[Docs] Steps to compile mmcv-full on MLU machine (#2571)

* [Docs] Steps to compile mmcv-full on MLU machine * [Docs] Adjust paragraph order * Update docs/zh_cn/get_started/build.md Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * Update docs/zh_cn/get_started/build.md Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * Update docs/en/get_started/build.md Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * Update docs/en/get_started/build.md Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * [Docs] Modify the format --------- Co-authored-by: budefei <budefei@cambricon.com> Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
open-mmlab · Jan 31, 2023 · 87768b7 · 87768b7
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diff --git a/docs/en/get_started/build.md b/docs/en/get_started/build.md
@@ -375,3 +375,60 @@ w = torch.ones(10).float().npu()
 output = softmax_focal_loss(x, y, 2.0, 0.25, w, 'none')
 print(output)
 ```
+
+### Build mmcv-full on Cambricon MLU Devices
+
+#### Install torch_mlu
+
+##### Option1: Install mmcv-full based on Cambricon docker image
+
+Firstly, install and pull Cambricon docker image (please email service@cambricon.com for the latest release docker):
+
+```bash
+docker pull ${docker image}
+```
+
+Run and attach to the docker, [Install mmcv-full on MLU device](#install-mmcv\-full-on-cambricon-mlu-device) and [make sure you've installed mmcv-full on MLU device successfully](#test-code)
+
+##### Option2: Install mmcv-full from compiling Cambricon PyTorch source code
+
+Please email service@cambricon.com or contact with Cambricon engineers for a suitable version of CATCH package. After you get the suitable version of CATCH package, please follow the steps in ${CATCH-path}/CONTRIBUTING.md to install Cambricon PyTorch.
+
+#### Install mmcv-full on Cambricon MLU device
+
+Clone the repo
+
+```bash
+git clone https://github.com/open-mmlab/mmcv.git
+```
+
+The mlu-ops library will be downloaded to the default directory (mmcv/mlu-ops) while building MMCV. You can also set `MMCV_MLU_OPS_PATH` to an existing mlu-ops library before building as follows:
+
+```bash
+export MMCV_MLU_OPS_PATH=/xxx/xxx/mlu-ops
+```
+
+Install mmcv-full
+
+```bash
+cd mmcv
+export MMCV_WITH_OPS=1
+export FORCE_MLU=1
+python setup.py install
+```
+
+#### Test Code
+
+After finishing previous steps, you can run the following python code to make sure that you've installed mmcv-full on MLU device successfully
+
+```python
+import torch
+import torch_mlu
+from mmcv.ops import sigmoid_focal_loss
+x = torch.randn(3, 10).mlu()
+x.requires_grad = True
+y = torch.tensor([1, 5, 3]).mlu()
+w = torch.ones(10).float().mlu()
+output = sigmoid_focal_loss(x, y, 2.0, 0.25, w, 'none')
+print(output)
+```
diff --git a/docs/zh_cn/get_started/build.md b/docs/zh_cn/get_started/build.md
@@ -389,3 +389,59 @@ w = torch.ones(10).float().npu()
 output = softmax_focal_loss(x, y, 2.0, 0.25, w, 'none')
 print(output)
 ```
+
+### 在寒武纪 MLU 机器编译 mmcv-full
+
+#### 安装 torch_mlu
+
+##### 选项1: 基于寒武纪 docker image 安装
+
+首先请下载并且拉取寒武纪 docker (请向 service@cambricon.com 发邮件以获得最新的寒武纪 pytorch 发布 docker)。
+
+```
+docker pull ${docker image}
+```
+
+进入 docker, [编译 MMCV MLU](#编译mmcv-mlu) 并[进行验证](#验证是否成功安装)。
+
+##### 选项2：基于 cambricon pytorch 源码编译安装
+
+请向 service@cambricon.com 发送邮件或联系 Cambricon 工程师以获取合适版本的 CATCH 软件包，在您获得合适版本的 CATCH 软件包后，请参照 ${CATCH-path}/CONTRIBUTING.md 中的步骤安装 CATCH。
+
+#### 编译 MMCV
+
+克隆代码仓库
+
+```bash
+git clone https://github.com/open-mmlab/mmcv.git
+```
+
+算子库 mlu-ops 在编译 MMCV 时自动下载到默认路径(mmcv/mlu-ops)，你也可以在编译前设置环境变量 MMCV_MLU_OPS_PATH 指向已经存在的 mlu-ops 算子库路径。
+
+```bash
+export MMCV_MLU_OPS_PATH=/xxx/xxx/mlu-ops
+```
+
+开始编译
+
+```bash
+cd mmcv
+export MMCV_WITH_OPS=1
+export FORCE_MLU=1
+python setup.py install
+```
+
+#### 验证是否成功安装
+
+完成上述安装步骤之后，您可以尝试运行下面的 Python 代码以测试您是否成功在 MLU 设备上安装了 mmcv-full
+
+```python
+import torch
+import torch_mlu
+from mmcv.ops import sigmoid_focal_loss
+x = torch.randn(3, 10).mlu()
+x.requires_grad = True
+y = torch.tensor([1, 5, 3]).mlu()
+w = torch.ones(10).float().mlu()
+output = sigmoid_focal_loss(x, y, 2.0, 0.25, w, 'none')
+```