del typos for R, add infor (#7589)

Author: enkilee

_typos.toml

@@ -37,9 +37,6 @@ Moible = "Moible"
 Operaton = "Operaton"
 Optimizaing = "Optimizaing"
 Optimzier = "Optimzier"
-REGISTE = "REGISTE"
-Reivew = "Reivew"
-Reuqest = "Reuqest"
 Setment = "Setment"
 Similarily = "Similarily"
 Simle = "Simle"
@@ -99,6 +96,7 @@ fundemental = "fundemental"
 funtion = "funtion"
 ilter = "ilter"
 inferface = "inferface"
+infor = "infor"
 instert = "instert"
 instrinsics = "instrinsics"
 interchangable = "interchangable"
@@ -135,15 +133,6 @@ overrided = "overrided"
 overwrited = "overwrited"
 porcess = "porcess"
 processer = "processer"
-recevied = "recevied"
-recomment = "recomment"
-registerd = "registerd"
-registring = "registring"
-reivew = "reivew"
-relase = "relase"
-requirments = "requirments"
-resut = "resut"
-resuts = "resuts"
 sacle = "sacle"
 samle = "samle"
 satifies = "satifies"

docs/api/gen_alias_api.py

@@ -121,7 +121,7 @@ def transform_file_to_api(self, file_path, api_last_name):
         return api
 
     # find the shortest path api which not starts with paddle.fluid
-    def _choose_recomment_api(self, api_list):
+    def _choose_recommend_api(self, api_list):
         min_len = len(api_list[0].split("."))
         rec_api = api_list[0]
         for api in api_list:
@@ -135,7 +135,7 @@ def sort_alias_name(self, api_list):
         real_api = self._choose_real_api(api_list)
         api_list.remove(real_api)
 
-        rec_api = self._choose_recomment_api(api_list)
+        rec_api = self._choose_recommend_api(api_list)
         api_list.remove(rec_api)
 
         # sort others api by path length

docs/design/mkldnn/acquire_api/acquire_api.md

@@ -70,7 +70,7 @@ Having memory objects and computational primitive we may trigger its execution .
 #### 5. Registering MKL-DNN memory format in corresponding Tensor
 Last step is to register MKL-DNN output memory object format inside of Output tensor eg. set Tensor::format_ to MKL-DNN enum that corresponds the way Tensor data is arranged (NCHW, NCHW16C etc.) This enum can be taken from dst memory object (wrapper to Output tensor) in Forward pass or from diff_src memory object (wrapper to X_grad Tensor).
 
-Example of registring MKL-DNN format in output tensor:
+Example of registering MKL-DNN format in output tensor:
 
     out->set_layout(framework::DataLayout::kMKLDNN);
     out->set_format(platform::GetMKLDNNFormat(*dst_memory));

docs/design/mkldnn/gru/gru.md

@@ -95,7 +95,7 @@ oneDNN `GRU` operator is based on Paddle Paddle `fusion_gru` operator. It uses p
 * PaddlePaddle Input LoD -> oneDNN TNC/NTC\
 Every time before executing `GRU`, each batch represented by PP tensor has to be converted into oneDNN tensor representation. It is done first by calculating length of the longest sentence in a batch to get `T` dimension and then creating oneDNN memory (or getting it from oneDNN cache). After that, based on the memory format chosen by oneDNN GRU primitive: `TNC` or `NTC` correct custom reorder is called.\
 \
-Because oneDNN assumes that all sentences are of equal length, before reorder, whole memory is set to 0 to add padding. Placement of this padding depends also on computation direction that is defined by `is_reverse` attribute. To get correct resuts, if computation is performed from left to right, the padding has to be on the right side of words. Otherwise, for right to left computation, it has to be on the left side.
+Because oneDNN assumes that all sentences are of equal length, before reorder, whole memory is set to 0 to add padding. Placement of this padding depends also on computation direction that is defined by `is_reverse` attribute. To get correct results, if computation is performed from left to right, the padding has to be on the right side of words. Otherwise, for right to left computation, it has to be on the left side.
 ![](images/input_is_reverse.svg)
 
 * PaddlePaddle WeightX -> oneDNN WeightX\

docs/design/others/releasing_process.md

@@ -43,7 +43,7 @@ PaddlePaddle 开发过程使用[git-flow](http://nvie.com/posts/a-successful-git
 * 其他用户的 fork 版本库并不需要严格遵守[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)分支规范，但所有 fork 的版本库的所有分支都相当于特性分支。
     * 建议，开发者 fork 的版本库使用`develop`分支同步主版本库的`develop`分支
     * 建议，开发者 fork 的版本库中，再基于`develop`版本 fork 出自己的功能分支。
-    * 当功能分支开发完毕后，向 PaddlePaddle 的主版本库提交`Pull Reuqest`，进而进行代码评审。
+    * 当功能分支开发完毕后，向 PaddlePaddle 的主版本库提交`Pull Request`，进而进行代码评审。
         * 在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。
 
 * BugFix 分支也是在开发者自己的 fork 版本库维护，与功能分支不同的是，BugFix 分支需要分别给主版本库的`master`、`develop`与可能有的`release/版本号`分支，同时提起`Pull Request`。

docs/design/phi/design_cn.md

@@ -1670,7 +1670,7 @@ Fc(const Context& dev_ctx, const Tensor& x, const Tensor& w, const Tensor& b, Te
     phi::add<T, Context>(phi::mul<T，Context>(x, w), b, out);
 }
 
-PT_REGISTE_KERNEL("fc", Fc, ...)
+PT_REGISTER_KERNEL("fc", Fc, ...)
 ```
 
 mul 和 add 操作的拼接，代码量很少，再加一个注册声明。

docs/design/phi/kernel_migrate_en.md

@@ -304,7 +304,7 @@ Differences include:
 1. The name of the registered macro is different, here is `PD_REGISTER_PLUGIN_KERNEL`.
 2. The name of the Backend is the name of the CustomDevice registered by the user, here is `ascend`.
 
-The kernel that registerd by REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE in Fluid is also registered using `PD_REGISTER_KERNEL` or `PD_REGISTER_PLUGIN_KERNEL` in PHI. It should be noted that if there are two types of kernel template parameters registered in fluid, since the kernel registered in PHI only supports one type, the first type is used in PHI registration. For example, the example under fluid:
+The kernel that registered by REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE in Fluid is also registered using `PD_REGISTER_KERNEL` or `PD_REGISTER_PLUGIN_KERNEL` in PHI. It should be noted that if there are two types of kernel template parameters registered in fluid, since the kernel registered in PHI only supports one type, the first type is used in PHI registration. For example, the example under fluid:
 
 ```c++
 

docs/dev_guides/git_guides/code_review_en.md

@@ -1,4 +1,4 @@
-# Code Reivew promise
+# Code Review promise
 
 In order that reviewers focus on code in the code review,please follow these rules every time you submit your code:
 
@@ -8,11 +8,11 @@ In order that reviewers focus on code in the code review,please follow these rul
 
 In addition,please follow the following regulations in response to the suggestion of reviewers:
 
-1）A reply to every comment of reviewers（It's a fundamental complimentary conduct in open source community.An expression of appreciation is a need for help from others):
+1）A reply to every comment of reviewers(It's a fundamental complimentary conduct in open source community.An expression of appreciation is a need for help from others):
 
    - If you adopt the suggestion of reviewer and make a modification accordingly, it's courteous to reply with a simple `Done` .
 
-   - Please clarify your reason to the disagreenment
+   - Please clarify your reason to the disagreement
 
 2）If there are many suggestions
 

docs/dev_guides/op_optimization/kernel_primitive_api/add_example_cn.md

@@ -39,7 +39,7 @@ __device__ void ElementwiseAddImpl(InT *in0, InT * in1, OutT * out, OpFunc func,
   ReadData<InT, InT, VecSize, 1, 1, IsBoundary>(arg0, in0, num);
   ReadData<InT, InT, VecSize, 1, 1, IsBoundary>(arg1, in1, num);
 
-  // compute resut[i] = args[i] + arg1[i]
+  // compute result[i] = args[i] + arg1[i]
   ElementwiseBinary<InT, OutT, VecSize, 1, 1, OpFunc>(result, arg0, arg1, func);
 
   // write data

docs/dev_guides/op_optimization/kernel_primitive_api/add_example_en.md

@@ -38,7 +38,7 @@ __device__ void ElementwiseAddImpl(InT *in0, InT * in1, OutT * out, OpFunc func,
   ReadData<InT, InT, VecSize, 1, 1, IsBoundary>(arg0, in0, num);
   ReadData<InT, InT, VecSize, 1, 1, IsBoundary>(arg1, in1, num);
 
-  // compute resut[i] = args[i] + arg1[i]
+  // compute result[i] = args[i] + arg1[i]
   ElementwiseBinary<InT, OutT, VecSize, 1, 1, OpFunc>(result, arg0, arg1, func);
 
   // write data