From 478fab5fa9213952393a6a8510d63686608a844a Mon Sep 17 00:00:00 2001 From: Winters Montagne <118546135+WintersMontagne10335@users.noreply.github.com> Date: Thu, 19 Oct 2023 21:44:58 +0800 Subject: [PATCH 1/7] Create 20231019_api_design_for_lp_pool.md --- rfcs/APIs/20231019_api_design_for_lp_pool.md | 300 +++++++++++++++++++ 1 file changed, 300 insertions(+) create mode 100644 rfcs/APIs/20231019_api_design_for_lp_pool.md diff --git a/rfcs/APIs/20231019_api_design_for_lp_pool.md b/rfcs/APIs/20231019_api_design_for_lp_pool.md new file mode 100644 index 000000000..53879fbfd --- /dev/null +++ b/rfcs/APIs/20231019_api_design_for_lp_pool.md @@ -0,0 +1,300 @@ +# LPPool1D / LPPool2D API 设计文档 + +| API 名称 | LPPool1D / LPPool2D | +| - | - | +| 提交作者 | WintersMontagne10335 | +| 提交时间 | 2023-10-19 | +| 版本号 | V1.0 | +| 依赖飞桨版本 | develop | +| 文件名 | 20231019_api_design_for_lp_pool.md | + +# 一、概述 + +## 1、相关背景 + +平均池化是将输入的图像划分为若干个矩形区域，对每个子区域输出所有元素的平均值。平均池化可以更多保留图像的背景信息。 + +最大池化是将输入的矩阵划分为若干个矩形区域，对每个子区域输出最大值。最大池化可以更多保留图像的纹理信息。 + +幂平均池化是基于平均池化和最大池化的结合，利用一个学习参数p来确定这两种方法的相对重要性。当 p=1 时，等同于累加池化；当 p=∞ 时，等同于最大池化。 + +幂平均池化每个窗口的计算过程： + +![39_1](https://github.com/WintersMontagne10335/doing/assets/118546135/eb928fe6-fff8-4c69-855a-82c933466583) + +## 2、功能目标 + +新增 `LPPool1D / LPPool2D` API。 + +调用形式： +- `paddle.nn.LPPool1D` +- `paddle.nn.LPPool2D` +- `paddle.nn.functional.lp_pool1d` +- `paddle.nn.functional.lp_pool2d` + +## 3、意义 + +为 `Paddle` 增加 `LPPool1D / LPPool2D` ，丰富 `Paddle` 中池化操作相关的 API。 + +# 二、飞桨现状 + +`Paddle` 目前已经提供了平均池化（ `AvgPool1d / AvgPool2d / AvgPool3d` ）、最大池化（ `MaxPool1d / MaxPool2d / MaxPool3d` ）等的池化方法。 + +目前 `Paddle` 在 `Python` 端缺少幂平均池化（ `LPPool1D / LPPool2D` ）相关接口的实现，而在底层也没有相关算子。 + +# 三、业内方案调研 + +## PyTorch + +`Pytorch` 底层并未实现 `LPPool1D / LPPool2D` 对应的Kernel，而是通过在 `Python` 端，基于 `AvgPool1d / AvgPool2d`，组合实现了 API。 + +### API 文档 + +- [torch.nn.LPPool1d(norm_type, kernel_size, stride=None, ceil_mode=False)](https://pytorch.org/docs/stable/generated/torch.nn.LPPool1d.html#lppool1d) + + - sketch + - Applies a 1D power-average pooling over an input signal composed of several input planes + - Note + > If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case + + - Parameters + - kernel_size + > a single int, the size of the window + - stride + > a single int, the stride of the window. Default value is kernel_size + - ceil_mode + >when True, will use ceil instead of floor to compute the output shape + +- [torch.nn.LPPool2d(norm_type, kernel_size, stride=None, ceil_mode=False)](https://pytorch.org/docs/stable/generated/torch.nn.LPPool2d.html#lppool2d) + + - sketch + - Applies a 2D power-average pooling over an input signal composed of several input planes + - Note + > If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case + + - Parameters + - kernel_size + > the size of the window + - stride + > the stride of the window. Default value is kernel_size + - ceil_mode + > when True, will use ceil instead of floor to compute the output shape + +### 实现逻辑 + +因为 `LPPool1D` 与 `LPPool2D` 实现逻辑基本相同，所以以下仅对 `LPPool1D` 进行分析。 + +#### `Python` 端 + +关键源码 + +- [torch/nn/modules/pooling.py](https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/pooling.py#L891) + +```Python +class _LPPoolNd(Module): + __constants__ = ['norm_type', 'kernel_size', 'stride', 'ceil_mode'] + + norm_type: float + ceil_mode: bool + + def __init__(self, norm_type: float, kernel_size: _size_any_t, stride: Optional[_size_any_t] = None, + ceil_mode: bool = False) -> None: + super().__init__() + self.norm_type = norm_type + self.kernel_size = kernel_size + self.stride = stride + self.ceil_mode = ceil_mode + + def extra_repr(self) -> str: + return 'norm_type={norm_type}, kernel_size={kernel_size}, stride={stride}, ' \ + 'ceil_mode={ceil_mode}'.format(**self.__dict__) + +class LPPool1d(_LPPoolNd): + kernel_size: _size_1_t + stride: _size_1_t + + def forward(self, input: Tensor) -> Tensor: + return F.lp_pool1d(input, float(self.norm_type), self.kernel_size, + self.stride, self.ceil_mode) +``` + +调用了 `torch/nn/functional.py` 中的 `lp_pool1d` 函数。 + +- [torch/nn/functional.py](https://github.com/pytorch/pytorch/blob/main/torch/nn/functional.py#L1050) + +```Python +def lp_pool1d( + input: Tensor, norm_type: Union[int, float], + kernel_size: int, + stride: Optional[BroadcastingList1[int]] = None, + ceil_mode: bool = False +) -> Tensor: + if has_torch_function_unary(input): + return handle_torch_function( + lp_pool1d, (input,), input, norm_type, kernel_size, stride=stride, ceil_mode=ceil_mode + ) + if stride is not None: + out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode) + else: + out = avg_pool1d(input.pow(norm_type), kernel_size, padding=0, ceil_mode=ceil_mode) + + return (torch.sign(out) * relu(torch.abs(out))).mul(kernel_size).pow(1.0 / norm_type) +``` + +`LPPool1d` 的公式如下: + +![image](https://github.com/WintersMontagne10335/doing/assets/118546135/14e5652f-521c-4b2c-bda4-7857e481ec34) + +`AVGPool1d` 的公式如下: +![image](https://github.com/WintersMontagne10335/doing/assets/118546135/9927757c-4c50-40aa-9e8a-ac3be01b54b0) + +对比可知，`LPool1d` 开方符号下的部分，是变形的累加池化，而累加池化又可以通过平均池化的结果乘以 `kernel_size` 来实现。所以，`PyTorch` 基于 `AVGPool1d` ，并与 `mul` 等相组合，实现了 `LPool1d` 。 + +#### CPU端 + +`PyTorch` 未实现。 + +#### GPU端 + +`PyTorch` 未实现。 + +## TensorFlow + +`TensorFlow` 未实现该算子。 + +## MXNet + +`MXNet` 未实现该算子。 + +## OneFlow + +`OneFlow` 未实现该算子。 + +# 四、对比分析 + +- 目前，主流深度学习框架仅有 `Pytorch` 实现了该算子。 + +- `Pytorch` 的 `LPPool1d` ,是通过在Python端，基于 `AVGPool1d` 组合实现的。 `Paddle` 的实现也可以沿用这个思路。这里存在一个小问题：[Paddle 的 AvgPool1d 与 PyTorch 的参数不一致](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/model_convert/convert_from_pytorch/api_difference/nn/torch.nn.AvgPool1d.html)，但是并不影响本API的实现。 + +- 组合实现的方式，会对性能有较大的负面影响。针对该算子， `Paddle` 最终应该实现底层 Kernel 。 + +# 五、设计思路与实现方案 + +分两步来实现： + +1. 基于 `AVGPool1d` 组合实现 API ，提交Python 实现代码、单测代码、中文API文档、英文API文档。 +2. 实现底层 Kernel ，提交一个性能优化的 PR 。 + +`本设计文档仅涉及第一部分。不久会补充第二部分的设计文档。` + +## 命名与参数设计 + +添加 python 上层接口: + +- `paddle.nn.functional.lp_pool1d` + + ``` python + paddle.nn.functional.lp_pool1d( + input:Tensor, + norm_type:Union[int, float], + kernel_size:int, + stride:Optional[BroadcastingList1[int]]=None, + ceil_mode:bool=False) + ``` + + |参数名|类型|描述| + |---|---|---| + |input|Tensor|Tensor of input| + |norm_type|Union[int, float]|exponent| + |kernel_size|int|the size of the window| + |stride|Optional[BroadcastingList1[int]]|the stride of the window| + |ceil_mode|bool|when True, will use ceil instead of floor to compute the output shape| + |output|Tensor|Tensor of output| + +- `paddle.nn.LPPool1D` + + 调用 `paddle.nn.functional.lp_pool1d` ，参数与之类似。 + +- `paddle.nn.functional.lp_pool2d` + + ``` python + paddle.nn.functional.lp_pool2d( + input:Tensor, + norm_type:Union[int, float], + kernel_size:BroadcastingList2[int], + stride:Optional[BroadcastingList2[int]]=None, + ceil_mode:bool=False) + ``` + + |参数名|类型|描述| + |---|---|---| + |input|Tensor|Tensor of input| + |norm_type|Union[int, float]|exponent| + |kernel_size|BroadcastingList2[int]|the size of the window| + |stride|Optional[BroadcastingList2[int]]|the stride of the window| + |ceil_mode|bool|when True, will use ceil instead of floor to compute the output shape| + |output|Tensor|Tensor of output| + +- `paddle.nn.LPPool2D` + + 调用 `paddle.nn.functional.lp_pool2d` ，参数与之类似。 + +## 底层 OP 设计 + +本设计文档不涉及。 + +## API实现方案 + +- `paddle.nn.functional.lp_pool1d` + +利用 `Paddle` 已有的 `paddle.nn.functional.avg_pool1d`组合实现。 + +- `paddle.nn.LPPool1D` + +调用 `paddle.nn.functional.lp_pool1d` 实现。 + +- `paddle.nn.functional.lp_pool2d` + +利用 `Paddle` 已有的 `paddle.nn.functional.avg_pool2d`组合实现。 + +- `paddle.nn.LPPool2D` + +调用 `paddle.nn.functional.lp_poo21d` 实现。 + +# 六、测试和验收的考量 + +测试考虑的case如下： + +- **编程范式场景** + 常规覆盖动态图和静态图的测试场景 + +- **硬件场景** + 常规需覆盖 CPU、GPU 两种测试场景 + +- **输出正确性** + 输出数值结果的一致性和数据类型是否正确，使用 PyTorch 作为参考标准 + +- **计算精度** + 需要保证 `前向/后向` 计算的精度正确性，使用 PyTorch 作为参考标准 + +- **维度测试** + 需要测试 `1d / 2d` 两类接口 + +# 七、可行性分析及规划排期 + +最晚下周末完成第一阶段的工作。 + +# 八、影响面 + +新增 API，对其他模块无影响。 + +# 名词解释 + +无 + +# 附件及参考资料 + +- [torch.nn.functional.lp_pool1d](https://pytorch.org/docs/stable/generated/torch.nn.functional.lp_pool1d.html) +- [torch.nn.LPPool1d](https://pytorch.org/docs/stable/generated/torch.nn.LPPool1d.html#lppool1d) +- [torch.nn.functional.lp_pool2d](https://pytorch.org/docs/stable/generated/torch.nn.functional.lp_pool2d.html) +- [torch.nn.LPPool2d](https://pytorch.org/docs/stable/generated/torch.nn.LPPool2d.html) From a00968124184acd99d9a07593223881eb49ef1f5 Mon Sep 17 00:00:00 2001 From: Winters Montagne <118546135+WintersMontagne10335@users.noreply.github.com> Date: Mon, 13 Nov 2023 23:22:40 +0800 Subject: [PATCH 2/7] Add files via upload --- rfcs/APIs/image/img_LPPool_0.png | Bin 0 -> 3611 bytes rfcs/APIs/image/img_LPPool_1.png | Bin 0 -> 2131 bytes 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