为了最大程度减少多次调用 cblas_?gemm 在Packing上的耗时，Intel® MKL 引入了以下四个API:
-通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。
TBD
+计划在paddle/utils.Flags中添加use_mkl_packed的flag，用于选择是否使用相关功能，并且当编译时WITH_MKL=ON的情况下，默认设置为true。
同时，在python/paddle/trainer/config_parser.py中对应的layer处，添加use_mkl_packed这个选择，方便用户在Python端选择是否启用这个功能。
具体实现方式比如：
+use_mkl_packed = bool(int(g_command_config_args.get("use_mkl_packed", 0)))
+if use_mkl_packed:
+ self.layer_type = mkl_packed_*
+所有相关的layer_type会以*mkl_packed_*开头，这些会在MKLPacked*Layer注册layer的时候保证，以示区分。
IfElse Operator","Design Doc: InferVarType","Intel\u00ae MKL Packed on PaddlePaddle: Design Doc","Intel\u00ae MKL-DNN on PaddlePaddle: Design Doc","Design Doc: Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","RNNOp design","Design: Sequence Decoder Generating LoDTensors","Optimizer Design","Design Doc: NCCL support in Paddle Fluid","Averaging Parameter in PaddlePaddle","Design Doc: The C++ Class Parameters","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design Doc: Supporting new Device/Library","Design for TensorArray","Background","Build from Sources","Run in Docker Containers","Install and Build","Install Using pip","GET STARTED","RNN Models","RNN Configuration","Build using Docker","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","Contribute Documentation","HOW TO","Profiling the Python Code","Tune GPU Performance","PaddlePaddle Fluid Source Code Overview","PaddlePaddle Distributed Training","Cluster Training Using Fabric","Distributed PaddlePaddle Training on AWS with Kubernetes","Paddle On Kubernetes","Cluster Training Using OpenMPI","<no title>","<no title>","Argument Outline","Detail Description","Set Command-line Parameters","Use Case","PaddlePaddle Documentation","Build PaddlePaddle for Android","PaddlePaddle Compiling Guide for iOS","Build PaddlePaddle for Raspberry Pi","MOBILE","Cluster bootstrapping tool survey","<no title>","Chinese Word Embedding Model Tutorial","Generative Adversarial Networks 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\ No newline at end of file
diff --git a/develop/doc_cn/_sources/design/mkl/mkl_packed.md.txt b/develop/doc_cn/_sources/design/mkl/mkl_packed.md.txt
index c07f7d0cbe9942e626bddbc37477e84e135f8e49..0123315ad4368e68b377f66119949bfd6c1c7860 100644
--- a/develop/doc_cn/_sources/design/mkl/mkl_packed.md.txt
+++ b/develop/doc_cn/_sources/design/mkl/mkl_packed.md.txt
@@ -30,10 +30,10 @@
由于在现有的某些情况下（例如RNN），多次调用 cblas_?gemm 会使用相同的原数据，因此，每次调用时对原数据的重复Packing便成为了冗余。
为了最大程度减少多次调用 cblas_?gemm 在Packing上的耗时，Intel® MKL 引入了以下四个API:
- * cblas_?gemm_alloc
- * cblas_?gemm_pack
- * cblas_?gemm_compute
- * cblas_?gemm_free
+ * [cblas_?gemm_alloc](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-alloc)
+ * [cblas_?gemm_pack](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-pack)
+ * [cblas_?gemm_compute](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-compute)
+ * [cblas_?gemm_free](https://software.intel.com/en-us/mkl-developer-reference-c-cblas-gemm-free)
通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。
@@ -84,7 +84,20 @@ PaddlePaddle/Paddle
2. 对比优化后layer与相对应的PaddlePaddle原有layer, 在batch mode下的结果。
### Python API
-TBD
+计划在`paddle/utils.Flags`中添加`use_mkl_packed`的flag，用于选择是否使用相关功能，并且当编译时`WITH_MKL=ON`的情况下，默认设置为`true`。
+
+同时，在`python/paddle/trainer/config_parser.py`中对应的layer处，添加`use_mkl_packed`这个选择，方便用户在Python端选择是否启用这个功能。
+
+具体实现方式比如：
+
+```python
+use_mkl_packed = bool(int(g_command_config_args.get("use_mkl_packed", 0)))
+if use_mkl_packed:
+ self.layer_type = mkl_packed_*
+```
+
+所有相关的`layer_type`会以*mkl_packed_*开头，这些会在`MKLPacked*Layer`注册layer的时候保证，以示区分。
+
### Benchmarking
会添加相应的脚本用于测试和对比在使用MKL Packed recurrent layers 前后的网络性能。
diff --git a/develop/doc_cn/design/mkl/mkl_packed.html b/develop/doc_cn/design/mkl/mkl_packed.html
index accad8029c81ca9fa03ede635bb09ab43e38412b..919a84ac74c672221c59194223bc61e3cfcc6c66 100644
--- a/develop/doc_cn/design/mkl/mkl_packed.html
+++ b/develop/doc_cn/design/mkl/mkl_packed.html
@@ -257,12 +257,14 @@
为了最大程度减少多次调用 cblas_?gemm 在Packing上的耗时，Intel® MKL 引入了以下四个API:
-通过使用这些API，我们可以先完成对原数据的Packing操作，再把已转换为Packed格式的数据传递给那些复用同一数据的gemm_compute函数，从而避免了Packing冗余。
TBD
+计划在paddle/utils.Flags中添加use_mkl_packed的flag，用于选择是否使用相关功能，并且当编译时WITH_MKL=ON的情况下，默认设置为true。
同时，在python/paddle/trainer/config_parser.py中对应的layer处，添加use_mkl_packed这个选择，方便用户在Python端选择是否启用这个功能。
具体实现方式比如：
+use_mkl_packed = bool(int(g_command_config_args.get("use_mkl_packed", 0)))
+if use_mkl_packed:
+ self.layer_type = mkl_packed_*
+所有相关的layer_type会以*mkl_packed_*开头，这些会在MKLPacked*Layer注册layer的时候保证，以示区分。
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