diff --git a/ci_scripts/hooks/pre-doc-compile.sh b/ci_scripts/hooks/pre-doc-compile.sh
index de1855f00c3..1d2b0b047bb 100755
--- a/ci_scripts/hooks/pre-doc-compile.sh
+++ b/ci_scripts/hooks/pre-doc-compile.sh
@@ -29,7 +29,7 @@ done
 
 ## 2 convert all ipynb files to markdown, and delete the ipynb files.
 # ../practices/**/*.ipynb
-for i in ${SCRIPT_DIR}/../../docs/practices/**/*.ipynb ; do
+for i in $(find ${SCRIPT_DIR}/../../docs/ -name '*.ipynb' -type f ) ; do
     echo "convert $i to markdown and delete ipynb"
     jupyter nbconvert --to markdown "$i"
     rm "$i"
diff --git a/docs/api/api_aliases.ini b/docs/api/api_aliases.ini
new file mode 100644
index 00000000000..2da6ad6e5e4
--- /dev/null
+++ b/docs/api/api_aliases.ini
@@ -0,0 +1,6 @@
+[help]
+help_zh=左侧是target name，右侧是origin name， 如paddle.device.cuda.Event=paddle.fluid.core_avx.CUDAEvent
+
+[en]
+paddle.device.cuda.Event=paddle.fluid.core_avx.CUDAEvent
+paddle.device.cuda.Stream=paddle.fluid.core_avx.CUDAStream
diff --git a/docs/api/gen_doc.py b/docs/api/gen_doc.py
index 45ca5e0b841..262e398f229 100755
--- a/docs/api/gen_doc.py
+++ b/docs/api/gen_doc.py
@@ -414,6 +414,7 @@ def set_api_sketch():
         paddle.nn.utils,
         paddle.static,
         paddle.static.nn,
+        paddle.signal,
         paddle.io,
         paddle.jit,
         paddle.metric,
@@ -427,6 +428,7 @@ def set_api_sketch():
         paddle.utils.profiler,
         paddle.utils.cpp_extension,
         paddle.utils.unique_name,
+        paddle.utils.dlpack,
         paddle.sysconfig,
         paddle.vision,
         paddle.vision.datasets,
diff --git a/docs/api/paddle/Overview_cn.rst b/docs/api/paddle/Overview_cn.rst
index 1d94b2583bb..62a1377748e 100755
--- a/docs/api/paddle/Overview_cn.rst
+++ b/docs/api/paddle/Overview_cn.rst
@@ -32,12 +32,14 @@ tensor数学操作
     :widths: 10, 30
 
     " :ref:`paddle.abs <cn_api_fluid_layers_abs>` ", "绝对值函数"
+    " :ref:`paddle.angle <cn_api_paddle_angle>` ", "相位角函数"
     " :ref:`paddle.acos <cn_api_fluid_layers_acos>` ", "arccosine函数"
     " :ref:`paddle.add <cn_api_tensor_add>` ", "Tensor逐元素相加"
     " :ref:`paddle.add_n <cn_api_tensor_add_n>` ", "对输入的一至多个Tensor或LoDTensor求和"
     " :ref:`paddle.addmm <cn_api_tensor_addmm>` ", "计算输入Tensor x和y的乘积，将结果乘以标量alpha，再加上input与beta的乘积，得到输出"
     " :ref:`paddle.all <cn_api_tensor_all>` ", "对指定维度上的Tensor元素进行逻辑与运算"
     " :ref:`paddle.allclose <cn_api_tensor_allclose>` ", "逐个检查输入Tensor x和y的所有元素是否均满足 ∣x−y∣≤atol+rtol×∣y∣"
+    " :ref:`paddle.isclose <cn_api_tensor_isclose>` ", "逐个检查输入Tensor x和y的所有元素是否满足 ∣x−y∣≤atol+rtol×∣y∣"
     " :ref:`paddle.any <cn_api_tensor_any>` ", "对指定维度上的Tensor元素进行逻辑或运算"
     " :ref:`paddle.asin <cn_api_fluid_layers_asin>` ", "arcsine函数"
     " :ref:`paddle.atan <cn_api_fluid_layers_atan>` ", "arctangent函数"
@@ -61,7 +63,6 @@ tensor数学操作
     " :ref:`paddle.greater_equal <cn_api_tensor_cn_greater_equal>` ", "逐元素地返回 x>=y 的逻辑值"
     " :ref:`paddle.greater_than <cn_api_tensor_cn_greater_than>` ", "逐元素地返回 x>y 的逻辑值"
     " :ref:`paddle.increment <cn_api_tensor_increment>` ", "在控制流程中用来让 x 的数值增加 value"
-    " :ref:`paddle.inverse <cn_api_tensor_inverse>` ", "计算方阵的逆"
     " :ref:`paddle.kron <cn_api_paddle_tensor_kron>` ", "计算两个张量的克罗内克积"
     " :ref:`paddle.less_equal <cn_api_tensor_cn_less_equal>` ", "逐元素地返回 x<=y 的逻辑值"
     " :ref:`paddle.less_than <cn_api_tensor_cn_less_than>` ", "逐元素地返回 x<y 的逻辑值"
@@ -112,6 +113,9 @@ tensor数学操作
     " :ref:`paddle.diagonal <cn_api_tensor_diagonal>` ", "根据给定的轴 axis 返回输入 Tensor 的局部视图"
     " :ref:`paddle.trunc <cn_api_tensor_trunc>` ", "对输入 Tensor 每个元素的小数部分进行截断"
     " :ref:`paddle.log1p <cn_api_paddle_tensor_log1p>` ", "该OP计算Log1p（加一的自然对数）结果"
+    " :ref:`paddle.diff <cn_api_tensor_diff>` ", "沿着指定维度对输入Tensor计算n阶的前向差值"
+    " :ref:`paddle.rad2deg <cn_api_paddle_tensor_rad2deg>` ", "将元素从弧度的角度转换为度"
+    " :ref:`paddle.deg2rad <cn_api_paddle_tensor_deg2rad>` ", "将元素从度的角度转换为弧度"
 
 .. _tensor_logic:
 
@@ -236,20 +240,16 @@ tensor线性代数相关
 
     " :ref:`paddle.bincount <cn_api_tensor_bincount>` ", "统计输入张量中元素的出现次数"
     " :ref:`paddle.bmm <cn_api_paddle_tensor_bmm>` ", "对输入x及输入y进行矩阵相乘"
-    " :ref:`paddle.cholesky <cn_api_tensor_cholesky>` ", "计算一个对称正定矩阵或一批对称正定矩阵的Cholesky分解"
     " :ref:`paddle.cross <cn_api_tensor_linalg_cross>` ", "计算张量 x 和 y 在 axis 维度上的向量积（叉积）"
     " :ref:`paddle.dist <cn_api_tensor_linalg_dist>` ", "计算 (x-y) 的 p 范数（p-norm）"
     " :ref:`paddle.dot <cn_api_paddle_tensor_linalg_dot>` ", "计算向量的内积"
     " :ref:`paddle.histogram <cn_api_tensor_histogram>` ", "计算输入张量的直方图"
     " :ref:`paddle.matmul <cn_api_tensor_matmul>` ", "计算两个Tensor的乘积，遵循完整的广播规则"
-    " :ref:`paddle.matrix_power <cn_api_tensor_matrix_power>` ", "计算一个（或一批）方阵的 n 次幂"
     " :ref:`paddle.mv <cn_api_tensor_mv>` ", "计算矩阵 x 和向量 vec 的乘积"
-    " :ref:`paddle.norm <cn_api_tensor_norm>` ", "计算给定Tensor的矩阵范数（Frobenius 范数）和向量范数（向量1范数、2范数、或者通常的p范数）"
     " :ref:`paddle.rank <cn_api_fluid_layers_rank>` ", "计算输入Tensor的维度（秩）"
     " :ref:`paddle.t <cn_api_paddle_tensor_t>` ", "对小于等于2维的Tensor进行数据转置"
     " :ref:`paddle.tril <cn_api_tensor_tril>` ", "返回输入矩阵 input 的下三角部分，其余部分被设为0"
     " :ref:`paddle.triu <cn_api_tensor_triu>` ", "返回输入矩阵 input 的上三角部分，其余部分被设为0"
-    " :ref:`paddle.multi_dot<cn_api_tensor_multi_dot>` ", "计算多个矩阵相乘"
 
 .. _tensor_manipulation:
 
@@ -299,7 +299,7 @@ tensor元素操作相关（如：转置，reshape等）
 .. einsum:
 
 爱因斯坦求和
-：：：：：：
+::::::::::::::::::
 
 .. csv-table::
     :header: "API名称", "API功能"
diff --git a/docs/api/paddle/Tensor/Overview_en.rst b/docs/api/paddle/Tensor/Overview_en.rst
index 0b66e16ee97..2e5b70819b8 100644
--- a/docs/api/paddle/Tensor/Overview_en.rst
+++ b/docs/api/paddle/Tensor/Overview_en.rst
@@ -69,6 +69,7 @@ Methods
     addmm
     all
     allclose
+    angle
     any
     argmax
     argmin
@@ -113,6 +114,7 @@ Methods
     dist
     divide
     dot
+    diff
     eigvals
     equal
     equal_all
@@ -143,6 +145,7 @@ Methods
     index_sample
     index_select
     inverse
+    isclose
     is_empty
     is_tensor
     isfinite
diff --git a/docs/api/paddle/Tensor_cn.rst b/docs/api/paddle/Tensor_cn.rst
index 032a0fade8a..a38bd8f8038 100755
--- a/docs/api/paddle/Tensor_cn.rst
+++ b/docs/api/paddle/Tensor_cn.rst
@@ -212,6 +212,15 @@ abs(name=None)
 
 请参考 :ref:`cn_api_fluid_layers_abs`
 
+angle(name=None)
+:::::::::
+
+返回：计算后的Tensor
+
+返回类型：Tensor
+
+请参考 :ref:`cn_api_paddle_angle`
+
 acos(name=None)
 :::::::::
 
@@ -271,6 +280,15 @@ allclose(y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None)
 
 请参考 :ref:`cn_api_tensor_allclose`
 
+isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None)
+:::::::::
+
+返回：计算后的Tensor
+
+返回类型：Tensor
+
+请参考 :ref:`cn_api_tensor_isclose`
+
 any(axis=None, keepdim=False, name=None)
 :::::::::
 
@@ -480,7 +498,7 @@ cholesky(upper=False, name=None)
 
 返回类型：Tensor
 
-请参考 :ref:`cn_api_tensor_cholesky`
+请参考 :ref:`cn_api_linalg_cholesky`
 
 chunk(chunks, axis=0, name=None)
 :::::::::
@@ -671,6 +689,17 @@ cumsum(axis=None, dtype=None, name=None)
 
 请参考 :ref:`cn_api_tensor_cn_cumsum`
 
+deg2rad(x, name=None)
+:::::::::
+
+将元素从度的角度转换为弧度
+
+返回：计算后的Tensor
+
+返回类型：Tensor
+
+请参考 :ref:`cn_api_paddle_tensor_deg2rad`
+
 detach()
 :::::::::
 
@@ -748,6 +777,15 @@ dot(y, name=None)
 
 请参考 :ref:`cn_api_paddle_tensor_linalg_dot`
 
+diff(x, n=1, axis=-1, prepend=None, append=None, name=None)
+:::::::::
+
+返回：计算后的Tensor
+
+返回类型：Tensor
+
+请参考 :ref:`cn_api_tensor_diff`
+
 equal(y, name=None)
 :::::::::
 
@@ -814,7 +852,7 @@ eigvals(y, name=None)
 
 返回类型：Tensor
 
-请参考 :ref:`cn_api_paddle_linalg_eigvals`
+请参考 :ref:`cn_api_linalg_eigvals`
 
 fill_(x, value, name=None)
 :::::::::
@@ -1076,14 +1114,14 @@ index_select(index, axis=0, name=None)
 
 请参考 :ref:`cn_api_tensor_search_index_select`
 
-inverse(name=None)
+inv(name=None)
 :::::::::
 
 返回：计算后的Tensor
 
 返回类型：Tensor
 
-请参考 :ref:`cn_api_tensor_inverse`
+请参考 :ref:`cn_api_linalg_inv`
 
 is_empty(cond=None)
 :::::::::
@@ -1263,7 +1301,7 @@ matrix_power(x, n, name=None)
 
 返回类型：Tensor
 
-请参考 :ref:`cn_api_tensor_matrix_power`
+请参考 :ref:`cn_api_linalg_matrix_power`
 
 max(axis=None, keepdim=False, name=None)
 :::::::::
@@ -1402,7 +1440,7 @@ norm(p=fro, axis=None, keepdim=False, name=None)
 
 返回类型：Tensor
 
-请参考 :ref:`cn_api_tensor_norm`
+请参考 :ref:`cn_api_linalg_norm`
 
 not_equal(y, name=None)
 :::::::::
@@ -1479,6 +1517,17 @@ prod(axis=None, keepdim=False, dtype=None, name=None)
 
 请参考 :ref:`cn_api_tensor_cn_prod`
 
+rad2deg(x, name=None)
+:::::::::
+
+将元素从弧度的角度转换为度
+
+返回：计算后的Tensor
+
+返回类型：Tensor
+
+请参考 :ref:`cn_api_paddle_tensor_rad2deg`
+
 rank()
 :::::::::
 
@@ -1950,6 +1999,15 @@ transpose(perm, name=None)
 
 请参考 :ref:`cn_api_fluid_layers_transpose`
 
+triangular_solve(b, upper=True, transpose=False, unitriangular=False, name=None)
+:::::::::
+
+返回：计算后的Tensor
+
+返回类型：Tensor
+
+请参考 :ref:`cn_api_linalg_triangular_solve`
+
 trunc(name=None)
 :::::::::
 
@@ -2064,7 +2122,7 @@ multi_dot(x, name=None)
 
 返回类型：Tensor
 
-请参考 :ref:`cn_api_tensor_multi_dot`
+请参考 :ref:`cn_api_linalg_multi_dot`
 
 solve(x, y name=None)
 :::::::::
diff --git a/docs/api/paddle/angle_cn.rst b/docs/api/paddle/angle_cn.rst
new file mode 100644
index 00000000000..8b56b58982e
--- /dev/null
+++ b/docs/api/paddle/angle_cn.rst
@@ -0,0 +1,27 @@
+.. _cn_api_paddle_angle:
+
+angle
+-------------------------------
+
+.. py:function:: paddle.angle(x, name=None)
+
+
+逐元素计算复数的相位角。对于非负实数，相位角为 0，而对于负实数，相位角为 :math:`\pi`.
+
+.. math::
+
+    angle(x) = arctan2(x.imag, x.real)
+
+参数
+:::::::::
+    - x (Tensor) - 输入的Tensor，数据类型为：complex64, complex128 或 float32, float64。
+    - name (str，可选） - 操作的名称(可选，默认值为None）。更多信息请参见 :ref:`api_guide_Name`。
+
+返回
+:::::::::
+输出实数Tensor，与 ``x`` 的数值精度一致。
+
+代码示例
+:::::::::
+
+COPY-FROM: paddle.angle
\ No newline at end of file
diff --git a/docs/api/paddle/bincount_cn.rst b/docs/api/paddle/bincount_cn.rst
index 7f121d086db..427d0912421 100644
--- a/docs/api/paddle/bincount_cn.rst
+++ b/docs/api/paddle/bincount_cn.rst
@@ -12,7 +12,7 @@ bincount
 
     - **x** (Tensor) - 输入Tensor。必须是一维Tensor，其中元素必须大于等于0，数据类型为int32, int64。
     - **weights** (Tensor, 可选) - weights Tensor，代表输入Tensor中每个元素的权重。长度必须与输入Tensor相同。数据类型为int32, int64, float32或float64。默认为None
-    - **minlength** (int, 可选) - 输出Tensor的最小长度，如果大于输入Tensor的长度，则多出的位置补0。该值必须大于等于0。默认为0。
+    - **minlength** (int, 可选) - 输出Tensor的最小长度，如果大于输入Tensor中的最大值，则多出的位置补0。该值必须大于等于0。默认为0。
     - **name** （str，可选）- 具体用法请参见 :ref:`api_guide_Name` ，一般无需设置，默认值为None。
 
 返回：
diff --git a/docs/api/paddle/broadcast_tensors_cn.rst b/docs/api/paddle/broadcast_tensors_cn.rst
index 227b9528def..0110f268601 100644
--- a/docs/api/paddle/broadcast_tensors_cn.rst
+++ b/docs/api/paddle/broadcast_tensors_cn.rst
@@ -9,12 +9,11 @@ broadcast_tensors
 输入应符合Broadcast规范
 
 .. note::
-    如您想了解更多Broadcasting内容，请参见 :ref:`cn_user_guide_broadcasting` 。
+    如想了解更多Broadcasting内容，请参见 :ref:`cn_user_guide_broadcasting` 。
 
 参数
 :::::::::
-    - inputs (list(Tensor)|tuple(Tensor)) - 一组输入Tensor，数据类型为：bool、float32、float64、int32或int64。
-                                          - 所有的输入Tensor均需要满足rank <= 5
+    - inputs (list(Tensor)|tuple(Tensor)) - 一组输入Tensor，数据类型为：bool、float32、float64、int32或int64。所有的输入Tensor均需要满足rank <= 5。
     - name (str，可选) - 操作的名称(可选，默认值为None）。更多信息请参见 :ref:`api_guide_Name` 。
 
 返回
diff --git a/docs/api/paddle/deg2rad_cn.rst b/docs/api/paddle/deg2rad_cn.rst
new file mode 100644
index 00000000000..026fd311ad3
--- /dev/null
+++ b/docs/api/paddle/deg2rad_cn.rst
@@ -0,0 +1,44 @@
+.. _cn_api_paddle_tensor_deg2rad:
+
+deg2rad
+-------------------------------
+
+.. py:function:: paddle.deg2rad(x, name=None)
+
+将元素从弧度的角度转换为度
+
+.. math::
+
+    deg2rad(x)=\pi * x / 180
+
+参数
+:::::::::
+
+- **x**  (Tensor) - 输入的Tensor，数据类型为：int32、int64、float32、float64。
+- **name**  (str，可选） - 操作的名称(可选，默认值为None）。更多信息请参见 :ref:`api_guide_Name`。
+
+返回
+:::::::::
+
+输出Tensor，与 ``x`` 维度相同、数据类型相同（输入为int时，输出数据类型为float32）。
+
+代码示例
+:::::::::
+
+.. code-block:: python
+
+  import paddle
+  import numpy as np
+  
+  x1 = paddle.to_tensor([180.0, -180.0, 360.0, -360.0, 90.0, -90.0])
+  result1 = paddle.deg2rad(x1)
+  print(result1)
+  # Tensor(shape=[6], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+  #         [3.14159274, -3.14159274,  6.28318548, -6.28318548,  1.57079637,
+  #           -1.57079637])
+
+  x2 = paddle.to_tensor(180)
+  result2 = paddle.deg2rad(x2)
+  print(result2)
+  # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+  #         [3.14159274])
diff --git a/docs/api/paddle/diff_cn.rst b/docs/api/paddle/diff_cn.rst
new file mode 100644
index 00000000000..53bd5f30243
--- /dev/null
+++ b/docs/api/paddle/diff_cn.rst
@@ -0,0 +1,54 @@
+.. _cn_api_tensor_diff:
+
+diff
+-------------------------------
+
+.. py:function:: paddle.diff(x, n=1, axis=-1, prepend=None, append=None, name=None)
+
+沿着指定轴计算输入Tensor的n阶前向差值，一阶的前向差值计算公式如下：
+
+..  math::
+    out[i] = x[i+1] - x[i]
+
+.. note::
+    高阶的前向差值可以通过递归的方式进行计算，目前只支持 n=1。
+
+参数:
+:::::::::
+
+    - **x** (Tensor) - 待计算前向差值的输入 `Tensor`。
+    - **n** (int, 可选) - 需要计算前向差值的次数，目前仅支持 `n=1`，默认值为1。
+    - **axis** (int, 可选) - 沿着哪一维度计算前向差值，默认值为-1，也即最后一个维度。
+    - **prepend** (Tensor, 可选) - 在计算前向差值之前，沿着指定维度axis附加到输入x的前面，它的维度需要和输入一致，并且除了axis维外，其他维度的形状也要和输入一致，默认值为None。
+    - **append** (Tensor, 可选) - 在计算前向差值之前，沿着指定维度axis附加到输入x的后面，它的维度需要和输入一致，并且除了axis维外，其他维度的形状也要和输入一致，默认值为None。
+    - **name** （str，可选）- 具体用法请参见 :ref:`api_guide_Name` ，一般无需设置，默认值为None。
+
+返回
+:::::::::
+前向差值计算后的Tensor，数据类型和输入一致。
+
+代码示例:
+:::::::::
+
+.. code-block:: python
+
+    import paddle
+    x = paddle.to_tensor([1, 4, 5, 2])
+    out = paddle.diff(x)
+    print(out)
+    # out:
+    # [3, 1, -3]
+    y = paddle.to_tensor([7, 9])
+    out = paddle.diff(x, append=y)
+    print(out)
+    # out: 
+    # [3, 1, -3, 5, 2]
+    z = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
+    out = paddle.diff(z, axis=0)
+    print(out)
+    # out:
+    # [[3, 3, 3]]
+    out = paddle.diff(z, axis=1)
+    print(out)
+    # out:
+    # [[1, 1], [1, 1]]
\ No newline at end of file
diff --git a/docs/api/paddle/distributed/all_gather_cn.rst b/docs/api/paddle/distributed/all_gather_cn.rst
index 72652d926b6..6bb37eecc02 100644
--- a/docs/api/paddle/distributed/all_gather_cn.rst
+++ b/docs/api/paddle/distributed/all_gather_cn.rst
@@ -7,6 +7,13 @@ all_gather
 .. py:function:: paddle.distributed.all_gather(tensor_list, tensor, group=0)
 
 进程组内所有进程的指定tensor进行聚合操作，并返回给所有进程聚合的结果。
+如下图所示，4个GPU分别开启4个进程，每张卡上的数据用卡号代表，
+经过all_gather算子后，每张卡都会拥有所有卡的数据。
+
+.. image:: ./img/allgather.png
+  :width: 800
+  :alt: all_gather
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/all_reduce_cn.rst b/docs/api/paddle/distributed/all_reduce_cn.rst
index 41970e8a727..c0d99a6f303 100644
--- a/docs/api/paddle/distributed/all_reduce_cn.rst
+++ b/docs/api/paddle/distributed/all_reduce_cn.rst
@@ -7,6 +7,13 @@ all_reduce
 .. py:function:: paddle.distributed.all_reduce(tensor, op=ReduceOp.SUM, group=0)
 
 进程组内所有进程的指定tensor进行归约操作，并返回给所有进程归约的结果。
+如下图所示，4个GPU分别开启4个进程，每张卡上的数据用卡号代表，规约操作为求和，
+经过all_reduce算子后，每张卡都会拥有所有卡数据的总和。
+
+.. image:: ./img/allreduce.png
+  :width: 800
+  :alt: all_reduce
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/alltoall_cn.rst b/docs/api/paddle/distributed/alltoall_cn.rst
index a2f61aad465..abb0d90e089 100644
--- a/docs/api/paddle/distributed/alltoall_cn.rst
+++ b/docs/api/paddle/distributed/alltoall_cn.rst
@@ -6,8 +6,15 @@ alltoall
 
 .. py:function:: paddle.distributed.alltoall(in_tensor_list, out_tensor_list, group=None, use_calc_stream=True)
 
-将in_tensor_list里面的tensors分发到所有参与的卡并将结果tensors汇总到out_tensor_list。
-
+将in_tensor_list里面的tensors按照卡数均分并按照卡的顺序分发到所有参与的卡并将结果tensors汇总到out_tensor_list。
+如下图所示，GPU0卡的in_tensor_list会按照两张卡拆分成0_0和0_1， GPU1卡的in_tensor_list同样拆分成1_0和1_1，经过alltoall算子后,
+GPU0卡的0_0会发送给GPU0，GPU0卡的0_1会发送给GPU1，GPU1卡的1_0会发送给GPU0，GPU1卡的1_1会发送给GPU1，所以GPU0卡的out_tensor_list包含0_0和1_0， 
+GPU1卡的out_tensor_list包含0_1和1_1。
+
+.. image:: ./img/alltoall.png
+  :width: 800
+  :alt: alltoall
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/broadcast_cn.rst b/docs/api/paddle/distributed/broadcast_cn.rst
index 2a2133ab58d..e771ad1c0f5 100644
--- a/docs/api/paddle/distributed/broadcast_cn.rst
+++ b/docs/api/paddle/distributed/broadcast_cn.rst
@@ -6,7 +6,13 @@ broadcast
 
 .. py:function:: paddle.distributed.broadcast(tensor, src, group=0)
 
-广播一个Tensor给其他所有进程
+广播一个Tensor给其他所有进程。
+如下图所示，4个GPU分别开启4个进程，GPU0卡拥有数据，经过broadcast算子后，会将这个数据传播到所有卡上。
+
+.. image:: ./img/broadcast.png
+  :width: 800
+  :alt: broadcast
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/fleet/utils/recompute_cn.rst b/docs/api/paddle/distributed/fleet/utils/recompute_cn.rst
new file mode 100644
index 00000000000..6269e5810f0
--- /dev/null
+++ b/docs/api/paddle/distributed/fleet/utils/recompute_cn.rst
@@ -0,0 +1,24 @@
+.. _cn_api_distributed_fleet_utils_recompute:
+
+recompute
+-------------------------------
+
+
+.. py:function:: paddle.distributed.fleet.utils.recompute(function, *args, **kwargs)
+
+重新计算中间激活函数值来节省显存。
+
+参数
+:::::::::
+    - function (paddle.nn.Sequential) - 模型前向传播的部分连续的层函数组成的序列，
+      它们的中间激活函数值将在前向传播过程中被释放掉来节省显存，并且在反向梯度计算的时候会重新被计算。
+    - args (Tensor) - function的输入。
+    - kwargs (Dict) - kwargs只应该包含preserve_rng_state的键值对，用来表示是否保存前向的rng，如果为True，那么在反向传播的重计算前向时会还原上次前向的rng值。默认preserve_rng_state为True。
+
+返回
+:::::::::
+function作用在输入的输出
+
+代码示例
+:::::::::
+COPY-FROM: paddle.distributed.fleet.utils.recompute
\ No newline at end of file
diff --git a/docs/api/paddle/distributed/img/allgather.png b/docs/api/paddle/distributed/img/allgather.png
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index 00000000000..e07b215c2c2
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index 00000000000..cadd52bd531
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diff --git a/docs/api/paddle/distributed/img/split_col_row.png b/docs/api/paddle/distributed/img/split_col_row.png
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index 00000000000..40db444b9f0
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diff --git a/docs/api/paddle/distributed/img/split_embedding_single.png b/docs/api/paddle/distributed/img/split_embedding_single.png
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index 00000000000..c81e79ea948
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diff --git a/docs/api/paddle/distributed/img/split_embedding_split.png b/docs/api/paddle/distributed/img/split_embedding_split.png
new file mode 100644
index 00000000000..2d89043fb31
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diff --git a/docs/api/paddle/distributed/img/split_row.png b/docs/api/paddle/distributed/img/split_row.png
new file mode 100644
index 00000000000..253d99780c5
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diff --git a/docs/api/paddle/distributed/img/split_single.png b/docs/api/paddle/distributed/img/split_single.png
new file mode 100644
index 00000000000..ad8bef08bb1
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diff --git a/docs/api/paddle/distributed/reduce_cn.rst b/docs/api/paddle/distributed/reduce_cn.rst
index 0a46e5a83eb..03cad54c6cb 100644
--- a/docs/api/paddle/distributed/reduce_cn.rst
+++ b/docs/api/paddle/distributed/reduce_cn.rst
@@ -7,6 +7,13 @@ reduce
 .. py:function:: paddle.distributed.reduce(tensor, dst, op=ReduceOp.SUM, group=0)
 
 进程组内所有进程的指定tensor进行归约操作，并返回给所有进程归约的结果。
+如下图所示，4个GPU分别开启4个进程，每张卡上的数据用卡号代表，reduce的目标是第0张卡，
+规约操作是求和，经过reduce操作后，第0张卡会得到所有卡数据的总和。
+
+.. image:: ./img/reduce.png
+  :width: 800
+  :alt: reduce
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/scatter_cn.rst b/docs/api/paddle/distributed/scatter_cn.rst
index 023111e8e71..a41ba8e5078 100644
--- a/docs/api/paddle/distributed/scatter_cn.rst
+++ b/docs/api/paddle/distributed/scatter_cn.rst
@@ -7,6 +7,13 @@ scatter
 .. py:function:: paddle.distributed.scatter(tensor, tensor_list=None, src=0, group=0)
 
 进程组内指定进程源的tensor列表分发到其他所有进程中。
+如下图所示，4个GPU分别开启4个进程，scatter的源选择为第0张卡，
+经过scatter算子后，会将第0张卡的数据平均分到所有卡上。
+
+.. image:: ./img/scatter.png
+  :width: 800
+  :alt: scatter
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/split_cn.rst b/docs/api/paddle/distributed/split_cn.rst
index 3c47a808277..3dd7a78ff3b 100644
--- a/docs/api/paddle/distributed/split_cn.rst
+++ b/docs/api/paddle/distributed/split_cn.rst
@@ -13,13 +13,61 @@ split
 情形1：并行Embedding
     Embedding操作的参数是个NxM的矩阵，行数为N，列数为M。并行Embedding情形下，参数切分到num_partitions个设备，每个设备上的参数是 (N/num_partitions + 1)行、M列的矩阵。其中，最后一行作为padding idx。
 
-    假设将NxM的参数矩阵切分到两个设备device_0和device_1。那么每个设置上的参数矩阵为(N/2+1)行和M列。device_0上，输入x中的值如果介于[0, N/2-1]，则其值保持不变；否则值变更为N/2，经过embedding映射为全0值。类似地，device_1上，输入x中的值V如果介于[N/2, N-1]之间，那么这些值将变更为(V-N/2)；否则，值变更为N/2，经过embedding映射为全0值。最后，使用all_reduce_sum操作汇聚各个卡上的结果。
+    假设将NxM的参数矩阵切分到两个设备device_0和device_1。那么每个设备上的参数矩阵为(N/2+1)行和M列。device_0上，输入x中的值如果介于[0, N/2-1]，则其值保持不变；否则值变更为N/2，经过embedding映射为全0值。类似地，device_1上，输入x中的值V如果介于[N/2, N-1]之间，那么这些值将变更为(V-N/2)；否则，值变更为N/2，经过embedding映射为全0值。最后，使用all_reduce_sum操作汇聚各个卡上的结果。
+
+    单卡Embedding情况如下图所示
+
+    .. image:: ./img/split_embedding_single.png
+        :width: 800
+        :height: 350
+        :alt: single_embedding
+        :align: center
+    
+    并行Embedding情况如下图所示
+
+    .. image:: ./img/split_embedding_split.png
+        :width: 800
+        :alt: split_embedding
+        :align: center
 
 情形2：行并行Linear
-    Linear操作的参数是个NxM的矩阵，行数为N，列数为M。行并行Linear情形下，参数切分到num_partitions个设备，每个设备上的参数是N/num_partitions行、M列的矩阵。
+    Linear操作是将输入变量X(N*N)与权重矩阵W(N*M)进行矩阵相乘。行并行Linear情形下，参数切分到num_partitions个设备，每个设备上的参数是N/num_partitions行、M列的矩阵。
+
+    单卡Linear情况如下图所示，输入变量用X表示，权重矩阵用W表示，输出变量用O表示，单卡Linear就是一个简单的矩阵乘操作，O = X * W。
+
+
+    .. image:: ./img/split_single.png
+        :width: 800
+        :alt: single_linear
+        :align: center
+
+    行并行Linear情况如下图所示，顾名思义，行并行是按照权重矩阵W的行切分权重矩阵为
+    [[W_row1], [W_row2]]，对应的输入X也按照列切成了两份[X_col1, X_col2]，分别与各自对应的权重矩阵相乘，
+    最后通过AllReduce规约每张卡的输出得到最终输出。
+
+    .. image:: ./img/split_row.png
+        :width: 800
+        :alt: split_row
+        :align: center
 
 情形3：列并行Linear
-    Linear操作的参数是个NxM的矩阵，行数为N，列数为M。列并行Linear情形下，参数切分到num_partitions个设备，每个设备上的参数是N行、M/num_partitions列的矩阵。
+    Linear操作是将输入变量X(N*N)与权重矩阵W(N*M)进行矩阵相乘。列并行Linear情形下，参数切分到num_partitions个设备，每个设备上的参数是N行、M/num_partitions列的矩阵。
+
+    单卡并行Linear可以看上面对应的图，列并行Linear情况如下图所示。列并行是按照权重矩阵W的列切分权重矩阵为[W_col1, W_col2]，
+    X分别与切分出来的矩阵相乘，最后通过AllGather拼接每张卡的输出得到最终输出。
+
+    .. image:: ./img/split_col.png
+        :width: 800
+        :alt: split_col
+        :align: center
+
+我们观察到，可以把上述按列切分矩阵乘法和按行切分矩阵乘法串联起来，从而省略掉一次AllGather通信操作，如下图所示。同时，我们注意到Transformer的Attention和MLP组件中各种两次矩阵乘法操作。因此，我们可以按照这种串联方式分别把Attention和MLP组件中的两次矩阵乘法串联起来，从而进一步优化性能。
+
+.. image:: ./img/split_col_row.png
+        :width: 800
+        :alt: split_col_row
+        :align: center
+
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/utils/global_gather_cn.rst b/docs/api/paddle/distributed/utils/global_gather_cn.rst
index c3e4057183b..3d3e88a951b 100644
--- a/docs/api/paddle/distributed/utils/global_gather_cn.rst
+++ b/docs/api/paddle/distributed/utils/global_gather_cn.rst
@@ -6,9 +6,32 @@ global_gather
 
 .. py:function:: paddle.distributed.utils.global_gather(x, local_count, global_count, group=None, use_calc_stream=True)
 
-根据global_count将x的数据收集到n_expert * world_size个expert，然后根据local_count接收数据。
+global_gather根据global_count将x的数据收集到n_expert * world_size个expert，然后根据local_count接收数据。
 其中expert是用户定义的专家网络，n_expert是指每张卡拥有的专家网络数目，world_size是指运行网络的显卡数目。
 
+如下图所示，world_size是2，n_expert是2，x的batch_size是4，local_count是[2, 0, 2, 0]，0卡的global_count是[2, 0, , ], 
+1卡的global_count是[2, 0, ,](因为篇幅问题，这里只展示在0卡运算的数据)，在global_gather算子里，
+global_count和local_count的意义与其在global_scatter里正好相反，
+global_count[i]代表向第 (i // n_expert)张卡的第 (i % n_expert)个expert发送local_expert[i]个数据，
+local_count[i]代表从第 (i // n_expert)张卡接收global_count[i]个数据给本卡的 第(i % n_expert)个expert。
+发送的数据会按照每张卡的每个expert排列。图中的rank0代表第0张卡，rank1代表第1张卡。
+
+global_gather发送数据的流程如下：
+
+第0张卡的global_count[0]代表向第0张卡的第0个expert发送2个数据；
+
+第0张卡的global_count[1]代表向第0张卡的第1个expert发送0个数据；
+
+第1张卡的global_count[0]代表向第0张卡的第0个expert发送2个数据；
+
+第1张卡的global_count[1]代表向第0张卡的第1个expert发送0个数据。
+
+
+.. image:: ../img/global_scatter_gather.png
+  :width: 800
+  :alt: global_scatter_gather
+  :align: center
+
 
 参数
 :::::::::
diff --git a/docs/api/paddle/distributed/utils/global_scatter_cn.rst b/docs/api/paddle/distributed/utils/global_scatter_cn.rst
index 0af3e015e97..8139ad60c7f 100644
--- a/docs/api/paddle/distributed/utils/global_scatter_cn.rst
+++ b/docs/api/paddle/distributed/utils/global_scatter_cn.rst
@@ -6,9 +6,37 @@ global_scatter
 
 .. py:function:: paddle.distributed.utils.global_scatter(x, local_count, global_count, group=None, use_calc_stream=True)
 
-根据local_count将x的数据分发到n_expert * world_size个expert，然后根据global_count接收数据。
+global_scatter根据local_count将x的数据分发到n_expert * world_size个expert，然后根据global_count接收数据。
 其中expert是用户定义的专家网络，n_expert是指每张卡拥有的专家网络数目，world_size是指运行网络的显卡数目。
 
+如下图所示，world_size是2，n_expert是2，x的batch_size是4，local_count是[2, 0, 2, 0]，0卡的global_count是[2, 0, , ], 
+1卡的global_count是[2, 0, ,](因为篇幅问题，这里只展示在0卡运算的数据)，在global_scatter算子里，
+local_count[i]代表向第 (i // n_expert)张卡的第 (i % n_expert)个expert发送local_expert[i]个数据，
+global_count[i]代表从第 (i // n_expert)张卡接收global_count[i]个数据给本卡的 第(i % n_expert)个expert。
+图中的rank0代表第0张卡，rank1代表第1张卡。
+global_scatter发送数据的流程如下：
+
+local_count[0]代表从x里取出2个batch的数据向第0张卡的第0个expert发送2个数据；
+
+local_count[1]代表从x里取出0个batch的数据向第0张卡的第1个expert发送0个数据；
+
+local_count[2]代表从x里取出2个batch的数据向第1张卡的第0个expert发送2个数据；
+
+local_count[3]代表从x里取出0个batch的数据向第1张卡的第1个expert发送0个数据；
+
+所以第0张卡的global_count[0]等于2，代表从第0张卡接收2个batch的数据给第0个expert；
+
+第0张卡的global_count[1]等于0，代表从第0张卡接收0个batch的数据给第1个expert；
+
+第1张卡的global_count[0]等于2，代表从第0张卡接收2个batch的数据给第0个expert；
+
+第1张卡的global_count[1]等与0，代表从第0张卡接收0个batch的数据给第1个expert。
+
+
+.. image:: ../img/global_scatter_gather.png
+  :width: 800
+  :alt: global_scatter_gather
+  :align: center
 
 参数
 :::::::::
diff --git a/docs/api/paddle/incubate/graph_send_recv_cn.rst b/docs/api/paddle/incubate/graph_send_recv_cn.rst
new file mode 100644
index 00000000000..22940d94ddb
--- /dev/null
+++ b/docs/api/paddle/incubate/graph_send_recv_cn.rst
@@ -0,0 +1,53 @@
+.. _cn_api_incubate_graph_send_recv:
+
+graph_send_recv
+-------------------------------
+
+.. py:function:: paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum", name=None)
+
+此API主要应用于图学习领域，目的是为了减少在消息传递过程中带来的中间变量显存或内存的损耗。其中， ``x`` 作为输入Tensor，首先利用 ``src_index`` 作为索引来gather出在 ``x`` 中相应位置的数据，随后再将gather出的结果利用 ``dst_index`` 来更新到对应的输出结果中，其中 ``pool_type`` 表示不同的更新方式，包括sum、mean、max、min共计4种处理模式。
+
+.. code-block:: text
+
+        X = [[0, 2, 3],
+             [1, 4, 5],
+             [2, 6, 7]]
+
+        src_index = [0, 1, 2, 0]
+
+        dst_index = [1, 2, 1, 0]
+
+        pool_type = "sum"
+
+        Then:
+
+        Out = [[0, 2, 3],
+               [2, 8, 10],
+               [1, 4, 5]]
+
+参数
+:::::::::
+    - x (Tensor) - 输入的 Tensor，数据类型为：float32、float64、int32、int64。
+    - src_index (Tensor) - 1-D Tensor，数据类型为：int32、int64。
+    - dst_index (Tensor) - 1-D Tensor，数据类型为：int32、int64。注意： ``dst_index`` 的形状应当与 ``src_index`` 一致。
+    - pool_type (str) - scatter结果的不同处理方式，包括sum、mean、max、min。 默认值为 sum。
+    - name (str，可选) - 操作的名称(可选，默认值为None）。更多信息请参见 :ref:`api_guide_Name` 。
+
+返回
+:::::::::
+``Tensor`` ，维度和数据类型都与 ``x`` 相同，存储运算后的结果。
+
+
+代码示例
+::::::::::
+
+.. code-block:: python
+
+    import paddle
+
+    x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+    indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
+    src_index = indexes[:, 0]
+    dst_index = indexes[:, 1]
+    out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")
+    # Outputs: [[0., 2., 3.], [2., 8., 10.], [1., 4., 5.]]
diff --git a/docs/api/paddle/incubate/nn/FusedMultiHeadAttention_cn.rst b/docs/api/paddle/incubate/nn/FusedMultiHeadAttention_cn.rst
index 3b7f8d56769..f4b82e95403 100644
--- a/docs/api/paddle/incubate/nn/FusedMultiHeadAttention_cn.rst
+++ b/docs/api/paddle/incubate/nn/FusedMultiHeadAttention_cn.rst
@@ -3,7 +3,7 @@
 FusedMultiHeadAttention
 -------------------------------
 
-.. py:class:: paddle.incubate.nn.FusedMultiHeadAttention(embed_dim, num_heads, dropout_rate=0.5, attn_dropout_rate=0.5, kdim=None, vdim=None, normalize_before=False, need_weights=False, weight_attr=None, bias_attr=None, name=None)
+.. py:class:: paddle.incubate.nn.FusedMultiHeadAttention(embed_dim, num_heads, dropout_rate=0.5, attn_dropout_rate=0.5, kdim=None, vdim=None, normalize_before=False, need_weights=False, weight_attr=None, bias_attr=None, epsilon=1e-5, name=None)
 
 
 
@@ -31,6 +31,7 @@ FusedMultiHeadAttention
     - **need_weights** (bool, 可选) - 表明是否返回注意力权重。默认值： ``False`` 。
     - **weight_attr** (ParamAttr，可选) - 指定权重参数属性的对象。默认值： ``None`` ，表示使用默认的权重参数属性。具体用法请参见 :ref:`cn_api_fluid_ParamAttr` 。
     - **bias_attr** （ParamAttr，可选）- 指定偏置参数属性的对象。默认值： ``None`` ，表示使用默认的偏置参数属性。具体用法请参见 :ref:`cn_api_fluid_ParamAttr` 。
+    - **epsilon** (float, 可选) - 为了数值稳定加在分母上的值。默认值：1e-05。
     - **name** (str，可选） - 操作的名称。默认值为： ``None`` 。更多信息请参见 :ref:`api_guide_Name`。
 
 形状
diff --git a/docs/api/paddle/incubate/nn/functional/fused_feedforward_cn.rst b/docs/api/paddle/incubate/nn/functional/fused_feedforward_cn.rst
index 49fb0c34d58..56fbc9fad76 100644
--- a/docs/api/paddle/incubate/nn/functional/fused_feedforward_cn.rst
+++ b/docs/api/paddle/incubate/nn/functional/fused_feedforward_cn.rst
@@ -3,7 +3,7 @@
 fused_feedforward
 -------------------------------
 
-.. py:function:: paddle.incubate.nn.functional.fused_feedforward(x, linear1_weight, linear2_weight, linear1_bias=None, linear2_bias=None, ln1_scale=None, ln1_bias=None, ln2_scale=None, ln2_bias=None, dropout1_rate=0.5, dropout2_rate=0.5,activation="relu", ln1_epsilon=1e-5, ln2_epsilon=1e-5, pre_layer_norm=False, name=None):
+.. py:function:: paddle.incubate.nn.functional.fused_feedforward(x, linear1_weight, linear2_weight, linear1_bias=None, linear2_bias=None, ln1_scale=None, ln1_bias=None, ln2_scale=None, ln2_bias=None, dropout1_rate=0.5, dropout2_rate=0.5,activation="relu", ln1_epsilon=1e-5, ln2_epsilon=1e-5, pre_layer_norm=False, training=True, mode='upscale_in_train', name=None):
 
 这是一个融合算子，该算子是对transformer模型中feed forward层的多个算子进行融合，该算子只支持在GPU下运行，该算子与如下伪代码表达一样的功能：
 
@@ -12,9 +12,10 @@ fused_feedforward
     residual = src;
     if pre_layer_norm:
         src = layer_norm(src)
-    src = linear(dropout(activation(dropout(linear(src)))))
+    src = linear(dropout(activation(linear(src))))
+    src = residual + dropout(src)
     if not pre_layer_norm:
-        src = layer_norm(out)
+        src = layer_norm(src)
 
 参数
 :::::::::
@@ -24,15 +25,28 @@ fused_feedforward
     - **linear1_bias** (Tensor, 可选) - 第一个linear算子的偏置数据，数据类型与 ``x`` 一样，形状是 ``[dim_feedforward]`` 。默认值为None。
     - **linear2_bias** (Tensor, 可选) - 第二个linear算子的偏置数据，数据类型与 ``x`` 一样，形状是 ``[d_model]`` 。默认值为None。
     - **ln1_scale** (Tensor, 可选) - 第一个layer_norm算子的权重数据，数据类型可以是float32或者float64，形状和 ``x`` 一样。默认值为None。
-    - **ln1_bias** (Tensor, 可选) - 第一个layer_norm算子的偏置数据，数据类型和 ``ln1_scale`` 一样， 形状是 ``[d_model]`` 。默认值为None。
+    - **ln1_bias** (Tensor, 可选) - 第一个layer_norm算子的偏置数据，数据类型和 ``ln1_scale`` 一样， 形状是 ``x.shape[-1]`` 。默认值为None。
     - **ln2_scale** (Tensor, 可选) - 第二个layer_norm算子的权重数据，数据类型可以是float32或者float64，形状和  ``x`` 一样。默认值为None。
-    - **ln2_bias** (Tensor, 可选) - 第二个layer_norm算子的偏置数据，数据类型和 ``ln2_scale`` 一样， 形状是 ``[d_model]`` 。默认值为None。
+    - **ln2_bias** (Tensor, 可选) - 第二个layer_norm算子的偏置数据，数据类型和 ``ln2_scale`` 一样， 形状是 ``x.shape[-1]`` 。默认值为None。
     - **dropout1_rate** (float, 可选) - 第一个dropout算子置零的概率。默认是0.5。
     - **dropout2_rate** (float, 可选) - 第二个dropout算子置零的概率。默认是0.5。
-    - **activation** (string, 可选) - 激活函数。默认值是relu。
+    - **activation** (string, 可选) - 激活函数，当前只支持relu和gelu。默认值是relu。
     - **ln1_epsilon** (float, 可选) - 一个很小的浮点数，被第一个layer_norm算子加到分母，避免出现除零的情况。默认值是1e-5。
     - **ln2_epsilon** (float, 可选) - 一个很小的浮点数，被第二个layer_norm算子加到分母，避免出现除零的情况。默认值是1e-5。
     - **pre_layer_norm** (bool, 可选) - 在预处理阶段加上layer_norm，或者在后处理阶段加上layer_norm。默认值是False。
+    - **training** (bool): 标记是否为训练阶段。 默认: True。
+    - **mode** (str): 丢弃单元的方式，有两种'upscale_in_train'和'downscale_in_infer'，默认: 'upscale_in_train'。计算方法如下:
+
+        1. upscale_in_train, 在训练时增大输出结果。
+
+            - train: out = input * mask / ( 1.0 - p )
+            - inference: out = input
+
+        2. downscale_in_infer, 在预测时减小输出结果
+
+            - train: out = input * mask
+            - inference: out = input * (1.0 - p)
+
     - **name** (string, 可选) – fused_feedforward的名称, 默认值为None。更多信息请参见 :ref:`api_guide_Name` 。
 
 返回
diff --git a/docs/api/paddle/incubate/nn/functional/fused_multi_head_attention_cn.rst b/docs/api/paddle/incubate/nn/functional/fused_multi_head_attention_cn.rst
index bcc34ccf4ec..c6a2f941e7c 100644
--- a/docs/api/paddle/incubate/nn/functional/fused_multi_head_attention_cn.rst
+++ b/docs/api/paddle/incubate/nn/functional/fused_multi_head_attention_cn.rst
@@ -3,7 +3,7 @@
 fused_multi_head_attention
 -------------------------------
 
-.. py:function:: paddle.incubate.nn.functional.fused_multi_head_attention(x, qkv_weight, linear_weight, pre_layer_norm=False, pre_ln_scale=None, pre_ln_bias=None, ln_scale=None, ln_bias=None, pre_ln_epsilon=1e-05, qkv_bias=None, linear_bias=None, attn_mask=None, dropout_rate=0.5, attn_dropout_rate=0.5, ln_epsilon=1e-05, name=None)
+.. py:function:: paddle.incubate.nn.functional.fused_multi_head_attention(x, qkv_weight, linear_weight, pre_layer_norm=False, pre_ln_scale=None, pre_ln_bias=None, ln_scale=None, ln_bias=None, pre_ln_epsilon=1e-05, qkv_bias=None, linear_bias=None, attn_mask=None, dropout_rate=0.5, attn_dropout_rate=0.5, ln_epsilon=1e-05, traing=True, mode='upscale_in_train', name=None)
 
 **多头注意力机制**
 
@@ -33,7 +33,10 @@ fused_multi_head_attention 算子目前只支持在GPU下运行，其包含的
     out = out * v
     out = transpose(out, perm=[0, 2, 1, 3])
     out = out_linear(out)
-    out = layer_norm(x + dropout(linear_bias + out))
+    if pre_layer_norm:
+        out = x + dropout(linear_bias + out)
+    else:
+        out = layer_norm(x + dropout(linear_bias + out))
 
 
 值得注意的是，该API中，q, k, v 的 weight 被统一存储在一个权重张量中，形状为 `[3, num_heads, head_dim, embed_dim]` , 
@@ -57,6 +60,18 @@ fused_multi_head_attention 算子目前只支持在GPU下运行，其包含的
     - **dropout_rate** (float, 可选) - 代表 multi-head attention 之后的 dropout 算子的 dropout 比例，默认为0.5。
     - **attn_dropout_rate** (float, 可选) - 代表 multi-head attention 中的 dropout 算子的 dropout 比例，默认为0.5。
     - **ln_epsilon** (float, 可选) - 代表 normalize_before 为True 时，multi-head attention 中第二个 （False时的第一个） ``layer_norm`` 为了数值稳定加在分母上的值。默认值为 1e-05 。
+    - **training** (bool): 标记是否为训练阶段。 默认: True。
+    - **mode** (str): 丢弃单元的方式，有两种'upscale_in_train'和'downscale_in_infer'，默认: 'upscale_in_train'。计算方法如下:
+
+        1. upscale_in_train, 在训练时增大输出结果。
+
+            - train: out = input * mask / ( 1.0 - p )
+            - inference: out = input
+
+        2. downscale_in_infer, 在预测时减小输出结果
+
+            - train: out = input * mask
+            - inference: out = input * (1.0 - p)
     - **name** (str, 可选) - 操作的名称(可选，默认值为 ``None`` ）。更多信息请参见 :ref:`api_guide_Name`。
 
 返回
diff --git a/docs/api/paddle/isclose_cn.rst b/docs/api/paddle/isclose_cn.rst
new file mode 100644
index 00000000000..7dfd07189fd
--- /dev/null
+++ b/docs/api/paddle/isclose_cn.rst
@@ -0,0 +1,54 @@
+.. _cn_api_tensor_isclose:
+
+isclose
+-------------------------------
+
+.. py:function:: paddle.isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None)
+
+逐个检查x和y的所有元素是否均满足如下条件：
+
+..  math::
+    \left| x - y \right| \leq atol + rtol \times \left| y \right|
+
+该API的行为类似于 :math:`numpy.isclose` ，即逐个比较两个Tensor的所有元素是否在一定容忍误差范围内视为相等。
+
+参数
+:::::::::
+
+    - **x** (Tensor) - 输入的 `Tensor` ，数据类型为：float32、float64。
+    - **y** (Tensor) - 输入的 `Tensor` ，数据类型为：float32、float64。
+    - **rtol** (float，可选) - 相对容忍误差，默认值为1e-5。
+    - **atol** (float，可选) - 绝对容忍误差，默认值为1e-8。
+    - **equal_nan** (bool，可选) - 如果设置为True，则两个NaN数值将被视为相等，默认值为False。
+    - **name** （str，可选）- 具体用法请参见 :ref:`api_guide_Name` ，一般无需设置，默认值为None。
+
+返回
+:::::::::
+计算得到的布尔类型Tensor。
+
+代码示例
+:::::::::
+
+.. code-block:: python
+
+    import paddle
+    x = paddle.to_tensor([10000., 1e-07])
+    y = paddle.to_tensor([10000.1, 1e-08])
+    result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
+                            equal_nan=False, name="ignore_nan")
+    np_result1 = result1.numpy()
+    # [True, False]
+    result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
+                                equal_nan=True, name="equal_nan")
+    np_result2 = result2.numpy()
+    # [True, False]
+    x = paddle.to_tensor([1.0, float('nan')])
+    y = paddle.to_tensor([1.0, float('nan')])
+    result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
+                            equal_nan=False, name="ignore_nan")
+    np_result1 = result1.numpy()
+    # [True, False]
+    result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08,
+                                equal_nan=True, name="equal_nan")
+    np_result2 = result2.numpy()
+    # [True, True]
diff --git a/docs/api/paddle/linalg/Overview_cn.rst b/docs/api/paddle/linalg/Overview_cn.rst
index 689b32da750..9e66691e3c9 100644
--- a/docs/api/paddle/linalg/Overview_cn.rst
+++ b/docs/api/paddle/linalg/Overview_cn.rst
@@ -21,9 +21,9 @@ paddle.linalg 目录下包含飞桨框架支持的线性代数相关API。具体
     :widths: 10, 30
 
     " :ref:`paddle.linalg.det <cn_api_linalg_det>` ", "计算方阵的行列式"
-    " :ref:`paddle.linalg.slogdet <cn_api_tensor_norm>` ", "计算方阵行列式的符号、绝对值的自然对数"
+    " :ref:`paddle.linalg.slogdet <cn_api_linalg_slogdet>` ", "计算方阵行列式的符号、绝对值的自然对数"
     " :ref:`paddle.linalg.cond <cn_api_linalg_cond>` ", "根据矩阵的范数，来计算矩阵的条件数"
-    " :ref:`paddle.linalg.norm <cn_api_tensor_norm>` ", "计算矩阵范数或向量范数"
+    " :ref:`paddle.linalg.norm <cn_api_linalg_norm>` ", "计算矩阵范数或向量范数"
     " :ref:`paddle.linalg.matrix_rank <cn_api_linalg_matrix_rank>` ", "计算矩阵的秩"
 
     
@@ -36,9 +36,9 @@ paddle.linalg 目录下包含飞桨框架支持的线性代数相关API。具体
     :header: "API名称", "API功能"
     :widths: 10, 30
 
-    " :ref:`paddle.linalg.multi_dot <cn_api_tensor_multi_dot>` ", "2个或更多矩阵的乘法，会自动选择计算量最少的乘法顺序"
-    " :ref:`paddle.linalg.matrix_power <cn_api_tensor_matrix_power>` ", "计算方阵的n次幂"
-    " :ref:`paddle.linalg.inv <cn_api_tensor_inverse>` ", "计算方阵的逆矩阵"
+    " :ref:`paddle.linalg.multi_dot <cn_api_linalg_multi_dot>` ", "2个或更多矩阵的乘法，会自动选择计算量最少的乘法顺序"
+    " :ref:`paddle.linalg.matrix_power <cn_api_linalg_matrix_power>` ", "计算方阵的n次幂"
+    " :ref:`paddle.linalg.inv <cn_api_linalg_inv>` ", "计算方阵的逆矩阵"
     " :ref:`paddle.linalg.pinv <cn_api_linalg_pinv>` ", "计算矩阵的广义逆"
 
 
@@ -52,10 +52,10 @@ paddle.linalg 目录下包含飞桨框架支持的线性代数相关API。具体
     :widths: 10, 30
 
     " :ref:`paddle.linalg.eig <cn_api_linalg_eig>` ", "计算一般方阵的特征值与特征向量"
-    " :ref:`paddle.linalg.eigvals <cn_api_paddle_linalg_eigvals>` ", "计算一般方阵的特征值"
+    " :ref:`paddle.linalg.eigvals <cn_api_linalg_eigvals>` ", "计算一般方阵的特征值"
     " :ref:`paddle.linalg.eigh <cn_api_linalg_eigh>` ", "计算厄米特矩阵或者实数对称矩阵的特征值和特征向量"
     " :ref:`paddle.linalg.eigvalsh <cn_api_linalg_eigvalsh>` ", "计算厄米特矩阵或者实数对称矩阵的特征值"
-    " :ref:`paddle.linalg.cholesky <cn_api_tensor_cholesky>` ", "计算一个实数对称正定矩阵的Cholesky分解"
+    " :ref:`paddle.linalg.cholesky <cn_api_linalg_cholesky>` ", "计算一个实数对称正定矩阵的Cholesky分解"
     " :ref:`paddle.linalg.svd <cn_api_linalg_svd>` ", "计算矩阵的奇异值分解"
     " :ref:`paddle.linalg.qr <cn_api_linalg_qr>` ", "计算矩阵的正交三角分解（也称QR分解）"
 
@@ -70,3 +70,4 @@ paddle.linalg 目录下包含飞桨框架支持的线性代数相关API。具体
     :widths: 10, 30
 
     " :ref:`paddle.linalg.solve <cn_api_linalg_solve>` ", "计算具有唯一解的线性方程组"
+    " :ref:`paddle.linalg.triangular_solve <cn_api_linalg_solve>` ", "计算具有唯一解的线性方程组"
diff --git a/docs/api/paddle/cholesky_cn.rst b/docs/api/paddle/linalg/cholesky_cn.rst
similarity index 90%
rename from docs/api/paddle/cholesky_cn.rst
rename to docs/api/paddle/linalg/cholesky_cn.rst
index 4393a33d2f5..0aa081fd454 100644
--- a/docs/api/paddle/cholesky_cn.rst
+++ b/docs/api/paddle/linalg/cholesky_cn.rst
@@ -1,9 +1,9 @@
-.. _cn_api_tensor_cholesky:
+.. _cn_api_linalg_cholesky:
 
 cholesky
 -------------------------------
 
-.. py:function:: paddle.cholesky(x, upper=False, name=None)
+.. py:function:: paddle.linalg.cholesky(x, upper=False, name=None)
 
 
 
@@ -32,7 +32,7 @@ cholesky
     a_t = np.transpose(a, [1, 0])
     x_data = np.matmul(a, a_t) + 1e-03
     x = paddle.to_tensor(x_data)
-    out = paddle.cholesky(x, upper=False)
+    out = paddle.linalg.cholesky(x, upper=False)
     print(out)
     # [[1.190523   0.         0.        ]
     #  [0.9906703  0.27676893 0.        ]
diff --git a/docs/api/paddle/linalg/eigvals_cn.rst b/docs/api/paddle/linalg/eigvals_cn.rst
index 30b239f9d86..2ae5627b49a 100644
--- a/docs/api/paddle/linalg/eigvals_cn.rst
+++ b/docs/api/paddle/linalg/eigvals_cn.rst
@@ -1,4 +1,4 @@
-.. _cn_api_paddle_linalg_eigvals:
+.. _cn_api_linalg_eigvals:
 
 eigvals
 -------------------------------
diff --git a/docs/api/paddle/inverse_cn.rst b/docs/api/paddle/linalg/inv_cn.rst
similarity index 87%
rename from docs/api/paddle/inverse_cn.rst
rename to docs/api/paddle/linalg/inv_cn.rst
index 7b75e82e958..4c79875f8df 100644
--- a/docs/api/paddle/inverse_cn.rst
+++ b/docs/api/paddle/linalg/inv_cn.rst
@@ -1,9 +1,9 @@
-.. _cn_api_tensor_inverse:
+.. _cn_api_linalg_inv:
 
-inverse
+inv
 -------------------------------
 
-.. py:function:: paddle.inverse(x, name=None)
+.. py:function:: paddle.linalg.inv(x, name=None)
 
 
 计算方阵的逆。方阵是行数和列数相等的矩阵。输入可以是一个方阵（2-D张量），或者是批次方阵（维数大于2时）。
@@ -26,5 +26,5 @@ Tensor, 输入方阵的逆。
     import paddle
 
     mat = paddle.to_tensor([[2, 0], [0, 2]], dtype='float32')
-    inv = paddle.inverse(mat)
+    inv = paddle.linalg.inv(mat)
     print(inv) # [[0.5, 0], [0, 0.5]]
diff --git a/docs/api/paddle/matrix_power_cn.rst b/docs/api/paddle/linalg/matrix_power_cn.rst
similarity index 84%
rename from docs/api/paddle/matrix_power_cn.rst
rename to docs/api/paddle/linalg/matrix_power_cn.rst
index 210b41e61c9..17d306d83c5 100644
--- a/docs/api/paddle/matrix_power_cn.rst
+++ b/docs/api/paddle/linalg/matrix_power_cn.rst
@@ -1,9 +1,9 @@
-.. _cn_api_tensor_matrix_power:
+.. _cn_api_linalg_matrix_power:
 
 matrix_power
 -------------------------------
 
-.. py:function:: paddle.matrix_power(x, n, name=None)
+.. py:function:: paddle.linalg.matrix_power(x, n, name=None)
 
 
 计算一个或一批方阵的 ``n`` 次幂。
@@ -41,17 +41,17 @@ matrix_power
     x = paddle.to_tensor([[1, 2, 3],
                           [1, 4, 9],
                           [1, 8, 27]], dtype='float64')
-    print(paddle.matrix_power(x, 2))
+    print(paddle.linalg.matrix_power(x, 2))
     # [[6.  , 34. , 102.],
     #  [14. , 90. , 282.],
     #  [36. , 250., 804.]]
 
-    print(paddle.matrix_power(x, 0))
+    print(paddle.linalg.matrix_power(x, 0))
     # [[1., 0., 0.],
     #  [0., 1., 0.],
     #  [0., 0., 1.]]
 
-    print(paddle.matrix_power(x, -2))
+    print(paddle.linalg.matrix_power(x, -2))
     # [[ 12.91666667, -12.75000000,  2.83333333 ],
     #  [-7.66666667 ,  8.         , -1.83333333 ],
-    #  [ 1.80555556 , -1.91666667 ,  0.44444444 ]]
\ No newline at end of file
+    #  [ 1.80555556 , -1.91666667 ,  0.44444444 ]]
diff --git a/docs/api/paddle/multi_dot_cn.rst b/docs/api/paddle/linalg/multi_dot_cn.rst
similarity index 96%
rename from docs/api/paddle/multi_dot_cn.rst
rename to docs/api/paddle/linalg/multi_dot_cn.rst
index 8dc63f4a419..294d630c84f 100755
--- a/docs/api/paddle/multi_dot_cn.rst
+++ b/docs/api/paddle/linalg/multi_dot_cn.rst
@@ -1,9 +1,9 @@
-.. _cn_api_tensor_multi_dot:
+.. _cn_api_linalg_multi_dot:
 
 multi_dot
 -------------------------------
 
-.. py:function:: paddle.multi_dot(x, name=None)
+.. py:function:: paddle.linalg.multi_dot(x, name=None)
 
 Multi_dot是一个计算多个矩阵乘法的算子。
 
diff --git a/docs/api/paddle/norm_cn.rst b/docs/api/paddle/linalg/norm_cn.rst
similarity index 73%
rename from docs/api/paddle/norm_cn.rst
rename to docs/api/paddle/linalg/norm_cn.rst
index f932883654c..941375509f8 100644
--- a/docs/api/paddle/norm_cn.rst
+++ b/docs/api/paddle/linalg/norm_cn.rst
@@ -1,9 +1,9 @@
-.. _cn_api_tensor_norm:
+.. _cn_api_linalg_norm:
 
 norm
 -------------------------------
 
-.. py:function:: paddle.norm(x, p='fro', axis=None, keepdim=False, name=None):
+.. py:function:: paddle.linalg.norm(x, p='fro', axis=None, keepdim=False, name=None):
 
 
 
@@ -12,7 +12,7 @@ norm
 
 .. note::
 
-    此API与`numpy.linalg.norm`存在差异。此API支持高阶张量（rank>=3）作为输入，输入`axis`对应的轴就可以计算出norm的值。但是`numpy.linalg.norm`仅支持一维向量和二维矩阵作为输入。特别需要注意的是，此API的P阶矩阵范数，实际上将矩阵摊平成向量计算。实际计算的是向量范数，而不是真正的矩阵范数。
+    此API与 ``numpy.linalg.norm`` 存在差异。此API支持高阶张量（rank>=3）作为输入，输入 ``axis`` 对应的轴就可以计算出norm的值。但是 ``numpy.linalg.norm`` 仅支持一维向量和二维矩阵作为输入。特别需要注意的是，此API的P阶矩阵范数，实际上将矩阵摊平成向量计算。实际计算的是向量范数，而不是真正的矩阵范数。
 
 参数
 :::::::::
@@ -43,27 +43,27 @@ norm
     # [[  0.   1.   2.   3.] [  4.   5.   6.   7.] [  8.   9.  10.  11.]]]
 
     # compute frobenius norm along last two dimensions.
-    out_fro = paddle.norm(x, p='fro', axis=[0,1])
+    out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])
     # out_fro.numpy() [17.435596 16.911535 16.7332   16.911535]
 
     # compute 2-order vector norm along last dimension.
-    out_pnorm = paddle.norm(x, p=2, axis=-1)
+    out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)
     #out_pnorm.numpy(): [[21.118711  13.190906   5.477226]
     #                    [ 3.7416575 11.224972  19.131126]]
 
     # compute 2-order  norm along [0,1] dimension.
-    out_pnorm = paddle.norm(x, p=2, axis=[0,1])
+    out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])
     #out_pnorm.numpy(): [17.435596 16.911535 16.7332   16.911535]
 
     # compute inf-order  norm
-    out_pnorm = paddle.norm(x, p=np.inf)
+    out_pnorm = paddle.linalg.norm(x, p=np.inf)
     #out_pnorm.numpy()  = [12.]
-    out_pnorm = paddle.norm(x, p=np.inf, axis=0)
+    out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)
     #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]
 
     # compute -inf-order  norm
-    out_pnorm = paddle.norm(x, p=-np.inf)
+    out_pnorm = paddle.linalg.norm(x, p=-np.inf)
     #out_pnorm.numpy(): [0.]
-    out_pnorm = paddle.norm(x, p=-np.inf, axis=0)
+    out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)
     #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]
  
diff --git a/docs/api/paddle/linalg/triangular_solve_cn.rst b/docs/api/paddle/linalg/triangular_solve_cn.rst
new file mode 100644
index 00000000000..4330a30eeb3
--- /dev/null
+++ b/docs/api/paddle/linalg/triangular_solve_cn.rst
@@ -0,0 +1,61 @@
+.. _cn_api_linalg_triangular_solve:
+
+triangular_solve
+-------------------------------
+
+.. py:function:: paddle.linalg.triangular_solve(x, y, upper=True, transpose=False, unitriangular=False, name=None)
+
+
+计算具有唯一解的线性方程组解，其中系数矩阵 `x` 是上(下)三角系数矩阵， `y` 是方程右边。
+
+记 :math:`X` 为一个或一批方阵，:math:`Y` 一个或一批矩阵。
+
+则方程组为：
+
+.. math::
+    X * Out = Y
+
+方程组的解为：
+
+.. math::
+    Out = X ^ {-1} * Y
+
+特别地，
+
+- 如果 ``x`` 不可逆 ， 则线性方程组不可解。
+
+参数
+:::::::::
+    - **x** (Tensor) : 线性方程组左边的系数方阵，其为一个或一批方阵。``x`` 的形状应为 ``[*, M, M]``，其中 ``*`` 为零或更大的批次维度，数据类型为float32， float64。
+    - **y** (Tensor) : 线性方程组右边的矩阵，其为一个或一批矩阵。``y`` 的形状应为 ``[*, M, K]``， 其中 ``*`` 为零或更大的批次维度，数据类型为float32， float64。
+    - **upper** (bool, 可选) - 对系数矩阵 ``x`` 取上三角还是下三角。默认为True，表示取上三角。
+    - **transpose** (bool, 可选) - 是否对系数矩阵 ``x`` 进行转置。默认为False，不进行转置。
+    - **unitriangular** (bool, 可选) - 如果为True，则将系数矩阵 ``x`` 对角线元素假设为1来求解方程。默认为False。
+    - **name** (str，可选) - 具体用法请参见 :ref:`api_guide_Name` ，一般无需设置，默认值为None。
+
+返回：
+:::::::::
+    - Tensor， 线程方程组的解， 数据类型和 ``x`` 一致。
+
+代码示例
+::::::::::
+
+.. code-block:: python
+
+    # a square system of linear equations:
+    # x1 +   x2  +   x3 = 0
+    #      2*x2  +   x3 = -9
+    #               -x3 = 5
+
+    import paddle
+    import numpy as np
+
+    x = paddle.to_tensor([[1, 1, 1], 
+                          [0, 2, 1],
+                          [0, 0,-1]], dtype="float64")
+    y = paddle.to_tensor([[0], [-9], [5]], dtype="float64")
+    out = paddle.linalg.triangular_solve(x, y, upper=True)
+
+    print(out)
+    # [7, -2, -5]
+
diff --git a/docs/api/paddle/nn/ELU_cn.rst b/docs/api/paddle/nn/ELU_cn.rst
index 06875ad3ad2..cf4e5e23422 100644
--- a/docs/api/paddle/nn/ELU_cn.rst
+++ b/docs/api/paddle/nn/ELU_cn.rst
@@ -10,7 +10,13 @@ ELU激活层（ELU Activation Operator）
 
 .. math::
 
-    ELU(x) = max(0, x) + min(0, \alpha * (e^{x} − 1))
+    ELU(x)=
+        \left\{
+            \begin{array}{lcl}
+            x,& &\text{if } \ x > 0 \\
+            alpha * (e^{x} - 1),& &\text{if } \ x <= 0
+            \end{array}
+        \right.
 
 其中，:math:`x` 为输入的 Tensor
 
diff --git a/docs/api/paddle/nn/Overview_cn.rst b/docs/api/paddle/nn/Overview_cn.rst
index c56093e1579..8f1d385f6be 100644
--- a/docs/api/paddle/nn/Overview_cn.rst
+++ b/docs/api/paddle/nn/Overview_cn.rst
@@ -102,6 +102,7 @@ Padding层
     " :ref:`paddle.nn.Pad1D <cn_api_nn_Pad1D>` ", "一维填充层"
     " :ref:`paddle.nn.Pad2D <cn_api_nn_Pad2D>` ", "二维填充层"
     " :ref:`paddle.nn.Pad3D <cn_api_nn_Pad3D>` ", "三维填充层"
+    " :ref:`paddle.nn.ZeroPad2D <cn_api_nn_ZeroPad2D>` ", "二维零填充层"
     
 .. _activation_layers:
 
@@ -344,6 +345,7 @@ Padding相关函数
     
 
     " :ref:`paddle.nn.functional.pad <cn_api_nn_cn_pad>` ", "依照 pad 和 mode 属性对input进行填充"
+    " :ref:`paddle.nn.functional.zeropad2d <cn_api_nn_functional_zeropad2d>` ", "依照 pad 对x进行零填充"
     
 .. _activation_functional:
 
diff --git a/docs/api/paddle/nn/ZeroPad2D_cn.rst b/docs/api/paddle/nn/ZeroPad2D_cn.rst
new file mode 100644
index 00000000000..27bc7339472
--- /dev/null
+++ b/docs/api/paddle/nn/ZeroPad2D_cn.rst
@@ -0,0 +1,50 @@
+.. _cn_api_nn_ZeroPad2D:
+
+ZeroPad2D
+-------------------------------
+.. py:class:: paddle.nn.ZeroPad2D(padding, data_format="NCHW", name=None)
+
+**ZeroPad2D**
+
+按照 padding 属性对输入进行零填充。
+
+参数
+:::::::::
+
+  - **padding** (Tensor | List[int] | int]) - 填充大小。如果是int，则在所有待填充边界使用相同的填充，
+    否则填充的格式为[pad_left, pad_right, pad_top, pad_bottom]。
+  - **data_format** (str)  - 指定输入的format，可为 ``'NCHW'`` 或者 ``'NHWC'``，默认值为 ``'NCHW'``。
+  - **name** (str, 可选) - 该参数供开发人员打印调试信息时使用，具体用法请参见 :ref:`api_guide_Name` ，缺省值为None。
+
+返回：无
+
+形状
+:::::::::
+
+  - x(Tensor): ZeroPadD层的输入，要求形状为4-D，dtype为 ``'float32'`` 或 ``'float64'``
+  - output(Tensor): 输出，形状为4-D，dtype与 ``'input'`` 相同
+
+代码示例
+:::::::::
+
+.. code-block:: python
+
+    import paddle
+    import paddle.nn as nn
+    import numpy as np
+
+    input_shape = (1, 1, 2, 3)
+    pad = [1, 0, 1, 2]
+    data = paddle.arange(np.prod(input_shape), dtype="float32").reshape(input_shape) + 1
+
+    my_pad = nn.ZeroPad2D(padding=pad)
+    result = my_pad(data)
+
+    print(result)
+    # [[[[0. 0. 0. 0.]
+    #    [0. 1. 2. 3.]
+    #    [0. 4. 5. 6.]
+    #    [0. 0. 0. 0.]
+    #    [0. 0. 0. 0.]]]]
+
+
diff --git a/docs/api/paddle/nn/functional/elu_cn.rst b/docs/api/paddle/nn/functional/elu_cn.rst
index 6a1bb9fe0d1..4fac0845e59 100644
--- a/docs/api/paddle/nn/functional/elu_cn.rst
+++ b/docs/api/paddle/nn/functional/elu_cn.rst
@@ -11,7 +11,13 @@ elu激活层（ELU Activation Operator）
 
 .. math::
 
-    elu(x) = max(0, x) + min(0, \alpha * (e^{x} − 1))
+    elu(x)=
+        \left\{
+            \begin{array}{lcl}
+            x,& &\text{if } \ x > 0 \\
+            alpha * (e^{x} - 1),& &\text{if } \ x <= 0
+            \end{array}
+        \right.
 
 其中，:math:`x` 为输入的 Tensor
 
diff --git a/docs/api/paddle/nn/functional/sparse_attention_cn.rst b/docs/api/paddle/nn/functional/sparse_attention_cn.rst
index ac95e6e6a3d..06e73583f75 100755
--- a/docs/api/paddle/nn/functional/sparse_attention_cn.rst
+++ b/docs/api/paddle/nn/functional/sparse_attention_cn.rst
@@ -14,6 +14,9 @@ sparse_attention
 
 其中，``Q``，``K``，``V`` 表示注意力模块的三个输入参数。这三个参数的维度是一样的。 ``d`` 代表这三个参数的最后一个维度的大小。
 
+.. warning::
+    目前该API只在CUDA11.3及以上版本中使用。
+
 参数：
 :::::::::
   - query (Tensor) - 输入的Tensor，代表注意力模块中的 ``query`` ，这是一个4维Tensor，形状为 :[batch_size, num_heads, seq_len, head_dim]，数据类型为float32或float64。
diff --git a/docs/api/paddle/nn/functional/zeropad2d_cn.rst b/docs/api/paddle/nn/functional/zeropad2d_cn.rst
new file mode 100644
index 00000000000..81336d91a78
--- /dev/null
+++ b/docs/api/paddle/nn/functional/zeropad2d_cn.rst
@@ -0,0 +1,37 @@
+.. _cn_api_nn_functional_zeropad2d:
+
+zeropad2d
+-------------------------------
+.. py:function:: paddle.nn.functional.zeropad2d(x, padding, data_format="NCHW", name=None)
+
+该OP返回一个按照 ``padding`` 属性对 ``x`` 进行零填充的Tensor，数据类型与 ``x`` 相同。
+
+参数
+::::::::::
+    - **x** (Tensor) - Tensor，format可以为 ``'NCHW'``, ``'NHWC'`` ，默认值为 ``'NCHW'``，数据类型支持float16, float32, float64, int32, int64。
+    - **padding** (Tensor | List[int] | Tuple[int]) - 填充大小。pad的格式为[pad_left, pad_right, pad_top, pad_bottom]；
+    - **data_format** (str)  - 指定 ``x`` 的format，可为 ``'NCHW'``, ``'NHWC'``, 默认值为 ``'NCHW'``。
+    - **name** (str, 可选) - 该参数供开发人员打印调试信息时使用，具体用法请参见 :ref:`api_guide_Name` ，缺省值为None。
+
+返回
+::::::::::
+    Tensor：对 ``x`` 进行 ``'pad'`` 的结果，数据类型和 ``x`` 相同。
+
+代码示例
+::::::::::
+
+.. code-block:: python
+
+    import paddle
+    import numpy as np
+
+    x_shape = (1, 1, 2, 3)
+    x = paddle.arange(np.prod(x_shape), dtype="float32").reshape(x_shape) + 1
+    y = paddle.nn.functional.zeropad2d(x, [1, 2, 1, 1])
+
+    # [[[[0. 0. 0. 0. 0. 0.]
+    #    [0. 1. 2. 3. 0. 0.]
+    #    [0. 4. 5. 6. 0. 0.]
+    #    [0. 0. 0. 0. 0. 0.]]]]
+
+
diff --git a/docs/api/paddle/rad2deg_cn.rst b/docs/api/paddle/rad2deg_cn.rst
new file mode 100644
index 00000000000..3baa5f80a3d
--- /dev/null
+++ b/docs/api/paddle/rad2deg_cn.rst
@@ -0,0 +1,50 @@
+.. _cn_api_paddle_tensor_rad2deg:
+
+rad2deg
+-------------------------------
+
+.. py:function:: paddle.rad2deg(x, name=None)
+
+将元素从弧度的角度转换为度
+
+.. math::
+
+    rad2deg(x)=180/ \pi * x
+
+参数
+:::::::::
+
+- **x**  (Tensor) - 输入的Tensor，数据类型为：int32、int64、float32、float64。
+- **name**  (str，可选） - 操作的名称(可选，默认值为None）。更多信息请参见 :ref:`api_guide_Name`。
+
+返回
+:::::::::
+
+输出Tensor，与 ``x`` 维度相同、数据类型相同（输入为int时，输出数据类型为float32）。
+
+代码示例
+:::::::::
+
+.. code-block:: python
+
+  import paddle
+  import numpy as np
+  
+  x1 = paddle.to_tensor([3.142, -3.142, 6.283, -6.283, 1.570, -1.570])
+  result1 = paddle.rad2deg(x1)
+  print(result1)
+  # Tensor(shape=[6], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+  #         [180.02334595, -180.02334595,  359.98937988, -359.98937988,
+  #           9.95437622 , -89.95437622])
+
+  x2 = paddle.to_tensor(np.pi/2)
+  result2 = paddle.rad2deg(x2)
+  print(result2)
+  # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+  #         [90.])
+           
+  x3 = paddle.to_tensor(1)
+  result3 = paddle.rad2deg(x3)
+  print(result3)
+  # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+  #         [57.29578018])
diff --git a/docs/api/paddle/shard_index_cn.rst b/docs/api/paddle/shard_index_cn.rst
index bdee00e8b48..292d5a4f940 100644
--- a/docs/api/paddle/shard_index_cn.rst
+++ b/docs/api/paddle/shard_index_cn.rst
@@ -5,25 +5,26 @@ shard_index
 
 .. py:function:: paddle.shard_index(input, index_num, nshards, shard_id, ignore_value=-1)
 
-
-该函数根据分片（shard）的偏移量重新计算分片的索引。索引长度被均分为N个分片，如果输入索引所在的分片跟分片ID对应，则该索引以分片的偏移量为界重新计算，否则更新为默认值（ignore_value）。具体计算为：
+根据当前shard重新设置输入参数\ `input`\ 的值。输入\ `input`\ 中的值需要为非负整型；参数\ `index_num`\ 为用户设置的大于\ `input`\ 最大值的整型值。因此，\ `input`\ 中的值属于区间[0, index_num)，且每个值可以被看作到区间起始的偏移量。区间可以被进一步划分为多个切片。具体地讲，我们首先根据下面的公式计算每个切片的大小：\ `shard_size`\ ，表示每个切片可以表示的整数的数量。因此，对于第\ `i`\ 个切片，其表示的区间为[i*shard_size, (i+1)*shard_size)。
 
 ::
 
     shard_size = (index_num + nshards - 1) // nshards
-    如果 shard_id == input // shard_size 则 output = input % shard_size  
-    否则 output = ignore_value
 	
-注意：若索引长度不能被分片数整除，则最后一个分片长度不足shard_size。
+对于输入\ `input`\ 中的每个值\ `v`\ ，我们根据下面的公式设置它新的值：
+
+::
+
+    v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id+1) * shard_size else ignore_value
 
 参数：
-    - input (Tensor）-  输入的索引，最后一维的维度值为1，数据类型为int64。
-    - index_num (int) - 定义索引长度的整型值。
+    - input (Tensor）-  输入tensor，最后一维的维度值为1，数据类型为int64或int32。
+    - index_num (int) - 用户设置的大于\ `input`\ 最大值的整型值。
     - nshards (int) - 分片数量。
     - shard_id (int) - 当前分片ID。
-    - ignore_value (int) - 超出分片索引范围的默认值。
+    - ignore_value (int) - 超出分片范围的默认值。
 
-返回：更新后的索引值Tensor
+返回：Tensor
 
 **代码示例：**
 
diff --git a/docs/api/paddle/vision/Overview_cn.rst b/docs/api/paddle/vision/Overview_cn.rst
index 5ccb752ac70..9c6520610bb 100644
--- a/docs/api/paddle/vision/Overview_cn.rst
+++ b/docs/api/paddle/vision/Overview_cn.rst
@@ -49,6 +49,8 @@ paddle.vision 目录是飞桨在视觉领域的高层API。具体如下：
     " :ref:`resnet50 <cn_api_paddle_vision_models_resnet50>` ", "50层的ResNet模型"
     " :ref:`resnet101 <cn_api_paddle_vision_models_resnet101>` ", "101层的ResNet模型"
     " :ref:`resnet152 <cn_api_paddle_vision_models_resnet152>` ", "152层的ResNet模型"
+    " :ref:`wide_resnet50_2 <_cn_api_paddle_vision_models_wide_resnet50_2>` ", "50层的WideResNet模型"
+    " :ref:`wide_resnet101_2 <_cn_api_paddle_vision_models_wide_resnet101_2>` ", "101层的WideResNet模型"
     " :ref:`ResNeXt <cn_api_paddle_vision_models_ResNeXt>` ", "ResNeXt模型"
     " :ref:`resnext50_32x4d <cn_api_paddle_vision_models_resnext50_32x4d>` ", "ResNeXt-50 32x4d模型"
     " :ref:`resnext50_64x4d <cn_api_paddle_vision_models_resnext50_64x4d>` ", "ResNeXt-50 64x4d模型"
diff --git a/docs/api/paddle/vision/models/ResNet_cn.rst b/docs/api/paddle/vision/models/ResNet_cn.rst
index 2370f6cb6a1..f3d4e9ca2e9 100644
--- a/docs/api/paddle/vision/models/ResNet_cn.rst
+++ b/docs/api/paddle/vision/models/ResNet_cn.rst
@@ -3,14 +3,15 @@
 ResNet
 -------------------------------
 
-.. py:class:: paddle.vision.models.ResNet(Block, depth=50, num_classes=1000, with_pool=True)
+.. py:class:: paddle.vision.models.ResNet(Block, depth=50, width=64, num_classes=1000, with_pool=True)
 
  ResNet模型，来自论文 `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ 。
 
 参数
 :::::::::
   - **Block** (BasicBlock|BottleneckBlock) - 模型的残差模块。
-  - **depth** (int，可选) - resnet模型的深度。默认值：50
+  - **depth** (int，可选) - resnet模型的深度。默认值：50。
+  - **width** (int，可选) - resnet模型的基础宽度。默认值：64。
   - **num_classes** (int, 可选) - 最后一个全连接层输出的维度。如果该值小于0，则不定义最后一个全连接层。默认值：1000。
   - **with_pool** (bool，可选) - 是否定义最后一个全连接层之前的池化层。默认值：True。
 
@@ -28,6 +29,8 @@ ResNet模型，Layer的实例。
 
     resnet50 = ResNet(BottleneckBlock, 50)
 
+    wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2)
+
     resnet18 = ResNet(BasicBlock, 18)
 
     x = paddle.rand([1, 3, 224, 224])
diff --git a/docs/api/paddle/vision/models/wide_resnet101_2_cn.rst b/docs/api/paddle/vision/models/wide_resnet101_2_cn.rst
new file mode 100644
index 00000000000..113db2965b4
--- /dev/null
+++ b/docs/api/paddle/vision/models/wide_resnet101_2_cn.rst
@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_wide_resnet101_2:
+
+wide_resnet101_2
+-------------------------------
+
+.. py:function:: paddle.vision.models.wide_resnet101_2(pretrained=False, **kwargs)
+
+ 101层的wide_resnet模型，来自论文 `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ 。
+
+参数
+:::::::::
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回
+:::::::::
+wide_resnet101_2模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import wide_resnet101_2
+
+    # build model
+    model = wide_resnet101_2()
+
+    # build model and load imagenet pretrained weight
+    # model = wide_resnet101_2(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/docs/api/paddle/vision/models/wide_resnet50_2_cn.rst b/docs/api/paddle/vision/models/wide_resnet50_2_cn.rst
new file mode 100644
index 00000000000..a775521412f
--- /dev/null
+++ b/docs/api/paddle/vision/models/wide_resnet50_2_cn.rst
@@ -0,0 +1,34 @@
+.. _cn_api_paddle_vision_models_wide_resnet50_2:
+
+wide_resnet50_2
+-------------------------------
+
+.. py:function:: paddle.vision.models.wide_resnet50_2(pretrained=False, **kwargs)
+
+ 50层的wide_resnet模型，来自论文 `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_ 。
+
+参数
+:::::::::
+  - **pretrained** (bool，可选) - 是否加载在imagenet数据集上的预训练权重。默认值：False。
+
+返回
+:::::::::
+wide_resnet50_2模型，Layer的实例。
+
+代码示例
+:::::::::
+.. code-block:: python
+
+    import paddle
+    from paddle.vision.models import wide_resnet50_2
+
+    # build model
+    model = wide_resnet50_2()
+
+    # build model and load imagenet pretrained weight
+    # model = wide_resnet50_2(pretrained=True)
+
+    x = paddle.rand([1, 3, 224, 224])
+    out = model(x)
+
+    print(out.shape)
diff --git a/docs/faq/2.0.md b/docs/faq/2.0.md
index ae327504dc8..8fcb03da613 100644
--- a/docs/faq/2.0.md
+++ b/docs/faq/2.0.md
@@ -48,7 +48,7 @@
 
 查看API变动的两种方法：
 
-1. 依据1.8版本API到2.0版本API的对应关系表对API进行升级，请参考文档 [飞桨框架API映射表](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/09_others_information/api_mapping_cn.html)
+1. 依据1.8版本API到2.0版本API的对应关系表对API进行升级，请参考文档 [飞桨框架API映射表](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_api_mapping/paddle_api_mapping_cn.html)
 
 2. 飞桨提供了迁移工具，来方便用户将旧版本的代码迁移为2.0.1版本的代码，详情请见：[版本迁移工具](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/migration_cn.html)
 
@@ -68,6 +68,18 @@
 
 ----------
 
+##### 问题：为什么 paddle2.0 以后的版本要废弃 LoDTensor ？
+
+- 答复：在 2.0 之前的版本的 paddle 中，向用户暴露了以下的数据表示的概念：
+  - [Tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.8/beginners_guide/basic_concept/tensor.html)： 类似于 numpy ndarray 的多维数组。
+  - [Variable](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.8/beginners_guide/basic_concept/variable.html)：可以简单理解为，在构建静态的计算图时的数据节点。
+  - [LodTensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.8/beginners_guide/basic_concept/lod_tensor.html)：用来表示嵌套的、每条数据长度不一的一组数据。（例：一个batch中包含了长度为3，10，7，50的四个句子）
+
+这三类不同类型的概念的同时存在，让使用 paddle 的开发者容易感到混淆，需要构建 LoDTensor 类型的数据的情况在具体的实践中，通常也可以使用 padding/bucketing 的最佳实践来达到同样的目的，因此 paddle 2.0 版本起，我们把这些概念统一为 [Tensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.html) 的概念。在 paddle 2.0 版本起，对于每条数据长度不一的一组数据的处理，您可以参看这篇 Tutorial： [使用注意力机制的LSTM的机器翻译](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/nlp/seq2seq_with_attention.html)。
+
+----------
+
+
 ##### 问题：1.8开发的静态图代码能在2.0版本中运行吗 ？
 
 + 答复：
diff --git a/docs/faq/params_cn.md b/docs/faq/params_cn.md
index ba23ffd3283..4124db0fce8 100644
--- a/docs/faq/params_cn.md
+++ b/docs/faq/params_cn.md
@@ -80,3 +80,22 @@ sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), gr
 sdg.step()                                      # 更新参数前，会先对参数的梯度进行裁剪
 ```
 [了解更多梯度裁剪知识](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.html)
+
+
+----------
+
+##### 问题：如何在同一个优化器中定义不同参数的优化策略，比如bias的参数weight_decay的值为0.0，非bias的参数weight_decay的值为0.01？
+
++ 答复：
+  1. [AdamW](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/AdamW_cn.html#adamw)的参数`apply_decay_param_fun`可以用来选择哪些参数使用decay_weight策略。  
+  2. 在创建`Param`的时候，可以通过设置[ParamAttr](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/ParamAttr_cn.html#paramattr)的属性来控制参数的属性。  
+
+----------
+
+##### 问题：paddle fluid如何自定义优化器，自定义更新模型参数的规则？
+ + 答复：
+   1. 要定义全新优化器，自定义优化器中参数的更新规则，可以通过继承fluid.Optimizer，重写_append_optimize_op方法实现。不同优化器实现原理各不相同，一般流程是先获取learning_rate，gradients参数，可训练参数，以及该优化器自身特别需要的参数，然后实现更新参数的代码，最后返回更新后的参数。  
+    在实现更新参数代码时，可以选择直接调用[paddle的API](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html)或者使用[自定义原生算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/index_cn.html)。在使用自定义原生算子时，要注意动态图与静态图调用方式有所区别：  
+    需要首先使用`framework.in_dygraph_mode()`判断是否为动态图模式，如果是动态图模式，则需要调用`paddle._C_ops`中相应的优化器算子；如果不是动态图模式，则需要调用`block.append_op` 来添加优化器算子。  
+    代码样例可参考[paddle源码](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/optimizer.py)中AdamOptimizer等优化器的实现。  
+    2. 使用现有的常用优化器，可以在创建`Param`的时候，可以通过设置[ParamAttr](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/ParamAttr_cn.html#paramattr)的属性来控制参数的属性，可以通过设置`regularizer`，`learning_rate`等参数简单设置参数的更新规则。  
diff --git a/docs/faq/save_cn.md b/docs/faq/save_cn.md
index f54c7cb67f8..3c6bd623856 100644
--- a/docs/faq/save_cn.md
+++ b/docs/faq/save_cn.md
@@ -84,6 +84,46 @@ adam.set_state_dict(opti_state_dict)
     2. 如果被保存的对象包含``numpy.ndarray``，尽量在``load``时设置``return_numpy = True``。
     3. 对于``Layer``对象，只保存参数的值和名字，如果需要其他信息（例如``stop_gradient``），请将手将这些信息打包成`dict`等，一并保存。
 
+##### 问题：paddle 2.x 如何保存模型文件？如何保存paddle 1.x 中的 model 文件?
++ 答复：
+
+    1. 在paddle2.x可使用``paddle.jit.save``接口以及``paddle.static.save_inference_model``,通过指定``path``来保存成为``path.pdmodel``和``path.pdiparams``,可对应paddle1.x中使用``save_inference_model``指定dirname和params_filename生成``dirname/__model__``和``dirname/params文件``。paddle2.x保存模型文件详情可参考:
+    - [paddle.jit.save/load](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/08_model_save_load_cn.html#dongtaitumoxing-canshubaocunzairu-xunliantuili)
+    - [paddle.static.save/load_inference_model](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/08_model_save_load_cn.html#jingtaitumoxing-canshubaocunzairu-tuilibushu)
+    2. 如果想要在paddle2.x中读取paddle 1.x中的model文件，可参考:
+    - [兼容载入旧格式模型](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.2rc/guides/01_paddle2.0_introduction/load_old_format_model.html#cn-guides-load-old-format-model)
+
+
+##### 问题：paddle如何单独load存下来所有模型变量中某一个变量，然后修改变量中的值？
++ 答复：
+
+    1. 如果目的是修改存储变量的值，可以使用``paddle.save``保存下来所有变量，然后再使用``paddle.load``将所有变量载入后，查找目标变量进行修改，示例代码如下：
+
+```python
+import paddle
+
+layer = paddle.nn.Linear(3, 4)
+path = 'example/model.pdparams'
+paddle.save(layer.state_dict(), path)
+layer_param = paddle.load(path)
+# 修改fc_0.b_0的值
+layer_param["fc_0.b_0"] = 10
+```
+
+    2. 如果目的是单独访问某个变量，需要单独存储然后再单独读取，示例代码如下：
+
+```python
+import paddle
+
+layer = paddle.nn.Linear(3, 4)
+path_w = 'example/weight.tensor'
+path_b = 'example/bias.tensor'
+paddle.save(layer.weight, path_w)
+paddle.save(layer.bias, path_b)
+tensor_bias = paddle.load(path_b)
+tensor_bias[0] = 10
+```
+
 更多介绍请参考以下API文档：
 
 - [paddle.save](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/framework/io/save_cn.html)
diff --git a/docs/faq/train_cn.md b/docs/faq/train_cn.md
index 2e9d715623f..b7a7deef3ed 100644
--- a/docs/faq/train_cn.md
+++ b/docs/faq/train_cn.md
@@ -12,6 +12,47 @@
 
 ----------
 
+##### 问题：请问`paddle.gather`和`torch.gather`有什么区别？
+
++ 答复：[`paddle.gather`](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/gather_cn.html#gather)和`torch.gather`的函数签名分别为：
+
+```python
+paddle.gather(x, index, axis=None, name=None)
+torch.gather(input, dim, index, *, sparse_grad=False, out=None)
+```
+
+其中，`paddle.gather`的参数`x`，`index`，`axis`分别与`torch.gather`的参数`input`，`index`，`dim`意义相同。
+
+两者在输入形状、输出形状、计算公式等方面都有区别，具体如下：
+
+- `paddle.gather`
+
+  - 输入形状：`x`可以是任意的`N`维Tensor。但`index`必须是形状为`[M]`的一维Tensor，或形状为`[M, 1]`的二维Tensor。
+
+  - 输出形状：输出Tensor `out`的形状`shape_out`和`x`的形状`shape_x`的关系为：`shape_out[i] = shape_x[i] if i != axis else M`。
+
+  - 计算公式：`out[i_1][i_2]...[i_axis]...[i_N] = x[i_1][i_2]...[index[i_axis]]...[i_N]` 。
+
+  - 举例说明：假设`x`的形状为`[N1, N2, N3]`，`index`的形状为`[M]`，`axis`的值为1，那么输出`out`的形状为`[N1, M, N3]`，且`out[i_1][i_2][i_3] = x[i_1][index[i_2]][i_3]`。
+
+- `torch.gather`
+
+  - 输入形状：`input`可以是任意的`N`维Tensor，且`index.rank`必须等于`input.rank`。
+
+  - 输出形状：输出Tensor `out`的形状与`index`相同。
+
+  - 计算公式：`out[i_1][i_2]...[i_dim]...[i_N] = input[i_1][i_2]...[index[i_1][i_2]...[i_N]]...[i_N]`。
+
+  - 举例说明：假设`x`的形状为`[N1, N2, N3]`，`index`的形状为`[M1, M2, M3]`，`dim`的值为1，那么输出`out`的形状为`[M1, M2, M3]`，且`out[i_1][i_2][i_3] = input[i_1][index[i_1][i_2][i_3]][i_3]`。
+
+- 异同比较
+
+  - 只有当`x.rank == 1`且`index.rank == 1`时，`paddle.gather`和`torch.gather`功能相同。其余情况两者无法直接互换使用。
+
+  - `paddle.gather`不支持`torch.gather`的`sparse_grad`参数。
+
+----------
+
 ##### 问题：在模型组网时，inplace参数的设置会影响梯度回传吗？经过不带参数的op之后，梯度是否会保留下来？
 
 + 答复：inplace 参数不会影响梯度回传。只要用户没有手动设置`stop_gradient=True`，梯度都会保留下来。
@@ -220,5 +261,37 @@ out = masked_fill(x, mask, 2)
 #        [[2.        , 2.        , 0.96637046],
 #         [2.        , 2.        , 2.        ],
 #         [2.        , 2.        , 2.        ]])
+```
+
+----------
+
+##### 问题：在paddle中如何实现`torch.nn.utils.rnn.pack_padded_sequence`和`torch.nn.utils.rnn.pad_packed_sequence`这两个API？
+
++ 答复：目前paddle中没有和上述两个API完全对应的实现。关于torch中这两个API的详细介绍可以参考知乎上的文章 [pack_padded_sequence 和 pad_packed_sequence](https://zhuanlan.zhihu.com/p/342685890) :
+`pack_padded_sequence`的功能是将mini-batch数据进行压缩，压缩掉无效的填充值，然后输入RNN网络中；`pad_packed_sequence`则是把RNN网络输出的压紧的序列再填充回来，便于进行后续的处理。
+在paddle中，大家可以在GRU、LSTM等RNN网络中输入含有填充值的mini-batch数据的同时传入对应的`sequence_length`参数实现上述等价功能，具体用法可以参考 [RNN](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/RNN_cn.html#rnn) 。
+
+----------
+
+##### 问题：paddle是否有爱因斯坦求和（einsum）这个api？
+
++ 答复：paddle在2.2rc 版本之后，新增了[paddle.einsum](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/einsum_cn.html#einsum)，在 develop 和2.2rc 之后的版本中都可以正常使用。
+
+----------
 
+
+----------
+
+##### 问题：[BatchNorm](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/BatchNorm_cn.html#batchnorm)在训练时加载预测时保存的模型参数时报错 AssertionError: Optimizer set error, batch_norm_1.w_0_moment_0 should in state dict.
+
++ 答复：BatchNorm在train模式和eval模式下需要的变量有差别，在train模式下要求传入优化器相关的变量，在eval模式下不管是保存参数还是加载参数都是不需要优化器相关变量的，因此如果在train模式下加载eval模式下保存的checkpoint，没有优化器相关的变量则会报错。如果想在train模式下加载eval模式下保存的checkpoint的话，用 ```paddle.load``` 加载进来参数之后，通过 ```set_state_dict``` 接口把参数赋值给模型，参考以下示例：
+
+```python
+import paddle
+
+bn = paddle.nn.BatchNorm(3)
+bn_param = paddle.load('./bn.pdparams')
+bn.set_state_dict()
 ```
+
+----------
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.ipynb b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.ipynb
new file mode 100644
index 00000000000..e5a5b2106b8
--- /dev/null
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.ipynb
@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# 自动混合精度训练\n",
+    "\n",
+    "一般情况下，训练深度学习模型时使用的数据类型为单精度（FP32）。2018年，百度与NVIDIA联合发表论文：[MIXED PRECISION TRAINING](https://arxiv.org/pdf/1710.03740.pdf)，提出了混合精度训练的方法。混合精度训练是指在训练过程中，同时使用单精度（FP32）和半精度（FP16），其目的是相较于使用单精度（FP32）训练模型，在保持精度持平的条件下，能够加速训练。本文将介绍如何使用飞桨框架，实现自动混合精度训练。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 一、半精度浮点类型 FP16\n",
+    "\n",
+    "首先介绍半精度（FP16）。如图1所示，半精度（FP16）是一种相对较新的浮点类型，在计算机中使用2字节（16位）存储。在IEEE 754-2008标准中，它亦被称作binary16。与计算中常用的单精度（FP32）和双精度（FP64）类型相比，FP16更适于在精度要求不高的场景中使用。\n",
+    "\n",
+    "<figure align=\"center\">\n",
+    "    <img src=\"https://paddleweb-static.bj.bcebos.com/images/fp16.png\" width=\"600\" alt='missing'/>\n",
+    "    <figcaption><center>图 1. 半精度和单精度数据示意图</center></figcaption>\n",
+    "</figure>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 二、NVIDIA GPU的FP16算力\n",
+    "在使用相同的超参数下，混合精度训练使用半精度浮点（FP16）和单精度（FP32）浮点即可达到与使用纯单精度训练相同的准确率，并可加速模型的训练速度。这主要得益于英伟达推出的Volta及Turing架构GPU在使用FP16计算时具有如下特点：\n",
+    "- FP16可降低一半的内存带宽和存储需求，这使得在相同的硬件条件下研究人员可使用更大更复杂的模型以及更大的batch size大小。\n",
+    "- FP16可以充分利用英伟达Volta及Turing架构GPU提供的Tensor Cores技术。在相同的GPU硬件上，Tensor Cores的FP16计算吞吐量是FP32的8倍。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 三、使用飞桨框架实现自动混合精度\n",
+    "使用飞桨框架提供的API，``paddle.amp.auto_cast`` 和 ``paddle.amp.decorate`` 和 ``paddle.amp.GradScaler`` 能够实现自动混合精度训练（Automatic Mixed Precision，AMP），即在相关OP的计算中，根据一定的规则，自动选择FP16或FP32计算。飞桨的AMP为用户提供了两种模式：\n",
+    "- level=’O1‘：采用黑名名单策略的混合精度训练，使用FP16与FP32进行计算的OP列表可见该[文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/amp/Overview_cn.html)。\n",
+    "- level=’O2‘：纯FP16训练，除用户自定义黑名单中指定的OP和不支持FP16计算的OP之外，全部使用FP16计算。\n",
+    "\n",
+    "下面来看一个具体的例子，来了解如果使用飞桨框架实现混合精度训练。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.1 辅助函数\n",
+    "首先定义辅助函数，用来计算训练时间。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "# 开始时间\n",
+    "start_time = None\n",
+    "\n",
+    "def start_timer():\n",
+    "    # 获取开始时间\n",
+    "    global start_time\n",
+    "    start_time = time.time()\n",
+    "\n",
+    "def end_timer_and_print(msg):\n",
+    "    # 打印信息并输出训练时间\n",
+    "    end_time = time.time()\n",
+    "    print(\"\\n\" + msg)\n",
+    "    print(\"共计耗时 = {:.3f} sec\".format(end_time - start_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.2 构建一个简单的网络\n",
+    "\n",
+    "构建一个简单的网络，用于对比使用普通方法进行训练与使用混合精度训练的训练速度。该网络由三层 ``Linear`` 组成，其中前两层 ``Linear`` 后接 ``ReLU`` 激活函数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import paddle\n",
+    "import paddle.nn as nn\n",
+    "\n",
+    "class SimpleNet(nn.Layer):\n",
+    "\n",
+    "    def __init__(self, input_size, output_size):\n",
+    "        \n",
+    "        super(SimpleNet, self).__init__()\n",
+    "        self.linear1 = nn.Linear(input_size, output_size)\n",
+    "        self.relu1 = nn.ReLU()\n",
+    "        self.linear2 = nn.Linear(input_size, output_size)\n",
+    "        self.relu2 = nn.ReLU()\n",
+    "        self.linear3 = nn.Linear(input_size, output_size)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "\n",
+    "        x = self.linear1(x)\n",
+    "        x = self.relu1(x)\n",
+    "        x = self.linear2(x)\n",
+    "        x = self.relu2(x)\n",
+    "        x = self.linear3(x)\n",
+    "\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "设置训练的相关参数，这里为了能有效的看出混合精度训练对于训练速度的提升，将 ``input_size`` 与 ``output_size`` 的值设为较大的值，为了使用GPU 提供的``Tensor Core`` 性能，还需将 ``batch_size`` 设置为 8 的倍数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W1110 18:42:02.362493   104 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
+      "W1110 18:42:02.367755   104 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 5\n",
+    "input_size = 4096   # 设为较大的值\n",
+    "output_size = 4096  # 设为较大的值\n",
+    "batch_size = 512    # batch_size 为8的倍数\n",
+    "nums_batch = 50\n",
+    "\n",
+    "train_data = [paddle.randn((batch_size, input_size)) for _ in range(nums_batch)]\n",
+    "labels = [paddle.randn((batch_size, output_size)) for _ in range(nums_batch)]\n",
+    "\n",
+    "mse = paddle.nn.MSELoss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.3 使用默认的训练方式进行训练"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.24519622])\n",
+      "\n",
+      "默认耗时:\n",
+      "共计耗时 = 2.926 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # 定义模型\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # 定义优化器\n",
+    "\n",
+    "start_timer() # 获取训练开始时间\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        output = model(data)\n",
+    "        loss = mse(output, label)\n",
+    "\n",
+    "        # 反向传播\n",
+    "        loss.backward()\n",
+    "\n",
+    "        # 训练模型\n",
+    "        optimizer.step()\n",
+    "        optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"默认耗时:\") # 获取结束时间并打印相关信息"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.4 使用AMP训练模型\n",
+    "\n",
+    "在飞桨框架中，使用自动混合精度训练，需要进行四个步骤：\n",
+    "\n",
+    "- Step1： 定义 ``GradScaler`` ，用于缩放 ``loss`` 比例，避免浮点数下溢\n",
+    "- Step2： 使用 ``decorate`` 在level=’O1‘模式下不做任何处理，无需调用该api，在level=’O2‘模式下，将网络参数从FP32转换为FP16\n",
+    "- Step3： 使用 ``auto_cast`` 用于创建AMP上下文环境，该上下文中自动会确定每个OP的输入数据类型（FP16或FP32）\n",
+    "- Step4： 使用 Step1中定义的 ``GradScaler`` 完成 ``loss`` 的缩放，用缩放后的 ``loss`` 进行反向传播，完成训练\n",
+    "\n",
+    "\n",
+    "采用level=’O1‘模式训练："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.24815702])\n",
+      "\n",
+      "使用AMP-O1模式耗时:\n",
+      "共计耗时 = 1.294 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # 定义模型\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # 定义优化器\n",
+    "\n",
+    "# Step1：定义 GradScaler，用于缩放loss比例，避免浮点数溢出\n",
+    "scaler = paddle.amp.GradScaler(init_loss_scaling=1024)\n",
+    "\n",
+    "start_timer() # 获取训练开始时间\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        # Step2：创建AMP上下文环境，开启自动混合精度训练\n",
+    "        with paddle.amp.auto_cast():\n",
+    "            output = model(data)\n",
+    "            loss = mse(output, label)\n",
+    "\n",
+    "        # Step3：使用 Step1中定义的 GradScaler 完成 loss 的缩放，用缩放后的 loss 进行反向传播\n",
+    "        scaled = scaler.scale(loss)\n",
+    "        scaled.backward()\n",
+    "\n",
+    "        # 训练模型\n",
+    "        scaler.minimize(optimizer, scaled)\n",
+    "        optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"使用AMP-O1模式耗时:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "采用level=’O2‘模式训练："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.25423336])\n",
+      "\n",
+      "使用AMP-O2模式耗时:\n",
+      "共计耗时 = 0.890 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # 定义模型\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # 定义优化器\n",
+    "\n",
+    "# Step1：定义 GradScaler，用于缩放loss比例，避免浮点数溢出\n",
+    "scaler = paddle.amp.GradScaler(init_loss_scaling=1024)\n",
+    "\n",
+    "# Step2：在level=’O2‘模式下，将网络参数从FP32转换为FP16\n",
+    "model, optimizer = paddle.amp.decorate(models=model, optimizers=optimizer, level='O2', master_weight=None, save_dtype=None)\n",
+    "\n",
+    "start_timer() # 获取训练开始时间\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        # Step3：创建AMP上下文环境，开启自动混合精度训练\n",
+    "        with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):\n",
+    "            output = model(data)\n",
+    "            loss = mse(output, label)\n",
+    "\n",
+    "        # Step4：使用 Step1中定义的 GradScaler 完成 loss 的缩放，用缩放后的 loss 进行反向传播\n",
+    "        scaled = scaler.scale(loss)\n",
+    "        scaled.backward()\n",
+    "\n",
+    "        # 训练模型\n",
+    "        scaler.minimize(optimizer, scaled)\n",
+    "        optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"使用AMP-O2模式耗时:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 四、进阶用法\n",
+    "### 4.1 使用梯度累加\n",
+    "梯度累加是指在模型训练过程中，训练一个batch的数据得到梯度后，不立即用该梯度更新模型参数，而是继续下一个batch数据的训练，得到梯度后继续循环，多次循环后梯度不断累加，直至达到一定次数后，用累加的梯度更新参数，这样可以起到变相扩大 batch_size 的作用。\n",
+    "\n",
+    "在自动混合精度训练中，也支持梯度累加，使用方式如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.25602019])\n",
+      "\n",
+      "使用AMP模式耗时:\n",
+      "共计耗时 = 1.026 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # 定义模型\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # 定义优化器\n",
+    "\n",
+    "accumulate_batchs_num = 10 # 梯度累加中 batch 的数量\n",
+    "\n",
+    "# 定义 GradScaler\n",
+    "scaler = paddle.amp.GradScaler(init_loss_scaling=1024)\n",
+    "\n",
+    "start_timer() # 获取训练开始时间\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        # 创建AMP上下文环境，开启自动混合精度训练\n",
+    "        with paddle.amp.auto_cast():\n",
+    "            output = model(data)\n",
+    "            loss = mse(output, label)\n",
+    "\n",
+    "        # 使用 GradScaler 完成 loss 的缩放，用缩放后的 loss 进行反向传播\n",
+    "        scaled = scaler.scale(loss)\n",
+    "        scaled.backward()\n",
+    "\n",
+    "        # 当累计的 batch 为 accumulate_batchs_num 时，更新模型参数\n",
+    "        if (i + 1) % accumulate_batchs_num == 0:\n",
+    "\n",
+    "            # 训练模型\n",
+    "            scaler.minimize(optimizer, scaled)\n",
+    "            optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"使用AMP模式耗时:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 五、总结\n",
+    "从上面的示例中可以看出，使用自动混合精度训练，O1模式共计耗时约 1.294s，O2模式共计耗时约 0.890s，而普通的训练方式则耗时 2.926s，O1模式训练速度提升约为 2.1倍，O2模式训练速度提升约为 3.0倍。如需更多使用混合精度训练的示例，请参考飞桨模型库： [paddlepaddle/models](https://github.com/PaddlePaddle/models)。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "py35-paddle1.2.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.md b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.md
index bc96b6736a4..646e01ecd37 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.md
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_cn.md
@@ -1,6 +1,6 @@
 # 自动混合精度训练
 
-一般情况下，训练深度学习模型时使用的数据类型为单精度（FP32）。2018年，百度与NVIDIA联合发表论文：[MIXED PRECISION TRAINING](https://arxiv.org/pdf/1710.03740.pdf)，提出了混合精度训练的方法。混合精度训练是指在训练过程中，同时使用单精度（FP32）和半精度（FP16），其目的是相较于使用单精度（FP32）训练模型，在保持精度持平的条件下，能够加速训练。本文将介绍如何使用飞桨框架，实现自动混合精度训练。
+一般情况下，训练深度学习模型时使用的数据类型为单精度（FP32）。2018年，百度与NVIDIA联合发表论文：[MIXED PRECISION TRAINING](https://arxiv.org/pdf/1710.03740.pdf)，提出了混合精度训练的方法。混合精度训练是指在训练过程中，同时使用单精度（FP32）和半精度（FP16），其目的是相较于使用单精度（FP32）训练模型，在保持精度持平的条件下，能够加速训练。本文将介绍如何使用飞桨框架，实现自动混合精度训练。  
 
 ## 一、半精度浮点类型 FP16
 
@@ -57,6 +57,7 @@ import paddle.nn as nn
 class SimpleNet(nn.Layer):
 
     def __init__(self, input_size, output_size):
+        
         super(SimpleNet, self).__init__()
         self.linear1 = nn.Linear(input_size, output_size)
         self.relu1 = nn.ReLU()
@@ -91,6 +92,10 @@ labels = [paddle.randn((batch_size, output_size)) for _ in range(nums_batch)]
 mse = paddle.nn.MSELoss()
 ```
 
+    W1110 18:42:02.362493   104 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
+    W1110 18:42:02.367755   104 device_context.cc:465] device: 0, cuDNN Version: 7.6.
+
+
 ### 3.3 使用默认的训练方式进行训练
 
 
@@ -120,10 +125,10 @@ end_timer_and_print("默认耗时:") # 获取结束时间并打印相关信息
 ```
 
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.24609220])
-
+           [1.24519622])
+    
     默认耗时:
-    共计耗时 = 2.819 sec
+    共计耗时 = 2.926 sec
 
 
 ### 3.4 使用AMP训练模型
@@ -138,6 +143,7 @@ end_timer_and_print("默认耗时:") # 获取结束时间并打印相关信息
 
 采用level=’O1‘模式训练：
 
+
 ```python
 model = SimpleNet(input_size, output_size)  # 定义模型
 
@@ -170,14 +176,15 @@ end_timer_and_print("使用AMP-O1模式耗时:")
 ```
 
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.24609900])
-
+           [1.24815702])
+    
     使用AMP-O1模式耗时:
-    共计耗时 = 1.324 sec
+    共计耗时 = 1.294 sec
 
 
 采用level=’O2‘模式训练：
 
+
 ```python
 model = SimpleNet(input_size, output_size)  # 定义模型
 
@@ -212,11 +219,17 @@ print(loss)
 end_timer_and_print("使用AMP-O2模式耗时:")
 ```
 
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.24997652])
-
+           [1.25423336])
+    
     使用AMP-O2模式耗时:
-    共计耗时 = 0.933 sec
+    共计耗时 = 0.890 sec
 
 
 ## 四、进阶用法
@@ -263,10 +276,11 @@ end_timer_and_print("使用AMP模式耗时:")
 ```
 
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.24623466])
-
+           [1.25602019])
+    
     使用AMP模式耗时:
-    共计耗时 = 1.020 sec
+    共计耗时 = 1.026 sec
+
 
 ## 五、总结
-从上面的示例中可以看出，使用自动混合精度训练，O1模式共计耗时约 1.324s，O2模式共计耗时约 0.933s，而普通的训练方式则耗时 2.819s，O1模式训练速度提升约为 2.1倍，O2模式训练速度提升约为 3.0倍。如需更多使用混合精度训练的示例，请参考飞桨模型库： [paddlepaddle/models](https://github.com/PaddlePaddle/models)。
+从上面的示例中可以看出，使用自动混合精度训练，O1模式共计耗时约 1.294s，O2模式共计耗时约 0.890s，而普通的训练方式则耗时 2.926s，O1模式训练速度提升约为 2.1倍，O2模式训练速度提升约为 3.0倍。如需更多使用混合精度训练的示例，请参考飞桨模型库： [paddlepaddle/models](https://github.com/PaddlePaddle/models)。
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.ipynb b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.ipynb
new file mode 100644
index 00000000000..22c12fcfed1
--- /dev/null
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.ipynb
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Automatic Mixed Precision Training\n",
+    "\n",
+    "In general, the datatype of training deep learning models is single-precision floating-point format(also called FP32). In 2018, Baidu and NVIDIA jointly published the paper: [MIXED PRECISION TRAINING](https://arxiv.org/pdf/1710.03740.pdf), which proposed mixed precision training. During the process of training, some operators use FP32 and other operators use half precision(also called FP16) in the same time. Its purpose is to speed up training, while compared with the FP32 training model, the same accuracy is maintained. This tutorial will introduce how to use automatic mixed precision training with PaddlePaddle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 1. Half Precision (FP16)\n",
+    "\n",
+    "First introduce FP16. As shown in Figure 1, FP16 occupies 16 bits (two bytes in modern computers) of computer memory. In the IEEE 754-2008 standard, it is also named binary16. Compared with FP32 and double precision (also called FP64) commonly used, FP16 is more suitable for the usage in scenarios with low precision requirements.\n",
+    "\n",
+    "<figure align=\"center\">\n",
+    "    <img src=\"https://paddleweb-static.bj.bcebos.com/images/fp16.png\" width=\"600\" alt='missing'/>\n",
+    "    <figcaption><center>Figure 1. Half precision(FP16) and single precision(FP32)</center></figcaption>\n",
+    "</figure>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 2. FP16 Computing Power of NVIDIA GPU\n",
+    "\n",
+    "When the same hyperparameters are used, mixed precision training using FP16 and FP32 can achieve the same accuracy as that of pure single precision used, and can accelerate the training speed. It mainly attributes to the features that NVIDIA Volta and NVIDIA Turing use FP16 to calculate:\n",
+    "- FP16 can reduce memory bandwidth and storage requirements by half, which allows researchers to use more complex models and larger batch sizes under the same hardware conditions.\n",
+    "- FP16 can make full use of Tensor Cores technology provided by NVIDIA Volta and NVIDIA Turing. On the same GPU hardware, the computing throughput of Tensor Cores' FP16 is 8 times bigger than that of FP32."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 3. Automatic Mixed Precision Training with PaddlePaddle\n",
+    "\n",
+    "Using PaddlePaddle's API ``paddle.amp.auto_cast`` and ``paddle.amp.GradScaler`` can realize automatic mixed precision training (AMP), which can automatically choose FP16 or FP32 for different operators' calculation. After the AMP mode is turned on, the operator list calculated by FP16 and FP32 can be found in this [document](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/amp/Overview_cn.html). This is a specific example to understand how to use PaddlePaddle to achieve mixed precision training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.1 Auxiliary Function\n",
+    "First define the auxiliary function to calculate the training time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "# start time\n",
+    "start_time = None\n",
+    "\n",
+    "def start_timer():\n",
+    "    # get start time\n",
+    "    global start_time\n",
+    "    start_time = time.time()\n",
+    "\n",
+    "def end_timer_and_print(msg):\n",
+    "    # print message and total training time\n",
+    "    end_time = time.time()\n",
+    "    print(\"\\n\" + msg)\n",
+    "    print(\"total time = {:.3f} sec\".format(end_time - start_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.2 A Simple Network\n",
+    "\n",
+    "Define a simple network to compare the training speed of common methods and mixed precision. The network is composed of three layers of ``Linear``. The first two layers of ``Linear`` are followed by the ``ReLU`` activation function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import paddle\n",
+    "import paddle.nn as nn\n",
+    "\n",
+    "class SimpleNet(nn.Layer):\n",
+    "\n",
+    "    def __init__(self, input_size, output_size):\n",
+    "        \n",
+    "        super(SimpleNet, self).__init__()\n",
+    "        self.linear1 = nn.Linear(input_size, output_size)\n",
+    "        self.relu1 = nn.ReLU()\n",
+    "        self.linear2 = nn.Linear(input_size, output_size)\n",
+    "        self.relu2 = nn.ReLU()\n",
+    "        self.linear3 = nn.Linear(input_size, output_size)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "\n",
+    "        x = self.linear1(x)\n",
+    "        x = self.relu1(x)\n",
+    "        x = self.linear2(x)\n",
+    "        x = self.relu2(x)\n",
+    "        x = self.linear3(x)\n",
+    "\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Set the parameters of training. In order to effectively show the improvement of training speed by mixed precision training, please set the larger values of ``input_size`` and ``output_size``. And in order to use the ``Tensor Core`` provided by GPU, ``batch_size`` needs to be set as a multiple of 8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "epochs = 5\n",
+    "input_size = 4096   # set to a larger value\n",
+    "output_size = 4096  # set to a larger value\n",
+    "batch_size = 512    # batch_size is a multiple of 8\n",
+    "nums_batch = 50\n",
+    "\n",
+    "train_data = [paddle.randn((batch_size, input_size)) for _ in range(nums_batch)]\n",
+    "labels = [paddle.randn((batch_size, output_size)) for _ in range(nums_batch)]\n",
+    "\n",
+    "mse = paddle.nn.MSELoss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.3 Training with Default Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.24072289])\n",
+      "\n",
+      "Default time:\n",
+      "total time = 2.935 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # define model\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # define optimizer\n",
+    "\n",
+    "start_timer() # get the start time of training\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        output = model(data)\n",
+    "        loss = mse(output, label)\n",
+    "\n",
+    "        # backpropagation\n",
+    "        loss.backward()\n",
+    "\n",
+    "        # update parameters\n",
+    "        optimizer.step()\n",
+    "        optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"Default time:\") # print massage and total time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.4 Training with AMP\n",
+    "\n",
+    "Using automatic mixed precision training with PaddlePaddle requires four steps:\n",
+    "\n",
+    "- Step1: Define ``GradScaler``, which is used to scale the ``loss`` to avoid underflow\n",
+    "- Step2: Use ``decorate``, to do nothing in level='O1' mode without using this api, and in level='O2' mode to convert network parameters from FP32 to FP16\n",
+    "- Step3: Use ``auto_cast`` to create an AMP context, in which the input datatype(FP16 or FP32) of each oprator will be automatically determined\n",
+    "- Step4: Use ``GradScaler`` defined in Step1 to complete the scaling of ``loss``, and use the scaled ``loss`` for backpropagation to complete the training\n",
+    "\n",
+    "In level=’O1‘ mode："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.24848151])\n",
+      "\n",
+      "AMP time in O1 mode:\n",
+      "total time = 1.299 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # define model\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # define optimizer\n",
+    "\n",
+    "# Step1：define GradScaler\n",
+    "scaler = paddle.amp.GradScaler(init_loss_scaling=1024)\n",
+    "\n",
+    "start_timer() # get start time\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        # Step2：create AMP context environment\n",
+    "        with paddle.amp.auto_cast():\n",
+    "            output = model(data)\n",
+    "            loss = mse(output, label)\n",
+    "\n",
+    "        # Step3：use GradScaler complete the loss scaling\n",
+    "        scaled = scaler.scale(loss)\n",
+    "        scaled.backward()\n",
+    "\n",
+    "        # update parameters\n",
+    "        scaler.minimize(optimizer, scaled)\n",
+    "        optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"AMP time in O1 mode:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "In level='O2' mode："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "in ParamBase copy_to func\n",
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.25075114])\n",
+      "\n",
+      "AMP time in O2 mode:\n",
+      "total time = 0.888 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # define model\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # define optimizer\n",
+    "\n",
+    "# Step1：define GradScaler\n",
+    "scaler = paddle.amp.GradScaler(init_loss_scaling=1024)\n",
+    "\n",
+    "# Step2：in level='O2' mode, convert network parameters from FP32 to FP16\n",
+    "model, optimizer = paddle.amp.decorate(models=model, optimizers=optimizer, level='O2', master_weight=None, save_dtype=None)\n",
+    "\n",
+    "start_timer() # get start time\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        # Step3：create AMP context environment\n",
+    "        with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):\n",
+    "            output = model(data)\n",
+    "            loss = mse(output, label)\n",
+    "\n",
+    "        # Step4：use GradScaler complete the loss scaling\n",
+    "        scaled = scaler.scale(loss)\n",
+    "        scaled.backward()\n",
+    "\n",
+    "        # update parameters\n",
+    "        scaler.minimize(optimizer, scaled)\n",
+    "        optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"AMP time in O2 mode:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 4. Advanced Usage\n",
+    "### 4.1 Gradient Accumulation\n",
+    "\n",
+    "Gradient accumulation means running a configured number of steps without updating the model variables. Until certain steps, use the accumulated gradients to update the variables.\n",
+    "\n",
+    "In automatic mixed precision training, gradient accumulation is also supported, and the usage is as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,\n",
+      "       [1.25853443])\n",
+      "\n",
+      "AMP time:\n",
+      "total time = 1.034 sec\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = SimpleNet(input_size, output_size)  # define model\n",
+    "\n",
+    "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # define optimizer\n",
+    "\n",
+    "accumulate_batchs_num = 10 # the batch numbers of gradients accumulation\n",
+    "\n",
+    "# define GradScaler\n",
+    "scaler = paddle.amp.GradScaler(init_loss_scaling=1024)\n",
+    "\n",
+    "start_timer() # get start time\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    datas = zip(train_data, labels)\n",
+    "    for i, (data, label) in enumerate(datas):\n",
+    "\n",
+    "        # create AMP context environment\n",
+    "        with paddle.amp.auto_cast():\n",
+    "            output = model(data)\n",
+    "            loss = mse(output, label)\n",
+    "\n",
+    "        # use GradScaler complete the loss scaling\n",
+    "        scaled = scaler.scale(loss)\n",
+    "        scaled.backward()\n",
+    "\n",
+    "        #  when the accumulated batch is accumulate_batchs_num, update the model parameters\n",
+    "        if (i + 1) % accumulate_batchs_num == 0:\n",
+    "\n",
+    "            # update parameters\n",
+    "            scaler.minimize(optimizer, scaled)\n",
+    "            optimizer.clear_grad()\n",
+    "\n",
+    "print(loss)\n",
+    "end_timer_and_print(\"AMP time:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 5. Conclusion\n",
+    "\n",
+    "As can be seen from the above example, using the automatic mixed precision training, in O1 mode the total time is about 1.299s, in O2 mode the total time is about 0.888s, while the ordinary training method takes 2.935s, and the training speed is increased by about 2.4 times in O1 mode and 2.4 times in O2 mode. For more examples of using mixed precision training, please refer to paddlepaddle's models: [paddlepaddle/models](https://github.com/PaddlePaddle/models)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "py35-paddle1.2.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.md b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.md
index ee31dc70ba1..6c5f15edfae 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.md
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/amp_en.md
@@ -1,6 +1,6 @@
 # Automatic Mixed Precision Training
 
-In general, the datatype of training deep learning models is single-precision floating-point format(also called FP32). In 2018, Baidu and NVIDIA jointly published the paper: [MIXED PRECISION TRAINING](https://arxiv.org/pdf/1710.03740.pdf), which proposed mixed precision training. During the process of training, some operators use FP32 and other operators use half precision(also called FP16) in the same time. Its purpose is to speed up training, while compared with the FP32 training model, the same accuracy is maintained. This tutorial will introduce how to use automatic mixed precision training with PaddlePaddle.
+In general, the datatype of training deep learning models is single-precision floating-point format(also called FP32). In 2018, Baidu and NVIDIA jointly published the paper: [MIXED PRECISION TRAINING](https://arxiv.org/pdf/1710.03740.pdf), which proposed mixed precision training. During the process of training, some operators use FP32 and other operators use half precision(also called FP16) in the same time. Its purpose is to speed up training, while compared with the FP32 training model, the same accuracy is maintained. This tutorial will introduce how to use automatic mixed precision training with PaddlePaddle.  
 
 ## 1. Half Precision (FP16)
 
@@ -55,6 +55,7 @@ import paddle.nn as nn
 class SimpleNet(nn.Layer):
 
     def __init__(self, input_size, output_size):
+        
         super(SimpleNet, self).__init__()
         self.linear1 = nn.Linear(input_size, output_size)
         self.relu1 = nn.ReLU()
@@ -118,19 +119,22 @@ end_timer_and_print("Default time:") # print massage and total time
 ```
 
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.25010288])
-
+           [1.24072289])
+    
     Default time:
-    total time = 2.943 sec
+    total time = 2.935 sec
 
 
 ### 3.4 Training with AMP
 
-Using automatic mixed precision training with PaddlePaddle requires three steps:
+Using automatic mixed precision training with PaddlePaddle requires four steps:
+
+- Step1: Define ``GradScaler``, which is used to scale the ``loss`` to avoid underflow
+- Step2: Use ``decorate``, to do nothing in level='O1' mode without using this api, and in level='O2' mode to convert network parameters from FP32 to FP16
+- Step3: Use ``auto_cast`` to create an AMP context, in which the input datatype(FP16 or FP32) of each oprator will be automatically determined
+- Step4: Use ``GradScaler`` defined in Step1 to complete the scaling of ``loss``, and use the scaled ``loss`` for backpropagation to complete the training
 
-- Step1: Define ``GradScaler``, which is used to scale the ``loss`` and ``gradients``to avoid underflow
-- Step2: Use ``auto_cast`` to create an AMP context, in which the input datatype(FP16 or FP32) of each oprator will be automatically determined
-- Step3: Use ``GradScaler`` defined in Step1 to complete the scaling of ``loss``, and use the scaled ``loss`` for backpropagation to complete the training
+In level=’O1‘ mode：
 
 
 ```python
@@ -161,14 +165,64 @@ for epoch in range(epochs):
         optimizer.clear_grad()
 
 print(loss)
-end_timer_and_print("AMP time:")
+end_timer_and_print("AMP time in O1 mode:")
 ```
 
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.23644269])
+           [1.24848151])
+    
+    AMP time in O1 mode:
+    total time = 1.299 sec
 
-    AMP time:
-    total time = 1.222 sec
+
+In level='O2' mode：
+
+
+```python
+model = SimpleNet(input_size, output_size)  # define model
+
+optimizer = paddle.optimizer.SGD(learning_rate=0.0001, parameters=model.parameters())  # define optimizer
+
+# Step1：define GradScaler
+scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
+
+# Step2：in level='O2' mode, convert network parameters from FP32 to FP16
+model, optimizer = paddle.amp.decorate(models=model, optimizers=optimizer, level='O2', master_weight=None, save_dtype=None)
+
+start_timer() # get start time
+
+for epoch in range(epochs):
+    datas = zip(train_data, labels)
+    for i, (data, label) in enumerate(datas):
+
+        # Step3：create AMP context environment
+        with paddle.amp.auto_cast(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
+            output = model(data)
+            loss = mse(output, label)
+
+        # Step4：use GradScaler complete the loss scaling
+        scaled = scaler.scale(loss)
+        scaled.backward()
+
+        # update parameters
+        scaler.minimize(optimizer, scaled)
+        optimizer.clear_grad()
+
+print(loss)
+end_timer_and_print("AMP time in O2 mode:")
+```
+
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    in ParamBase copy_to func
+    Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
+           [1.25075114])
+    
+    AMP time in O2 mode:
+    total time = 0.888 sec
 
 
 ## 4. Advanced Usage
@@ -204,7 +258,7 @@ for epoch in range(epochs):
         scaled = scaler.scale(loss)
         scaled.backward()
 
-        # when the accumulated batch is accumulate_batchs_num, update the model parameters
+        #  when the accumulated batch is accumulate_batchs_num, update the model parameters
         if (i + 1) % accumulate_batchs_num == 0:
 
             # update parameters
@@ -216,12 +270,12 @@ end_timer_and_print("AMP time:")
 ```
 
     Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
-           [1.25127280])
-
+           [1.25853443])
+    
     AMP time:
-    total time = 1.006 sec
+    total time = 1.034 sec
 
 
 ## 5. Conclusion
 
-As can be seen from the above example, using the automatic mixed precision training, the total time is about 1.222s, while the ordinary training method takes 2.943s, and the training speed is increased by about 2.4 times. For more examples of using mixed precision training, please refer to paddlepaddle's models: [paddlepaddle/models](https://github.com/PaddlePaddle/models).
+As can be seen from the above example, using the automatic mixed precision training, in O1 mode the total time is about 1.299s, in O2 mode the total time is about 0.888s, while the ordinary training method takes 2.935s, and the training speed is increased by about 2.4 times in O1 mode and 2.4 times in O2 mode. For more examples of using mixed precision training, please refer to paddlepaddle's models: [paddlepaddle/models](https://github.com/PaddlePaddle/models).
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/autograd_cn.rst b/docs/guides/01_paddle2.0_introduction/basic_concept/autograd_cn.rst
index 3951f03c09d..fcf36e1d774 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/autograd_cn.rst
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/autograd_cn.rst
@@ -35,7 +35,7 @@ PaddlePaddle的神经网络核心是自动微分，本篇文章主要为你介
 
 .. parsed-literal::
 
-    2.1.1
+    2.2.0
 
 
 本案例首先定义网络。因为本示例着重展示如何使用飞桨进行自动微分，故组网部分不过多展开，直接使用高层API中封装好的模型\ ``vgg11``\ 。
@@ -291,4 +291,4 @@ PaddlePaddle的神经网络核心是自动微分，本篇文章主要为你介
 五、总结
 ------------------------
 
-本文章主要介绍了如何使用飞桨的自动微分，以及飞桨的自动微分机制。
+本文章主要介绍了如何使用飞桨的自动微分，以及飞桨的自动微分机制。
\ No newline at end of file
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.rst b/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.rst
index 5f32441212d..7d5cd89b959 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.rst
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.rst
@@ -20,6 +20,8 @@ Paddle提供了三种梯度裁剪方式：
 
 .. code:: ipython3
 
+    import paddle
+
     linear = paddle.nn.Linear(10, 10)
     clip = paddle.nn.ClipGradByValue(min=-1, max=1)
     sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_en.rst b/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_en.rst
index b6d58570b4f..31fd73f8b11 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_en.rst
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_en.rst
@@ -20,6 +20,8 @@ By default, Gradients of all parameters in SGD optimizer will be clipped:
 
 .. code:: ipython3
 
+    import paddle
+    
     linear = paddle.nn.Linear(10, 10)
     clip = paddle.nn.ClipGradByValue(min=-1, max=1)
     sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_cn.md b/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_cn.md
index 9872d55e8f9..0f89e8308c4 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_cn.md
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_cn.md
@@ -303,8 +303,9 @@ Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True,
 同样的也可以使用 ``register_forward_pre_hook()`` 来注册**pre_hook**：
 
 ```python
-def forward_pre_hook(layer, input, output):
-    return 2*output
+def forward_pre_hook(layer, input):
+    print(input)
+    return input
 
 x = paddle.ones([10, 1], 'float32')
 model = Model()
@@ -313,10 +314,17 @@ out = model(x)
 ```
 
 ```text
-Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True,
-       [[2.],
-        [2.],
-        ...
+(Tensor(shape=[10, 1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+       [[1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.]]),)
 ```
 
 ## 模型数据保存
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_en.md b/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_en.md
index 3a667fc8c33..e96637cbb05 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_en.md
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_en.md
@@ -311,8 +311,9 @@ Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True,
 Similarly, we can also register a **pre_hook** through ``register_forward_pre_hook()``
 
 ```python
-def forward_pre_hook(layer, input, output):
-    return 2*output
+def forward_pre_hook(layer, input):
+    print(input)
+    return input
 
 x = paddle.ones([10, 1], 'float32')
 model = Model()
@@ -321,10 +322,17 @@ out = model(x)
 ```
 
 ```text
-Tensor(shape=[10, 1], dtype=float32, place=CPUPlace, stop_gradient=True,
-       [[2.],
-        [2.],
-        ...
+(Tensor(shape=[10, 1], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+       [[1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.],
+        [1.]]),)
 ```
 
 ## Save a model's data
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.md b/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.md
index 3eb03db37b8..00efa373a39 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.md
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.md
@@ -81,8 +81,8 @@ array([[1., 2., 3.],
 **Tensor**不仅支持 floats、ints 类型数据，也支持 complex numbers数据，如果输入为复数数据，则**Tensor**的dtype为 ``complex64`` 或 ``complex128`` ，其每个元素均为1个复数：
 
 ```python
-ndim_2_tensor = paddle.to_tensor([[1.0, 2.0, 3.0],
-                                  [4.0, 5.0, 6.0]])
+ndim_2_tensor = paddle.to_tensor([[(1+1j), (2+2j)],
+                                  [(3+3j), (4+4j)]])
 print(ndim_2_tensor)                                    
 ```
 
@@ -473,7 +473,6 @@ x.logical_not(y)              #对两个bool型tensor逐元素进行逻辑非操
 
 ### 线性代数相关
 ```python
-x.cholesky()                  #矩阵的cholesky分解
 x.t()                         #矩阵转置
 x.transpose([1, 0])           #交换axis 0 与axis 1的顺序
 x.norm('fro')                 #矩阵的Frobenius 范数
diff --git a/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_en.md b/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_en.md
index 9e44ad029a7..f9dfcde4c58 100644
--- a/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_en.md
+++ b/docs/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_en.md
@@ -80,8 +80,8 @@ array([[1., 2., 3.],
 **Tensor** supports not only floats and ints but also complex numbers data, If input complex number data, the dtype of **Tensor** is ``complex64`` or ``complex128`` : 
 
 ```python
-ndim_2_tensor = paddle.to_tensor([[1.0, 2.0, 3.0],
-                                  [4.0, 5.0, 6.0]])
+ndim_2_tensor = paddle.to_tensor([[(1+1j), (2+2j)],
+                                  [(3+3j), (4+4j)]])
 print(ndim_2_tensor)
 ```
 
@@ -482,7 +482,6 @@ x.logical_not(y)              #logic not operation for two bool tensor
 
 ### linear algebra operators
 ```python
-x.cholesky()                  #cholesky decomposition of a matrix
 x.t()                         #matrix transpose
 x.transpose([1, 0])           #swap axis 0 with axis 1
 x.norm('fro')                 #Frobenius Norm of matrix
diff --git a/docs/guides/01_paddle2.0_introduction/load_old_format_model.rst b/docs/guides/01_paddle2.0_introduction/load_old_format_model_cn.rst
similarity index 100%
rename from docs/guides/01_paddle2.0_introduction/load_old_format_model.rst
rename to docs/guides/01_paddle2.0_introduction/load_old_format_model_cn.rst
diff --git a/docs/guides/01_paddle2.0_introduction/migration_cn.rst b/docs/guides/01_paddle2.0_introduction/migration_cn.rst
index f04a2ee8835..94f9e2ee60d 100644
--- a/docs/guides/01_paddle2.0_introduction/migration_cn.rst
+++ b/docs/guides/01_paddle2.0_introduction/migration_cn.rst
@@ -66,7 +66,7 @@ paddle_upgrade_tool 可以使用下面的方式，快速使用:
 开始
 ^^^^
 
-在使用paddle_upgrade_tool前，需要确保已经安装了Paddle 2.0.0版本。
+在使用paddle_upgrade_tool前，需要确保已经安装了Paddle 2.0.0+版本。
 
 .. code:: ipython3
 
diff --git a/docs/guides/01_paddle2.0_introduction/update_cn.md b/docs/guides/01_paddle2.0_introduction/update_cn.md
index 2e1c44ab4ac..7f367547d13 100644
--- a/docs/guides/01_paddle2.0_introduction/update_cn.md
+++ b/docs/guides/01_paddle2.0_introduction/update_cn.md
@@ -558,5 +558,5 @@ https://github.com/PaddlePaddle/paddle_upgrade_tool
 ### 2.0文档教程
 以下提供了2.0版本的一些示例教程：
 
-你可以在官网[应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/tutorial/index_cn.html)栏目内进行在线浏览，也可以下载在这里提供的源代码:
-https://github.com/PaddlePaddle/book/tree/develop/paddle2.0_docs
+你可以在官网[应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/practices/index_cn.html)栏目内进行在线浏览，也可以下载在这里提供的源代码:
+https://github.com/PaddlePaddle/docs/tree/develop/docs/practices
diff --git a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst
index acda7be5e86..b354c0c995d 100644
--- a/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst
+++ b/docs/guides/02_paddle2.0_develop/01_quick_start_cn.rst
@@ -22,7 +22,7 @@
 
 .. parsed-literal::
 
-    2.1.1
+    2.2.0
 
 
 三、实践：手写数字识别任务
diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst
index 789be2a9394..3c2182c9b33 100644
--- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst
+++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.rst
@@ -7,7 +7,7 @@
 
 .. note::
 
-    高层API实现的模型训练与预测如\ ``Model.fit()、Model.evaluate()、Model.predict()``\ 都可以通过基础API实现，本文先介绍高层API的训练方式，然后会将高层API拆解为基础API的方式，方便对比学习。最后会补充介绍如何使用paddle inference进行预测。
+    高层API实现的模型训练与预测如\ ``Model.fit()、Model.evaluate()、Model.predict()``\ 都可以通过基础API实现，本文先介绍高层API的训练方式，然后会将高层API拆解为基础API的方式，方便对比学习。
 
 一、训练前准备
 ---------------------
@@ -137,11 +137,6 @@ numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据
 
 除了通过第一部分的高层API实现模型的训练与预测，飞桨框架也同样支持通过基础API对模型进行训练与预测。简单来说，\ ``Model.prepare()、Model.fit()、Model.evaluate()、Model.predict()``\ 都是由基础API封装而来。下面通过拆解高层API到基础API的方式，来了解如何用基础API完成模型的训练与预测。
 
-
-.. note::
-
-    对于网络模型的创建你依旧可以选择Sequential组网方式，也可以采用SubClass组网方式，为方便后续使用paddle inference进行预测，我们使用SubClass组网方式创建网络，若后续使用paddle inference预测，需通过paddle.jit.save保存适用于预测部署的模型，并在forward函数前加@paddle.jit.to_static装饰器，将函数内的动态图API转化为静态图API。
-
 .. code:: ipython3
 
     # 定义网络结构( 采用SubClass 组网 )
@@ -153,9 +148,7 @@ numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据
             self.linear_2 = paddle.nn.Linear(512, 10)
             self.relu = paddle.nn.ReLU()
             self.dropout = paddle.nn.Dropout(0.2)
-       
-        #后续若不使用paddle inferece，可对 @paddle.jit.to_static 进行注释  
-        @paddle.jit.to_static       
+            
         def forward(self, inputs):
             y = self.flatten(inputs)
             y = self.linear_1(y)
@@ -214,9 +207,6 @@ numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据
 
             # 梯度清零
             optim.clear_grad()
-    ##保存模型，会生成*.pdmodel、*.pdiparams、*.pdiparams.info三个模型文件
-    path='./mnist/inference_model'
-    paddle.jit.save(layer=mnist,path=path)
 
 
 .. parsed-literal::
@@ -284,101 +274,3 @@ numpy_ndarray_n是对应原始数据经过模型计算后得到的预测数据
 .. parsed-literal::
 
     predict finished
-    
-
-部署预测模型
-=====================
-其中预测方法除以上两种外，还可采用原生推理库paddle inference 进行推理部署，该方法支持TeansorRT加速，支持第三方框架模型，支持量化、裁剪后的模型，适合于工业部署或对推理性能、通用性有要求的用户。
-
- 
-四、通过paddle inference实现预测
------------------------------------------
-
-paddle inference与model.predict()以及基础API的预测相比，可使用MKLDNN、CUDNN、TensorRT进行预测加速，同时支持用 X2Paddle 工具从第三方框架（TensorFlow、Pytorh 、 Caffe 等）产出的模型，可联动PaddleSlim，支持加载量化、裁剪和蒸馏后的模型部署。针对不同平台不同的应用场景进行了深度的适配优化，保证模型在服务器端即训即用，快速部署。在这里，我们只简单的展示如何用paddle inference实现该模型的部署预测。
-
-4.1 准备预测部署模型
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-要使用paddle inference预测需得到paddle预测格式的模型，所以你需要在训练过程中通过 paddle.jit.save(layer=mnist,path=path) 来保存模型，注意在训练时在forward函数前加@paddle.jit.to_static装饰器，将函数内的动态图API转化为静态图API。在第三章节基础API模型的训练中已加入相关配置。
-
-.. code:: ipython3
-
-    #模型目录如下：
-                mnist/
-            ├── inference.pdmodel
-            ├── inference.pdiparams.info
-            └── inference.pdiparams
-4.2 准备预测部署程序
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-将以下代码保存为python_demo.py文件：
-
-.. code:: ipython3
-
-    import argparse
-    import numpy as np
-    from skimage import transform,data
-
-    # 引用 paddle inference 预测库
-    import paddle.inference as paddle_infer
-    from PIL import Image
-
-    def main():
-        args = parse_args()
-
-        # 创建 config
-        config = paddle_infer.Config(args.model_file, args.params_file)
-
-        # 根据 config 创建 predictor
-        predictor = paddle_infer.create_predictor(config)
-
-        # 获取输入的名称
-        input_names = predictor.get_input_names()
-        input_handle = predictor.get_input_handle(input_names[0])
-
-        # 设置输入，自定义一张输入照片，图片大小为28*28
-        im=Image.open('./img3.png').convert('L')
-        im=np.array(im).reshape(1,1,28,28).astype(np.float32)
-        input_handle.copy_from_cpu(im)
-
-        # 运行predictor
-        predictor.run()
-
-        # 获取输出
-        output_names = predictor.get_output_names()
-        output_handle = predictor.get_output_handle(output_names[0])
-        output_data = output_handle.copy_to_cpu() # numpy.ndarray类型，是10个分类的概率
-        print(output_data)
-        print("Output data size is {}".format(output_data.size))
-        print("Output data shape is {}".format(output_data.shape))
-        pred=np.argmax(output_data) #选出概率最大的一个
-        print("The predicted data is ： {}".format(pred.item()))
-
-    def parse_args():
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--model_file", type=str, help="model filename")
-        parser.add_argument("--params_file", type=str, help="parameter filename")
-        parser.add_argument("--batch_size", type=int, default=1, help="batch size")
-        return parser.parse_args()
-
-    if __name__ == "__main__":
-        main()
-        
-
-4.3 执行预测程序
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. code:: ipython3
-
-   python python_demo.py --model_file ./mnist/inference_model.pdmodel --params_file ./mnist/inference_model.pdiparams --batch_size 2
-
-.. parsed-literal::
-    
-    #输出如下
-    
-    [[-1347.5923  -1156.918    -774.73865  3387.0623  -1553.3696    107.96879
-      -2631.2185   -701.50323 -1094.3896    206.71666]]
-    Output data size is 10
-    Output data shape is (1, 10)
-    The predicted data is ： 3
-    
-详细教程可参照paddle inference文档：https://paddle-inference.readthedocs.io/en/latest/quick_start/python_demo.html
-
diff --git a/docs/guides/04_dygraph_to_static/basic_usage_cn.md b/docs/guides/04_dygraph_to_static/basic_usage_cn.md
index b3159c24a2a..eb45f0d83b2 100644
--- a/docs/guides/04_dygraph_to_static/basic_usage_cn.md
+++ b/docs/guides/04_dygraph_to_static/basic_usage_cn.md
@@ -1,475 +1,491 @@
-# 基础接口用法
+# 使用样例
 
 
-## 一、 @to_static 概览
+## 一、 使用 @to_static 进行动静转换
 
-动静转换（@to_static）通过解析 Python 代码（抽象语法树，下简称：AST） 实现一行代码即可转为静态图功能，即只需在待转化的函数前添加一个装饰器 ``@paddle.jit.to_static`` 。
+动静转换（@to_static）通过解析 Python 代码（抽象语法树，下简称：AST） 实现一行代码即可将动态图转为静态图的功能，只需在待转化的函数前添加一个装饰器 ``@paddle.jit.to_static`` 。
 
-如下是一个使用 @to_static 装饰器的 ``Model`` 示例：
+如下是使用 @to_static 进行动静转换的两种方式：
 
-```python
-import paddle
-from paddle.jit import to_static
+- 方式一：使用 @to_static 装饰器装饰 ``SimpleNet`` (继承了 ``nn.Layer``)  的 ``forward`` 函数:
 
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
+    ```python
+    import paddle
+    from paddle.jit import to_static
+
+    class SimpleNet(paddle.nn.Layer):
+        def __init__(self):
+            super(SimpleNet, self).__init__()
+            self.linear = paddle.nn.Linear(10, 3)
+
+        @to_static # 动静转换
+        def forward(self, x, y):
+            out = self.linear(x)
+            out = out + y
+            return out
 
-    # 方式一：装饰 forward 函数（支持训练）
-    @to_static
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        return out
+    net = SimpleNet()
+    net.eval()
+    x = paddle.rand([2, 10])
+    y = paddle.rand([2, 3])
+    out = net(x, y)
+    paddle.jit.save(net, './net')
+    ```
 
-net = SimpleNet()
-# 方式二：(推荐)仅做预测模型导出时，推荐此种用法
-net = paddle.jit.to_static(net)  # 动静转换
-```
+- 方式二：调用 ``paddle.jit.to_static()`` 函数，仅做预测模型导出时推荐此种用法。
 
-动转静 @to_static 除了支持预测模型导出，还兼容转为静态图子图训练，仅需要在 ``forward`` 函数上添加此装饰器即可，不需要修改任何其他的代码。
+    ```python
+    import paddle
+    from paddle.jit import to_static
+
+    class SimpleNet(paddle.nn.Layer):
+        def __init__(self):
+            super(SimpleNet, self).__init__()
+            self.linear = paddle.nn.Linear(10, 3)
+
+        def forward(self, x, y):
+            out = self.linear(x)
+            out = out + y
+            return out
+
+    net = SimpleNet()
+    net.eval()
+    net = paddle.jit.to_static(net)  # 动静转换
+    x = paddle.rand([2, 10])
+    y = paddle.rand([2, 3])
+    out = net(x, y)
+    paddle.jit.save(net, './net')
+    ```
 
-基本执行流程如下：
+方式一和方式二的主要区别是，使用 @to_static 除了支持预测模型导出外，在模型训练时，还会转为静态图子图训练，而方式二仅支持预测模型导出。@to_static 的基本执行流程如下图：
 
 <img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/to_static_train.png" style="zoom:50%" />
 
 
 
-## 二、 输入层 InputSpec
+## 二、动转静模型导出
 
+动转静模块**是架在动态图与静态图的一个桥梁**，旨在打破动态图模型训练与静态部署的鸿沟，消除部署时对模型代码的依赖，打通与预测端的交互逻辑。下图展示了**动态图模型训练——>动转静模型导出——>静态预测部署**的流程。
 
-静态图下，模型起始的 Placeholder 信息是通过 ``paddle.static.data`` 来指定的，并以此作为编译期的 ``InferShape`` 推导起点。
+<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/to_static_export.png" style="zoom:50%" />
 
-```python
-import paddle
-# 开启静态图模式
-paddle.enable_static()
 
-# placeholder 信息
-x = paddle.static.data(shape=[None, 10], dtype='float32', name='x')
-y = paddle.static.data(shape=[None, 3], dtype='float32', name='y')
 
-out = paddle.static.nn.fc(x, 3)
-out = paddle.add(out, y)
-```
+在处理逻辑上，动转静主要包含两个主要模块：
+
++ **代码层面**：将模型中所有的 ``layers`` 接口在静态图模式下执行以转为 ``Op`` ，从而生成完整的静态 ``Program``
++ **Tensor层面**：将所有的 ``Parameters`` 和 ``Buffers`` 转为**可导出的 ``Variable`` 参数**（ ``persistable=True`` ）
 
 
-动转静代码示例，通过 ``InputSpec`` 设置 ``Placeholder`` 信息：
+### 2.1 通过 ``forward`` 导出预测模型
+
+通过 ``forward`` 导出预测模型导出一般包括三个步骤：
+
++ **切换 `eval()` 模式**：类似 `Dropout` 、`LayerNorm` 等接口在 `train()` 和 `eval()` 的行为存在较大的差异，在模型导出前，**请务必确认模型已切换到正确的模式，否则导出的模型在预测阶段可能出现输出结果不符合预期的情况。**
++ **构造 `InputSpec` 信息**：InputSpec 用于表示输入的shape、dtype、name信息，且支持用 `None` 表示动态shape（如输入的 batch_size 维度），是辅助动静转换的必要描述信息。
++ **调用 `save` 接口**：调用 `paddle.jit.save`接口，若传入的参数是类实例，则默认对 `forward` 函数进行 `@to_static` 装饰，并导出其对应的模型文件和参数文件。
+
+如下是一个简单的示例：
 
 ```python
 import paddle
 from paddle.jit import to_static
+from paddle.static import InputSpec
 
 class SimpleNet(paddle.nn.Layer):
     def __init__(self):
         super(SimpleNet, self).__init__()
         self.linear = paddle.nn.Linear(10, 3)
 
-    # 方式一：在函数定义处装饰
-    @to_static
     def forward(self, x, y):
         out = self.linear(x)
         out = out + y
         return out
 
+    def another_func(self, x):
+        out = self.linear(x)
+        out = out * 2
+        return out
+
 net = SimpleNet()
+# train(net)  模型训练 (略)
+
+# step 1: 切换到 eval() 模式
+net.eval()
 
-# 方式二：(推荐)仅做预测模型导出时，推荐此种用法
-x_spec = InputSpec(shape=[None, 10], name='x')
-y_spec = InputSpec(shape=[3], name='y')
+# step 2: 定义 InputSpec 信息
+x_spec = InputSpec(shape=[None, 3], dtype='float32', name='x')
+y_spec = InputSpec(shape=[3], dtype='float32', name='y')
 
-net = paddle.jit.to_static(net, input_spec=[x_spec, y_spec])  # 动静转换
+# step 3: 调用 jit.save 接口
+net = paddle.jit.save(net, path='simple_net', input_spec=[x_spec, y_spec])  # 动静转换
 ```
 
+执行上述代码样例后，在当前目录下会生成三个文件，即代表成功导出预测模型：
+```
+simple_net.pdiparams        // 存放模型中所有的权重数据
+simple_net.pdimodel         // 存放模型的网络结构
+simple_net.pdiparams.info   // 存放额外的其他信息
+```
 
-在导出模型时，需要显式地指定输入 ``Tensor`` 的**签名信息**，优势是：
+### 2.2 使用 InputSpec 指定模型输入 Tensor 信息
 
+动静转换在生成静态图 Program 时，依赖输入 Tensor 的 shape、dtype 和 name 信息。因此，Paddle 提供了 InputSpec 接口，用于指定输入 Tensor 的描述信息，并支持动态 shape 特性。
 
-+ 可以指定某些维度为 ``None`` ， 如 ``batch_size`` ，``seq_len`` 维度
-+ 可以指定 Placeholder 的 ``name`` ，方面预测时根据 ``name`` 输入数据
 
-> 注：InputSpec 接口的高阶用法，请参看 [【InputSpec 功能介绍】](./export_model_cn.html#inputspec)
+#### 2.2.1 构造 InputSpec
 
 
-## 三、函数转写
+**方式一：直接构造**
 
-在 NLP、CV 领域中，一个模型常包含层层复杂的子函数调用，动转静中是如何实现**只需装饰最外层的 ``forward`` 函数**，就能递归处理所有的函数。
 
-如下是一个模型样例：
+InputSpec 接口在 ``paddle.static`` 目录下， 只有 ``shape`` 是必须参数， ``dtype`` 和 ``name`` 可以缺省，默认取值分别为 ``float32`` 和 ``None`` 。使用样例如下：
 
 ```python
-import paddle
-from paddle.jit import to_static
+from paddle.static import InputSpec
 
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
+x = InputSpec([None, 784], 'float32', 'x')
+label = InputSpec([None, 1], 'int64', 'label')
 
-    @to_static
-    def forward(self, x, y):
-        out = self.my_fc(x)       # <---- self.other_func
-        out = add_two(out, y)     # <---- other plain func
-        return out
+print(x)      # InputSpec(shape=(-1, 784), dtype=VarType.FP32, name=x)
+print(label)  # InputSpec(shape=(-1, 1), dtype=VarType.INT64, name=label)
+```
 
-    def my_fc(self, x):
-        out = self.linear(x)
-        return out
 
-# 此函数可以在任意文件
-def add_two(x, y):
-    out = x + y
-    return out
+**方式二：由 Tensor 构造**
 
-net = SimpleNet()
-# 查看转写的代码内容
-paddle.jit.set_code_level(100)
+可以借助 ``InputSpec.from_tensor`` 方法，从一个 Tensor 直接创建 InputSpec 对象，其拥有与源 Tensor 相同的 ``shape`` 和 ``dtype`` 。 使用样例如下：
 
-x = paddle.zeros([2,10], 'float32')
-y = paddle.zeros([3], 'float32')
+```python
+import numpy as np
+import paddle
+from paddle.static import InputSpec
 
-out = net(x, y)
+x = paddle.to_tensor(np.ones([2, 2], np.float32))
+x_spec = InputSpec.from_tensor(x, name='x')
+print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
 ```
 
-可以通过 ``paddle.jit.set_code_level(100)`` 在执行时打印代码转写的结果到终端，转写代码如下：
+> 注：若未在 ``from_tensor`` 中指定新的 ``name``，则默认使用与源 Tensor 相同的 ``name``。
 
-```python
-def forward(self, x, y):
-    out = paddle.jit.dy2static.convert_call(self.my_fc)(x)
-    out = paddle.jit.dy2static.convert_call(add_two)(out, y)
-    return out
-
-def my_fc(self, x):
-    out = paddle.jit.dy2static.convert_call(self.linear)(x)
-    return out
-
-def add_two(x, y):
-    out = x + y
-    return out
-```
 
+**方式三：由 numpy.ndarray**
 
-如上所示，所有的函数调用都会被转写如下形式：
+也可以借助 ``InputSpec.from_numpy`` 方法，从一个 `Numpy.ndarray` 直接创建 InputSpec 对象，其拥有与源 ndarray 相同的 ``shape`` 和 ``dtype`` 。使用样例如下：
 
 ```python
- out = paddle.jit.dy2static.convert_call( self.my_fc )( x )
-  ^                    ^                      ^         ^
-  |                    |                      |         |
-返回列表           convert_call             原始函数    参数列表
+import numpy as np
+from paddle.static import InputSpec
+
+x = np.ones([2, 2], np.float32)
+x_spec = InputSpec.from_numpy(x, name='x')
+print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
 ```
 
-即使函数定义分布在不同的文件中， ``convert_call`` 函数也会递归地处理和转写所有嵌套的子函数。
+> 注：若未在 ``from_numpy`` 中指定新的 ``name``，则默认使用 ``None`` 。
 
-## 四、控制流转写
+#### 2.2.2 基本用法
 
-控制流 ``if/for/while`` 的转写和处理是动转静中比较重要的模块，也是动态图模型和静态图模型实现上差别最大的一部分。
+**方式一： 在 @to_static 装饰器中调用**
+
+如下是一个简单的使用样例：
+
+```python
+import paddle
+from paddle.nn import Layer
+from paddle.jit import to_static
+from paddle.static import InputSpec
 
-**转写上有两个基本原则：**
+class SimpleNet(Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
 
-+ **并非**所有动态图中的 ``if/for/while`` 都会转写为 ``cond_op/while_op``
-+ **只有**控制流的判断条件 **依赖了``Tensor``**（如 ``shape`` 或 ``value`` ），才会转写为对应 Op
+    @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')])
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
+        return out
 
+net = SimpleNet()
 
-<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/convert_cond.png" style="zoom:80%" />
+# save static model for inference directly
+paddle.jit.save(net, './simple_net')
+```
 
+在上述的样例中， ``@to_static`` 装饰器中的 ``input_spec`` 为一个 InputSpec 对象组成的列表，用于依次指定参数 x 和 y 对应的 Tensor 签名信息。在实例化 SimpleNet 后，可以直接调用 ``paddle.jit.save`` 保存静态图模型，不需要执行任何其他的代码。
 
+> 注：
+>    1. input_spec 参数中不仅支持 InputSpec 对象，也支持 int 、 float 等常见 Python 原生类型。
+>    2. 若指定 input_spec 参数，则需为被装饰函数的所有必选参数都添加对应的 InputSpec 对象，如上述样例中，不支持仅指定 x 的签名信息。
+>    3. 若被装饰函数中包括非 Tensor 参数，推荐函数的非 Tensor 参数设置默认值，如 ``forward(self, x, use_bn=False)``
 
-### 4.1 IfElse
 
-无论是否会转写为 ``cond_op`` ，动转静都会首先对代码进行处理，**转写为 ``cond`` 接口可以接受的写法**
+**方式二：在 to_static 函数中调用**
 
-**示例一：不依赖 Tensor 的控制流**
+若期望在动态图下训练模型，在训练完成后保存预测模型，并指定预测时需要的签名信息，则可以选择在保存模型时，直接调用 ``to_static`` 函数。使用样例如下：
 
 ```python
-def not_depend_tensor_if(x, label=None):
-    out = x + 1
-    if label is not None:              # <----- python bool 类型
-        out = paddle.nn.functional.cross_entropy(out, label)
-    return out
-
-print(to_static(not_depend_tensor_ifw).code)
-# 转写后的代码：
-"""
-def not_depend_tensor_if(x, label=None):
-    out = x + 1
-
-    def true_fn_1(label, out):  # true 分支
-        out = paddle.nn.functional.cross_entropy(out, label)
-        return out
+class SimpleNet(Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
 
-    def false_fn_1(out):        # false 分支
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
         return out
 
-    out = paddle.jit.dy2static.convert_ifelse(label is not None, true_fn_1,
-        false_fn_1, (label, out), (out,), (out,))
-
-    return out
-"""
-```
+net = SimpleNet()
 
+# train process (Pseudo code)
+for epoch_id in range(10):
+    train_step(net, train_reader)
 
-**示例二：依赖 Tensor 的控制流**
+net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')])
 
-```python
-def depend_tensor_if(x):
-    if paddle.mean(x) > 5.:         # <---- Bool Tensor 类型
-        out = x - 1
-    else:
-        out = x + 1
-    return out
-
-print(to_static(depend_tensor_if).code)
-# 转写后的代码：
-"""
-def depend_tensor_if(x):
-    out = paddle.jit.dy2static.data_layer_not_check(name='out', shape=[-1],
-        dtype='float32')
-
-    def true_fn_0(x):      # true 分支
-        out = x - 1
-        return out
+# save static model for inference directly
+paddle.jit.save(net, './simple_net')
+```
 
-    def false_fn_0(x):     # false 分支
-        out = x + 1
-        return out
+如上述样例代码中，在完成训练后，可以借助 ``to_static(net, input_spec=...)`` 形式对模型实例进行处理。Paddle 会根据 input_spec 信息对 forward 函数进行递归的动转静，得到完整的静态图，且包括当前训练好的参数数据。
 
-    out = paddle.jit.dy2static.convert_ifelse(paddle.mean(x) > 5.0,
-        true_fn_0, false_fn_0, (x,), (x,), (out,))
 
-    return out
-"""
-```
+**方式三：通过 list 和 dict 推导**
 
+上述两个样例中，被装饰的 forward 函数的参数均为 Tensor 。这种情况下，参数个数必须与 InputSpec 个数相同。但当被装饰的函数参数为 list 或 dict 类型时，``input_spec`` 需要与函数参数保持相同的嵌套结构。
 
-规范化代码之后，所有的 ``IfElse`` 均转为了如下形式：
+当函数的参数为 list 类型时，input_spec 列表中对应元素的位置，也必须是包含相同元素的 InputSpec 列表。使用样例如下：
 
 ```python
- out = convert_ifelse(paddle.mean(x) > 5.0, true_fn_0, false_fn_0, (x,), (x,), (out,))
-  ^          ^                   ^             ^           ^        ^      ^      ^
-  |          |                   |             |           |        |      |      |
- 输出   convert_ifelse          判断条件       true分支   false分支  分支输入 分支输入 输出
+class SimpleNet(Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+
+    @to_static(input_spec=[[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]])
+    def forward(self, inputs):
+        x, y = inputs[0], inputs[1]
+        out = self.linear(x)
+        out = out + y
+        return out
 ```
 
+其中 ``input_spec`` 参数是长度为 1 的 list ，对应 forward 函数的 inputs 参数。 ``input_spec[0]`` 包含了两个 InputSpec 对象，对应于参数 inputs 的两个 Tensor 签名信息。
 
-``convert_ifelse`` 是框架底层的函数，在逐行执行用户代码生成 ``Program`` 时，执行到此处时，会根据**判断条件**的类型（ ``bool`` 还是 ``Bool Tensor`` ），自适应决定是否转为 ``cond_op`` 。
+当函数的参数为dict时， ``input_spec`` 列表中对应元素的位置，也必须是包含相同键（key）的 InputSpec 列表。使用样例如下：
 
 ```python
-def convert_ifelse(pred, true_fn, false_fn, true_args, false_args, return_vars):
+class SimpleNet(Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
 
-    if isinstance(pred, Variable):  # 触发 cond_op 的转换
-        return _run_paddle_cond(pred, true_fn, false_fn, true_args, false_args,
-                                return_vars)
-    else:                           # 正常的 python if
-        return _run_py_ifelse(pred, true_fn, false_fn, true_args, false_args)
+    @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), {'x': InputSpec(shape=[3], name='bias')}])
+    def forward(self, x, bias_info):
+        x_bias = bias_info['x']
+        out = self.linear(x)
+        out = out + x_bias
+        return out
 ```
 
+其中 ``input_spec`` 参数是长度为 2 的 list ，对应 forward 函数的 x 和 bias_info 两个参数。 ``input_spec`` 的最后一个元素是包含键名为 x 的 InputSpec 对象的 dict ，对应参数 bias_info 的 Tensor 签名信息。
 
-### 4.2 For/While
 
-``For/While`` 也会先进行代码层面的规范化，在逐行执行用户代码时，才会决定是否转为 ``while_op``。
+**方式四：指定非Tensor参数类型**
 
-**示例一：不依赖 Tensor 的控制流**
+目前，``to_static`` 装饰器中的 ``input_spec`` 参数仅接收 ``InputSpec`` 类型对象。若被装饰函数的参数列表除了 Tensor 类型，还包含其他如 Int、 String 等非 Tensor 类型时，推荐在函数中使用 kwargs 形式定义非 Tensor 参数，如下述样例中的 use_act 参数。
 
 ```python
-def not_depend_tensor_while(x):
-    a = 1
-
-    while a < 10:           # <---- a is python scalar
-        x = x + 1
-        a += 1
 
-    return x
-
-print(to_static(not_depend_tensor_while).code)
-"""
-def not_depend_tensor_while(x):
-    a = 1
+class SimpleNet(Layer):
+    def __init__(self, ):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+        self.relu = paddle.nn.ReLU()
 
-    def while_condition_0(a, x):
-        return a < 10
+    def forward(self, x, use_act=False):
+        out = self.linear(x)
+        if use_act:
+            out = self.relu(out)
+        return out
 
-    def while_body_0(a, x):
-        x = x + 1
-        a += 1
-        return a, x
+net = SimpleNet()
+# 方式一：save inference model with use_act=False
+net = to_static(input_spec=[InputSpec(shape=[None, 10], name='x')])
+paddle.jit.save(net, path='./simple_net')
 
-    [a, x] = paddle.jit.dy2static.convert_while_loop(while_condition_0,
-        while_body_0, [a, x])
 
-    return x
-"""
+# 方式二：save inference model with use_act=True
+net = to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), True])
+paddle.jit.save(net, path='./simple_net')
 ```
 
 
-**示例二：依赖 Tensor 的控制流**
+在上述样例中，假设 step 为奇数时，use_act 取值为 False ； step 为偶数时， use_act 取值为 True 。动转静支持非 Tensor 参数在训练时取不同的值，且保证了取值不同的训练过程都可以更新模型的网络参数，行为与动态图一致。
 
-```python
-def depend_tensor_while(x):
-    bs = paddle.shape(x)[0]
+在借助 ``paddle.jit.save`` 保存预测模型时，动转静会根据 input_spec 和 kwargs 的默认值保存推理模型和网络参数。**建议将 kwargs 参数默认值设置为预测时的取值。**
 
-    for i in range(bs):       # <---- bas is a Tensor
-        x = x + 1
 
-    return x
+更多关于动转静 ``to_static`` 搭配 ``paddle.jit.save/load`` 的使用方式，可以参考  [【模型的存储与载入】](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/02_paddle2.0_develop/08_model_save_load_cn.html)。
 
-print(to_static(depend_tensor_while).code)
-"""
-def depend_tensor_while(x):
-    bs = paddle.shape(x)[0]
-    i = 0
 
-    def for_loop_condition_0(x, i, bs):
-        return i < bs
+## 三、动、静态图部署区别
 
-    def for_loop_body_0(x, i, bs):
-        x = x + 1
-        i += 1
-        return x, i, bs
+当训练完一个模型后，下一阶段就是保存导出，实现**模型**和**参数**的分发，进行多端部署。如下两小节，将介绍动态图和静态图的概念和差异性，以帮助理解动转静如何起到**桥梁作用**的。
+### 3.1 动态图预测部署
 
-    [x, i, bs] = paddle.jit.dy2static.convert_while_loop(for_loop_condition_0,
-        for_loop_body_0, [x, i, bs])
-    return x
-"""
-```
+动态图下，**模型**指的是 Python 前端代码；**参数**指的是 ``model.state_dict()`` 中存放的权重数据。
 
+```python
+net = SimpleNet()
 
-``convert_while_loop`` 的底层的逻辑同样会根据 **判断条件是否为``Tensor``** 来决定是否转为 ``while_op``
+# .... 训练过程(略)
 
-## 五、 Parameters 与 Buffers
+layer_state_dict = net.state_dict()
+paddle.save(layer_state_dict, "net.pdiparams") # 导出模型
+```
 
-### 5.1 动态图 layer 生成 Program
+<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/dygraph_export.png" style="zoom:50%" />
 
-文档开始的样例中 ``forward`` 函数包含两行组网代码： ``Linear`` 和 ``add`` 操作。以 ``Linear`` 为例，在 Paddle 的框架底层，每个 Paddle 的组网 API 的实现包括两个分支：
+上图展示了动态图下**模型训练——>参数导出——>预测部署**的流程。如图中所示，动态图预测部署时，除了已经序列化的参数文件，还须提供**最初的模型组网代码**。
+
+在动态图下，模型代码是 **逐行被解释执行** 的。如：
 
 ```python
+import paddle
 
-class Linear(...):
-    def __init__(self, ...):
-        # ...(略)
+zeros = paddle.zeros(shape=[1,2], dtype='float32')
+print(zeros)
 
-    def forward(self, input):
+#Tensor(shape=[1, 2], dtype=float32, place=CPUPlace, stop_gradient=True,
+#       [[0., 0.]])
+```
 
-        if in_dygraph_mode():  # 动态图分支
-            core.ops.matmul(input, self.weight, pre_bias, ...)
-            return out
-        else:                  # 静态图分支
-            self._helper.append_op(type="matmul", inputs=inputs, ...)     # <----- 生成一个 Op
-            if self.bias is not None:
-                self._helper.append_op(type='elementwise_add', ...)       # <----- 生成一个 Op
 
-            return out
-```
+**从框架层面上，上述的调用链是：**
 
-动态图 ``layer`` 生成 ``Program`` ，其实是开启 ``paddle.enable_static()`` 时，在静态图下逐行执行用户定义的组网代码，依次添加(对应 ``append_op`` 接口) 到默认的主 Program（即 ``main_program`` ） 中。
+> 前端 zeros 接口 &rarr; core.ops.fill_constant (Pybind11)  &rarr; 后端 Kernel  &rarr; 前端 Tensor 输出
 
-### 5.2 动态图 Tensor 转为静态图 Variable
+如下是一个简单的 Model 示例：
 
-上面提到，所有的组网代码都会在静态图模式下执行，以生成完整的 ``Program`` 。**但静态图 ``append_op`` 有一个前置条件必须满足：**
+```python
 
-> **前置条件**：append_op() 时，所有的 inputs，outputs 必须都是静态图的 Variable 类型，不能是动态图的 Tensor 类型。
+import paddle
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
 
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
+        return out
 
-**原因**：静态图下，操作的都是**描述类单元**：计算相关的 ``OpDesc`` ，数据相关的 ``VarDesc`` 。可以分别简单地理解为 ``Program`` 中的 ``Op`` 和 ``Variable`` 。
+net = SimpleNet()
+```
 
-因此，在动转静时，我们在需要在**某个统一的入口处**，将动态图 ``Layers`` 中 ``Tensor`` 类型（包含具体数据）的 ``Weight`` 、``Bias`` 等变量转换为**同名的静态图 ``Variable``**。
+动态图下，当实例化一个 ``SimpleNet()`` 对象时，隐式地执行了如下几个步骤：
 
-+ ParamBase &rarr; Parameters
-+ VarBase &rarr;   Variable
++ 创建一个 ``Linear`` 对象，记录到 ``self._sub_layer`` 中（dict 类型）
 
-技术实现上，我们选取了框架层面两个地方作为类型**转换的入口**：
+    + 创建一个 ``ParamBase`` 类型的 ``weight`` ，记录到 ``self._parameters`` 中（dict类型）
+    + 创建一个 ``ParamBase`` 类型的 ``bias`` ，记录到 ``self._parameters`` 中
 
-+ ``Paddle.nn.Layer`` 基类的 ``__call__`` 函数
-    ```python
-    def __call__(self, *inputs, **kwargs):
-        # param_guard 会对将 Tensor 类型的 Param 和 buffer 转为静态图 Variable
-        with param_guard(self._parameters), param_guard(self._buffers):
-            # ... forward_pre_hook 逻辑
+一个复杂模型可能包含很多子类，框架层就是通过 ``self._sub_layer`` 和 ``self._parameters`` 两个核心数据结构关联起来的，这也是后续动转静原理上操作的两个核心属性。
 
-            outputs = self.forward(*inputs, **kwargs) # 此处为forward函数
+```python
+sgd = paddle.optimizer.SGD(learning_rate=0.1, parameters=net.parameters())
+                                                              ^
+                                                              |
+                                                         所有待更新参数
+```
 
-            # ... forward_post_hook 逻辑
+### 3.2 静态图预测部署
 
-            return outputs
-    ```
+静态图部署时，**模型**指的是 ``Program`` ；参数指的是所有的 ``Persistable=True`` 的 ``Variable`` 。二者都可以序列化导出为磁盘文件，**与前端代码完全解耦**。
 
-+ ``Block.append_op`` 函数中，生成 ``Op`` 之前
-    ```python
-    def append_op(self, *args, **kwargs):
-        if in_dygraph_mode():
-            # ... (动态图分支)
-        else:
-            inputs=kwargs.get("inputs", None)
-            outputs=kwargs.get("outputs", None)
-            # param_guard 会确保将 Tensor 类型的 inputs 和 outputs 转为静态图 Variable
-            with param_guard(inputs), param_guard(outputs):
-                op = Operator(
-                    block=self,
-                    desc=op_desc,
-                    type=kwargs.get("type", None),
-                    inputs=inputs,
-                    outputs=outputs,
-                    attrs=kwargs.get("attrs", None))
-    ```
+```python
+main_program = paddle.static.default_main_program()
 
+# ...... 训练过程（略）
 
-以上，是动态图转为静态图的两个核心逻辑，总结如下：
+prog_path='main_program.pdimodel'
+paddle.save(main_program, prog_path) # 导出为 .pdimodel
 
-+ 动态图 ``layer`` 调用在动转静时会走底层 ``append_op`` 的分支，以生成 ``Program``
-+ 动态图 ``Tensor`` 转为静态图 ``Variable`` ，并确保编译期的 ``InferShape`` 正确执行
+para_path='main_program.pdiparams'
+paddle.save(main_program.state_dict(), para_path) # 导出为 .pdiparams
+```
 
+<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/static_export.png" style="zoom:50%" />
 
-### 5.3 Buffer 变量
+上图展示了静态图下**模型训练——>模型导出——>预测部署**的流程。如图所示，静态图模型导出时将``Program``和模型参数都导出为磁盘文件，``Program`` 中包含了模型所有的计算描述（ ``OpDesc`` ），不存在计算逻辑有遗漏的地方。
 
-**什么是 ``Buffers`` 变量？**
 
-+ **Parameters**：``persistable`` 为 ``True`` ，且每个 batch 都被 Optimizer 更新的变量
-+ **Buffers**：``persistable`` 为 ``True`` ，``is_trainable = False`` ，不参与更新，但与预测相关；如 ``BatchNorm`` 层中的均值和方差
+**静态图编程，总体上包含两个部分：**
 
-在动态图模型代码中，若一个 ``paddle.to_tensor`` 接口生成的 ``Tensor`` 参与了最终预测结果的的计算，则此 ``Tensor`` 需要在转换为静态图预测模型时，也需要作为一个 ``persistable`` 的变量保存到 ``.pdiparam`` 文件中。
++ **编译期**：组合各个 ``Layer`` 接口，搭建网络结构，执行每个 Op 的 ``InferShape`` 逻辑，最终生成 ``Program``
++ **执行期**：构建执行器，输入数据，依次执行每个 ``OpKernel`` ，进行训练和评估
 
-**举一个例子（错误写法）：**
+在静态图编译期，变量 ``Variable`` 只是**一个符号化表示**，并不像动态图 ``Tensor`` 那样持有实际数据。
 
 ```python
 import paddle
-from paddle.jit import to_static
+# 开启静态图模式
+paddle.enable_static()
 
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self, mask):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
+zeros = paddle.zeros(shape=[1,2], dtype='float32')
+print(zeros)
+# var fill_constant_1.tmp_0 : LOD_TENSOR.shape(1, 2).dtype(float32).stop_gradient(True)
+```
 
-        # mask value，此处不会保存到预测模型文件中
-        self.mask = mask # 假设为 [0, 1, 1]
+**从框架层面上，静态图的调用链：**
 
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        mask = paddle.to_tensor(self.mask)    # <----- 每次执行都转为一个 Tensor
-        out = out * mask
-        return out
-```
+> layer 组网（前端） &rarr; InferShape 检查（编译期） &rarr;  Executor（执行期） &rarr; 逐个执行 OP
 
 
-**推荐的写法是：**
+如下是 ``SimpleNet`` 的静态图模式下的组网代码：
 
 ```python
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self, mask):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
+import paddle
+# 开启静态图模式
+paddle.enable_static()
 
-        # 此处的 mask 会当做一个 buffer Tensor，保存到 .pdiparam 文件
-        self.mask = paddle.to_tensor(mask) # 假设为 [0, 1, 1]
+# placeholder 信息
+x = paddle.static.data(shape=[None, 10], dtype='float32', name='x')
+y = paddle.static.data(shape=[None, 3], dtype='float32', name='y')
 
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        out = out * self.mask                 # <---- 直接使用 self.mask
-        return out
+out = paddle.static.nn.fc(x, 3)
+out = paddle.add(out, y)
+# 打印查看 Program 信息
+print(paddle.static.default_main_program())
+
+# { // block 0
+#    var x : LOD_TENSOR.shape(-1, 10).dtype(float32).stop_gradient(True)
+#    var y : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(True)
+#    persist trainable param fc_0.w_0 : LOD_TENSOR.shape(10, 3).dtype(float32).stop_gradient(False)
+#    var fc_0.tmp_0 : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(False)
+#    persist trainable param fc_0.b_0 : LOD_TENSOR.shape(3,).dtype(float32).stop_gradient(False)
+#    var fc_0.tmp_1 : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(False)
+#    var elementwise_add_0 : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(False)
+
+#    {Out=['fc_0.tmp_0']} = mul(inputs={X=['x'], Y=['fc_0.w_0']}, force_fp32_output = False, op_device = , op_namescope = /, op_role = 0, op_role_var = [], scale_out = 1.0, scale_x = 1.0, scale_y = [1.0], use_mkldnn = False, x_num_col_dims = 1, y_num_col_dims = 1)
+#    {Out=['fc_0.tmp_1']} = elementwise_add(inputs={X=['fc_0.tmp_0'], Y=['fc_0.b_0']}, Scale_out = 1.0, Scale_x = 1.0, Scale_y = 1.0, axis = 1, mkldnn_data_type = float32, op_device = , op_namescope = /, op_role = 0, op_role_var = [], use_mkldnn = False, use_quantizer = False, x_data_format = , y_data_format = )
+#    {Out=['elementwise_add_0']} = elementwise_add(inputs={X=['fc_0.tmp_1'], Y=['y']}, Scale_out = 1.0, Scale_x = 1.0, Scale_y = 1.0, axis = -1, mkldnn_data_type = float32, op_device = , op_namescope = /, op_role = 0, op_role_var = [], use_mkldnn = False, use_quantizer = False, x_data_format = , y_data_format = )
+}
 ```
 
 
-总结一下 ``buffers`` 的用法：
+静态图中的一些概念：
+
++ **Program**：与 ``Model`` 对应，描述网络的整体结构，内含一个或多个 ``Block``
++ **Block**
+    + **global_block**：全局 ``Block`` ，包含所有 ``Parameters`` 、全部 ``Ops`` 和 ``Variables``
+    + **sub_block**：控制流，包含控制流分支内的所有 ``Ops`` 和必要的 ``Variables``
++ **OpDesc**：对应每个前端 API 的计算逻辑描述
++ **Variable**：对应所有的数据变量，如 ``Parameter`` ，临时中间变量等，全局唯一 ``name`` 。
+
+
 
-+  若某个非 ``Tensor`` 数据需要当做 ``Persistable`` 的变量序列化到磁盘，则最好在 ``__init__`` 中调用 ``self.XX= paddle.to_tensor(xx)`` 接口转为 ``buffer`` 变量
+> 注：更多细节，请参考 [【官方文档】模型的存储与载入](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/02_paddle2.0_develop/08_model_save_load_cn.html)。
diff --git a/docs/guides/04_dygraph_to_static/case_analysis_cn.md b/docs/guides/04_dygraph_to_static/case_analysis_cn.md
index 4f1d09cf0fc..3307e753f6f 100644
--- a/docs/guides/04_dygraph_to_static/case_analysis_cn.md
+++ b/docs/guides/04_dygraph_to_static/case_analysis_cn.md
@@ -1,7 +1,7 @@
-# 常见案例解析
+# 案例解析
 
 
-在[【基础接口用法】](./basic_usage_cn.html)章节我们介绍了动转静的用法和机制，下面会结合一些具体的模型代码，解答动转静中比较常见的问题。
+在[【使用样例】](./basic_usage_cn.html)章节介绍了动转静的用法和机制，下面会结合一些具体的模型代码，解答动转静中比较常见的问题。
 
 ## 一、 @to_static 放在哪里？
 
@@ -77,7 +77,7 @@
 
 
 
-> 注：InputSpec 接口的高阶用法，请参看 [【InputSpec 功能介绍】](./input_spec_cn.html#inputspec)
+> 注：InputSpec 接口的高阶用法，请参看 [【使用InputSpec指定模型输入Tensor信息】](./basic_usage_cn.html#inputspec)
 
 ## 三、内嵌 Numpy 操作？
 
@@ -273,7 +273,58 @@ jit.save(mode, model_path)
 
 此 flag 继承自 ``nn.Layer`` ，因此可通过 ``model.train()`` 和 ``model.eval()`` 来全局切换所有 sublayers 的分支状态。
 
-## 七、再谈控制流
+## 七、非forward函数导出
+
+`@to_static` 与 `jit.save` 接口搭配也支持导出非forward 的其他函数，具体使用方式如下：
+
+```python
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
+        return out
+
+    def another_func(self, x):
+        out = self.linear(x)
+        out = out * 2
+        return out
+
+net = SimpleNet()
+# train(net)  # 模型训练
+
+# step 1: 切换到 eval() 模式 （同上）
+net.eval()
+
+# step 2: 定义 InputSpec 信息 （同上）
+x_spec = InputSpec(shape=[None, 3], dtype='float32', name='x')
+
+# step 3: @to_static 装饰
+static_func = to_static(net.another_func, input_spec=[x_spec])
+
+# step 4: 调用 jit.save 接口
+net = paddle.jit.save(static_func, path='another_func')
+```
+
+使用上的区别主要在于：
+
++ **`@to_static` 装饰**：导出其他函数时需要显式地用 `@to_static` 装饰，以告知动静转换模块将其识别、并转为静态图 Program；
++ **`save`接口参数**：调用`jit.save`接口时，需将上述被`@to_static` 装饰后的函数作为**参数**；
+
+执行上述代码样例后，在当前目录下会生成三个文件：
+```
+another_func.pdiparams        // 存放模型中所有的权重数据
+another_func.pdimodel         // 存放模型的网络结构
+another_func.pdiparams.info   // 存放额外的其他信息
+```
+
+
+> 关于动转静 @to_static 的用法，以及搭配 `paddle.jit.save` 接口导出预测模型的用法案例，可以参考 [使用样例](./basic_usage_cn.html) 。
+
+## 八、再谈控制流
 
 前面[【控制流转写】(./basic_usage_cn.html#sikongzhiliuzhuanxie)]提到，不论控制流 ``if/for/while`` 语句是否需要转为静态图中的 ``cond_op/while_op`` ，都会先进行代码规范化，如 ``IfElse`` 语句会规范为如下范式：
 
@@ -293,7 +344,7 @@ out = convert_ifelse(paddle.mean(x) > 5.0, true_fn_0, false_fn_0, (x,), (x,), (o
 ```
 
 
-### 7.1 list 与 LoDTensorArray
+### 8.1 list 与 LoDTensorArray
 
 当控制流中，出现了 ``list.append`` 类似语法时，情况会有一点点特殊。
 
@@ -359,7 +410,7 @@ def forward(x):
 > 因为框架底层的 ``LoDTensorArray = std::vector< LoDTensor >`` ，不支持两层以上 ``vector`` 嵌套
 
 
-### 7.2 x.shape 与 paddle.shape(x)
+### 8.2 x.shape 与 paddle.shape(x)
 
 模型中比较常见的控制流转写大多数与 ``batch_size`` 或者 ``x.shape`` 相关。
 
@@ -381,7 +432,7 @@ def forward(self, x)：
 
 > 动态 shape 推荐使用 ``paddle.shape(x)[i]`` ，动转静也对 ``x.shape[i]`` 做了很多兼容处理。前者写法出错率可能更低些。
 
-## 八、jit.save 与默认参数
+## 九、jit.save 与默认参数
 
 
 最后一步是预测模型的导出，Paddle 提供了 ``paddle.jit.save`` 接口，搭配 ``@to_static`` 可以导出预测模型。
diff --git a/docs/guides/04_dygraph_to_static/debugging_cn.md b/docs/guides/04_dygraph_to_static/debugging_cn.md
index 6b53dd82235..2fc3cf77d86 100644
--- a/docs/guides/04_dygraph_to_static/debugging_cn.md
+++ b/docs/guides/04_dygraph_to_static/debugging_cn.md
@@ -1,4 +1,4 @@
-# 报错调试经验
+# 报错调试
 
 ## 一、动转静报错日志
 ### 1.1 错误日志怎么看
diff --git a/docs/guides/04_dygraph_to_static/export_model_cn.md b/docs/guides/04_dygraph_to_static/export_model_cn.md
deleted file mode 100644
index 44e5dea3c73..00000000000
--- a/docs/guides/04_dygraph_to_static/export_model_cn.md
+++ /dev/null
@@ -1,472 +0,0 @@
-# 预测模型导出
-
-
-## 一、动转静模型导出
-
-动转静模块**是架在动态图与静态图的一个桥梁**，旨在打破动态图与静态部署的鸿沟，消除部署时对模型代码的依赖，打通与预测端的交互逻辑。
-
-<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/to_static_export.png" style="zoom:50%" />
-
-
-
-在处理逻辑上，动转静主要包含两个主要模块：
-
-+ **代码层面**：将所有的 Paddle ``layers`` 接口在静态图模式下执行以转为 ``Op`` ，从而生成完整的静态 ``Program``
-+ **Tensor层面**：将所有的 ``Parameters`` 和 ``Buffers`` 转为**可导出的 ``Variable`` 参数**（ ``persistable=True`` ）
-
-
-### 1.1 forward 函数导出
-
-如下是一个简单的 ``Model`` 的代码：
-
-```python
-import paddle
-from paddle.jit import to_static
-from paddle.static import InputSpec
-
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        return out
-
-    def another_func(self, x):
-        out = self.linear(x)
-        out = out * 2
-        return out
-
-net = SimpleNet()
-# train(net)  模型训练 (略)
-
-# step 1: 切换到 eval() 模式
-net.eval()
-
-# step 2: 定义 InputSpec 信息
-x_spec = InputSpec(shape=[None, 3], dtype='float32', name='x')
-y_spec = InputSpec(shape=[3], dtype='float32', name='y')
-
-# step 3: 调用 jit.save 接口
-net = paddle.jit.save(net, path='simple_net', input_spec=[x_spec, y_spec])  # 动静转换
-```
-
-执行上述代码样例后，在当前目录下会生成三个文件：
-```
-simple_net.pdiparams        // 存放模型中所有的权重数据
-simple_net.pdimodel         // 存放模型的网络结构
-simple_net.pdiparams.info   // 存放额外的其他信息
-```
-
-
-预测模型导出一般包括三个步骤：
-
-+ **切换 `eval()` 模式**：类似 `Dropout` 、`LayerNorm` 等接口在 `train()` 和 `eval()` 的行为存在较大的差异，在模型导出前，**请务必确认模型已切换到正确的模式，否则导出的模型在预测阶段可能出现输出结果不符合预期的情况。**
-+ **构造 `InputSpec` 信息**：InputSpec 用于表示输入的shape、dtype、name信息，且支持用 `None` 表示动态shape（如输入的 batch_size 维度），是辅助动静转换的必要描述信息。
-+ **调用 `save` 接口**：调用 `paddle.jit.save`接口，若传入的参数是类实例，则默认对 `forward` 函数进行 `@to_static` 装饰，并导出其对应的模型文件和参数文件。
-
-
-### 1.2 其他函数导出
-
-`@to_static` 与 `jit.save` 接口搭配也支持导出非forward 的其他函数，具体使用方式如下：
-
-```python
-# SimpleNet 类的定义见 1.1
-
-net = SimpleNet()
-# train(net)  # 模型训练
-
-# step 1: 切换到 eval() 模式 （同上）
-net.eval()
-
-# step 2: 定义 InputSpec 信息 （同上）
-x_spec = InputSpec(shape=[None, 3], dtype='float32', name='x')
-
-# step 3: @to_static 装饰
-static_func = to_static(net.another_func, input_spec=[x_spec])
-
-# step 4: 调用 jit.save 接口
-net = paddle.jit.save(static_func, path='another_func')
-```
-
-使用上的区别主要在于：
-
-+ **`@to_static` 装饰**：导出其他函数时需要显式地用 `@to_static` 装饰，以告知动静转换模块将其识别、并转为静态图 Program；
-+ **`save`接口参数**：调用`jit.save`接口时，需将上述被`@to_static` 装饰后的函数作为**参数**；
-
-执行上述代码样例后，在当前目录下会生成三个文件：
-```
-another_func.pdiparams        // 存放模型中所有的权重数据
-another_func.pdimodel         // 存放模型的网络结构
-another_func.pdiparams.info   // 存放额外的其他信息
-```
-
-
-> 关于动转静 @to_static 的用法，可以参考 [基本用法](./basic_usage_cn.html)；搭配 `paddle.jit.save` 接口导出预测模型的用法案例，可以参考 [案例解析](./case_analysis_cn.html) 。
-
-
-### 1.3 InputSpec 功能介绍
-
-动静转换在生成静态图 Program 时，依赖输入 Tensor 的 shape、dtype 和 name 信息。因此，Paddle 提供了 InputSpec 接口，用于指定输入 Tensor 的描述信息，并支持动态 shape 特性。
-
-
-#### 1.3.1 InputSpec 构造
-
-
-**方式一：直接构造**
-
-
-InputSpec 接口在 ``paddle.static`` 目录下， 只有 ``shape`` 是必须参数， ``dtype`` 和 ``name`` 可以缺省，默认取值分别为 ``float32`` 和 ``None`` 。使用样例如下：
-
-```python
-from paddle.static import InputSpec
-
-x = InputSpec([None, 784], 'float32', 'x')
-label = InputSpec([None, 1], 'int64', 'label')
-
-print(x)      # InputSpec(shape=(-1, 784), dtype=VarType.FP32, name=x)
-print(label)  # InputSpec(shape=(-1, 1), dtype=VarType.INT64, name=label)
-```
-
-
-**方式二：由 Tensor 构造**
-
-可以借助 ``InputSpec.from_tensor`` 方法，从一个 Tensor 直接创建 InputSpec 对象，其拥有与源 Tensor 相同的 ``shape`` 和 ``dtype`` 。 使用样例如下：
-
-```python
-import numpy as np
-import paddle
-from paddle.static import InputSpec
-
-x = paddle.to_tensor(np.ones([2, 2], np.float32))
-x_spec = InputSpec.from_tensor(x, name='x')
-print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
-```
-
-> 注：若未在 ``from_tensor`` 中指定新的name，则默认使用与源Tensor相同的name。
-
-
-**方式三：由 numpy.ndarray**
-
-也可以借助 ``InputSpec.from_numpy`` 方法，从一个 `Numpy.ndarray` 直接创建 InputSpec 对象，其拥有与源 ndarray 相同的 ``shape`` 和 ``dtype`` 。使用样例如下：
-
-```python
-import numpy as np
-from paddle.static import InputSpec
-
-x = np.ones([2, 2], np.float32)
-x_spec = InputSpec.from_numpy(x, name='x')
-print(x_spec)  # InputSpec(shape=(2, 2), dtype=VarType.FP32, name=x)
-```
-
-> 注：若未在 ``from_numpy`` 中指定新的 name，则默认使用 None 。
-
-
-#### 1.3.2 基本用法
-
-**方式一： @to_static 装饰器模式**
-
-如下是一个简单的使用样例：
-
-```python
-import paddle
-from paddle.jit import to_static
-from paddle.static import InputSpec
-from paddle.fluid.dygraph import Layer
-
-class SimpleNet(Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-
-    @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')])
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        return out
-
-net = SimpleNet()
-
-# save static model for inference directly
-paddle.jit.save(net, './simple_net')
-```
-
-在上述的样例中， ``@to_static`` 装饰器中的 ``input_spec`` 为一个 InputSpec 对象组成的列表，用于依次指定参数 x 和 y 对应的 Tensor 签名信息。在实例化 SimpleNet 后，可以直接调用 ``paddle.jit.save`` 保存静态图模型，不需要执行任何其他的代码。
-
-> 注：
->    1. input_spec 参数中不仅支持 InputSpec 对象，也支持 int 、 float 等常见 Python 原生类型。
->    2. 若指定 input_spec 参数，则需为被装饰函数的所有必选参数都添加对应的 InputSpec 对象，如上述样例中，不支持仅指定 x 的签名信息。
->    3. 若被装饰函数中包括非 Tensor 参数，推荐函数的非 Tensor 参数设置默认值，如 ``forward(self, x, use_bn=False)``
-
-
-**方式二：to_static函数调用**
-
-若期望在动态图下训练模型，在训练完成后保存预测模型，并指定预测时需要的签名信息，则可以选择在保存模型时，直接调用 ``to_static`` 函数。使用样例如下：
-
-```python
-class SimpleNet(Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        return out
-
-net = SimpleNet()
-
-# train process (Pseudo code)
-for epoch_id in range(10):
-    train_step(net, train_reader)
-
-net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')])
-
-# save static model for inference directly
-paddle.jit.save(net, './simple_net')
-```
-
-如上述样例代码中，在完成训练后，可以借助 ``to_static(net, input_spec=...)`` 形式对模型实例进行处理。Paddle 会根据 input_spec 信息对 forward 函数进行递归的动转静，得到完整的静态图，且包括当前训练好的参数数据。
-
-
-**方式三：支持 list 和 dict 推导**
-
-上述两个样例中，被装饰的 forward 函数的参数均为 Tensor 。这种情况下，参数个数必须与 InputSpec 个数相同。但当被装饰的函数参数为 list 或 dict 类型时，``input_spec`` 需要与函数参数保持相同的嵌套结构。
-
-当函数的参数为 list 类型时，input_spec 列表中对应元素的位置，也必须是包含相同元素的 InputSpec 列表。使用样例如下：
-
-```python
-class SimpleNet(Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-
-    @to_static(input_spec=[[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]])
-    def forward(self, inputs):
-        x, y = inputs[0], inputs[1]
-        out = self.linear(x)
-        out = out + y
-        return out
-```
-
-其中 ``input_spec`` 参数是长度为 1 的 list ，对应 forward 函数的 inputs 参数。 ``input_spec[0]`` 包含了两个 InputSpec 对象，对应于参数 inputs 的两个 Tensor 签名信息。
-
-当函数的参数为dict时， ``input_spec`` 列表中对应元素的位置，也必须是包含相同键（key）的 InputSpec 列表。使用样例如下：
-
-```python
-class SimpleNet(Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-
-    @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), {'x': InputSpec(shape=[3], name='bias')}])
-    def forward(self, x, bias_info):
-        x_bias = bias_info['x']
-        out = self.linear(x)
-        out = out + x_bias
-        return out
-```
-
-其中 ``input_spec`` 参数是长度为 2 的 list ，对应 forward 函数的 x 和 bias_info 两个参数。 ``input_spec`` 的最后一个元素是包含键名为 x 的 InputSpec 对象的 dict ，对应参数 bias_info 的 Tensor 签名信息。
-
-
-**方式四：指定非Tensor参数类型**
-
-目前，``to_static`` 装饰器中的 ``input_spec`` 参数仅接收 ``InputSpec`` 类型对象。若被装饰函数的参数列表除了 Tensor 类型，还包含其他如 Int、 String 等非 Tensor 类型时，推荐在函数中使用 kwargs 形式定义非 Tensor 参数，如下述样例中的 use_act 参数。
-
-```python
-
-class SimpleNet(Layer):
-    def __init__(self, ):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-        self.relu = paddle.nn.ReLU()
-
-    def forward(self, x, use_act=False):
-        out = self.linear(x)
-        if use_act:
-            out = self.relu(out)
-        return out
-
-net = SimpleNet()
-# 方式一：save inference model with use_act=False
-net = to_static(input_spec=[InputSpec(shape=[None, 10], name='x')])
-paddle.jit.save(net, path='./simple_net')
-
-
-# 方式二：save inference model with use_act=True
-net = to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), True])
-paddle.jit.save(net, path='./simple_net')
-```
-
-
-在上述样例中，假设 step 为奇数时，use_act 取值为 False ； step 为偶数时， use_act 取值为 True 。动转静支持非 Tensor 参数在训练时取不同的值，且保证了取值不同的训练过程都可以更新模型的网络参数，行为与动态图一致。
-
-在借助 ``paddle.jit.save`` 保存预测模型时，动转静会根据 input_spec 和 kwargs 的默认值保存推理模型和网络参数。**建议将 kwargs 参数默认值设置为预测时的取值。**
-
-
-更多关于动转静 ``to_static`` 搭配 ``paddle.jit.save/load`` 的使用方式，可以参考  [【模型的存储与载入】](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/02_paddle2.0_develop/08_model_save_load_cn.html)。
-
-
-## 二、动、静态图部署区别
-
-当训练完一个模型后，下一阶段就是保存导出，实现**模型**和**参数**的分发，进行多端部署。如下两小节，将介绍动态图和静态图的概念和差异性，以帮助理解动转静如何起到**桥梁作用**的。
-### 2.1 动态图预测部署
-
-动态图下，**模型**指的是 Python 前端代码；**参数**指的是 ``model.state_dict()`` 中存放的权重数据。
-
-```python
-net = SimpleNet()
-
-# .... 训练过程(略)
-
-layer_state_dict = net.state_dict()
-paddle.save(layer_state_dict, "net.pdiparams") # 导出模型
-```
-
-<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/dygraph_export.png" style="zoom:50%" />
-
-即意味着，动态图预测部署时，除了已经序列化的参数文件，还须提供**最初的模型组网代码**。
-
-在动态图下，模型代码是 **逐行被解释执行** 的。如：
-
-```python
-import paddle
-
-zeros = paddle.zeros(shape=[1,2], dtype='float32')
-print(zeros)
-
-#Tensor(shape=[1, 2], dtype=float32, place=CPUPlace, stop_gradient=True,
-#       [[0., 0.]])
-```
-
-
-**从框架层面上，上述的调用链是：**
-
-> 前端 zeros 接口 &rarr; core.ops.fill_constant (Pybind11)  &rarr; 后端 Kernel  &rarr; 前端 Tensor 输出
-
-如下是一个简单的 Model 示例：
-
-```python
-
-import paddle
-
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 3)
-
-    def forward(self, x, y):
-        out = self.linear(x)
-        out = out + y
-        return out
-
-net = SimpleNet()
-```
-
-动态图下，当实例化一个 ``SimpleNet()`` 对象时，隐式地执行了如下几个步骤：
-
-+ 创建一个 ``Linear`` 对象，记录到 ``self._sub_layer`` 中（dict 类型）
-
-    + 创建一个 ``ParamBase`` 类型的 ``weight`` ，记录到 ``self._parameters`` 中（dict类型）
-    + 创建一个 ``ParamBase`` 类型的 ``bias`` ，记录到 ``self._parameters`` 中
-
-一个复杂模型可能包含很多子类，框架层就是通过 ``self._sub_layer`` 和 ``self._parameters`` 两个核心数据结构关联起来的，这也是后续动转静原理上操作的两个核心属性。
-
-```python
-sgd = paddle.optimizer.SGD(learning_rate=0.1, parameters=net.parameters())
-                                                              ^
-                                                              |
-                                                         所有待更新参数
-```
-
-### 2.2 静态图预测部署
-
-静态图部署时，**模型**指的是 ``Program`` ；参数指的是所有的 ``Persistable=True`` 的 ``Variable`` 。二者都可以序列化导出为磁盘文件，**与前端代码完全解耦**。
-
-```python
-main_program = paddle.static.default_main_program()
-
-# ...... 训练过程（略）
-
-prog_path='main_program.pdimodel'
-paddle.save(main_program, prog_path) # 导出为 .pdimodel
-
-para_path='main_program.pdiparams'
-paddle.save(main_program.state_dict(), para_path) # 导出为 .pdiparams
-```
-
-<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/static_export.png" style="zoom:50%" />
-
-
-即意味着， ``Program`` 中包含了模型所有的计算描述（ ``OpDesc`` ），不存在计算逻辑有遗漏的地方。
-
-
-**静态图编程，总体上包含两个部分：**
-
-+ **编译期**：组合各个 ``Layer`` 接口，搭建网络结构，执行每个 Op 的 ``InferShape`` 逻辑，最终生成 ``Program``
-+ **执行期**：构建执行器，输入数据，依次执行每个 ``OpKernel`` ，进行训练和评估
-
-在静态图编译期，变量 ``Variable`` 只是**一个符号化表示**，并不像动态图 ``Tensor`` 那样持有实际数据。
-
-```python
-import paddle
-# 开启静态图模式
-paddle.enable_static()
-
-zeros = paddle.zeros(shape=[1,2], dtype='float32')
-print(zeros)
-# var fill_constant_1.tmp_0 : LOD_TENSOR.shape(1, 2).dtype(float32).stop_gradient(True)
-```
-
-**从框架层面上，静态图的调用链：**
-
-> layer 组网（前端） &rarr; InferShape 检查（编译期） &rarr;  Executor（执行期） &rarr; 逐个执行 OP
-
-
-如下是 ``SimpleNet`` 的静态图模式下的组网代码：
-
-```python
-import paddle
-# 开启静态图模式
-paddle.enable_static()
-
-# placeholder 信息
-x = paddle.static.data(shape=[None, 10], dtype='float32', name='x')
-y = paddle.static.data(shape=[None, 3], dtype='float32', name='y')
-
-out = paddle.static.nn.fc(x, 3)
-out = paddle.add(out, y)
-# 打印查看 Program 信息
-print(paddle.static.default_main_program())
-
-# { // block 0
-#    var x : LOD_TENSOR.shape(-1, 10).dtype(float32).stop_gradient(True)
-#    var y : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(True)
-#    persist trainable param fc_0.w_0 : LOD_TENSOR.shape(10, 3).dtype(float32).stop_gradient(False)
-#    var fc_0.tmp_0 : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(False)
-#    persist trainable param fc_0.b_0 : LOD_TENSOR.shape(3,).dtype(float32).stop_gradient(False)
-#    var fc_0.tmp_1 : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(False)
-#    var elementwise_add_0 : LOD_TENSOR.shape(-1, 3).dtype(float32).stop_gradient(False)
-
-#    {Out=['fc_0.tmp_0']} = mul(inputs={X=['x'], Y=['fc_0.w_0']}, force_fp32_output = False, op_device = , op_namescope = /, op_role = 0, op_role_var = [], scale_out = 1.0, scale_x = 1.0, scale_y = [1.0], use_mkldnn = False, x_num_col_dims = 1, y_num_col_dims = 1)
-#    {Out=['fc_0.tmp_1']} = elementwise_add(inputs={X=['fc_0.tmp_0'], Y=['fc_0.b_0']}, Scale_out = 1.0, Scale_x = 1.0, Scale_y = 1.0, axis = 1, mkldnn_data_type = float32, op_device = , op_namescope = /, op_role = 0, op_role_var = [], use_mkldnn = False, use_quantizer = False, x_data_format = , y_data_format = )
-#    {Out=['elementwise_add_0']} = elementwise_add(inputs={X=['fc_0.tmp_1'], Y=['y']}, Scale_out = 1.0, Scale_x = 1.0, Scale_y = 1.0, axis = -1, mkldnn_data_type = float32, op_device = , op_namescope = /, op_role = 0, op_role_var = [], use_mkldnn = False, use_quantizer = False, x_data_format = , y_data_format = )
-}
-```
-
-
-静态图中的一些概念：
-
-+ **Program**：与 ``Model`` 对应，描述网络的整体结构，内含一个或多个 ``Block``
-+ **Block**
-    + **global_block**：全局 ``Block`` ，包含所有 ``Parameters`` 、全部 ``Ops`` 和 ``Variables``
-    + **sub_block**：控制流，包含控制流分支内的所有 ``Ops`` 和必要的 ``Variables``
-+ **OpDesc**：对应每个前端 API 的计算逻辑描述
-+ **Variable**：对应所有的数据变量，如 ``Parameter`` ，临时中间变量等，全局唯一 ``name`` 。
-
-
-
-> 注：更多细节，请参考 [【官方文档】模型的存储与载入](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/02_paddle2.0_develop/08_model_save_load_cn.html)。
diff --git a/docs/guides/04_dygraph_to_static/grammar_list_cn.md b/docs/guides/04_dygraph_to_static/grammar_list_cn.md
index a82c4a47163..bc9e73f7fdf 100644
--- a/docs/guides/04_dygraph_to_static/grammar_list_cn.md
+++ b/docs/guides/04_dygraph_to_static/grammar_list_cn.md
@@ -1,8 +1,8 @@
-# 语法支持列表
+# 支持语法
 
 ## 一、主要针对场景
 
-本文档概览性介绍了飞桨动转静功能的语法支持情况，旨在向用户提供一个便捷的语法速查表，**主要适用于如下场景**：
+本文档概览性介绍了飞桨动转静功能的语法支持情况，旨在提供一个便捷的语法速查表，**主要适用于如下场景**：
 
 1. 不确定当前动态图模型是否可以正确转化为静态图
 
@@ -11,11 +11,9 @@
 3. 当出现不支持的语法时，如何修改源码适配动转静语法
 
 
-若您初次接触动转静功能，或对此功能尚不熟悉，推荐您阅读：[基础接口用法](./basic_usage_cn.html)；
+若你初次接触动转静功能，或对此功能尚不熟悉，推荐阅读：[使用样例](./basic_usage_cn.html)；
 
-若您想进行预测模型导出，或想深入了解此模块，推荐您阅读：[预测模型导出](./export_model_cn.html)；
-
-若您动静转换遇到了问题，或想学习调试的技巧，推荐您阅读：[报错调试经验](./debugging_cn.html)。
+若你动静转换遇到了问题，或想学习调试的技巧，推荐阅读：[报错调试](./debugging_cn.html)。
 
 ## 二、语法支持速查列表
 
diff --git a/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png b/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png
new file mode 100644
index 00000000000..9db5392c5b2
Binary files /dev/null and b/docs/guides/04_dygraph_to_static/images/dygraph_to_static.png differ
diff --git a/docs/guides/04_dygraph_to_static/index_cn.rst b/docs/guides/04_dygraph_to_static/index_cn.rst
index b54141fa883..bf443b324a9 100644
--- a/docs/guides/04_dygraph_to_static/index_cn.rst
+++ b/docs/guides/04_dygraph_to_static/index_cn.rst
@@ -2,21 +2,57 @@
 动态图转静态图
 ###############
 
-动态图在接口易用性，交互式调试等方面具有诸多优势，但在工业界的许多部署场景中（如大型推荐系统、移动端）Python执行开销较大，与C++有一定的差距，静态图部署更具优势。
+=========================
+什么是动态图和静态图？
+=========================
 
-PaddlePaddle 在2.0版本之后，正式支持动态图转静态图（@to_static）的功能，对动态图代码进行智能化分析，自动转换为静态图网络结构，兼顾了动态图易用性和静态图部署性能两方面的优势。
+在深度学习模型构建上，飞桨框架支持动态图编程和静态图编程两种方式，其代码编写和执行方式均存在差异。
 
-如下将详细地介绍动静转换的各个模块内容：
+* **动态图编程：** 采用 Python 的编程风格，解析式地执行每一行网络代码，并同时返回计算结果。在 `模型开发 <../02_paddle2.0_develop/index_cn.html>`_ 章节中，介绍的都是动态图编程方式。
 
-- `基础接口用法 <basic_usage_cn.html>`_ : 介绍了动静转换 @to_static 的基本用法
+* **静态图编程：** 采用先编译后执行的方式。需先在代码中预定义完整的神经网络结构，飞桨框架会将神经网络描述为 `Program` 的数据结构，并对 `Program` 进行编译优化，再调用执行器获得计算结果。
 
-- `语法支持列表 <grammar_list_cn.html>`_ ：介绍了动静转换功能已支持的语法概况
+动态图编程体验更佳、更易调试，但是因为采用 Python 实时执行的方式，开销较大，在性能方面与 C++ 有一定差距；静态图调试难度大，但是将前端 Python 编写的神经网络预定义为 Program描述，转到 C++ 端重新解析执行，脱离了 Python 依赖，往往执行性能更佳，并且预先拥有完整网络结构也更利于全局优化。
 
-- `预测模型导出 <./export_model/index_cn.html>`_ ：介绍了导出动态图预测模型的详细教程
+=========================
+什么场景下需要动态图转静态图？
+=========================
 
-- `常见案例解析 <./case_analysis_cn.html>`_ : 介绍使用 @to_static 时常见的问题和案例解析
+飞桨框架在设计时，考虑同时兼顾动态图的高易用性和静态图的高性能优势，采用『动静统一』的方案：
 
-- `报错调试经验 <debugging_cn.html>`_ ：介绍了动静转换 @to_static 的调试方法和经验
+* **在模型开发时，推荐采用动态图编程。** 可获得更好的编程体验、更易用的接口、更友好的调试交互机制。
+
+* **在模型训练或者推理部署时，只需添加一行装饰器 @to_static，即可将动态图代码转写为静态图代码，并在底层自动使用静态图执行器运行。** 可获得更好的模型运行性能。
+
+方案如下图所示：
+
+.. image:: images/dygraph_to_static.png
+    :width: 800px
+    :class: center
+
+.. rst-class:: center
+
+图1 飞桨框架动静统一方案示意图
+
+
+.. note::
+    飞桨框架 2.0 及以上版本默认的编程模式是动态图模式，包括使用高层 API 编程和基础的 API 编程。如果想切换到静态图模式编程，可以在程序的开始执行 `enable_static()` 函数。如果程序已经使用动态图的模式编写了，想转成静态图模式训练或者保存模型用于部署，可以使用装饰器 @to_static。
+
+想了解动态图和静态图的详细对比介绍，可参见
+`动态图和静态图的差异 <https://www.paddlepaddle.org.cn/tutorials/projectdetail/2134396#anchor-8>`_。
+
+
+**以下将详细地介绍动静转换的各个模块内容：**
+
+- `使用样例 <basic_usage_cn.html>`_ : 介绍了动静转换 @to_static 的基本用法
+
+- `转换原理 <principle_cn..html>`_ ：介绍了动静转换的内部原理
+
+- `支持语法 <grammar_list_cn.html>`_ ：介绍了动静转换功能已支持的语法概况
+
+- `案例解析 <./case_analysis_cn.html>`_ : 介绍使用 @to_static 时常见的问题和案例解析
+
+- `报错调试 <debugging_cn.html>`_ ：介绍了动静转换 @to_static 的调试方法和经验
 
 
 
@@ -24,8 +60,7 @@ PaddlePaddle 在2.0版本之后，正式支持动态图转静态图（@to_static
     :hidden:
 
     basic_usage_cn.rst    
+    principle_cn.md
     grammar_list_cn.md
-    export_model_cn.md
     case_analysis_cn.md
-    debugging_cn.md
-
+    debugging_cn.md
\ No newline at end of file
diff --git a/docs/guides/04_dygraph_to_static/principle_cn.md b/docs/guides/04_dygraph_to_static/principle_cn.md
new file mode 100644
index 00000000000..39fad087ac7
--- /dev/null
+++ b/docs/guides/04_dygraph_to_static/principle_cn.md
@@ -0,0 +1,440 @@
+# 转换原理
+
+在框架内部，动转静模块在转换上主要包括对InputSpec的处理，对函数调用的递归转写，对IfElse、For、While控制语句的转写，以及Layer的Parameters和Buffers变量的转换。下面将从这四个方面介绍动转静模块的转换原理。
+
+## 一、 设置 Placeholder 信息
+
+
+静态图下，模型起始的 Placeholder 信息是通过 ``paddle.static.data`` 来指定的，并以此作为编译期的 ``InferShape`` 推导起点。
+
+```python
+import paddle
+# 开启静态图模式
+paddle.enable_static()
+
+# placeholder 信息
+x = paddle.static.data(shape=[None, 10], dtype='float32', name='x')
+y = paddle.static.data(shape=[None, 3], dtype='float32', name='y')
+
+out = paddle.static.nn.fc(x, 3)
+out = paddle.add(out, y)
+```
+
+
+动转静代码示例，通过 ``InputSpec`` 设置 ``Placeholder`` 信息：
+
+```python
+import paddle
+from paddle.jit import to_static
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+
+    @to_static
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
+        return out
+
+net = SimpleNet()
+
+# 通过 InputSpec 设置 Placeholder 信息
+x_spec = InputSpec(shape=[None, 10], name='x') 
+y_spec = InputSpec(shape=[3], name='y')
+
+net = paddle.jit.to_static(net, input_spec=[x_spec, y_spec])  # 动静转换
+```
+
+
+在导出模型时，需要显式地指定输入 ``Tensor`` 的**签名信息**，优势是：
+
+
++ 可以指定某些维度为 ``None`` ， 如 ``batch_size`` ，``seq_len`` 维度
++ 可以指定 Placeholder 的 ``name`` ，方面预测时根据 ``name`` 输入数据
+
+> 注：InputSpec 接口的高阶用法，请参看 [【使用InputSpec指定模型输入Tensor信息】](./basic_usage_cn.html#inputspec)
+
+
+## 二、函数转写
+
+在 NLP、CV 领域中，一个模型常包含层层复杂的子函数调用，动转静中是如何实现**只需装饰最外层的 ``forward`` 函数**，就能递归处理所有的函数。
+
+如下是一个模型样例：
+
+```python
+import paddle
+from paddle.jit import to_static
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+
+    @to_static
+    def forward(self, x, y):
+        out = self.my_fc(x)       # <---- self.other_func
+        out = add_two(out, y)     # <---- other plain func
+        return out
+
+    def my_fc(self, x):
+        out = self.linear(x)
+        return out
+
+# 此函数可以在任意文件
+def add_two(x, y):
+    out = x + y
+    return out
+
+net = SimpleNet()
+# 查看转写的代码内容
+paddle.jit.set_code_level(100)
+
+x = paddle.zeros([2,10], 'float32')
+y = paddle.zeros([3], 'float32')
+
+out = net(x, y)
+```
+
+可以通过 ``paddle.jit.set_code_level(100)`` 在执行时打印代码转写的结果到终端，转写代码如下：
+
+```python
+def forward(self, x, y):
+    out = paddle.jit.dy2static.convert_call(self.my_fc)(x)
+    out = paddle.jit.dy2static.convert_call(add_two)(out, y)
+    return out
+
+def my_fc(self, x):
+    out = paddle.jit.dy2static.convert_call(self.linear)(x)
+    return out
+
+def add_two(x, y):
+    out = x + y
+    return out
+```
+
+
+如上所示，所有的函数调用都会被转写如下形式：
+
+```python
+ out = paddle.jit.dy2static.convert_call( self.my_fc )( x )
+  ^                    ^                      ^         ^
+  |                    |                      |         |
+返回列表           convert_call             原始函数    参数列表
+```
+
+即使函数定义分布在不同的文件中， ``convert_call`` 函数也会递归地处理和转写所有嵌套的子函数。
+
+## 三、控制流转写
+
+控制流 ``if/for/while`` 的转写和处理是动转静中比较重要的模块，也是动态图模型和静态图模型实现上差别最大的一部分。如下图所示，对于控制流的转写分为两个阶段：转写期和执行期。在转写期，动转静模块将控制流语句转写为统一的形式；在执行期，根据控制流是否依赖 ``Tensor`` 来决定是否将控制流转写为相应的 ``cond_op/while_op`` 。
+
+<img src="https://raw.githubusercontent.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/convert_cond.png" style="zoom:80%" />
+
+**转写上有两个基本原则：**
+
++ **并非**所有动态图中的 ``if/for/while`` 都会转写为 ``cond_op/while_op``
++ **只有**控制流的判断条件 **依赖了``Tensor``**（如 ``shape`` 或 ``value`` ），才会转写为对应 Op
+
+
+
+
+### 3.1 IfElse
+
+无论是否会转写为 ``cond_op`` ，动转静都会首先对代码进行处理，**转写为 ``cond`` 接口可以接受的写法**
+
+**示例一：不依赖 Tensor 的控制流**
+
+```python
+def not_depend_tensor_if(x, label=None):
+    out = x + 1
+    if label is not None:              # <----- python bool 类型
+        out = paddle.nn.functional.cross_entropy(out, label)
+    return out
+
+print(to_static(not_depend_tensor_ifw).code)
+# 转写后的代码：
+"""
+def not_depend_tensor_if(x, label=None):
+    out = x + 1
+
+    def true_fn_1(label, out):  # true 分支
+        out = paddle.nn.functional.cross_entropy(out, label)
+        return out
+
+    def false_fn_1(out):        # false 分支
+        return out
+
+    out = paddle.jit.dy2static.convert_ifelse(label is not None, true_fn_1,
+        false_fn_1, (label, out), (out,), (out,))
+
+    return out
+"""
+```
+
+
+**示例二：依赖 Tensor 的控制流**
+
+```python
+def depend_tensor_if(x):
+    if paddle.mean(x) > 5.:         # <---- Bool Tensor 类型
+        out = x - 1
+    else:
+        out = x + 1
+    return out
+
+print(to_static(depend_tensor_if).code)
+# 转写后的代码：
+"""
+def depend_tensor_if(x):
+    out = paddle.jit.dy2static.data_layer_not_check(name='out', shape=[-1],
+        dtype='float32')
+
+    def true_fn_0(x):      # true 分支
+        out = x - 1
+        return out
+
+    def false_fn_0(x):     # false 分支
+        out = x + 1
+        return out
+
+    out = paddle.jit.dy2static.convert_ifelse(paddle.mean(x) > 5.0,
+        true_fn_0, false_fn_0, (x,), (x,), (out,))
+
+    return out
+"""
+```
+
+
+规范化代码之后，所有的 ``IfElse`` 均转为了如下形式：
+
+```python
+ out = convert_ifelse(paddle.mean(x) > 5.0, true_fn_0, false_fn_0, (x,), (x,), (out,))
+  ^          ^                   ^             ^           ^        ^      ^      ^
+  |          |                   |             |           |        |      |      |
+ 输出   convert_ifelse          判断条件       true分支   false分支  分支输入 分支输入 输出
+```
+
+
+``convert_ifelse`` 是框架底层的函数，在逐行执行用户代码生成 ``Program`` 时，执行到此处时，会根据**判断条件**的类型（ ``bool`` 还是 ``Bool Tensor`` ），自适应决定是否转为 ``cond_op`` 。
+
+```python
+def convert_ifelse(pred, true_fn, false_fn, true_args, false_args, return_vars):
+
+    if isinstance(pred, Variable):  # 触发 cond_op 的转换
+        return _run_paddle_cond(pred, true_fn, false_fn, true_args, false_args,
+                                return_vars)
+    else:                           # 正常的 python if
+        return _run_py_ifelse(pred, true_fn, false_fn, true_args, false_args)
+```
+
+
+### 3.2 For/While
+
+``For/While`` 也会先进行代码层面的规范化，在逐行执行用户代码时，才会决定是否转为 ``while_op``。
+
+**示例一：不依赖 Tensor 的控制流**
+
+```python
+def not_depend_tensor_while(x):
+    a = 1
+
+    while a < 10:           # <---- a is python scalar
+        x = x + 1
+        a += 1
+
+    return x
+
+print(to_static(not_depend_tensor_while).code)
+"""
+def not_depend_tensor_while(x):
+    a = 1
+
+    def while_condition_0(a, x):
+        return a < 10
+
+    def while_body_0(a, x):
+        x = x + 1
+        a += 1
+        return a, x
+
+    [a, x] = paddle.jit.dy2static.convert_while_loop(while_condition_0,
+        while_body_0, [a, x])
+
+    return x
+"""
+```
+
+
+**示例二：依赖 Tensor 的控制流**
+
+```python
+def depend_tensor_while(x):
+    bs = paddle.shape(x)[0]
+
+    for i in range(bs):       # <---- bas is a Tensor
+        x = x + 1
+
+    return x
+
+print(to_static(depend_tensor_while).code)
+"""
+def depend_tensor_while(x):
+    bs = paddle.shape(x)[0]
+    i = 0
+
+    def for_loop_condition_0(x, i, bs):
+        return i < bs
+
+    def for_loop_body_0(x, i, bs):
+        x = x + 1
+        i += 1
+        return x, i, bs
+
+    [x, i, bs] = paddle.jit.dy2static.convert_while_loop(for_loop_condition_0,
+        for_loop_body_0, [x, i, bs])
+    return x
+"""
+```
+
+
+``convert_while_loop`` 的底层的逻辑同样会根据 **判断条件是否为``Tensor``** 来决定是否转为 ``while_op``
+
+## 四、 Parameters 与 Buffers
+
+### 4.1 动态图 layer 生成 Program
+
+文档开始的样例中 ``forward`` 函数包含两行组网代码： ``Linear`` 和 ``add`` 操作。以 ``Linear`` 为例，在 Paddle 的框架底层，每个 Paddle 的组网 API 的实现包括两个分支：
+
+```python
+
+class Linear(...):
+    def __init__(self, ...):
+        # ...(略)
+
+    def forward(self, input):
+
+        if in_dygraph_mode():  # 动态图分支
+            core.ops.matmul(input, self.weight, pre_bias, ...)
+            return out
+        else:                  # 静态图分支
+            self._helper.append_op(type="matmul", inputs=inputs, ...)     # <----- 生成一个 Op
+            if self.bias is not None:
+                self._helper.append_op(type='elementwise_add', ...)       # <----- 生成一个 Op
+
+            return out
+```
+
+动态图 ``layer`` 生成 ``Program`` ，其实是开启 ``paddle.enable_static()`` 时，在静态图下逐行执行用户定义的组网代码，依次添加(对应 ``append_op`` 接口) 到默认的主 Program（即 ``main_program`` ） 中。
+
+### 4.2 动态图 Tensor 转为静态图 Variable
+
+上面提到，所有的组网代码都会在静态图模式下执行，以生成完整的 ``Program`` 。**但静态图 ``append_op`` 有一个前置条件必须满足：**
+
+> **前置条件**：append_op() 时，所有的 inputs，outputs 必须都是静态图的 Variable 类型，不能是动态图的 Tensor 类型。
+
+
+**原因**：静态图下，操作的都是**描述类单元**：计算相关的 ``OpDesc`` ，数据相关的 ``VarDesc`` 。可以分别简单地理解为 ``Program`` 中的 ``Op`` 和 ``Variable`` 。
+
+因此，在动转静时，我们在需要在**某个统一的入口处**，将动态图 ``Layers`` 中 ``Tensor`` 类型（包含具体数据）的 ``Weight`` 、``Bias`` 等变量转换为**同名的静态图 ``Variable``**。
+
++ ParamBase &rarr; Parameters
++ VarBase &rarr;   Variable
+
+技术实现上，我们选取了框架层面两个地方作为类型**转换的入口**：
+
++ ``Paddle.nn.Layer`` 基类的 ``__call__`` 函数
+    ```python
+    def __call__(self, *inputs, **kwargs):
+        # param_guard 会对将 Tensor 类型的 Param 和 buffer 转为静态图 Variable
+        with param_guard(self._parameters), param_guard(self._buffers):
+            # ... forward_pre_hook 逻辑
+
+            outputs = self.forward(*inputs, **kwargs) # 此处为forward函数
+
+            # ... forward_post_hook 逻辑
+
+            return outputs
+    ```
+
++ ``Block.append_op`` 函数中，生成 ``Op`` 之前
+    ```python
+    def append_op(self, *args, **kwargs):
+        if in_dygraph_mode():
+            # ... (动态图分支)
+        else:
+            inputs=kwargs.get("inputs", None)
+            outputs=kwargs.get("outputs", None)
+            # param_guard 会确保将 Tensor 类型的 inputs 和 outputs 转为静态图 Variable
+            with param_guard(inputs), param_guard(outputs):
+                op = Operator(
+                    block=self,
+                    desc=op_desc,
+                    type=kwargs.get("type", None),
+                    inputs=inputs,
+                    outputs=outputs,
+                    attrs=kwargs.get("attrs", None))
+    ```
+
+
+以上，是动态图转为静态图的两个核心逻辑，总结如下：
+
++ 动态图 ``layer`` 调用在动转静时会走底层 ``append_op`` 的分支，以生成 ``Program``
++ 动态图 ``Tensor`` 转为静态图 ``Variable`` ，并确保编译期的 ``InferShape`` 正确执行
+
+
+### 4.3 Buffer 变量
+
+**什么是 ``Buffers`` 变量？**
+
++ **Parameters**：``persistable`` 为 ``True`` ，且每个 batch 都被 Optimizer 更新的变量
++ **Buffers**：``persistable`` 为 ``True`` ，``is_trainable = False`` ，不参与更新，但与预测相关；如 ``BatchNorm`` 层中的均值和方差
+
+在动态图模型代码中，若一个 ``paddle.to_tensor`` 接口生成的 ``Tensor`` 参与了最终预测结果的的计算，则此 ``Tensor`` 需要在转换为静态图预测模型时，也需要作为一个 ``persistable`` 的变量保存到 ``.pdiparam`` 文件中。
+
+**举一个例子（错误写法）：**
+
+```python
+import paddle
+from paddle.jit import to_static
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self, mask):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+
+        # mask value，此处不会保存到预测模型文件中
+        self.mask = mask # 假设为 [0, 1, 1]
+
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
+        mask = paddle.to_tensor(self.mask)    # <----- 每次执行都转为一个 Tensor
+        out = out * mask
+        return out
+```
+
+
+**推荐的写法是：**
+
+```python
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self, mask):
+        super(SimpleNet, self).__init__()
+        self.linear = paddle.nn.Linear(10, 3)
+
+        # 此处的 mask 会当做一个 buffer Tensor，保存到 .pdiparam 文件
+        self.mask = paddle.to_tensor(mask) # 假设为 [0, 1, 1]
+
+    def forward(self, x, y):
+        out = self.linear(x)
+        out = out + y
+        out = out * self.mask                 # <---- 直接使用 self.mask
+        return out
+```
+
+
+总结一下 ``buffers`` 的用法：
+
++  若某个非 ``Tensor`` 数据需要当做 ``Persistable`` 的变量序列化到磁盘，则最好在 ``__init__`` 中调用 ``self.XX= paddle.to_tensor(xx)`` 接口转为 ``buffer`` 变量
diff --git a/docs/guides/performance_improving/index_cn.rst b/docs/guides/performance_improving/index_cn.rst
index 241893eca6b..64faa2caf93 100644
--- a/docs/guides/performance_improving/index_cn.rst
+++ b/docs/guides/performance_improving/index_cn.rst
@@ -2,6 +2,11 @@
 性能调优
 ########
 
+你可以通过以下内容，了解飞桨框架性能调优相关的内容：
+
+- `模型量化 <./quantization.html>`_ : 使用飞桨框架进行模型量化。
+
 ..  toctree::
-    :maxdepth: 1
+    :hidden:
 
+    quantization.md
\ No newline at end of file
diff --git a/docs/install/Tables.md b/docs/install/Tables.md
index 378f9c5eb27..7f3b1a67ff8 100644
--- a/docs/install/Tables.md
+++ b/docs/install/Tables.md
@@ -228,11 +228,11 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。
     </thead>
     <tbody>
     <tr>
-        <td> paddlepaddle==[版本号] 例如 paddlepaddle==2.1.3 </td>
+        <td> paddlepaddle==[版本号] 例如 paddlepaddle==2.2.0 </td>
         <td> 只支持CPU对应版本的PaddlePaddle，具体版本请参见<a href=https://pypi.org/project/paddlepaddle/#history>Pypi</a> </td>
     </tr>
     <tr>
-        <td> paddlepaddle-gpu==[版本号] 例如 paddlepaddle-gpu==2.1.3 </td>
+        <td> paddlepaddle-gpu==[版本号] 例如 paddlepaddle-gpu==2.2.0 </td>
         <td> 默认安装支持CUDA 10.2和cuDNN 7的对应[版本号]的PaddlePaddle安装包 </td>
     </tr>
    </tbody>
@@ -242,7 +242,7 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。
 您可以在 [Release History](https://pypi.org/project/paddlepaddle-gpu/#history) 中找到PaddlePaddle-gpu的各个发行版本。
 > 其中`postXX` 对应的是CUDA和cuDNN的版本，`postXX`之前的数字代表Paddle的版本
 
-需要注意的是，命令中<code> paddlepaddle-gpu==2.1.3 </code> 在windows环境下，会默认安装支持CUDA 10.2和cuDNN 7的对应[版本号]的PaddlePaddle安装包
+需要注意的是，命令中<code> paddlepaddle-gpu==2.2.0 </code> 在windows环境下，会默认安装支持CUDA 10.2和cuDNN 7的对应[版本号]的PaddlePaddle安装包
 
 <a name="ciwhls-release"></a>
 </br></br>
@@ -263,124 +263,190 @@ PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。
     <tbody>
     <tr>
         <td> cpu-mkl-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp36-cp36m-linux_x86_64.whl"> paddlepaddle-2.1.3-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp37-cp37m-linux_x86_64.whl"> paddlepaddle-2.1.3-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp39-cp39-linux_x86_64.whl"> paddlepaddle-2.1.3-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp36-cp36m-linux_x86_64.whl"> paddlepaddle-2.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp37-cp37m-linux_x86_64.whl"> paddlepaddle-2.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp39-cp39-linux_x86_64.whl"> paddlepaddle-2.2.0-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cpu-openblas-avx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-openblas-avx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-openblas-avx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
     <tr>
         <td> cpu-mkl-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-noavx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-noavx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
     <tr>
         <td> cpu-openblas-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-openblas-noavx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-openblas-noavx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
     <tr>
         <td> cuda10.1-cudnn7-mkl-gcc5.4-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cuda10.1-cudnn7-mkl-gcc5.4-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-noavx/paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-noavx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
-    <tr>
-        <td> cuda10.1-cudnn7-mkl-gcc8.2-avx-trt6.0.1.5 </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl</a></td>
-    </tr>
     <tr>
         <td> cuda10.2-cudnn7-mkl-gcc8.2-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cuda10.2-cudnn7-mkl-gcc8.2-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-noavx/paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-noavx/paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
-    <tr>
-        <td> cuda10.2-cudnn8.1-mkl-gcc8.2-trt7.1.3.4 </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl</a></td>
-    </tr>
     <tr>
         <td> cuda11.0-cudnn8-mkl-gcc8.2-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp39-cp39-linux_x86_64.whl</a></td>
-    </tr>
-    <tr>
-        <td> cuda11.1-cudnn8.1-mkl-gcc8.2-trt7.1.3.4 </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp39-cp39-linux_x86_64.whl</a></td>
+    </tr>
+    <tr>
+        <td> cuda11.1-cudnn8.1-mkl-gcc8.2-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cuda11.2-cudnn8-mkl-gcc8.2-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp39-cp39-linux_x86_64.whl</a></td>
+    </tr>
+    <tr>
+        <td> macos-cpu-openblas </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp36-cp36m-macosx_10_6_intel.whl">
+        paddlepaddle-2.2.0-cp36-cp36m-macosx_10_6_intel.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp37-cp37m-macosx_10_6_intel.whl">
+        paddlepaddle-2.2.0-cp37-cp37m-macosx_10_6_intel.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp38-cp38-macosx_10_14_x86_64.whl">
+        paddlepaddle-2.2.0-cp38-cp38-macosx_10_14_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp39-cp39-macosx_10_14_x86_64.whl">
+        paddlepaddle-2.2.0-cp39-cp39-macosx_10_14_x86_64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cpu-mkl-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp36-cp36m-win_amd64.whl"> paddlepaddle-2.2.0-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp37-cp37m-win_amd64.whl"> paddlepaddle-2.2.0-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp39-cp39-win_amd64.whl"> paddlepaddle-2.2.0-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cpu-mkl-noavx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-noavx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cpu-openblas-avx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-openblas-avx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cpu-openblas-noavx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-openblas-noavx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cuda10.1-cudnn7-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda10.1-cudnn7-mkl-vs2017-noavx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-noavx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cuda10.2-cudnn7-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda10.2-cudnn7-mkl-vs2017-noavx </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-noavx/paddlepaddle_gpu-2.2.0-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-noavx/paddlepaddle_gpu-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cuda11.0-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda11.1-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda11.2-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp39-cp39-win_amd64.whl</a></td>
     </tr>
    </tbody>
 </table>
@@ -469,6 +535,13 @@ platform tag: 类似 'linux_x86_64', 'any'
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.0-cudnn8-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post110-cp38-cp38-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.0-cudnn8-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post110-cp39-cp39-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
+    <tr>
+        <td> cuda11.1-cudnn8.1-mkl </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp38-cp38-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp39-cp39-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl</a></td>
+    </tr>
     <tr>
         <td> cuda11.2-cudnn8-mkl </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.2-cudnn8-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post112-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl</a></td>
@@ -495,7 +568,7 @@ platform tag: 类似 'linux_x86_64', 'any'
         <td> - </td>
         <td> - </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/cpu-mkl-noavx/paddlepaddle-0.0.0-cp38-cp38-win_amd64.whl"> paddlepaddle-latest-cp38-cp38-win_amd64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/cpu-mkl-noavx/paddlepaddle-0.0.0-cp39-cp39-win_amd64.whl"> paddlepaddle-latest-cp39-cp39-win_amd64.whl</a></td>
+        <td> - </td>
     </tr>
     <tr>
         <td> win-cpu-openblas-avx </td>
@@ -540,14 +613,21 @@ platform tag: 类似 'linux_x86_64', 'any'
         <td> - </td>
     </tr>
     <tr>
-        <td> win-cuda11.0-cudnn7-mkl-vs2017-avx </td>
+        <td> win-cuda11.0-cudnn8-mkl-vs2017-avx </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl</a></td>
     </tr>
     <tr>
-        <td> win-cuda11.2-cudnn7-mkl-vs2017-avx </td>
+        <td> win-cuda11.1-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda11.2-cudnn8-mkl-vs2017-avx </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.2-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post112-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.2-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post112-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.2-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post112-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl</a></td>
diff --git a/docs/install/Tables_en.md b/docs/install/Tables_en.md
index 3649ca05235..61325954bab 100644
--- a/docs/install/Tables_en.md
+++ b/docs/install/Tables_en.md
@@ -220,11 +220,11 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif
     </thead>
     <tbody>
     <tr>
-        <td> paddlepaddle==[version code] such as paddlepaddle==2.1.3 </td>
+        <td> paddlepaddle==[version code] such as paddlepaddle==2.2.0 </td>
         <td> Only support the corresponding version of the CPU PaddlePaddle, please refer to <a href=https://pypi.org/project/paddlepaddle/#history>Pypi</a> for the specific version. </td>
     </tr>
     <tr>
-        <td> paddlepaddle-gpu==[version code], such as paddlepaddle-gpu==2.1.3 </td>
+        <td> paddlepaddle-gpu==[version code], such as paddlepaddle-gpu==2.2.0 </td>
         <td> The default installation supports the PaddlePaddle installation package corresponding to [version number] of CUDA 10.2 and cuDNN 7 </td>
     </tr>
    </tbody>
@@ -234,7 +234,7 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif
 You can find various distributions of PaddlePaddle-gpu in [the Release History](https://pypi.org/project/paddlepaddle-gpu/#history).
 > 'postxx' corresponds to CUDA and cuDNN versions, and the number before 'postxx' represents the version of Paddle
 
-Please note that: in the commands, <code> paddlepaddle-gpu==2.1.3 </code> will install the installation package of PaddlePaddle that supports CUDA 10.2 and cuDNN 7 by default under Windows environment.
+Please note that: in the commands, <code> paddlepaddle-gpu==2.2.0 </code> will install the installation package of PaddlePaddle that supports CUDA 10.2 and cuDNN 7 by default under Windows environment.
 
 
 <a name="ciwhls-release"></a>
@@ -257,124 +257,190 @@ Please note that: in the commands, <code> paddlepaddle-gpu==2.1.3 </code> will i
     <tbody>
     <tr>
         <td> cpu-mkl-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp36-cp36m-linux_x86_64.whl"> paddlepaddle-2.1.3-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp37-cp37m-linux_x86_64.whl"> paddlepaddle-2.1.3-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-avx/paddlepaddle-2.1.3-cp39-cp39-linux_x86_64.whl"> paddlepaddle-2.1.3-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp36-cp36m-linux_x86_64.whl"> paddlepaddle-2.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp37-cp37m-linux_x86_64.whl"> paddlepaddle-2.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-avx/paddlepaddle-2.2.0-cp39-cp39-linux_x86_64.whl"> paddlepaddle-2.2.0-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cpu-openblas-avx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-openblas-avx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-openblas-avx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
     <tr>
         <td> cpu-mkl-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-mkl-noavx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-mkl-noavx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
     <tr>
         <td> cpu-openblas-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-cpu-openblas-noavx/paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-cpu-openblas-noavx/paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl"> paddlepaddle-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
     <tr>
         <td> cuda10.1-cudnn7-mkl-gcc5.4-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-avx/paddlepaddle_gpu-2.2.0.post101-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cuda10.1-cudnn7-mkl-gcc5.4-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-noavx/paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.1-cudnn7-mkl-gcc5.4-noavx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post101-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
-    <tr>
-        <td> cuda10.1-cudnn7-mkl-gcc8.2-avx-trt6.0.1.5 </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post101-cp39-cp39-linux_x86_64.whl</a></td>
-    </tr>
     <tr>
         <td> cuda10.2-cudnn7-mkl-gcc8.2-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cuda10.2-cudnn7-mkl-gcc8.2-noavx </td>
         <td> - </td>
         <td> - </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-noavx/paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda10.2-cudnn7-mkl-gcc8.2-noavx/paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0-cp38-cp38-linux_x86_64.whl</a></td>
         <td> - </td>
     </tr>
-    <tr>
-        <td> cuda10.2-cudnn8.1-mkl-gcc8.2-trt7.1.3.4 </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3-cp39-cp39-linux_x86_64.whl</a></td>
-    </tr>
     <tr>
         <td> cuda11.0-cudnn8-mkl-gcc8.2-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post110-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post110-cp39-cp39-linux_x86_64.whl</a></td>
-    </tr>
-    <tr>
-        <td> cuda11.1-cudnn8.1-mkl-gcc8.2-trt7.1.3.4 </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-trt7-avx/paddlepaddle_gpu-2.1.3.post111-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post111-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post110-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post110-cp39-cp39-linux_x86_64.whl</a></td>
+    </tr>
+    <tr>
+        <td> cuda11.1-cudnn8.1-mkl-gcc8.2-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.1-cudnn8.1-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post111-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post111-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
     <tr>
         <td> cuda11.2-cudnn8-mkl-gcc8.2-avx </td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp36-cp36m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp36-cp36m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp37-cp37m-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp37-cp37m-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp38-cp38-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp38-cp38-linux_x86_64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.1.3/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.1.3.post112-cp39-cp39-linux_x86_64.whl">
-        paddlepaddle_gpu-2.1.3.post112-cp39-cp39-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp38-cp38-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/linux/linux-gpu-cuda11.2-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.2.0.post112-cp39-cp39-linux_x86_64.whl">
+        paddlepaddle_gpu-2.2.0.post112-cp39-cp39-linux_x86_64.whl</a></td>
+    </tr>
+    <tr>
+        <td> macos-cpu-openblas </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp36-cp36m-macosx_10_6_intel.whl">
+        paddlepaddle-2.2.0-cp36-cp36m-macosx_10_6_intel.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp37-cp37m-macosx_10_6_intel.whl">
+        paddlepaddle-2.2.0-cp37-cp37m-macosx_10_6_intel.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp38-cp38-macosx_10_14_x86_64.whl">
+        paddlepaddle-2.2.0-cp38-cp38-macosx_10_14_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/macos/macos-cpu-openblas/paddlepaddle-2.2.0-cp39-cp39-macosx_10_14_x86_64.whl">
+        paddlepaddle-2.2.0-cp39-cp39-macosx_10_14_x86_64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cpu-mkl-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp36-cp36m-win_amd64.whl"> paddlepaddle-2.2.0-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp37-cp37m-win_amd64.whl"> paddlepaddle-2.2.0-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-avx/paddlepaddle-2.2.0-cp39-cp39-win_amd64.whl"> paddlepaddle-2.2.0-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cpu-mkl-noavx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-mkl-noavx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cpu-openblas-avx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-openblas-avx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cpu-openblas-noavx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-cpu-openblas-noavx/paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle-2.2.0-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cuda10.1-cudnn7-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0.post101-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda10.1-cudnn7-mkl-vs2017-noavx </td>
+        <td> - </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.1-cudnn7-mkl-noavx/paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post101-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cuda10.2-cudnn7-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-avx/paddlepaddle_gpu-2.2.0-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda10.2-cudnn7-mkl-vs2017-noavx </td>
+        <td> - </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-noavx/paddlepaddle_gpu-2.2.0-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda10.2-cudnn7-mkl-noavx/paddlepaddle_gpu-2.2.0-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post102-cp38-cp38-win_amd64.whl</a></td>
+        <td> - </td>
+    </tr>
+    <tr>
+        <td> win-cuda11.0-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.0-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post110-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post110-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda11.1-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.1-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post111-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post111-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda11.2-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/2.2.0/windows/windows-gpu-cuda11.2-cudnn8-mkl-avx/paddlepaddle_gpu-2.2.0.post112-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-2.2.0.post112-cp39-cp39-win_amd64.whl</a></td>
     </tr>
     </tbody>
 </table>
@@ -467,6 +533,13 @@ platform tag: similar to 'linux_x86_64', 'any'
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.0-cudnn8-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post110-cp38-cp38-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.0-cudnn8-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post110-cp39-cp39-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl</a></td>
     </tr>
+    <tr>
+        <td> cuda11.1-cudnn8.1-mkl </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp37-cp37m-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp38-cp38-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp38-cp38-linux_x86_64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.1-cudnn8.1-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post111-cp39-cp39-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp39-cp39-linux_x86_64.whl</a></td>
+    </tr>
     <tr>
         <td> cuda11.2-cudnn8-mkl </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/linux/gpu-cuda11.2-cudnn8-mkl_gcc8.2/paddlepaddle_gpu-0.0.0.post112-cp36-cp36m-linux_x86_64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl</a></td>
@@ -493,7 +566,7 @@ platform tag: similar to 'linux_x86_64', 'any'
         <td> - </td>
         <td> - </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/cpu-mkl-noavx/paddlepaddle-0.0.0-cp38-cp38-win_amd64.whl"> paddlepaddle-latest-cp38-cp38-win_amd64.whl</a></td>
-        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/cpu-mkl-noavx/paddlepaddle-0.0.0-cp39-cp39-win_amd64.whl"> paddlepaddle-latest-cp39-cp39-win_amd64.whl</a></td>
+        <td> - </td>
     </tr>
     <tr>
         <td> win-cpu-openblas-avx </td>
@@ -538,14 +611,21 @@ platform tag: similar to 'linux_x86_64', 'any'
         <td> - </td>
     </tr>
     <tr>
-        <td> win-cuda11.0-cudnn7-mkl-vs2017-avx </td>
+        <td> win-cuda11.0-cudnn8-mkl-vs2017-avx </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.0-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post110-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl</a></td>
     </tr>
     <tr>
-        <td> win-cuda11.2-cudnn7-mkl-vs2017-avx </td>
+        <td> win-cuda11.1-cudnn8-mkl-vs2017-avx </td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl</a></td>
+        <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.1-cudnn8-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post111-cp39-cp39-win_amd64.whl"> paddlepaddle_gpu-latest-cp39-cp39-win_amd64.whl</a></td>
+    </tr>
+    <tr>
+        <td> win-cuda11.2-cudnn8-mkl-vs2017-avx </td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.2-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post112-cp36-cp36m-win_amd64.whl"> paddlepaddle_gpu-latest-cp36-cp36m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.2-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post112-cp37-cp37m-win_amd64.whl"> paddlepaddle_gpu-latest-cp37-cp37m-win_amd64.whl</a></td>
         <td> <a href="https://paddle-wheel.bj.bcebos.com/develop/windows/gpu-cuda11.2-cudnn7-mkl-vs2017-avx/paddlepaddle_gpu-0.0.0.post112-cp38-cp38-win_amd64.whl"> paddlepaddle_gpu-latest-cp38-cp38-win_amd64.whl</a></td>
diff --git a/docs/install/docker/fromdocker.rst b/docs/install/docker/fromdocker.rst
index aa25d82d3d7..62905f664d7 100644
--- a/docs/install/docker/fromdocker.rst
+++ b/docs/install/docker/fromdocker.rst
@@ -5,5 +5,4 @@
 ..	toctree::
 	:maxdepth: 1
 
-	linux-docker.md
 	macos-docker.md
diff --git a/docs/install/docker/fromdocker_en.rst b/docs/install/docker/fromdocker_en.rst
index c0b2b487411..af6a1a7fafe 100644
--- a/docs/install/docker/fromdocker_en.rst
+++ b/docs/install/docker/fromdocker_en.rst
@@ -5,5 +5,4 @@
 ..	toctree::
 	
 
-	linux-docker_en.md
 	macos-docker_en.md
diff --git a/docs/install/pip/linux-pip.md b/docs/install/pip/linux-pip.md
index 50aef641f10..e7a25a32a83 100644
--- a/docs/install/pip/linux-pip.md
+++ b/docs/install/pip/linux-pip.md
@@ -145,8 +145,22 @@
   python -m pip install paddlepaddle-gpu==0.0.0.post102 -f https://www.paddlepaddle.org.cn/whl/linux/gpu/develop.html
   ```
 
+2.2.3 CUDA11.0的PaddlePaddle
 
-2.2.3 CUDA11.2的PaddlePaddle
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post110 -f https://www.paddlepaddle.org.cn/whl/linux/gpu/develop.html
+  ```
+
+2.2.4 CUDA11.1的PaddlePaddle
+
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post111 -f https://www.paddlepaddle.org.cn/whl/linux/gpu/develop.html
+  ```
+
+
+2.2.5 CUDA11.2的PaddlePaddle
 
 
   ```
diff --git a/docs/install/pip/linux-pip_en.md b/docs/install/pip/linux-pip_en.md
index e8e529a70af..739a9b771a5 100644
--- a/docs/install/pip/linux-pip_en.md
+++ b/docs/install/pip/linux-pip_en.md
@@ -137,7 +137,7 @@ You can choose the following version of PaddlePaddle to start installation:
 
 
 
-2.2.1 CUDA10.1的PaddlePaddle
+2.2.1 If you are using CUDA 10.1
 
 
   ```
@@ -146,7 +146,7 @@ You can choose the following version of PaddlePaddle to start installation:
 
 
 
-2.2.2 CUDA10.2的PaddlePaddle
+2.2.2 If you are using CUDA 10.2
 
 
   ```
@@ -154,7 +154,24 @@ You can choose the following version of PaddlePaddle to start installation:
   ```
 
 
-2.2.3 CUDA11.2的PaddlePaddle
+2.2.3 If you are using CUDA 11.0
+
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post110 -f https://www.paddlepaddle.org.cn/whl/linux/gpu/develop.html
+  ```
+
+
+2.2.4 If you are using CUDA 11.1
+
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post111 -f https://www.paddlepaddle.org.cn/whl/linux/gpu/develop.html
+  ```
+
+
+
+2.2.5 If you are using CUDA 11.2
 
 
   ```
diff --git a/docs/install/pip/windows-pip.md b/docs/install/pip/windows-pip.md
index 5bfa3495b10..cbceead859a 100644
--- a/docs/install/pip/windows-pip.md
+++ b/docs/install/pip/windows-pip.md
@@ -111,7 +111,23 @@
   ```
 
 
-2.2.3 CUDA11.2的PaddlePaddle
+2.2.3 CUDA11.0的PaddlePaddle
+
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post110 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
+  ```
+
+
+2.2.4 CUDA11.1的PaddlePaddle
+
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post111 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
+  ```
+
+
+2.2.5 CUDA11.2的PaddlePaddle
 
   ```
   python -m pip install paddlepaddle-gpu==0.0.0.post112 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
diff --git a/docs/install/pip/windows-pip_en.md b/docs/install/pip/windows-pip_en.md
index eefcffc5d13..cc652e95d9b 100644
--- a/docs/install/pip/windows-pip_en.md
+++ b/docs/install/pip/windows-pip_en.md
@@ -95,7 +95,7 @@ You can choose the following version of PaddlePaddle to start installation:
 #### 2.2 GPU Version of PaddlePaddle
 
 
-2.2.1 CUDA10.1的PaddlePaddle
+2.2.1 If you are using CUDA 10.1
 
 
   ```
@@ -103,14 +103,29 @@ You can choose the following version of PaddlePaddle to start installation:
   ```
 
 
-2.2.2 CUDA10.2的PaddlePaddle
+2.2.2 If you are using CUDA 10.2
 
   ```
   python -m pip install paddlepaddle-gpu==0.0.0.post102 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
   ```
 
+2.2.3 If you are using CUDA 11.0
 
-2.2.3 CUDA11.2的PaddlePaddle
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post110 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
+  ```
+
+
+2.2.4 If you are using CUDA 11.1
+
+
+  ```
+  python -m pip install paddlepaddle-gpu==0.0.0.post111 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
+  ```
+
+
+2.2.5 If you are using CUDA 11.2
 
   ```
   python -m pip install paddlepaddle-gpu==0.0.0.post112 -f https://www.paddlepaddle.org.cn/whl/windows/gpu/develop.html
diff --git a/docs/practices/cv/image_ocr.ipynb b/docs/practices/cv/image_ocr.ipynb
new file mode 100644
index 00000000000..d3b9c516c16
--- /dev/null
+++ b/docs/practices/cv/image_ocr.ipynb
@@ -0,0 +1,722 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# 通过OCR实现验证码识别\n",
+    "\n",
+    "**作者:** [GT_老张](https://github.com/GT-ZhangAcer)  \n",
+    "\n",
+    "**时间:** 2021.11\n",
+    "\n",
+    "**摘要:** 本篇将介绍如何通过飞桨实现简单的CRNN+CTC自定义数据集OCR识别模型，数据集采用[CaptchaDataset](https://github.com/GT-ZhangAcer/CaptchaDataset)中OCR部分的9453张图像，其中前8453张图像在本案例中作为训练集，后1000张则作为测试集。  \n",
+    "在更复杂的场景中推荐使用[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)产出工业级模型，模型轻量且精度大幅提升。  \n",
+    "同样也可以在[PaddleHub](https://www.paddlepaddle.org.cn/hubdetail?name=chinese_ocr_db_crnn_mobile&en_category=TextRecognition)中快速使用PaddleOCR。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 一、环境配置\n",
+    "\n",
+    "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) PaddlePaddle 2.2 。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import paddle\n",
+    "print(paddle.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 二、自定义数据集读取器\n",
+    "\n",
+    "常见的开发任务中，我们并不一定会拿到标准的数据格式，好在我们可以通过自定义Reader的形式来随心所欲读取自己想要数据。 \n",
+    "\n",
+    "设计合理的Reader往往可以带来更好的性能，我们可以将读取标签文件列表、制作图像文件列表等必要操作在`__init__`特殊方法中实现。这样就可以在实例化`Reader`时装入内存，避免使用时频繁读取导致增加额外开销。同样我们可以在`__getitem__`特殊方法中实现如图像增强、归一化等个性操作，完成数据读取后即可释放该部分内存。    \n",
+    "需要我们注意的是，如果不能保证自己数据十分纯净，可以通过`try`和`expect`来捕获异常并指出该数据的位置。当然也可以制定一个策略，使其在发生数据读取异常后依旧可以正常进行训练。  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 2.1 数据展示\n",
+    "<div align=\"center\">\n",
+    "<img src=https://ai-studio-static-online.cdn.bcebos.com/57d6c77aa5194cdca5c7edc533cc57e4d5070de95f6a4454b3cd1ca1e0eebe98 width=\"500px\">\n",
+    "</div>\n",
+    "\n",
+    "点此[快速获取本节数据集](https://aistudio.baidu.com/aistudio/datasetdetail/57285)，待数据集下载完毕后可使用`!unzip OCR_Dataset.zip -d data/`命令或熟悉的解压软件进行解压，待数据准备工作完成后修改本文“训练准备”中的`DATA_PATH = 解压后数据集路径`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 下载数据集 \n",
+    "!wget -O OCR_Dataset.zip https://bj.bcebos.com/v1/ai-studio-online/c91f50ef72de43b090298a38281e9c59a2d741eadd334f1cba7c710c5496e342?responseContentDisposition=attachment%3B%20filename%3DOCR_Dataset.zip&authorization=bce-auth-v1%2F0ef6765c1e494918bc0d4c3ca3e5c6d1%2F2020-10-27T09%3A50%3A21Z%2F-1%2F%2Fddc4aebed803af6c57dac46abba42d207961b78e7bc81744e8388395979b66fa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 解压数据集\n",
+    "!unzip OCR_Dataset.zip -d data/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import PIL.Image as Image\n",
+    "import numpy as np\n",
+    "from paddle.io import Dataset\n",
+    "\n",
+    "# 图片信息配置 - 通道数、高度、宽度\n",
+    "IMAGE_SHAPE_C = 3\n",
+    "IMAGE_SHAPE_H = 30\n",
+    "IMAGE_SHAPE_W = 70\n",
+    "# 数据集图片中标签长度最大值设置 - 因图片中均为4个字符，故该处填写为4即可\n",
+    "LABEL_MAX_LEN = 4\n",
+    "\n",
+    "\n",
+    "class Reader(Dataset):\n",
+    "    def __init__(self, data_path: str, is_val: bool = False):\n",
+    "        \"\"\"\n",
+    "        数据读取Reader\n",
+    "        :param data_path: Dataset路径\n",
+    "        :param is_val: 是否为验证集\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.data_path = data_path\n",
+    "        # 读取Label字典\n",
+    "        with open(os.path.join(self.data_path, \"label_dict.txt\"), \"r\", encoding=\"utf-8\") as f:\n",
+    "            self.info = eval(f.read())\n",
+    "        # 获取文件名列表\n",
+    "        self.img_paths = [img_name for img_name in self.info]\n",
+    "        # 将数据集后1024张图片设置为验证集，当is_val为真时img_path切换为后1024张\n",
+    "        self.img_paths = self.img_paths[-1024:] if is_val else self.img_paths[:-1024]\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        # 获取第index个文件的文件名以及其所在路径\n",
+    "        file_name = self.img_paths[index]\n",
+    "        file_path = os.path.join(self.data_path, file_name)\n",
+    "        # 捕获异常 - 在发生异常时终止训练\n",
+    "        try:\n",
+    "            # 使用Pillow来读取图像数据\n",
+    "            img = Image.open(file_path)\n",
+    "            # 转为Numpy的array格式并整体除以255进行归一化\n",
+    "            img = np.array(img, dtype=\"float32\").reshape((IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W)) / 255\n",
+    "        except Exception as e:\n",
+    "            raise Exception(file_name + \"\\t文件打开失败，请检查路径是否准确以及图像文件完整性，报错信息如下:\\n\" + str(e))\n",
+    "        # 读取该图像文件对应的Label字符串，并进行处理\n",
+    "        label = self.info[file_name]\n",
+    "        label = list(label)\n",
+    "        # 将label转化为Numpy的array格式\n",
+    "        label = np.array(label, dtype=\"int32\")\n",
+    "\n",
+    "        return img, label\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        # 返回每个Epoch中图片数量\n",
+    "        return len(self.img_paths)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 三、模型配置"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 3.1 定义模型结构以及模型输入\n",
+    "\n",
+    "模型方面使用的简单的CRNN-CTC结构，输入形为CHW的图像在经过CNN->Flatten->Linear->RNN->Linear后输出图像中每个位置所对应的字符概率。考虑到CTC解码器在面对图像中元素数量不一、相邻元素重复时会存在无法正确对齐等情况，故额外添加一个类别代表“分隔符”进行改善。\n",
+    "\n",
+    "CTC相关论文：[Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu](http://people.idsia.ch/~santiago/papers/icml2006.pdf) \n",
+    "\n",
+    "<div align=\"center\">\n",
+    "<img src=https://ai-studio-static-online.cdn.bcebos.com/f9458cedbb4441d682f15fefd3f3cae5e49d499bcf0a4bbdb976dfdff5a2e656 width=\"500px\">\n",
+    "</div>\n",
+    "\n",
+    "网络部分，因本篇采用数据集较为简单且图像尺寸较小并不适合较深层次网络。若在对尺寸较大的图像进行模型构建，可以考虑使用更深层次网络/注意力机制来完成。当然也可以通过目标检测形式先检出文本位置，然后进行OCR部分模型构建。\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "<img src=https://ai-studio-static-online.cdn.bcebos.com/19ddf6107e7f47ee9b3b84ee0c12de1e15f7ab8b88f04eed95232440c92fe0d7 width=\"500px\">\n",
+    "</div>\n",
+    "\n",
+    "<a href=\"https://github.com/PaddlePaddle/PaddleOCR\">PaddleOCR效果图</a>\n",
+    "</p>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import paddle\n",
+    "\n",
+    "# 分类数量设置 - 因数据集中共包含0~9共10种数字+分隔符，所以是11分类任务\n",
+    "CLASSIFY_NUM = 11\n",
+    "\n",
+    "# 定义输入层，shape中第0维使用-1则可以在预测时自由调节batch size\n",
+    "input_define = paddle.static.InputSpec(shape=[-1, IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W],\n",
+    "                                   dtype=\"float32\",\n",
+    "                                   name=\"img\")\n",
+    "\n",
+    "# 定义网络结构\n",
+    "class Net(paddle.nn.Layer):\n",
+    "    def __init__(self, is_infer: bool = False):\n",
+    "        super().__init__()\n",
+    "        self.is_infer = is_infer\n",
+    "\n",
+    "        # 定义一层3x3卷积+BatchNorm\n",
+    "        self.conv1 = paddle.nn.Conv2D(in_channels=IMAGE_SHAPE_C,\n",
+    "                                  out_channels=32,\n",
+    "                                  kernel_size=3)\n",
+    "        self.bn1 = paddle.nn.BatchNorm2D(32)\n",
+    "        # 定义一层步长为2的3x3卷积进行下采样+BatchNorm\n",
+    "        self.conv2 = paddle.nn.Conv2D(in_channels=32,\n",
+    "                                  out_channels=64,\n",
+    "                                  kernel_size=3,\n",
+    "                                  stride=2)\n",
+    "        self.bn2 = paddle.nn.BatchNorm2D(64)\n",
+    "        # 定义一层1x1卷积压缩通道数，输出通道数设置为比LABEL_MAX_LEN稍大的定值可获取更优效果，当然也可设置为LABEL_MAX_LEN\n",
+    "        self.conv3 = paddle.nn.Conv2D(in_channels=64,\n",
+    "                                  out_channels=LABEL_MAX_LEN + 4,\n",
+    "                                  kernel_size=1)\n",
+    "        # 定义全连接层，压缩并提取特征（可选）\n",
+    "        self.linear = paddle.nn.Linear(in_features=429,\n",
+    "                                   out_features=128)\n",
+    "        # 定义RNN层来更好提取序列特征，此处为双向LSTM输出为2 x hidden_size，可尝试换成GRU等RNN结构\n",
+    "        self.lstm = paddle.nn.LSTM(input_size=128,\n",
+    "                               hidden_size=64,\n",
+    "                               direction=\"bidirectional\")\n",
+    "        # 定义输出层，输出大小为分类数\n",
+    "        self.linear2 = paddle.nn.Linear(in_features=64 * 2,\n",
+    "                                    out_features=CLASSIFY_NUM)\n",
+    "\n",
+    "    def forward(self, ipt):\n",
+    "        # 卷积 + ReLU + BN\n",
+    "        x = self.conv1(ipt)\n",
+    "        x = paddle.nn.functional.relu(x)\n",
+    "        x = self.bn1(x)\n",
+    "        # 卷积 + ReLU + BN\n",
+    "        x = self.conv2(x)\n",
+    "        x = paddle.nn.functional.relu(x)\n",
+    "        x = self.bn2(x)\n",
+    "        # 卷积 + ReLU\n",
+    "        x = self.conv3(x)\n",
+    "        x = paddle.nn.functional.relu(x)\n",
+    "        # 将3维特征转换为2维特征 - 此处可以使用reshape代替\n",
+    "        x = paddle.tensor.flatten(x, 2)\n",
+    "        # 全连接 + ReLU\n",
+    "        x = self.linear(x)\n",
+    "        x = paddle.nn.functional.relu(x)\n",
+    "        # 双向LSTM - [0]代表取双向结果，[1][0]代表forward结果,[1][1]代表backward结果，详细说明可在官方文档中搜索'LSTM'\n",
+    "        x = self.lstm(x)[0]\n",
+    "        # 输出层 - Shape = (Batch Size, Max label len, Signal) \n",
+    "        x = self.linear2(x)\n",
+    "\n",
+    "        # 在计算损失时ctc-loss会自动进行softmax，所以在预测模式中需额外做softmax获取标签概率\n",
+    "        if self.is_infer:\n",
+    "            # 输出层 - Shape = (Batch Size, Max label len, Prob) \n",
+    "            x = paddle.nn.functional.softmax(x)\n",
+    "            # 转换为标签\n",
+    "            x = paddle.argmax(x, axis=-1)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 四、训练准备"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 4.1 定义label输入以及超参数\n",
+    "监督训练需要定义label，预测则不需要该步骤。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 数据集路径设置\n",
+    "DATA_PATH = \"./data/OCR_Dataset\"\n",
+    "# 训练轮数\n",
+    "EPOCH = 10\n",
+    "# 每批次数据大小\n",
+    "BATCH_SIZE = 16\n",
+    "\n",
+    "label_define = paddle.static.InputSpec(shape=[-1, LABEL_MAX_LEN],\n",
+    "                                    dtype=\"int32\",\n",
+    "                                    name=\"label\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 4.2 定义CTC Loss\n",
+    "\n",
+    "了解CTC解码器效果后，我们需要在训练中让模型尽可能接近这种类型输出形式，那么我们需要定义一个CTC Loss来计算模型损失。不必担心，在飞桨框架中内置了多种Loss，无需手动复现即可完成损失计算。\n",
+    " \n",
+    "使用文档：[CTCLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-beta/api/paddle/nn/functional/loss/ctc_loss_cn.html#ctc-loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class CTCLoss(paddle.nn.Layer):\n",
+    "    def __init__(self):\n",
+    "        \"\"\"\n",
+    "        定义CTCLoss\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "\n",
+    "    def forward(self, ipt, label):\n",
+    "        input_lengths = paddle.full(shape=[BATCH_SIZE],fill_value=LABEL_MAX_LEN + 4,dtype= \"int64\")\n",
+    "        label_lengths = paddle.full(shape=[BATCH_SIZE],fill_value=LABEL_MAX_LEN,dtype= \"int64\")\n",
+    "        # 按文档要求进行转换dim顺序\n",
+    "        ipt = paddle.tensor.transpose(ipt, [1, 0, 2])\n",
+    "        # 计算loss\n",
+    "        loss = paddle.nn.functional.ctc_loss(ipt, label, input_lengths, label_lengths, blank=10)\n",
+    "        return loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 4.3 实例化模型并配置优化策略"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 实例化模型\n",
+    "model = paddle.Model(Net(), inputs=input_define, labels=label_define)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 定义优化器\n",
+    "optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())\n",
+    "\n",
+    "# 为模型配置运行环境并设置该优化策略\n",
+    "model.prepare(optimizer=optimizer,\n",
+    "                loss=CTCLoss())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 五、开始训练\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
+      "Epoch 1/10\n",
+      "step 526/526 [==============================] - loss: 0.2182 - 13ms/step         \n",
+      "save checkpoint at /home/aistudio/output/0\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.1953 - 6ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 2/10\n",
+      "step 526/526 [==============================] - loss: 0.1394 - 10ms/step        \n",
+      "save checkpoint at /home/aistudio/output/1\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0416 - 5ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 3/10\n",
+      "step 526/526 [==============================] - loss: 0.0296 - 9ms/step        \n",
+      "save checkpoint at /home/aistudio/output/2\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0327 - 6ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 4/10\n",
+      "step 526/526 [==============================] - loss: 0.0150 - 9ms/step        \n",
+      "save checkpoint at /home/aistudio/output/3\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0228 - 5ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 5/10\n",
+      "step 526/526 [==============================] - loss: 0.0102 - 9ms/step        \n",
+      "save checkpoint at /home/aistudio/output/4\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0161 - 6ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 6/10\n",
+      "step 526/526 [==============================] - loss: 0.1300 - 10ms/step        \n",
+      "save checkpoint at /home/aistudio/output/5\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0164 - 5ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 7/10\n",
+      "step 526/526 [==============================] - loss: 0.0199 - 9ms/step        \n",
+      "save checkpoint at /home/aistudio/output/6\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0121 - 5ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 8/10\n",
+      "step 526/526 [==============================] - loss: 0.0060 - 9ms/step        \n",
+      "save checkpoint at /home/aistudio/output/7\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0133 - 5ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 9/10\n",
+      "step 526/526 [==============================] - loss: 0.0084 - 11ms/step        \n",
+      "save checkpoint at /home/aistudio/output/8\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0098 - 5ms/step        \n",
+      "Eval samples: 1024\n",
+      "Epoch 10/10\n",
+      "step 526/526 [==============================] - loss: 0.0100 - 9ms/step        \n",
+      "save checkpoint at /home/aistudio/output/9\n",
+      "Eval begin...\n",
+      "step 64/64 [==============================] - loss: 0.0109 - 10ms/step        \n",
+      "Eval samples: 1024\n",
+      "save checkpoint at /home/aistudio/output/final\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 执行训练\n",
+    "model.fit(train_data=Reader(DATA_PATH),\n",
+    "            eval_data=Reader(DATA_PATH, is_val=True),\n",
+    "            batch_size=BATCH_SIZE,\n",
+    "            epochs=EPOCH,\n",
+    "            save_dir=\"output/\",\n",
+    "            save_freq=1,\n",
+    "            verbose=1,\n",
+    "            drop_last=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 六、预测前准备"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 6.1 像定义训练Reader一样定义预测Reader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 与训练近似，但不包含Label\n",
+    "class InferReader(Dataset):\n",
+    "    def __init__(self, dir_path=None, img_path=None):\n",
+    "        \"\"\"\n",
+    "        数据读取Reader(预测)\n",
+    "        :param dir_path: 预测对应文件夹（二选一）\n",
+    "        :param img_path: 预测单张图片（二选一）\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        if dir_path:\n",
+    "            # 获取文件夹中所有图片路径\n",
+    "            self.img_names = [i for i in os.listdir(dir_path) if os.path.splitext(i)[1] == \".jpg\"]\n",
+    "            self.img_paths = [os.path.join(dir_path, i) for i in self.img_names]\n",
+    "        elif img_path:\n",
+    "            self.img_names = [os.path.split(img_path)[1]]\n",
+    "            self.img_paths = [img_path]\n",
+    "        else:\n",
+    "            raise Exception(\"请指定需要预测的文件夹或对应图片路径\")\n",
+    "\n",
+    "    def get_names(self):\n",
+    "        \"\"\"\n",
+    "        获取预测文件名顺序 \n",
+    "        \"\"\"\n",
+    "        return self.img_names\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        # 获取图像路径\n",
+    "        file_path = self.img_paths[index]\n",
+    "        # 使用Pillow来读取图像数据并转成Numpy格式\n",
+    "        img = Image.open(file_path)\n",
+    "        img = np.array(img, dtype=\"float32\").reshape((IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W)) / 255\n",
+    "        return img\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.img_paths)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 6.2 参数设置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# 待预测目录 - 可在测试数据集中挑出\\b3张图像放在该目录中进行推理\n",
+    "INFER_DATA_PATH = \"./sample_img\"\n",
+    "# 训练后存档点路径 - final 代表最终训练所得模型\n",
+    "CHECKPOINT_PATH = \"./output/final.pdparams\"\n",
+    "# 每批次处理数量\n",
+    "BATCH_SIZE = 32"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### 6.3 展示待预测数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "sample_idxs = np.random.choice(50000, size=25, replace=False)\n",
+    "\n",
+    "for img_id, img_name in enumerate(os.listdir(INFER_DATA_PATH)):\n",
+    "    plt.subplot(1, 3, img_id + 1)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    im = Image.open(os.path.join(INFER_DATA_PATH, img_name))\n",
+    "    plt.imshow(im, cmap=plt.cm.binary)\n",
+    "    plt.xlabel(\"Img name: \" + img_name)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## 七、开始预测\n",
+    "> 飞桨2.2 CTC Decoder 相关API正在迁移中，本节暂时使用简易版解码器。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predict begin...\n",
+      "step 1/1 [==============================] - 7ms/step\n",
+      "Predict samples: 3\n",
+      "文件名：9451.jpg，推理结果为：[3, 4, 6, 3]\n",
+      "文件名：9450.jpg，推理结果为：[8, 2, 0, 5]\n",
+      "文件名：9452.jpg，推理结果为：[0, 3, 0, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 编写简易版解码器\n",
+    "def ctc_decode(text, blank=10):\n",
+    "    \"\"\"\n",
+    "    简易CTC解码器\n",
+    "    :param text: 待解码数据\n",
+    "    :param blank: 分隔符索引值\n",
+    "    :return: 解码后数据\n",
+    "    \"\"\"\n",
+    "    result = []\n",
+    "    cache_idx = -1\n",
+    "    for char in text:\n",
+    "        if char != blank and char != cache_idx:\n",
+    "            result.append(char)\n",
+    "        cache_idx = char\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "# 实例化推理模型\n",
+    "model = paddle.Model(Net(is_infer=True), inputs=input_define)\n",
+    "# 加载训练好的参数模型\n",
+    "model.load(CHECKPOINT_PATH)\n",
+    "# 设置运行环境\n",
+    "model.prepare()\n",
+    "\n",
+    "# 加载预测Reader\n",
+    "infer_reader = InferReader(INFER_DATA_PATH)\n",
+    "img_names = infer_reader.get_names()\n",
+    "results = model.predict(infer_reader, batch_size=BATCH_SIZE)\n",
+    "index = 0\n",
+    "for text_batch in results[0]:\n",
+    "    for prob in text_batch:\n",
+    "        out = ctc_decode(prob, blank=10)\n",
+    "        print(f\"文件名：{img_names[index]}，推理结果为：{out}\")\n",
+    "        index += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "py35-paddle1.2.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/practices/cv/image_ocr/image_ocr.ipynb b/docs/practices/cv/image_ocr/image_ocr.ipynb
deleted file mode 100644
index 95f6699855b..00000000000
--- a/docs/practices/cv/image_ocr/image_ocr.ipynb
+++ /dev/null
@@ -1,739 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# 通过OCR实现验证码识别\n",
-    "\n",
-    "**作者:** [GT_老张](https://github.com/GT-ZhangAcer)  \n",
-    "\n",
-    "**时间:** 2021.11\n",
-    "\n",
-    "**摘要:** 本篇将介绍如何通过飞桨实现简单的CRNN+CTC自定义数据集OCR识别模型，数据集采用[CaptchaDataset](https://github.com/GT-ZhangAcer/CaptchaDataset)中OCR部分的9453张图像，其中前8453张图像在本案例中作为训练集，后1000张则作为测试集。  \n",
-    "在更复杂的场景中推荐使用[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)产出工业级模型，模型轻量且精度大幅提升。  \n",
-    "同样也可以在[PaddleHub](https://www.paddlepaddle.org.cn/hubdetail?name=chinese_ocr_db_crnn_mobile&en_category=TextRecognition)中快速使用PaddleOCR。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 一、环境配置\n",
-    "\n",
-    "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) PaddlePaddle 2.2 。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.2.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import paddle\n",
-    "print(paddle.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 二、自定义数据集读取器\n",
-    "\n",
-    "常见的开发任务中，我们并不一定会拿到标准的数据格式，好在我们可以通过自定义Reader的形式来随心所欲读取自己想要数据。 \n",
-    "\n",
-    "设计合理的Reader往往可以带来更好的性能，我们可以将读取标签文件列表、制作图像文件列表等必要操作在`__init__`特殊方法中实现。这样就可以在实例化`Reader`时装入内存，避免使用时频繁读取导致增加额外开销。同样我们可以在`__getitem__`特殊方法中实现如图像增强、归一化等个性操作，完成数据读取后即可释放该部分内存。    \n",
-    "需要我们注意的是，如果不能保证自己数据十分纯净，可以通过`try`和`expect`来捕获异常并指出该数据的位置。当然也可以制定一个策略，使其在发生数据读取异常后依旧可以正常进行训练。  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 2.1 数据展示\n",
-    "<div align=\"center\">\n",
-    "<img src=https://ai-studio-static-online.cdn.bcebos.com/57d6c77aa5194cdca5c7edc533cc57e4d5070de95f6a4454b3cd1ca1e0eebe98 width=\"500px\">\n",
-    "</div>\n",
-    "\n",
-    "点此[快速获取本节数据集](https://aistudio.baidu.com/aistudio/datasetdetail/57285)，待数据集下载完毕后可使用`!unzip OCR_Dataset.zip -d data/`命令或熟悉的解压软件进行解压，待数据准备工作完成后修改本文“训练准备”中的`DATA_PATH = 解压后数据集路径`。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 下载数据集 \n",
-    "!wget -O OCR_Dataset.zip https://bj.bcebos.com/v1/ai-studio-online/c91f50ef72de43b090298a38281e9c59a2d741eadd334f1cba7c710c5496e342?responseContentDisposition=attachment%3B%20filename%3DOCR_Dataset.zip&authorization=bce-auth-v1%2F0ef6765c1e494918bc0d4c3ca3e5c6d1%2F2020-10-27T09%3A50%3A21Z%2F-1%2F%2Fddc4aebed803af6c57dac46abba42d207961b78e7bc81744e8388395979b66fa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 解压数据集\n",
-    "!unzip OCR_Dataset.zip -d data/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import PIL.Image as Image\n",
-    "import numpy as np\n",
-    "from paddle.io import Dataset\n",
-    "\n",
-    "# 图片信息配置 - 通道数、高度、宽度\n",
-    "IMAGE_SHAPE_C = 3\n",
-    "IMAGE_SHAPE_H = 30\n",
-    "IMAGE_SHAPE_W = 70\n",
-    "# 数据集图片中标签长度最大值设置 - 因图片中均为4个字符，故该处填写为4即可\n",
-    "LABEL_MAX_LEN = 4\n",
-    "\n",
-    "\n",
-    "class Reader(Dataset):\n",
-    "    def __init__(self, data_path: str, is_val: bool = False):\n",
-    "        \"\"\"\n",
-    "        数据读取Reader\n",
-    "        :param data_path: Dataset路径\n",
-    "        :param is_val: 是否为验证集\n",
-    "        \"\"\"\n",
-    "        super().__init__()\n",
-    "        self.data_path = data_path\n",
-    "        # 读取Label字典\n",
-    "        with open(os.path.join(self.data_path, \"label_dict.txt\"), \"r\", encoding=\"utf-8\") as f:\n",
-    "            self.info = eval(f.read())\n",
-    "        # 获取文件名列表\n",
-    "        self.img_paths = [img_name for img_name in self.info]\n",
-    "        # 将数据集后1024张图片设置为验证集，当is_val为真时img_path切换为后1024张\n",
-    "        self.img_paths = self.img_paths[-1024:] if is_val else self.img_paths[:-1024]\n",
-    "\n",
-    "    def __getitem__(self, index):\n",
-    "        # 获取第index个文件的文件名以及其所在路径\n",
-    "        file_name = self.img_paths[index]\n",
-    "        file_path = os.path.join(self.data_path, file_name)\n",
-    "        # 捕获异常 - 在发生异常时终止训练\n",
-    "        try:\n",
-    "            # 使用Pillow来读取图像数据\n",
-    "            img = Image.open(file_path)\n",
-    "            # 转为Numpy的array格式并整体除以255进行归一化\n",
-    "            img = np.array(img, dtype=\"float32\").reshape((IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W)) / 255\n",
-    "        except Exception as e:\n",
-    "            raise Exception(file_name + \"\\t文件打开失败，请检查路径是否准确以及图像文件完整性，报错信息如下:\\n\" + str(e))\n",
-    "        # 读取该图像文件对应的Label字符串，并进行处理\n",
-    "        label = self.info[file_name]\n",
-    "        label = list(label)\n",
-    "        # 将label转化为Numpy的array格式\n",
-    "        label = np.array(label, dtype=\"int32\")\n",
-    "\n",
-    "        return img, label\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        # 返回每个Epoch中图片数量\n",
-    "        return len(self.img_paths)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 三、模型配置"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 3.1 定义模型结构以及模型输入\n",
-    "\n",
-    "模型方面使用的简单的CRNN-CTC结构，输入形为CHW的图像在经过CNN->Flatten->Linear->RNN->Linear后输出图像中每个位置所对应的字符概率。考虑到CTC解码器在面对图像中元素数量不一、相邻元素重复时会存在无法正确对齐等情况，故额外添加一个类别代表“分隔符”进行改善。\n",
-    "\n",
-    "CTC相关论文：[Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neu](http://people.idsia.ch/~santiago/papers/icml2006.pdf) \n",
-    "\n",
-    "<div align=\"center\">\n",
-    "<img src=https://ai-studio-static-online.cdn.bcebos.com/f9458cedbb4441d682f15fefd3f3cae5e49d499bcf0a4bbdb976dfdff5a2e656 width=\"500px\">\n",
-    "</div>\n",
-    "\n",
-    "网络部分，因本篇采用数据集较为简单且图像尺寸较小并不适合较深层次网络。若在对尺寸较大的图像进行模型构建，可以考虑使用更深层次网络/注意力机制来完成。当然也可以通过目标检测形式先检出文本位置，然后进行OCR部分模型构建。\n",
-    "\n",
-    "<div align=\"center\">\n",
-    "<img src=https://ai-studio-static-online.cdn.bcebos.com/19ddf6107e7f47ee9b3b84ee0c12de1e15f7ab8b88f04eed95232440c92fe0d7 width=\"500px\">\n",
-    "</div>\n",
-    "\n",
-    "<a href=\"https://github.com/PaddlePaddle/PaddleOCR\">PaddleOCR效果图</a>\n",
-    "</p>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import paddle\n",
-    "\n",
-    "# 分类数量设置 - 因数据集中共包含0~9共10种数字+分隔符，所以是11分类任务\n",
-    "CLASSIFY_NUM = 11\n",
-    "\n",
-    "# 定义输入层，shape中第0维使用-1则可以在预测时自由调节batch size\n",
-    "input_define = paddle.static.InputSpec(shape=[-1, IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W],\n",
-    "                                   dtype=\"float32\",\n",
-    "                                   name=\"img\")\n",
-    "\n",
-    "# 定义网络结构\n",
-    "class Net(paddle.nn.Layer):\n",
-    "    def __init__(self, is_infer: bool = False):\n",
-    "        super().__init__()\n",
-    "        self.is_infer = is_infer\n",
-    "\n",
-    "        # 定义一层3x3卷积+BatchNorm\n",
-    "        self.conv1 = paddle.nn.Conv2D(in_channels=IMAGE_SHAPE_C,\n",
-    "                                  out_channels=32,\n",
-    "                                  kernel_size=3)\n",
-    "        self.bn1 = paddle.nn.BatchNorm2D(32)\n",
-    "        # 定义一层步长为2的3x3卷积进行下采样+BatchNorm\n",
-    "        self.conv2 = paddle.nn.Conv2D(in_channels=32,\n",
-    "                                  out_channels=64,\n",
-    "                                  kernel_size=3,\n",
-    "                                  stride=2)\n",
-    "        self.bn2 = paddle.nn.BatchNorm2D(64)\n",
-    "        # 定义一层1x1卷积压缩通道数，输出通道数设置为比LABEL_MAX_LEN稍大的定值可获取更优效果，当然也可设置为LABEL_MAX_LEN\n",
-    "        self.conv3 = paddle.nn.Conv2D(in_channels=64,\n",
-    "                                  out_channels=LABEL_MAX_LEN + 4,\n",
-    "                                  kernel_size=1)\n",
-    "        # 定义全连接层，压缩并提取特征（可选）\n",
-    "        self.linear = paddle.nn.Linear(in_features=429,\n",
-    "                                   out_features=128)\n",
-    "        # 定义RNN层来更好提取序列特征，此处为双向LSTM输出为2 x hidden_size，可尝试换成GRU等RNN结构\n",
-    "        self.lstm = paddle.nn.LSTM(input_size=128,\n",
-    "                               hidden_size=64,\n",
-    "                               direction=\"bidirectional\")\n",
-    "        # 定义输出层，输出大小为分类数\n",
-    "        self.linear2 = paddle.nn.Linear(in_features=64 * 2,\n",
-    "                                    out_features=CLASSIFY_NUM)\n",
-    "\n",
-    "    def forward(self, ipt):\n",
-    "        # 卷积 + ReLU + BN\n",
-    "        x = self.conv1(ipt)\n",
-    "        x = paddle.nn.functional.relu(x)\n",
-    "        x = self.bn1(x)\n",
-    "        # 卷积 + ReLU + BN\n",
-    "        x = self.conv2(x)\n",
-    "        x = paddle.nn.functional.relu(x)\n",
-    "        x = self.bn2(x)\n",
-    "        # 卷积 + ReLU\n",
-    "        x = self.conv3(x)\n",
-    "        x = paddle.nn.functional.relu(x)\n",
-    "        # 将3维特征转换为2维特征 - 此处可以使用reshape代替\n",
-    "        x = paddle.tensor.flatten(x, 2)\n",
-    "        # 全连接 + ReLU\n",
-    "        x = self.linear(x)\n",
-    "        x = paddle.nn.functional.relu(x)\n",
-    "        # 双向LSTM - [0]代表取双向结果，[1][0]代表forward结果,[1][1]代表backward结果，详细说明可在官方文档中搜索'LSTM'\n",
-    "        x = self.lstm(x)[0]\n",
-    "        # 输出层 - Shape = (Batch Size, Max label len, Signal) \n",
-    "        x = self.linear2(x)\n",
-    "\n",
-    "        # 在计算损失时ctc-loss会自动进行softmax，所以在预测模式中需额外做softmax获取标签概率\n",
-    "        if self.is_infer:\n",
-    "            # 输出层 - Shape = (Batch Size, Max label len, Prob) \n",
-    "            x = paddle.nn.functional.softmax(x)\n",
-    "            # 转换为标签\n",
-    "            x = paddle.argmax(x, axis=-1)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 四、训练准备"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 4.1 定义label输入以及超参数\n",
-    "监督训练需要定义label，预测则不需要该步骤。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 数据集路径设置\n",
-    "DATA_PATH = \"./data/OCR_Dataset\"\n",
-    "# 训练轮数\n",
-    "EPOCH = 10\n",
-    "# 每批次数据大小\n",
-    "BATCH_SIZE = 16\n",
-    "\n",
-    "label_define = paddle.static.InputSpec(shape=[-1, LABEL_MAX_LEN],\n",
-    "                                    dtype=\"int32\",\n",
-    "                                    name=\"label\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 4.2 定义CTC Loss\n",
-    "\n",
-    "了解CTC解码器效果后，我们需要在训练中让模型尽可能接近这种类型输出形式，那么我们需要定义一个CTC Loss来计算模型损失。不必担心，在飞桨框架中内置了多种Loss，无需手动复现即可完成损失计算。\n",
-    " \n",
-    "使用文档：[CTCLoss](https://www.paddlepaddle.org.cn/documentation/docs/zh/2.0-beta/api/paddle/nn/functional/loss/ctc_loss_cn.html#ctc-loss)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "class CTCLoss(paddle.nn.Layer):\n",
-    "    def __init__(self):\n",
-    "        \"\"\"\n",
-    "        定义CTCLoss\n",
-    "        \"\"\"\n",
-    "        super().__init__()\n",
-    "\n",
-    "    def forward(self, ipt, label):\n",
-    "        input_lengths = paddle.full(shape=[BATCH_SIZE],fill_value=LABEL_MAX_LEN + 4,dtype= \"int64\")\n",
-    "        label_lengths = paddle.full(shape=[BATCH_SIZE],fill_value=LABEL_MAX_LEN,dtype= \"int64\")\n",
-    "        # 按文档要求进行转换dim顺序\n",
-    "        ipt = paddle.tensor.transpose(ipt, [1, 0, 2])\n",
-    "        # 计算loss\n",
-    "        loss = paddle.nn.functional.ctc_loss(ipt, label, input_lengths, label_lengths, blank=10)\n",
-    "        return loss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 4.3 实例化模型并配置优化策略"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 实例化模型\n",
-    "model = paddle.Model(Net(), inputs=input_define, labels=label_define)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 定义优化器\n",
-    "optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())\n",
-    "\n",
-    "# 为模型配置运行环境并设置该优化策略\n",
-    "model.prepare(optimizer=optimizer,\n",
-    "                loss=CTCLoss())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 五、开始训练\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
-      "Epoch 1/10\n",
-      "step 529/529 [==============================] - loss: 0.0891 - 9ms/step         \n",
-      "save checkpoint at /home/aistudio/output/0\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0830 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 2/10\n",
-      "step 529/529 [==============================] - loss: 0.0199 - 10ms/step        \n",
-      "save checkpoint at /home/aistudio/output/1\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0353 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 3/10\n",
-      "step 529/529 [==============================] - loss: 0.2133 - 10ms/step        \n",
-      "save checkpoint at /home/aistudio/output/2\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0259 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 4/10\n",
-      "step 529/529 [==============================] - loss: 0.0133 - 9ms/step        \n",
-      "save checkpoint at /home/aistudio/output/3\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0210 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 5/10\n",
-      "step 529/529 [==============================] - loss: 0.0110 - 10ms/step        \n",
-      "save checkpoint at /home/aistudio/output/4\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0130 - 5ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 6/10\n",
-      "step 529/529 [==============================] - loss: 0.0150 - 9ms/step         \n",
-      "save checkpoint at /home/aistudio/output/5\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0111 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 7/10\n",
-      "step 529/529 [==============================] - loss: 0.0039 - 9ms/step        \n",
-      "save checkpoint at /home/aistudio/output/6\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0093 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 8/10\n",
-      "step 529/529 [==============================] - loss: 0.0100 - 9ms/step         \n",
-      "save checkpoint at /home/aistudio/output/7\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0059 - 5ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 9/10\n",
-      "step 529/529 [==============================] - loss: 0.0096 - 9ms/step        \n",
-      "save checkpoint at /home/aistudio/output/8\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0061 - 5ms/step        \n",
-      "Eval samples: 1000\n",
-      "Epoch 10/10\n",
-      "step 529/529 [==============================] - loss: 0.0066 - 10ms/step        \n",
-      "save checkpoint at /home/aistudio/output/9\n",
-      "Eval begin...\n",
-      "step 63/63 [==============================] - loss: 0.0054 - 6ms/step        \n",
-      "Eval samples: 1000\n",
-      "save checkpoint at /home/aistudio/output/final\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 执行训练\n",
-    "model.fit(train_data=Reader(DATA_PATH),\n",
-    "            eval_data=Reader(DATA_PATH, is_val=True),\n",
-    "            batch_size=BATCH_SIZE,\n",
-    "            epochs=EPOCH,\n",
-    "            save_dir=\"output/\",\n",
-    "            save_freq=1,\n",
-    "            verbose=1,\n",
-    "            drop_last=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 六、预测前准备"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 6.1 像定义训练Reader一样定义预测Reader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 与训练近似，但不包含Label\n",
-    "class InferReader(Dataset):\n",
-    "    def __init__(self, dir_path=None, img_path=None):\n",
-    "        \"\"\"\n",
-    "        数据读取Reader(预测)\n",
-    "        :param dir_path: 预测对应文件夹（二选一）\n",
-    "        :param img_path: 预测单张图片（二选一）\n",
-    "        \"\"\"\n",
-    "        super().__init__()\n",
-    "        if dir_path:\n",
-    "            # 获取文件夹中所有图片路径\n",
-    "            self.img_names = [i for i in os.listdir(dir_path) if os.path.splitext(i)[1] == \".jpg\"]\n",
-    "            self.img_paths = [os.path.join(dir_path, i) for i in self.img_names]\n",
-    "        elif img_path:\n",
-    "            self.img_names = [os.path.split(img_path)[1]]\n",
-    "            self.img_paths = [img_path]\n",
-    "        else:\n",
-    "            raise Exception(\"请指定需要预测的文件夹或对应图片路径\")\n",
-    "\n",
-    "    def get_names(self):\n",
-    "        \"\"\"\n",
-    "        获取预测文件名顺序 \n",
-    "        \"\"\"\n",
-    "        return self.img_names\n",
-    "\n",
-    "    def __getitem__(self, index):\n",
-    "        # 获取图像路径\n",
-    "        file_path = self.img_paths[index]\n",
-    "        # 使用Pillow来读取图像数据并转成Numpy格式\n",
-    "        img = Image.open(file_path)\n",
-    "        img = np.array(img, dtype=\"float32\").reshape((IMAGE_SHAPE_C, IMAGE_SHAPE_H, IMAGE_SHAPE_W)) / 255\n",
-    "        return img\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.img_paths)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 6.2 参数设置"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# 待预测目录 - 可在测试数据集中挑出\\b3张图像放在该目录中进行推理\n",
-    "INFER_DATA_PATH = \"./sample_img\"\n",
-    "# 训练后存档点路径 - final 代表最终训练所得模型\n",
-    "CHECKPOINT_PATH = \"./output/final.pdparams\"\n",
-    "# 每批次处理数量\n",
-    "BATCH_SIZE = 32"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### 6.3 展示待预测数据"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x720 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(10, 10))\n",
-    "sample_idxs = np.random.choice(50000, size=25, replace=False)\n",
-    "\n",
-    "for img_id, img_name in enumerate(os.listdir(INFER_DATA_PATH)):\n",
-    "    plt.subplot(1, 3, img_id + 1)\n",
-    "    plt.xticks([])\n",
-    "    plt.yticks([])\n",
-    "    im = Image.open(os.path.join(INFER_DATA_PATH, img_name))\n",
-    "    plt.imshow(im, cmap=plt.cm.binary)\n",
-    "    plt.xlabel(\"Img name: \" + img_name)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## 七、开始预测\n",
-    "> 飞桨2.1 CTC Decoder 相关API正在迁移中，本节暂时使用简易版解码器。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Detect dataset only contains single fileds, return format changed since Paddle 2.1. In Paddle <= 2.0, DataLoader add a list surround output data(e.g. return [data]), and in Paddle >= 2.1, DataLoader return the single filed directly (e.g. return data). For example, in following code: \n",
-      "\n",
-      "import numpy as np\n",
-      "from paddle.io import DataLoader, Dataset\n",
-      "\n",
-      "class RandomDataset(Dataset):\n",
-      "    def __getitem__(self, idx):\n",
-      "        data = np.random.random((2, 3)).astype('float32')\n",
-      "\n",
-      "        return data\n",
-      "\n",
-      "    def __len__(self):\n",
-      "        return 10\n",
-      "\n",
-      "dataset = RandomDataset()\n",
-      "loader = DataLoader(dataset, batch_size=1)\n",
-      "data = next(loader())\n",
-      "\n",
-      "In Paddle <= 2.0, data is in format '[Tensor(shape=(1, 2, 3), dtype=float32)]', and in Paddle >= 2.1, data is in format 'Tensor(shape=(1, 2, 3), dtype=float32)'\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predict begin...\n",
-      "step 1/1 [==============================] - 10ms/step\n",
-      "Predict samples: 3\n",
-      "文件名：9451.jpg，推理结果为：[3, 4, 6, 3]\n",
-      "文件名：9452.jpg，推理结果为：[0, 3, 0, 0]\n",
-      "文件名：9450.jpg，推理结果为：[8, 2, 0, 5]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 编写简易版解码器\n",
-    "def ctc_decode(text, blank=10):\n",
-    "    \"\"\"\n",
-    "    简易CTC解码器\n",
-    "    :param text: 待解码数据\n",
-    "    :param blank: 分隔符索引值\n",
-    "    :return: 解码后数据\n",
-    "    \"\"\"\n",
-    "    result = []\n",
-    "    cache_idx = -1\n",
-    "    for char in text:\n",
-    "        if char != blank and char != cache_idx:\n",
-    "            result.append(char)\n",
-    "        cache_idx = char\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# 实例化推理模型\n",
-    "model = paddle.Model(Net(is_infer=True), inputs=input_define)\n",
-    "# 加载训练好的参数模型\n",
-    "model.load(CHECKPOINT_PATH)\n",
-    "# 设置运行环境\n",
-    "model.prepare()\n",
-    "\n",
-    "# 加载预测Reader\n",
-    "infer_reader = InferReader(INFER_DATA_PATH)\n",
-    "img_names = infer_reader.get_names()\n",
-    "results = model.predict(infer_reader, batch_size=BATCH_SIZE)\n",
-    "index = 0\n",
-    "for text_batch in results[0]:\n",
-    "    for prob in text_batch:\n",
-    "        out = ctc_decode(prob, blank=10)\n",
-    "        print(f\"文件名：{img_names[index]}，推理结果为：{out}\")\n",
-    "        index += 1"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "py35-paddle1.2.0"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
diff --git a/docs/practices/cv/image_ocr/images/image1.png b/docs/practices/cv/image_ocr/images/image1.png
deleted file mode 100644
index 8163e6d5df9..00000000000
Binary files a/docs/practices/cv/image_ocr/images/image1.png and /dev/null differ
diff --git a/docs/practices/cv/image_ocr/images/image2.png b/docs/practices/cv/image_ocr/images/image2.png
deleted file mode 100644
index 715085e1b99..00000000000
Binary files a/docs/practices/cv/image_ocr/images/image2.png and /dev/null differ
diff --git a/docs/practices/cv/image_ocr/images/image3.png b/docs/practices/cv/image_ocr/images/image3.png
deleted file mode 100644
index 66c7d3758f6..00000000000
Binary files a/docs/practices/cv/image_ocr/images/image3.png and /dev/null differ
diff --git a/docs/practices/cv/index_cn.rst b/docs/practices/cv/index_cn.rst
index 2a7aa441493..a432fec5e74 100644
--- a/docs/practices/cv/index_cn.rst
+++ b/docs/practices/cv/index_cn.rst
@@ -9,7 +9,7 @@
     - `图像分类 <./convnet_image_classification.html>`_ ：介绍使用 PaddlePaddle 在Cifar10数据集上完成图像分类。    
     - `以图搜图 <./image_search.html>`_ : 介绍使用 PaddlePaddle 实现以图搜图。
     - `图像分割 <./image_segmentation.html>`_ : 介绍使用 PaddlePaddle 实现U-Net模型完成图像分割。
-    - `OCR <./image_ocr/image_ocr.html>`_ : 介绍使用 PaddlePaddle 实现 OCR。
+    - `OCR <./image_ocr.html>`_ : 介绍使用 PaddlePaddle 实现 OCR。
     - `图像超分 <./super_resolution_sub_pixel.html>`_ : 介绍使用 PaddlePaddle 完成图像超分。
     - `人脸关键点检测 <./landmark_detection.html>`_ : 介绍使用 PaddlePaddle 完成人脸关键点检测。
     - `点云分类 <./pointnet.html>`_ :介绍使用 PaddlePaddle 完成点云分类。
@@ -23,7 +23,7 @@
     convnet_image_classification.ipynb
     image_search.ipynb    
     image_segmentation.ipynb
-    image_ocr/image_ocr.ipynb
+    image_ocr.ipynb
     super_resolution_sub_pixel.ipynb
     landmark_detection.ipynb
-    pointnet.ipynb
\ No newline at end of file
+    pointnet.ipynb
diff --git a/docs/practices/cv/image_ocr/sample_img/9450.jpg b/docs/practices/cv/sample_img/9450.jpg
similarity index 100%
rename from docs/practices/cv/image_ocr/sample_img/9450.jpg
rename to docs/practices/cv/sample_img/9450.jpg
diff --git a/docs/practices/cv/image_ocr/sample_img/9451.jpg b/docs/practices/cv/sample_img/9451.jpg
similarity index 100%
rename from docs/practices/cv/image_ocr/sample_img/9451.jpg
rename to docs/practices/cv/sample_img/9451.jpg
diff --git a/docs/practices/cv/image_ocr/sample_img/9452.jpg b/docs/practices/cv/sample_img/9452.jpg
similarity index 100%
rename from docs/practices/cv/image_ocr/sample_img/9452.jpg
rename to docs/practices/cv/sample_img/9452.jpg
diff --git a/docs/practices/index_cn.rst b/docs/practices/index_cn.rst
index 08f43970778..992bb490f53 100644
--- a/docs/practices/index_cn.rst
+++ b/docs/practices/index_cn.rst
@@ -19,7 +19,7 @@
     - `图像分类 <./cv/convnet_image_classification.html>`_ ：介绍使用 PaddlePaddle 在Cifar10数据集上完成图像分类。    
     - `以图搜图 <./cv/image_search.html>`_ : 介绍使用 PaddlePaddle 实现以图搜图。
     - `图像分割 <./cv/image_segmentation.html>`_ : 介绍使用 PaddlePaddle 实现U-Net模型完成图像分割。
-    - `OCR <./cv/image_ocr/image_ocr.html>`_ : 介绍使用 PaddlePaddle 实现 OCR。
+    - `OCR <./cv/image_ocr.html>`_ : 介绍使用 PaddlePaddle 实现 OCR。
     - `图像超分 <./cv/super_resolution_sub_pixel.html>`_ : 介绍使用 PaddlePaddle 完成图像超分。
     - `人脸关键点检测 <./cv/landmark_detection.html>`_ : 介绍使用 PaddlePaddle 完成人脸关键点检测。
     - `点云分类 <./cv/pointnet.html>`_ :介绍使用 PaddlePaddle 完成点云分类。
@@ -46,6 +46,9 @@
 
     - `异常数据检测 <./time_series/autoencoder.html>`_ : 介绍使用 PaddlePaddle 完成时序数据异常点检测。
 
+动转静：
+    - `使用动转静完成以图搜图 <./jit/image_search_with_jit.html>`_ : 介绍使用 PaddlePaddle 通过动转静完成以图搜图。
+
 
 ..  toctree::
     :hidden:
@@ -56,3 +59,4 @@
     recommendations/index_cn.rst
     reinforcement_learning/index_cn.rst
     time_series/index_cn.rst
+    jit/index_cn.rst
diff --git a/docs/practices/jit/image_search_with_jit.ipynb b/docs/practices/jit/image_search_with_jit.ipynb
new file mode 100644
index 00000000000..ffddbfec3ca
--- /dev/null
+++ b/docs/practices/jit/image_search_with_jit.ipynb
@@ -0,0 +1,780 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 使用动转静完成以图搜图\n",
+    "\n",
+    "作者: [PaddlePaddle](https://github.com/PaddlePaddle)\n",
+    "\n",
+    "日期: 2021.12\n",
+    "\n",
+    "摘要: 本示例简要介绍如何通过飞桨开源框架，使用动转静功能，完成图片搜索。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 一、简要介绍\n",
+    "\n",
+    "在深度学习模型开发中，动态图代码更易编写和debug，但在部署性能上，静态图更具优势。而飞桨框架支持动态图转静态图（下文简称动转静）的功能，支持使用动态图编写组网代码，飞桨框架会对代码进行分析，自动转换为静态图网络结构，兼顾了动态图易用性和静态图部署性能两方面优势。\n",
+    "\n",
+    "本示例简要介绍如何通过飞桨开源框架，使用动转静功能，完成图片搜索的部署。\n",
+    "\n",
+    "本示例中的的大部分代码都源于 [基于图片相似度的图片搜索](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/cv_case/image_search/image_search.html) ，如果你想要了解关于组网和训练的更多信息，可以参考该示例。本示例将重点介绍，如何使用动转静完成模型的部署。\n",
+    "\n",
+    "关于动转静的更多文档，可以参考：[动态图转静态图-使用文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/index_cn.html)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 二、环境配置"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import paddle\n",
+    "import paddle.nn.functional as F\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "print(paddle.__version__)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.0\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 三、数据加载\n",
+    "\n",
+    "### 3.1 数据集介绍\n",
+    "\n",
+    "本示例采用 [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) 数据集。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "import paddle.vision.transforms as T\n",
+    "\n",
+    "transform = T.Compose([T.Transpose((2, 0, 1))])\n",
+    "\n",
+    "cifar10_train = paddle.vision.datasets.Cifar10(mode='train', transform=transform)\n",
+    "x_train = np.zeros((50000, 3, 32, 32))\n",
+    "y_train = np.zeros((50000, 1), dtype='int32')\n",
+    "\n",
+    "for i in range(len(cifar10_train)):\n",
+    "    train_image, train_label = cifar10_train[i]\n",
+    "    \n",
+    "    # normalize the data\n",
+    "    x_train[i,:, :, :] = train_image / 255.\n",
+    "    y_train[i, 0] = train_label\n",
+    "\n",
+    "y_train = np.squeeze(y_train)\n",
+    "\n",
+    "cifar10_test = paddle.vision.datasets.cifar.Cifar10(mode='test', transform=transform)\n",
+    "x_test = np.zeros((10000, 3, 32, 32), dtype='float32')\n",
+    "y_test = np.zeros((10000, 1), dtype='int64')\n",
+    "\n",
+    "for i in range(len(cifar10_test)):\n",
+    "    test_image, test_label = cifar10_test[i]\n",
+    "   \n",
+    "    # normalize the data\n",
+    "    x_test[i,:, :, :] = test_image / 255.\n",
+    "    y_test[i, 0] = test_label\n",
+    "\n",
+    "y_test = np.squeeze(y_test)\n",
+    "\n",
+    "height_width = 32\n",
+    "\n",
+    "def show_collage(examples):\n",
+    "    box_size = height_width + 2\n",
+    "    num_rows, num_cols = examples.shape[:2]\n",
+    "\n",
+    "    collage = Image.new(\n",
+    "        mode=\"RGB\",\n",
+    "        size=(num_cols * box_size, num_rows * box_size),\n",
+    "        color=(255, 255, 255),\n",
+    "    )\n",
+    "    for row_idx in range(num_rows):\n",
+    "        for col_idx in range(num_cols):\n",
+    "            array = (np.array(examples[row_idx, col_idx]) * 255).astype(np.uint8)\n",
+    "            array = array.transpose(1,2,0)\n",
+    "            collage.paste(\n",
+    "                Image.fromarray(array), (col_idx * box_size, row_idx * box_size)\n",
+    "            )\n",
+    "\n",
+    "    collage = collage.resize((2 * num_cols * box_size, 2 * num_rows * box_size))\n",
+    "    return collage\n",
+    "\n",
+    "sample_idxs = np.random.randint(0, 50000, size=(5, 5))\n",
+    "examples = x_train[sample_idxs]\n",
+    "show_collage(examples)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 3.2 构建训练数据\n",
+    "图片检索的模型的训练样本跟常见的分类任务的训练样本不太一样的地方在于，每个训练样本并不是一个(image, class)这样的形式。而是（image0, image1, similary_or_not)的形式，即，每一个训练样本由两张图片组成，而其label是这两张图片是否相似的标志位（0或者1）。\n",
+    "\n",
+    "很自然的能够想到，来自同一个类别的两张图片，是相似的图片，而来自不同类别的两张图片，应该是不相似的图片。\n",
+    "\n",
+    "为了能够方便的抽样出相似图片（以及不相似图片）的样本，先建立能够根据类别找到该类别下所有图片的索引。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "class_idx_to_train_idxs = defaultdict(list)\n",
+    "for y_train_idx, y in enumerate(y_train):\n",
+    "    class_idx_to_train_idxs[y].append(y_train_idx)\n",
+    "\n",
+    "class_idx_to_test_idxs = defaultdict(list)\n",
+    "for y_test_idx, y in enumerate(y_test):\n",
+    "    class_idx_to_test_idxs[y].append(y_test_idx)\n",
+    "\n",
+    "num_classes = 10\n",
+    "\n",
+    "def reader_creator(num_batchs):\n",
+    "    def reader():\n",
+    "        iter_step = 0\n",
+    "        while True:\n",
+    "            if iter_step >= num_batchs:\n",
+    "                break\n",
+    "            iter_step += 1\n",
+    "            x = np.empty((2, num_classes, 3, height_width, height_width), dtype=np.float32)\n",
+    "            for class_idx in range(num_classes):\n",
+    "                examples_for_class = class_idx_to_train_idxs[class_idx]\n",
+    "                anchor_idx = random.choice(examples_for_class)\n",
+    "                positive_idx = random.choice(examples_for_class)\n",
+    "                while positive_idx == anchor_idx:\n",
+    "                    positive_idx = random.choice(examples_for_class)\n",
+    "                x[0, class_idx] = x_train[anchor_idx]\n",
+    "                x[1, class_idx] = x_train[positive_idx]\n",
+    "            yield x\n",
+    "\n",
+    "    return reader\n",
+    "\n",
+    "def anchor_positive_pairs(num_batchs=100):\n",
+    "    return reader_creator(num_batchs)\n",
+    "\n",
+    "pairs_train_reader = anchor_positive_pairs(num_batchs=1000)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 四、模型组网：把图片转换为高维的向量表示的网络\n",
+    "目标是首先把图片转换为高维空间的表示，然后计算图片在高维空间表示时的相似度。 下面的网络结构用来把一个形状为(3, 32, 32)的图片转换成形状为(8,)的向量。在有些资料中也会把这个转换成的向量称为Embedding，请注意，这与自然语言处理领域的词向量的区别。 下面的模型由三个连续的卷积加一个全局均值池化，然后用一个线性全链接层映射到维数为8的向量空间。为了后续计算余弦相似度时的便利，还在最后做了归一化。（即，余弦相似度的分母部分）"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "class MyNet(paddle.nn.Layer):\n",
+    "    def __init__(self):\n",
+    "        super(MyNet, self).__init__()\n",
+    "\n",
+    "        self.conv1 = paddle.nn.Conv2D(in_channels=3, \n",
+    "                                      out_channels=32, \n",
+    "                                      kernel_size=(3, 3),\n",
+    "                                      stride=2)\n",
+    "         \n",
+    "        self.conv2 = paddle.nn.Conv2D(in_channels=32, \n",
+    "                                      out_channels=64, \n",
+    "                                      kernel_size=(3,3), \n",
+    "                                      stride=2)       \n",
+    "        \n",
+    "        self.conv3 = paddle.nn.Conv2D(in_channels=64, \n",
+    "                                      out_channels=128, \n",
+    "                                      kernel_size=(3,3),\n",
+    "                                      stride=2)\n",
+    "       \n",
+    "        self.gloabl_pool = paddle.nn.AdaptiveAvgPool2D((1,1))\n",
+    "\n",
+    "        self.fc1 = paddle.nn.Linear(in_features=128, out_features=8)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.conv2(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.conv3(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.gloabl_pool(x)\n",
+    "        x = paddle.squeeze(x, axis=[2, 3])\n",
+    "        x = self.fc1(x)\n",
+    "        x = x / paddle.norm(x, axis=1, keepdim=True)\n",
+    "        return x"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 五、模型训练"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "def train(model):\n",
+    "    print('start training ... ')\n",
+    "    model.train()\n",
+    "\n",
+    "    inverse_temperature = paddle.to_tensor(np.array([1.0/0.2], dtype='float32'))\n",
+    "\n",
+    "    epoch_num = 20\n",
+    "    \n",
+    "    opt = paddle.optimizer.Adam(learning_rate=0.0001,\n",
+    "                                parameters=model.parameters())\n",
+    "    \n",
+    "    for epoch in range(epoch_num):\n",
+    "        for batch_id, data in enumerate(pairs_train_reader()):\n",
+    "            anchors_data, positives_data = data[0], data[1]\n",
+    "\n",
+    "            anchors = paddle.to_tensor(anchors_data)\n",
+    "            positives = paddle.to_tensor(positives_data)\n",
+    "            \n",
+    "            anchor_embeddings = model(anchors)\n",
+    "            positive_embeddings = model(positives)\n",
+    "            \n",
+    "            similarities = paddle.matmul(anchor_embeddings, positive_embeddings, transpose_y=True) \n",
+    "            similarities = paddle.multiply(similarities, inverse_temperature)\n",
+    "            \n",
+    "            sparse_labels = paddle.arange(0, num_classes, dtype='int64')\n",
+    "\n",
+    "            loss = F.cross_entropy(similarities, sparse_labels)\n",
+    "            \n",
+    "            if batch_id % 500 == 0:\n",
+    "                print(\"epoch: {}, batch_id: {}, loss is: {}\".format(epoch, batch_id, loss.numpy()))\n",
+    "            loss.backward()\n",
+    "            opt.step()\n",
+    "            opt.clear_grad()\n",
+    "\n",
+    "model = MyNet()\n",
+    "train(model)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "W1203 09:42:06.354787   104 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
+      "W1203 09:42:06.359786   104 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "start training ... \n",
+      "epoch: 0, batch_id: 0, loss is: [2.3075619]\n",
+      "epoch: 0, batch_id: 500, loss is: [1.9298995]\n",
+      "epoch: 1, batch_id: 0, loss is: [1.7443665]\n",
+      "epoch: 1, batch_id: 500, loss is: [2.2623153]\n",
+      "epoch: 2, batch_id: 0, loss is: [2.2922273]\n",
+      "epoch: 2, batch_id: 500, loss is: [2.2985363]\n",
+      "epoch: 3, batch_id: 0, loss is: [2.2139223]\n",
+      "epoch: 3, batch_id: 500, loss is: [1.7750809]\n",
+      "epoch: 4, batch_id: 0, loss is: [2.2329998]\n",
+      "epoch: 4, batch_id: 500, loss is: [2.202569]\n",
+      "epoch: 5, batch_id: 0, loss is: [1.8858416]\n",
+      "epoch: 5, batch_id: 500, loss is: [2.0760386]\n",
+      "epoch: 6, batch_id: 0, loss is: [1.750076]\n",
+      "epoch: 6, batch_id: 500, loss is: [1.9625857]\n",
+      "epoch: 7, batch_id: 0, loss is: [2.0362983]\n",
+      "epoch: 7, batch_id: 500, loss is: [1.9722912]\n",
+      "epoch: 8, batch_id: 0, loss is: [2.1468532]\n",
+      "epoch: 8, batch_id: 500, loss is: [1.8924134]\n",
+      "epoch: 9, batch_id: 0, loss is: [2.0176272]\n",
+      "epoch: 9, batch_id: 500, loss is: [1.874192]\n",
+      "epoch: 10, batch_id: 0, loss is: [1.670248]\n",
+      "epoch: 10, batch_id: 500, loss is: [2.1149437]\n",
+      "epoch: 11, batch_id: 0, loss is: [1.6959581]\n",
+      "epoch: 11, batch_id: 500, loss is: [1.7163551]\n",
+      "epoch: 12, batch_id: 0, loss is: [2.1149023]\n",
+      "epoch: 12, batch_id: 500, loss is: [1.5345385]\n",
+      "epoch: 13, batch_id: 0, loss is: [1.5874553]\n",
+      "epoch: 13, batch_id: 500, loss is: [1.9915801]\n",
+      "epoch: 14, batch_id: 0, loss is: [2.3038936]\n",
+      "epoch: 14, batch_id: 500, loss is: [1.9974185]\n",
+      "epoch: 15, batch_id: 0, loss is: [2.2840767]\n",
+      "epoch: 15, batch_id: 500, loss is: [2.654189]\n",
+      "epoch: 16, batch_id: 0, loss is: [1.4491551]\n",
+      "epoch: 16, batch_id: 500, loss is: [1.9145182]\n",
+      "epoch: 17, batch_id: 0, loss is: [1.6488547]\n",
+      "epoch: 17, batch_id: 500, loss is: [1.8082515]\n",
+      "epoch: 18, batch_id: 0, loss is: [2.1301975]\n",
+      "epoch: 18, batch_id: 500, loss is: [2.1468956]\n",
+      "epoch: 19, batch_id: 0, loss is: [1.6691527]\n",
+      "epoch: 19, batch_id: 500, loss is: [1.8820274]\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 六、模型预测\n",
+    "前述的模型训练训练结束之后，就可以用该网络结构来计算出任意一张图片的高维向量表示（embedding)，通过计算该图片与图片库中其他图片的高维向量表示之间的相似度，就可以按照相似程度进行排序，排序越靠前，则相似程度越高。\n",
+    "\n",
+    "下面对测试集中所有的图片都两两计算相似度，然后选一部分相似的图片展示出来。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "near_neighbours_per_example = 10\n",
+    "\n",
+    "x_test_t = paddle.to_tensor(x_test)\n",
+    "test_images_embeddings = model(x_test_t)\n",
+    "similarities_matrix = paddle.matmul(test_images_embeddings, test_images_embeddings, transpose_y=True) \n",
+    "\n",
+    "indicies = paddle.argsort(similarities_matrix, descending=True)\n",
+    "indicies = indicies.numpy()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "examples = np.empty(\n",
+    "    (\n",
+    "        num_classes,\n",
+    "        near_neighbours_per_example + 1,\n",
+    "        3,\n",
+    "        height_width,\n",
+    "        height_width,\n",
+    "    ),\n",
+    "    dtype=np.float32,\n",
+    ")\n",
+    "\n",
+    "for row_idx in range(num_classes):\n",
+    "    examples_for_class = class_idx_to_test_idxs[row_idx]\n",
+    "    anchor_idx = random.choice(examples_for_class)\n",
+    "    \n",
+    "    examples[row_idx, 0] = x_test[anchor_idx]\n",
+    "    anchor_near_neighbours = indicies[anchor_idx][1:near_neighbours_per_example+1]\n",
+    "    for col_idx, nn_idx in enumerate(anchor_near_neighbours):\n",
+    "        examples[row_idx, col_idx + 1] = x_test[nn_idx]\n",
+    "\n",
+    "show_collage(examples)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=748x680 at 0x7F9DDF8F89D0>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 七、使用 paddle.jit.to_static 实现动转静\n",
+    "\n",
+    "飞桨推荐使用 `@paddle.jit.to_static` 实现动转静，也被称为基于源代码转写的动态图转静态图，其基本原理是通过分析 Python 代码来将动态图代码转写为静态图代码，并在底层自动使用执行器运行，使用起来非常方便，只需要在原网络结构的 `forward` 前添加一个装饰器 `paddle.jit.to_static` 即可。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 7.1 改写组网代码"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "class MyNet2(paddle.nn.Layer):\n",
+    "    def __init__(self):\n",
+    "        super(MyNet2, self).__init__()\n",
+    "\n",
+    "        self.conv1 = paddle.nn.Conv2D(in_channels=3, \n",
+    "                                      out_channels=32, \n",
+    "                                      kernel_size=(3, 3),\n",
+    "                                      stride=2)\n",
+    "         \n",
+    "        self.conv2 = paddle.nn.Conv2D(in_channels=32, \n",
+    "                                      out_channels=64, \n",
+    "                                      kernel_size=(3,3), \n",
+    "                                      stride=2)       \n",
+    "        \n",
+    "        self.conv3 = paddle.nn.Conv2D(in_channels=64, \n",
+    "                                      out_channels=128, \n",
+    "                                      kernel_size=(3,3),\n",
+    "                                      stride=2)\n",
+    "       \n",
+    "        self.gloabl_pool = paddle.nn.AdaptiveAvgPool2D((1,1))\n",
+    "\n",
+    "        self.fc1 = paddle.nn.Linear(in_features=128, out_features=8)\n",
+    "    \n",
+    "    # 在forward 前添加 paddle.jit.to_static 装饰器\n",
+    "    @paddle.jit.to_static()\n",
+    "    def forward(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.conv2(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.conv3(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.gloabl_pool(x)\n",
+    "        x = paddle.squeeze(x, axis=[2, 3])\n",
+    "        x = self.fc1(x)\n",
+    "        x = x / paddle.norm(x, axis=1, keepdim=True)\n",
+    "        return x"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "通过 `model.summary` 查看网络结构。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "model_2 = MyNet2()\n",
+    "model_info = paddle.summary(model_2, (10, 3, 32, 32))\n",
+    "print(model_info)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "-------------------------------------------------------------------------------\n",
+      "   Layer (type)         Input Shape          Output Shape         Param #    \n",
+      "===============================================================================\n",
+      "     Conv2D-4        [[10, 3, 32, 32]]     [10, 32, 15, 15]         896      \n",
+      "     Conv2D-5        [[10, 32, 15, 15]]     [10, 64, 7, 7]        18,496     \n",
+      "     Conv2D-6         [[10, 64, 7, 7]]     [10, 128, 3, 3]        73,856     \n",
+      "AdaptiveAvgPool2D-2  [[10, 128, 3, 3]]     [10, 128, 1, 1]           0       \n",
+      "     Linear-2           [[10, 128]]            [10, 8]             1,032     \n",
+      "===============================================================================\n",
+      "Total params: 94,280\n",
+      "Trainable params: 94,280\n",
+      "Non-trainable params: 0\n",
+      "-------------------------------------------------------------------------------\n",
+      "Input size (MB): 0.12\n",
+      "Forward/backward pass size (MB): 0.89\n",
+      "Params size (MB): 0.36\n",
+      "Estimated Total Size (MB): 1.36\n",
+      "-------------------------------------------------------------------------------\n",
+      "\n",
+      "{'total_params': 94280, 'trainable_params': 94280}\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
+      "  return (isinstance(seq, collections.Sequence) and\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 7.2 模型训练\n",
+    "使用 paddle.jit.to_static 装饰器后，训练方式仍与原动态图训练一致。因此这里直接传入 `model_2` 完成模型的训练。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "train(model_2)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "start training ... \n",
+      "epoch: 0, batch_id: 0, loss is: [2.2664194]\n",
+      "epoch: 0, batch_id: 500, loss is: [2.1221528]\n",
+      "epoch: 1, batch_id: 0, loss is: [1.9064648]\n",
+      "epoch: 1, batch_id: 500, loss is: [2.015831]\n",
+      "epoch: 2, batch_id: 0, loss is: [2.1349819]\n",
+      "epoch: 2, batch_id: 500, loss is: [1.7188847]\n",
+      "epoch: 3, batch_id: 0, loss is: [1.7870805]\n",
+      "epoch: 3, batch_id: 500, loss is: [1.9015355]\n",
+      "epoch: 4, batch_id: 0, loss is: [2.0960422]\n",
+      "epoch: 4, batch_id: 500, loss is: [1.9041044]\n",
+      "epoch: 5, batch_id: 0, loss is: [1.9104369]\n",
+      "epoch: 5, batch_id: 500, loss is: [1.9426862]\n",
+      "epoch: 6, batch_id: 0, loss is: [1.9272857]\n",
+      "epoch: 6, batch_id: 500, loss is: [2.003079]\n",
+      "epoch: 7, batch_id: 0, loss is: [2.0555334]\n",
+      "epoch: 7, batch_id: 500, loss is: [2.0897827]\n",
+      "epoch: 8, batch_id: 0, loss is: [1.735752]\n",
+      "epoch: 8, batch_id: 500, loss is: [1.6519189]\n",
+      "epoch: 9, batch_id: 0, loss is: [1.892964]\n",
+      "epoch: 9, batch_id: 500, loss is: [2.1541076]\n",
+      "epoch: 10, batch_id: 0, loss is: [2.1418836]\n",
+      "epoch: 10, batch_id: 500, loss is: [2.3189983]\n",
+      "epoch: 11, batch_id: 0, loss is: [1.9001983]\n",
+      "epoch: 11, batch_id: 500, loss is: [1.8500187]\n",
+      "epoch: 12, batch_id: 0, loss is: [1.7524569]\n",
+      "epoch: 12, batch_id: 500, loss is: [2.2970917]\n",
+      "epoch: 13, batch_id: 0, loss is: [1.8639088]\n",
+      "epoch: 13, batch_id: 500, loss is: [1.9846786]\n",
+      "epoch: 14, batch_id: 0, loss is: [2.0572317]\n",
+      "epoch: 14, batch_id: 500, loss is: [1.8811153]\n",
+      "epoch: 15, batch_id: 0, loss is: [1.7000355]\n",
+      "epoch: 15, batch_id: 500, loss is: [1.9591945]\n",
+      "epoch: 16, batch_id: 0, loss is: [1.6536798]\n",
+      "epoch: 16, batch_id: 500, loss is: [2.035933]\n",
+      "epoch: 17, batch_id: 0, loss is: [1.7236006]\n",
+      "epoch: 17, batch_id: 500, loss is: [1.8770548]\n",
+      "epoch: 18, batch_id: 0, loss is: [1.5971379]\n",
+      "epoch: 18, batch_id: 500, loss is: [1.5866497]\n",
+      "epoch: 19, batch_id: 0, loss is: [1.4629636]\n",
+      "epoch: 19, batch_id: 500, loss is: [1.6511686]\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 7.3 使用 `paddle.jit.save` 保存动转静模型\n",
+    "使用 `paddle.jit.to_static` 转换模型后，需要调用 `paddle.jit.save` 将保存模型，以供后续的预测部署。保存后，会产生 `model.pdmodel` 、`model.pdiparams.info`、`model.pdiparams` 三个文件。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "source": [
+    "paddle.jit.save(model_2, 'model')"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 7.4 使用 `paddle.jit.load` 加载动转静模型\n",
+    "\n",
+    "将模型导出后，需要使用 `paddle.jit.load` 加载模型。加载后的模型可以直接用于预测。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "source": [
+    "model_2 = paddle.jit.load('model')"
+   ],
+   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 7.5 使用动转静模型\n",
+    "\n",
+    "前述的模型训练训练结束之后，就可以用该网络结构来计算出任意一张图片的高维向量表示（embedding)，通过计算该图片与图片库中其他图片的高维向量表示之间的相似度，就可以按照相似程度进行排序，排序越靠前，则相似程度越高。\n",
+    "\n",
+    "下面对测试集中所有的图片都两两计算相似度，然后选一部分相似的图片展示出来。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "near_neighbours_per_example = 10\n",
+    "\n",
+    "x_test_t = paddle.to_tensor(x_test)\n",
+    "test_images_embeddings = model_2(x_test_t)\n",
+    "similarities_matrix = paddle.matmul(test_images_embeddings, test_images_embeddings, transpose_y=True) \n",
+    "\n",
+    "indicies = paddle.argsort(similarities_matrix, descending=True)\n",
+    "indicies = indicies.numpy()\n",
+    "\n",
+    "examples = np.empty(\n",
+    "    (\n",
+    "        num_classes,\n",
+    "        near_neighbours_per_example + 1,\n",
+    "        3,\n",
+    "        height_width,\n",
+    "        height_width,\n",
+    "    ),\n",
+    "    dtype=np.float32,\n",
+    ")\n",
+    "\n",
+    "for row_idx in range(num_classes):\n",
+    "    examples_for_class = class_idx_to_test_idxs[row_idx]\n",
+    "    anchor_idx = random.choice(examples_for_class)\n",
+    "    \n",
+    "    examples[row_idx, 0] = x_test[anchor_idx]\n",
+    "    anchor_near_neighbours = indicies[anchor_idx][1:near_neighbours_per_example+1]\n",
+    "    for col_idx, nn_idx in enumerate(anchor_near_neighbours):\n",
+    "        examples[row_idx, col_idx + 1] = x_test[nn_idx]\n",
+    "\n",
+    "show_collage(examples)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=748x680 at 0x7F9DDC20EED0>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## The End\n",
+    "\n",
+    "通过上述的内容，就使用 `@jit.to_static` 完成了动转静并使用该模型进行了预测，如果想了解更多关于动转静的内容，可以参考 [动态图转静态图](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/index_cn.html)。"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "py35-paddle1.2.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/docs/practices/jit/index_cn.rst b/docs/practices/jit/index_cn.rst
new file mode 100644
index 00000000000..be6d4b4afc5
--- /dev/null
+++ b/docs/practices/jit/index_cn.rst
@@ -0,0 +1,13 @@
+################
+动转静
+################
+
+这里提供了一篇动转静的示例：
+    
+     - `使用动转静完成以图搜图 <./image_search_with_jit.html>`_ : 介绍使用 PaddlePaddle 通过动转静完成以图搜图。
+
+..  toctree::
+    :hidden:
+    :titlesonly:
+
+    image_search_with_jit.ipynb
\ No newline at end of file
diff --git a/docs/release_note_cn.md b/docs/release_note_cn.md
index fc56944eac8..781b1b76957 100644
--- a/docs/release_note_cn.md
+++ b/docs/release_note_cn.md
@@ -1,13 +1,13 @@
 
-# 2.2.0 rc0 Release Note
+# Release Note
 
 ## 1. 重要更新
 
-我们很高兴的发布飞桨框架2.2.0-rc0版本，本版本包含如下重要更新。
+我们很高兴的发布飞桨框架2.2.0版本，本版本包含如下重要更新。
 
 ### API
 
-- 新增100+个API，包含24个傅里叶变换API、14个线性代数计算 API 等，更好地支持科学计算类、信号处理类模型。
+- 新增100+个API，包含24个傅里叶变换API、17个线性代数计算 API 等，更好地支持科学计算类、信号处理类模型。
 - 新增多种索引类型的支持，新增的索引类型包括：省略号（…）、维度扩增（None）、布尔类型数组（Bool Mask）、整数数组(（list)，以及张量（Tensor) ），可以更加方便的对张量（Tensor）进行操作。
 - 新增 `paddle.einsum` API，可以以更加简洁的方式来表达多维张量（Tensor）的计算。
 - 动态图混合精度功能增强，新增整个任务使用半精度（float16）训练的方式，主要任务下的计算效率提升20%左右。												
@@ -290,7 +290,9 @@ paddle.int64
 	- 新增 ``paddle.linalg.multi_dot``，支持多个矩阵连乘的计算。([#35224](https://github.com/PaddlePaddle/Paddle/pull/35224))
 	- 新增 ``paddle.linalg.solve``，支持计算线性方程组的解。([#35715](https://github.com/PaddlePaddle/Paddle/pull/35715))
 	- 新增``paddle.linalg.matrix_power``，支持矩阵的幂运算操作。([#34667](https://github.com/PaddlePaddle/Paddle/pull/34667))
-
+	-  新增`paddle.linalg.eigvalsh`，用于计算厄米特矩阵或者实数对称矩阵的特征值。([#36680](https://github.com/PaddlePaddle/Paddle/pull/36680))
+	- 新增`paddle.linalg.eig`，用于计算一般方阵的特征值和特征向量。([#35674](https://github.com/PaddlePaddle/Paddle/pull/35674))
+	- 新增`paddle.linalg.qr`，用于计算矩阵的QR分解（暂不支持反向）。([#36627](https://github.com/PaddlePaddle/Paddle/pull/36627))
 - 新增傅里叶变换相关API ([#35665](https://github.com/PaddlePaddle/Paddle/pull/35665)) 
     - 新增快速傅立叶变换系列函数
         - 可微分的 1d 到 nd 复数到复数快速傅里叶变换。(``paddle.fft.fft``, ``paddle.fft.fft2``, ``paddle.fft.fftn``, ``paddle.fft.ifft``, ``paddle.fft.ifft2``, ``paddle.fft.ifftn``)
@@ -303,19 +305,21 @@ paddle.int64
         - 短时傅里叶逆变换。(``paddle.signal.istft``)
 
 - 新增高层API
-	  - 新增 ``paddle.vision.ops.roi_pool`` 和 ``paddle.vision.ops.RoIPool``，支持检测任务中 RoI 区域池化操作。 ([#36154](https://github.com/PaddlePaddle/Paddle/pull/36154))
-	  -  新增 ``paddle.vision.ops.roi_align`` 和 ``paddle.vision.ops.RoIAlign``，支持检测任务中 RoI 区域 Align 操作。([#36207](https://github.com/PaddlePaddle/Paddle/pull/36207))
-	  - 新增 ``paddle.vision.ops.psroi_pool`` 和 ``paddle.vision.ops.PSRoIPool``，支持检测任务中位置敏感的 RoI 区域池化操作。 ([#36111](https://github.com/PaddlePaddle/Paddle/pull/36111))
-	  - 新增 ``paddle.vision.models.vgg19`` 预训练权重。 ([#35788](https://github.com/PaddlePaddle/Paddle/pull/35788))
-	  - 新增 ``paddle.vision.datasets.*`` 中数据集 API 下载进度条。([#33302](https://github.com/PaddlePaddle/Paddle/pull/33302))
-	  - 新增 ``paddle.Model.predict`` 参数 ``verbose``，支持是否显示日志。([#33405](https://github.com/PaddlePaddle/Paddle/pull/33405))
-	  - 新增 ``paddle.hub`` 下载选项 `wget` 方式。([#33379](https://github.com/PaddlePaddle/Paddle/pull/33379))
-	  -  新增 ``paddle.Model`` 动态图模式下梯度累加功能。([#32702](https://github.com/PaddlePaddle/Paddle/pull/32702))
-	  - 新增 ``paddle.Model.fit`` 和 ``paddle.Model.evaluate``  动态图模式下 ``num_iters`` 参数，控制训练迭代轮数。([#33986](https://github.com/PaddlePaddle/Paddle/pull/33986))
-	  - 新增 ``paddle.vision.ops.yolo_box`` 参数 ``iou_aware`` 和 ``iou_aware_factor``，支持 YoloBox 使用预测的 IOU 作为置信度的因子。([#33400](https://github.com/PaddlePaddle/Paddle/pull/33400))
-	  - 新增 ``paddle.summary`` 参数``input``，支持给定输入。([#34165](https://github.com/PaddlePaddle/Paddle/pull/34165))
+	- 新增 ``paddle.vision.ops.roi_pool`` 和 ``paddle.vision.ops.RoIPool``，支持检测任务中 RoI 区域池化操作。 ([#36154](https://github.com/PaddlePaddle/Paddle/pull/36154))
+	-  新增 ``paddle.vision.ops.roi_align`` 和 ``paddle.vision.ops.RoIAlign``，支持检测任务中 RoI 区域 Align 操作。([#36207](https://github.com/PaddlePaddle/Paddle/pull/36207))
+	- 新增 ``paddle.vision.ops.psroi_pool`` 和 ``paddle.vision.ops.PSRoIPool``，支持检测任务中位置敏感的 RoI 区域池化操作。 ([#36111](https://github.com/PaddlePaddle/Paddle/pull/36111))
+	- 新增 ``paddle.vision.models.vgg19`` 预训练权重。 ([#35788](https://github.com/PaddlePaddle/Paddle/pull/35788))
+	- 新增 ``paddle.vision.datasets.*`` 中数据集 API 下载进度条。([#33302](https://github.com/PaddlePaddle/Paddle/pull/33302))
+	- 新增 ``paddle.Model.predict`` 参数 ``verbose``，支持是否显示日志。([#33405](https://github.com/PaddlePaddle/Paddle/pull/33405))
+	- 新增 ``paddle.hub`` 下载选项 `wget` 方式。([#33379](https://github.com/PaddlePaddle/Paddle/pull/33379))
+	-  新增 ``paddle.Model`` 动态图模式下梯度累加功能。([#32702](https://github.com/PaddlePaddle/Paddle/pull/32702))
+	- 新增 ``paddle.Model.fit`` 和 ``paddle.Model.evaluate``  动态图模式下 ``num_iters`` 参数，控制训练迭代轮数。([#33986](https://github.com/PaddlePaddle/Paddle/pull/33986))
+	- 新增 ``paddle.vision.ops.yolo_box`` 参数 ``iou_aware`` 和 ``iou_aware_factor``，支持 YoloBox 使用预测的 IOU 作为置信度的因子。([#33400](https://github.com/PaddlePaddle/Paddle/pull/33400))
+	- 新增 ``paddle.summary`` 参数``input``，支持给定输入。([#34165](https://github.com/PaddlePaddle/Paddle/pull/34165))
+	- 新增`paddle.text.viterbi_decode`，支持动态图下CPU、GPU的Viterbi解码功能。([#35778](https://github.com/PaddlePaddle/Paddle/pull/35778))
 
 - 新增组网类 API
+	- 新增`paddle.nn.functional.sparse_attention`，用于计算稀疏的Transformer Attention模块。([#35757](https://github.com/PaddlePaddle/Paddle/pull/35757))
 	- 新增 ``paddle.nn.MaxUnPool2D`` 和 ``paddle.nn.functional.max_unpool2d``，支持根据输入的input和最大值位置计算出池化的逆结果。([#35056](https://github.com/PaddlePaddle/Paddle/pull/35056))
 	- 新增 ``paddle.nn.functional.gumbel_softmax``，支持 ``gumbel softmax`` 采样。([#35506](https://github.com/PaddlePaddle/Paddle/pull/35506), [#36065](https://github.com/PaddlePaddle/Paddle/pull/36065), [#36094](https://github.com/PaddlePaddle/Paddle/pull/36094))
 	- 新增 ``paddle.nn.functional.class_center_sample``，支持 PartialFC 类中心采样功能。([#34106](https://github.com/PaddlePaddle/Paddle/pull/34106))
@@ -332,9 +336,13 @@ paddle.int64
 	- 新增 ``paddle.device.cuda.empty_cache``，支持清理空闲的显存。([#35427](https://github.com/PaddlePaddle/Paddle/pull/35427))
 	- 新增 ``paddle.device.cuda.get_device_properties``，支持返回给定的设备属性。([#35875](https://github.com/PaddlePaddle/Paddle/pull/35875))
 	- 新增 ``paddle.device.cuda.stream_guard``，用于动态图下 CUDA Stream的灵活切换。([#35623](https://github.com/PaddlePaddle/Paddle/pull/35623))
-
+	- 新增`paddle.device.cuda.get_device_name`，支持返回给定设备的名称。([#36172](https://github.com/PaddlePaddle/Paddle/pull/36172))
+	- 新增`paddle.device.cuda.get_device_capability`，支持返回给定设备计算能力的版本号。([#36172](https://github.com/PaddlePaddle/Paddle/pull/36172))
+	- 新增`paddle.framework.core.async_read`和`paddle.framework.core.async_write`，可支持非默认 CUDA `Stream`下`CUDAPinnedPlace` 和 `CUDAPlace` 的 `Tensor` 数据异步读写。([#36501](https://github.com/PaddlePaddle/Paddle/pull/36501))
 
 - 新增Tensor操作API
+ - 新增`paddle.tensordot`，支持对高维张量做缩并(Tensor Contraction)运算。([#36454](https://github.com/PaddlePaddle/Paddle/pull/36454))
+ - 新增`paddle.bincount`，支持对一维张量内元素进行计数。([#36709](https://github.com/PaddlePaddle/Paddle/pull/36709))
  - 新增 `paddle.broadcast_tensors` ，支持对一组 `Tensor` 进行广播操作。([#33294](https://github.com/PaddlePaddle/Paddle/pull/33294), [#34874](https://github.com/PaddlePaddle/Paddle/pull/34874))
  - 新增 `paddle.einsum` 。([#33821](https://github.com/PaddlePaddle/Paddle/pull/34874))
  - 增强``paddle.tensor.gradient``接口，支持sigmoid_op的二阶求导算子。([#32971](https://github.com/PaddlePaddle/Paddle/pull/32971))
@@ -373,6 +381,7 @@ paddle.int64
     - 新增  ``paddle.static.ExponentialMovingAverage``，支持用指数衰减计算参数的滑动平均值。([#35673](https://github.com/PaddlePaddle/Paddle/pull/35673))
     - 新增 `` paddle::Tensor::slice`` C++ API， 支持 slice 操作，允许用户对外部 Tensor 切片操作。([#34227](https://github.com/PaddlePaddle/Paddle/pull/34227))
     - 新增``paddle.incubate.segment_*``系列API，包含 ``paddle.incubate.segment_sum, paddle.incubate.segment_mean,  paddle.incubate.segment_max, paddle.incubate.segment_min``。支持对`Tensor`按照分段求和、求均值、求最大值、求最小值。 ([#35759](https://github.com/PaddlePaddle/Paddle/pull/35759))
+    - 新增`paddle.version.cuda`和`paddle.version.cudnn`，用于获取 paddle 安装包所使用的 `CUDA`和 `cuDNN`的版本号。([#36556](https://github.com/PaddlePaddle/Paddle/pull/36556))
 
 #### IR(Intermediate Representation)
 - 动态图转静态图 
@@ -388,13 +397,15 @@ paddle.int64
     - 提供分析 `Program` 中控制流需要的依赖辅助函数。 ([#33439](https://github.com/PaddlePaddle/Paddle/pull/33439))
     - `Program` 和 `Graph` 相互转换后保留训练所需要的 `stop_gradient` ,  `persistable` 属性值。([#33771](https://github.com/PaddlePaddle/Paddle/pull/33771)) 
     - 原 `Pass` 只处理主`Graph`，忽略子图，现`Pass` 支持处理主 `Graph`及其所有子图。 ([#34158](https://github.com/PaddlePaddle/Paddle/pull/34158)) 
-    - 处理了在预测情况下 `Program` 和 `Graph` 互转的一些拓扑排序问题。([#34121](https://github.com/PaddlePaddle/Paddle/pull/34121), [#34521](https://github.com/PaddlePaddle/Paddle/pull/34521)). **《== **
+    - 处理了在预测情况下 `Program` 和 `Graph` 互转的一些拓扑排序问题。([#34121](https://github.com/PaddlePaddle/Paddle/pull/34121), [#34521](https://github.com/PaddlePaddle/Paddle/pull/34521))
 
 - Pass开发
     - 新增 Python 侧针对 fusion 等子图替换场景下的 Pass 开发方式。([#35708](https://github.com/PaddlePaddle/Paddle/pull/35708), [#35602](https://github.com/PaddlePaddle/Paddle/pull/35602))
 
 - Kernel Primitive API	
     - 对算子 Kernel 实现中的底层代码进行了抽象与功能封装，提供高性能的 Block 级 IO 运算和 Compute 运算。使用 Kernel Primitive API 进行 Kernel 开发可以更加专注计算逻辑的实现，在保证性能的同时大幅减少代码量，同时实现了算子计算与硬件解耦。([#34672](https://github.com/PaddlePaddle/Paddle/pull/34672),  [#35075](https://github.com/PaddlePaddle/Paddle/pull/35075),  [#34456](https://github.com/PaddlePaddle/Paddle/pull/34456),  [#35282](https://github.com/PaddlePaddle/Paddle/pull/35282),  [#35743](https://github.com/PaddlePaddle/Paddle/pull/35743),  [#34208](https://github.com/PaddlePaddle/Paddle/pull/34208))
+    - 在 Kernel Primitive API中添加一元和二元计算Functor共13个。 ([#36418](https://github.com/PaddlePaddle/Paddle/pull/36418))
+    - 修改 Kernel Primitive API 中 ReadData 实现方式，修复`NX !=1`访存越界的问题。 ([#36373](https://github.com/PaddlePaddle/Paddle/pull/36373))
 
 #### 混合精度训练
 - 动态图混合精度功能增强，新增整个任务使用半精度（float16）训练的方式，主要任务下的计算效率提升20%左右。 ([#35521](https://github.com/PaddlePaddle/Paddle/pull/35521))
@@ -512,7 +523,13 @@ paddle.int64
 - 优化``l2_normalize``,``p_norm``,``elementwise_max``,``prelu``,``clip_by_norm``,``lars optimizer``算子支持float16计算。 ([#35576](https://github.com/PaddlePaddle/Paddle/pull/35576), [#35888](https://github.com/PaddlePaddle/Paddle/pull/35888), [#35888](https://github.com/PaddlePaddle/Paddle/pull/35888), [35532](https://github.com/PaddlePaddle/Paddle/pull/35532), [#35446](https://github.com/PaddlePaddle/Paddle/pull/35446), [#33280](https://github.com/PaddlePaddle/Paddle/pull/33280))
 - 优化flowers数据集的读取速度，从每批次数分钟优化至1~3秒。([#31408](https://github.com/PaddlePaddle/Paddle/pull/31408))
 - 支持`paddle.distributed.fleet.DistributedStrategy` 中 `without_graph_optimize` 开关打开后的fuse allreduce sum功能。FP32下性能提升3%，AMP下性能提升8%。([#34446](https://github.com/PaddlePaddle/Paddle/pull/34446)) 
-
+- `paddle.matmul` 将底层Op算子由matmul op 切换到 matmul_v2 op。 ([#36374](https://github.com/PaddlePaddle/Paddle/pull/36374))
+- `paddle.fft` 模块添加了 mkl_cdft 和 hipfft 两个计算后端。 ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- `paddle.roll` 的参数 `shifts` 支持 `Tensor` 作为输入。 ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- `paddle.shape` 支持复数类型的输入。([#36835](https://github.com/PaddlePaddle/Paddle/pull/36835))
+- matmul_v2 支持量化。([#36469](https://github.com/PaddlePaddle/Paddle/pull/36469))
+- 新增 `clip_op` 对 `float16` 的支持。 ([#36672](https://github.com/PaddlePaddle/Paddle/pull/36672))
+- `paddle.fft` 模块为 cufft 后端添加了缓存 plan 的功能，优化性能。([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
 
 #### IR(Intermediate Representation)
 - 动态图转静态图
@@ -521,6 +538,9 @@ paddle.int64
     - 优化了动转静训练代码逻辑，升级内部 ``Program`` 缓存机制，新增输入 ``Tensor`` 的提前 copy 策略，提升训练性能。 ([#34181](https://github.com/PaddlePaddle/Paddle/pull/34181), [#33796](https://github.com/PaddlePaddle/Paddle/pull/33796))
     - 优化动转静内部执行器显存回收策略，减少训练时显存占用量。 ([#34177](https://github.com/PaddlePaddle/Paddle/pull/34177))
     - 集成了 ``Gast`` 三方依赖库的源码，解耦了版本依赖。 ([#34556](https://github.com/PaddlePaddle/Paddle/pull/34556)) 
+    - 动转静报错时显示部分框架层报错信息，使得定位问题更加容易。([#36765](https://github.com/PaddlePaddle/Paddle/pull/36765))
+    - 移除动转静报错模块中重复的临时文件删除函数`remove_static_file()`。([#36375](https://github.com/PaddlePaddle/Paddle/pull/36375))
+    - 优化对RegisterPass中`input_specs`参数处理，支持图优化时作为匹配子图条件。([#36453](https://github.com/PaddlePaddle/Paddle/pull/36453))
 
 
 #### 分布式训练
@@ -534,7 +554,13 @@ paddle.int64
     - `paddle.io.Dataset` 支持动态库解析数据。 ([#33969](https://github.com/PaddlePaddle/Paddle/pull/33969))
     - 新增 `paddle.distributed.fleet.dataset.DatasetBase` 中对`use_var_list`和 `pipe_command` 生成数据的一致性检查函数。 ([#34463](https://github.com/PaddlePaddle/Paddle/pull/34463))
     - 新增 `paddle.fluid.layers.embedding` 的 `emd` 维度与 `fleet` 中` sparse table` 的 `emb` 维度的一致性检查。 ([#34249](https://github.com/PaddlePaddle/Paddle/pull/34249))
-
+    - 动态图混合并行支持Pure FP16训练。([#36707](https://github.com/PaddlePaddle/Paddle/pull/36707))
+    - 静态图混合并行支持dropout使用固定随机种子生成器，以确保模型并行中全局变量的一致性与局部变量的随机性。([#36682](https://github.com/PaddlePaddle/Paddle/pull/36682))
+    ‘
+    - 实现了CPU并行，并支持调用 spawn 或 launch 时可以添加自定义的backend参数。可用的backend选择为 "gloo", "nccl", "bkcl", "auto" ，分别表示CPU并行，GPU并行，XPU并行和按照Paddle版本自动选择。([#35745](https://github.com/PaddlePaddle/Paddle/pull/35745))
+    - 优化动态图混合并行 HybridParallelClipGrad 策略，支持4D混合并行+Pure FP16训练。([#36707](https://github.com/PaddlePaddle/Paddle/pull/36707))
+    - 添加 SlotRecordDataset 类支持GPU参数服务器训练。([#36710](https://github.com/PaddlePaddle/Paddle/pull/36710))
+    - GPU参数服务器构建阶段支持使用SlotRecordDataset。([#36723](https://github.com/PaddlePaddle/Paddle/pull/36723))
 
 - 静态图混合并行
     - 优化混合并行 loss scale，减少 scale op 插入个数。([#35775](https://github.com/PaddlePaddle/Paddle/pull/35775))
@@ -555,6 +581,14 @@ paddle.int64
 	- 修正 ``paddle.jit.save``  接口和模型裁剪的逻辑，不再为输出变量增加一个关联的 ``scale_op``，可以正确导出含有 ``bool``，``float16`` 类型输出的模型。([#35730](https://github.com/PaddlePaddle/Paddle/pull/35730), [#36132](https://github.com/PaddlePaddle/Paddle/pull/36132))
 - 自定义OP
 	- 移除 ``paddle::Tensor`` 的 ``copy`` 方法中不必要的 ``cudaStreamSynchronize`` 操作，以提升性能。([#35802](https://github.com/PaddlePaddle/Paddle/pull/35802))
+- 新增C++对GeneratePass开发注册的支持，开发方式与Python侧对齐。([#36302](https://github.com/PaddlePaddle/Paddle/pull/36302))
+- 自动稀疏化训练(Automic SParsity)
+	- 新增`paddle.static.sparsity`，支持生成`n:m`稀疏模式的稀疏参数，目前只支持静态图ASP训练。A100上FP32、FP16分别设置`1:2`、`2:4`的稀疏模式，训练保存的稀疏模型，可通过调用TensorRT 8利用Ampere架构的稀疏Tensor Core加速推理任务。当前版本共提供了5个API：([#32995](https://github.com/PaddlePaddle/Paddle/pull/32995)、[#33132](https://github.com/PaddlePaddle/Paddle/pull/33132)、[#33558](https://github.com/PaddlePaddle/Paddle/pull/33558)、[#36525](https://github.com/PaddlePaddle/Paddle/pull/36525))
+	- `paddle.static.sparsity.calculate_density`，计算输入Tensor的密度。
+	- `paddle.static.sparsity.decorate`，将给定的优化器包装为`OptimizerWithSparsityGuarantee`，在调用 `optimizer.minimize()`时自动为ASP工作流插入必要的操作。
+	- `paddle.static.sparsity.prune_model`，依据`mask_algo`指定的掩码生成函数裁剪`main_program`中支持的层的参数。
+	- `paddle.static.sparsity.set_excluded_layers`，设置不会被裁剪的层的参数名称。
+	- `paddle.static.sparsity.reset_excluded_layers`，重置与`main_program`相对应的`excluded_layers`设置。
 
 
 
@@ -594,6 +628,18 @@ paddle.int64
 - 优化动态图性能，将只在静态图执行的逻辑从动态图的执行路径中剥离。([#34024](https://github.com/PaddlePaddle/Paddle/pull/34024))
 - IR Pass优化能力作为通用能力露出，同时支持单机和分布式优化。在GPT混合并行场景性能提升3%-5%。([#34955](https://github.com/PaddlePaddle/Paddle/pull/34955), [#35704](https://github.com/PaddlePaddle/Paddle/pull/35704), [#34730](https://github.com/PaddlePaddle/Paddle/pull/34730), [#34524](https://github.com/PaddlePaddle/Paddle/pull/34524))
 - 优化 ctc loss grad 计算速度，提速~3x，但相应增加了GPU显存占用。([#34729](https://github.com/PaddlePadle/Paddle/pull/34729))
+- transformer encoder 性能优化
+	- 优化思路：通过新增 `paddle.incubate.nn.FusedMultiHeadAttention` 和 `paddle.incubate.nn.FusedFeedForward` 的方式，在实现中采用 q, k, v gemm融合及多种kernel融合优化技术，提升transformer encoder的性能。
+		- FusedAttention
+			- 新增 `paddle.incubate.nn.functional.fused_multi_head_attention` ，支持multi-head attention的融合计算。([#35905](https://github.com/PaddlePaddle/Paddle/pull/35905) [35903](https://github.com/PaddlePaddle/Paddle/pull/35903) [#36803](https://github.com/PaddlePaddle/Paddle/pull/36803) [#36793](https://github.com/PaddlePaddle/Paddle/pull/36793) [36185](https://github.com/PaddlePaddle/Paddle/pull/36185))
+			- 新增 `paddle.incubate.nn.FusedMultiHeadAttention` ，用于融合multi-head attention的layer层组网。 ([#36498](https://github.com/PaddlePaddle/Paddle/pull/36498) )
+			- 该模块使用q, k, v gemm融合和bias add + dropout + residual add + layer_norm kernel融合优化技术，可带来1.08x-1.45x加速。
+		
+		- FusedFeedForward
+			- 新增 `paddle.incubate.nn.functional.fused_feedforward` ，支持 feedforward的融合计算。([#36729](https://github.com/PaddlePaddle/Paddle/pull/36729) [#36730](https://github.com/PaddlePaddle/Paddle/pull/36730))
+			- 新增 `paddle.incubate.nn.FusedFeedForward` ，用于融合feedforward的layer层组网。 ([#36776](https://github.com/PaddlePaddle/Paddle/pull/36776))
+			- 性能较优化前有1.04x~1.22x左右的提升。
+		- 新增 `paddle.incubate.nn.FusedTransformerEncoderLayer`，支持使用融合multi-head attention和融合feedforward计算的layer层组网。 ([#36776](https://github.com/PaddlePaddle/Paddle/pull/36776))
 
 
 ### （4）问题修复
@@ -687,12 +733,27 @@ paddle.int64
 - 迁移``paddle.nn.functional.dice_loss``API中的`one_hot`算子到`one_hot_v2`算子。([#35734](https://github.com/PaddlePaddle/Paddle/pull/35734))
 - 修复 ``paddle.summary`` 静态图模式下使用 bug。([#35303](https://github.com/PaddlePaddle/Paddle/pull/35303))
 - 修复 ``paddle.Model.prepare`` 静态图模式下多卡启动的 bug。([#34311](https://github.com/PaddlePaddle/Paddle/pull/34311))
+- 修复`paddle.nn.functional.cross_entropy` 给定`weight`，且指定`axis`为除-1外的其他合法维度时会报错的问题。([#36647](https://github.com/PaddlePaddle/Paddle/pull/36647))
+- 修复`paddle.utils.dlpack.to_dlpack`无法编码多维 `Tensor` 的问题，修复其所生成的 DLPack 对象无法进行跨深度学习框架共享的问题。([#36177](https://github.com/PaddlePaddle/Paddle/pull/36177))
+- 修复使用`paddle.distribution.Categorical`的`sample`方法报错的问题，具体原因是multinomial op的cuda kernel中数组访问越界，该bug会导致访问超出数组下标的值，引起报错。 ([#36511](https://github.com/PaddlePaddle/Paddle/pull/36511))
+- 修复动态图`_BatchNormBase`基类中修改了 default_dtype，导致后续组网参数类型错误的问题，受影响的API有`paddle.nn.BatchNorm1D`，`paddle.nn.BatchNorm2D`，`paddle.nn.BatchNorm3D`，`paddle.nn.SyncBatchNorm`。具体原因是当 `get_default_dtype() == 'float16'` 时，通过 `set_default_dtype('float32')`修改默认参数数据类型，动态图组网的参数类型是通过 default_dtype 来创建的，因此当默认参数类型被修改后导致后续的组网参数类型错误。 ([#36376](https://github.com/PaddlePaddle/Paddle/pull/36376))
+- 修复`paddle.nn.functional.grid_sample`因特殊输入导致的异常问题。([#36625](https://github.com/PaddlePaddle/Paddle/pull/36625))
+- 修复 `paddle.fft.fft`, `paddle.fft.ifft`, `paddle.fft.rfft` , `paddle.fft.irfft`, `paddle.fft.hfft`, `paddle.fft.ihfft` 在输入 `axis=0` 情况下的计算错误问题。([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- 修复 `paddle.fft.fftshift`  和 `paddle.fft.ifftshift` 在静态图下出错的问题。([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- 修复 `paddle.fft.ifftshift` 计算结果不正确的问题。([#36835](https://github.com/PaddlePaddle/Paddle/pull/36835))
+- 修复`paddle.nn.functional.pad`在`replicate`模式下的报错信息提示。([#36531](https://github.com/PaddlePaddle/Paddle/pull/36531))
+
 
 #### IR(Intermediate Representation)
 
 - 动态图转静态图
     - 修复了动转静后，在 ``paddle.no_grad`` 语义下显存异常增长的问题。([#35725](https://github.com/PaddlePaddle/Paddle/pull/35725))
     - 修复了对 ``paddle.no_grad`` 接口的错误识别和转换问题。([#34136](https://github.com/PaddlePaddle/Paddle/pull/34136)) 
+    - 修复了部分场景下模型中间设置 stop_gradient=True 时，动转静训练报错的问题。([#36353](https://github.com/PaddlePaddle/Paddle/pull/36353))
+    - 修复了在控制流 if 的部分场景转换时，对返回结果检查会报错的问题。([#36830](https://github.com/PaddlePaddle/Paddle/pull/36830))
+    - 修复了在 ifelse 分支返回不等长结果时，动转静会额外对齐返回长度导致返回类型意外改变的问题。([#36565](https://github.com/PaddlePaddle/Paddle/pull/36565))
+    - 修复使用 jit.save/load 接口加载模型后，在 train 模式和 no_grad 上下文中，显存会一直增长的问题。([#36463](https://github.com/PaddlePaddle/Paddle/pull/36463))
+
 
 #### 分布式训练
 
@@ -727,6 +788,10 @@ paddle.int64
     - 修复 GPU 参数服务器使用非0卡训练报错问题。([#33078](https://github.com/PaddlePaddle/Paddle/pull/33078))
     - 修复 GPU 参数服务器 delta score，scale show问题。([#33492](https://github.com/PaddlePaddle/Paddle/pull/33078), [#33492](https://github.com/PaddlePaddle/Paddle/pull/33492))
     - 修复 GPU 参数服务器训练结束后未 merge dense，g2sum 计算有误，data norm 添加了optimize op 等问题。 ([#35029](https://github.com/PaddlePaddle/Paddle/pull/35029))
+    - 修复使用 fuse all reduce ops 开关时，如果梯度出现 empty 时会报错的问题。([#36231](https://github.com/PaddlePaddle/Paddle/pull/36231))
+    - 修复 dist_transformer 文件出现未定义的变量问题。([#36211](https://github.com/PaddlePaddle/Paddle/pull/36211))
+	
+	
 	
 - 动态图混合并行
 	- 修复流水线并行计算错误的问题。([#35556](https://github.com/PaddlePaddle/Paddle/pull/35556))
@@ -767,6 +832,8 @@ paddle.int64
     - 子图通过支持Paddle-Lite NNAdapter接入ascend310硬件预测 [#35226](https://github.com/PaddlePaddle/Paddle/pull/35226)， 示例可参考[demo](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/c%2B%2B/ascend310_lite_subgraph/image_classification_demo)。
     - 新增晟腾910 推理支持 [#34101](https://github.com/PaddlePaddle/Paddle/pull/34101)
 
+- 新增pool3d算子支持TensorRT的功能。([#36545](https://github.com/PaddlePaddle/Paddle/pull/36545))
+
 ### （2）功能优化
 
 #### 框架及API更新
@@ -774,6 +841,7 @@ paddle.int64
 - 量化支持 
     - 动态图量化推理 pass 的重构，支持非模拟量化的 OP和模拟量化的 OP。([#35907](https://github.com/PaddlePaddle/Paddle/pull/35907))
   - 增加 int8 的模拟量化OP matmul（权重乘以 tensor的情况）。([#34359](https://github.com/PaddlePaddle/Paddle/pull/34359))
+  - 修复MobileNetV3模型在量化训练过程中因量化参数为0导致的Loss出NAN问题。([#36763](https://github.com/PaddlePaddle/Paddle/pull/36763))
 
 
 - API 增强
@@ -810,16 +878,18 @@ paddle.int64
   - 增加TensorRT `qkv_context` plugin 对int8的支持([#34917](https://github.com/PaddlePaddle/Paddle/pull/34917), [#35504](https://github.com/PaddlePaddle/Paddle/pull/35504))
   - 增加TensorRT conv3d的支持。([#35507](https://github.com/PaddlePaddle/Paddle/pull/35507))
   - 增加对 `multihead_matmul` 融合算子的输入进行广播的支持。([#35780](https://github.com/PaddlePaddle/Paddle/pull/35780))
+  - Inference 支持 TensorRT8 稀疏推理，[测试环境](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/c%2B%2B/sparsity)下，ERNIE 模型变长输入在不同的 batch_size 下性能提升10%-30%，ResNeXt101_32x4d模型在不同的batch_size下性能提升10%。([#36659](https://github.com/PaddlePaddle/Paddle/pull/36659))
 
 - Nvidia Jetson 原生支持能力增强
     - 新增 Op 支持，针对Jetson Nano/TX2这两款算力较低的设备，我们做了针对性的优化，目前新增了 `pool2d`, `pool_max`, `conv3d_transpose` 等 17个OP的支持。([#35378](https://github.com/PaddlePaddle/Paddle/pull/35378))
     - 针对Jetson Nano，新增模型：DPN68, EfficientNetB0, ttfnet, fcn_hrnetw18, hardnet。([#35378](https://github.com/PaddlePaddle/Paddle/pull/35378))
     - 针对Jetson TX2，新增模型：deeplabv3p_resnet50, deeplabv3_resnet50, fcn_hrnetw18, hardnet, pspnet, ttfnet, unet。([#35378](https://github.com/PaddlePaddle/Paddle/pull/35378))
 
-
 - 昆仑XPU接口功能扩展 
   - 新增 `set_xpu_device_id` 接口，支持设置推理时的昆仑芯片的设备号([#35572](https://github.com/PaddlePaddle/Paddle/pull/35572))
 
+- Inference python `copy_from_cpu`接口加入输入类型检查，错误类型输入下提前报错。([#36552](https://github.com/PaddlePaddle/Paddle/pull/36552))
+
 ### （3）问题修复
 
 #### 框架及API修复
@@ -842,6 +912,16 @@ paddle.int64
     - 修复ernie变长情况下，输入的顺序不一致导致输出不对的问题。([#33575](https://github.com/PaddlePaddle/Paddle/pull/33575))
     - 修复多流状态下分配器功能异常的问题。([#32932](https://github.com/PaddlePaddle/Paddle/pull/33575))
 
+- 修复 ERNIE 模型在 TRT8 下可能出现的崩溃问题。([#36769](https://github.com/PaddlePaddle/Paddle/pull/36769))
+- 修复使用 Pool, Slice 时可能出现的崩溃及精度问题。([#36666](https://github.com/PaddlePaddle/Paddle/pull/36666))
+- 修复 yolo_box op因为计算公式错误导致的精度问题。([#36365](https://github.com/PaddlePaddle/Paddle/pull/36365))
+- 修复量化后的 matmul_v2 在TRT下无法正常推理的问题。([#36821](https://github.com/PaddlePaddle/Paddle/pull/36821))
+- 修复了量化 matmul_v2 时错误地添加量化op的问题。([#36820](https://github.com/PaddlePaddle/Paddle/pull/36820))
+- 修复算子 batch_norm 和 elementwise_add 在3D应用场景下开启 TRT 报错的问题。([#36446](https://github.com/PaddlePaddle/Paddle/pull/36446))
+- 修复高层 linear api保存得到的预测模型无法被 Pass 融合优化的问题。([#36500](https://github.com/PaddlePaddle/Paddle/pull/36500))
+- 修改 MatmulV2ToMul 的 Pass，重新限定 (matmul_v2 to mul) 映射的 Pass，增加 MatmulV2ToMatmul 的 Pass，限定 (matmul_v2 to matmul) 映射的 Pass条件(不支持广播)，修改 (matmul, mul) 的 op_teller 映射条件。([#36652](https://github.com/PaddlePaddle/Paddle/pull/36652))
+
+
 #### 后端能力修复
 
 - TensorRT 子图引擎修复
@@ -907,4 +987,5 @@ paddle.int64
 
 This release contains contributions from:
 
-0x45f, 123malin, Adam Osewski, Aganlengzi, Aurelius84, Baibaifan, Bo Liu, CheQiXiao, Chen Long, Chen Weihang, CtfGo, Double\_V, Ethanzjp, Fan Zhang, Feiyu Chan, Feng Xing, From00, GT-Zhang, Guanghua Yu, Guoxia Wang, Haipeng Wang, Hao Lin, Haohongxiang, Hui Zhang, Huihuang Zheng, HydrogenSulfate, IMMORTAL, JYChen, JZ-LIANG, Jacek Czaja, Jack Zhou, Jackwaterveg, Jeng Bai-Cheng, Jiangxinz, Jiaqi Liu, Jiawei Wang, JingZhuangzhuang, June Weng, Kaipeng Deng, Kqnonrime, LJQ❤️, Leo Chen, Li Min, LielinJiang, Lijunhui, Linjie Chen, Liu-xiandong, LiuWei, Ming-Xu Huang, MissPenguin, PaddlePM, Pei Yang, Peihan, Qi Li, QingshuChen, Ren Wei (任卫), Roc, Shang Zhizhou, ShenLiang, Shibo Tao, Siming Dai, Sing\_chan, TCChenLong, TTerror, TeslaZhao, Thomas Young, Thunderbrook, Tongxin Bai, WJJ1995, WangXi, Wangzheee, Wei Shengyu, WeiXin, Weilong Wu, Wenyu, Wilber, XGZhang, XYZ, XYZ916829, XiangGao, Xiaoxu Chen, YUNSHEN XIE, Yanxing Shi, Yiqun Liu, YuanRisheng, Yuang Liu, Yulong Ao, Zeng Jinle, Zhang Ting, Zhang Zheng, Zhanlue Yang, Zhen Wang, Zhong Hui, Zhou Wei, andreazanetti, andyjpaddle, arlesniak, baoachun, cc, ceci3, chajchaj, chenenquan, chenjian, chentianyu03, crystal, cuicheng01, danleifeng, denglin-github, duanboqiang, dyning, feng626, feng_shuai, furnace, gongweibao, heliqi, hlygit66666, hong, hong19860320, houj04, huangjun12, huangxu96, huzhiqiang, iducn, jakpiase, jiangcheng, joanna.wozna.intel, jzhang533, kuizhiqing, levi131, lidanqing, lilong12, limingshu, littletomatodonkey, liu zhengxi, liutiexing, liuyuhui, liym27, lyuwenyu, lzzyzlbb, niuliling123, pangyoki, parap1uie-s, ronnywang, root, seemingwang, shangliang Xu, shiyutang, smallv0221, sunli, sunzhongkai588, taixiurong, tangwei12, tianshuo78520a, veyron95, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, wangxinxin08, wangzhen38, wangzhuang01, wawltor, wenbin, whs, will-jl944, wuhuachaocoding, wuhuanzhou, xiaoting, xiaoxiaohehe001, xiayanming, xiegegege, xiemoyuan, xiongkun, yaoxuefeng, yeliang2258, yingyibiao, zhangbo9674, zhangchunle, zhangkaihuo, zhaoyingli, zhiboniu, zhoujun, zhouzj, zhulei, zhupengyang, zlsh80826, zmx, zyfncg, 李季, 津, 王明冬, 石晓伟
\ No newline at end of file
+0x45f, 123malin, Adam Osewski, Aganlengzi, Aurelius84, Baibaifan, Bo Liu, CheQiXiao, Chen Long, Chen Weihang, CtfGo, Double\_V, Ethanzjp, Fan Zhang, Feiyu Chan, Feng Xing, From00, GT-Zhang, Guanghua Yu, Guoxia Wang, Haipeng Wang, Hao Lin, Haohongxiang, Hui Zhang, Huihuang Zheng, HydrogenSulfate, IMMORTAL, JYChen, JZ-LIANG, Jacek Czaja, Jack Zhou, Jackwaterveg, Jeng Bai-Cheng, Jiangxinz, Jiaqi Liu, Jiawei Wang, JingZhuangzhuang, June Weng, Kaipeng Deng, Kqnonrime, LJQ❤️, Leo Chen, Li Min, LielinJiang, Lijunhui, Linjie Chen, Liu-xiandong, LiuWei, Ming-Xu Huang, MissPenguin, PaddlePM, Pei Yang, Peihan, Qi Li, QingshuChen, Ren Wei (任卫), Roc, Shang Zhizhou, ShenLiang, Shibo Tao, Siming Dai, Sing\_chan, TCChenLong, TTerror, TeslaZhao, Thomas Young, Thunderbrook, Tongxin Bai, WJJ1995, WangXi, Wangzheee, Wei Shengyu, WeiXin, Weilong Wu, Wenyu, Wilber, XGZhang, XYZ, XYZ916829, XiangGao, Xiaoxu Chen, YUNSHEN XIE, Yanxing Shi, Yiqun Liu, YuanRisheng, Yuang Liu, Yulong Ao, Zeng Jinle, Zhang Ting, Zhang Zheng, Zhanlue Yang, Zhen Wang, Zhong Hui, Zhou Wei, andreazanetti, andyjpaddle, arlesniak, baoachun, cc, ceci3, chajchaj, chenenquan, chenjian, chentianyu03, crystal, cuicheng01, danleifeng, denglin-github, duanboqiang, dyning, feng626, feng_shuai, furnace, gongweibao, heliqi, hlygit66666, hong, hong19860320, houj04, huangjun12, huangxu96, huzhiqiang, iducn, jakpiase, jiangcheng, joanna.wozna.intel, jzhang533, kuizhiqing, levi131, lidanqing, lilong12, limingshu, littletomatodonkey, liu zhengxi, liutiexing, liuyuhui, liym27, lyuwenyu, lzzyzlbb, niuliling123, pangyoki, parap1uie-s, ronnywang, root, seemingwang, shangliang Xu, shiyutang, smallv0221, sunli, sunzhongkai588, taixiurong, tangwei12, tianshuo78520a, veyron95, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, wangxinxin08, wangzhen38, wangzhuang01, wawltor, wenbin, whs, will-jl944, wuhuachaocoding, wuhuanzhou, xiaoting, xiaoxiaohehe001, xiayanming, xiegegege, xiemoyuan, xiongkun, yaoxuefeng, yeliang2258, yingyibiao, zhangbo9674, zhangchunle, zhangkaihuo, zhaoyingli, zhiboniu, zhoujun, zhouzj, zhulei, zhupengyang, zlsh80826, zmx, zyfncg, 李季, 津, 王明冬, 石晓伟
+
diff --git a/docs/release_note_en.md b/docs/release_note_en.md
index 9848c8de754..349796fdebb 100644
--- a/docs/release_note_en.md
+++ b/docs/release_note_en.md
@@ -1,13 +1,13 @@
 
-# 2.2.0 rc0 Release Note
+# Release Note
 
 ## **1. Highlights**
 
-We are excited to release the PaddlePaddle Framework V2.2.0-rc0. This version contains the following highlights.
+We are excited to release the PaddlePaddle Framework V2.2.0. This version contains the following highlights.
 
 ### API
 
-- Added 100+ APIs, including 24 Fourier transform APIs, 14 linear algebra APIs, etc., to better facilitate developing of scientific computing and signal processing models.
+- Added 100+ APIs, including 24 Fourier transform APIs, 17 linear algebra APIs, etc., to better facilitate developing of scientific computing and signal processing models.
 - Added the support for multiple indexing syntax, including ellipsis (...), dimension expansion (None), boolean arrays (Bool Mask), and integer arrays (list and tensor), making it easier to operate on tensor.
 - Added the `paddle.einsum` API, to express multi-dimensional tensor computation in a more concise way.  
 - Enhanced the dynamic graph mixed precision. Added a way to use half-precision (float16) training for the whole task. The computational efficiency under the main tasks increased by 20%.
@@ -289,6 +289,9 @@ paddle.int64
    - Add the ``paddle.linalg.multi_dot``, to support the computing of concatenated multiplication of multiple matrices. ([#35224](https://github.com/PaddlePaddle/Paddle/pull/35224))
    - Add the ``paddle.linalg.solve``, to support the computing of the solutions of linear equations.  ([#35715](https://github.com/PaddlePaddle/Paddle/pull/35715))
    - Add the ``paddle.linalg.matrix_power``, to support the power operations on matrices.  ([#34667](https://github.com/PaddlePaddle/Paddle/pull/34667))
+   - Add `paddle.linalg.eigvalsh` for computing eigenvalues of Hermite Matrix or real symmetric matrices.  ([#36680](https://github.com/PaddlePaddle/Paddle/pull/36680))
+   - Add `paddle.linalg.eig` for computing eigenvalues and eigenvectors of general square matrices.  ([#35674](https://github.com/PaddlePaddle/Paddle/pull/35674))
+   - Add `paddle.linalg.qr` for computing QR decomposition of matrices (inverse is not supported yet).  ([#36627](https://github.com/PaddlePaddle/Paddle/pull/36627))
 
 - Add new Fourier transform related API ([#35665](https://github.com/PaddlePaddle/Paddle/pull/35665)) 
   - Add fast Fourier transform family functions  
@@ -303,18 +306,20 @@ paddle.int64
 
 - Add new high-level APIs  
   - Add the ``paddle.vision.ops.roi_pool`` and ``paddle.vision.ops.RoIPool``, support RoI region pooling operations in detection tasks. ([#36154](https://github.com/PaddlePaddle/Paddle/pull/36154))
-    -  Add the ``paddle.vision.ops.roi_align`` and ``paddle.vision.ops.RoIAlign``, to support RoI region Align operations in detection tasks.  ([#36207](https://github.com/PaddlePaddle/Paddle/pull/36207))
-    -  Add the ``paddle.vision.ops.psroi_pool`` and ``paddle.vision.ops.PSRoIPool``, to support location-sensitive RoI region pooling operations in detection tasks. ([#36111](https://github.com/PaddlePaddle/Paddle/pull/36111))
-    -  Add the ``paddle.vision.models.vgg19`` pre-training weights. ([#35788](https://github.com/PaddlePaddle/Paddle/pull/35788))
-    -  Add thedatasets API download progress bar in ``paddle.vision.datasets.*``. ([#33302](https://github.com/PaddlePaddle/Paddle/pull/33302))
-    -  Add the ``paddle.Model.predict`` parameter ``verbose``, to support whether to show logs or not. ([#33405](https://github.com/PaddlePaddle/Paddle/pull/33405))
-    -  Add the ``paddle.hub`` download option ``wget`` method. ([#33379](https://github.com/PaddlePaddle/Paddle/pull/33379))
-    -  Add the ``paddle.Model`` gradient accumulation in dynamic graph mode. ([#32702](https://github.com/PaddlePaddle/Paddle/pull/32702))
-    -  Add the ``paddle.Model.fit`` and ``paddle.Model.evaluate`` ``num_iters`` parameters in dynamic graph mode to control the number of training iterations. ([#33986](https://github.com/PaddlePaddle/Paddle/pull/33986))
-    -  Add the ``paddle.vision.ops.yolo_box`` parameters ``iou_aware`` and ``iou_aware_factor``, to support YoloBox using predicted IOUs as confidence factors. ([#33400](https://github.com/PaddlePaddle/Paddle/pull/33400))
-    -  Add the ``paddle.summary`` parameter input to support the given ``input``. ([#34165](https://github.com/PaddlePaddle/Paddle/pull/34165))
+  -  Add the ``paddle.vision.ops.roi_align`` and ``paddle.vision.ops.RoIAlign``, to support RoI region Align operations in detection tasks.  ([#36207](https://github.com/PaddlePaddle/Paddle/pull/36207))
+  -  Add the ``paddle.vision.ops.psroi_pool`` and ``paddle.vision.ops.PSRoIPool``, to support location-sensitive RoI region pooling operations in detection tasks. ([#36111](https://github.com/PaddlePaddle/Paddle/pull/36111))
+  -  Add the ``paddle.vision.models.vgg19`` pre-training weights. ([#35788](https://github.com/PaddlePaddle/Paddle/pull/35788))
+  -  Add the datasets API download progress bar in ``paddle.vision.datasets.*``. ([#33302](https://github.com/PaddlePaddle/Paddle/pull/33302))
+  -  Add the ``paddle.Model.predict`` parameter ``verbose``, to support whether to show logs or not. ([#33405](https://github.com/PaddlePaddle/Paddle/pull/33405))
+  -  Add the ``paddle.hub`` download option ``wget`` method. ([#33379](https://github.com/PaddlePaddle/Paddle/pull/33379))
+  -  Add the ``paddle.Model`` gradient accumulation in dynamic graph mode. ([#32702](https://github.com/PaddlePaddle/Paddle/pull/32702))
+  -  Add the ``paddle.Model.fit`` and ``paddle.Model.evaluate`` ``num_iters`` parameters in dynamic graph mode to control the number of training iterations. ([#33986](https://github.com/PaddlePaddle/Paddle/pull/33986))
+  -  Add the ``paddle.vision.ops.yolo_box`` parameters ``iou_aware`` and ``iou_aware_factor``, to support YoloBox using predicted IOUs as confidence factors. ([#33400](https://github.com/PaddlePaddle/Paddle/pull/33400))
+  -  Add the ``paddle.summary`` parameter input to support the given ``input``. ([#34165](https://github.com/PaddlePaddle/Paddle/pull/34165))
+  - Add `paddle.text.viterbi_decode`, to support Viterbi decoding for CPU and GPU under dynamic graphs.  ([#35778](https://github.com/PaddlePaddle/Paddle/pull/35778))
 
 - Add networking class APIs  
+  - Add `paddle.nn.functional.sparse_attention` for computing sparse Transformer Attention modules.  ([#35757](https://github.com/PaddlePaddle/Paddle/pull/35757))
   - Add the ``paddle.nn.MaxUnPool2D`` and ``paddle.nn.functional.max_unpool2d``, to support the computing of the inverse of the pooling result based on the input and maximum position.  ([#35056](https://github.com/PaddlePaddle/Paddle/pull/35056))
   - Add the ``paddle.nn.functional.gumbel_softmax``, to support ``gumbel softmax`` sampling.  ([#35506](https://github.com/PaddlePaddle/Paddle/pull/35506), [#36065](https://github.com/PaddlePaddle/Paddle/pull/36065), [#36094](https://github.com/PaddlePaddle/Paddle/pull/36094))
   - Add the ``paddle.nn.functional.class_center_sample``, to support PartialFC class center sampling. ([#34106](https://github.com/PaddlePaddle/Paddle/pull/34106))
@@ -331,9 +336,14 @@ paddle.int64
   - Add the ``paddle.device.cuda.empty_cache``, to support for clearing free GPU memory.  ([#35427](https://github.com/PaddlePaddle/Paddle/pull/35427))
   - Add the ``paddle.device.cuda.get_device_properties``, to support for returning the given device properties. ([#35875](https://github.com/PaddlePaddle/Paddle/pull/35875))
   - Add the ``paddle.device.cuda.stream_guard`` for flexible switching of CUDA Streams under dynamic graphs. ([#35623](https://github.com/PaddlePaddle/Paddle/pull/35623))
+  - Add `paddle.device.cuda.get_device_name`, to support returning the name of a given device.  ([#36172](https://github.com/PaddlePaddle/Paddle/pull/36172))
+  - Add `paddle.device.cuda.get_device_capability`, to support returning version number of the computational capability of a given device.  ([#36172](https://github.com/PaddlePaddle/Paddle/pull/36172))
+  - Add `paddle.framework.core.async_read` and `paddle.framework.core.async_write`, to support `Tensor` data asynchronous read and write of `CUDAPinnedPlace` and ` CUDAPlace` under non-default CUDA `Stream`.  ([#36501](https://github.com/PaddlePaddle/Paddle/pull/36501))
 
 
 - Add Tensor operation APIs  
+ - Add `paddle.tensordot`, to support Tensor Contraction for high dimension.  ([#36454](https://github.com/PaddlePaddle/Paddle/pull/36454))
+ - Add `paddle.bincount`, to support counting elements in a one-dimensional tensor.  ([#36709](https://github.com/PaddlePaddle/Paddle/pull/36709))
  - Add the `paddle.broadcast_tensors`, to support broadcast operations on a set of `Tensors`.  ([#33294](https://github.com/PaddlePaddle/Paddle/pull/33294), [#34874](https://github.com/PaddlePaddle/Paddle/pull/34874))
  - Add the `paddle.einsum`.  ([#33821](https://github.com/PaddlePaddle/Paddle/pull/34874))
  - Enhance the ``paddle.tensor.gradient`` interface to support second-order derivative operators for sigmoid_op. ([#32971](https://github.com/PaddlePaddle/Paddle/pull/32971))
@@ -372,6 +382,8 @@ paddle.int64
   -  Add the ``paddle.static.ExponentialMovingAverage``, to support the computing of the sliding average of parameters with exponential decay. ([#35673](https://github.com/PaddlePaddle/Paddle/pull/35673))
   -  Add the ``paddle::Tensor::slice`` C++ API, to support the slice operation, and allow users to perform slice operations for the external Tensor. ([#34227](https://github.com/PaddlePaddle/Paddle/pull/34227))
   -  Add the ``paddle.incubate.segment_*`` series APIs, including ``paddle.incubate.segment_sum``, ``paddle.incubate.segment_mean``, ``paddle.incubate.segment_max``, and ``paddle. incubate.segment_min``. Support the summing, averaging, maximizing, and minimizing of ``Tensor`` by segment.  ([#35759](https://github.com/PaddlePaddle/Paddle/pull/35759))
+  - Add `paddle.version.cuda` and `paddle.version.cudnn` to get version numbers of `CUDA` and `cuDNN` used by paddle installer.  ([#36556](https://github.com/PaddlePaddle/Paddle/pull/36556))
+
 
 #### IR(Intermediate Representation)
 
@@ -388,13 +400,15 @@ paddle.int64
   - Provide dependent helper functions needed to analyze the control flow in `Program`.  ([#33439](https://github.com/PaddlePaddle/Paddle/pull/33439))
   - `Program` and `Graph` retain the values of the `stop_gradient` and `persistable` attributes needed for training after converting each other.  ([#33771](https://github.com/PaddlePaddle/Paddle/pull/33771)) 
   - `Pass` now supports processing the main `Graph` and all its sub-graphs, while the original `Pass` only processed the main `Graph` and ignored the sub-graphs.  ([#34158](https://github.com/PaddlePaddle/Paddle/pull/34158)) 
-  - Handle some topological ordering problems for `Program` and `Graph` inter-conversion in the prediction cases. ([#34121](https://github.com/PaddlePaddle/Paddle/pull/34121), [#34521](https://github.com/PaddlePaddle/Paddle/pull/34521)). **《== **
+  - Handle some topological ordering problems for `Program` and `Graph` inter-conversion in the prediction cases. ([#34121](https://github.com/PaddlePaddle/Paddle/pull/34121), [#34521](https://github.com/PaddlePaddle/Paddle/pull/34521)).
 
 - Pass development  
   - Add the Pass development for subgraph replacement scenarios such as fusion on the Python side.  ([#35708](https://github.com/PaddlePaddle/Paddle/pull/35708), [#35602](https://github.com/PaddlePaddle/Paddle/pull/35602))
 
 - Kernel Primitive API	
   - Abstract and encapsulate the underlying codes in the operator Kernel implementation, to provide high-performance Block-level IO and Compute operations. The Kernel development using the Kernel Primitive API allows you to focus more on the implementation of the computational logic, significantly reducing the amount of codes while ensuring performance, and decoupling operator computation from hardware.  ([#34672](https://github.com/PaddlePaddle/Paddle/pull/34672),  [#35075](https://github.com/PaddlePaddle/Paddle/pull/35075),  [#34456](https://github.com/PaddlePaddle/Paddle/pull/34456),  [#35282](https://github.com/PaddlePaddle/Paddle/pull/35282),  [#35743](https://github.com/PaddlePaddle/Paddle/pull/35743),  [#34208](https://github.com/PaddlePaddle/Paddle/pull/34208))
+  - Add a total of 13 monadic and binary computation Functors to the Kernel Primitive API.  ([#36418](https://github.com/PaddlePaddle/Paddle/pull/36418))
+  - Modify the ReadData implementation in the Kernel Primitive API to fix the NX ! =1 access memory out-of-bound bug.  ([#36373](https://github.com/PaddlePaddle/Paddle/pull/36373))
 
 #### **Mixed Precision Training**
 
@@ -513,8 +527,16 @@ paddle.int64
 -   `paddle.equal`: Add the support for `int`, `float`, and `bool` types for the second input. ([#35695](https://github.com/PaddlePaddle/Paddle/pull/35695))
 -   ``paddle.io.DataLoader``: Add the support for persistent_worker mode. ([#34017](https://github.com/PaddlePaddle/Paddle/pull/34017))
 -   Optimize ``l2_normalize``, ``p_norm``, ``elementwise_max``, ``prelu,clip_by_norm``, ``lars optimizer`` operators support the float16 computation.  ([#35576](https://github.com/PaddlePaddle/Paddle/pull/35576), [#35888](https://github.com/PaddlePaddle/Paddle/pull/35888), [#35888](https://github.com/PaddlePaddle/Paddle/pull/35888), [35532](https://github.com/PaddlePaddle/Paddle/pull/35532), [#35446](https://github.com/PaddlePaddle/Paddle/pull/35446), [#33280](https://github.com/PaddlePaddle/Paddle/pull/33280))
--   Optimize the reading speed of flowers dataset from several minutes per batch to 1~3 seconds per batch.  ([#31408](https://github.com/PaddlePaddle/Paddle/pull/31408))
--   Support the fuse allreduce sum function in `paddle.distributed.fleet.DistributedStrategy` when the `without_graph_optimize` switch is on.In the FP32, the performance increases by 3%. In the AMP, the performance increases by 8%. ([#34446](https://github.com/PaddlePaddle/Paddle/pull/34446)) 
+- Optimize the reading speed of flowers dataset from several minutes per batch to 1~3 seconds per batch.  ([#31408](https://github.com/PaddlePaddle/Paddle/pull/31408))
+- Support the fuse allreduce sum function in `paddle.distributed.fleet.DistributedStrategy` when the `without_graph_optimize` switch is on.In the FP32, the performance increases by 3%. In the AMP, the performance increases by 8%. ([#34446](https://github.com/PaddlePaddle/Paddle/pull/34446)) 
+- In `paddle.matmul`, switch underlying Op from matmul op to matmul_v2 op.  ([#36374](https://github.com/PaddlePaddle/Paddle/pull/36374))
+- In `paddle.fft` module, add mkl_cdft and hipfft two computational backends.  ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- Parameter `shifts` of `paddle.roll` supports `Tensor` as input.  ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- `paddle.shape` supports plural type inputs. ([#36835](https://github.com/PaddlePaddle/Paddle/pull/36835))
+- matmul_v2 supports quantization. ([#36469](https://github.com/PaddlePaddle/Paddle/pull/36469))
+- Add `clip_op` support for `float16`. ([#36672](https://github.com/PaddlePaddle/Paddle/pull/36672))
+- In `paddle.fft` module, add cache plan functionality to the cufft backend, optimizing performance. ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+
 
 
 #### IR(Intermediate Representation)
@@ -525,7 +547,9 @@ paddle.int64
   - Optimize the logic of dynamic to static training codes, upgrade the internal ``Program`` cache mechanism, and add an advance copy policy for input ``Tensor`` to improve training performance.   ([#34181](https://github.com/PaddlePaddle/Paddle/pull/34181), [#33796](https://github.com/PaddlePaddle/Paddle/pull/33796))
   - Optimize the internal actuator memory recycling strategy for dynamic to static graphs, reducing the GPU memory usage during training.  ([#34177](https://github.com/PaddlePaddle/Paddle/pull/34177))
   - Integrate the source codes of ``Gast`` triple dependency library, decoupling version dependencies.  ([#34556](https://github.com/PaddlePaddle/Paddle/pull/34556)) 
-
+  - Display partial frame level error reporting information in case of dynamic-to-static error reporting. It is easier to locate the problem. ([#36765](https://github.com/PaddlePaddle/Paddle/pull/36765))
+  - Remove duplicate temporary file removal function `remove_static_file()` in the dynamic to static error reporting module. ([#36375](https://github.com/PaddlePaddle/Paddle/pull/36375))
+  - Optimize processing of `input_specs` parameter in RegisterPass, to support graph optimization as a matching subgraph condition.  ([#36453](https://github.com/PaddlePaddle/Paddle/pull/36453))
 
 #### **Distributed training**
 
@@ -539,6 +563,12 @@ paddle.int64
   - `paddle.io.Dataset`: Support the dynamic library parsing data. ([#33969](https://github.com/PaddlePaddle/Paddle/pull/33969))
   - In the `paddle.distributed.fleet.dataset.DatasetBase`, add the consistency check function for generated data of the `use_var_list` and `pipe_command`.  ([#34463](https://github.com/PaddlePaddle/Paddle/pull/34463))
   - Add the consistency check between the `emd` dimension of `paddle.fluid.layers.embedding` and `emb` dimension of `sparse table` in `fleet`.  ([#34249](https://github.com/PaddlePaddle/Paddle/pull/34249))
+  - Dynamic graph hybrid parallel supports for Pure FP16 training. ([#36707](https://github.com/PaddlePaddle/Paddle/pull/36707))
+  - Static graph hybrid parallel supports dropout using a fixed random seed generator to ensure consistency of global variables and randomness of local variables in model parallel.  ([#36682](https://github.com/PaddlePaddle/Paddle/pull/36682))
+  - Implement CPU parallelism and support for adding custom backend parameters when calling spawn or launch.  Available backend options are "gloo", "nccl", "bkcl", and "auto", for CPU parallel, GPU parallel, XPU parallel, and automatic selection by Paddle version, respectively.  ([#35745](https://github.com/PaddlePaddle/Paddle/pull/35745))
+  - Optimize dynamic graph hybrid parallel HybridParallelClipGrad policy, to support 4D hybrid parallel + Pure FP16 training.  ([#36707](https://github.com/PaddlePaddle/Paddle/pull/36707))
+  - Add SlotRecordDataset class to support GPU parameter server training.  ([#36710](https://github.com/PaddlePaddle/Paddle/pull/36710))
+  - In the GPU parameter server building phase, support use of SlotRecordDataset. ([#36723](https://github.com/PaddlePaddle/Paddle/pull/36723))
 
 
 - Static graph hybrid parallel
@@ -561,7 +591,15 @@ paddle.int64
   - Fix the ``paddle.jit.save`` interface and model pruning logic. It is unnecessary to add an associated ``scale_op`` for output variables, and to properly export models containing outputs of type ``bool`` and ``float16``.  ([#35730](https://github.com/PaddlePaddle/Paddle/pull/35730), [#36132](https://github.com/PaddlePaddle/Paddle/pull/36132))
 - Custom OP
   - Remove unnecessary ``cudaStreamSynchronize`` operations from ``paddle::Tensor's`` ``copy`` method, to improve performance.  ([#35802](https://github.com/PaddlePaddle/Paddle/pull/35802))
+- Add C++ to support for GeneratePass development registration. The development mode is aligned with Python side. ([#36302](https://github.com/PaddlePaddle/Paddle/pull/36302))
+- Automic SParsity
 
+- Add `paddle.static.sparsity`, to support generating sparse parameters for `n:m` sparse mode. Currently, it only supports static graph ASP training. FP32 and FP16 on A100 are set with `1:2` and `2:4` sparse modes, respectively, to train saved sparse models, which can be used to accelerate inference tasks by calling TensorRT 8 based on the sparse Tensor Core of Ampere architecture. The current version provides a total of 5 APIs:  ([#32995](https://github.com/PaddlePaddle/Paddle/pull/32995)、[#33132](https://github.com/PaddlePaddle/Paddle/pull/33132)、[#33558](https://github.com/PaddlePaddle/Paddle/pull/33558)、[#36525](https://github.com/PaddlePaddle/Paddle/pull/36525))
+  - `paddle.static.sparsity.calculate_density`: calculates the density of the input Tensor.  
+  - `paddle.static.sparsity.decorate`: wraps the given optimizer as `OptimizerWithSparsityGuarantee`, automatically inserting necessary operations for the ASP workflow when calling `optimizer.minimize()`.    
+  - `paddle.static.sparsity.prune_model`: prunes the parameters of the supported layers in `main_program` based on the mask generator function specified by `mask_algo`. 
+  - `paddle.static.sparsity.set_excluded_layers`: sets the names of the parameters of layers that will not be trimmed.   
+  - `paddle.static.sparsity.reset_excluded_layers`: resets the `excluded_layers` setting corresponding to `main_program`. 
 
 
 ### **(3) Performance optimization**
@@ -600,6 +638,20 @@ paddle.int64
 - Optimize the dynamic graph performance by stripping logic executed only on static graphs from the execution path of dynamic graphs. ([#34024](https://github.com/PaddlePaddle/Paddle/pull/34024))
 - For the IR Pass, optimize the capability exposed as a general-purpose capability. Support both single machine and distributed optimization.The performance improves by 3%-5% in GPT mixed parallel scenarios. ([#34955](https://github.com/PaddlePaddle/Paddle/pull/34955), [#35704](https://github.com/PaddlePaddle/Paddle/pull/35704), [#34730](https://github.com/PaddlePaddle/Paddle/pull/34730), [#34524](https://github.com/PaddlePaddle/Paddle/pull/34524))
 - Optimize the ctc loss grad computation, increase the speed by ~3x. Correspondingly, the GPU memory usage increases.  ([#34729](https://github.com/PaddlePadle/Paddle/pull/34729))
+- transformer encoder Performance Optimization
+  - Optimization method: add `paddle.incubate.nn.FusedMultiHeadAttention` and `paddle.incubate.nn.FusedFeedForward`. In the implementation, q, k, v gemm fusion and multiple kernel fusion optimization techniques are used to improve performance of the transformer encoder.    
+    - FusedAttention
+      - Add `paddle.incubate.nn.functional.fused_multi_head_attention`, to support fusion computation of multi-head attention.  ([#35905](https://github.com/PaddlePaddle/Paddle/pull/35905) [35903](https://github.com/PaddlePaddle/Paddle/pull/35903) [#36803](https://github.com/PaddlePaddle/Paddle/pull/36803) [#36793](https://github.com/PaddlePaddle/Paddle/pull/36793) [36185](https://github.com/PaddlePaddle/Paddle/pull/36185))
+      - Add `paddle.incubate.nn.FusedMultiHeadAttention` for layer networking of the fused multi-head attention.  ([#36498](https://github.com/PaddlePaddle/Paddle/pull/36498) )
+      - This module uses q, k, v gemm fusion and bias add + dropout + residual add + layer_norm kernel fusion optimization techniques, resulting in 1.08x-1.45x acceleration. 
+
+    - FusedFeedForward
+      - Add `paddle.incubate.nn.functional.fused_feedforward`, to support feedforward fusion computation.  ([#36729](https://github.com/PaddlePaddle/Paddle/pull/36729) [#36730](https://github.com/PaddlePaddle/Paddle/pull/36730))
+      - Add `paddle.incubate.nn.FusedFeedForward` for layer networking of fused feedforward.  ([#36776](https://github.com/PaddlePaddle/Paddle/pull/36776))
+      - Performance is improved by about 1.04x~1.22x over pre-optimization.
+      - Add `paddle.incubate.nn.FusedTransformerEncoderLayer`, to support layer networking by using fused multi-head attention and fused feedforward computation.  ([#36776](https://github.com/PaddlePaddle/Paddle/pull/36776))
+
+
 
 
 ### **(4) Troubleshooting**
@@ -693,12 +745,27 @@ paddle.int64
 -  Migrate the one_hot operator in ``paddle.nn.functional.dice_loss`` API to the ``one_hot_v2`` operator. ([#35734](https://github.com/PaddlePaddle/Paddle/pull/35734))
 -  Fix the bug of usage in the static graph mode in ``paddle.summary``. ([#35303](https://github.com/PaddlePaddle/Paddle/pull/35303))
 -  Fix the multi-card startup bug in ``paddle.Model.prepare`` static graph mode.  ([#34311](https://github.com/PaddlePaddle/Paddle/pull/34311))
+- Fix error report of `paddle.nn.functional.cross_entropy` when `weight` is given and `axis` is specified as a legal dimension other than -1. ([#36647](https://github.com/PaddlePaddle/Paddle/pull/36647))
+- Fix a bug with `paddle.utils.dlpack.to_dlpack` that prevents it from encoding multidimensional `Tensor`, and fix a bug with its generated DLPack objects not being shared across deep learning frameworks. ([#36177](https://github.com/PaddlePaddle/Paddle/pull/36177))
+- Fix a bug in the `sample` method using `paddle.distribution.Categorical`, specifically, due to an out-of-bounds array access in the multinomial op's cuda kernel. The bug causes access to values beyond the subscript of the array, causing an error to be reported. ([#36511](https://github.com/PaddlePaddle/Paddle/pull/36511))
+- Fix a bug in the dynamic graph `_BatchNormBase` base class where the default_dtype is modified, resulting in the wrong type of subsequent networking parameters. Affected APIs are `paddle.nn.BatchNorm1D`, `paddle.nn.BatchNorm2D`, ` paddle.nn.BatchNorm3D`, and `paddle.nn.SyncBatchNorm`. The specific reason is that when `get_default_dtype() == 'float16'`, the default parameter data type is modified by `set_default_dtype('float32')`. The parameter type of dynamic graph networking is created by default_dtype. Therefore, when the default parameter type is modified, subsequent networking parameter type is consequently incorrect.  ([#36376](https://github.com/PaddlePaddle/Paddle/pull/36376))
+- Fix an exception in `paddle.nn.functional.grid_sample` caused by special input. ([#36625](https://github.com/PaddlePaddle/Paddle/pull/36625))
+- Fix calculation error of `paddle.fft.ffft`, `paddle.fft.ifft`, `paddle.fft.rfft` , `paddle.fft.irfft`, `paddle.fft.hfft`, and `paddle.fft.ihfft` when input ` axis=0`. ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- Fix a bug of errors of `paddle.fft.fftshift` and `paddle.fft.ifftshift` under static graphs.  ([#36537](https://github.com/PaddlePaddle/Paddle/pull/36537))
+- Fix a bug where `paddle.fft.ifftshift` is not calculated correctly. ([#36835](https://github.com/PaddlePaddle/Paddle/pull/36835))
+- Fix error message prompt for `paddle.nn.functional.pad` in `replicate` mode. ([#36531](https://github.com/PaddlePaddle/Paddle/pull/36531))
+
+
 
 #### IR(Intermediate Representation)
 
 - Dynamic graph to static graph
   - Fix an abnormal growth of GPU memory under ``paddle.no_grad`` semantics after dynamic to static. ([#35725](https://github.com/PaddlePaddle/Paddle/pull/35725))
   - Fix a misidentification and conversion bug in the ``paddle.no_grad`` interface.  ([#34136](https://github.com/PaddlePaddle/Paddle/pull/34136)) 
+  - Fix a bug of reporting an error in dynamic to static training when stop_gradient=True is set in the middle of the model in some scenarios. ([#36353](https://github.com/PaddlePaddle/Paddle/pull/36353))
+  - Fix a bug of reporting an error when checking the return result in some scenarios where the control flow “if” is converted. ([#36830](https://github.com/PaddlePaddle/Paddle/pull/36830))
+  - Fix a bug that the return type changes unexpectedly due to additional dynamic to static aligning in the return length when “ifelse” branch returns unequal results. ([#36565](https://github.com/PaddlePaddle/Paddle/pull/36565))
+  - Fix a bug where video memory will keep growing in train mode and no_grad contexts after loading a model via the jit.save/load interface. ([#36463](https://github.com/PaddlePaddle/Paddle/pull/36463))
 
 #### **Distributed training**
 
@@ -733,6 +800,8 @@ paddle.int64
   - Fix the GPU parameter server error reported by using non-0 card training. ([#33078](https://github.com/PaddlePaddle/Paddle/pull/33078))
   - Fix the bug of the delta score and scale show in the GPU Parameter Server. ([#33492](https://github.com/PaddlePaddle/Paddle/pull/33078), [#33492](https://github.com/PaddlePaddle/Paddle/pull/33492))
   - Fix the bug with GPU Parameter Server not merging dense after training, in incorrect g2sum calculation. For data norm, add the optimize op. ([#35029](https://github.com/PaddlePaddle/Paddle/pull/35029))
+  - Fix an error reported if the gradient is empty when using the fuse all reduce ops switch.  ([#36231](https://github.com/PaddlePaddle/Paddle/pull/36231))
+  - Fix a bug with dist_transformer files showing undefined variables. ([#36211](https://github.com/PaddlePaddle/Paddle/pull/36211))
 
 - Dynamic graph hybrid parallel
   - Fix the precision error in pipeline parallel due to communication asynchronization. [#35556](https://github.com/PaddlePaddle/Paddle/pull/35556)
@@ -774,6 +843,7 @@ paddle.int64
 - Add native support for Ascend series hardware
   - sub-graphs are accessed to ascend310 hardware [#35226](https://github.com/PaddlePaddle/Paddle/pull/35226) by supporting Paddle-Lite NNAdapter. For the example, see the [demo](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/c%2B%2B/ascend310_lite_subgraph/image_classification_demo).
   - New Ascend 910 inference support [#34101](https://github.com/PaddlePaddle/Paddle/pull/34101)
+- Add pool3d OP to support for TensorRT. ([#36545](https://github.com/PaddlePaddle/Paddle/pull/36545))
 
 ### **(2) Function optimization**
 
@@ -782,7 +852,7 @@ paddle.int64
 - Quantification support
   - Refactor dynamic graph quantization inference pass, to support non-analog quantization OP and analog quantization OP. ([#35907](https://github.com/PaddlePaddle/Paddle/pull/35907))
   - Add int8 for analog quantized OP matmul (the case where weights are multiplied by tensor).  ([#34359](https://github.com/PaddlePaddle/Paddle/pull/34359))
-
+  - Fix a bug that MobileNetV3 model "Loss” out of NAN during quantization training due to the quantization parameter being 0.  ([#36763](https://github.com/PaddlePaddle/Paddle/pull/36763))
 
 - API enhancements
   - Refactor GO API based on new version of CAPI, [#33113](https://github.com/PaddlePaddle/Paddle/pull/33113). For the example, see the [demo](https://github.com/PaddlePaddle/Paddle-Inference-Demo/tree/master/go/resnet50).
@@ -818,6 +888,7 @@ paddle.int64
   - Add support for int8 in TensorRT `qkv_context` plugin ([#34917](https://github.com/PaddlePaddle/Paddle/pull/34917), [#35504](https://github.com/PaddlePaddle/Paddle/pull/35504))
   - Add support for TensorRT conv3d. ([#35507](https://github.com/PaddlePaddle/Paddle/pull/35507))
   - Add support for broadcasting the input of the `multihead_matmul` fusion operator. ([#35780](https://github.com/PaddlePaddle/Paddle/pull/35780))
+  - Inference supports for TensorRT8 sparse inference, with performance improved by 10%-30% for ERNIE model with variable-length input at different batch_sizes, and performance improved by 10% for ResNeXt101_32x4d model at different batch_sizes under test environment.  ([#36659](https://github.com/PaddlePaddle/Paddle/pull/36659))
 
 - Nvidia Jetson native support enhancements
   - Add the Op support, for the Jetson Nano/TX2, two devices with lower arithmetic power. We made targeted optimizations. Now add the support for 17 OPs such as `pool2d`, `pool_max`, `conv3d_transpose`, etc. ([#35378](https://github.com/PaddlePaddle/Paddle/pull/35378))
@@ -827,6 +898,7 @@ paddle.int64
 
 - Kunlun XPU interface feature extensions
   - Add the `set_xpu_device_id` interface to support setting the device number of the Kunlun chip in the inference ([#35572](https://github.com/PaddlePaddle/Paddle/pull/35572))
+- In Inference python `copy_from_cpu` interface, add input type check. Report errors in advance for wrong type inputs.  ([#36552](https://github.com/PaddlePaddle/Paddle/pull/36552))
 
 ### **(3) Troubleshooting**
 
@@ -849,6 +921,14 @@ paddle.int64
   - Fix a possible accuracy bug in the running of the ernie model FP16 with precision. ([#34771](https://github.com/PaddlePaddle/Paddle/pull/34711))
   - Fix the incorrect output bug due to an inconsistent order of inputs when the ernie becomes longer. ([#33575](https://github.com/PaddlePaddle/Paddle/pull/33575))
   - Fix a bug where the allocator function is abnormal in multi-stream state. ([#32932](https://github.com/PaddlePaddle/Paddle/pull/33575))
+- Fix a possible crash bug of ERNIE model under TRT8. ([#36769](https://github.com/PaddlePaddle/Paddle/pull/36769))
+- Fix a bug of crash and accuracy when Pool and Slice are used. ([#36666](https://github.com/PaddlePaddle/Paddle/pull/36666))
+- Fix an accuracy bug of yolo_box op caused by a wrong formula. ([#36365](https://github.com/PaddlePaddle/Paddle/pull/36365))
+- Fix a bug where quantized matmul_v2 does not infer properly under TRT. ([#36821](https://github.com/PaddlePaddle/Paddle/pull/36821))
+- Fix a bug where quantized op is incorrectly added when quantizing matmul_v2. ([#36820](https://github.com/PaddlePaddle/Paddle/pull/36820))
+- Fix a bug with the operators batch_norm and elementwise_add reporting an error when TRT is enabled in 3D application scenarios. ([#36446](https://github.com/PaddlePaddle/Paddle/pull/36446))
+- Fix a bug where the prediction model saved by the high-level linear api cannot not be optimized by Pass fusion. ([#36500](https://github.com/PaddlePaddle/Paddle/pull/36500))
+- Fix the Pass of MatmulV2ToMul, re-qualify (matmul_v2 to mul) mapping pass, add Pass of MatmulV2ToMatmul, qualify (matmul_v2 to matmul) mapping pass condition (not supporting broadcast), and modify (matmul, mul) op_teller mapping condition.  ([#36652](https://github.com/PaddlePaddle/Paddle/pull/36652)
 
 #### **Back-end capability fixing**