• If you want to have no-op incremental rebuilds (which are fast), see Make no-op build fast below.

• If you don't need CUDA, build using USE_CUDA=0: the build is significantly faster. There are also a lot of other build flags that help get rid of components that you might not work on. Below is an opinionated build command that gets rid of a lot of different options that don't get used very often.

  USE_KINETO=0 BUILD_CAFFE2=0 USE_DISTRIBUTED=0 USE_NCCL=0 BUILD_TEST=0 USE_XNNPACK=0 USE_FBGEMM=0 USE_QNNPACK=0 USE_MKLDNN=0 USE_MIOPEN=0 USE_NNPACK=0 BUILD_CAFFE2_OPS=0 USE_TENSORPIPE=0 python setup.py develop

  See Build only what you need for a list of useful build flags.

• When developing PyTorch, instead of branching off of master, you can branch off of viable/strict. viable/strict is a branch that lags behind master and guarantees that all PyTorch tests are passing on the branch. Basing your work off of viable/strict gives you confidence that any test failures are actually your code's fault.

  # Creating a new feature branch off of viable/strict
  git checkout viable/strict
  git checkout -b my_new_feature
  
  # Rebasing your work to appear on top of viable/strict, assuming upstream points to pytorch/pytorch.
  # (Some people develop with origin pointing to pytorch/pytorch)
  git pull --rebase upstream viable/strict
  

python setup.py develop will build everything by default, but sometimes you are only interested in a specific component.

• Working on a test binary? Run (cd build && ninja bin/test_binary_name) to rebuild only that test binary (without rerunning cmake). (Replace ninja with make if you don't have ninja installed).
• Don't need Caffe2? Pass BUILD_CAFFE2=0 to disable Caffe2 build.

On the initial build, you can also speed things up with the environment variables DEBUG, USE_DISTRIBUTED, USE_MKLDNN, USE_CUDA, BUILD_TEST, USE_FBGEMM, USE_NNPACK and USE_QNNPACK.

• DEBUG=1 will enable debug builds (-g -O0)
• REL_WITH_DEB_INFO=1 will enable debug symbols with optimizations (-g -O3)
• USE_DISTRIBUTED=0 will disable distributed (c10d, gloo, mpi, etc.) build.
• USE_MKLDNN=0 will disable using MKL-DNN.
• USE_CUDA=0 will disable compiling CUDA (in case you are developing on something not CUDA related), to save compile time.
• BUILD_TEST=0 will disable building C++ test binaries.
• USE_FBGEMM=0 will disable using FBGEMM (quantized 8-bit server operators).
• USE_NNPACK=0 will disable compiling with NNPACK.
• USE_QNNPACK=0 will disable QNNPACK build (quantized 8-bit operators).
• USE_XNNPACK=0 will disable compiling with XNNPACK.

For example:

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

For subsequent builds (i.e., when build/CMakeCache.txt exists), the build options passed for the first time will persist; please run ccmake build/, run cmake-gui build/, or directly edit build/CMakeCache.txt to adapt build options.

When installing with python setup.py develop (in contrast to python setup.py install) Python runtime will use the current local source-tree when importing torch package. (This is done by creating .egg-link file in site-packages folder) This way you do not need to repeatedly install after modifying Python files (.py). However, you would need to reinstall if you modify Python interface (.pyi, .pyi.in) or non-Python files (.cpp, .cc, .cu, .h, ...).

One way to avoid running python setup.py develop every time one makes a change to C++/CUDA/ObjectiveC files on Linux/Mac, is to create a symbolic link from build folder to torch/lib, for example, by issuing following: bash pushd torch/lib; sh -c "ln -sf ../../build/lib/libtorch_cpu.* ."; popd Afterwards rebuilding a library (for example to rebuild libtorch_cpu.so issue ninja torch_cpu from build folder), would be sufficient to make change visible in torch package.

If you are working on the C++ code, there are a few important things that you will want to keep in mind:

1. How to rebuild only the code you are working on.
2. How to make rebuilds in the absence of changes go faster.

When using python setup.py develop, PyTorch will generate a compile_commands.json file that can be used by many editors to provide command completion and error highlighting for PyTorch's C++ code. You need to pip install ninja to generate accurate information for the code in torch/csrc. More information at:

• https://sarcasm.github.io/notes/dev/compilation-database.html

By default, cmake will use its Makefile generator to generate your build system. You can get faster builds if you install the ninja build system with pip install ninja. If PyTorch was already built, you will need to run python setup.py clean once after installing ninja for builds to succeed.

Even when dependencies are tracked with file modification, there are many 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:

conda install ccache -c conda-forge
sudo apt install ccache
sudo yum install ccache
brew install ccache

You may also find the default cache size in ccache is too small to be useful. The cache sizes can be increased from the command line:

# config: cache dir is ~/.ccache, conf file ~/.ccache/ccache.conf
# max size of cache
ccache -M 25Gi  # -M 0 for unlimited
# unlimited number of files
ccache -F 0

To check this is working, do two clean builds of pytorch in a row. The second 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.

export CMAKE_C_COMPILER_LAUNCHER=ccache
export CMAKE_CXX_COMPILER_LAUNCHER=ccache
export CMAKE_CUDA_COMPILER_LAUNCHER=ccache
python setup.py develop

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.

People on Mac, follow this guide instead.

The easiest way to use lld this is download the latest LLVM binaries and run:

ln -s /path/to/downloaded/ld.lld /usr/local/bin/ld

Sometimes there's no way of getting around rebuilding lots of files, for example editing native_functions.yaml usually means 1000+ files being rebuilt. If you're using CMake newer than 3.16, you can enable pre-compiled headers by setting USE_PRECOMPILED_HEADERS=1 either on first setup, or in the CMakeCache.txt file.

USE_PRECOMPILED_HEADERS=1 python setup.py develop

This adds a build step where the compiler takes <ATen/ATen.h> and essentially dumps it's internal AST to a file so the compiler can avoid repeating itself for every .cpp file.

One caveat is that when enabled, this header gets included in every file by default. Which may change what code is legal, for example:

• internal functions can never alias existing names in <ATen/ATen.h>
• names in <ATen/ATen.h> will work even if you don't explicitly include it.

If re-building without modifying any files results in several CUDA files being re-compiled, you may be running into an nvcc bug where header dependencies are not converted to absolute paths before reporting it to the build system. This makes ninja think one of the header files has been deleted, so it runs the build again.

A compiler-wrapper to fix this is provided in tools/nvcc_fix_deps.py. You can use this as a compiler launcher, similar to ccache

export CMAKE_CUDA_COMPILER_LAUNCHER="python;`pwd`/tools/nvcc_fix_deps.py;ccache"
python setup.py develop

We have very extensive tests in the test/cpp/api folder. The tests are a great way to see how certain components are intended to be used. When compiling PyTorch from source, the test runner binary will be written to build/bin/test_api. The tests use the GoogleTest framework, which you can read up about to learn how to configure the test runner. When submitting a new feature, we care very much that you write appropriate tests. Please follow the lead of the other tests to see how to write a new test case.

If you are debugging pytorch inside GDB, you might be interested in pytorch-gdb. This script introduces some pytorch-specific commands which you can use from the GDB prompt. In particular, torch-tensor-repr prints a human-readable repr of an at::Tensor object. Example of usage:

$ gdb python
GNU gdb (GDB) 9.2
[...]
(gdb) # insert a breakpoint when we call .neg()
(gdb) break at::Tensor::neg
Function "at::Tensor::neg" not defined.
Make breakpoint pending on future shared library load? (y or [n]) y
Breakpoint 1 (at::Tensor::neg) pending.

(gdb) run
[...]
>>> import torch
>>> t = torch.tensor([1, 2, 3, 4], dtype=torch.float64)
>>> t
tensor([1., 2., 3., 4.], dtype=torch.float64)
>>> t.neg()

Thread 1 "python" hit Breakpoint 1, at::Tensor::neg (this=0x7ffb118a9c88) at aten/src/ATen/core/TensorBody.h:3295
3295    inline at::Tensor Tensor::neg() const {
(gdb) # the default repr of 'this' is not very useful
(gdb) p this
$1 = (const at::Tensor * const) 0x7ffb118a9c88
(gdb) p *this
$2 = {impl_ = {target_ = 0x55629b5cd330}}
(gdb) torch-tensor-repr *this
Python-level repr of *this:
tensor([1., 2., 3., 4.], dtype=torch.float64)

GDB tries to automatically load pytorch-gdb thanks to the .gdbinit at the root of the pytorch repo. However, auto-loadings is disabled by default, because of security reasons:

$ 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
        add-auto-load-safe-path /path/to/pytorch/.gdbinit
line to your configuration file "/home/YOUR-USERNAME/.gdbinit".
To completely disable this security protection add
        set auto-load safe-path /
line to your configuration file "/home/YOUR-USERNAME/.gdbinit".
For more information about this security protection see the
"Auto-loading safe path" section in the GDB manual.  E.g., run from the shell:
        info "(gdb)Auto-loading safe path"
(gdb)

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):

add-auto-load-safe-path /path/to/pytorch/.gdbinit

Set TORCH_SHOW_CPP_STACKTRACES=1 to get the C++ stacktrace when an error occurs in Python.

If you are working on the CUDA code, here are some useful CUDA debugging tips:

1. CUDA_DEVICE_DEBUG=1 will enable CUDA device function debug symbols (-g -G). This will be particularly helpful in debugging device code. However, it will slow down the build process for about 50% (compared to only DEBUG=1), so use wisely.
2. cuda-gdb and cuda-memcheck are your best CUDA debugging friends. Unlikegdb, cuda-gdb can display actual values in a CUDA tensor (rather than all zeros).
3. CUDA supports a lot of C++11/14 features such as, std::numeric_limits, std::nextafter, std::tuple etc. in device code. Many of such features are possible because of the --expt-relaxed-constexpr nvcc flag. There is a known issue that ROCm errors out on device code, which uses such stl functions.
4. A good performance metric for a CUDA kernel is the Effective Memory Bandwidth. It is useful for you to measure this metric whenever you are writing/optimizing a CUDA kernel. Following script shows how we can measure the effective bandwidth of CUDA uniform_ kernel.

   import torch
   from torch.utils.benchmark import Timer
   size = 128*512
   nrep = 100
   nbytes_read_write = 4 # this is number of bytes read + written by a kernel. Change this to fit your kernel.
   
   for i in range(10):
       a=torch.empty(size).cuda().uniform_()
       torch.cuda.synchronize()
       out = a.uniform_()
       torch.cuda.synchronize()
       t = Timer(stmt="a.uniform_()", globals=globals())
       res = t.blocked_autorange()
       timec = res.median
       print("uniform, size, elements", size, "forward", timec, "bandwidth (GB/s)", size*(nbytes_read_write)*1e-9/timec)
       size *=2

See more cuda development tips here

For building from source on Windows, consult our documentation on it.

Occasionally, you will write a patch which works on Linux, but fails CI on Windows. There are a few aspects in which MSVC (the Windows compiler toolchain we use) is stricter than Linux, which are worth keeping in mind when fixing these problems.

1. Symbols are NOT exported by default on Windows; instead, you have to explicitly mark a symbol as exported/imported in a header file with __declspec(dllexport) / __declspec(dllimport). We have codified this pattern into a set of macros which follow the convention *_API, e.g., TORCH_API inside Caffe2, Aten and Torch. (Every separate shared library needs a unique macro name, because symbol visibility is on a per shared library basis. See c10/macros/Macros.h for more details.)

   The upshot is if you see an "unresolved external" error in your Windows build, this is probably because you forgot to mark a function with *_API. However, there is one important counterexample to this principle: if you want a templated function to be instantiated at the call site, do NOT mark it with *_API (if you do mark it, you'll have to explicitly instantiate all of the specializations used by the call sites.)

2. If you link against a library, this does not make its dependencies transitively visible. You must explicitly specify a link dependency against every library whose symbols you use. (This is different from Linux where in most environments, transitive dependencies can be used to fulfill unresolved symbols.)

3. If you have a Windows box (we have a few on EC2 which you can request access to) and you want to run the build, the easiest way is to just run .ci/pytorch/win-build.sh. If you need to rebuild, run REBUILD=1 .ci/pytorch/win-build.sh (this will avoid blowing away your Conda environment.)

Even if you don't know anything about MSVC, you can use cmake to build simple programs on Windows; this can be helpful if you want to learn more about some peculiar linking behavior by reproducing it on a small example. Here's a simple example cmake file that defines two dynamic libraries, one linking with the other:

project(myproject CXX)
set(CMAKE_CXX_STANDARD 14)
add_library(foo SHARED foo.cpp)
add_library(bar SHARED bar.cpp)
# NB: don't forget to __declspec(dllexport) at least one symbol from foo,
# otherwise foo.lib will not be created.
target_link_libraries(bar PUBLIC foo)

You can build it with:

mkdir build
cd build
cmake ..
cmake --build .

The PyTorch codebase sometimes likes to use exciting C++ features, and these exciting features lead to exciting bugs in Windows compilers. To add insult to injury, the error messages will often not tell you which line of code actually induced the erroring template instantiation.

We've found the most effective way to debug these problems is to carefully read over diffs, keeping in mind known bugs in MSVC/NVCC. Here are a few well known pitfalls and workarounds:

• This is not actually a bug per se, but in general, code generated by MSVC is more sensitive to memory errors; you may have written some code that does a use-after-free or stack overflows; on Linux the code might work, but on Windows your program will crash. ASAN may not catch all of these problems: stay vigilant to the possibility that your crash is due to a real memory problem.

• (NVCC) c10::optional does not work when used from device code. Don't use it from kernels. Upstream issue: https://github.com/akrzemi1/Optional/issues/58 and our local issue #10329.

• constexpr generally works less well on MSVC.

  • The idiom static_assert(f() == f()) to test if f is constexpr does not work; you'll get "error C2131: expression did not evaluate to a constant". Don't use these asserts on Windows. (Example: c10/util/intrusive_ptr.h)

• (NVCC) Code you access inside a static_assert will eagerly be evaluated as if it were device code, and so you might get an error that the code is "not accessible".

class A {
  static A singleton_;
  static constexpr inline A* singleton() {
    return &singleton_;
  }
};
static_assert(std::is_same(A*, decltype(A::singleton()))::value, "hmm");

• The compiler will run out of heap space if you attempt to compile files that are too large. Splitting such files into separate files helps. (Example: THTensorMath, THTensorMoreMath, THTensorEvenMoreMath.)

• MSVC's preprocessor (but not the standard compiler) has a bug where it incorrectly tokenizes raw string literals, ending when it sees a ". This causes preprocessor tokens inside the literal like an#endif to be incorrectly treated as preprocessor directives. See https://godbolt.org/z/eVTIJq as an example.

• Either MSVC or the Windows headers have a PURE macro defined and will replace any occurrences of the PURE token in code with an empty string. This is why we have AliasAnalysisKind::PURE_FUNCTION and not AliasAnalysisKind::PURE. The same is likely true for other identifiers that we just didn't try to use yet.

CUDA, MSVC, and PyTorch versions are interdependent; please install matching versions from this table:

Note: There's a compilation issue in several Visual Studio 2019 versions since 16.7.1, so please make sure your Visual Studio 2019 version is not in 16.7.1 ~ 16.7.5

ASAN is very useful for debugging memory errors in C++. We run it in CI, but here's how to get the same thing to run on your local machine.

First, install LLVM 8. The easiest way is to get prebuilt binaries and extract them to folder (later called $LLVM_ROOT).

Then set up the appropriate scripts. You can put this in your .bashrc:

LLVM_ROOT=<wherever your llvm install is>
PYTORCH_ROOT=<wherever your pytorch checkout is>

LIBASAN_RT="$LLVM_ROOT/lib/clang/8.0.0/lib/linux/libclang_rt.asan-x86_64.so"
build_with_asan()
{
  LD_PRELOAD=${LIBASAN_RT} \
  CC="$LLVM_ROOT/bin/clang" \
  CXX="$LLVM_ROOT/bin/clang++" \
  LDSHARED="clang --shared" \
  LDFLAGS="-stdlib=libstdc++" \
  CFLAGS="-fsanitize=address -fno-sanitize-recover=all -shared-libasan -pthread" \
  CXX_FLAGS="-pthread" \
  USE_CUDA=0 USE_OPENMP=0 BUILD_CAFFE2_OPS=0 USE_DISTRIBUTED=0 DEBUG=1 \
  python setup.py develop
}

run_with_asan()
{
  LD_PRELOAD=${LIBASAN_RT} $@
}

# you can look at build-asan.sh to find the latest options the CI uses
export ASAN_OPTIONS=detect_leaks=0:symbolize=1:strict_init_order=true
export UBSAN_OPTIONS=print_stacktrace=1:suppressions=$PYTORCH_ROOT/ubsan.supp
export ASAN_SYMBOLIZER_PATH=$LLVM_ROOT/bin/llvm-symbolizer

Then you can use the scripts like:

suo-devfair ~/pytorch ❯ build_with_asan
suo-devfair ~/pytorch ❯ run_with_asan python test/test_jit.py

The scripts above specify the clang and clang++ binaries directly, which bypasses ccache. Here's how to get ccache to work:

1. Make sure the ccache symlinks for clang and clang++ are set up (see CONTRIBUTING.md)
2. Make sure $LLVM_ROOT/bin is available on your $PATH.
3. Change the CC and CXX variables in build_with_asan() to point directly to clang and clang++.

The “standard” workflow for ASAN assumes you have a standalone binary:

1. Recompile your binary with -fsanitize=address.
2. Run the binary, and ASAN will report whatever errors it find.

Unfortunately, PyTorch is a distributed as a shared library that is loaded by a third-party executable (Python). It’s too much of a hassle to recompile all of Python every time we want to use ASAN. Luckily, the ASAN folks have a workaround for cases like this:

1. Recompile your library with -fsanitize=address -shared-libasan. The extra -shared-libasan tells the compiler to ask for the shared ASAN runtime library.
2. Use LD_PRELOAD to tell the dynamic linker to load the ASAN runtime library before anything else.

More information can be found here.

We need LD_PRELOAD because there is a cmake check that ensures that a simple program builds and runs. If we are building with ASAN as a shared library, we need to LD_PRELOAD the runtime library, otherwise there will dynamic linker errors and the check will fail.

We don’t actually need either of these if we fix the cmake checks.

Python leaks a lot of memory. Possibly we could configure a suppression file, but we haven’t gotten around to it.