[CodeStyle][Typos][S-20,S-22,S-[24-29]] Fix typo(skiped,'specail','splited','stradegy','stirng','subract','sucess','sucessor','sucessors')

_typos.toml

@@ -36,16 +36,7 @@ setted = "setted"
 shoule = "shoule"
 similary = "similary"
 simplier = "simplier"
-skiped = "skiped"
 softwares = "softwares"
-specail = "specail"
 sperated = "sperated"
-splited = "splited"
 splitted = "splitted"
-stirng = "stirng"
-stradegy = "stradegy"
-subract = "subract"
-sucess = "sucess"
-sucessor = "sucessor"
-sucessors = "sucessors"
 szie = "szie"

ci_scripts/check_api_parameters.py

@@ -184,7 +184,7 @@ def check_api_parameters(rstfiles, apiinfo):
     """check function's parameters same as its origin definition.
 
     TODO:
-    1. All the documents of classes are skiped now. As
+    1. All the documents of classes are skipped now. As
         (1) there ars many class methods in documents, may break the scripts.
         (2) parameters of Class should be checked with its `__init__` method.
     2. Some COMPLICATED annotations may break the scripts.

docs/api/gen_doc.py

@@ -262,8 +262,8 @@ def parse_module_file(mod):
             return
         logger.debug("parsing %s:%s", mod_name, src_file)
         if len(mod_name) >= 6 and mod_name[:6] == "paddle":
-            fn_splited = os.path.splitext(src_file)
-            if len(fn_splited) > 1 and fn_splited[1].lower() == ".py":
+            fn_split = os.path.splitext(src_file)
+            if len(fn_split) > 1 and fn_split[1].lower() == ".py":
                 mod_ast = ast.parse(open(src_file, "r").read())
                 for node in mod_ast.body:
                     short_names = []

docs/design/memory/memory_optimization.md

@@ -3,7 +3,7 @@
 
 ## Problem
 
-In a lecture from Andrew Ng, he attributes the recent sucess of AI due to a combination of these:
+In a lecture from Andrew Ng, he attributes the recent success of AI due to a combination of these:
 
 - Availability of Big Data
 - Supercomputing power to process this Big Data over very large neural networks
@@ -74,7 +74,7 @@ A simple way to perform data-flow analysis of programs is to set up dataflow equ
 
 - Flow Graph Terminology
 
-A flow graph node has out-edges that lead to sucessor nodes, and in-edges that come from predecessor nodes. The set *pred[n]* is all the predecessors of node n, and *succ[n]* is the set of sucessors.
+A flow graph node has out-edges that lead to successor nodes, and in-edges that come from predecessor nodes. The set *pred[n]* is all the predecessors of node n, and *succ[n]* is the set of successors.
 In former control flow graph, the out-edges of node 5 are 5 --> 6 and 5 --> 2, and *succ[5]* = {2, 6}. The in-edges of 2 are 5 --> 2 and 1 --> 2, and *pred[2]* = {1, 5}.
 
 - Uses and Defs
@@ -147,7 +147,7 @@ We can transfer all the operators and variables in ProgramDesc to build a contro
 ```python
 class ControlFlowGraph:
     def __init__(self, Program):
-        self._sucessors = defaultdict(set)
+        self._successors = defaultdict(set)
         self._presucessors = defaultdict(set)
         self._uses = defaultdict(set)
         self._defs = defaultdict(set)

docs/design/modules/python_api.md

@@ -116,7 +116,7 @@ class Operator:
                  block,  # Block
                  type,   # string
                  inputs, # dict<string, Variable>
-                 outputs,# dict<stirng, Variable>
+                 outputs,# dict<string, Variable>
                  attrs   # dict<string, Any>
                  ):
         self.desc = core.NewOpDesc(block.desc, type, inputs, outputs, attrs)

docs/dev_guides/api_contributing_guides/api_design_guidelines_standard_cn.md

@@ -242,7 +242,7 @@
    |  paddle2.0 之前  | pytorch |  numpy   | tensorflow  |   paddle2.0 之后   |
    | :------------- | :----- | :------ | :--------- | :--------------- |
    | elementwise_add |   add   |   add    |     add     |        add        |
-   | elementwise_sub |   sub   | subtract |  subtract   |      subract      |
+   | elementwise_sub |   sub   | subtract |  subtract   |      subtract      |
    | elementwise_mul |   mul   | multiply |  multiply   |     multiply      |
    | elementwise_div |   div   |  divide  |   divide    | divide |
    | elementwise_min |   min   | minimum  |   minimum   |      minimum      |

docs/eval/【Hackathon No.113】 PR.md

@@ -105,13 +105,13 @@ else:
 ```python
 if self._nranks > 1:
     dist.init_parallel_env()
-    stradegy = fluid.dygraph.parallel.ParallelStrategy()
-    stradegy.nranks = ParallelEnv().nranks
-    stradegy.local_rank = ParallelEnv().local_rank
-    stradegy.trainer_endpoints = ParallelEnv().trainer_endpoints
-    stradegy.current_endpoint = ParallelEnv().current_endpoint
+    strategy = fluid.dygraph.parallel.ParallelStrategy()
+    strategy.nranks = ParallelEnv().nranks
+    strategy.local_rank = ParallelEnv().local_rank
+    strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
+    strategy.current_endpoint = ParallelEnv().current_endpoint
     self.ddp_model = fluid.dygraph.parallel.DataParallel(
-        self.model.network, stradegy)
+        self.model.network, strategy)
 
 ```
 这样的话，分布式的环境与分布式的模型就已经在 paddle.Model 内的内部构建完成了。

docs/guides/advanced/auto_type_promotion_en.md

@@ -230,7 +230,7 @@ Number | API | Tensor-to-Tensor | Tensor-to-Scalar |
 31 | huber_loss | Common Rule | - |
 32 | mse_loss | Common Rule | - |
 
-There are two specail rules in this table above:
+There are two special rules in this table above:
 
 -  Divide Rule: For divide API, it will not return dtype smaller than float. Such as int32 / Scalar returns float32.