
NaN gradient in model.layers.26.linear_attn.dt_bias
NaN gradient in model.layers.26.linear_attn.A_log
NaN gradient in model.layers.26.linear_attn.dt_bias
NaN gradient in model.layers.26.linear_attn.dt_bias
NaN gradient in model.layers.26.linear_attn.A_logNaN gradient in model.layers.26.linear_attn.conv1d.weight

NaN gradient in model.layers.26.linear_attn.A_log
NaN gradient in model.layers.26.linear_attn.in_proj_a.weight
NaN gradient in model.layers.26.linear_attn.dt_biasNaN gradient in model.layers.26.linear_attn.in_proj_b.weight

NaN gradient in model.layers.26.linear_attn.conv1d.weight
NaN gradient in model.layers.26.linear_attn.conv1d.weight
NaN gradient in model.layers.26.linear_attn.A_log
NaN gradient in model.layers.26.linear_attn.in_proj_a.weight
NaN gradient in model.layers.26.linear_attn.in_proj_a.weight
NaN gradient in model.layers.26.linear_attn.in_proj_b.weight
NaN gradient in model.layers.26.linear_attn.in_proj_b.weight
NaN gradient in model.layers.26.linear_attn.conv1d.weight
NaN gradient in model.layers.26.linear_attn.in_proj_z.weight
NaN gradient in model.layers.26.linear_attn.in_proj_a.weight
NaN gradient in model.layers.26.linear_attn.in_proj_b.weight
NaN gradient in model.layers.26.linear_attn.in_proj_z.weight
NaN gradient in model.layers.26.linear_attn.in_proj_z.weight
NaN gradient in model.layers.26.linear_attn.in_proj_z.weight
NaN gradient in model.layers.26.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.26.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.26.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.26.input_layernorm.weight
NaN gradient in model.layers.26.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.26.input_layernorm.weight
NaN gradient in model.layers.26.input_layernorm.weight
NaN gradient in model.layers.26.input_layernorm.weight
NaN gradient in model.layers.25.mlp.down_proj.weight
NaN gradient in model.layers.25.mlp.down_proj.weight
NaN gradient in model.layers.25.mlp.down_proj.weight
NaN gradient in model.layers.25.mlp.up_proj.weight
NaN gradient in model.layers.25.mlp.down_proj.weight
NaN gradient in model.layers.25.mlp.up_proj.weight
NaN gradient in model.layers.25.mlp.up_proj.weight
NaN gradient in model.layers.25.mlp.up_proj.weight
NaN gradient in model.layers.25.mlp.gate_proj.weight
NaN gradient in model.layers.25.post_attention_layernorm.weight
NaN gradient in model.layers.25.mlp.gate_proj.weight
NaN gradient in model.layers.25.mlp.gate_proj.weight
NaN gradient in model.layers.25.post_attention_layernorm.weight
NaN gradient in model.layers.25.post_attention_layernorm.weight
NaN gradient in model.layers.25.mlp.gate_proj.weight
NaN gradient in model.layers.25.post_attention_layernorm.weight
NaN gradient in model.layers.25.linear_attn.out_proj.weight
NaN gradient in model.layers.25.linear_attn.out_proj.weight
NaN gradient in model.layers.25.linear_attn.out_proj.weight
NaN gradient in model.layers.25.linear_attn.out_proj.weight
NaN gradient in model.layers.25.linear_attn.dt_bias
NaN gradient in model.layers.25.linear_attn.A_log
NaN gradient in model.layers.25.linear_attn.dt_bias
NaN gradient in model.layers.25.linear_attn.dt_bias
NaN gradient in model.layers.25.linear_attn.A_log
NaN gradient in model.layers.25.linear_attn.conv1d.weight
NaN gradient in model.layers.25.linear_attn.A_log
NaN gradient in model.layers.25.linear_attn.in_proj_a.weight
NaN gradient in model.layers.25.linear_attn.conv1d.weight
NaN gradient in model.layers.25.linear_attn.in_proj_b.weightNaN gradient in model.layers.25.linear_attn.dt_bias

NaN gradient in model.layers.25.linear_attn.conv1d.weight
NaN gradient in model.layers.25.linear_attn.in_proj_a.weight
NaN gradient in model.layers.25.linear_attn.A_log
NaN gradient in model.layers.25.linear_attn.in_proj_a.weight
NaN gradient in model.layers.25.linear_attn.in_proj_b.weight
NaN gradient in model.layers.25.linear_attn.in_proj_b.weight
NaN gradient in model.layers.25.linear_attn.conv1d.weight
NaN gradient in model.layers.25.linear_attn.in_proj_z.weight
NaN gradient in model.layers.25.linear_attn.in_proj_a.weight
NaN gradient in model.layers.25.linear_attn.in_proj_b.weight
NaN gradient in model.layers.25.linear_attn.in_proj_z.weight
NaN gradient in model.layers.25.linear_attn.in_proj_z.weight
NaN gradient in model.layers.25.linear_attn.in_proj_z.weight
NaN gradient in model.layers.25.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.25.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.25.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.25.input_layernorm.weight
NaN gradient in model.layers.25.linear_attn.in_proj_qkv.weight
NaN gradient in model.layers.25.input_layernorm.weight
NaN gradient in model.layers.25.input_layernorm.weight
NaN gradient in model.layers.25.input_layernorm.weight
[Qwen3_5RMSNorm] NaN in OUTPUT
NaN gradient in model.layers.24.mlp.down_proj.weight
NaN gradient in model.layers.24.mlp.down_proj.weight
NaN gradient in model.layers.24.mlp.down_proj.weight
NaN gradient in model.layers.24.mlp.up_proj.weight
NaN gradient in model.layers.24.mlp.up_proj.weight
NaN gradient in model.layers.24.mlp.up_proj.weight
```
这下就很奇怪了：我的Qwen代码和LLaVA-1.5代码并没有本质区别，但一个能正常训练一个并不能. 区别肯定是在模型侧: `Qwen3.5`使用了线性注意力，并且原生是用`BF16`存储的；LLaVA-1.5是比较古早的架构，原生用`FP32`存储的，但训练的时候也是用的`bf16`. 基于这些差别，在Copilot的指导下，也根据上面nan出现的顺序，自然怀疑到了`linear_attn`的数值精度问题，强制将其转成`fp32`又在网络里添加了一层`RMSNorm`。

结果是，训练倒是能正常训练，推理又出问题了！推理的时候会出现NaN，这又是什么玩意...

我感觉不能这样下去了，毕竟`Qwen3.5`原生就是使用的bf16,用bf16理论上不会出问题。

在机缘巧合之下，我改用`torchrun`进行训练，这下就好了！所以问题可以锁定在`Deepspeed`的配置上了。又在Codex的帮助下，找到了最终解决办法,zero2配置如下:
```json
{
    "fp16": {
        "enabled": "auto"
    },
    "bf16": {
        "enabled": "auto",
        "check_grad_overflow": true
    },
    "communication_data_type": "fp32",
    "grad_accum_dtype": "fp32",
    "train_micro_batch_size_per_gpu": "auto",
    "train_batch_size": "auto",
    "gradient_accumulation_steps": "auto",
    "gradient_clipping": "auto",
    "zero_optimization": {
        "stage": 2,
        "overlap_comm": false,
        "contiguous_gradients": false,
        "sub_group_size": 1e9,
        "reduce_bucket_size": 50000000
    }
}
```
可以看出，应该是数据交流的时候的精度问题，deepspeed没有默认开启bf16的溢出检查，将其改成fp32后就能正常训练了

### ImageProcessor
LLaVA使用CLIP作为visual encoder，但clip期待的是恒定大小的图片，Copilot在修改的时候没有正确发现qwen是动态图片大小的，导致qwen一直在不看图片下进行训练..


# 总结
人类唯一对ai的优势是上下文比ai长，能够自动压缩记忆。ai工具的改进也能真正地促进生产力(copilot->codex).所以怎么用好ai改代码还真是一个学问，希望这种错误少犯.。

Qwen3.5微调中数值精度问题

### D4RT
+ https://d4rt-paper.github.io/
+ 一句话总结: 用一个Encoder得到视频的`Representation`, 再用轻量化的Decoder查询对应像素的3d位置，实现3维重建。
+ 
![](https://ys.al/static/img/84555fa9f2461ca70035b787c32876bb.image.webp)
+ 训练形式：
    + Encoder得到`Global Scene Representation`**F**
    + Decoder的Query使用$(u,v,t_{src},t_{tgt},t_{cam})$,对F进行查询
        + $(u,v,t_{src})$代表在$t_{src}$时刻的图像的$(u,v)$像素
        + $(t_{tgt},t_{cam})$代表希望查询这个像素的点在$t_{tgt}$时刻在$t_{cam}$相机坐标系下的点
    + 好处: 监督信号的提供较为容易，推理时可以平行推理
### VGGT
+ https://arxiv.org/abs/2503.11651
+ 一句话总结: 在加入`Camera Token`后用交替注意力得到表示，用不同的head解码`Camera Ex/Intrinics/point map/depth map..`
+ 
![](https://ys.al/static/img/599f8d74ba393abcf53cd4df264fae64.image.webp)
+ 训练形式：
    + Camera/Depth maps/Point maps都是稠密的，即一次forward全部算出来
    + Point Tracking得到的是feature,再用下游方法进行追踪。

3d重建论文阅读

# 课程信息:
+ [MIT课程](https://diffusion.csail.mit.edu/2025/index.html)
+ 我只上过微积分/概率论，希望能够以不那么数学的方式学懂




# Introduction
+ 为什么我们需要概率分布去建模图片生成? 
    + 对于同一个prompt, 可能合法的图片有很多，不能够单纯地区分正例负例
    + 和前一点类似，在数据集中，对于同一个prompt，有许多对应的图片，我们可以说，这些图片服从一个概率分布；由于这个概率和prompt相关，因此若想要生成的东西为$z$,prompt本身是$y$, 那么$z\sim p_{data}(·|y)$
    + 为了保证生成图片的多样性，我们可以从一个初始分布$p_{init}$出发,一步一步到达$p_{data}$,在实际操作中，我们从$x\sim p_{init}$中随机采样一个$x_0$, 每次对x的值进行变换再加上噪声，最终得到的$x_n$可能就符合$p_{data}$
+ 从$p_{init}$到$p_{data}$
    + 既然$x$是一个向量，那么不考虑随机噪声的情况下x变换的过程其实是在一个向量场中进行运动($\mu_t(x)$代表x这一点前进的方向)
    + 我们可以列出方程$dX_t=\mu_t(X_t)dt$建模这一个确定的过程
    + 在计算机模拟的时候我们可以用欧拉算法进行建模:
        + Set $t=0$
        + Set step size h=$\frac{1}{n}$
        + Set $X_0=x_0$
        + $\textbf{for}\ i=1,\dots,n-1\ \textbf{do}$
            + $X_{t+h}=X_t+h\mu_t(X_t)$
            + Update $t\leftarrow t+h$
        + $\textbf{end for}$
    + 现在我们希望这个变换不再是确定性的，而是在每次变换的时候也添加一定的噪声；而高斯分布是最常见的噪声，因此添加高斯噪声。布朗运动是高斯噪声对应的随机过程(神秘名词)，其满足性质
        + $W_t-W_s\sim N(0,(t-s)I_d)$ 也就是它是高斯噪声的叠加
        + 由于布朗运动实际上不可导，所以我们希望能用一个不依赖导数的方程去建模它:
        + $dX_t=\mu_t(X_t)dt+\sigma_tdW_t$(实际上不合法)对应的是$X_{t+h}=X_t+h\mu_t(X_t)+h(W_{t+h}-W_t)+hR(t)$&&$lim_{h\rightarrow0}E(R(t))=0$
    + 所以我们可以这样模拟:
        + Set $t=0$
        + Set step size h=$\frac{1}{n}$
        + Set $X_0=x_0$
        + $\textbf{for}\ i=1,\dots,n-1\ \textbf{do}$
            + Sample $\epsilon \sim N(0,I_d)$
            + $X_{t+h}=X_t+h\mu_t(X_t)+\sigma_t\sqrt{h}\epsilon$ $\sqrt{h}$是为了保证方差为h
            + Update $t\leftarrow t+h$
        + $\textbf{end for}$