# 报错ValueError:For 'Pad', all elements of paddings must be \>= 0.

**1. 报错描述**

1.1 系统环境

```
Hardware Environment(Ascend/GPU/CPU): Ascend Software Environment: -- MindSpore version (source or binary): 1.6.0 -- Python version (e.g., Python 3.7.5): 3.7.6 -- OS platform and distribution (e.g., Linux Ubuntu 16.04): Ubuntu 4.15.0-74-generic -- GCC/Compiler version (if compiled from source):
```

1.2 基本信息

1.2.1 脚本

训练脚本是通过构建Pad的单算子网络，对输入张量进行边界填充。脚本如下：

```
 01 class Net(nn.Cell):
 02     def __init__(self):
 03         super(Net, self).__init__()
 04         self.pad_op = nn.Pad(((0, 0), (-1, 1)))
 05
 06     def construct(self, input_x):
 07        output = self.pad_op(input_x)
 08        return output
 09 net = Net()
 10
 11 input_x = Tensor(np.ones([3, 3]),mindspore.float32)
 12 output = net(input_x)
 13 print('output shape',output.shape)
```

1.2.2 报错

这里报错信息如下：

```
[INFO] ANALYZER(169538,ffffb713b010,python):2022-04-07-11:09:58.692.494 [mindspore/ccsrc/pipeline/jit/static_analysis/async_eval_result.cc:103] Wait] Infer finished.
Traceback (most recent call last):
  File &quot;pad.py&quot;, line 12, in &lt;module&gt;
    output = net(input_x)
  File &quot;/lib/python3.7/site-packages/mindspore/nn/cell.py&quot;, line 569, in __call__
    out = self.compile_and_run(*args)
  File &quot;/lib/python3.7/site-packages/mindspore/nn/cell.py&quot;, line 899, in compile_and_run
    self.compile(*inputs)
  File &quot;/lib/python3.7/site-packages/mindspore/nn/cell.py&quot;, line 884, in compile
    _cell_graph_executor.compile(self, *inputs, phase=self.phase, auto_parallel_mode=self._auto_parallel_mode)
  File &quot;/lib/python3.7/site-packages/mindspore/common/api.py&quot;, line 784, in compile
    result = self._graph_executor.compile(obj, args_list, phase, self._use_vm_mode())
  File &quot;/lib/python3.7/site-packages/mindspore/ops/primitive.py&quot;, line 575, in __infer__
    out[track] = fn(*(x[track] for x in args))
  File &quot;/lib/python3.7/site-packages/mindspore/ops/operations/nn_ops.py&quot;, line 3944, in infer_shape
    raise ValueError(f&quot;For '{self.name}', all elements of paddings must be &gt;= 0.&quot;)
ValueError: For 'Pad', all elements of paddings must be &gt;= 0.
```

原因分析

我们看报错信息，在ValueError中，写到For 'Pad', all elements of paddings must be \>= 0，意思是传的paddings必须大于等于0，根据这个报错和报错代码行很容易回溯到04行代码初始化padding属性值时，传入了负数-1，此时在API映射中查询pad可知，pytorch与MindSpore的pad算子存在使用差异（如图），

![](figures/8-57.png)

详见[https://www.mindspore. cn/docs/migration\_guide/zh-CN/r1.6/api\_mapping/pytorch\_diff/Pad.html](https://www.mindspore. cn/docs/migration_guide/zh-CN/r1.6/api_mapping/pytorch_diff/Pad.html)。值得注意的是，在官网r1.3版本中写的是torch.nn.functional.pad和mindspore.ops.Pad功能一致，该处描述存在一定的缺陷，在r1.5分支及其以后分支中均已对两者的异同点进行了描述。

pytorch中能够传入负数，示例脚本如下：

```
 1 x = torch.empty(3, 3)
 2 pad = (-1, 1)
 3 output = torch.nn.functional.pad(x, pad)
 4 print('output shape',output.shape)
```

执行成功的输出如下：

```
output shape torch.Size([3, 3])
```

**2. 解决方法**

基于上面已知的原因，很容易做出如下修改：

```
 01 class Net(nn.Cell):
 02     def __init__(self):
 03         super(Net, self).__init__()
 04         self.pad_op = nn.Pad(((0, 0), (1, 1)))
 05
 06     def construct(self, input_x):
 07        output = self.pad_op(input_x)
 08        return output
 09 net = Net()
 10
 11 input_x = Tensor(np.ones([3, 3]),mindspore.float32)
 12 output = net(input_x)
 13 print('output shape',output.shape)
```

此时执行成功，输出如下：

```
output shape (3, 5)
```

**3. 总结**

定位报错问题的步骤：

1、 找到报错的用户代码行：output = self.pad\_op\(input\_x\);

2、 根据日志报错信息中的关键字，缩小分析问题的范围： For 'Pad', all elements of paddings must be \>= 0;

3、 查询不同版本上的API映射，与标杆算子进行对比；

4、 需要重点关注变量定义、初始化的正确性。

**4. 参考文档**

4.1  [API映射](https://www.mindspore.cn/docs/migration_guide/zh-CN/r1.6/api_mapping/pytorch_api_mapping.html)

