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【Hackathon 7th No.31】NO.31 为 paddle.sparse.sparse_csr_tensor进行功能增强 -part #6876

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【Hackathon 7th No.31】NO.31 为 paddle.sparse.sparse_csr_tensor进行功能增强 -part #6876

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4 changes: 2 additions & 2 deletions docs/api/paddle/sparse/sparse_csr_tensor_cn.rst
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Expand Up @@ -3,7 +3,7 @@
sparse_csr_tensor
-------------------------------

.. py:function:: paddle.sparse.sparse_csr_tensor(crows, cols, values, shape, dtype=None, place=None, stop_gradient=True)
.. py:function:: paddle.sparse.sparse_csr_tensor(crows, cols, values, shape=None, dtype=None, place=None, stop_gradient=True)

该 API 通过已知的非零元素的 ``crows`` , ``cols`` 和 ``values`` 来创建一个 CSR(Compressed Sparse Row) 格式的稀疏 tensor，tensor 类型为 ``paddle.Tensor`` 。

Expand All @@ -26,7 +26,7 @@ sparse_csr_tensor
list，tuple，numpy\.ndarray，paddle\.Tensor 类型。
- **values** (list|tuple|ndarray|Tensor) - 一维数组，存储非零元素，可以是
list，tuple，numpy\.ndarray，paddle\.Tensor 类型。
- **shape** (list|tuple) - 稀疏 Tensor 的形状，也是 Tensor 的形状，如果没有提供，将自动推测出最小的形状。
- **shape** (list|tuple，可选) - 稀疏 Tensor 的形状，也是 Tensor 的形状，如果没有提供，将自动推测出最小的形状。
- **dtype** (str|np.dtype，可选) - 创建 tensor 的数据类型，可以是 'bool' ，'float16'，'float32'，
'float64' ，'int8'，'int16'，'int32'，'int64'，'uint8'，'complex64'，'complex128'。
默认值为 None，如果 ``values`` 为 python 浮点类型，则从
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24 changes: 12 additions & 12 deletions ...el_convert/convert_from_pytorch/api_difference/torch/torch.sparse_csr_tensor.md
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Expand Up @@ -19,24 +19,24 @@ torch.sparse_csr_tensor(crow_indices,
### [paddle.sparse.sparse_csr_tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/sparse/sparse_csr_tensor_cn.html#sparse-csr-tensor)

```python
paddle.sparse.sparse_csr_tensor(crows, cols, values, shape, dtype=None, place=None, stop_gradient=True)
paddle.sparse.sparse_csr_tensor(crows, cols, values, shape=None, dtype=None, place=None, stop_gradient=True)
```

PyTorch 相比 Paddle 支持更多其他参数，具体如下：

### 参数映射

| PyTorch | PaddlePaddle | 备注 |
| ---------------- | ------------- | -------------------------------------------------------------- |
| crow_indices | crows | 每行第一个非零元素在 values 的起始位置，仅参数名不一致。 |
| col_indices | cols | 一维数组，存储每个非零元素的列信息，仅参数名不一致。 |
| values | values | 一维数组，存储非零元素。 |
| size | shape | 稀疏 Tensor 的形状，仅参数名不一致。 |
| dtype | dtype | 创建 tensor 的数据类型。 |
| layout |- |表示布局方式，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。|
| device | place | 创建 tensor 的设备位置，仅参数名不一致。 |
| pin_memory | - | 表示是否使用锁页内存， Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。|
| requires_grad | stop_gradient | 是否阻断 Autograd 的梯度传导，两者参数功能相反，需要转写。 |
| PyTorch | PaddlePaddle | 备注 |
| ---------------- | ------------- | ----------------------------------------------------------------------------------- |
| crow_indices | crows | 每行第一个非零元素在 values 的起始位置，仅参数名不一致。 |
| col_indices | cols | 一维数组，存储每个非零元素的列信息，仅参数名不一致。 |
| values | values | 一维数组，存储非零元素。 |
| size | shape | 稀疏 Tensor 的形状，仅参数名不一致。 |
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验证一下，这个自动推导的结果与Pytorch是完全一致的

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验证过了的

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然后paddle和pytorch不同的是，paddle是只能一维向量，pytorch是多维向量

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所以paddle在切割一维向量后，需要判断每个子向量包含的数字个数是不是相同的

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@zhwesky2010 zhwesky2010 Sep 23, 2024 •
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然后paddle和pytorch不同的是，paddle是只能一维向量，pytorch是多维向量

什么是一维向量，没看明白差异。

要从torch角度往paddle看，torch的所有用法情况都可以被paddle覆盖，则视为一致，即使paddle有更多功能也不用管

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是指输入的参数维度

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稍等，我给个例子

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image

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而在paddle中，输入是这样的
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OK，看起来是paddle与torch的csr tensor，在3D情形下的设计是不一致的，paddle为展平式的设计

| dtype | dtype | 创建 tensor 的数据类型。 |
| layout | - | 表示布局方式，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 |
| device | place | 创建 tensor 的设备位置，仅参数名不一致。 |
| pin_memory | - | 表示是否使用锁页内存， Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 |
| requires_grad | stop_gradient | 是否阻断 Autograd 的梯度传导，两者参数功能相反，需要转写。 |
| check_invariants | - | 是否检查稀疏 Tensor 变量，Paddle 无此参数，一般对网络训练结果影响不大，可直接删除。 |

### 转写示例
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