[Docathon][Add CN Doc No.30、31] #6447
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感谢你贡献飞桨文档，文档预览构建中，Docs-New 跑完后即可预览，预览链接：http://preview-pr-6447.paddle-docs-preview.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html |
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| - **q** (Tensor) - 输入张量。 数据类型可以是 bfloat16, float16, float32 或 float64. q 的形状必须是 [batch_size, seq_len, num_heads, head_dim] 并且 head_dim 必须是 2 的倍数。 |
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float64.标点符号需要改成中文符号。q 的形状必须是 [batch_size, seq_len, num_heads, head_dim] 并且 head_dim 必须是 2 的倍数。->q 的形状必须是 [batch_size, seq_len, num_heads, head_dim]，并且 head_dim 必须是 2 的倍数。- 下面的参数说明也存在相同问题
| 输入(Input)和输出(Output)是多维稀疏张量(SparseCooTensors), 其形状为 :math: `[N, H, W, C]` 。 | ||
| 其中 N 是批次大小, C 是通道数, H 是特征的高度, W 是特征的宽度。 | ||
| 如果提供了偏差归因，则将偏差添加到卷积的输出中。 | ||
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一些名词描述上，可以参考下 https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/Conv2D_cn.html#conv2d文档，名词与其保持一致。例如：
滤波器 -> 卷积核
步幅 -> 步长（stride）
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| - x (Tensor): 输入是形状为 [N, H, W, C] 的四维稀疏张量, 输入数据类型为 float16、float32 或 float64。 | ||
| - weight (Tensor): 形状为 [kH, kW, C/g, M] 的卷积核, | ||
| 其中 M 是滤波器(输出通道)的数量, g 是组的数量, kD、kH、kW 分别是滤波器的高度和宽度。 |
| 如果 dilation 是列表/元组，则它必须包含两个整数 (dilation_height、dilation_width)。否则, dilation_height = dilation_width = dilation。 | ||
| dilation 的默认值为 1。 | ||
| - groups (int, optional): 二维卷积层的组号。根据 Alex Krizhevsky 的 Deep CNN 论文中的卷积分组： | ||
| 当 group=2 时，滤波器的前半部分仅连接到前半部分的输入通道，而滤波器的后半部分仅连接到输入通道的后半部分。 |
…on_embedding_cn.rst modified: docs/api/paddle/sparse/nn/functional/subm_conv2d_cn.rst
zhangting2020
approved these changes
Jan 19, 2024
Co-authored-by: zachary sun <70642955+sunzhongkai588@users.noreply.github.com>
Co-authored-by: zachary sun <70642955+sunzhongkai588@users.noreply.github.com>
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Description
补充缺失的中文api文档 ：
#6193
添加中文文档：
paddle.incubate.nn.functional.fused_rotary_position_embedding
paddle.sparse.nn.functional.subm_conv2d
英文文档链接：
https://www.paddlepaddle.org.cn/documentation/docs/en/develop/api/paddle/incubate/nn/functional/fused_rotary_position_embedding_en.html
https://www.paddlepaddle.org.cn/documentation/docs/en/develop/api/paddle/sparse/nn/functional/subm_conv2d_en.html
@sunzhongkai588 @niuliling123