MODULE.bazel

module(
    name = "torch_tensorrt",
    version = "2.8.0a0",
    repo_name = "org_pytorch_tensorrt",
)

bazel_dep(name = "googletest", version = "1.16.0")
bazel_dep(name = "platforms", version = "0.0.11")
bazel_dep(name = "rules_cc", version = "0.1.1")
bazel_dep(name = "rules_python", version = "1.3.0")

python = use_extension("@rules_python//python/extensions:python.bzl", "python")
python.toolchain(
    ignore_root_user_error = True,
    python_version = "3.11",
)

bazel_dep(name = "rules_pkg", version = "1.0.1")
git_override(
    module_name = "rules_pkg",
    commit = "17c57f4",
    remote = "https://github.com/narendasan/rules_pkg",
)

local_repository = use_repo_rule("@bazel_tools//tools/build_defs/repo:local.bzl", "local_repository")

# External dependency for torch_tensorrt if you already have precompiled binaries.
local_repository(
    name = "torch_tensorrt",
    path = "/opt/conda/lib/python3.11/site-packages/torch_tensorrt",
)

new_local_repository = use_repo_rule("@bazel_tools//tools/build_defs/repo:local.bzl", "new_local_repository")

# CUDA should be installed on the system locally
# for linux x86_64 and aarch64
new_local_repository(
    name = "cuda",
    build_file = "@//third_party/cuda:BUILD",
    path = "/usr/local/cuda-12.8/",
)

# for Jetson
# These versions can be selected using the flag `--//toolchains/dep_collection:compute_libs="jetpack"`
new_local_repository(
    name = "cuda_l4t",
    build_file = "@//third_party/cuda:BUILD",
    path = "/usr/local/cuda-12.6",
)

# for windows
new_local_repository(
    name = "cuda_win",
    build_file = "@//third_party/cuda:BUILD",
    path = "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.8/",
)

http_archive = use_repo_rule("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

#############################################################################################################
# Tarballs and fetched dependencies (default - use in cases when building from precompiled bin and tarballs)
#############################################################################################################

# this is used for linux x86_64
http_archive(
    name = "libtorch",
    build_file = "@//third_party/libtorch:BUILD",
    strip_prefix = "libtorch",
    urls = ["https://download.pytorch.org/libtorch/nightly/cu128/libtorch-cxx11-abi-shared-with-deps-latest.zip"],
)

# in aarch64 platform you can get libtorch via either local or wheel file
# It is possible to specify a wheel file to use as the libtorch source by providing the URL below and
# using the build flag `--//toolchains/dep_src:torch="whl"`
#http_archive(
#    name = "torch_whl",
#    build_file = "@//third_party/libtorch:BUILD",
#    strip_prefix = "torch",
#    type = "zip",
#    urls = ["https://download.pytorch.org/whl/nightly/cu128/torch-2.8.0.dev20250415%2Bcu128-cp310-cp310-manylinux_2_28_aarch64.whl"],
#)

http_archive(
    name = "libtorch_win",
    build_file = "@//third_party/libtorch:BUILD",
    strip_prefix = "libtorch",
    urls = ["https://download.pytorch.org/libtorch/nightly/cu128/libtorch-win-shared-with-deps-latest.zip"],
)

http_archive(
    name = "torch_l4t",
    build_file = "@//third_party/libtorch:BUILD",
    strip_prefix = "torch",
    type = "zip",
    urls = ["https://pypi.jetson-ai-lab.dev/jp6/cu126/+f/6ef/f643c0a7acda9/torch-2.7.0-cp310-cp310-linux_aarch64.whl"],
    sha256 = "6eff643c0a7acda92734cc798338f733ff35c7df1a4434576f5ff7c66fc97319"
)

# Download these tarballs manually from the NVIDIA website
# Either place them in the distdir directory in third_party and use the --distdir flag
# or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz

http_archive(
    name = "tensorrt",
    build_file = "@//third_party/tensorrt/archive:BUILD",
    strip_prefix = "TensorRT-10.9.0.34",
    urls = [
        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.9.0/tars/TensorRT-10.9.0.34.Linux.x86_64-gnu.cuda-12.8.tar.gz",
    ],
)

http_archive(
    name = "tensorrt_sbsa",
    build_file = "@//third_party/tensorrt/archive:BUILD",
    strip_prefix = "TensorRT-10.9.0.34",
    urls = [
        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.9.0/tars/TensorRT-10.9.0.34.Linux.aarch64-gnu.cuda-12.8.tar.gz",
    ],
)

http_archive(
    name = "tensorrt_l4t",
    build_file = "@//third_party/tensorrt/archive:BUILD",
    strip_prefix = "TensorRT-10.3.0.26",
    urls = [
        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.3.0/tars/TensorRT-10.3.0.26.l4t.aarch64-gnu.cuda-12.6.tar.gz",
    ],
)

http_archive(
    name = "tensorrt_win",
    build_file = "@//third_party/tensorrt/archive:BUILD",
    strip_prefix = "TensorRT-10.9.0.34",
    urls = [
        "https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.9.0/zip/TensorRT-10.9.0.34.Windows.win10.cuda-12.8.zip",
    ],
)

####################################################################################
# Locally installed dependencies (use in cases of custom dependencies or aarch64)
####################################################################################

# NOTE: If you are using a local build of torch, just point the Libtorch dep to that path.

#new_local_repository(
#    name = "libtorch",
# please modify this path according to your local python wheel path
#    path = "/usr/local/lib/python3.11/dist-packages/torch",
#    build_file = "third_party/libtorch/BUILD"
#)

#new_local_repository(
#   name = "tensorrt",
#   path = "/usr/",
#   build_file = "@//third_party/tensorrt/local:BUILD"
#)