Fix building docs instructions (pytorch#64508)

Summary:
Fixes #{64507}

Removed duplicate instruction and linted the file a bit (consistent spacing around codeblocks/headers, adding code types in codeblocks, remove `$` from bash code blocks when uncecessary).

Pull Request resolved: pytorch#64508

Reviewed By: raghuramank100

Differential Revision: D30791164

Pulled By: mrshenli

fbshipit-source-id: a00db32dcfdd1ecc194c836f31174c806062eb6d

CONTRIBUTING.md

@@ -85,7 +85,6 @@ installed`. (You should only have to `pip uninstall` a few times, but
 you can always `uninstall` with `timeout` or in a loop if you're feeling
 lazy.)
 
-
 ```bash
 conda uninstall pytorch -y
 yes | pip uninstall torch
@@ -108,10 +107,8 @@ git submodule update --init --recursive --jobs 0
 
 If you want to have no-op incremental rebuilds (which are fast), see the section below titled "Make no-op build fast."
 
-
 3. Install PyTorch in `develop` mode:
 
-
 The change you have to make is to replace
 
 ```bash
@@ -147,6 +144,7 @@ torch as it is not installed`; next run `python setup.py clean`. After
 that, you can install in `develop` mode again.
 
 ### Tips and Debugging
+
 * A prerequisite to installing PyTorch is CMake. We recommend installing it with [Homebrew](https://brew.sh/)
 with `brew install cmake` if you are developing on MacOS or Linux system.
 * Our `setup.py` requires Python >= 3.6
@@ -188,7 +186,7 @@ with `brew install cmake` if you are developing on MacOS or Linux system.
     ```
     this is likely that you are using HTTP proxying and the certificate expired. To check if the certificate is valid, run
     `git config --global --list` and search for config like `http.proxysslcert=<cert_file>`. Then check certificate valid date by running
-    ```
+    ```bash
     openssl x509 -noout -in <cert_file> -dates
     ```
 
@@ -354,6 +352,7 @@ an optional dependency, and `pytest` may help run tests more selectively.
 All these packages can be installed with `conda` or `pip`.
 
 ### Better local unit tests with `pytest`
+
 We don't officially support `pytest`, but it works well with our
 `unittest` tests and offers a number of useful features for local
 developing. Install it via `pip install pytest`.
@@ -369,7 +368,6 @@ The above is an example of testing a change to all Loss functions: this
 command runs tests such as `TestNN.test_BCELoss` and
 `TestNN.test_MSELoss` and can be useful to save keystrokes.
 
-
 ### Local linting
 
 You can run the same linting steps that are used in CI locally via `make`:
@@ -407,6 +405,7 @@ make clang-tidy
 ```
 
 To run a lint only on changes, add the `CHANGED_ONLY` option:
+
 ```bash
 make <name of lint> CHANGED_ONLY=--changed-only
 ```
@@ -501,14 +500,13 @@ pip install -r requirements.txt
 
 > Note that if you are a Facebook employee using a devserver, yarn may be more convenient to install katex:
 
-```
+```bash
 yarn global add katex
 ```
 
 3. Generate the documentation HTML files. The generated files will be in `docs/build/html`.
 
 ```bash
-cd docs
 make html
 ```
 
@@ -533,6 +531,7 @@ git add index.rst jit.rst
 ```
 
 #### Building C++ Documentation
+
 For C++ documentation (https://pytorch.org/cppdocs), we use
 [Doxygen](http://www.doxygen.nl/) and then convert it to
 [Sphinx](http://www.sphinx-doc.org/) via
@@ -571,12 +570,13 @@ Then navigate to `localhost:8000` in your web browser.
 **Tip:**
 You can start a lightweight HTTP server on the remote machine with:
 
-```
+```bash
 python -m http.server 8000 <path_to_html_output>
 ```
 
 Alternatively, you can run `rsync` on your local machine to copy the files from
 your remote machine:
+
 ```bash
 mkdir -p build cpp/build
 rsync -az me@my_machine:/path/to/pytorch/docs/build/html build
@@ -614,7 +614,7 @@ that has the ability to profile native code and Python code in the same session.
 `py-spy` can be installed via `pip`:
 
 ```bash
-$ pip install py-spy
+pip install py-spy
 ```
 
 To use `py-spy`, first write a Python test script that exercises the
@@ -637,7 +637,7 @@ number of times to get good statistics. The most straightforward way to use
 graph](http://www.brendangregg.com/flamegraphs.html):
 
 ```bash
-$ py-spy record -o profile.svg --native -- python test_tensor_tensor_add.py
+py-spy record -o profile.svg --native -- python test_tensor_tensor_add.py
 ```
 
 This will output a file named `profile.svg` containing a flame graph you can
@@ -706,6 +706,7 @@ variables `DEBUG`, `USE_DISTRIBUTED`, `USE_MKLDNN`, `USE_CUDA`, `BUILD_TEST`, `U
 - `USE_XNNPACK=0` will disable compiling with XNNPACK.
 
 For example:
+
 ```bash
 DEBUG=1 USE_DISTRIBUTED=0 USE_MKLDNN=0 USE_CUDA=0 BUILD_TEST=0 USE_FBGEMM=0 USE_NNPACK=0 USE_QNNPACK=0 USE_XNNPACK=0 python setup.py develop
 ```
@@ -741,6 +742,7 @@ situations where files get rebuilt when a previous compilation was exactly the
 same. Using ccache in a situation like this is a real time-saver.
 
 Before building pytorch, install ccache from your package manager of choice:
+
 ```bash
 conda install ccache -f conda-forge
 sudo apt install ccache
@@ -764,12 +766,14 @@ build should be substantially and noticeably faster than the first build. If
 this doesn't seem to be the case, check the `CMAKE_<LANG>_COMPILER_LAUNCHER`
 rules in `build/CMakeCache.txt`, where `<LANG>` is `C`, `CXX` and `CUDA`.
 Each of these 3 variables should contain ccache, e.g.
+
 ```
 //CXX compiler launcher
 CMAKE_CXX_COMPILER_LAUNCHER:STRING=/usr/bin/ccache
 ```
 
 If not, you can define these variables on the command line before invoking `setup.py`.
+
 ```bash
 export CMAKE_C_COMPILER_LAUNCHER=ccache
 export CMAKE_CXX_COMPILER_LAUNCHER=ccache
@@ -778,6 +782,7 @@ python setup.py develop
 ```
 
 #### Use a faster linker
+
 If you are editing a single file and rebuilding in a tight loop, the time spent
 linking will dominate. The system linker available in most Linux distributions
 (GNU `ld`) is quite slow. Use a faster linker, like [lld](https://lld.llvm.org/).
@@ -786,7 +791,8 @@ People on Mac, follow [this guide](https://stackoverflow.com/questions/42730345/
 
 The easiest way to use `lld` this is download the
 [latest LLVM binaries](http://releases.llvm.org/download.html#8.0.0) and run:
-```
+
+```bash
 ln -s /path/to/downloaded/ld.lld /usr/local/bin/ld
 ```
 
@@ -862,7 +868,7 @@ 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. However, auto-loadings is disabled by default, because of security reasons:
 
-```
+```bash
 $ gdb
 warning: File "/path/to/pytorch/.gdbinit" auto-loading has been declined by your `auto-load safe-path' set to "$debugdir:$datadir/auto-load".
 To enable execution of this file add
@@ -880,11 +886,11 @@ For more information about this security protection see the
 As gdb itself suggests, the best way to enable auto-loading of `pytorch-gdb`
 is to add the following line to your `~/.gdbinit` (i.e., the `.gdbinit` file
 which is in your home directory, **not** `/path/to/pytorch/.gdbinit`):
-```
+
+```bash
 add-auto-load-safe-path /path/to/pytorch/.gdbinit
 ```
 
-
 ## CUDA development tips
 
 If you are working on the CUDA code, here are some useful CUDA debugging tips:
@@ -1119,7 +1125,7 @@ folder (later called `$LLVM_ROOT`).
 
 Then set up the appropriate scripts. You can put this in your `.bashrc`:
 
-```
+```bash
 LLVM_ROOT=<wherever your llvm install is>
 PYTORCH_ROOT=<wherever your pytorch checkout is>