fix up wrong link (PaddlePaddle#762)

Lausichen · May 20, 2021 · 508fcbb · 508fcbb
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diff --git a/README_en.md b/README_en.md
@@ -82,19 +82,19 @@ pip install paddleslim==1.2.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
 
 ## Usage
 
-- [QuickStart](https://paddlepaddle.github.io/PaddleSlim/quick_start/index_en.html): Introduce how to use PaddleSlim by simple examples.
+- [QuickStart](https://paddleslim.readthedocs.io/en/latest/quick_start/index_en.html): Introduce how to use PaddleSlim by simple examples.
 - Dynamic graph
-  - Pruning: [Tutorial](dygraph_docs/), [Demo](demo/dygraph/pruning)
+  - Pruning: [Tutorial](https://paddleslim.readthedocs.io/en/latest/tutorials/image_classification_sensitivity_analysis_tutorial_en.html), [Demo](demo/dygraph/pruning)
   - Quantization: [Demo](demo/dygraph/quant)
 
 
-- [Advanced Tutorials](https://paddlepaddle.github.io/PaddleSlim/tutorials/index_en.html)：Tutorials about advanced usage of PaddleSlim.
+- [Advanced Tutorials](https://paddleslim.readthedocs.io/en/latest/tutorials/index_en.html)：Tutorials about advanced usage of PaddleSlim.
 
-- [Model Zoo](https://paddlepaddle.github.io/PaddleSlim/model_zoo_en.html)：Benchmark and pretrained models.
+- [Model Zoo](https://paddleslim.readthedocs.io/en/latest/model_zoo_en.html)：Benchmark and pretrained models.
 
-- [API Documents](https://paddlepaddle.github.io/PaddleSlim/api_en/index_en.html)
+- [API Documents](https://paddleslim.readthedocs.io/en/latest/api_en/index_en.html)
 
-- [Algorithm Background](https://paddlepaddle.github.io/PaddleSlim/algo/algo.html): Introduce the background of quantization, pruning, distillation, NAS.
+- [Algorithm Background](https://paddleslim.readthedocs.io/en/latest/intro_en.html): Introduce the background of quantization, pruning, distillation, NAS.
 
 - [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/master/slim): Introduce how to use PaddleSlim in PaddleDetection library.
 

diff --git a/demo/distillation/README.md b/demo/distillation/README.md
@@ -4,7 +4,7 @@
 
 ## 接口介绍
 
-请参考 [知识蒸馏API文档](https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/)。
+请参考 [知识蒸馏API文档](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/static/dist/single_distiller_api.html)。
 
 ### 1. 蒸馏训练配置
 
@@ -37,4 +37,4 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python distill.py
 
 经过120轮的蒸馏训练，MobileNet模型的Top-1/Top-5准确率达到72.77%/90.68%, Top-1/Top-5性能提升+1.78%/+1.00%
 
-详细实验数据请参见[PaddleSlim模型库蒸馏部分](https://paddlepaddle.github.io/PaddleSlim/model_zoo/#13)
+详细实验数据请参见[PaddleSlim模型库蒸馏部分](https://paddleslim.readthedocs.io/zh_CN/latest/model_zoo.html#id5)
diff --git a/demo/mkldnn_quant/README.md b/demo/mkldnn_quant/README.md
@@ -38,15 +38,15 @@ import numpy as np
 
 #### 2.1 量化训练
 
-量化训练流程可以参考 [分类模型的量化训练流程](https://paddlepaddle.github.io/PaddleSlim/tutorials/quant_aware_demo/)
+量化训练流程可以参考 [分类模型的量化训练流程](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/static/quant_aware_tutorial.html)
 
 **量化训练过程中config参数：**
 - **quantize_op_types:** 目前CPU上量化支持的算子为 `depthwise_conv2d`, `conv2d`, `mul`, `matmul`, `transpose2`, `reshape2`, `pool2d`, `scale`, `concat`。但是在量化训练阶段插入fake_quantize/fake_dequantize算子时，只需在前四种op前后插入fake_quantize/fake_dequantize 算子，因为后面四种算子 `transpose2`, `reshape2`, `pool2d`, `scale`, `concat`的scales将从其他op的`out_threshold`属性获取。所以，在使用PaddleSlim量化训练时，只可以对 `depthwise_conv2d`, `conv2d`, `mul`, `matmul`进行量化，不支持其他op。
-- **其他参数:** 请参考 [PaddleSlim quant_aware API](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/#quant_aware)
+- **其他参数:** 请参考 [PaddleSlim quant_aware API](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/static/quant/quantization_api.html#quant-aware)
 
 #### 2.2 离线量化
 
-离线量化模型产出可以参考[分类模型的静态离线量化流程](https://paddlepaddle.github.io/PaddleSlim/tutorials/quant_post_demo/#_1)
+离线量化模型产出可以参考[分类模型的静态离线量化流程](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/static/quant_post_tutorial.html)
 
 在使用PaddleSlim离线量化时，只可以对 `depthwise_conv2d`, `conv2d`, `mul`, `matmul`进行量化，不支持其他op。
 

diff --git a/demo/mkldnn_quant/README_en.md b/demo/mkldnn_quant/README_en.md
@@ -13,7 +13,7 @@ The process comprises the following steps:
 
 #### Install PaddleSlim
 
-For PaddleSlim installation, please see [Paddle Installation Document](https://paddlepaddle.github.io/PaddleSlim/install.html)
+For PaddleSlim installation, please see [Paddle Installation Document](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/static/quant/quantization_api.html#quant-aware)
 ```
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
 cd PaddleSlim
@@ -34,15 +34,15 @@ One can generate fake-quantized model with post-training or quant-aware strategy
 
 #### 2.1 Quant-aware training
 
-To generate fake quantized model with quant-aware strategy, see [Quant-aware training tutorial](https://paddlepaddle.github.io/PaddleSlim/tutorials/quant_aware_demo/)
+To generate fake quantized model with quant-aware strategy, see [Quant-aware training tutorial](https://paddleslim.readthedocs.io/en/latest/quick_start/quant_aware_tutorial_en.html)
 
 **The parameters during quant-aware training:**
 - **quantize_op_types:** A list of operators to insert `fake_quantize` and `fake_dequantize` ops around them. In PaddlePaddle, quantization of following operators is supported for CPU: `depthwise_conv2d`, `conv2d`, `fc`, `matmul`, `transpose2`, `reshape2`, `pool2d`, `scale`, `concat`. However, inserting fake_quantize/fake_dequantize operators during training is needed only for the first four of them (`depthwise_conv2d`, `conv2d`, `fc`, `matmul`), so setting the `quantize_op_types` parameter to the list of those four ops is enough. Scala data needed for quantization of the other five operators is reused from the fake ops or gathered from the `out_threshold` attributes of the operators.
-- **Other parameters:** Please read [PaddleSlim quant_aware API](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/#quant_aware)
+- **Other parameters:** Please read [PaddleSlim quant_aware API](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/static/quant/quantization_api.html#quant-aware)
 
 #### 2.2 Post-training quantization
 
-To generate post-training fake quantized model, see [Offline post-training quantization tutorial](https://paddlepaddle.github.io/PaddleSlim/tutorials/quant_post_demo/#_1)
+To generate post-training fake quantized model, see [Offline post-training quantization tutorial](https://paddleslim.readthedocs.io/en/latest/quick_start/index_en.html)
 
 ## 3. Convert the fake quantized model to DNNL INT8 model
 In order to deploy an INT8 model on the CPU, we need to collect scales, remove all fake_quantize/fake_dequantize operators, optimize the graph and quantize it, turning it into the final DNNL INT8 model. This is done by the script [save_quant_model.py](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py). Copy the script to the directory where the demo is located: `/PATH_TO_PaddleSlim/demo/mkldnn_quant/` and run it as follows:
@@ -55,7 +55,7 @@ python save_quant_model.py --quant_model_path=/PATH/TO/SAVE/FLOAT32/quant/MODEL
 - **int8_model_save_path:** The final INT8 model output path after the quant model is optimized and quantized by DNNL.
 - **ops_to_quantize:** A comma separated list of specified op types to be quantized. It is optional. If the option is skipped, all quantizable operators will be quantized. Skipping the option is recommended in the first approach as it usually yields best performance and accuracy for image classification models and NLP models listed in the Benchmark..
 - **--op_ids_to_skip:** "A comma-separated list of operator ID numbers. It is optional. Default value is none. The op ids in this list will not be quantized and will adopt FP32 type. To get the ID of a specific op, first run the script using the `--debug` option, and open the generated file `int8_<number>_cpu_quantize_placement_pass.dot` to find the op that does not need to be quantified, and the ID number is in parentheses after the Op name.
-- **--debug:** Generate models graph or not. If this option is present, .dot files with graphs of the model will be generated after each optimization step that modifies the graph. For the description of DOT format, please read [DOT](https://graphviz.gitlab.io/_pages/doc/info/lang.html). To open the `*.dot` file, please use any Graphviz tool available on the system(such as the `xdot` tool on Linux or the `dot` tool on Windows. For Graphviz documentation, see [Graphviz](http://www. graphviz.org/documentation/).
+- **--debug:** Generate models graph or not. If this option is present, .dot files with graphs of the model will be generated after each optimization step that modifies the graph. For the description of DOT format, please read [DOT](https://graphviz.gitlab.io/_pages/doc/info/lang.html). To open the `*.dot` file, please use any Graphviz tool available on the system(such as the `xdot` tool on Linux or the `dot` tool on Windows. For Graphviz documentation, see [Graphviz](http://www.graphviz.org/documentation/).
 
 - **Note:**
   - The DNNL supported quantizable ops are `conv2d`, `depthwise_conv2d`, `fc`, `matmul`, `pool2d`, `reshape2`, `transpose2`, `scale`, `concat`.

diff --git a/demo/ocr/README.md b/demo/ocr/README.md
@@ -1,4 +1,4 @@
-[English](README_en.md) | 简体中文
+English | 简体中文
 
 # SlimOCR模型库
 

diff --git a/demo/ofa/bert/README.md b/demo/ofa/bert/README.md
@@ -1,6 +1,6 @@
 # OFA压缩PaddleNLP-BERT模型
 
-BERT-base模型是一个迁移能力很强的通用语义表示模型，但是模型中也有一些参数冗余。本教程将介绍如何使用PaddleSlim对[PaddleNLP](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/)中BERT-base模型进行压缩。  
+BERT-base模型是一个迁移能力很强的通用语义表示模型，但是模型中也有一些参数冗余。本教程将介绍如何使用PaddleSlim对[PaddleNLP](https://paddlenlp.readthedocs.io/zh/latest/)中BERT-base模型进行压缩。  
 本教程只会演示如何快速启动相应训练，详细教程请参考: [BERT](https://github.com/PaddlePaddle/PaddleSlim/blob/release/2.0.0/docs/zh_cn/nlp/paddlenlp_slim_ofa_tutorial.md)
 
 ## 1. 压缩结果
@@ -79,8 +79,8 @@ BERT-base模型是一个迁移能力很强的通用语义表示模型，但是
                                 <span style="font-size:18px">14.93</span>
                         </td>
                 </tr>
-		<tr>
-			<td rowspan=4 align=center> 40 </td>
+                <tr>
+                        <td rowspan=4 align=center> 40 </td>
                         <td rowspan=2 align=center> BERT </td>
                         <td style="text-align:center">
                                 <span style="font-size:18px">N</span>
@@ -185,7 +185,7 @@ pip install paddlepaddle_gpu>=2.0rc1
 ```
 
 ### 2.2 Fine-tuing
-首先需要对Pretrain-Model在实际的下游任务上进行Fine-tuning，得到需要压缩的模型。Fine-tuning流程参考[Fine-tuning教程](https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/examples/bert)
+首先需要对Pretrain-Model在实际的下游任务上进行Fine-tuning，得到需要压缩的模型。Fine-tuning流程参考[Fine-tuning教程](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/bert)
 Fine-tuning 在dev上的结果如压缩结果表1-1『Baseline』那一列所示。
 
 ### 2.3 压缩训练
@@ -268,10 +268,10 @@ python -u ./run_glue_ofa.py --model_type bert \
 python3.7 -u ./export_model.py --model_type bert \
                              --model_name_or_path ${PATH_OF_QQP_MODEL_AFTER_OFA} \
                              --max_seq_length 128     \
-			     --sub_model_output_dir ./tmp/$TASK_NAME/dynamic_model \
+                             --sub_model_output_dir ./tmp/$TASK_NAME/dynamic_model \
                              --static_sub_model ./tmp/$TASK_NAME/static_model \
-			     --n_gpu 1 \
-			     --width_mult  0.6666666666666666
+                             --n_gpu 1 \
+                             --width_mult  0.6666666666666666
 ```
 
 其中参数释义如下：

diff --git a/demo/pantheon/lexical_anlysis/README.md b/demo/pantheon/lexical_anlysis/README.md
@@ -11,7 +11,7 @@ BiGRU is to train a BiGRU based LAC model from scratch; BERT fine-tuned is to fi
 
 ## Introduction
 
-Lexical Analysis of Chinese, or LAC for short, is a lexical analysis model that completes the tasks of Chinese word segmentation, part-of-speech tagging, and named entity recognition in a single model. We conduct an overall evaluation of word segmentation, part-of-speech tagging, and named entity recognition on a self-built dataset. We use the finetuned [ERNIE](https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE) model as the Teacher model and GRU as the Student model, which are needed by the Pantheon framework for online distillation.
+Lexical Analysis of Chinese, or LAC for short, is a lexical analysis model that completes the tasks of Chinese word segmentation, part-of-speech tagging, and named entity recognition in a single model. We conduct an overall evaluation of word segmentation, part-of-speech tagging, and named entity recognition on a self-built dataset. We use the finetuned ERNIE model as the Teacher model and GRU as the Student model, which are needed by the Pantheon framework for online distillation.
 
 #### 1. Download the training data set
 
@@ -37,4 +37,4 @@ bash run_teacher.sh
 bash run_student.sh
 ```
 
-> If you want to learn more about LAC, you can refer to this repo: https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis
+> If you want to learn more about LAC, you can refer to this repo: https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/lexical_analysis
diff --git a/demo/pantheon/lexical_anlysis/README_cn.md b/demo/pantheon/lexical_anlysis/README_cn.md
@@ -12,7 +12,7 @@ BiGRU 是使用双向GRU网络从头训练LAC任务；BERT fine-tuned 是在BERT
 
 ## 简介
 
-Lexical Analysis of Chinese，简称 LAC，是一个联合的词法分析模型，在单个模型中完成中文分词、词性标注、专名识别任务。我们在自建的数据集上对分词、词性标注、专名识别进行整体的评估效果。我们使用经过finetune的 [ERNIE](https://github.com/PaddlePaddle/LARK/tree/develop/ERNIE) 模型作为Teacher模型，使用GRU作为Student模型，使用Pantheon框架进行在线蒸馏。
+Lexical Analysis of Chinese，简称 LAC，是一个联合的词法分析模型，在单个模型中完成中文分词、词性标注、专名识别任务。我们在自建的数据集上对分词、词性标注、专名识别进行整体的评估效果。我们使用经过finetune的 ERNIE 模型作为Teacher模型，使用GRU作为Student模型，使用Pantheon框架进行在线蒸馏。
 
 #### 1. 下载训练数据集
 

diff --git a/demo/prune/README.md b/demo/prune/README.md
@@ -69,7 +69,7 @@ python eval.py \
 
 ## 5. 接口介绍
 
-该示例使用了`paddleslim.Pruner`工具类，用户接口使用介绍请参考：[API文档](https://paddlepaddle.github.io/PaddleSlim/api/prune_api/)
+该示例使用了`paddleslim.Pruner`工具类，用户接口使用介绍请参考：[API文档](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/index.html)
 
 在调用`paddleslim.Pruner`工具类时，需要指定待裁卷积层的参数名称。不同模型的参数命名不同，
 在`train.py`脚本中，提供了`get_pruned_params`方法，根据用户设置的选项`--model`确定要裁剪的参数。
diff --git a/demo/quant/deploy/TensorRT/README.md b/demo/quant/deploy/TensorRT/README.md
@@ -79,7 +79,7 @@ LIB_ROOT/
 
 ### 2.1 将模型导出为inference model
 
-* 可以参考[量化训练教程](https://paddleslim.readthedocs.io/zh_CN/latest/quick_start/quant_aware_tutorial.html#id9)，在训练完成后导出inference model。
+* 可以参考[量化训练教程](https://paddleslim.readthedocs.io/zh_CN/latest/tutorials/quant/index.html)，在训练完成后导出inference model。
 
 ```
 inference/

diff --git a/docs/en/model_zoo_en.md b/docs/en/model_zoo_en.md
@@ -134,7 +134,7 @@ Dataset：WIDER-FACE
 |   BlazeFace    | quant_post  |     8     |    640     | 87.8/85.1/74.9 (-3.7/-4.1/-4.8) |       228        | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_post.tar) |
 |   BlazeFace    | quant_aware |     8     |    640     | 90.5/87.9/77.6 (-1.0/-1.3/-2.1) |       228        | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_aware.tar) |
 | BlazeFace-Lite |      -      |     8     |    640     |         90.9/88.5/78.1          |       711        | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) |
-| BlazeFace-Lite | quant_post  |     8     |    640     | 89.4/86.7/75.7 (-1.5/-1.8/-2.4) |       211        | [model]((https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_post.tar)) |
+| BlazeFace-Lite | quant_post  |     8     |    640     | 89.4/86.7/75.7 (-1.5/-1.8/-2.4) |       211        | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_post.tar) |
 | BlazeFace-Lite | quant_aware |     8     |    640     | 89.7/87.3/77.0 (-1.2/-1.2/-1.1) |       211        | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_aware.tar) |
 | BlazeFace-NAS  |      -      |     8     |    640     |         83.7/80.7/65.8          |       244        | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
 | BlazeFace-NAS  | quant_post  |     8     |    640     | 81.6/78.3/63.6 (-2.1/-2.4/-2.2) |        71        | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_post.tar) |

diff --git a/docs/en/quick_start/nas_tutorial_en.md b/docs/en/quick_start/nas_tutorial_en.md
@@ -1,6 +1,6 @@
 # Nerual Architecture Search for Image Classification
 
-This tutorial shows how to use [API](../api/nas_api.md) about SANAS in PaddleSlim. We start experiment based on MobileNetV2 as example. The tutorial contains follow section.
+This tutorial shows how to use [API](https://paddleslim.readthedocs.io/en/latest/api_en/paddleslim.nas.html) about SANAS in PaddleSlim. We start experiment based on MobileNetV2 as example. The tutorial contains follow section.
 
 1. necessary imports
 2. initial SANAS instance