[CodeStyle][Typos][B-[1-5]] Fix typo(startswith B)

.gitignore

@@ -6,6 +6,7 @@
 # virtualenv
 venv/
 ENV/
+.venv/
 
 # Compiled Python files
 __pycache__/

_typos.toml

@@ -26,7 +26,6 @@ Archetecture = "Archetecture"
 Asynchoronous = "Asynchoronous"
 Attrbute = "Attrbute"
 Attribtue = "Attribtue"
-Bounary = "Bounary"
 Classfication = "Classfication"
 Comparision = "Comparision"
 Contructing = "Contructing"
@@ -83,13 +82,6 @@ Wether = "Wether"
 accordding = "accordding"
 accoustic = "accoustic"
 accpetance = "accpetance"
-baisc = "baisc"
-basci = "basci"
-beacuse = "beacuse"
-bechmark = "bechmark"
-benckmark = "benckmark"
-boradcast = "boradcast"
-brodcast = "brodcast"
 caculate = "caculate"
 cantains = "cantains"
 choosen = "choosen"

docs/api/paddle/incubate/autograd/Overview_cn.rst

@@ -225,7 +225,7 @@ _________________________
 - 输入数据不支持可变形状写法，如[None, 1]、[-1, 1]。如果训练数据形状是变化的，一种可行 Workaround 方案是根据不同数据形状创建不同网络，即在组网阶段将形状固定，具体参考附 1 代码。
 - 我们尚未在 windows 平台进行完整验证和支持。
 - 目前只支持使用 default_main_program 和 default_startup_program。
-- boradcast 语意尚未完整支持。
+- broadcast 语意尚未完整支持。
 
 
 .. _autograd_design_details:

docs/design/others/graph_survey.md

@@ -168,9 +168,9 @@ Expression pred = W * in;
 Expression loss = square(pred - label);
 ```
 
-The input data and parameter are also represented by Expression. Every basci Expression corresponds to a Node. And input data is also a Node.
+The input data and parameter are also represented by Expression. Every basic Expression corresponds to a Node. And input data is also a Node.
 
-Expression has a data member ComputationGraph, and ComputationGraph will be modified in users' configuring process. Expression can be a running target, beacuse Expression contains all dependency.
+Expression has a data member ComputationGraph, and ComputationGraph will be modified in users' configuring process. Expression can be a running target, because Expression contains all dependency.
 
 
 Here is a detailed example:

docs/design/phi/design_cn.md

@@ -253,7 +253,7 @@ enum class Backend : uint8_t {
   NUM_BACKENDS,
 
   /**
-   * [ Why we need ALL in baisc kernel key member? ]
+   * [ Why we need ALL in basic kernel key member? ]
    *
    * For Tensor, ALL represents an illegal Backend, but for Kernel, some
    * kernels may be device-independent by nature, such as reshape;
@@ -382,7 +382,7 @@ enum class DataType {
   BFLOAT16,
 
   NUM_DATA_TYPES,
-  // See Note [ Why we need ALL in baisc kernel key member? ]
+  // See Note [ Why we need ALL in basic kernel key member? ]
   ALL_DTYPE = UNDEFINED,
 };
 ```

docs/design/phi/design_en.md

@@ -253,7 +253,7 @@ enum class Backend : uint8_t {
   NUM_BACKENDS,
 
   /**
-   * [ Why we need ALL in baisc kernel key member? ]
+   * [ Why we need ALL in basic kernel key member? ]
    *
    * For Tensor, ALL represents an illegal Backend, but for Kernel, some
    * kernels may be device-independent by nature, such as reshape;
@@ -382,7 +382,7 @@ enum class DataType {
   BFLOAT16,
 
   NUM_DATA_TYPES,
-  // See Note [ Why we need ALL in baisc kernel key member? ]
+  // See Note [ Why we need ALL in basic kernel key member? ]
   ALL_DTYPE = UNDEFINED,
 };
 ```

docs/dev_guides/api_contributing_guides/api_design_guidelines_standard_cn.md

@@ -629,7 +629,7 @@
 | 最近邻回归 | nearest neighbor regression | |
 | 最近邻 | nearest neighbor | |
 | 验证集 | validation set | |
-| 基准 | bechmark | |
+| 基准 | benchmark | |
 | 基准 | baseline | |
 | 点估计 | point estimator | |
 | 估计量 | estimator | |

docs/dev_guides/op_optimization/kernel_primitive_api/io_api_cn.md

@@ -79,7 +79,7 @@ __device__ void ReadDataBc(T* dst, const T* src,
 
 ### 函数说明
 
-将需要进行 brodcast 的 2D 数据按照 T 类型从全局内存 src 中读取到寄存器 dst 中，其中 src 为原始输入数据指针，根据 config 计算当前输出数据对应的输入数据坐标，并将坐标对应的数据读取到寄存器中。</br>
+将需要进行 broadcast 的 2D 数据按照 T 类型从全局内存 src 中读取到寄存器 dst 中，其中 src 为原始输入数据指针，根据 config 计算当前输出数据对应的输入数据坐标，并将坐标对应的数据读取到寄存器中。</br>
 数据处理过程如下：</br>
 ![ReadDataBc](./images/io_read_data_broadcast_stride.png)
 
@@ -116,7 +116,7 @@ __device__ void ReadDataBc(T* dst, const T* src,
 
 ### 函数说明
 
-将需要进行 brodcast 的 1D 数据按照 T 类型从全局内存 src 中读取到寄存器 dst 中，其中 src 为原始输入数据指针，根据 config 计算当前输出数据对应的输入数据坐标，并将坐标对应的数据读取到寄存器中。</br>
+将需要进行 broadcast 的 1D 数据按照 T 类型从全局内存 src 中读取到寄存器 dst 中，其中 src 为原始输入数据指针，根据 config 计算当前输出数据对应的输入数据坐标，并将坐标对应的数据读取到寄存器中。</br>
 数据处理过程如下：</br>
 ![ReadDataBc](./images/io_read_data_broadcast.png)
 

docs/dev_guides/op_optimization/kernel_primitive_api/io_api_en.md

@@ -77,7 +77,7 @@ __device__ void ReadDataBc(T* dst, const T* src,
 
 ### Description
 
-Read the 2D data that needs to be brodcast from the global memory src into the register dst according to the T type, where src is the original input data pointer, calculate the input data coordinates corresponding to the current output data according to config, and read the data corresponding to the coordinates to the register. </br>
+Read the 2D data that needs to be broadcast from the global memory src into the register dst according to the T type, where src is the original input data pointer, calculate the input data coordinates corresponding to the current output data according to config, and read the data corresponding to the coordinates to the register. </br>
 The data processing process is as follows:</br>
 ![ReadDataBc](./images/io_read_data_broadcast_stride.png)
 
@@ -114,7 +114,7 @@ __device__ void ReadDataBc(T* dst, const T* src,
 
 ### Description
 
-Read the 1D data that needs to be brodcast from the global memory src into the register dst according to the T type, where src is the original input data pointer, calculate the input data coordinates corresponding to the current output data according to config, and read the data corresponding to the coordinates to the register. </br>
+Read the 1D data that needs to be broadcast from the global memory src into the register dst according to the T type, where src is the original input data pointer, calculate the input data coordinates corresponding to the current output data according to config, and read the data corresponding to the coordinates to the register. </br>
 The data processing process is as follows:</br>
 ![ReadDataBc](./images/io_read_data_broadcast.png)
 

docs/dev_guides/op_optimization/kernel_primitive_api/reduce_example_cn.md

@@ -84,7 +84,7 @@ __global__ void ReduceHigherDimKernel(const Tx* x, Ty* y, ReduceOp reducer,
 
   } else {
 
-    // The remaining data is smaller than blockdim.x, IsBounary must be true
+    // The remaining data is smaller than blockdim.x, IsBoundary must be true
     HigherDimImpl<Tx, Ty, MPType, AddFunctor<Tx, Ty>, IdentityFunctor<Tx, Ty>, true>(
         x, y, AddFunctor<Tx, Ty>(), IdentityFunctor<Tx, Ty>(), init, reduce_num, left_num, blocking_num);
 

docs/dev_guides/op_optimization/kernel_primitive_api/reduce_example_en.md

@@ -83,7 +83,7 @@ __global__ void ReduceHigherDimKernel(const Tx* x, Ty* y, ReduceOp reducer,
 
   } else {
 
-    // The remaining data is smaller than blockdim.x, IsBounary must be true
+    // The remaining data is smaller than blockdim.x, IsBoundary must be true
     HigherDimImpl<Tx, Ty, MPType, AddFunctor<Tx, Ty>, IdentityFunctor<Tx, Ty>, true>(
         x, y, AddFunctor<Tx, Ty>(), IdentityFunctor<Tx, Ty>(), init, reduce_num, left_num, blocking_num);
 

docs/guides/infer/paddleslim/paddle_slim_en.rst

@@ -3,7 +3,7 @@ Model Compression
 
 PaddleSlim is a toolkit for model compression. It contains a collection of compression strategies, such as pruning, fixed point quantization, knowledge distillation, hyperparameter searching and neural architecture search.
 
-PaddleSlim provides solutions of compression on computer vision models, such as image classification, object detection and semantic segmentation. Meanwhile, PaddleSlim Keeps exploring advanced compression strategies for language model. Furthermore, benckmark of compression strategies on some open tasks is available for your reference.
+PaddleSlim provides solutions of compression on computer vision models, such as image classification, object detection and semantic segmentation. Meanwhile, PaddleSlim Keeps exploring advanced compression strategies for language model. Furthermore, benchmark of compression strategies on some open tasks is available for your reference.
 
 PaddleSlim also provides auxiliary and primitive API for developer and researcher to survey, implement and apply the method in latest papers. PaddleSlim will support developer in ability of framework and technology consulting.