For correct import and export of functions when the dynamic linkage is used for HIP libraries on windows, the appropriate export/import macros need to be put in place. This Pull Request utilizes existing CUDA import/export macros by converting them to corresponding HIP macros during the hipification process.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144098
Approved by: https://github.com/jeffdaily
Adds `C10_UBSAN_ENABLED` macro and use it to disable `SymIntTest::Overflows` (fails under `signed-integer-overflow` UBSAN check).
Also cleans up UBSAN guard in `jit/test_misc.cpp` to use `C10_UBSAN_ENABLED` and the existing `C10_ASAN_ENABLED` instead of locally defining `HAS_ASANUBSAN`.
> NOTE: This should fix `SymIntTest::Overflows` failing under ubsan in fbcode too...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127967
Approved by: https://github.com/atalman, https://github.com/d4l3k, https://github.com/malfet
# Motivation
As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), The first runtime component we would like to upstream is `Device` which contains the device management functions of Intel GPU's runtime. To facilitate the code review, we split the code changes into 4 PRs. This is one of the 4 PRs and covers the changes under `c10`.
# Design
Intel GPU device is a wrapper of sycl device on which kernels can be executed. In our design, we will maintain a sycl device pool containing all the GPU devices of the current machine, and manage the status of the device pool by PyTorch. The thread local safe is considered in this design. The corresponding C++ files related to `Device` will be placed in c10/xpu folder. And we provide the c10 device runtime APIs, like
- `c10::xpu::device_count`
- `c10::xpu::set_device`
- ...
# Additional Context
In our plan, 4 PRs should be submitted to PyTorch for `Device`:
1. for c10
2. for aten
3. for python frontend
4. for lazy initialization shared with CUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116019
Approved by: https://github.com/gujinghui, https://github.com/jgong5, https://github.com/EikanWang, https://github.com/malfet
# Motivation
As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), The first runtime component we would like to upstream is `Device` which contains the device management functions of Intel GPU's runtime. To facilitate the code review, we split the code changes into 4 PRs. This is one of the 4 PRs and covers the changes under `c10`.
# Design
Intel GPU device is a wrapper of sycl device on which kernels can be executed. In our design, we will maintain a sycl device pool containing all the GPU devices of the current machine, and manage the status of the device pool by PyTorch. The thread local safe is considered in this design. The corresponding C++ files related to `Device` will be placed in c10/xpu folder. And we provide the c10 device runtime APIs, like
- `c10::xpu::device_count`
- `c10::xpu::set_device`
- ...
# Additional Context
In our plan, 4 PRs should be submitted to PyTorch for `Device`:
1. for c10
2. for aten
3. for python frontend
4. for lazy initialization shared with CUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116019
Approved by: https://github.com/gujinghui, https://github.com/jgong5, https://github.com/EikanWang, https://github.com/malfet
Related to #103973#110532#108404#94891
**Context:**
As commented in 6ae0554d11/cmake/Dependencies.cmake (L1198)
Kernel asserts are enabled by default for CUDA and disabled for ROCm.
However it is somewhat broken, and Kernel assert was still enabled for ROCm.
Disabling kernel assert is also needed for users who do not have PCIe atomics support. These community users have verified that disabling the kernel assert in PyTorch/ROCm platform fixed their pytorch workflow, like torch.sum script, stable-diffusion. (see the related issues)
**Changes:**
This pull request serves the following purposes:
* Refactor and clean up the logic, make it simpler for ROCm to enable and disable Kernel Asserts
* Fix the bug that Kernel Asserts for ROCm was not disabled by default.
Specifically,
- Renamed `TORCH_DISABLE_GPU_ASSERTS` to `C10_USE_ROCM_KERNEL_ASSERT` for the following reasons:
(1) This variable only applies to ROCm.
(2) The new name is more align with #define CUDA_KERNEL_ASSERT function.
(3) With USE_ in front of the name, we can easily control it with environment variable to turn on and off this feature during build (e.g. `USE_ROCM_KERNEL_ASSERT=1 python setup.py develop` will enable kernel assert for ROCm build).
- Get rid of the `ROCM_FORCE_ENABLE_GPU_ASSERTS' to simplify the logic and make it easier to understand and maintain
- Added `#cmakedefine` to carry over the CMake variable to C++
**Tests:**
(1) build with default mode and verify that USE_ROCM_KERNEL_ASSERT is OFF(0), and kernel assert is disabled:
```
python setup.py develop
```
Verify CMakeCache.txt has correct value.
```
/xxxx/pytorch/build$ grep USE_ROCM_KERNEL_ASSERT CMakeCache.txt
USE_ROCM_KERNEL_ASSERT:BOOL=0
```
Tested the following code in ROCm build and CUDA build, and expected the return code differently.
```
subprocess.call([sys.executable, '-c', "import torch;torch._assert_async(torch.tensor(0,device='cuda'));torch.cuda.synchronize()"])
```
This piece of code is adapted from below unit test to get around the limitation that this unit test now was skipped for ROCm. (We will check to enable this unit test in the future)
```
python test/test_cuda_expandable_segments.py -k test_fixed_cuda_assert_async
```
Ran the following script, expecting r ==0 since the CUDA_KERNEL_ASSERT is defined as nothing:
```
>> import sys
>>> import subprocess
>>> r=subprocess.call([sys.executable, '-c', "import torch;torch._assert_async(torch.tensor(0,device='cuda'));torch.cuda.synchronize()"])
>>> r
0
```
(2) Enable the kernel assert by building with USE_ROCM_KERNEL_ASSERT=1, or USE_ROCM_KERNEL_ASSERT=ON
```
USE_ROCM_KERNEL_ASSERT=1 python setup.py develop
```
Verify `USE_ROCM_KERNEL_ASSERT` is `1`
```
/xxxx/pytorch/build$ grep USE_ROCM_KERNEL_ASSERT CMakeCache.txt
USE_ROCM_KERNEL_ASSERT:BOOL=1
```
Run the assert test, and expected return code not equal to 0.
```
>> import sys
>>> import subprocess
>>> r=subprocess.call([sys.executable, '-c', "import torch;torch._assert_async(torch.tensor(0,device='cuda'));torch.cuda.synchronize()"])
>>>/xxxx/pytorch/aten/src/ATen/native/hip/TensorCompare.hip:108: _assert_async_cuda_kernel: Device-side assertion `input[0] != 0' failed.
:0:rocdevice.cpp :2690: 2435301199202 us: [pid:206019 tid:0x7f6cf0a77700] Callback: Queue 0x7f64e8400000 aborting with error : HSA_STATUS_ERROR_EXCEPTION: An HSAIL operation resulted in a hardware exception. code: 0x1016
>>> r
-6
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114660
Approved by: https://github.com/jeffdaily, https://github.com/malfet, https://github.com/jithunnair-amd
Compiler behavior when non-zero offset is added to a null pointer is undefined and is a bad habit.
- When `lapackEig` is called with to estimate a workspace size, do not add matrix size to the W pointer.
- When `unpack_pivots_cpu_kernel` with zero `dim_size` exit early.
- When `topk_impl_loop` is called with `k` is zero, exit right away as output tensors are empty anyway.
- Ignore adding non-zero storage-offset in `TensorImpl::data_ptr_impl_impl`, which can be the case if tensor is created as `torch.empty(3)[4:]`.
- In `s_addmm_out_sparse_dense_worker` do not call `axpy` over an empty vector.
- In `_sparse_binary_op_intersection_kernel_impl` do skip computing `ptr_indices_dim` when `sparse_dim` is empty.
- Exit `grid_sample` forward/backward kernels earlier if either `input` or `grid` are empty tensors.
Found by asan in clang-12
Before the change UBSan report looks as follows:
```
ASAN_SYMBOLIZER_PATH=/usr/lib/llvm-12/bin/llvm-symbolizer UBSAN_OPTIONS=print_stacktrace=1 LD_PRELOAD=/usr/lib/llvm-12/lib/clang/12.0.1/lib/linux/libclang_rt.asan-x86_64.so python test_fx_experimental.py -v -k test_normalize_operator_exhaustive_linalg_eig_cpu_float32
Test results will be stored in test-reports/python-unittest/test_fx_experimental
Running tests...
----------------------------------------------------------------------
test_normalize_operator_exhaustive_linalg_eig_cpu_float32 (__main__.TestNormalizeOperatorsCPU) ... /opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/overrides.py:111: UserWarning: 'has_cuda' is deprecated, please use 'torch.backends.cuda.is_built()'
torch.has_cuda,
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/overrides.py:112: UserWarning: 'has_cudnn' is deprecated, please use 'torch.backends.cudnn.is_available()'
torch.has_cudnn,
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/overrides.py:118: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'
torch.has_mps,
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/overrides.py:119: UserWarning: 'has_mkldnn' is deprecated, please use 'torch.backends.mkldnn.is_available()'
torch.has_mkldnn,
/var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:937:17: runtime error: applying non-zero offset 20 to null pointer
#0 0x7f2025794888 in void at::native::lapackEig<float, float>(char, char, int, float*, int, float*, float*, int, float*, int, float*, int, float*, int*) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x9945888)
#1 0x7f20257da256 in void at::native::(anonymous namespace)::apply_linalg_eig<float>(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor&, bool) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x998b256)
#2 0x7f20257d902d in at::native::(anonymous namespace)::linalg_eig_kernel(at::Tensor&, at::Tensor&, at::Tensor&, at::Tensor const&, bool) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x998a02d)
#3 0x7f20257b5b3d in at::native::linalg_eig_out_info(at::Tensor const&, at::Tensor&, at::Tensor&, at::Tensor&, bool) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x9966b3d)
#4 0x7f20257b4770 in at::native::linalg_eig_out(at::Tensor const&, at::Tensor&, at::Tensor&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x9965770)
#5 0x7f20280710e6 in c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<std::tuple<at::Tensor&, at::Tensor&> (at::Tensor const&, at::Tensor&, at::Tensor&), &(at::(anonymous namespace)::(anonymous namespace)::wrapper_CPU_out_linalg_eig_out(at::Tensor const&, at::Tensor&, at::Tensor&))>, std::tuple<at::Tensor&, at::Tensor&>, c10::guts::typelist::typelist<at::Tensor const&, at::Tensor&, at::Tensor&> >, std::tuple<at::Tensor&, at::Tensor&> (at::Tensor const&, at::Tensor&, at::Tensor&)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, at::Tensor&, at::Tensor&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0xc2220e6)
#6 0x7f202727a045 in at::_ops::linalg_eig_out::call(at::Tensor const&, at::Tensor&, at::Tensor&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0xb42b045)
#7 0x7f20257b7e29 in at::native::linalg_eig(at::Tensor const&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x9968e29)
#8 0x7f2028070bf0 in c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<std::tuple<at::Tensor, at::Tensor> (at::Tensor const&), &(at::(anonymous namespace)::(anonymous namespace)::wrapper_CPU__linalg_eig(at::Tensor const&))>, std::tuple<at::Tensor, at::Tensor>, c10::guts::typelist::typelist<at::Tensor const&> >, std::tuple<at::Tensor, at::Tensor> (at::Tensor const&)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0xc221bf0)
#9 0x7f2026b1f787 in std::tuple<at::Tensor, at::Tensor> c10::Dispatcher::redispatch<std::tuple<at::Tensor, at::Tensor>, at::Tensor const&>(c10::TypedOperatorHandle<std::tuple<at::Tensor, at::Tensor> (at::Tensor const&)> const&, c10::DispatchKeySet, at::Tensor const&) const (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0xacd0787)
#10 0x7f20273230a7 in at::_ops::linalg_eig::redispatch(c10::DispatchKeySet, at::Tensor const&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0xb4d40a7)
#11 0x7f202c3cc32d in torch::autograd::VariableType::(anonymous namespace)::linalg_eig(c10::DispatchKeySet, at::Tensor const&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x1057d32d)
#12 0x7f202c3cba96 in c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<std::tuple<at::Tensor, at::Tensor> (c10::DispatchKeySet, at::Tensor const&), &(torch::autograd::VariableType::(anonymous namespace)::linalg_eig(c10::DispatchKeySet, at::Tensor const&))>, std::tuple<at::Tensor, at::Tensor>, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&> >, std::tuple<at::Tensor, at::Tensor> (c10::DispatchKeySet, at::Tensor const&)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0x1057ca96)
#13 0x7f20272798e0 in at::_ops::linalg_eig::call(at::Tensor const&) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so+0xb42a8e0)
#14 0x7f2043d97ae3 in torch::autograd::THPVariable_linalg_eig(_object*, _object*, _object*) (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib/libtorch_python.so+0x23feae3)
#15 0x5072d6 in cfunction_call /usr/local/src/conda/python-3.9.17/Objects/methodobject.c:543:19
...
SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /var/lib/jenkins/workspace/aten/src/ATen/native/BatchLinearAlgebra.cpp:937:17 in
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106354
Approved by: https://github.com/huydhn, https://github.com/lezcano
Summary:
Fix this warning:
```
caffe2\c10\macros\Macros.h(138): warning C4067: unexpected tokens following preprocessor directive - expected a newline
```
`caffe2/c10/util/variant.h` already has a similar to check and define a stub for `__has_attribute(x)`, so this would not be new to caffe2/pytorch.
Test Plan: CI should complete, still with plenty of caffe2 warnings but this one should be gone from the Windows build log
Differential Revision: D47735319
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105922
Approved by: https://github.com/kit1980
Basically the same as #88644, to fix warnings like `ptxas warning : Value of threads per SM for entry _ZN2at6native13reduce_kernelILi512ELi1ENS0_8ReduceOpIfNS0_10NormTwoffEEjfLi4EEEEEvT1_ is out of range. .minnctapersm will be ignored`
CC @ptrblck @ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91972
Approved by: https://github.com/ngimel
### Bug description
When `__SYCL_DEVICE_ONLY__` is defined, while building PyTorch, the output of the preprocessing step would not have the closing curly brace of the `extern "C"` block, as it has been incorrectly placed. Compilers don't seem to report an error or a warning for a missing closing brace of an `extern "C"` block.
### Impact of the bug
If `c10/macros/Macros.h` would be included in a C++ file, and after the preprocessing stage, if the preprocessed source file would have some templated code after `extern "C" {`, then, after compilation, linking might fail with the error `templates must have c++ linkage`). eg. https://stackoverflow.com/questions/61717819/template-with-c-linkage-error-when-using-template-keyword-in-main-cpp/61717908#61717908 (its answer also has a small snippet of code to reproduce such an issue).
### Solution in this PR
one-liner bug fix that rectifies the placement of closing curly brace (`}`), so that the `extern "C"` block ends properly when `__SYCL_DEVICE_ONLY__` is defined.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87853
Approved by: https://github.com/jgong5, https://github.com/kit1980, https://github.com/malfet
# Motivation:
We need a device version assertation that can be used in SYCL kernel. SYCL_KERNEL_ASSERT will be used in the kernel launched on device XPU.
# Solution:
We add a macro SYCL_KERNEL_ASSERT via __assert_fail declaration for Linux and _wassert declaration for Windows even though NDEBUG is enabled.
# Additional context:
`__assert_fail` in SYCL kernel
`extern SYCL_EXTERNAL void __assert_fail(const char *expr, const char *file, unsigned int line, const char *func);`
`_wassert` in SYCL kernel
`extern SYCL_EXTERNAL void _wassert(const wchar_t *wexpr, const wchar_t *wfile, unsigned line);`
No additional unit test because this change could not affect PyTorch's functionality. It only affects assertation in kernel on XPU backend. So it is difficult to add ut to test it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84106
Approved by: https://github.com/malfet
This problem updates the the PR [#73040](https://github.com/pytorch/pytorch/pull/73040)
The compilation error in pyTorch with ROCm is successful with these changes when `NDEBUG` is enabled.
Solution:
For HIP we keep `__device__ __assert_fail()`
and for host side compilation we want to use the `__assert_fail()` from the glibc library.
Tested the code by compiling with below steps
```
python3 tools/amd_build/build_amd.py
python3 setup.py develop --cmake-only
cmake -DHIP_HIPCC_FLAGS_RELEASE="-DNDEBUG" build
cmake --build build
```
The UT test_fixed_cuda_assert_async is still skipped due performance overhead.
cc @jithunnair-amd
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81790
Approved by: https://github.com/shintaro-iwasaki, https://github.com/jeffdaily, https://github.com/malfet
I noticed that when `SymInt` was introduced, `jit_type_base.h` was
added as an include to the `Operator.h` template which is supposed to
be kept extremely clean and only use forward declarations. Also,
that forward declarations for `OptionalArrayRef` were missing.
So, I've refactored the forward declarations into
`ATen/core/ATen_fwd.h` and cleaned up some of the `c10`
headers that were masking these missing declarations. I've also
re-generated the pre-compiled header so `SymInt` is included.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76576
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76366
caffe2 is not currently being built for XROS.
Test Plan: CI
Reviewed By: kimishpatel
Differential Revision: D35923922
fbshipit-source-id: 260dacadf0bd5b6bab7833a4ce81e896d280b053
(cherry picked from commit 8370b8dd2519d55a79fa8d45e7951ca8dc0b21a8)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73589
Long-form flags do not work and neither does the \w character class.
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D34558345
Pulled By: dagitses
fbshipit-source-id: 73e1b18bdd55d67fd3936428400c3835684549b0
(cherry picked from commit fc85796fadb8dac0043ba0bf43fd54cf817665b7)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73040
This patch fixes a compilation error in PyTorch with ROCm when `NDEBUG` is passed.
## Problem
Forward declaration of `__host__ __device__ __assert_fail()` is used in `c10/macros/Macros.h` for HIP compilation when `NDEBUG` is set However, HIP has `__device__ __assert_fail()` in `hip/amd_detail/amd_device_functions.h`, causing a function type error.
This issue does not appear in ROCm CI tests since it happens only when `NDEBUG` is passed.
## Solution
[EDIT] After the discussion on GitHub, we chose to entirely disable `CUDA_KERNEL_ASSERT()` for ROCm.
---
To solve this compilation error, this patch disables `CUDA_KERNEL_ASSERT()`, which uses `__assert_fail()` when
1. `c10/macros/Macros.h` is included for `*.hip` (precisely speaking, `__HIP__` or `__HIP_ARCH__` is defined), and
2. `NDEBUG` is passed.
Note that there's no impact on default compilation because, without a special compilation flag, those HIP files are compiled without `-NDEBUG`. And that's why this issue has not been found.
### Justification
[1] We cannot declare one host-and-device function for two separate host and device functions.
```
__device__ int func() {return 0};
__host__ int func() {return 0};
// Compile error (hipcc)
// __device__ __host__ int func();
```
[2] Forward declaration of a correct `__device__` only `__assert_fail()` for `__HIP__` causes the following error:
```
pytorch/c10/util/TypeCast.h:135:7: error: reference to __device__ function '__assert_fail' in __host__ __device__ function
ERROR_UNSUPPORTED_CAST
^
pytorch/c10/util/TypeCast.h:118:32: note: expanded from macro 'ERROR_UNSUPPORTED_CAST'
#define ERROR_UNSUPPORTED_CAST CUDA_KERNEL_ASSERT(false);
^
pytorch/c10/macros/Macros.h:392:5: note: expanded from macro 'CUDA_KERNEL_ASSERT'
__assert_fail(
```
[3] Maybe there's a way to properly define `__assert_fail()` for HIP + NDEBUG, but this might be too much. Please let me just disable it.
### Technical details
Error
```
pytorch/c10/macros/Macros.h:368:5: error: __host__ __device__ function '__assert_fail' cannot overload __device__ function '__assert_fail'
__assert_fail(
^
/opt/rocm/hip/include/hip/amd_detail/amd_device_functions.h:1173:6: note: previous declaration is here
void __assert_fail(const char *assertion,
```
CUDA definition (9.x) of `__assert_fail()`
```
#elif defined(__GNUC__)
extern __host__ __device__ __cudart_builtin__ void __assert_fail(
const char *, const char *, unsigned int, const char *)
__THROW;
```
ROCm definition (the latest version)
```
// 2b59661f3e/include/hip/amd_detail/amd_device_functions.h (L1172-L1177)
extern "C" __device__ __attribute__((noinline)) __attribute__((weak))
void __assert_fail(const char *assertion,
const char *file,
unsigned int line,
const char *function);
```
Test Plan:
CI + reproducer
```
python3 tools/amd_build/build_amd.py
python3 setup.py develop --cmake-only
cmake -DHIP_HIPCC_FLAGS_RELEASE="-DNDEBUG" build
cmake --build build
```
Reviewed By: xw285cornell
Differential Revision: D34310555
fbshipit-source-id: 7542288912590533ced3f20afd2e704b6551991b
(cherry picked from commit 9e52196e36820abe36bf6427cabc7389d3ea6cb5)
