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Update on "De-prioritise Dimname and DimnameList in python overload resolution"
 `None` being a valid `Dimname` is awkward for optional `dim` arguments, as found on NumPy's reduction functions like `std` and `var`. In these cases `dim=None` should mean an all-reduction, but instead you get an error "Please look up dimensions by name". I've also had to fix `FunctionParameter::check` to actually check the first element of `INT_LIST` arguments and reject non-int types. Otherwise, the dim names end up calling the `int[]` overload and fail. [ghstack-poisoned]
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797 files changed

Lines changed: 27865 additions & 8424 deletions

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‎.circleci/cimodel/data/binary_build_data.py‎

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@@ -52,6 +52,14 @@ def get_processor_arch_name(gpu_version):
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"3.7",
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],
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)),
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macos_arm64=([None], OrderedDict(
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wheel=[
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"3.8",
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],
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conda=[
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"3.8",
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],
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)),
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# Skip CUDA-9.2 builds on Windows
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windows=(
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[v for v in dimensions.GPU_VERSIONS if v not in ['cuda92'] + dimensions.ROCM_VERSION_LABELS],

‎.circleci/cimodel/data/binary_build_definitions.py‎

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@@ -164,7 +164,7 @@ def gen_build_env_list(smoke):
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c.find_prop("gpu"),
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c.find_prop("package_format"),
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[c.find_prop("pyver")],
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c.find_prop("smoke"),
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c.find_prop("smoke") and not (c.find_prop("os_name") == "macos_arm64"), # don't test arm64
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c.find_prop("libtorch_variant"),
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c.find_prop("gcc_config_variant"),
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c.find_prop("libtorch_config_variant"),
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configs = gen_build_env_list(smoke)
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phase = "build" if toplevel_key == "binarybuilds" else "test"
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for build_config in configs:
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jobs_list.append(build_config.gen_workflow_job(phase, nightly=True))
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# don't test for macos_arm64 as it's cross compiled
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if phase != "test" or build_config.os != "macos_arm64":
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jobs_list.append(build_config.gen_workflow_job(phase, nightly=True))
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return jobs_list
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‎.circleci/cimodel/data/dimensions.py‎

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CUDA_VERSIONS = [
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"101",
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"102",
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"110",
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"111",
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]
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ROCM_VERSIONS = [

‎.circleci/cimodel/data/pytorch_build_data.py‎

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]),
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]),
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]),
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("11.2", [
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("3.8", [
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X(True),
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("libtorch", [
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(True, [
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('build_only', [X(True)]),
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]),
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]),
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]),
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]),
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]),
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]),
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("bionic", [

‎.circleci/cimodel/data/simple/docker_definitions.py‎

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# TODO: make this generated from a matrix rather than just a static list
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IMAGE_NAMES = [
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"pytorch-linux-bionic-cuda11.1-cudnn8-py3.6-gcc9",
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"pytorch-linux-bionic-cuda11.1-cudnn8-py3.8-gcc9",
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"pytorch-linux-bionic-cuda11.0-cudnn8-py3.6-gcc9",
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"pytorch-linux-bionic-cuda11.0-cudnn8-py3.8-gcc9",
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"pytorch-linux-bionic-cuda10.2-cudnn7-py3.8-gcc9",
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"pytorch-linux-bionic-py3.6-clang9",
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"pytorch-linux-bionic-cuda10.2-cudnn7-py3.6-clang9",
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"pytorch-linux-bionic-py3.8-gcc9",
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"pytorch-linux-xenial-cuda10-cudnn7-py3-gcc7",
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"pytorch-linux-xenial-cuda10.1-cudnn7-py3-gcc7",
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"pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7",
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"pytorch-linux-xenial-cuda11.0-cudnn8-py3-gcc7",
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"pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7",
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"pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7",
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"pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc5.4",
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"pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc7",
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"pytorch-linux-xenial-py3-clang5-android-ndk-r19c",
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"pytorch-linux-xenial-py3.6-gcc7",
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"pytorch-linux-bionic-rocm3.9-py3.6",
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"pytorch-linux-bionic-rocm3.10-py3.6",
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"pytorch-linux-bionic-rocm4.0.1-py3.6",
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]
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‎.circleci/cimodel/data/simple/macos_definitions.py‎

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return [{full_job_name: props_dict}]
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WORKFLOW_DATA = [MacOsJob("10_13"), MacOsJob("10_13", True)]
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WORKFLOW_DATA = [MacOsJob("10_15"), MacOsJob("10_13"), MacOsJob("10_13", True)]
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def get_workflow_jobs():

‎.circleci/cimodel/data/simple/mobile_definitions.py‎

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["custom", "build", "dynamic"]
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),
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MobileJob(
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DOCKER_IMAGE_NDK,
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[DOCKER_REQUIREMENT_NDK],
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["custom", "build", "static"]
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),
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# Use LLVM-DEV toolchain in android-ndk-r19c docker image
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# Most of this CI is already covered by "mobile-custom-build-dynamic" job
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MobileJob(

‎.circleci/cimodel/data/windows_build_definitions.py‎

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WindowsJob(None, _VC2019, CudaVersion(11, 1)),
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WindowsJob(1, _VC2019, CudaVersion(11, 1), master_only_pred=TruePred),
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WindowsJob(2, _VC2019, CudaVersion(11, 1), master_only_pred=TruePred),
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# VS2019 CUDA-11.2
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WindowsJob(None, _VC2019, CudaVersion(11, 2), master_only_pred=TruePred),
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WindowsJob(1, _VC2019, CudaVersion(11, 2), master_only_pred=TruePred),
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WindowsJob(2, _VC2019, CudaVersion(11, 2), master_only_pred=TruePred),
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# VS2019 CPU-only
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WindowsJob(None, _VC2019, None),
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WindowsJob(1, _VC2019, None, master_only_pred=TruePred),

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