As we are linking with cuDNN and cuBLAS dynamically for all configs anyway, as statically linked cuDNN is different library than dynamically linked one, increases default memory footprint, etc, and libtorch_cuda even if compiled for all GPU architectures is no longer approaching 2Gb binary size limit, so BUILD_SPLIT_CUDA can go away.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87502
Approved by: https://github.com/atalman
The function checks the output of e.g. `c++ -v` for "gcc version". But on another locale than English it might be "gcc-Version" which makes the check fail.
This causes the function to wrongly return false on systems where `c++` is a hardlink to `g++` and the current locale returns another output format.
Fix this by setting `LC_ALL=C`.
I found this as `test_utils.py` was failing in `test_cpp_compiler_is_ok`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85891
Approved by: https://github.com/ezyang
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
### Description
<!-- What did you change and why was it needed? -->
Listed in the commit message:
> The user may want to use `python3 -c "..."` to get the torch library
> path and the include path. Printing messages to stdout will mess up
> the output.
I'm using the command:
```bash
LIBTORCH_PATH="$(
python3 -c 'print(":".join(__import__("torch.utils.cpp_extension", fromlist=[None]).library_paths()))'
)"
export LD_LIBRARY_PATH="${LIBTORCH_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
```
To let the command line tools find the torch shared libraries. I think this would be a common use case for users who writing C/C++ extensions.
I got:
```console
$ LIBTORCH_PATH="$(python3 -c 'print(":".join(__import__("torch.utils.cpp_extension", fromlist=[None]).library_paths()))')"
$ export LD_LIBRARY_PATH="${LIBTORCH_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
$ echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}"
LD_LIBRARY_PATH=No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda-11.6'
/opt/hostedtoolcache/Python/3.7.13/x64/lib/python3.7/site-packages/torch/lib:/usr/local/cuda-11.6/lib64:
$ ls -alh "${LIBTORCH_PATH}"
ls: cannot access 'No CUDA runtime is found, using CUDA_HOME='\''/usr/local/cuda-11.6'\'''$'\n''/opt/hostedtoolcache/Python/3.7.13/x64/lib/python3.7/site-packages/torch/lib': No such file or directory
```
This PR prints messages in `torch.utils.cpp_extension` to `stderr`, which allows users to get correct result using `VAR="$(python3 -c '...')"`
### Issue
<!-- Link to Issue ticket or RFP -->
N/A
### Testing
<!-- How did you test your change? -->
N/A
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82097
Approved by: https://github.com/ezyang
*even if no GPUs are available*
When building PyTorch extensions for ROCm Pytorch, if the user doesn't specify a list of archs using PYTORCH_ROCM_ARCH env var, we would like to use the list of gfx archs that PyTorch was built for as the default value. To do this successfully even in an environment where no GPUs are available eg. a build-only CPU node, we need to be able to get the list of archs. `torch.cuda.get_arch_list()` doesn't work here because it calls `torch.cuda.available()` first: 0922cc024e/torch/cuda/__init__.py (L463), which will return `False` if no GPUs are available, resulting in an empty list being returned by `torch.cuda.get_arch_list()`. To get around this issue, we call the underlying API `torch._C._cuda_getArchFlags()`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80498
Approved by: https://github.com/ezyang, https://github.com/malfet
Summary:
hip/hip_runtime.h and libamdhip64.so may be required to compile
extension such as torch_ucc. They are in $ROCM_HOME/hip by default,
and may not be symlinked to $ROCM_HOME/include and $ROCM_HOME/lib.
This commit defines $ROCM_HOME/hip as $HIP_HOME, and adds its include
and lib paths when building hipified extension.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75548
Test Plan:
## Verify OSS pytorch + TorchUCC on an AMD GPU machine (MI100)
- step 1. Install OSS pytorch
```
export ROCM_PATH=/opt/rocm-4.5.2
git clone https://github.com/pytorch/pytorch.git
cd pytorch
python3 tools/amd_build/build_amd.py
USE_NCCL=0 USE_RCCL=0 USE_KINETO=0 with-proxy python3 setup.py develop
USE_NCCL=0 USE_RCCL=0 USE_KINETO=0 with-proxy python3 setup.py install
```
- step2. Install torchUCC extension
```
# /opt/rocm-4.5.2/include/hip does not exist, need include /opt/rocm-4.5.2/hip/include at compile time
export ROCM_PATH=/opt/rocm-4.5.2
export RCCL_INSTALL_DIR=/opt/rccl-rocm-rel-4.4-rdc
git clone https://github.com/facebookresearch/torch_ucc.git
cd torch_ucc
UCX_HOME=$RCCL_INSTALL_DIR UCC_HOME=$RCCL_INSTALL_DIR WITH_CUDA=$ROCM_PATH python setup.py
```
Build log before fix (error "hip/hip_runtime.h: No such file or directory"): P493038915
Build log after fix: P493037572
Reviewed By: ezyang
Differential Revision: D35506098
Pulled By: minsii
fbshipit-source-id: 76cbb6d4eaa6549a00898c9d9ebaca47a55330e9
(cherry picked from commit d684c080edf1fbd293e3321151976812c1da8533)
Summary:
Adding documentation about compiling extension with CUDA 11.5 and Windows
Example of failure: https://github.com/pytorch/pytorch/runs/4408796098?check_suite_focus=true
Note: Don't use torch/extension.h In CUDA 11.5 under windows in your C++ code:
Use aten instead of torch interface in all cuda 11.5 code under windows. It has been failing with errors, due to a bug in nvcc.
Example use:
>>> #include <ATen/ATen.h>
>>> at::Tensor SigmoidAlphaBlendForwardCuda(....)
Instead of:
>>> #include <torch/extension.h>
>>> torch::Tensor SigmoidAlphaBlendForwardCuda(...)
Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
Complete Workaround code example: cb170ac024
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73013
Reviewed By: malfet, seemethere
Differential Revision: D34306134
Pulled By: atalman
fbshipit-source-id: 3c5b9d7a89c91bd1920dc63dbd356e45dc48a8bd
(cherry picked from commit 87098e7f17fca1b98c90fafe2dde1defb6633f49)
Summary:
Remove all hardcoded AMD gfx targets
PyTorch build and Magma build will use rocm_agent_enumerator as
backup if PYTORCH_ROCM_ARCH env var is not defined
PyTorch extensions will use same gfx targets as the PyTorch build,
unless PYTORCH_ROCM_ARCH env var is defined
torch.cuda.get_arch_list() now works for ROCm builds
PyTorch CI dockers will continue to be built for gfx900 and gfx906 for now.
PYTORCH_ROCM_ARCH env var can be a space or semicolon separated list of gfx archs eg. "gfx900 gfx906" or "gfx900;gfx906"
cc jeffdaily sunway513 jithunnair-amd ROCmSupport KyleCZH
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61706
Reviewed By: seemethere
Differential Revision: D32735862
Pulled By: malfet
fbshipit-source-id: 3170e445e738e3ce373203e1e4ae99c84e645d7d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65610
- Replace HIP_PLATFORM_HCC with USE_ROCM
- Dont rely on CUDA_VERSION or HIP_VERSION and use USE_ROCM and ROCM_VERSION.
- In the next PR
- Will be removing the mapping from CUDA_VERSION to HIP_VERSION and CUDA to HIP in hipify.
- HIP_PLATFORM_HCC is deprecated, so will add HIP_PLATFORM_AMD to support HIP host code compilation on gcc.
cc jeffdaily sunway513 jithunnair-amd ROCmSupport amathews-amd
Reviewed By: jbschlosser
Differential Revision: D30909053
Pulled By: ezyang
fbshipit-source-id: 224a966ebf1aaec79beccbbd686fdf3d49267e06
Summary:
I think this may be related to https://app.circleci.com/pipelines/github/pytorch/vision/9352/workflows/9c8afb1c-6157-4c82-a5c8-105c5adac57d/jobs/687003
Apparently `pkg_resource.parse_version` returns a type of `pkg_resources.extern.packaging.version.Version` instead of `packaging.version.Version` and seems on some older version of the setuptools it doesn't support `.major/minor` operation. changing it back to using `packaging.version.parse`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61053
Test Plan: CI
Reviewed By: samestep
Differential Revision: D29494322
Pulled By: walterddr
fbshipit-source-id: 294572a10b167677440d7404e5ebe007ab59d299
Summary:
[distutils](https://docs.python.org/3/library/distutils.html) is on its way out and will be deprecated-on-import for Python 3.10+ and removed in Python 3.12 (see [PEP 632](https://www.python.org/dev/peps/pep-0632/)). There's no reason for us to keep it around since all the functionality we want from it can be found in `setuptools` / `sysconfig`. `setuptools` includes a copy of most of `distutils` (which is fine to use according to the PEP), that it uses under the hood, so this PR also uses that in some places.
Fixes#56527
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57040
Pulled By: driazati
Reviewed By: nikithamalgifb
Differential Revision: D28051356
fbshipit-source-id: 1ca312219032540e755593e50da0c9e23c62d720
Summary:
- This change is required to handle the case when hipcc is
updated to the latest using update-alternatives.
- Update-alternatives support for few ROCm binaries is available
from ROCm 4.1 onwards.
- This change doesnt not affect any previous versions of ROCm.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55968
Reviewed By: mruberry
Differential Revision: D27785123
Pulled By: ezyang
fbshipit-source-id: 8467e468d8d51277fab9b0c8cbd57e80bbcfc7f7
Summary:
Allows extensions to override ROCm gfx arch targets. Reuses the same env var used during cmake build for consistency.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54341
Reviewed By: bdhirsh
Differential Revision: D27244010
Pulled By: heitorschueroff
fbshipit-source-id: 279e1a41ee395a0596aa7f696b6e908cf7f5bb83
Summary:
This fixes the previous erroring out by adding stricter conditions in cpp_extension.py.
To test, run a split torch_cuda build on Windows with export BUILD_SPLIT_CUDA=ON && python setup.py develop and then run the following test: python test/test_utils.py TestStandaloneCPPJIT.test_load_standalone. It should pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51596
Reviewed By: malfet
Differential Revision: D26213816
Pulled By: janeyx99
fbshipit-source-id: a752ce7f9ab9d73dcf56f952bed2f2e040614443
Summary:
On all non-Windows platforms we should use 'posix_prefix' schema to discover location of Python.h header
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51586
Reviewed By: ezyang
Differential Revision: D26208684
Pulled By: malfet
fbshipit-source-id: bafa6d79de42231629960c642d535f1fcf7a427f
Summary:
Because of the size of our `libtorch_cuda.so`, linking with other hefty binaries presents a problem where 32bit relocation markers are too small and end up overflowing. This PR attempts to break up `torch_cuda` into `torch_cuda_cu` and `torch_cuda_cpp`.
`torch_cuda_cu`: all the files previously in `Caffe2_GPU_SRCS` that are
* pure `.cu` files in `aten`match
* all the BLAS files
* all the THC files, except for THCAllocator.cpp, THCCachingHostAllocator.cpp and THCGeneral.cpp
* all files in`detail`
* LegacyDefinitions.cpp and LegacyTHFunctionsCUDA.cpp
* Register*CUDA.cpp
* CUDAHooks.cpp
* CUDASolver.cpp
* TensorShapeCUDA.cpp
`torch_cuda_cpp`: all other files in `Caffe2_GPU_SRCS`
Accordingly, TORCH_CUDA_API and TORCH_CUDA_BUILD_MAIN_LIB usages are getting split as well to TORCH_CUDA_CU_API and TORCH_CUDA_CPP_API.
To test this locally, you can run `export BUILD_SPLIT_CUDA=ON && python setup.py develop`. In your `build/lib` folder, you should find binaries for both `torch_cuda_cpp` and `torch_cuda_cu`. To see that the SPLIT_CUDA option was toggled, you can grep the Summary of running cmake and make sure `Split CUDA` is ON.
This build option is tested on CI for CUDA 11.1 builds (linux for now, but windows soon).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49050
Reviewed By: walterddr
Differential Revision: D26114310
Pulled By: janeyx99
fbshipit-source-id: 0180f2519abb5a9cdde16a6fb7dd3171cff687a6
Summary:
Bugs:
1) would introduce -I* in compile commands
2) wouldn't hipify source code directly in build_dir, only one level down or more
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50703
Reviewed By: mrshenli
Differential Revision: D25949070
Pulled By: ngimel
fbshipit-source-id: 018c2a056b68019a922e20e5db2eb8435ad147fe
Summary:
_resubmission of gh-49654, which was reverted due to a cross-merge conflict_
This caught one incorrect annotation in `cpp_extension.load`.
xref gh-16574.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50278
Reviewed By: walterddr
Differential Revision: D25865278
Pulled By: ezyang
fbshipit-source-id: 25489191628af5cf9468136db36f5a0f72d9d54d
Summary:
tldr: current version of `is_ninja_available` of `torch/utils/cpp_extension.py` fails to run in the recent incarnations of pip w/ new build isolation feature which is now a default. This PR fixes this problem.
The full story follows:
--------------------------
Currently trying to build https://github.com/facebookresearch/fairscale/ which builds cuda extensions fails with the recent pip versions. The build is failing to perform `is_ninja_available`, which runs a simple subprocess to run `ninja --version` but does it with some /dev/null stream override which seems to break with the new pip versions. Currently I have `pip==20.3.3`. The recent pip performs build isolation which first fetches all dependencies to somewhere under /tmp/pip-install-xyz and then builds the package.
If I build:
```
pip install fairscale --no-build-isolation
```
everything works.
When building normally (i.e. without `--no-build-isolation`), the failure is a long long trace,
<details>
<summary>Full log</summary>
<pre>
pip install fairscale
Collecting fairscale
Downloading fairscale-0.1.1.tar.gz (83 kB)
|████████████████████████████████| 83 kB 562 kB/s
Installing build dependencies ... done
Getting requirements to build wheel ... error
ERROR: Command errored out with exit status 1:
command: /home/stas/anaconda3/envs/main-38/bin/python /home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmpjvw00c7v
cwd: /tmp/pip-install-1wq9f8fp/fairscale_347f218384a64f24b8d5ce846641213e
Complete output (55 lines):
running egg_info
writing fairscale.egg-info/PKG-INFO
writing dependency_links to fairscale.egg-info/dependency_links.txt
writing requirements to fairscale.egg-info/requires.txt
writing top-level names to fairscale.egg-info/top_level.txt
Traceback (most recent call last):
File "/home/stas/anaconda3/envs/main-38/bin/ninja", line 5, in <module>
from ninja import ninja
ModuleNotFoundError: No module named 'ninja'
Traceback (most recent call last):
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py", line 280, in <module>
main()
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py", line 263, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py", line 114, in get_requires_for_build_wheel
return hook(config_settings)
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 149, in get_requires_for_build_wheel
return self._get_build_requires(
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 130, in _get_build_requires
self.run_setup()
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/build_meta.py", line 145, in run_setup
exec(compile(code, __file__, 'exec'), locals())
File "setup.py", line 56, in <module>
setuptools.setup(
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/__init__.py", line 153, in setup
return distutils.core.setup(**attrs)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/core.py", line 148, in setup
dist.run_commands()
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/command/egg_info.py", line 298, in run
self.find_sources()
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/command/egg_info.py", line 305, in find_sources
mm.run()
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/command/egg_info.py", line 536, in run
self.add_defaults()
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/setuptools/command/egg_info.py", line 572, in add_defaults
sdist.add_defaults(self)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/command/sdist.py", line 228, in add_defaults
self._add_defaults_ext()
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/command/sdist.py", line 311, in _add_defaults_ext
build_ext = self.get_finalized_command('build_ext')
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/cmd.py", line 298, in get_finalized_command
cmd_obj = self.distribution.get_command_obj(command, create)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/distutils/dist.py", line 858, in get_command_obj
cmd_obj = self.command_obj[command] = klass(self)
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 351, in __init__
if not is_ninja_available():
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1310, in is_ninja_available
subprocess.check_call('ninja --version'.split(), stdout=devnull)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/subprocess.py", line 364, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['ninja', '--version']' returned non-zero exit status 1.
----------------------------------------
ERROR: Command errored out with exit status 1: /home/stas/anaconda3/envs/main-38/bin/python /home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmpjvw00c7v Check the logs for full command output.
</pre>
</details>
and the middle of it is what we want:
```
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 351, in __init__
if not is_ninja_available():
File "/tmp/pip-build-env-a5x2icen/overlay/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1310, in is_ninja_available
subprocess.check_call('ninja --version'.split(), stdout=devnull)
File "/home/stas/anaconda3/envs/main-38/lib/python3.8/subprocess.py", line 364, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['ninja', '--version']' returned non-zero exit status 1.
```
For some reason pytorch fails to run this simple code:
```
# torch/utils/cpp_extension.py
def is_ninja_available():
r'''
Returns ``True`` if the `ninja <https://ninja-build.org/>`_ build system is
available on the system, ``False`` otherwise.
'''
with open(os.devnull, 'wb') as devnull:
try:
subprocess.check_call('ninja --version'.split(), stdout=devnull)
except OSError:
return False
else:
return True
```
I suspect that pip does something to `os.devnull` and that's why it fails.
This PR proposes a simpler code which doesn't rely on anything but `subprocess.check_output`:
```
def is_ninja_available():
r'''
Returns ``True`` if the `ninja <https://ninja-build.org/>`_ build system is
available on the system, ``False`` otherwise.
'''
try:
subprocess.check_output('ninja --version'.split())
except Exception:
return False
else:
return True
```
which doesn't use `os.devnull` and performs the same function. There could be a whole bunch of different exceptions there I think, so I went for the generic one - we don't care why it failed, since this function's only purpose is to suggest whether ninja can be used or not.
Let's check
```
python -c "import torch.utils.cpp_extension; print(torch.utils.cpp_extension.is_ninja_available())"
True
```
Look ma - no std noise to take care of. (i.e. no need for /dev/null).
I was editing the installed environment-wide `cpp_extension.py` file directly, so didn't need to tweak `PYTHONPATH` - I made sure to replace `'ninja --version'.` with something that should fail and I did get `False` for the above command line.
I next did a somewhat elaborate cheat to re-package an already existing binary wheel with this corrected version of `cpp_extension.py`, rather than building from source:
```
mkdir /tmp/pytorch-local-channel
cd /tmp/pytorch-local-channel
# get the latest nightly wheel
wget https://download.pytorch.org/whl/nightly/cu110/torch-1.8.0.dev20201215%2Bcu110-cp38-cp38-linux_x86_64.whl
# unpack it
unzip torch-1.8.0.dev20201215+cu110-cp38-cp38-linux_x86_64.whl
# edit torch/utils/cpp_extension.py to fix the python code with the new version as in this PR
emacs torch/utils/cpp_extension.py &
# pack the files back
zip -r torch-1.8.0.dev20201215+cu110-cp38-cp38-linux_x86_64.whl caffe2 torch torch-1.8.0.dev20201215+cu110.dist-info
```
Now I tell pip to use my local channel, plus `--pre` for it to pick up the pre-release as an acceptable wheel
```
# install using this local channel
git clone https://github.com/facebookresearch/fairscale/
cd fairscale
pip install -v --disable-pip-version-check -e . -f file:///tmp/pytorch-local-channel --pre
```
and voila all works.
```
[...]
Successfully installed fairscale
```
I noticed a whole bunch of ninja not found errors in the log, which I think is the same problem with other parts of the build system packages which also use this old check copied all over various projects and build tools, and which the recent pip breaks.
```
writing manifest file '/tmp/pip-modern-metadata-_nsdesbq/fairscale.egg-info/SOURCES.txt'
Traceback (most recent call last):
File "/home/stas/anaconda3/envs/main-38/bin/ninja", line 5, in <module>
from ninja import ninja
ModuleNotFoundError: No module named 'ninja'
[...]
/tmp/pip-build-env-fqflyevr/overlay/lib/python3.8/site-packages/torch/utils/cpp_extension.py:364: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.
warnings.warn(msg.format('we could not find ninja.'))
```
but these don't prevent from the build completing and installing.
I suppose these need to be identified and reported to various other projects, but that's another story.
The new pip does something to `os.devnull` I think which breaks any code relying on it - I haven't tried to figure out what happens to that stream object, but this PR which removes its usage solves the problem.
Also do notice that:
```
git clone https://github.com/facebookresearch/fairscale/
cd fairscale
python setup.py bdist_wheel
pip install dist/fairscale-0.1.1-cp38-cp38-linux_x86_64.whl
```
works too. So it is really a pip issue.
Apologies if the notes are too many, I tried to give the complete picture and probably other projects will need those details as well.
Thank you for reading.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49443
Reviewed By: mruberry
Differential Revision: D25592109
Pulled By: ezyang
fbshipit-source-id: bfce4420c28b614ead48e9686f4153c6e0fbe8b7
Summary:
I am submitting this PR on behalf of Janne Hellsten(nurpax) from NVIDIA, for the convenience of CLA. Thanks Janne a lot for the contribution!
Currently, the ninja build decides whether to rebuild a .cu file or not pretty randomly. And there are actually two issues:
First, the arch list in the building command is ordered randomly. When the order changes, it will unconditionally rebuild regardless of the timestamp.
Second, the header files are not included in the dependency list, so if the header file changes, it is possible that ninja will not rebuild.
This PR fixes both issues. The fix for the second issue requires nvcc >= 10.2. nvcc < 10.2 can still build CUDA extension as it used to be, but it will be unable to see the changes in header files.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49344
Reviewed By: glaringlee
Differential Revision: D25540157
Pulled By: ezyang
fbshipit-source-id: 197541690d7f25e3ac5ebe3188beb1f131a4c51f
