Summary:
Fixes https://github.com/pytorch/pytorch/issues/35316
On master, bazel cuda build is disabled due to lack of a proper `cu_library` rule. This PR:
- Add `rules_cuda` to the WORKSPACE and forward `cu_library` to `rules_cuda`.
- Use a simple local cuda and cudnn repositories (adopted from TRTorch) for cuda 11.3.
- Fix current broken cuda build.
- Enable cuda build in CI, not just for `:torch` target but all the test binaries to catch undefined symbols.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66241
Reviewed By: ejguan
Differential Revision: D31544091
Pulled By: malfet
fbshipit-source-id: fd3c34d0e8f80fee06f015694a4c13a8e9e12206
Summary:
## Context
We take the first step at tackling the GPU-bazel support by adding bazel external workspaces `local_config_cuda` and `cuda`, where the first one has some hardcoded values and lists of files, and the second one provides a nicer, high-level wrapper that maps into the already expected by pytorch bazel targets that are guarded with `if_cuda` macro.
The prefix `local_config_` signifies the fact that we are breaking the bazel hermeticity philosophy by explicitly relaying on the CUDA installation that is present on the machine.
## Testing
Notice an important scenario that is unlocked by this change: compilation of cpp code that depends on cuda libraries (i.e. cuda.h and so on).
Before:
```
sergei.vorobev@cs-sv7xn77uoy-gpu-1628706590:~/src/pytorch4$ bazelisk build --define=cuda=true //:c10
ERROR: /home/sergei.vorobev/src/pytorch4/tools/config/BUILD:12:1: no such package 'tools/toolchain': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.
- /home/sergei.vorobev/src/pytorch4/tools/toolchain and referenced by '//tools/config:cuda_enabled_and_capable'
ERROR: While resolving configuration keys for //:c10: Analysis failed
ERROR: Analysis of target '//:c10' failed; build aborted: Analysis failed
INFO: Elapsed time: 0.259s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (2 packages loaded, 2 targets configured)
```
After:
```
sergei.vorobev@cs-sv7xn77uoy-gpu-1628706590:~/src/pytorch4$ bazelisk build --define=cuda=true //:c10
INFO: Analyzed target //:c10 (6 packages loaded, 246 targets configured).
INFO: Found 1 target...
Target //:c10 up-to-date:
bazel-bin/libc10.lo
bazel-bin/libc10.so
INFO: Elapsed time: 0.617s, Critical Path: 0.04s
INFO: 0 processes.
INFO: Build completed successfully, 1 total action
```
The `//:c10` target is a good testing one for this, because it has such cases where the [glob is different](075024b9a3/BUILD.bazel (L76-L81)), based on do we compile for CUDA or not.
## What is out of scope of this PR
This PR is a first in a series of providing the comprehensive GPU bazel build support. Namely, we don't tackle the [cu_library](11a40ad915/tools/rules/cu.bzl (L2)) implementation here. This would be a separate large chunk of work.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63604
Reviewed By: soulitzer
Differential Revision: D30442083
Pulled By: malfet
fbshipit-source-id: b2a8e4f7e5a25a69b960a82d9e36ba568eb64595
