【Hackathon No.24】add RFC for ChannelShuffle #40

BrilliantYuKaimin · 2022-03-17T00:37:16Z

增加paddle.nn.ChannelShuffle设计文档。

paddle-bot-old · 2022-03-17T00:37:24Z

Thanks for your contribution!

dingjiaweiww · 2022-03-17T02:05:37Z

你的 PR 提交成功，感谢你对于开源项目的贡献，请检查 PR 提交格式和内容是否完备，具体请参考示例和模版。

dingjiaweiww · 2022-03-17T02:05:55Z

PR 格式检查通过，你的PR 将接受Paddle 专家以及开源社区的review，请及时关注PR 动态

shiyutang

方案整体完备，但是实现细节等需要步骤化说明，请修改方案后再提交代码。修改示例参照：https://github.com/PaddlePaddle/community/blob/master/rfcs/APIs/20200301_api_design_for_quantile.md

shiyutang · 2022-03-17T12:19:58Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+飞桨目前不支持此功能，但可以通过组合API的方式实现此功能。
+


请展开描述组合API进行的方式

shiyutang · 2022-03-17T12:25:05Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+Tensor math_channel_shuffle(const Tensor& self, int64_t groups) {
+  int64_t b = self.size(0);
+  int64_t c = self.size(1);
+  int64_t oc = c / groups;
+
+  auto input_reshaped = self.view({b, groups, oc, -1});
+  Tensor output_tensor =
+      input_reshaped.permute({0 /* b */, 2 /* oc */, 1 /* groups */, 3})
+      .contiguous()
+      .reshape(self.sizes());
+  return namedinference::propagate_names_if_nonempty(
+      output_tensor,
+      self.has_names() ? self.names() : at::ArrayRef<Dimname>{});
+}
+```
+
+## TensorFlow
+
+TensorFlow目前没有直接提供`ChannelShuffle`的API，但是也有[网友](https://blog.csdn.net/baidu_23388287/article/details/94456951)通过组合API的方式实现了该操作：
+
+```python
+def shuffle_unit(self, x, groups):
+    with tf.variable_scope('shuffle_unit'):
+        n, h, w, c = x.get_shape().as_list()
+        x = tf.reshape(x, shape=tf.convert_to_tensor([tf.shape(x)[0], h, w, groups, c // groups]))
+        x = tf.transpose(x, tf.convert_to_tensor([0, 1, 2, 4, 3]))
+        x = tf.reshape(x, shape=tf.convert_to_tensor([tf.shape(x)[0], h, w, c]))
+```
+


请就这两种方案的实现方式的整体逻辑进行分步骤阐述

请就这两种方案的实现方式的整体逻辑进行分步骤阐述

完成

shiyutang · 2022-03-17T12:26:40Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+无论是C++实现还是组合API实现，其逻辑都是十分简单的，故考虑使用C++编写新的算子以期取得更高的效率。
+


请就 torch/tensorflow 两种实现方案进行对比评价和对比分析，论述优势、劣势。

请就 torch/tensorflow 两种实现方案进行对比评价和对比分析，论述优势、劣势。

完成

shiyutang · 2022-03-17T12:30:24Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+## API实现方案
+
+参考`paddle.nn.PixelShuffle`来实现`paddle.nn.ChannelShuffle`，顺便实现`paddle.nn.functional.channel_shuffle`。
+


请对实现方案包含实现文件存放位置，代码内部逻辑进行步骤化的描述

请对实现方案包含实现文件存放位置，代码内部逻辑进行步骤化的描述

完成

shiyutang · 2022-03-17T12:31:18Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+考虑测试的情况：
+- 与PyTorch的结果的一致性；
+- 反向传播的正确性；
+- 错误检查：`groups`不合法或不整除通道数时能正确抛出异常。


测试内容需要进一步参考这里：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/dev_guides/api_contributing_guides/api_accpetance_criteria_cn.html#id1

测试内容需要进一步参考这里：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/dev_guides/api_contributing_guides/api_accpetance_criteria_cn.html#id1

完成

paddle-bot-old · 2022-03-18T08:32:54Z

你的PR有最新反馈，请及时修改。
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shiyutang · 2022-03-18T08:38:01Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+- PyTorch在C++层面为ChannelShuffle设计了底层算子，执行效率高，但不利于（不参与框架设计）开发者从源码层面了解该API的行为。
+- 使用组合API的方式来实现ChannelShuffle是简洁易懂的，开发者可以直接从Python源码的层面来了解该API的行为，但在执行效率上可能会逊色于原生算子实现方案。
+


此处的对比分析应当对比pytorch 和 tf 两种方案的优劣，此处两种方法差异不大，则可以直接说明分Channel形式来进行和两种方案一致的的设计

shiyutang · 2022-03-18T08:46:55Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+- 是否同时支持CPU和GPU平台；
+- 测试不同张量类型下的表现；
+- 对全部入参进行参数有效性和边界值测试，确定每个入参都可以正确生效；
+- 前向计算的正确性（与组合API实现比较、与PyTorch比较）；


可以在本地和pytorch实现比较，在test由于CI环境没有pytorch，需要使用 numpy 实现进行比较。

shiyutang · 2022-03-18T08:50:08Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+    def extra_repr(self):
+        pass


这个方法是否需要？

其他API均有此方法。

shiyutang · 2022-03-18T08:51:20Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+- 前向计算的正确性（与组合API实现比较、与PyTorch比较）；
+- 反向计算的正确性；
+- 当传入的`group`不合法（不是正整数、不整除通道数）时会抛出异常并有友好的提示。
+


错误检查：data_format不合法，x不是tensor，x维度检查等

根据评审意见修改了文档

shiyutang

LGTM

shiyutang · 2022-03-22T12:03:31Z

rfcs/APIs/20220317_api_design_for_ChannelShuffle.md

+它们的实现定义在`paddle/phi/kernels/impl/channel_shuffle_kernel_impl.h`和`paddle/phi/kernels/impl/channel_shuffle_grad_kernel_impl.h`，它们的注册放在
+
+- `paddle/phi/kernels/cpu/channel_shuffle_kernel.cc`
+- `paddle/phi/kernels/cpu/channel_shuffle_grad_kernel.cc`
+- `paddle/phi/kernels/gpu/channel_shuffle_kernel.cu`
+- `paddle/phi/kernels/gpu/channel_shuffle_grad_kernel.cu`


实现内容可以存放在paddle/phi/kernels/cpu(gpu)/ 下，注册则在fluid/oprerator下。如下图所示：

c++ op开发方式可以参见开发指南：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/dev_guides/api_contributing_guides/new_cpp_op_cn.html

paddle-bot-old · 2022-03-24T02:48:38Z

你的PR已合入community库，请进行后续代码开发，并将代码提交至Paddle仓库。
Your PR has been merged into community repository. Please move on coding part and submit your code to corresponding repo.

Create 20220317_api_design_for_ChannelShuffle.md

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BrilliantYuKaimin mentioned this pull request Mar 17, 2022

【PaddlePaddle Hackathon 第二期】任务总览 PaddlePaddle/Paddle#40234

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【Hackathon No.29】add RFC for PixelUnshuffle #44

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Update 20220317_api_design_for_ChannelShuffle.md

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Update 20220317_api_design_for_ChannelShuffle.md

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根据评审意见修改了文档

BrilliantYuKaimin requested a review from shiyutang March 18, 2022 09:48

yang131313 assigned shiyutang Mar 21, 2022

修改核函数的实现的位置

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【PaddlePaddle Hackathon 2】24、为 Paddle 新增 nn.ChannelShuffle 组网 API PaddlePaddle/Paddle#40743

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【Hackathon No.24】add RFC for ChannelShuffle #40

【Hackathon No.24】add RFC for ChannelShuffle #40

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paddle-bot-old bot commented Mar 24, 2022

		飞桨目前不支持此功能，但可以通过组合API的方式实现此功能。

		无论是C++实现还是组合API实现，其逻辑都是十分简单的，故考虑使用C++编写新的算子以期取得更高的效率。

		## API实现方案

		参考`paddle.nn.PixelShuffle`来实现`paddle.nn.ChannelShuffle`，顺便实现`paddle.nn.functional.channel_shuffle`。

		- PyTorch在C++层面为ChannelShuffle设计了底层算子，执行效率高，但不利于（不参与框架设计）开发者从源码层面了解该API的行为。
		- 使用组合API的方式来实现ChannelShuffle是简洁易懂的，开发者可以直接从Python源码的层面来了解该API的行为，但在执行效率上可能会逊色于原生算子实现方案。

【Hackathon No.24】add RFC for ChannelShuffle #40

【Hackathon No.24】add RFC for ChannelShuffle #40

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