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# buildifier: disable=load-on-top
workspace(name = "xla")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# Initialize toolchains for ML projects.
#
# A hermetic build system is designed to produce completely reproducible builds for C++.
# Details: https://github.com/google-ml-infra/rules_ml_toolchain
http_archive(
name = "rules_ml_toolchain",
sha256 = "5f17275397752b666adbf8f0a81a3ebfb1e26a970b459cac33a06a8f03caa537",
strip_prefix = "rules_ml_toolchain-a2626615e1277a635b43dd268e1d4bc892afea10",
urls = [
"https://github.com/google-ml-infra/rules_ml_toolchain/archive/a2626615e1277a635b43dd268e1d4bc892afea10.tar.gz",
],
)
load(
"@rules_ml_toolchain//cc/deps:cc_toolchain_deps.bzl",
"cc_toolchain_deps",
)
cc_toolchain_deps()
register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64")
register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64_cuda")
register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64_sycl")
register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64")
register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")
# Initialize the XLA repository and all dependencies.
#
# The cascade of load() statements and xla_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load(":workspace4.bzl", "xla_workspace4")
xla_workspace4()
load(":workspace3.bzl", "xla_workspace3")
xla_workspace3()
# Initialize hermetic Python
load("//third_party/py:python_init_rules.bzl", "python_init_rules")
python_init_rules()
load("//third_party/py:python_init_repositories.bzl", "python_init_repositories")
python_init_repositories(
requirements = {
"3.11": "//:requirements_lock_3_11.txt",
"3.12": "//:requirements_lock_3_12.txt",
},
)
load("//third_party/py:python_init_toolchains.bzl", "python_init_toolchains")
python_init_toolchains()
load("//third_party/py:python_init_pip.bzl", "python_init_pip")
python_init_pip()
load("@pypi//:requirements.bzl", "install_deps")
install_deps()
load(":workspace2.bzl", "xla_workspace2")
xla_workspace2()
load(":workspace1.bzl", "xla_workspace1")
xla_workspace1()
load(":workspace0.bzl", "xla_workspace0")
xla_workspace0()
load(
"@rules_ml_toolchain//gpu/cuda:cuda_json_init_repository.bzl",
"cuda_json_init_repository",
)
cuda_json_init_repository()
load(
"@cuda_redist_json//:distributions.bzl",
"CUDA_REDISTRIBUTIONS",
"CUDNN_REDISTRIBUTIONS",
)
load(
"@rules_ml_toolchain//gpu/cuda:cuda_redist_init_repositories.bzl",
"cuda_redist_init_repositories",
"cudnn_redist_init_repository",
)
cuda_redist_init_repositories(
cuda_redistributions = CUDA_REDISTRIBUTIONS,
)
cudnn_redist_init_repository(
cudnn_redistributions = CUDNN_REDISTRIBUTIONS,
)
load(
"@rules_ml_toolchain//gpu/cuda:cuda_configure.bzl",
"cuda_configure",
)
cuda_configure(name = "local_config_cuda")
load(
"@rules_ml_toolchain//gpu/nccl:nccl_redist_init_repository.bzl",
"nccl_redist_init_repository",
)
nccl_redist_init_repository()
load(
"@rules_ml_toolchain//gpu/nccl:nccl_configure.bzl",
"nccl_configure",
)
nccl_configure(name = "local_config_nccl")
load(
"@rules_ml_toolchain//gpu/nvshmem:nvshmem_json_init_repository.bzl",
"nvshmem_json_init_repository",
)
nvshmem_json_init_repository()
load(
"@nvshmem_redist_json//:distributions.bzl",
"NVSHMEM_REDISTRIBUTIONS",
)
load(
"@rules_ml_toolchain//gpu/nvshmem:nvshmem_redist_init_repository.bzl",
"nvshmem_redist_init_repository",
)
nvshmem_redist_init_repository(
nvshmem_redistributions = NVSHMEM_REDISTRIBUTIONS,
)
# This is used for building nightly PJRT wheels.
load("//build_tools/pjrt_wheels:nightly.bzl", "nightly_timestamp_repo")
nightly_timestamp_repo(name = "nightly_timestamp")
# This is used for building release candidate PJRT wheels.
load("//build_tools/pjrt_wheels:release_candidate.bzl", "rc_number_repo")
rc_number_repo(name = "rc_number")