Adds build option and supporting macros for oneDNN-reference builds on Aarch64.
For now, oneDNN will link to GCC's libgomp.so. See, related issue: https://github.com/tensorflow/tensorflow/issues/42858
Signed-off-by: cfRod <crefeda.rodrigues@arm.com>
MLIR generated gpu kernels will continue to be disabled by default.
This commit provides a way to enable them, if conifugre.py is run with the env `TF_ENABLE_MLIR_GENERATED_GPU_KERNELS=1`
Currently, if the version check fails, the error message is:
```
subprocess.CalledProcessError: Command '['bazel', '--batch', '--bazelrc=/dev/null', 'version']' returned non-zero exit status 1.
```
After this patch, it becomes:
```
Error checking bazel version: ERROR: The project you're trying to build requires Bazel 3.0.0 (specified in /usr/local/google/home/cheshire/code/opensource/docker_tf/tensorflow/.bazelversion), but it wasn't found in /usr/bin.
You can install the required Bazel version via apt:
sudo apt update && sudo apt install bazel-3.0.0
```
PiperOrigin-RevId: 312520687
Change-Id: I41523f7defa3db10aa34b6b313d6b65c792b2020
Avoid using `--enable_platform_specific_config` when cross-compiling for
iOS/Android, as this pulls in host build flags, which may not be
appropriate (e.g., when cross-compiling for Android on a Windows host).
Also fix an issue when building tensorflowlite_c for iOS.
Fixes#38525.
PiperOrigin-RevId: 311767770
Change-Id: I80b817fd89a6889dc78be50f1def8b899f091cb6
Use `bazel --version` instead of `bazel --batch version`.
`bazel --batch` fails on Windows with a "command line too long" error, if user's environment variable is too large.
Example: https://buildkite.com/bazel/bazelisk-plus-incompatible-flags/builds/496#f5fbd8db-7421-43f4-a018-555af9856be4
`bazel --version` can also print the Bazel version without starting Bazel server, it's even faster than `bazel --batch version`.
PiperOrigin-RevId: 310570885
Change-Id: Iafc4c90f0ff57610e5f77bee230e81e78d9f1289
Environment variables are strings, not integers.'0' does not evaluate to false.
PiperOrigin-RevId: 297916971
Change-Id: I9df143690b16a181fa5f0c4a60ac2f944b396672
Remove the prompt for XLA in configure. But still allow users to build without it for platforms like raspberry pi.
PiperOrigin-RevId: 297713015
Change-Id: I7a12b073389c1cf3d265e00952f28e98470c80ff
This is necessary to start using bazel's experimental cc_shared_library support.
This change is part of the refactoring described in https://github.com/tensorflow/community/pull/179
PiperOrigin-RevId: 294736824
Change-Id: I8d13c71545cf2f2edd47818b40fa413e3e9c3254
Today if you run the ./configure script without setting the
variable TF_NEED_CUDA, in the resulting .tf_configure.bazelrc
you get:
test:v1 --test_tag_filters=-benchmark-test,-no_oss,-gpu,-oss_serial
test:v1 --build_tag_filters=-benchmark-test,-no_oss,-gpu
test:v2 --test_tag_filters=-benchmark-test,-no_oss,-gpu,-oss_serial,-v1only
test:v2 --build_tag_filters=-benchmark-test,-no_oss,-gpu,-v1only
This is incorrect because -gpu means exclude the gpu test. It should be
-no_gpu.
Debugging the problem I found when the code was switched from using
os.env to using environ_cp, that the method system_specific_test_config
was never updated. So unless the environment variable TF_NEED_CUDA was
set before running ./configure then answering yes for CUDA would not
select the correct test filter for gpus.
With this change the test filters are correct:
test:v1 --test_tag_filters=-benchmark-test,-no_oss,-no_gpu,-oss_serial
test:v1 --build_tag_filters=-benchmark-test,-no_oss,-no_gpu
test:v2 --test_tag_filters=-benchmark-test,-no_oss,-no_gpu,-oss_serial,-v1only
test:v2 --build_tag_filters=-benchmark-test,-no_oss,-no_gpu,-v1only
This is necessary to start using bazel's experimental cc_shared_library support.
This change is part of the refactoring described in https://github.com/tensorflow/community/pull/179
PiperOrigin-RevId: 288808507
Change-Id: Ie78fb4ff8dad128ebef280037cce4d3c4f42addc
1. Rewrote hipcc_cc_toolchain_config.bzl.tpl.oss based on third_party/bazel/tools/cpp/unix_cc_toolchain_config.bzl
2. Cleaned up non-Linux stuff in toolchain configuration
3. Added support for parameter file in the compiler wrapper script
4. Re-generated preconfigured toolchain by third_party/tensorflow/third_party/toolchains/preconfig/generate/update.sh ubuntu16.04-py3_opt-gcc5-rocm
5. Bumped min Bazel version to 0.27.1 because toolchain configure requires newer Bazel
6. Removed --noincompatible_do_not_split_linking_cmdline
PiperOrigin-RevId: 278844463
Change-Id: I477ec5b44e6c634db7c6d65d02b3e307f0be338b
This may be valuable to enable globally (and disable via a user flag), but for now I've enabled it for one specific call which was causing an error in #32776.
Resolves#32776.
PiperOrigin-RevId: 276094797
Change-Id: I6f007a50311c00885cf0db2d291f4d00294d53d7
Besides upgrading the Bazel version, we also refactored all build scripts to use rbe options in .bazelrc file.
In order to migrate for https://github.com/bazelbuild/bazel/issues/7480, we have to specify the complete strategies list in .bazelrc file.
PiperOrigin-RevId: 275459466
Change-Id: Iaec997da7862245955a36ebb1018d901f61c591d
