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f922b58b5f
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
42 lines
972 B
Python
42 lines
972 B
Python
load("@bazel_skylib//lib:selects.bzl", "selects")
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config_setting(
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name = "cuda",
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define_values = {
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"cuda": "true",
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},
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)
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# Even when building with --config=cuda, host targets should be built with cuda disabled
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# as these targets will run on CI machines that have no GPUs.
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selects.config_setting_group(
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name = "cuda_enabled_and_capable",
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match_all = [
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":cuda",
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],
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)
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# Configures the system to build with cuda using clang.
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config_setting(
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name = "cuda_clang",
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define_values = {
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"cuda_clang": "true",
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},
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)
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# Indicates that cuda code should be compiled with nvcc
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# Mostly exists to support _analysis_ of tensorflow; more work is needed to actually make this
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# setting work.
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config_setting(
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name = "cuda_nvcc",
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define_values = {
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"cuda_nvcc": "true",
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},
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)
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config_setting(
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name = "thread_sanitizer",
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define_values = {"thread_sanitizer": "1"},
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visibility = ["//visibility:public"],
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)
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