mirror of
https://github.com/zebrajr/tensorflow.git
synced 2025-12-06 00:19:58 +01:00
160 lines
3.9 KiB
Python
160 lines
3.9 KiB
Python
# buildifier: disable=load-on-top
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workspace(name = "xla")
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load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
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# Initialize toolchains for ML projects.
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#
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# A hermetic build system is designed to produce completely reproducible builds for C++.
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# Details: https://github.com/google-ml-infra/rules_ml_toolchain
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http_archive(
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name = "rules_ml_toolchain",
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sha256 = "5a9586022d8e9bdf7d5a7a36ec0232dc3a795ae6344ee3a3625b9fd290436669",
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strip_prefix = "rules_ml_toolchain-08019214a1acf479496692743366faa6fdea9e61",
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urls = [
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"https://github.com/google-ml-infra/rules_ml_toolchain/archive/08019214a1acf479496692743366faa6fdea9e61.tar.gz",
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],
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)
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load(
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"@rules_ml_toolchain//cc/deps:cc_toolchain_deps.bzl",
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"cc_toolchain_deps",
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)
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cc_toolchain_deps()
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register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64")
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register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64_cuda")
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register_toolchains("@rules_ml_toolchain//cc:linux_x86_64_linux_x86_64_sycl")
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register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64")
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register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")
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# Initialize the XLA repository and all dependencies.
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#
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# The cascade of load() statements and xla_workspace?() calls works around the
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# restriction that load() statements need to be at the top of .bzl files.
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# E.g. we can not retrieve a new repository with http_archive and then load()
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# a macro from that repository in the same file.
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load(":workspace4.bzl", "xla_workspace4")
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xla_workspace4()
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load(":workspace3.bzl", "xla_workspace3")
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xla_workspace3()
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# Initialize hermetic Python
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load("//third_party/py:python_init_rules.bzl", "python_init_rules")
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python_init_rules()
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load("//third_party/py:python_init_repositories.bzl", "python_init_repositories")
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python_init_repositories(
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requirements = {
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"3.11": "//:requirements_lock_3_11.txt",
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"3.12": "//:requirements_lock_3_12.txt",
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},
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)
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load("//third_party/py:python_init_toolchains.bzl", "python_init_toolchains")
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python_init_toolchains()
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load("//third_party/py:python_init_pip.bzl", "python_init_pip")
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python_init_pip()
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load("@pypi//:requirements.bzl", "install_deps")
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install_deps()
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load(":workspace2.bzl", "xla_workspace2")
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xla_workspace2()
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load(":workspace1.bzl", "xla_workspace1")
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xla_workspace1()
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load(":workspace0.bzl", "xla_workspace0")
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xla_workspace0()
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load(
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"@rules_ml_toolchain//gpu/cuda:cuda_json_init_repository.bzl",
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"cuda_json_init_repository",
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)
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cuda_json_init_repository()
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load(
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"@cuda_redist_json//:distributions.bzl",
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"CUDA_REDISTRIBUTIONS",
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"CUDNN_REDISTRIBUTIONS",
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)
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load(
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"@rules_ml_toolchain//gpu/cuda:cuda_redist_init_repositories.bzl",
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"cuda_redist_init_repositories",
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"cudnn_redist_init_repository",
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)
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cuda_redist_init_repositories(
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cuda_redistributions = CUDA_REDISTRIBUTIONS,
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)
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cudnn_redist_init_repository(
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cudnn_redistributions = CUDNN_REDISTRIBUTIONS,
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)
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load(
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"@rules_ml_toolchain//gpu/cuda:cuda_configure.bzl",
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"cuda_configure",
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)
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cuda_configure(name = "local_config_cuda")
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load(
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"@rules_ml_toolchain//gpu/nccl:nccl_redist_init_repository.bzl",
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"nccl_redist_init_repository",
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)
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nccl_redist_init_repository()
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load(
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"@rules_ml_toolchain//gpu/nccl:nccl_configure.bzl",
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"nccl_configure",
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)
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nccl_configure(name = "local_config_nccl")
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load(
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"@rules_ml_toolchain//gpu/nvshmem:nvshmem_json_init_repository.bzl",
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"nvshmem_json_init_repository",
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)
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nvshmem_json_init_repository()
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load(
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"@nvshmem_redist_json//:distributions.bzl",
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"NVSHMEM_REDISTRIBUTIONS",
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)
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load(
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"@rules_ml_toolchain//gpu/nvshmem:nvshmem_redist_init_repository.bzl",
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"nvshmem_redist_init_repository",
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)
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nvshmem_redist_init_repository(
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nvshmem_redistributions = NVSHMEM_REDISTRIBUTIONS,
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)
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# This is used for building nightly PJRT wheels.
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load("//build_tools/pjrt_wheels:nightly.bzl", "nightly_timestamp_repo")
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nightly_timestamp_repo(name = "nightly_timestamp")
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