对readme改名，将开源的readme.md改为readme_origin，DCU上的readme_HIP.md改为readme.md

README.md

-# PyTorch bindings for Warp-ctc
+# DLIB
 
-[![Build Status](https://travis-ci.org/SeanNaren/warp-ctc.svg?branch=pytorch_bindings)](https://travis-ci.org/SeanNaren/warp-ctc)
+## 环境配置
 
-This is an extension onto the original repo found [here](https://github.com/baidu-research/warp-ctc).
+使用DCU编译之前，需要准备编译环境。参考
+[environment prepare](environment_prepare.md)
 
-## Installation
+## 使用源码安装
 
-Install [PyTorch](https://github.com/pytorch/pytorch#installation) v0.4.
+### 编译环境准备（以dtk-23.04版本为例）
 
-`WARP_CTC_PATH` should be set to the location of a built WarpCTC
-(i.e. `libwarpctc.so`).  This defaults to `../build`, so from within a
-new warp-ctc clone you could build WarpCTC like this:
+- 拉取代码
 
-```bash
-git clone https://github.com/SeanNaren/warp-ctc.git
-cd warp-ctc
-mkdir build; cd build
-cmake ..
-make
-```
+  ```
+  git clone -b develop http://developer.hpccube.com/codes/aicomponent/warpctc.git
+  ```
+- 在[开发者社区](https://developer.hpccube.com/tool/#sdk) DCU Toolkit 中下载 DTK-23.04 解压至 /opt/ 路径下，并建立软链接
+
+  ```
+  cd /opt && ln -s dtk-23.04 dtk
+  ```
+
+- 导入环境变量以及安装必要依赖库
+
+  ```shell
+  source /opt/dtk/env.sh
+  ```
+
+### 编译安装
 
-Now install the bindings:
-```bash
+#### 编译 Python API
+
+- 使用python安装
+
+```shell
 cd pytorch_binding
 python setup.py install
 ```
 
-If you try the above and get a dlopen error on OSX with anaconda3 (as recommended by pytorch):
-```bash
-cd ../pytorch_binding
-python setup.py install
-cd ../build
-cp libwarpctc.dylib /Users/$WHOAMI/anaconda3/lib
-```
-This will resolve the library not loaded error. This can be easily modified to work with other python installs if needed.
-
-Example to use the bindings below.
-
-```python
-import torch
-from warpctc_pytorch import CTCLoss
-ctc_loss = CTCLoss()
-# expected shape of seqLength x batchSize x alphabet_size
-probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous()
-labels = torch.IntTensor([1, 2])
-label_sizes = torch.IntTensor([2])
-probs_sizes = torch.IntTensor([2])
-probs.requires_grad_(True)  # tells autograd to compute gradients for probs
-cost = ctc_loss(probs, labels, probs_sizes, label_sizes)
-cost.backward()
+- 使用python编译whl包
+
+```shell
+cd pytorch_binding
+python setup.py bdist_wheel
 ```
 
-## Documentation
+### 测试
+
+- 验证warpctc的loss正确性（CPU和GPU的一致性）
 
+```shell
+cd pytorch_binding/tests
+python3 test_gpu.py
 ```
-CTCLoss(size_average=False, length_average=False)
-    # size_average (bool): normalize the loss by the batch size (default: False)
-    # length_average (bool): normalize the loss by the total number of frames in the batch. If True, supersedes size_average (default: False)
-
-forward(acts, labels, act_lens, label_lens)
-    # acts: Tensor of (seqLength x batch x outputDim) containing output activations from network (before softmax)
-    # labels: 1 dimensional Tensor containing all the targets of the batch in one large sequence
-    # act_lens: Tensor of size (batch) containing size of each output sequence from the network
-    # label_lens: Tensor of (batch) containing label length of each example
+
+- 验证warpctc的loss的GPU加速效果
+
+```shell
+cd pytorch_binding/tests
+python3 test_gpu_speed.py
 ```
+ 
+
+
+

README_origin.md

+# PyTorch bindings for Warp-ctc
+
+[![Build Status](https://travis-ci.org/SeanNaren/warp-ctc.svg?branch=pytorch_bindings)](https://travis-ci.org/SeanNaren/warp-ctc)
+
+This is an extension onto the original repo found [here](https://github.com/baidu-research/warp-ctc).
+
+## Installation
+
+Install [PyTorch](https://github.com/pytorch/pytorch#installation) v0.4.
+
+`WARP_CTC_PATH` should be set to the location of a built WarpCTC
+(i.e. `libwarpctc.so`).  This defaults to `../build`, so from within a
+new warp-ctc clone you could build WarpCTC like this:
+
+```bash
+git clone https://github.com/SeanNaren/warp-ctc.git
+cd warp-ctc
+mkdir build; cd build
+cmake ..
+make
+```
+
+Now install the bindings:
+```bash
+cd pytorch_binding
+python setup.py install
+```
+
+If you try the above and get a dlopen error on OSX with anaconda3 (as recommended by pytorch):
+```bash
+cd ../pytorch_binding
+python setup.py install
+cd ../build
+cp libwarpctc.dylib /Users/$WHOAMI/anaconda3/lib
+```
+This will resolve the library not loaded error. This can be easily modified to work with other python installs if needed.
+
+Example to use the bindings below.
+
+```python
+import torch
+from warpctc_pytorch import CTCLoss
+ctc_loss = CTCLoss()
+# expected shape of seqLength x batchSize x alphabet_size
+probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous()
+labels = torch.IntTensor([1, 2])
+label_sizes = torch.IntTensor([2])
+probs_sizes = torch.IntTensor([2])
+probs.requires_grad_(True)  # tells autograd to compute gradients for probs
+cost = ctc_loss(probs, labels, probs_sizes, label_sizes)
+cost.backward()
+```
+
+## Documentation
+
+```
+CTCLoss(size_average=False, length_average=False)
+    # size_average (bool): normalize the loss by the batch size (default: False)
+    # length_average (bool): normalize the loss by the total number of frames in the batch. If True, supersedes size_average (default: False)
+
+forward(acts, labels, act_lens, label_lens)
+    # acts: Tensor of (seqLength x batch x outputDim) containing output activations from network (before softmax)
+    # labels: 1 dimensional Tensor containing all the targets of the batch in one large sequence
+    # act_lens: Tensor of size (batch) containing size of each output sequence from the network
+    # label_lens: Tensor of (batch) containing label length of each example
+```
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