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narendasannarendasan
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refactor: CI toolchains for different platforms
Signed-off-by: Naren Dasan <[email protected]>
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‎toolchains/ci_workspaces/WORKSPACE.sbsa

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workspace(name = "Torch-TensorRT")
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load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
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load("@bazel_tools//tools/build_defs/repo:git.bzl", "git_repository")
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http_archive(
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name = "rules_python",
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sha256 = "778197e26c5fbeb07ac2a2c5ae405b30f6cb7ad1f5510ea6fdac03bded96cc6f",
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url = "https://github.com/bazelbuild/rules_python/releases/download/0.2.0/rules_python-0.2.0.tar.gz",
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)
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load("@rules_python//python:pip.bzl", "pip_install")
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http_archive(
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name = "rules_pkg",
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sha256 = "038f1caa773a7e35b3663865ffb003169c6a71dc995e39bf4815792f385d837d",
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urls = [
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"https://mirror.bazel.build/github.com/bazelbuild/rules_pkg/releases/download/0.4.0/rules_pkg-0.4.0.tar.gz",
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"https://github.com/bazelbuild/rules_pkg/releases/download/0.4.0/rules_pkg-0.4.0.tar.gz",
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],
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)
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load("@rules_pkg//:deps.bzl", "rules_pkg_dependencies")
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rules_pkg_dependencies()
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git_repository(
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name = "googletest",
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commit = "703bd9caab50b139428cea1aaff9974ebee5742e",
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remote = "https://github.com/google/googletest",
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shallow_since = "1570114335 -0400",
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)
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# External dependency for torch_tensorrt if you already have precompiled binaries.
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local_repository(
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name = "torch_tensorrt",
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path = "/opt/circleci/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch_tensorrt"
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)
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# CUDA should be installed on the system locally
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new_local_repository(
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name = "cuda",
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build_file = "@//third_party/cuda:BUILD",
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path = "/usr/local/cuda/",
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)
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new_local_repository(
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name = "cublas",
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build_file = "@//third_party/cublas:BUILD",
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path = "/usr",
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)
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#############################################################################################################
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# Tarballs and fetched dependencies (default - use in cases when building from precompiled bin and tarballs)
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#############################################################################################################
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#http_archive(
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# name = "libtorch",
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# build_file = "@//third_party/libtorch:BUILD",
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# sha256 = "8d9e829ce9478db4f35bdb7943308cf02e8a2f58cf9bb10f742462c1d57bf287",
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# strip_prefix = "libtorch",
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# urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.11.0%2Bcu113.zip"],
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#)
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#
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#http_archive(
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# name = "libtorch_pre_cxx11_abi",
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# build_file = "@//third_party/libtorch:BUILD",
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# sha256 = "90159ecce3ff451f3ef3f657493b6c7c96759c3b74bbd70c1695f2ea2f81e1ad",
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# strip_prefix = "libtorch",
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# urls = ["https://download.pytorch.org/libtorch/cu113/libtorch-shared-with-deps-1.11.0%2Bcu113.zip"],
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#)
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# Download these tarballs manually from the NVIDIA website
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# Either place them in the distdir directory in third_party and use the --distdir flag
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# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
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#http_archive(
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# name = "cudnn",
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# build_file = "@//third_party/cudnn/archive:BUILD",
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# sha256 = "0e5d2df890b9967efa6619da421310d97323565a79f05a1a8cb9b7165baad0d7",
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# strip_prefix = "cuda",
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# urls = [
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# "https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.4/11.4_20210831/cudnn-11.4-linux-x64-v8.2.4.15.tgz",
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# ],
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#)
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#
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#http_archive(
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# name = "tensorrt",
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# build_file = "@//third_party/tensorrt/archive:BUILD",
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# sha256 = "826180eaaecdf9a7e76116855b9f1f3400ea9b06e66b06a3f6a0747ba6f863ad",
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# strip_prefix = "TensorRT-8.2.4.2",
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# urls = [
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# "https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.2.4/tars/tensorrt-8.2.4.2.linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz",
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# ],
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#)
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####################################################################################
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# Locally installed dependencies (use in cases of custom dependencies or aarch64)
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####################################################################################
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# NOTE: In the case you are using just the pre-cxx11-abi path or just the cxx11 abi path
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# with your local libtorch, just point deps at the same path to satisfy bazel.
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# NOTE: NVIDIA's aarch64 PyTorch (python) wheel file uses the CXX11 ABI unlike PyTorch's standard
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# x86_64 python distribution. If using NVIDIA's version just point to the root of the package
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# for both versions here and do not use --config=pre-cxx11-abi
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new_local_repository(
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name = "libtorch",
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path = "/opt/circleci/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch",
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build_file = "third_party/libtorch/BUILD"
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)
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new_local_repository(
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name = "libtorch_pre_cxx11_abi",
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path = "/opt/circleci/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch",
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build_file = "third_party/libtorch/BUILD"
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)
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new_local_repository(
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name = "cudnn",
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path = "/usr/",
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build_file = "@//third_party/cudnn/local:BUILD"
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)
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new_local_repository(
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name = "tensorrt",
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path = "/usr/",
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build_file = "@//third_party/tensorrt/local:BUILD"
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)
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# #########################################################################
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# # Testing Dependencies (optional - comment out on aarch64)
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# #########################################################################
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# pip_install(
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# name = "torch_tensorrt_py_deps",
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# requirements = "//py:requirements.txt",
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# )
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# pip_install(
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# name = "py_test_deps",
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# requirements = "//tests/py:requirements.txt",
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# )
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pip_install(
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name = "pylinter_deps",
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requirements = "//tools/linter:requirements.txt",
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)

‎WORKSPACE.ci renamed to ‎toolchains/ci_workspaces/WORKSPACE.x86_64

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@@ -34,7 +34,7 @@ git_repository(
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# External dependency for torch_tensorrt if you already have precompiled binaries.
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local_repository(
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name = "torch_tensorrt",
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path = "/opt/conda/lib/python3.8/site-packages/torch_tensorrt"
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path = "/opt/circleci/.pyenv/versions/3.9.4/lib/python3.9/site-packages/torch_tensorrt"
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)
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# CUDA should be installed on the system locally

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