Fix typo. (pytorch#58728)

Summary:
Fix typo in docs and comments.

Pull Request resolved: pytorch#58728

Reviewed By: mruberry

Differential Revision: D28603612

Pulled By: H-Huang

fbshipit-source-id: b3cd8f6f19354201d597254d0b3cb4e2062471ab

CONTRIBUTING.md

@@ -910,7 +910,7 @@ tensor([1., 2., 3., 4.], dtype=torch.float64)
 ```
 
 GDB tries to automatically load `pytorch-gdb` thanks to the
-[.gdbinit](.gdbinit) at the root of the pytorch repo. Howevever, auto-loadings is disabled by default, because of security reasons:
+[.gdbinit](.gdbinit) at the root of the pytorch repo. However, auto-loadings is disabled by default, because of security reasons:
 
 ```
 $ gdb

README.md

@@ -270,13 +270,13 @@ Sometimes there are regressions in new versions of Visual Studio, so
 it's best to use the same Visual Studio Version [16.8.5](https://github.com/pytorch/pytorch/blob/master/.circleci/scripts/vs_install.ps1) as Pytorch CI's.
 You can use Visual Studio Enterprise, Professional or Community though PyTorch CI uses Visual Studio BuildTools.
 
-If you want to build legacy python code, please refert to [Building on legacy code and CUDA](https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md#building-on-legacy-code-and-cuda)
+If you want to build legacy python code, please refer to [Building on legacy code and CUDA](https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md#building-on-legacy-code-and-cuda)
 
 Build with CPU
 
 It's fairly easy to build with CPU.
 
-Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the buliding environment by tweaking `CMAKE_INCLUDE_PATH` and `LIB`. The instruction [here](https://github.com/pytorch/pytorch/blob/master/docs/source/notes/windows.rst#building-from-source) is an example for setting up both MKL and Intel OpenMP. Without these configuraions for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.
+Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking `CMAKE_INCLUDE_PATH` and `LIB`. The instruction [here](https://github.com/pytorch/pytorch/blob/master/docs/source/notes/windows.rst#building-from-source) is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.
 
 Build with CUDA
 

RELEASE.md

@@ -37,7 +37,7 @@ An example of this would look like:
 release/1.8
 ```
 
-Please make sure to create branch that pins divergent point of release branch from the main thunk, i.e. `orig/release/{MAJOR}.{MINOR}`
+Please make sure to create branch that pins divergent point of release branch from the main branch, i.e. `orig/release/{MAJOR}.{MINOR}`
 ### Making release branch specific changes
 
 These are examples of changes that should be made to release branches so that CI / tooling can function normally on

tools/build_variables.bzl

@@ -786,7 +786,7 @@ aten_cpu_source_codegen_list = [
     "aten/src/ATen/native/cpu/AdaptiveAvgPoolKernel.cpp",
 ]
 
-# When buliding lite interpreter in OSS, "aten/src/ATen/native/cpu/AdaptiveAvgPoolKernel.cpp" will go through
+# When building lite interpreter in OSS, "aten/src/ATen/native/cpu/AdaptiveAvgPoolKernel.cpp" will go through
 # codegen process. The codegen version of this file, like Activation.cpp.DEFAULT.cpp, will be included
 # in ${cpu_kernel_cpp} in aten/src/ATen/CMakeLists.txt. As a result, in aten/src/ATen/CMakeLists.txt,
 # only aten_cpu_source_non_codegen_list need to be added to ${all_cpu_cpp}.

torch/utils/benchmark/utils/timer.py

@@ -443,7 +443,7 @@ def collect_callgrind(
         jitter from the Python interpreter.) This makes them ideal for detailed
         performance analysis. This method runs `stmt` in a separate process
         so that Valgrind can instrument the program. Performance is severely
-        degraded due to the instrumentation, howevever this is ameliorated by
+        degraded due to the instrumentation, however this is ameliorated by
         the fact that a small number of iterations is generally sufficient to
         obtain good measurements.