fix: correct typos I-10 to I-16 (infor→info etc.)

Author: liangqi520

_typos.toml

@@ -56,13 +56,6 @@ functinal = "functinal"
 fundemental = "fundemental"
 funtion = "funtion"
 ilter = "ilter"
-inferface = "inferface"
-infor = "infor"
-instert = "instert"
-instrinsics = "instrinsics"
-interchangable = "interchangable"
-intializers = "intializers"
-intput = "intput"
 lable = "lable"
 learing = "learing"
 legth = "legth"

docs/api/copy_codes_from_en_doc.py

@@ -237,7 +237,7 @@ def find_codeblock_needed(cf_info):
         return None
 
 
-def instert_codes_into_cn_rst_if_need(cnrstfilename):
+def insert_codes_into_cn_rst_if_need(cnrstfilename):
     """
     Analyse the cn rst file, if need, extract code-blocks from en docstring.
     """
@@ -290,7 +290,7 @@ def instert_codes_into_cn_rst_if_need(cnrstfilename):
 
 
 def filter_all_files(
-    rootdir, ext="_cn.rst", action=instert_codes_into_cn_rst_if_need
+    rootdir, ext="_cn.rst", action=insert_codes_into_cn_rst_if_need
 ):
     """
     find all the _en.html file, and do the action.

docs/design/data_type/float16.md

@@ -93,7 +93,7 @@ To support the above features, two fundamental conversion functions are provided
 float16 float_to_half_rn(float f);  // convert to half precision in round-to-nearest-even mode
 float half_to_float(float16 h);
 ```
-which provides one-to-one conversion between float32 and float16. These twos functions will do different conversion routines based on the current hardware. CUDA/ARM instrinsics will be used when the corresponding hardware is available. If the hardware or compiler level does not support float32 to float16 conversion, software emulation will be performed to do the conversion.
+which provides one-to-one conversion between float32 and float16. These twos functions will do different conversion routines based on the current hardware. CUDA/ARM intrinsics will be used when the corresponding hardware is available. If the hardware or compiler level does not support float32 to float16 conversion, software emulation will be performed to do the conversion.
 
 ## float16 inference
 In Fluid, a neural network is represented as a protobuf message called [ProgramDesc](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/concepts/program.md), whose Python wrapper is a [Program](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/modules/python_api.md#program). The basic structure of a program is some nested [blocks](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/modules/python_api.md#block), where each block consists of some [variable](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/modules/python_api.md#variable) definitions and a sequence of [operators](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/modules/python_api.md#operator). An [executor](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/concepts/executor.md) will run a given program desc by executing the sequence of operators in the entrance block of the program one by one.

docs/design/modules/infer_var_type.md

@@ -10,7 +10,7 @@ The variable type will be constant at runtime. Every variable's type can either
 
 ## Proposed Solution
 
-The `InferVarType` is a compile-time function which is registered to each operator. The inferface of that function is:
+The `InferVarType` is a compile-time function which is registered to each operator. The interface of that function is:
 
 
 ```c++

docs/design/others/graph.md

@@ -56,7 +56,7 @@ For each parameter, like W and b created by `layer.fc`, marked as double circles
 
 ## Block and Graph
 
-The word block and graph are interchangable in the desgin of PaddlePaddle.  A [Block](https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
+The word block and graph are interchangeable in the desgin of PaddlePaddle.  A [Block](https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions.  A graph of operators and variables is a representation of the block.
 
 A Block keeps operators in an array `BlockDesc::ops`
 

docs/guides/model_convert/convert_from_pytorch/api_difference/torch_more_args/torch.nn.FractionalMaxPool2d.md

@@ -24,7 +24,7 @@ PyTorch 参数更多，具体如下：
 ### 转写示例
 #### output_ratio:目标输出比例
 ```python
-# 假设 intput 的 with=7, height=7，
+# 假设 input 的 with=7, height=7，
 # output_ratio = 0.75, 则目标 output 的 width = int(7*0.75) = 5, height = int(7*0.75) = 5
 # Pytorch 写法
 torch.nn.FractionalMaxPool2d(2, output_ratio=[0.75, 0.75], return_indices=True)

docs/guides/model_convert/convert_from_pytorch/api_difference/torch_more_args/torch.nn.FractionalMaxPool3d.md

@@ -24,7 +24,7 @@ PyTorch 参数更多，具体如下：
 ### 转写示例
 #### output_ratio:目标输出比例
 ```python
-# 假设 intput 的 depth=7, with=7, height=7，
+# 假设 input 的 depth=7, with=7, height=7，
 # output_ratio = 0.75, 则目标 output 的 depth = int(7*0.75) = 5, width = int(7*0.75) = 5, height = int(7*0.75) = 5
 # Pytorch 写法
 torch.nn.FractionalMaxPool3d(2, output_ratio=[0.75, 0.75, 0.75], return_indices=True)

docs/guides/model_convert/convert_from_pytorch/api_difference/torch_more_args/torch.nn.functional.fractional_max_pool2d.md

@@ -25,7 +25,7 @@ PyTorch 参数更多，具体如下：
 ### 转写示例
 #### output_ratio:目标输出比例
 ```python
-# 假设 intput 的 with=7, height=7，
+# 假设 input 的 with=7, height=7，
 # output_ratio = 0.75, 则目标 output 的 width = int(7*0.75) = 5, height = int(7*0.75) = 5
 # Pytorch 写法
 torch.nn.functional.fractional_max_pool2d(input, 2, output_ratio=[0.75, 0.75], return_indices=True)

docs/guides/model_convert/convert_from_pytorch/api_difference/torch_more_args/torch.nn.functional.fractional_max_pool3d.md

@@ -25,7 +25,7 @@ PyTorch 参数更多，具体如下：
 ### 转写示例
 #### output_ratio:目标输出比例
 ```python
-# 假设 intput 的 depth=7, with=7, height=7，
+# 假设 input 的 depth=7, with=7, height=7，
 # output_ratio = 0.75, 则目标 output 的 depth = int(7*0.75) = 5, width = int(7*0.75) = 5, height = int(7*0.75) = 5
 # Pytorch 写法
 torch.nn.functional.fractional_max_pool3d(input, 2, output_ratio=[0.75, 0.75, 0.75], return_indices=True)