fix lint

This reverts commit 9ee513365121cd387e11987c66db6599ac53ded7.

.circleci/.gitignore

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-*.svg
-*.png

.circleci/README.md

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-Structure of CI
-===============
-
-setup job:
-1. Does a git checkout
-2. Persists CircleCI scripts (everything in `.circleci`) into a workspace.  Why?
-   We don't always do a Git checkout on all subjobs, but we usually
-   still want to be able to call scripts one way or another in a subjob.
-   Persisting files this way lets us have access to them without doing a
-   checkout.  This workspace is conventionally mounted on `~/workspace`
-   (this is distinguished from `~/project`, which is the conventional
-   working directory that CircleCI will default to starting your jobs
-   in.)
-3. Write out the commit message to `.circleci/COMMIT_MSG`.  This is so
-   we can determine in subjobs if we should actually run the jobs or
-   not, even if there isn't a Git checkout.
-
-
-
-
-CircleCI configuration generator
-================================
-
-One may no longer make changes to the `.circleci/config.yml` file directly.
-Instead, one must edit these Python scripts or files in the `verbatim-sources/` directory.
-
-
-Usage
-----------
-
-1. Make changes to these scripts.
-2. Run the `regenerate.sh` script in this directory and commit the script changes and the resulting change to `config.yml`.
-
-You'll see a build failure on TravisCI if the scripts don't agree with the checked-in version.
-
-
-Motivation
-----------
-
-These scripts establish a single, authoritative source of documentation for the CircleCI configuration matrix.
-The documentation, in the form of diagrams, is automatically generated and cannot drift out of sync with the YAML content.
-
-Furthermore, consistency is enforced within the YAML config itself, by using a single source of data to generate
-multiple parts of the file.
-
-* Facilitates one-off culling/enabling of CI configs for testing PRs on special targets
-
-Also see https://github.com/pytorch/pytorch/issues/17038
-
-
-Future direction
-----------------
-
-### Declaring sparse config subsets
-See comment [here](https://github.com/pytorch/pytorch/pull/17323#pullrequestreview-206945747):
-
-In contrast with a full recursive tree traversal of configuration dimensions,
-> in the future future I think we actually want to decrease our matrix somewhat and have only a few mostly-orthogonal builds that taste as many different features as possible on PRs, plus a more complete suite on every PR and maybe an almost full suite nightly/weekly (we don't have this yet). Specifying PR jobs in the future might be easier to read with an explicit list when we come to this.
-
-----------------
-----------------
-
-# How do the binaries / nightlies / releases work?
-
-### What is a binary?
-
-A binary or package (used interchangeably) is a pre-built collection of c++ libraries, header files, python bits, and other files. We build these and distribute them so that users do not need to install from source.
-
-A **binary configuration** is a collection of
-
-* release or nightly
-    * releases are stable, nightlies are beta and built every night
-* python version
-    * linux: 2.7m, 2.7mu, 3.5m, 3.6m 3.7m (mu is wide unicode or something like that. It usually doesn't matter but you should know that it exists)
-    * macos and windows: 2.7, 3.5, 3.6, 3.7
-* cpu version
-    * cpu, cuda 9.0, cuda 10.0
-    * The supported cuda versions occasionally change
-* operating system
-    * Linux - these are all built on CentOS. There haven't been any problems in the past building on CentOS and using on Ubuntu
-    * MacOS
-    * Windows - these are built on Azure pipelines
-* devtoolset version (gcc compiler version)
-    * This only matters on Linux cause only Linux uses gcc. tldr is gcc made a backwards incompatible change from gcc 4.8 to gcc 5, because it had to change how it implemented std::vector and std::string
-
-### Where are the binaries?
-
-The binaries are built in CircleCI. There are nightly binaries built every night at 9pm PST (midnight EST) and release binaries corresponding to Pytorch releases, usually every few months.
-
-We have 3 types of binary packages
-
-* pip packages - nightlies are stored on s3 (pip install -f <a s3 url>). releases are stored in a pip repo (pip install torch) (ask Soumith about this)
-* conda packages - nightlies and releases are both stored in a conda repo. Nighty packages have a '_nightly' suffix
-* libtorch packages - these are zips of all the c++ libraries, header files, and sometimes dependencies. These are c++ only
-    * shared with dependencies
-    * static with dependencies
-    * shared without dependencies
-    * static without dependencies
-
-All binaries are built in CircleCI workflows. There are checked-in workflows (committed into the .circleci/config.yml) to build the nightlies every night. Releases are built by manually pushing a PR that builds the suite of release binaries (overwrite the config.yml to build the release)
-
-# CircleCI structure of the binaries
-
-Some quick vocab:
-
-* A\**workflow** is a CircleCI concept; it is a DAG of '**jobs**'. ctrl-f 'workflows' on\https://github.com/pytorch/pytorch/blob/master/.circleci/config.yml to see the workflows.
-* **jobs** are a sequence of '**steps**'
-* **steps** are usually just a bash script or a builtin CircleCI command.* All steps run in new environments, environment variables declared in one script DO NOT persist to following steps*
-* CircleCI has a **workspace**, which is essentially a cache between steps of the *same job* in which you can store artifacts between steps.
-
-## How are the workflows structured?
-
-The nightly binaries have 3 workflows. We have one job (actually 3 jobs:  build, test, and upload) per binary configuration
-
-1. binarybuilds
-    1. every day midnight EST
-    2. linux: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/linux-binary-build-defaults.yml
-    3. macos: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/macos-binary-build-defaults.yml
-    4. For each binary configuration, e.g. linux_conda_3.7_cpu there is a
-        1. binary_linux_conda_3.7_cpu_build
-            1. Builds the build. On linux jobs this uses the 'docker executor'.
-            2. Persists the package to the workspace
-        2. binary_linux_conda_3.7_cpu_test
-            1. Loads the package to the workspace
-            2. Spins up a docker image (on Linux), mapping the package and code repos into the docker
-            3. Runs some smoke tests in the docker
-            4. (Actually, for macos this is a step rather than a separate job)
-        3. binary_linux_conda_3.7_cpu_upload
-            1. Logs in to aws/conda
-            2. Uploads the package
-2. update_s3_htmls
-    1. every day 5am EST
-    2. https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/binary_update_htmls.yml
-    3. See below for what these are for and why they're needed
-    4. Three jobs that each examine the current contents of aws and the conda repo and update some html files in s3
-3. binarysmoketests
-    1. every day
-    2. https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml
-    3. For each binary configuration, e.g. linux_conda_3.7_cpu there is a
-        1. smoke_linux_conda_3.7_cpu
-            1. Downloads the package from the cloud, e.g. using the official pip or conda instructions
-            2. Runs the smoke tests
-
-## How are the jobs structured?
-
-The jobs are in https://github.com/pytorch/pytorch/tree/master/.circleci/verbatim-sources . Jobs are made of multiple steps. There are some shared steps used by all the binaries/smokes. Steps of these jobs are all delegated to scripts in https://github.com/pytorch/pytorch/tree/master/.circleci/scripts .
-
-* Linux jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/linux-binary-build-defaults.yml
-    * binary_linux_build.sh
-    * binary_linux_test.sh
-    * binary_linux_upload.sh
-* MacOS jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/macos-binary-build-defaults.yml
-    * binary_macos_build.sh
-    * binary_macos_test.sh
-    * binary_macos_upload.sh
-* Update html jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/binary_update_htmls.yml
-    * These delegate from the pytorch/builder repo
-    * https://github.com/pytorch/builder/blob/master/cron/update_s3_htmls.sh
-    * https://github.com/pytorch/builder/blob/master/cron/upload_binary_sizes.sh
-* Smoke jobs (both linux and macos): https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml
-    * These delegate from the pytorch/builder repo
-    * https://github.com/pytorch/builder/blob/master/run_tests.sh
-    * https://github.com/pytorch/builder/blob/master/smoke_test.sh
-    * https://github.com/pytorch/builder/blob/master/check_binary.sh
-* Common shared code (shared across linux and macos): https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-binary-build-defaults.yml
-    * binary_checkout.sh - checks out pytorch/builder repo. Right now this also checks out pytorch/pytorch, but it shouldn't. pytorch/pytorch should just be shared through the workspace. This can handle being run before binary_populate_env.sh
-    * binary_populate_env.sh - parses BUILD_ENVIRONMENT into the separate env variables that make up a binary configuration. Also sets lots of default values, the date, the version strings, the location of folders in s3, all sorts of things. This generally has to be run before other steps.
-    * binary_install_miniconda.sh - Installs miniconda, cross platform. Also hacks this for the update_binary_sizes job that doesn't have the right env variables
-    * binary_run_in_docker.sh - Takes a bash script file (the actual test code) from a hardcoded location, spins up a docker image, and runs the script inside the docker image
-
-### **Why do the steps all refer to scripts?**
-
-CircleCI creates a  final yaml file by inlining every <<* segment, so if we were to keep all the code in the config.yml itself then the config size would go over 4 MB and cause infra problems.
-
-### **What is binary_run_in_docker for?**
-
-So, CircleCI has several executor types: macos, machine, and docker are the ones we use. The 'machine' executor gives you two cores on some linux vm. The 'docker' executor gives you considerably more cores (nproc was 32 instead of 2 back when I tried in February). Since the dockers are faster, we try to run everything that we can in dockers. Thus
-
-* linux build jobs use the docker executor. Running them on the docker executor was at least 2x faster than running them on the machine executor
-* linux test jobs use the machine executor and spin up their own docker. Why this nonsense? It's cause we run nvidia-docker for our GPU tests; any code that calls into the CUDA runtime needs to be run on nvidia-docker. To run a nvidia-docker you need to install some nvidia packages on the host machine and then call docker with the '—runtime nvidia' argument. CircleCI doesn't support this, so we have to do it ourself.
-    * This is not just a mere inconvenience. **This blocks all of our linux tests from using more than 2 cores.** But there is nothing that we can do about it, but wait for a fix on circleci's side. Right now, we only run some smoke tests (some simple imports) on the binaries, but this also affects non-binary test jobs.
-* linux upload jobs use the machine executor. The upload jobs are so short that it doesn't really matter what they use
-* linux smoke test jobs use the machine executor for the same reason as the linux test jobs
-
-binary_run_in_docker.sh is a way to share the docker start-up code between the binary test jobs and the binary smoke test jobs
-
-### **Why does binary_checkout also checkout pytorch? Why shouldn't it?**
-
-We want all the nightly binary jobs to run on the exact same git commit, so we wrote our own checkout logic to ensure that the same commit was always picked. Later circleci changed that to use a single pytorch checkout and persist it through the workspace (they did this because our config file was too big, so they wanted to take a lot of the setup code into scripts, but the scripts needed the code repo to exist to be called, so they added a prereq step called 'setup' to checkout the code and persist the needed scripts to the workspace). The changes to the binary jobs were not properly tested, so they all broke from missing pytorch code no longer existing. We hotfixed the problem by adding the pytorch checkout back to binary_checkout, so now there's two checkouts of pytorch on the binary jobs. This problem still needs to be fixed, but it takes careful tracing of which code is being called where.
-
-# Code structure of the binaries (circleci agnostic)
-
-## Overview
-
-The code that runs the binaries lives in two places, in the normal [github.com/pytorch/pytorch](http://github.com/pytorch/pytorch), but also in [github.com/pytorch/builder](http://github.com/pytorch/builder) , which is a repo that defines how all the binaries are built. The relevant code is
-
-
-```
-# All code needed to set-up environments for build code to run in,
-# but only code that is specific to the current CI system
-pytorch/pytorch
-- .circleci/                # Folder that holds all circleci related stuff
-  - config.yml              # GENERATED file that actually controls all circleci behavior
-  - verbatim-sources        # Used to generate job/workflow sections in ^
-  - scripts/                # Code needed to prepare circleci environments for binary build scripts
-
-- setup.py                  # Builds pytorch. This is wrapped in pytorch/builder
-- cmake files               # used in normal building of pytorch
-
-# All code needed to prepare a binary build, given an environment
-# with all the right variables/packages/paths.
-pytorch/builder
-
-# Given an installed binary and a proper python env, runs some checks
-# to make sure the binary was built the proper way. Checks things like
-# the library dependencies, symbols present, etc.
-- check_binary.sh
-
-# Given an installed binary, runs python tests to make sure everything
-# is in order. These should be de-duped. Right now they both run smoke
-# tests, but are called from different places. Usually just call some
-# import statements, but also has overlap with check_binary.sh above
-- run_tests.sh
-- smoke_test.sh
-
-# Folders that govern how packages are built. See paragraphs below
-
-- conda/
-  - build_pytorch.sh          # Entrypoint. Delegates to proper conda build folder
-  - switch_cuda_version.sh    # Switches activate CUDA installation in Docker
-  - pytorch-nightly/          # Build-folder
-- manywheel/
-  - build_cpu.sh              # Entrypoint for cpu builds
-  - build.sh                  # Entrypoint for CUDA builds
-  - build_common.sh           # Actual build script that ^^ call into
-- wheel/
-  - build_wheel.sh            # Entrypoint for wheel builds
-```
-
-Every type of package has an entrypoint build script that handles the all the important logic.
-
-## Conda
-
-Both Linux and MacOS use the same code flow for the conda builds.
-
-Conda packages are built with conda-build, see https://conda.io/projects/conda-build/en/latest/resources/commands/conda-build.html
-
-Basically, you pass `conda build` a build folder (pytorch-nightly/ above) that contains a build script and a meta.yaml. The meta.yaml specifies in what python environment to build the package in, and what dependencies the resulting package should have, and the build script gets called in the env to build the thing.
-tldr; on conda-build is
-
-1. Creates a brand new conda environment, based off of deps in the meta.yaml
-    1. Note that environment variables do not get passed into this build env unless they are specified in the meta.yaml
-    2. If the build fails this environment will stick around. You can activate it for much easier debugging. The “General Python” section below explains what exactly a python “environment” is.
-2. Calls build.sh in the environment
-3. Copies the finished package to a new conda env, also specified by the meta.yaml
-4. Runs some simple import tests (if specified in the meta.yaml)
-5. Saves the finished package as a tarball
-
-The build.sh we use is essentially a wrapper around ```python setup.py build``` , but it also manually copies in some of our dependent libraries into the resulting tarball and messes with some rpaths.
-
-The entrypoint file `builder/conda/build_conda.sh` is complicated because
-
-* It works for both Linux and MacOS
-    * The mac builds used to create their own environments, since they all used to be on the same machine. There’s now a lot of extra logic to handle conda envs. This extra machinery could be removed
-* It used to handle testing too, which adds more logic messing with python environments too. This extra machinery could be removed.
-
-## Manywheels (linux pip and libtorch packages)
-
-Manywheels are pip packages for linux distros. Note that these manywheels are not actually manylinux compliant.
-
-`builder/manywheel/build_cpu.sh` and `builder/manywheel/build.sh` (for CUDA builds) just set different env vars and then call into `builder/manywheel/build_common.sh`
-
-The entrypoint file `builder/manywheel/build_common.sh` is really really complicated because
-
-* This used to handle building for several different python versions at the same time. The loops have been removed, but there's still unnecessary folders and movements here and there.
-    * The script is never used this way anymore. This extra machinery could be removed.
-* This used to handle testing the pip packages too. This is why there’s testing code at the end that messes with python installations and stuff
-    * The script is never used this way anymore. This extra machinery could be removed.
-* This also builds libtorch packages
-    * This should really be separate. libtorch packages are c++ only and have no python. They should not share infra with all the python specific stuff in this file.
-* There is a lot of messing with rpaths. This is necessary, but could be made much much simpler if the above issues were fixed.
-
-## Wheels (MacOS pip and libtorch packages)
-
-The entrypoint file `builder/wheel/build_wheel.sh` is complicated because
-
-* The mac builds used to all run on one machine (we didn’t have autoscaling mac machines till circleci). So this script handled siloing itself by setting-up and tearing-down its build env and siloing itself into its own build directory.
-    * The script is never used this way anymore. This extra machinery could be removed.
-* This also builds libtorch packages
-    * Ditto the comment above. This should definitely be separated out.
-
-Note that the MacOS Python wheels are still built in conda environments. Some of the dependencies present during build also come from conda.
-
-## General notes
-
-### Note on run_tests.sh, smoke_test.sh, and check_binary.sh
-
-* These should all be consolidated
-* These must run on all OS types: MacOS, Linux, and Windows
-* These all run smoke tests at the moment. They inspect the packages some, maybe run a few import statements. They DO NOT run the python tests nor the cpp tests. The idea is that python tests on master and PR merges will catch all breakages. All these tests have to do is make sure the special binary machinery didn’t mess anything up.
-* There are separate run_tests.sh and smoke_test.sh because one used to be called by the smoke jobs and one used to be called by the binary test jobs (see circleci structure section above). This is still true actually, but these could be united into a single script that runs these checks, given an installed pytorch package.
-
-### Note on libtorch
-
-Libtorch packages are built in the wheel build scripts: manywheel/build_*.sh for linux and build_wheel.sh for mac. There are several things wrong with this
-
-* It’s confusing. Most of those scripts deal with python specifics.
-* The extra conditionals everywhere severely complicate the wheel build scripts
-* The process for building libtorch is different from the official instructions (a plain call to cmake, or a call to a script)
-
-### Note on docker images / Dockerfiles
-
-All linux builds occur in docker images. The docker images are
-
-* soumith/conda-cuda
-    * Has ALL CUDA versions installed. The script pytorch/builder/conda/switch_cuda_version.sh sets /usr/local/cuda to a symlink to e.g. /usr/local/cuda-10.0 to enable different CUDA builds
-    * Also used for cpu builds
-* soumith/manylinux-cuda90
-* soumith/manylinux-cuda92
-* soumith/manylinux-cuda100
-    * Also used for cpu builds
-
-The Dockerfiles are available in pytorch/builder, but there is no circleci job or script to build these docker images, and they cannot be run locally (unless you have the correct local packages/paths). Only Soumith can build them right now.
-
-### General Python
-
-* This is still a good explanation of python installations https://caffe2.ai/docs/faq.html#why-do-i-get-import-errors-in-python-when-i-try-to-use-caffe2
-
-# How to manually rebuild the binaries
-
-tldr; make a PR that looks like https://github.com/pytorch/pytorch/pull/21159
-
-Sometimes we want to push a change to master and then rebuild all of today's binaries after that change. As of May 30, 2019 there isn't a way to manually run a workflow in the UI. You can manually re-run a workflow, but it will use the exact same git commits as the first run and will not include any changes. So we have to make a PR and then force circleci to run the binary workflow instead of the normal tests. The above PR is an example of how to do this; essentially you copy-paste the binarybuilds workflow steps into the default workflow steps. If you need to point the builder repo to a different commit then you'd need to change https://github.com/pytorch/pytorch/blob/master/.circleci/scripts/binary_checkout.sh#L42-L45 to checkout what you want.
-
-## How to test changes to the binaries via .circleci
-
-Writing PRs that test the binaries is annoying, since the default circleci jobs that run on PRs are not the jobs that you want to run. Likely, changes to the binaries will touch something under .circleci/ and require that .circleci/config.yml be regenerated (.circleci/config.yml controls all .circleci behavior, and is generated using ```.circleci/regenerate.sh``` in python 3.7). But you also need to manually hardcode the binary jobs that you want to test into the .circleci/config.yml workflow, so you should actually make at least two commits, one for your changes and one to temporarily hardcode jobs. See https://github.com/pytorch/pytorch/pull/22928 as an example of how to do this.
-
-```
-# Make your changes
-touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
-
-# Regenerate the yaml, has to be in python 3.7
-.circleci/regenerate.sh
-
-# Make a commit
-git add .circleci *
-git commit -m "My real changes"
-git push origin my_branch
-
-# Now hardcode the jobs that you want in the .circleci/config.yml workflows section
-# Also eliminate ensure-consistency and should_run_job checks
-# e.g. https://github.com/pytorch/pytorch/commit/2b3344bfed8772fe86e5210cc4ee915dee42b32d
-
-# Make a commit you won't keep
-git add .circleci
-git commit -m "[DO NOT LAND] testing binaries for above changes"
-git push origin my_branch
-
-# Now you need to make some changes to the first commit.
-git rebase -i HEAD~2 # mark the first commit as 'edit'
-
-# Make the changes
-touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
-.circleci/regenerate.sh
-
-# Ammend the commit and recontinue
-git add .circleci
-git commit --amend
-git rebase --continue
-
-# Update the PR, need to force since the commits are different now
-git push origin my_branch --force
-```
-
-The advantage of this flow is that you can make new changes to the base commit and regenerate the .circleci without having to re-write which binary jobs you want to test on. The downside is that all updates will be force pushes.
-
-## How to build a binary locally
-
-### Linux
-
-You can build Linux binaries locally easily using docker.
-
-```
-# Run the docker
-# Use the correct docker image, soumith/conda-cuda used here as an example
-#
-# -v path/to/foo:path/to/bar makes path/to/foo on your local machine (the
-#    machine that you're running the command on) accessible to the docker
-#    container at path/to/bar. So if you then run `touch path/to/bar/baz`
-#    in the docker container then you will see path/to/foo/baz on your local
-#    machine. You could also clone the pytorch and builder repos in the docker.
-#
-# If you're building a CUDA binary then use `nvidia-docker run` instead, see below.
-#
-# If you know how, add ccache as a volume too and speed up everything
-docker run \
-    -v your/pytorch/repo:/pytorch \
-    -v your/builder/repo:/builder \
-    -v where/you/want/packages/to/appear:/final_pkgs \
-    -it soumith/conda-cuda /bin/bash
-
-# Export whatever variables are important to you. All variables that you'd
-# possibly need are in .circleci/scripts/binary_populate_env.sh
-# You should probably always export at least these 3 variables
-export PACKAGE_TYPE=conda
-export DESIRED_PYTHON=3.6
-export DESIRED_CUDA=cpu
-
-# Call the entrypoint
-# `|& tee foo.log` just copies all stdout and stderr output to foo.log
-# The builds generate lots of output so you probably need this when
-# building locally.
-/builder/conda/build_pytorch.sh |& tee build_output.log
-```
-
-**Building CUDA binaries on docker**
-
-To build a CUDA binary you need to use `nvidia-docker run` instead of just `docker run` (or you can manually pass `--runtime=nvidia`). This adds some needed libraries and things to build CUDA stuff.
-
-You can build CUDA binaries on CPU only machines, but you can only run CUDA binaries on CUDA machines. This means that you can build a CUDA binary on a docker on your laptop if you so choose (though it’s gonna take a loong time).
-
-For Facebook employees, ask about beefy machines that have docker support and use those instead of your laptop; it will be 5x as fast.
-
-### MacOS
-
-There’s no easy way to generate reproducible hermetic MacOS environments. If you have a Mac laptop then you can try emulating the .circleci environments as much as possible, but you probably have packages in /usr/local/, possibly installed by brew, that will probably interfere with the build. If you’re trying to repro an error on a Mac build in .circleci and you can’t seem to repro locally, then my best advice is actually to iterate on .circleci    :/
-
-But if you want to try, then I’d recommend
-
-```
-# Create a new terminal
-# Clear your LD_LIBRARY_PATH and trim as much out of your PATH as you
-# know how to do
-
-# Install a new miniconda
-# First remove any other python or conda installation from your PATH
-# Always install miniconda 3, even if building for Python <3
-new_conda="~/my_new_conda"
-conda_sh="$new_conda/install_miniconda.sh"
-curl -o "$conda_sh" https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-chmod +x "$conda_sh"
-"$conda_sh" -b -p "$MINICONDA_ROOT"
-rm -f "$conda_sh"
-export PATH="~/my_new_conda/bin:$PATH"
-
-# Create a clean python env
-# All MacOS builds use conda to manage the python env and dependencies
-# that are built with, even the pip packages
-conda create -yn binary python=2.7
-conda activate binary
-
-# Export whatever variables are important to you. All variables that you'd
-# possibly need are in .circleci/scripts/binary_populate_env.sh
-# You should probably always export at least these 3 variables
-export PACKAGE_TYPE=conda
-export DESIRED_PYTHON=3.6
-export DESIRED_CUDA=cpu
-
-# Call the entrypoint you want
-path/to/builder/wheel/build_wheel.sh
-```
-
-N.B. installing a brand new miniconda is important. This has to do with how conda installations work. See the “General Python” section above, but tldr; is that
-
-1. You make the ‘conda’ command accessible by prepending `path/to/conda_root/bin` to your PATH.
-2. You make a new env and activate it, which then also gets prepended to your PATH. Now you have `path/to/conda_root/envs/new_env/bin:path/to/conda_root/bin:$PATH`
-3. Now say you (or some code that you ran) call python executable `foo`
-    1. if you installed `foo` in `new_env`, then `path/to/conda_root/envs/new_env/bin/foo` will get called, as expected.
-    2. But if you forgot to installed `foo` in `new_env` but happened to previously install it in your root conda env (called ‘base’), then unix/linux will still find `path/to/conda_root/bin/foo` . This is dangerous, since `foo` can be a different version than you want; `foo` can even be for an incompatible python version!
-
-Newer conda versions and proper python hygiene can prevent this, but just install a new miniconda to be safe.
-
-### Windows
-
-Maybe @peterjc123 can fill this section in.

.circleci/cimodel/data/binary_build_data.py

@@ -1,178 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-This module models the tree of configuration variants
-for "smoketest" builds.
-
-Each subclass of ConfigNode represents a layer of the configuration hierarchy.
-These tree nodes encapsulate the logic for whether a branch of the hierarchy
-should be "pruned".
-
-In addition to generating config.yml content, the tree is also traversed
-to produce a visualization of config dimensions.
-"""
-
-from collections import OrderedDict
-
-from cimodel.lib.conf_tree import ConfigNode
-import cimodel.data.dimensions as dimensions
-
-
-LINKING_DIMENSIONS = [
-    "shared",
-    "static",
-]
-
-
-DEPS_INCLUSION_DIMENSIONS = [
-    "with-deps",
-    "without-deps",
-]
-
-
-def get_processor_arch_name(cuda_version):
-    return "cpu" if not cuda_version else "cu" + cuda_version
-
-
-LINUX_PACKAGE_VARIANTS = OrderedDict(
-    manywheel=[
-        "2.7m",
-        "2.7mu",
-        "3.5m",
-        "3.6m",
-        "3.7m",
-    ],
-    conda=dimensions.STANDARD_PYTHON_VERSIONS,
-    libtorch=[
-        "2.7m",
-    ],
-)
-
-CONFIG_TREE_DATA = OrderedDict(
-    linux=(dimensions.CUDA_VERSIONS, LINUX_PACKAGE_VARIANTS),
-    macos=([None], OrderedDict(
-        wheel=dimensions.STANDARD_PYTHON_VERSIONS,
-        conda=dimensions.STANDARD_PYTHON_VERSIONS,
-        libtorch=[
-            "2.7",
-        ],
-    )),
-)
-
-
-# Why is this an option?
-# All the nightlies used to be devtoolset3 and built with the old gcc ABI. We
-# added a devtoolset7 option so that we could build nightlies with the new gcc
-# ABI. That didn't work since devtoolset7 can't build with the new gcc ABI. But
-# then we set devtoolset7 to be the default anyways, since devtoolset7
-# understands avx512, which is needed for good fbgemm performance.
-# This should be removed. The base dockers should just be upgraded to
-# devtoolset7 so we don't have to reinstall this in every build job.
-# The same machinery that this uses, though, should be retooled for a different
-# compiler toolchain that can build with the new gcc ABI.
-DEVTOOLSET_VERSIONS = [
-    7,
-]
-
-
-class TopLevelNode(ConfigNode):
-    def __init__(self, node_name, config_tree_data, smoke):
-        super(TopLevelNode, self).__init__(None, node_name)
-
-        self.config_tree_data = config_tree_data
-        self.props["smoke"] = smoke
-
-    def get_children(self):
-        return [OSConfigNode(self, x, c, p) for (x, (c, p)) in self.config_tree_data.items()]
-
-
-class OSConfigNode(ConfigNode):
-    def __init__(self, parent, os_name, cuda_versions, py_tree):
-        super(OSConfigNode, self).__init__(parent, os_name)
-
-        self.py_tree = py_tree
-        self.props["os_name"] = os_name
-        self.props["cuda_versions"] = cuda_versions
-
-    def get_children(self):
-        packaging_variants = [PackageFormatConfigNode(self, k, v) for k, v in self.py_tree.items()]
-
-        if self.find_prop("smoke"):
-            filtered_packaging_variants = list(filter(lambda x: x.get_label() != "libtorch", packaging_variants))
-            return filtered_packaging_variants
-        else:
-            return packaging_variants
-
-
-class PackageFormatConfigNode(ConfigNode):
-    def __init__(self, parent, package_format, python_versions):
-        super(PackageFormatConfigNode, self).__init__(parent, package_format)
-
-        self.props["python_versions"] = python_versions
-        self.props["package_format"] = package_format
-
-    def get_children(self):
-        if self.find_prop("os_name") == "linux":
-            return [LinuxGccConfigNode(self, v) for v in DEVTOOLSET_VERSIONS]
-        else:
-            return [ArchConfigNode(self, v) for v in self.find_prop("cuda_versions")]
-
-
-class LinuxGccConfigNode(ConfigNode):
-    def __init__(self, parent, devtoolset_version):
-        super(LinuxGccConfigNode, self).__init__(parent, "DEVTOOLSET=" + str(devtoolset_version))
-
-        self.props["devtoolset_version"] = devtoolset_version
-
-    def get_children(self):
-        cuda_versions = self.find_prop("cuda_versions")
-
-        # XXX devtoolset7 on CUDA 9.0 is temporarily disabled
-        # see https://github.com/pytorch/pytorch/issues/20066
-        if self.find_prop("devtoolset_version") == 7:
-            cuda_versions = filter(lambda x: x != "90", cuda_versions)
-
-        return [ArchConfigNode(self, v) for v in cuda_versions]
-
-
-class ArchConfigNode(ConfigNode):
-    def __init__(self, parent, cu):
-        super(ArchConfigNode, self).__init__(parent, get_processor_arch_name(cu))
-
-        self.props["cu"] = cu
-
-    def get_children(self):
-        return [PyVersionConfigNode(self, v) for v in self.find_prop("python_versions")]
-
-
-class PyVersionConfigNode(ConfigNode):
-    def __init__(self, parent, pyver):
-        super(PyVersionConfigNode, self).__init__(parent, pyver)
-
-        self.props["pyver"] = pyver
-
-    def get_children(self):
-
-        smoke = self.find_prop("smoke")
-        package_format = self.find_prop("package_format")
-        os_name = self.find_prop("os_name")
-
-        has_libtorch_variants = package_format == "libtorch" and os_name == "linux"
-        linking_variants = LINKING_DIMENSIONS if has_libtorch_variants else []
-
-        return [LinkingVariantConfigNode(self, v) for v in linking_variants]
-
-
-class LinkingVariantConfigNode(ConfigNode):
-    def __init__(self, parent, linking_variant):
-        super(LinkingVariantConfigNode, self).__init__(parent, linking_variant)
-
-    def get_children(self):
-        return [DependencyInclusionConfigNode(self, v) for v in DEPS_INCLUSION_DIMENSIONS]
-
-
-class DependencyInclusionConfigNode(ConfigNode):
-    def __init__(self, parent, deps_variant):
-        super(DependencyInclusionConfigNode, self).__init__(parent, deps_variant)
-
-        self.props["libtorch_variant"] = "-".join([self.parent.get_label(), self.get_label()])

.circleci/cimodel/data/binary_build_definitions.py

@@ -1,213 +0,0 @@
-#!/usr/bin/env python3
-
-from collections import OrderedDict
-
-import cimodel.data.binary_build_data as binary_build_data
-import cimodel.lib.conf_tree as conf_tree
-import cimodel.lib.miniutils as miniutils
-import cimodel.lib.visualization as visualization
-
-
-class Conf(object):
-    def __init__(self, os, cuda_version, pydistro, parms, smoke, libtorch_variant, devtoolset_version):
-
-        self.os = os
-        self.cuda_version = cuda_version
-        self.pydistro = pydistro
-        self.parms = parms
-        self.smoke = smoke
-        self.libtorch_variant = libtorch_variant
-        self.devtoolset_version = devtoolset_version
-
-    def gen_build_env_parms(self):
-        elems = [self.pydistro] + self.parms + [binary_build_data.get_processor_arch_name(self.cuda_version)]
-        if self.devtoolset_version is not None:
-            elems.append("devtoolset" + str(self.devtoolset_version))
-        return elems
-
-    def gen_docker_image(self):
-
-        docker_word_substitution = {
-            "manywheel": "manylinux",
-            "libtorch": "manylinux",
-        }
-
-        docker_distro_prefix = miniutils.override(self.pydistro, docker_word_substitution)
-
-        # The cpu nightlies are built on the soumith/manylinux-cuda100 docker image
-        alt_docker_suffix = self.cuda_version or "100"
-        docker_distro_suffix = "" if self.pydistro == "conda" else alt_docker_suffix
-        return miniutils.quote("soumith/" + docker_distro_prefix + "-cuda" + docker_distro_suffix)
-
-    def get_name_prefix(self):
-        return "smoke" if self.smoke else "binary"
-
-    def gen_build_name(self, build_or_test):
-
-        parts = [self.get_name_prefix(), self.os] + self.gen_build_env_parms()
-
-        if self.libtorch_variant:
-            parts.append(self.libtorch_variant)
-
-        if not self.smoke:
-            parts.append(build_or_test)
-
-        return "_".join(parts)
-
-    def gen_yaml_tree(self, build_or_test):
-
-        env_tuples = [("BUILD_ENVIRONMENT", miniutils.quote(" ".join(self.gen_build_env_parms())))]
-
-        if self.libtorch_variant:
-            env_tuples.append(("LIBTORCH_VARIANT", miniutils.quote(self.libtorch_variant)))
-
-        os_name = miniutils.override(self.os, {"macos": "mac"})
-        d = {"<<": "*" + "_".join([self.get_name_prefix(), os_name, build_or_test])}
-
-        if build_or_test == "test":
-
-            if not (self.smoke and self.os == "macos"):
-                env_tuples.append(("DOCKER_IMAGE", self.gen_docker_image()))
-
-            if self.cuda_version:
-                env_tuples.append(("USE_CUDA_DOCKER_RUNTIME", miniutils.quote("1")))
-
-        else:
-            if self.os == "linux" and build_or_test != "upload":
-                d["docker"] = [{"image": self.gen_docker_image()}]
-
-        d["environment"] = OrderedDict(env_tuples)
-
-        if build_or_test == "test":
-            if self.cuda_version:
-                d["resource_class"] = "gpu.medium"
-
-        return d
-
-
-def get_root(smoke, name):
-
-    return binary_build_data.TopLevelNode(
-        name,
-        binary_build_data.CONFIG_TREE_DATA,
-        smoke,
-    )
-
-
-def gen_build_env_list(smoke):
-
-    root = get_root(smoke, "N/A")
-    config_list = conf_tree.dfs(root)
-
-    newlist = []
-    for c in config_list:
-        conf = Conf(
-            c.find_prop("os_name"),
-            c.find_prop("cu"),
-            c.find_prop("package_format"),
-            [c.find_prop("pyver")],
-            c.find_prop("smoke"),
-            c.find_prop("libtorch_variant"),
-            c.find_prop("devtoolset_version"),
-        )
-        newlist.append(conf)
-
-    return newlist
-
-
-def predicate_exclude_nonlinux_and_libtorch(config):
-    return config.os == "linux" and (config.smoke or config.pydistro != "libtorch")
-
-
-def add_build_entries(jobs_dict, phase, smoke, filter_predicate=lambda x: True):
-
-    configs = gen_build_env_list(smoke)
-    for conf_options in filter(filter_predicate, configs):
-        jobs_dict[conf_options.gen_build_name(phase)] = conf_options.gen_yaml_tree(phase)
-
-
-def add_binary_build_specs(jobs_dict):
-    add_build_entries(jobs_dict, "build", False)
-
-
-def add_binary_build_tests(jobs_dict):
-    add_build_entries(jobs_dict, "test", False, predicate_exclude_nonlinux_and_libtorch)
-
-
-def add_binary_build_uploads(jobs_dict):
-    add_build_entries(jobs_dict, "upload", False)
-
-
-def add_smoke_test_specs(jobs_dict):
-    add_build_entries(jobs_dict, "test", True)
-
-
-def get_nightly_tests():
-
-    configs = gen_build_env_list(False)
-    filtered_configs = filter(predicate_exclude_nonlinux_and_libtorch, configs)
-
-    tests = []
-    for conf_options in filtered_configs:
-        params = {"requires": ["setup", conf_options.gen_build_name("build")]}
-        tests.append({conf_options.gen_build_name("test"): params})
-
-    return tests
-
-def get_nightly_uploads():
-
-    configs = gen_build_env_list(False)
-
-    def gen_config(conf, phase_dependency):
-        return {
-            conf.gen_build_name("upload"): OrderedDict([
-                ("context", "org-member"),
-                ("requires", ["setup", conf.gen_build_name(phase_dependency)]),
-            ]),
-        }
-
-    mylist = []
-    for conf in configs:
-        phase_dependency = "test" if predicate_exclude_nonlinux_and_libtorch(conf) else "build"
-        mylist.append(gen_config(conf, phase_dependency))
-
-    return mylist
-
-
-def gen_schedule_tree(cron_timing):
-    return [{
-        "schedule": {
-            "cron": miniutils.quote(cron_timing),
-            "filters": {
-                "branches": {
-                    "only": ["master"],
-                },
-            },
-        },
-    }]
-
-
-def add_jobs_and_render(jobs_dict, toplevel_key, smoke, cron_schedule):
-
-    jobs_list = ["setup"]
-
-    configs = gen_build_env_list(smoke)
-    for build_config in configs:
-        build_name = build_config.gen_build_name("build")
-        jobs_list.append({build_name: {"requires": ["setup"]}})
-
-    jobs_dict[toplevel_key] = OrderedDict(
-        triggers=gen_schedule_tree(cron_schedule),
-        jobs=jobs_list,
-    )
-
-    graph = visualization.generate_graph(get_root(smoke, toplevel_key))
-    graph.draw(toplevel_key + "-config-dimensions.png", prog="twopi")
-
-
-def add_binary_build_jobs(jobs_dict):
-    add_jobs_and_render(jobs_dict, "binarybuilds", False, "5 5 * * *")
-
-
-def add_binary_smoke_test_jobs(jobs_dict):
-    add_jobs_and_render(jobs_dict, "binarysmoketests", True, "15 16 * * *")

.circleci/cimodel/data/caffe2_build_data.py

@@ -1,109 +0,0 @@
-#!/usr/bin/env python3
-
-from cimodel.lib.conf_tree import ConfigNode, X, XImportant
-from cimodel.lib.conf_tree import Ver
-
-
-CONFIG_TREE_DATA = [
-    (Ver("ubuntu", "14.04"), [
-        (Ver("gcc", "4.8"), [X("py2")]),
-        (Ver("gcc", "4.9"), [X("py2")]),
-    ]),
-    (Ver("ubuntu", "16.04"), [
-        (Ver("cuda", "9.0"), [
-            # TODO make explicit that this is a "secret TensorRT build"
-            #  (see https://github.com/pytorch/pytorch/pull/17323#discussion_r259446749)
-            # TODO Uh oh, were we supposed to make this one important?!
-            X("py2"),
-            XImportant("cmake"),
-        ]),
-        (Ver("cuda", "9.1"), [XImportant("py2")]),
-        (Ver("mkl"), [XImportant("py2")]),
-        (Ver("gcc", "5"), [XImportant("onnx_py2")]),
-        (Ver("clang", "3.8"), [X("py2")]),
-        (Ver("clang", "3.9"), [X("py2")]),
-        (Ver("clang", "7"), [XImportant("py2"), XImportant("onnx_py3.6")]),
-        (Ver("android"), [XImportant("py2")]),
-    ]),
-    (Ver("centos", "7"), [
-        (Ver("cuda", "9.0"), [X("py2")]),
-    ]),
-    (Ver("macos", "10.13"), [
-        # TODO ios and system aren't related. system qualifies where the python comes
-        #  from (use the system python instead of homebrew or anaconda)
-        (Ver("ios"), [X("py2")]),
-        (Ver("system"), [XImportant("py2")]),
-    ]),
-]
-
-
-class TreeConfigNode(ConfigNode):
-    def __init__(self, parent, node_name, subtree):
-        super(TreeConfigNode, self).__init__(parent, self.modify_label(node_name))
-        self.subtree = subtree
-        self.init2(node_name)
-
-    # noinspection PyMethodMayBeStatic
-    def modify_label(self, label):
-        return str(label)
-
-    def init2(self, node_name):
-        pass
-
-    def get_children(self):
-        return [self.child_constructor()(self, k, v) for (k, v) in self.subtree]
-
-    def is_build_only(self):
-        if str(self.find_prop("language_version")) == "onnx_py3.6":
-            return False
-        return str(self.find_prop("compiler_version")) in [
-            "gcc4.9",
-            "clang3.8",
-            "clang3.9",
-            "clang7",
-            "android",
-        ] or self.find_prop("distro_version").name == "macos"
-
-
-class TopLevelNode(TreeConfigNode):
-    def __init__(self, node_name, subtree):
-        super(TopLevelNode, self).__init__(None, node_name, subtree)
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return DistroConfigNode
-
-
-class DistroConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["distro_version"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return CompilerConfigNode
-
-
-class CompilerConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["compiler_version"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return LanguageConfigNode
-
-
-class LanguageConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["language_version"] = node_name
-        self.props["build_only"] = self.is_build_only()
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class ImportantConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["important"] = True
-
-    def get_children(self):
-        return []

.circleci/cimodel/data/caffe2_build_definitions.py

@@ -1,187 +0,0 @@
-#!/usr/bin/env python3
-
-from collections import OrderedDict
-
-import cimodel.data.dimensions as dimensions
-import cimodel.lib.conf_tree as conf_tree
-from cimodel.lib.conf_tree import Ver
-import cimodel.lib.miniutils as miniutils
-import cimodel.lib.visualization as visualization
-from cimodel.data.caffe2_build_data import CONFIG_TREE_DATA, TopLevelNode
-
-
-from dataclasses import dataclass
-
-
-DOCKER_IMAGE_PATH_BASE = "308535385114.dkr.ecr.us-east-1.amazonaws.com/caffe2/"
-
-DOCKER_IMAGE_VERSION = 287
-
-
-@dataclass
-class Conf:
-    language: str
-    distro: Ver
-    compiler: Ver
-    build_only: bool
-    is_important: bool
-
-    # TODO: Eventually we can probably just remove the cudnn7 everywhere.
-    def get_cudnn_insertion(self):
-
-        omit = self.language == "onnx_py2" \
-            or self.language == "onnx_py3.6" \
-            or self.compiler.name in ["android", "mkl", "clang"] \
-            or str(self.distro) in ["ubuntu14.04", "macos10.13"]
-
-        return [] if omit else ["cudnn7"]
-
-    def get_build_name_root_parts(self):
-        return [
-            "caffe2",
-            self.language,
-        ] + self.get_build_name_middle_parts()
-
-    def get_build_name_middle_parts(self):
-        return [str(self.compiler)] + self.get_cudnn_insertion() + [str(self.distro)]
-
-    def construct_phase_name(self, phase):
-        root_parts = self.get_build_name_root_parts()
-        return "_".join(root_parts + [phase]).replace(".", "_")
-
-    def get_platform(self):
-        platform = self.distro.name
-        if self.distro.name != "macos":
-            platform = "linux"
-        return platform
-
-    def gen_docker_image(self):
-
-        lang_substitutions = {
-            "onnx_py2": "py2",
-            "onnx_py3.6": "py3.6",
-            "cmake": "py2",
-        }
-
-        lang = miniutils.override(self.language, lang_substitutions)
-        parts = [lang] + self.get_build_name_middle_parts()
-        return miniutils.quote(DOCKER_IMAGE_PATH_BASE + "-".join(parts) + ":" + str(DOCKER_IMAGE_VERSION))
-
-    def gen_yaml_tree(self, phase):
-
-        tuples = []
-
-        lang_substitutions = {
-            "onnx_py2": "onnx-py2",
-            "onnx_py3.6": "onnx-py3.6",
-        }
-
-        lang = miniutils.override(self.language, lang_substitutions)
-
-        parts = [
-            "caffe2",
-            lang,
-        ] + self.get_build_name_middle_parts() + [phase]
-
-        build_env = "-".join(parts)
-        if not self.distro.name == "macos":
-            build_env = miniutils.quote(build_env)
-
-        tuples.append(("BUILD_ENVIRONMENT", build_env))
-
-        if self.compiler.name == "ios":
-            tuples.append(("BUILD_IOS", miniutils.quote("1")))
-
-        if phase == "test":
-            # TODO cuda should not be considered a compiler
-            if self.compiler.name == "cuda":
-                tuples.append(("USE_CUDA_DOCKER_RUNTIME", miniutils.quote("1")))
-
-        if self.distro.name == "macos":
-            tuples.append(("PYTHON_VERSION", miniutils.quote("2")))
-
-        else:
-            tuples.append(("DOCKER_IMAGE", self.gen_docker_image()))
-            if self.build_only:
-                tuples.append(("BUILD_ONLY", miniutils.quote("1")))
-
-        d = OrderedDict({"environment": OrderedDict(tuples)})
-
-        if phase == "test":
-            resource_class = "large" if self.compiler.name != "cuda" else "gpu.medium"
-            d["resource_class"] = resource_class
-
-        d["<<"] = "*" + "_".join(["caffe2", self.get_platform(), phase, "defaults"])
-
-        return d
-
-
-def get_root():
-    return TopLevelNode("Caffe2 Builds", CONFIG_TREE_DATA)
-
-
-def instantiate_configs():
-
-    config_list = []
-
-    root = get_root()
-    found_configs = conf_tree.dfs(root)
-    for fc in found_configs:
-
-        c = Conf(
-            language=fc.find_prop("language_version"),
-            distro=fc.find_prop("distro_version"),
-            compiler=fc.find_prop("compiler_version"),
-            build_only=fc.find_prop("build_only"),
-            is_important=fc.find_prop("important"),
-        )
-
-        config_list.append(c)
-
-    return config_list
-
-
-def add_caffe2_builds(jobs_dict):
-    configs = instantiate_configs()
-    for conf_options in configs:
-        phases = ["build"]
-        if not conf_options.build_only:
-            phases = dimensions.PHASES
-        for phase in phases:
-            jobs_dict[conf_options.construct_phase_name(phase)] = conf_options.gen_yaml_tree(phase)
-
-    graph = visualization.generate_graph(get_root())
-    graph.draw("caffe2-config-dimensions.png", prog="twopi")
-
-
-def get_caffe2_workflows():
-
-    configs = instantiate_configs()
-
-    # TODO Why don't we build this config?
-    # See https://github.com/pytorch/pytorch/pull/17323#discussion_r259450540
-    filtered_configs = filter(lambda x: not (str(x.distro) == "ubuntu14.04" and str(x.compiler) == "gcc4.9"), configs)
-
-    x = []
-    for conf_options in filtered_configs:
-
-        phases = ["build"]
-        if not conf_options.build_only:
-            phases = dimensions.PHASES
-
-        for phase in phases:
-
-            requires = ["setup"]
-            sub_d = {"requires": requires}
-
-            if phase == "test":
-                requires.append(conf_options.construct_phase_name("build"))
-
-            if not conf_options.is_important:
-                # If you update this, update
-                # pytorch_build_definitions.py too
-                sub_d["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/"]}}
-
-            x.append({conf_options.construct_phase_name(phase): sub_d})
-
-    return x

.circleci/cimodel/data/dimensions.py

@@ -1,17 +0,0 @@
-#!/usr/bin/env python3
-
-
-PHASES = ["build", "test"]
-
-CUDA_VERSIONS = [
-    None,  # cpu build
-    "92",
-    "100",
-]
-
-STANDARD_PYTHON_VERSIONS = [
-    "2.7",
-    "3.5",
-    "3.6",
-    "3.7",
-]

.circleci/cimodel/data/pytorch_build_data.py

@@ -1,201 +0,0 @@
-#!/usr/bin/env python3
-
-from cimodel.lib.conf_tree import ConfigNode, X, XImportant
-
-
-CONFIG_TREE_DATA = [
-    ("trusty", [
-        (None, [
-            XImportant("2.7.9"),
-            X("2.7"),
-            X("3.5"),
-            X("nightly"),
-        ]),
-        ("gcc", [
-            ("4.8", [X("3.6")]),
-            ("5.4", [
-                XImportant("3.6"),
-                ("3.6", [
-                    ("xla", [XImportant(True)]),
-                    ("namedtensor", [XImportant(True)]),
-                ]),
-            ]),
-            ("7", [X("3.6")]),
-        ]),
-    ]),
-    ("xenial", [
-        ("clang", [
-            ("5", [
-                XImportant("3.6"),  # This is actually the ASAN build
-                ("3.6", [
-                    ("namedtensor", [XImportant(True)]),  # ASAN
-                ]),
-            ]),
-        ]),
-        ("cuda", [
-            ("9", [
-                # Note there are magic strings here
-                # https://github.com/pytorch/pytorch/blob/master/.jenkins/pytorch/build.sh#L21
-                # and
-                # https://github.com/pytorch/pytorch/blob/master/.jenkins/pytorch/build.sh#L143
-                # and
-                # https://github.com/pytorch/pytorch/blob/master/.jenkins/pytorch/build.sh#L153
-                # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453144)
-                X("2.7"),
-                XImportant("3.6"),
-                ("2.7", [
-                    ("namedtensor", [XImportant(True)]),
-                ]),
-            ]),
-            ("9.2", [X("3.6")]),
-            ("10", [X("3.6")]),
-            ("10.1", [X("3.6")]),
-        ]),
-        ("android", [
-            ("r19c", [XImportant("3.6")]),
-        ]),
-    ]),
-]
-
-
-def get_major_pyver(dotted_version):
-    parts = dotted_version.split(".")
-    return "py" + parts[0]
-
-
-class TreeConfigNode(ConfigNode):
-    def __init__(self, parent, node_name, subtree):
-        super(TreeConfigNode, self).__init__(parent, self.modify_label(node_name))
-        self.subtree = subtree
-        self.init2(node_name)
-
-    def modify_label(self, label):
-        return label
-
-    def init2(self, node_name):
-        pass
-
-    def get_children(self):
-        return [self.child_constructor()(self, k, v) for (k, v) in self.subtree]
-
-
-class TopLevelNode(TreeConfigNode):
-    def __init__(self, node_name, subtree):
-        super(TopLevelNode, self).__init__(None, node_name, subtree)
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return DistroConfigNode
-
-
-class DistroConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["distro_name"] = node_name
-
-    def child_constructor(self):
-        distro = self.find_prop("distro_name")
-
-        next_nodes = {
-            "trusty": TrustyCompilerConfigNode,
-            "xenial": XenialCompilerConfigNode,
-        }
-        return next_nodes[distro]
-
-
-class TrustyCompilerConfigNode(TreeConfigNode):
-
-    def modify_label(self, label):
-        return label or "<unspecified>"
-
-    def init2(self, node_name):
-        self.props["compiler_name"] = node_name
-
-    def child_constructor(self):
-        return TrustyCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
-
-
-class TrustyCompilerVersionConfigNode(TreeConfigNode):
-
-    def init2(self, node_name):
-        self.props["compiler_version"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return PyVerConfigNode
-
-
-class PyVerConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["pyver"] = node_name
-        self.props["abbreviated_pyver"] = get_major_pyver(node_name)
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-
-class ExperimentalFeatureConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["experimental_feature"] = node_name
-
-    def child_constructor(self):
-        experimental_feature = self.find_prop("experimental_feature")
-
-        next_nodes = {
-            "xla": XlaConfigNode,
-            "namedtensor": NamedTensorConfigNode,
-            "important": ImportantConfigNode,
-        }
-        return next_nodes[experimental_feature]
-
-
-class XlaConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "XLA=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_xla"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class NamedTensorConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "NAMEDTENSOR=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_namedtensor"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class ImportantConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "IMPORTANT=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_important"] = node_name
-
-    def get_children(self):
-        return []
-
-
-class XenialCompilerConfigNode(TreeConfigNode):
-
-    def init2(self, node_name):
-        self.props["compiler_name"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return XenialCompilerVersionConfigNode
-
-
-class XenialCompilerVersionConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["compiler_version"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return PyVerConfigNode

.circleci/cimodel/data/pytorch_build_definitions.py

@@ -1,310 +0,0 @@
-#!/usr/bin/env python3
-
-from collections import OrderedDict
-
-from cimodel.data.pytorch_build_data import TopLevelNode, CONFIG_TREE_DATA
-import cimodel.data.dimensions as dimensions
-import cimodel.lib.conf_tree as conf_tree
-import cimodel.lib.miniutils as miniutils
-import cimodel.lib.visualization as visualization
-
-from dataclasses import dataclass, field
-from typing import List, Optional
-
-
-DOCKER_IMAGE_PATH_BASE = "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/"
-
-DOCKER_IMAGE_VERSION = 327
-
-
-@dataclass
-class Conf:
-    distro: str
-    parms: List[str]
-    pyver: Optional[str] = None
-    cuda_version: Optional[str] = None
-    # TODO expand this to cover all the USE_* that we want to test for
-    #  tesnrorrt, leveldb, lmdb, redis, opencv, mkldnn, ideep, etc.
-    # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453608)
-    is_xla: bool = False
-    restrict_phases: Optional[List[str]] = None
-    gpu_resource: Optional[str] = None
-    dependent_tests: List = field(default_factory=list)
-    parent_build: Optional['Conf'] = None
-    is_namedtensor: bool = False
-    is_important: bool = False
-
-    # TODO: Eliminate the special casing for docker paths
-    # In the short term, we *will* need to support special casing as docker images are merged for caffe2 and pytorch
-    def get_parms(self, for_docker):
-        leading = []
-        # We just don't run non-important jobs on pull requests;
-        # previously we also named them in a way to make it obvious
-        # if self.is_important and not for_docker:
-        #    leading.append("AAA")
-        leading.append("pytorch")
-        if self.is_xla and not for_docker:
-            leading.append("xla")
-        if self.is_namedtensor and not for_docker:
-            leading.append("namedtensor")
-
-        cuda_parms = []
-        if self.cuda_version:
-            cuda_parms.extend(["cuda" + self.cuda_version, "cudnn7"])
-        return leading + ["linux", self.distro] + cuda_parms + self.parms
-
-    def gen_docker_image_path(self):
-
-        parms_source = self.parent_build or self
-        base_build_env_name = "-".join(parms_source.get_parms(True))
-
-        return miniutils.quote(DOCKER_IMAGE_PATH_BASE + base_build_env_name + ":" + str(DOCKER_IMAGE_VERSION))
-
-    def get_build_job_name_pieces(self, build_or_test):
-        return self.get_parms(False) + [build_or_test]
-
-    def gen_build_name(self, build_or_test):
-        return ("_".join(map(str, self.get_build_job_name_pieces(build_or_test)))).replace(".", "_").replace("-", "_")
-
-    def get_dependents(self):
-        return self.dependent_tests or []
-
-    def gen_yaml_tree(self, build_or_test):
-
-        build_job_name_pieces = self.get_build_job_name_pieces(build_or_test)
-
-        build_env_name = "-".join(map(str, build_job_name_pieces))
-
-        env_dict = OrderedDict([
-            ("BUILD_ENVIRONMENT", build_env_name),
-            ("DOCKER_IMAGE", self.gen_docker_image_path()),
-        ])
-
-        if self.pyver:
-            env_dict["PYTHON_VERSION"] = miniutils.quote(self.pyver)
-
-        if build_or_test == "test" and self.gpu_resource:
-            env_dict["USE_CUDA_DOCKER_RUNTIME"] = miniutils.quote("1")
-
-        d = {
-            "environment": env_dict,
-            "<<": "*" + "_".join(["pytorch", "linux", build_or_test, "defaults"]),
-        }
-
-        if build_or_test == "test":
-            resource_class = "large"
-            if self.gpu_resource:
-                resource_class = "gpu." + self.gpu_resource
-
-                if self.gpu_resource == "large":
-                    env_dict["MULTI_GPU"] = miniutils.quote("1")
-
-            d["resource_class"] = resource_class
-
-        return d
-
-    def gen_workflow_yaml_item(self, phase):
-
-        # All jobs require the setup job
-        parameters = OrderedDict({"requires": ["setup"]})
-
-        if phase == "test":
-
-            # TODO When merging the caffe2 and pytorch jobs, it might be convenient for a while to make a
-            #  caffe2 test job dependent on a pytorch build job. This way we could quickly dedup the repeated
-            #  build of pytorch in the caffe2 build job, and just run the caffe2 tests off of a completed
-            #  pytorch build job (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259452641)
-
-            dependency_build = self.parent_build or self
-            parameters["requires"].append(dependency_build.gen_build_name("build"))
-
-        if not self.is_important:
-            # If you update this, update
-            # caffe2_build_definitions.py too
-            parameters["filters"] = {"branches": {"only": ["master", r"/ci-all\/.*/"]}}
-
-        return {self.gen_build_name(phase): parameters}
-
-
-# TODO This is a hack to special case some configs just for the workflow list
-class HiddenConf(object):
-    def __init__(self, name, parent_build=None):
-        self.name = name
-        self.parent_build = parent_build
-
-    def gen_workflow_yaml_item(self, phase):
-
-        return {self.gen_build_name(phase): {"requires": [self.parent_build.gen_build_name("build")]}}
-
-    def gen_build_name(self, _):
-        return self.name
-
-
-# TODO Convert these to graph nodes
-def gen_dependent_configs(xenial_parent_config):
-
-    extra_parms = [
-        (["multigpu"], "large"),
-        (["NO_AVX2"], "medium"),
-        (["NO_AVX", "NO_AVX2"], "medium"),
-        (["slow"], "medium"),
-        (["nogpu"], None),
-    ]
-
-    configs = []
-    for parms, gpu in extra_parms:
-
-        c = Conf(
-            xenial_parent_config.distro,
-            ["py3"] + parms,
-            pyver="3.6",
-            cuda_version=xenial_parent_config.cuda_version,
-            restrict_phases=["test"],
-            gpu_resource=gpu,
-            parent_build=xenial_parent_config,
-            is_important=xenial_parent_config.is_important,
-        )
-
-        configs.append(c)
-
-    for x in ["pytorch_short_perf_test_gpu", "pytorch_python_doc_push", "pytorch_cpp_doc_push"]:
-        configs.append(HiddenConf(x, parent_build=xenial_parent_config))
-
-    return configs
-
-
-def get_root():
-    return TopLevelNode("PyTorch Builds", CONFIG_TREE_DATA)
-
-
-def gen_tree():
-    root = get_root()
-    configs_list = conf_tree.dfs(root)
-    return configs_list
-
-
-def instantiate_configs():
-
-    config_list = []
-
-    root = get_root()
-    found_configs = conf_tree.dfs(root)
-    restrict_phases = None
-    for fc in found_configs:
-
-        distro_name = fc.find_prop("distro_name")
-
-        python_version = None
-        if distro_name == "xenial":
-            python_version = fc.find_prop("pyver")
-            parms_list = [fc.find_prop("abbreviated_pyver")]
-        else:
-            parms_list = ["py" + fc.find_prop("pyver")]
-
-        compiler_name = fc.find_prop("compiler_name")
-
-        cuda_version = None
-        if compiler_name == "cuda":
-            cuda_version = fc.find_prop("compiler_version")
-
-        elif compiler_name == "android":
-            android_ndk_version = fc.find_prop("compiler_version")
-            # TODO: do we need clang to compile host binaries like protoc?
-            parms_list.append("clang5")
-            parms_list.append("android-ndk-" + android_ndk_version)
-            restrict_phases = ["build"]
-
-        elif compiler_name:
-            gcc_version = compiler_name + (fc.find_prop("compiler_version") or "")
-            parms_list.append(gcc_version)
-
-            # TODO: This is a nasty special case
-            if compiler_name == "clang":
-                parms_list.append("asan")
-
-        if cuda_version in ["9.2", "10", "10.1"]:
-            # TODO The gcc version is orthogonal to CUDA version?
-            parms_list.append("gcc7")
-
-        is_xla = fc.find_prop("is_xla") or False
-        is_namedtensor = fc.find_prop("is_namedtensor") or False
-        is_important = fc.find_prop("is_important") or False
-
-        gpu_resource = None
-        if cuda_version and cuda_version != "10":
-            gpu_resource = "medium"
-
-        c = Conf(
-            distro_name,
-            parms_list,
-            python_version,
-            cuda_version,
-            is_xla,
-            restrict_phases,
-            gpu_resource,
-            is_namedtensor=is_namedtensor,
-            is_important=is_important,
-        )
-
-        if cuda_version == "9" and python_version == "3.6":
-            c.dependent_tests = gen_dependent_configs(c)
-
-        config_list.append(c)
-
-    return config_list
-
-
-def add_build_env_defs(jobs_dict):
-
-    mydict = OrderedDict()
-
-    config_list = instantiate_configs()
-    for c in config_list:
-
-        phases = c.restrict_phases or dimensions.PHASES
-
-        for phase in phases:
-
-            # TODO why does this not have a test?
-            if phase == "test" and c.cuda_version == "10":
-                continue
-
-            d = c.gen_yaml_tree(phase)
-            mydict[c.gen_build_name(phase)] = d
-
-            if phase == "test":
-                for x in filter(lambda x: type(x) is not HiddenConf, c.get_dependents()):
-
-                    d = x.gen_yaml_tree(phase)
-                    mydict[x.gen_build_name(phase)] = d
-
-    # this is the circleci api version and probably never changes
-    jobs_dict["version"] = 2
-    jobs_dict["jobs"] = mydict
-
-    graph = visualization.generate_graph(get_root())
-    graph.draw("pytorch-config-dimensions.png", prog="twopi")
-
-
-def get_workflow_list():
-
-    config_list = instantiate_configs()
-
-    x = ["setup"]
-    for conf_options in config_list:
-
-        phases = conf_options.restrict_phases or dimensions.PHASES
-
-        for phase in phases:
-
-            # TODO why does this not have a test?
-            if phase == "test" and conf_options.cuda_version == "10":
-                continue
-
-            x.append(conf_options.gen_workflow_yaml_item(phase))
-
-        # TODO convert to recursion
-        for conf in conf_options.get_dependents():
-            x.append(conf.gen_workflow_yaml_item("test"))
-
-    return x

.circleci/cimodel/lib/conf_tree.py

@@ -1,110 +0,0 @@
-#!/usr/bin/env python3
-
-
-from dataclasses import dataclass, field
-from typing import Optional, Dict
-
-
-def X(val):
-    """
-    Compact way to write a leaf node
-    """
-    return val, []
-
-
-def XImportant(name):
-    """Compact way to write an important (run on PRs) leaf node"""
-    return (name, [("important", [X(True)])])
-
-
-@dataclass
-class Ver:
-    """
-    Represents a product with a version number
-    """
-    name: str
-    version: str = ""
-
-    def __str__(self):
-        return self.name + self.version
-
-
-@dataclass
-class ConfigNode:
-    parent: Optional['ConfigNode']
-    node_name: str
-    props: Dict[str, str] = field(default_factory=dict)
-
-    def get_label(self):
-        return self.node_name
-
-    # noinspection PyMethodMayBeStatic
-    def get_children(self):
-        return []
-
-    def get_parents(self):
-        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []
-
-    def get_depth(self):
-        return len(self.get_parents())
-
-    def get_node_key(self):
-        return "%".join(self.get_parents() + [self.get_label()])
-
-    def find_prop(self, propname, searched=None):
-        """
-        Checks if its own dictionary has
-        the property, otherwise asks parent node.
-        """
-
-        if searched is None:
-            searched = []
-
-        searched.append(self.node_name)
-
-        if propname in self.props:
-            return self.props[propname]
-        elif self.parent:
-            return self.parent.find_prop(propname, searched)
-        else:
-            # raise Exception('Property "%s" does not exist anywhere in the tree! Searched: %s' % (propname, searched))
-            return None
-
-
-def dfs_recurse(
-        node,
-        leaf_callback=lambda x: None,
-        discovery_callback=lambda x, y, z: None,
-        child_callback=lambda x, y: None,
-        sibling_index=0,
-        sibling_count=1):
-
-    discovery_callback(node, sibling_index, sibling_count)
-
-    node_children = node.get_children()
-    if node_children:
-        for i, child in enumerate(node_children):
-            child_callback(node, child)
-
-            dfs_recurse(
-                child,
-                leaf_callback,
-                discovery_callback,
-                child_callback,
-                i,
-                len(node_children),
-            )
-    else:
-        leaf_callback(node)
-
-
-def dfs(toplevel_config_node):
-
-    config_list = []
-
-    def leaf_callback(node):
-        config_list.append(node)
-
-    dfs_recurse(toplevel_config_node, leaf_callback)
-
-    return config_list

.circleci/cimodel/lib/miniutils.py

@@ -1,13 +0,0 @@
-#!/usr/bin/env python3
-
-
-def quote(s):
-    return sandwich('"', s)
-
-
-def sandwich(bread, jam):
-    return bread + jam + bread
-
-
-def override(word, substitutions):
-    return substitutions.get(word, word)

.circleci/cimodel/lib/miniyaml.py

@@ -1,64 +0,0 @@
-#!/usr/bin/env python3
-
-
-from collections import OrderedDict
-
-
-LIST_MARKER = "- "
-INDENTATION_WIDTH = 2
-
-
-def is_dict(data):
-    return type(data) is dict or type(data) is OrderedDict
-
-
-def is_collection(data):
-    return is_dict(data) or type(data) is list
-
-
-# TODO can eventually drop this custom sorting
-def sortkey(x):
-    k = x[0]
-    return (
-        k == "<<",
-        k != "environment",
-        k,
-    )
-
-
-def render(fh, data, depth, is_list_member=False):
-    """
-    PyYaml does not allow precise control over the quoting
-    behavior, especially for merge references.
-    Therefore, we use this custom YAML renderer.
-    """
-
-    indentation = " " * INDENTATION_WIDTH * depth
-
-    if is_dict(data):
-
-        tuples = list(data.items())
-        if type(data) is not OrderedDict:
-            tuples.sort(key=sortkey)
-
-        for i, (k, v) in enumerate(tuples):
-
-            # If this dict is itself a list member, the first key gets prefixed with a list marker
-            list_marker_prefix = LIST_MARKER if is_list_member and not i else ""
-
-            trailing_whitespace = "\n" if is_collection(v) else " "
-            fh.write(indentation + list_marker_prefix + k + ":" + trailing_whitespace)
-
-            render(fh, v, depth + 1 + int(is_list_member))
-
-        # TODO Could eventually drop this cosmetic convention
-        if depth == 2:
-            fh.write("\n")
-
-    elif type(data) is list:
-        for v in data:
-            render(fh, v, depth, True)
-
-    else:
-        list_member_prefix = indentation + LIST_MARKER if is_list_member else ""
-        fh.write(list_member_prefix + str(data) + "\n")

.circleci/cimodel/lib/visualization.py

@@ -1,86 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-This module encapsulates dependencies on pygraphviz
-"""
-
-import colorsys
-
-import cimodel.lib.conf_tree as conf_tree
-
-
-def rgb2hex(rgb_tuple):
-    def to_hex(f):
-        return "%02x" % int(f * 255)
-
-    return "#" + "".join(map(to_hex, list(rgb_tuple)))
-
-
-def handle_missing_graphviz(f):
-    """
-    If the user has not installed pygraphviz, this causes
-    calls to the draw() method of the returned object to do nothing.
-    """
-    try:
-        import pygraphviz  # noqa: F401
-        return f
-
-    except ModuleNotFoundError:
-
-        class FakeGraph:
-            def draw(self, *args, **kwargs):
-                pass
-
-        return lambda _: FakeGraph()
-
-
-@handle_missing_graphviz
-def generate_graph(toplevel_config_node):
-    """
-    Traverses the graph once first just to find the max depth
-    """
-
-    config_list = conf_tree.dfs(toplevel_config_node)
-
-    max_depth = 0
-    for config in config_list:
-        max_depth = max(max_depth, config.get_depth())
-
-    # color the nodes using the max depth
-
-    from pygraphviz import AGraph
-    dot = AGraph()
-
-    def node_discovery_callback(node, sibling_index, sibling_count):
-        depth = node.get_depth()
-
-        sat_min, sat_max = 0.1, 0.6
-        sat_range = sat_max - sat_min
-
-        saturation_fraction = sibling_index / float(sibling_count - 1) if sibling_count > 1 else 1
-        saturation = sat_min + sat_range * saturation_fraction
-
-        # TODO Use a hash of the node label to determine the color
-        hue = depth / float(max_depth + 1)
-
-        rgb_tuple = colorsys.hsv_to_rgb(hue, saturation, 1)
-
-        this_node_key = node.get_node_key()
-
-        dot.add_node(
-            this_node_key,
-            label=node.get_label(),
-            style="filled",
-            # fillcolor=hex_color + ":orange",
-            fillcolor=rgb2hex(rgb_tuple),
-            penwidth=3,
-            color=rgb2hex(colorsys.hsv_to_rgb(hue, saturation, 0.9))
-        )
-
-    def child_callback(node, child):
-        this_node_key = node.get_node_key()
-        child_node_key = child.get_node_key()
-        dot.add_edge((this_node_key, child_node_key))
-
-    conf_tree.dfs_recurse(toplevel_config_node, lambda x: None, node_discovery_callback, child_callback)
-    return dot

.circleci/ensure-consistency.py

@@ -1,39 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import subprocess
-import sys
-import tempfile
-
-import generate_config_yml
-
-
-CHECKED_IN_FILE = "config.yml"
-REGENERATION_SCRIPT = "regenerate.sh"
-
-PARENT_DIR = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
-README_PATH = os.path.join(PARENT_DIR, "README.md")
-
-ERROR_MESSAGE_TEMPLATE = """
-The checked-in CircleCI "%s" file does not match what was generated by the scripts.
-Please re-run the "%s" script in the "%s" directory and commit the result. See "%s" for more information.
-"""
-
-
-def check_consistency():
-
-    _, temp_filename = tempfile.mkstemp("-generated-config.yml")
-
-    with open(temp_filename, "w") as fh:
-        generate_config_yml.stitch_sources(fh)
-
-    try:
-        subprocess.check_call(["cmp", temp_filename, CHECKED_IN_FILE])
-    except subprocess.CalledProcessError:
-        sys.exit(ERROR_MESSAGE_TEMPLATE % (CHECKED_IN_FILE, REGENERATION_SCRIPT, PARENT_DIR, README_PATH))
-    finally:
-        os.remove(temp_filename)
-
-
-if __name__ == "__main__":
-    check_consistency()

.circleci/generate_config_yml.py

@@ -1,127 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-This script is the source of truth for config.yml.
-Please see README.md in this directory for details.
-"""
-
-import os
-import sys
-import shutil
-from collections import namedtuple, OrderedDict
-
-import cimodel.data.pytorch_build_definitions as pytorch_build_definitions
-import cimodel.data.binary_build_definitions as binary_build_definitions
-import cimodel.data.caffe2_build_definitions as caffe2_build_definitions
-import cimodel.lib.miniutils as miniutils
-import cimodel.lib.miniyaml as miniyaml
-
-
-class File(object):
-    """
-    Verbatim copy the contents of a file into config.yml
-    """
-    def __init__(self, filename):
-        self.filename = filename
-
-    def write(self, output_filehandle):
-        with open(os.path.join("verbatim-sources", self.filename)) as fh:
-            shutil.copyfileobj(fh, output_filehandle)
-
-
-class FunctionGen(namedtuple('FunctionGen', 'function depth')):
-    __slots__ = ()
-
-
-class Treegen(FunctionGen):
-    """
-    Insert the content of a YAML tree into config.yml
-    """
-
-    def write(self, output_filehandle):
-        build_dict = OrderedDict()
-        self.function(build_dict)
-        miniyaml.render(output_filehandle, build_dict, self.depth)
-
-
-class Listgen(FunctionGen):
-    """
-    Insert the content of a YAML list into config.yml
-    """
-    def write(self, output_filehandle):
-        miniyaml.render(output_filehandle, self.function(), self.depth)
-
-
-def horizontal_rule():
-    return "".join("#" * 78)
-
-
-class Header(object):
-
-    def __init__(self, title, summary=None):
-        self.title = title
-        self.summary_lines = summary or []
-
-    def write(self, output_filehandle):
-        text_lines = [self.title] + self.summary_lines
-        comment_lines = ["# " + x for x in text_lines]
-        lines = miniutils.sandwich([horizontal_rule()], comment_lines)
-
-        for line in filter(None, lines):
-            output_filehandle.write(line + "\n")
-
-
-# Order of this list matters to the generated config.yml.
-YAML_SOURCES = [
-    File("header-section.yml"),
-    File("linux-build-defaults.yml"),
-    File("macos-build-defaults.yml"),
-    File("nightly-binary-build-defaults.yml"),
-    File("linux-binary-build-defaults.yml"),
-    File("macos-binary-build-defaults.yml"),
-    File("nightly-build-smoke-tests-defaults.yml"),
-    Header("Job specifications job specs"),
-    Treegen(pytorch_build_definitions.add_build_env_defs, 0),
-    File("job-specs-setup.yml"),
-    File("job-specs-custom.yml"),
-    Treegen(caffe2_build_definitions.add_caffe2_builds, 1),
-    File("binary_update_htmls.yml"),
-    Header("Binary build specs individual job specifications"),
-    Treegen(binary_build_definitions.add_binary_build_specs, 1),
-    Header(
-        "Binary build tests", [
-            "These are the smoke tests run right after the build, before the upload.",
-            "If these fail, the upload doesn't happen."
-        ]
-    ),
-    Treegen(binary_build_definitions.add_binary_build_tests, 1),
-    File("binary-build-tests.yml"),
-    Header("Binary build uploads"),
-    Treegen(binary_build_definitions.add_binary_build_uploads, 1),
-    Header("Smoke test specs individual job specifications"),
-    Treegen(binary_build_definitions.add_smoke_test_specs, 1),
-    File("workflows.yml"),
-    Listgen(pytorch_build_definitions.get_workflow_list, 3),
-    File("workflows-pytorch-macos-builds.yml"),
-    Listgen(caffe2_build_definitions.get_caffe2_workflows, 3),
-    File("workflows-binary-builds-smoke-subset.yml"),
-    Header("Daily smoke test trigger"),
-    Treegen(binary_build_definitions.add_binary_smoke_test_jobs, 1),
-    Header("Daily binary build trigger"),
-    Treegen(binary_build_definitions.add_binary_build_jobs, 1),
-    Header("Nightly tests"),
-    Listgen(binary_build_definitions.get_nightly_tests, 3),
-    File("workflows-nightly-uploads-header.yml"),
-    Listgen(binary_build_definitions.get_nightly_uploads, 3),
-    File("workflows-s3-html.yml"),
-]
-
-
-def stitch_sources(output_filehandle):
-    for f in YAML_SOURCES:
-        f.write(output_filehandle)
-
-
-if __name__ == "__main__":
-
-    stitch_sources(sys.stdout)

.circleci/regenerate.sh

@@ -1,8 +0,0 @@
-#!/bin/bash -xe
-
-# Allows this script to be invoked from any directory:
-cd $(dirname "$0")
-
-NEW_FILE=$(mktemp)
-./generate_config_yml.py > $NEW_FILE
-cp $NEW_FILE config.yml

.circleci/scripts/README.md

@@ -1,4 +0,0 @@
-All the scripts in this directory are callable from `~/workspace/.circleci/scripts/foo.sh`.
-Don't try to call them as `.circleci/scripts/foo.sh`, that won't
-(necessarily) work.  See Note [Workspace for CircleCI scripts] in
-job-specs-setup.yml for more details.

.circleci/scripts/binary_checkout.sh

@@ -1,46 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-# This step runs on multiple executors with different envfile locations
-if [[ "$(uname)" == Darwin ]]; then
-  # macos executor (builds and tests)
-  workdir="/Users/distiller/project"
-elif [[ -d "/home/circleci/project" ]]; then
-  # machine executor (binary tests)
-  workdir="/home/circleci/project"
-else
-  # docker executor (binary builds)
-  workdir="/"
-fi
-
-# It is very important that this stays in sync with binary_populate_env.sh
-export PYTORCH_ROOT="$workdir/pytorch"
-export BUILDER_ROOT="$workdir/builder"
-
-# Clone the Pytorch branch
-git clone https://github.com/pytorch/pytorch.git "$PYTORCH_ROOT"
-pushd "$PYTORCH_ROOT"
-if [[ -n "${CIRCLE_PR_NUMBER:-}" ]]; then
-  # "smoke" binary build on PRs
-  git fetch --force origin "pull/${CIRCLE_PR_NUMBER}/head:remotes/origin/pull/${CIRCLE_PR_NUMBER}"
-  git reset --hard "$CIRCLE_SHA1"
-  git checkout -q -B "$CIRCLE_BRANCH"
-  git reset --hard "$CIRCLE_SHA1"
-elif [[ -n "${CIRCLE_SHA1:-}" ]]; then
-  # Scheduled workflows & "smoke" binary build on master on PR merges
-  git reset --hard "$CIRCLE_SHA1"
-  git checkout -q -B master
-else
-  echo "Can't tell what to checkout"
-  exit 1
-fi
-git submodule update --init --recursive --quiet
-echo "Using Pytorch from "
-git --no-pager log --max-count 1
-popd
-
-# Clone the Builder master repo
-git clone -q https://github.com/pytorch/builder.git "$BUILDER_ROOT"
-pushd "$BUILDER_ROOT"
-echo "Using builder from "
-git --no-pager log --max-count 1
-popd

.circleci/scripts/binary_install_miniconda.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-set -eux -o pipefail
-
-# This step runs on multiple executors with different envfile locations
-if [[ "$(uname)" == Darwin ]]; then
-  envfile="/Users/distiller/project/env"
-elif [[ -d "/home/circleci/project" ]]; then
-  # machine executor (binary tests)
-  envfile="/home/circleci/project/env"
-else
-  # docker executor (binary builds)
-  envfile="/env"
-fi
-
-# TODO this is super hacky and ugly. Basically, the binary_update_html job does
-# not have an env file, since it does not call binary_populate_env.sh, since it
-# does not have a BUILD_ENVIRONMENT. So for this one case, which we detect by a
-# lack of an env file, we manually export the environment variables that we
-# need to install miniconda
-if [[ ! -f "$envfile" ]]; then
-  MINICONDA_ROOT="/home/circleci/project/miniconda"
-  workdir="/home/circleci/project"
-  retry () {
-      $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-  }
-  export -f retry
-else
-  source "$envfile"
-fi
-
-conda_sh="$workdir/install_miniconda.sh"
-if [[ "$(uname)" == Darwin ]]; then
-  retry curl -o "$conda_sh" https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-else
-  retry curl -o "$conda_sh" https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
-fi
-chmod +x "$conda_sh"
-"$conda_sh" -b -p "$MINICONDA_ROOT"
-rm -f "$conda_sh"
-
-# We can't actually add miniconda to the PATH in the envfile, because that
-# breaks 'unbuffer' in Mac jobs. This is probably because conda comes with
-# a tclsh, which then gets inserted before the tclsh needed in /usr/bin

.circleci/scripts/binary_linux_build.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-
-echo "RUNNING ON $(uname -a) WITH $(nproc) CPUS AND $(free -m)"
-set -eux -o pipefail
-source /env
-
-# Defaults here so they can be changed in one place
-export MAX_JOBS=12
-
-# Parse the parameters
-if [[ "$PACKAGE_TYPE" == 'conda' ]]; then
-  build_script='conda/build_pytorch.sh'
-elif [[ "$DESIRED_CUDA" == cpu ]]; then
-  build_script='manywheel/build_cpu.sh'
-else
-  build_script='manywheel/build.sh'
-fi
-
-# We want to call unbuffer, which calls tclsh which finds the expect
-# package. The expect was installed by yum into /usr/bin so we want to
-# find /usr/bin/tclsh, but this is shadowed by /opt/conda/bin/tclsh in
-# the conda docker images, so we prepend it to the path here.
-if [[ "$PACKAGE_TYPE" == 'conda' ]]; then
-  mkdir /just_tclsh_bin
-  ln -s /usr/bin/tclsh /just_tclsh_bin/tclsh
-  export PATH=/just_tclsh_bin:$PATH
-fi
-
-# Build the package
-SKIP_ALL_TESTS=1 unbuffer "/builder/$build_script" | ts

.circleci/scripts/binary_linux_test.sh

@@ -1,53 +0,0 @@
-#!/bin/bash
-
-source /home/circleci/project/env
-cat >/home/circleci/project/ci_test_script.sh <<EOL
-# =================== The following code will be executed inside Docker container ===================
-set -eux -o pipefail
-
-# Set up Python
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  retry conda create -qyn testenv python="$DESIRED_PYTHON"
-  source activate testenv >/dev/null
-elif [[ "$DESIRED_PYTHON" == 2.7mu ]]; then
-  export PATH="/opt/python/cp27-cp27mu/bin:\$PATH"
-else
-  python_nodot="\$(echo $DESIRED_PYTHON | tr -d m.u)"
-  export PATH="/opt/python/cp\$python_nodot-cp\${python_nodot}m/bin:\$PATH"
-fi
-
-# Install the package
-# These network calls should not have 'retry's because they are installing
-# locally and aren't actually network calls
-# TODO there is duplicated and inconsistent test-python-env setup across this
-#   file, builder/smoke_test.sh, and builder/run_tests.sh, and also in the
-#   conda build scripts themselves. These should really be consolidated
-pkg="/final_pkgs/\$(ls /final_pkgs)"
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  conda install -y "\$pkg" --offline
-  if [[ "$DESIRED_CUDA" == 'cpu' ]]; then
-    conda install -y cpuonly -c pytorch
-  fi
-  retry conda install -yq future numpy protobuf six
-  if [[ "$DESIRED_CUDA" != 'cpu' ]]; then
-    # DESIRED_CUDA is in format cu90 or cu100
-    if [[ "${#DESIRED_CUDA}" == 4 ]]; then
-      cu_ver="${DESIRED_CUDA:2:1}.${DESIRED_CUDA:3}"
-    else
-      cu_ver="${DESIRED_CUDA:2:2}.${DESIRED_CUDA:4}"
-    fi
-    retry conda install -yq -c pytorch "cudatoolkit=\${cu_ver}"
-  fi
-else
-  pip install "\$pkg"
-  retry pip install -q future numpy protobuf six
-fi
-
-# Test the package
-/builder/check_binary.sh
-# =================== The above code will be executed inside Docker container ===================
-EOL
-echo
-echo
-echo "The script that will run in the next step is:"
-cat /home/circleci/project/ci_test_script.sh

.circleci/scripts/binary_linux_upload.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-# Do NOT set -x
-source /home/circleci/project/env
-set -eu -o pipefail
-set +x
-declare -x "AWS_ACCESS_KEY_ID=${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}"
-declare -x "AWS_SECRET_ACCESS_KEY=${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}"
-cat >/home/circleci/project/login_to_anaconda.sh <<EOL
-set +x
-echo "Trying to login to Anaconda"
-yes | anaconda login \
-    --username "$PYTORCH_BINARY_PJH5_CONDA_USERNAME" \
-    --password "$PYTORCH_BINARY_PJH5_CONDA_PASSWORD"
-set -x
-EOL
-chmod +x /home/circleci/project/login_to_anaconda.sh
-
-#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!
-# DO NOT TURN -x ON BEFORE THIS LINE
-#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!
-set -eux -o pipefail
-export PATH="$MINICONDA_ROOT/bin:$PATH"
-
-# Upload the package to the final location
-pushd /home/circleci/project/final_pkgs
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  retry conda install -yq anaconda-client
-  retry timeout 30 /home/circleci/project/login_to_anaconda.sh
-  anaconda upload "$(ls)" -u pytorch-nightly --label main --no-progress --force
-elif [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  retry pip install -q awscli
-  s3_dir="s3://pytorch/libtorch/${PIP_UPLOAD_FOLDER}${DESIRED_CUDA}/"
-  for pkg in $(ls); do
-    retry aws s3 cp "$pkg" "$s3_dir" --acl public-read
-  done
-else
-  retry pip install -q awscli
-  s3_dir="s3://pytorch/whl/${PIP_UPLOAD_FOLDER}${DESIRED_CUDA}/"
-  retry aws s3 cp "$(ls)" "$s3_dir" --acl public-read
-fi

.circleci/scripts/binary_macos_build.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-source "/Users/distiller/project/env"
-mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR"
-
-# For some reason `unbuffer` breaks if we change the PATH here, so we
-# write a script with the PATH change in it and unbuffer the whole
-# thing
-build_script="$workdir/build_script.sh"
-touch "$build_script"
-chmod +x "$build_script"
-
-# Build
-cat >"$build_script" <<EOL
-export PATH="$workdir/miniconda/bin:$PATH"
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  "$workdir/builder/conda/build_pytorch.sh"
-else
-  export TORCH_PACKAGE_NAME="$(echo $TORCH_PACKAGE_NAME | tr '-' '_')"
-  "$workdir/builder/wheel/build_wheel.sh"
-fi
-EOL
-unbuffer "$build_script" | ts

.circleci/scripts/binary_macos_test.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-source "/Users/distiller/project/env"
-export "PATH=$workdir/miniconda/bin:$PATH"
-pkg="$workdir/final_pkgs/$(ls $workdir/final_pkgs)"
-
-# Don't test libtorch
-# TODO we should test libtorch
-if [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  exit 0
-fi
-
-# Create a new test env
-# TODO cut all this out into a separate test job and have an entirely different
-# miniconda
-source deactivate || true
-conda create -qyn test python="$DESIRED_PYTHON"
-source activate test >/dev/null
-
-# Install the package
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  conda install -y "$pkg" --offline
-else
-  pip install "$pkg" --no-index --no-dependencies -v
-fi
-
-# Test
-pushd "$workdir/pytorch"
-$workdir/builder/run_tests.sh "$PACKAGE_TYPE" "$DESIRED_PYTHON" "$DESIRED_CUDA"

.circleci/scripts/binary_macos_upload.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-# Do NOT set -x
-set -eu -o pipefail
-set +x
-export AWS_ACCESS_KEY_ID="${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}"
-export AWS_SECRET_ACCESS_KEY="${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}"
-cat >/Users/distiller/project/login_to_anaconda.sh <<EOL
-set +x
-echo "Trying to login to Anaconda"
-yes | anaconda login \
-    --username "$PYTORCH_BINARY_PJH5_CONDA_USERNAME" \
-    --password "$PYTORCH_BINARY_PJH5_CONDA_PASSWORD"
-set -x
-EOL
-chmod +x /Users/distiller/project/login_to_anaconda.sh
-
-#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!
-# DO NOT TURN -x ON BEFORE THIS LINE
-#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!#!
-set -eux -o pipefail
-
-source "/Users/distiller/project/env"
-export "PATH=$workdir/miniconda/bin:$PATH"
-
-pushd "$workdir/final_pkgs"
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  retry conda install -yq anaconda-client
-  retry /Users/distiller/project/login_to_anaconda.sh
-  retry anaconda upload "$(ls)" -u pytorch-nightly --label main --no-progress --force
-elif [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  retry pip install -q awscli
-  s3_dir="s3://pytorch/libtorch/${PIP_UPLOAD_FOLDER}${DESIRED_CUDA}/"
-  for pkg in $(ls); do
-    retry aws s3 cp "$pkg" "$s3_dir" --acl public-read
-  done
-else
-  retry pip install -q awscli
-  s3_dir="s3://pytorch/whl/${PIP_UPLOAD_FOLDER}${DESIRED_CUDA}/"
-  retry aws s3 cp "$(ls)" "$s3_dir" --acl public-read
-fi

.circleci/scripts/binary_populate_env.sh

@@ -1,107 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-export TZ=UTC
-
-# We need to write an envfile to persist these variables to following
-# steps, but the location of the envfile depends on the circleci executor
-if [[ "$(uname)" == Darwin ]]; then
-  # macos executor (builds and tests)
-  workdir="/Users/distiller/project"
-elif [[ -d "/home/circleci/project" ]]; then
-  # machine executor (binary tests)
-  workdir="/home/circleci/project"
-else
-  # docker executor (binary builds)
-  workdir="/"
-fi
-envfile="$workdir/env"
-touch "$envfile"
-chmod +x "$envfile"
-
-# Parse the BUILD_ENVIRONMENT to package type, python, and cuda
-configs=($BUILD_ENVIRONMENT)
-export PACKAGE_TYPE="${configs[0]}"
-export DESIRED_PYTHON="${configs[1]}"
-export DESIRED_CUDA="${configs[2]}"
-export DESIRED_DEVTOOLSET="${configs[3]:-}"
-if [[ "$PACKAGE_TYPE" == 'libtorch' ]]; then
-  export BUILD_PYTHONLESS=1
-fi
-
-# Pick docker image
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  export DOCKER_IMAGE="soumith/conda-cuda"
-elif [[ "$DESIRED_CUDA" == cpu ]]; then
-  export DOCKER_IMAGE="soumith/manylinux-cuda100"
-else
-  export DOCKER_IMAGE="soumith/manylinux-cuda${DESIRED_CUDA:2}"
-fi
-
-# Upload to parallel folder for gcc abis
-# All nightlies used to be devtoolset3, then devtoolset7 was added as a build
-# option, so the upload was redirected to nightly/devtoolset7 to avoid
-# conflicts with other binaries (there shouldn't be any conflicts). Now we are
-# making devtoolset7 the default.
-if [[ "$DESIRED_DEVTOOLSET" == 'devtoolset7' || "$(uname)" == 'Darwin' ]]; then
-  export PIP_UPLOAD_FOLDER='nightly/'
-else
-  # On linux machines, this shouldn't actually be called anymore. This is just
-  # here for extra safety.
-  export PIP_UPLOAD_FOLDER='nightly/devtoolset3/'
-fi
-
-# We put this here so that OVERRIDE_PACKAGE_VERSION below can read from it
-export DATE="$(date -u +%Y%m%d)"
-if [[ "$(uname)" == 'Darwin' ]] || [[ "$DESIRED_CUDA" == "cu100" ]]; then
-  export PYTORCH_BUILD_VERSION="1.3.0.dev$DATE"
-else
-  export PYTORCH_BUILD_VERSION="1.3.0.dev$DATE+$DESIRED_CUDA"
-fi
-export PYTORCH_BUILD_NUMBER=1
-
-cat >>"$envfile" <<EOL
-# =================== The following code will be executed inside Docker container ===================
-export TZ=UTC
-echo "Running on $(uname -a) at $(date)"
-
-export PACKAGE_TYPE="$PACKAGE_TYPE"
-export DESIRED_PYTHON="$DESIRED_PYTHON"
-export DESIRED_CUDA="$DESIRED_CUDA"
-export LIBTORCH_VARIANT="${LIBTORCH_VARIANT:-}"
-export BUILD_PYTHONLESS="${BUILD_PYTHONLESS:-}"
-export DESIRED_DEVTOOLSET="$DESIRED_DEVTOOLSET"
-
-export DATE="$DATE"
-export NIGHTLIES_DATE_PREAMBLE=1.3.0.dev
-export PYTORCH_BUILD_VERSION="$PYTORCH_BUILD_VERSION"
-export PYTORCH_BUILD_NUMBER="$PYTORCH_BUILD_NUMBER"
-export OVERRIDE_PACKAGE_VERSION="$PYTORCH_BUILD_VERSION"
-
-# TODO: We don't need this anymore IIUC
-export TORCH_PACKAGE_NAME='torch'
-export TORCH_CONDA_BUILD_FOLDER='pytorch-nightly'
-
-export USE_FBGEMM=1
-export PIP_UPLOAD_FOLDER="$PIP_UPLOAD_FOLDER"
-export DOCKER_IMAGE="$DOCKER_IMAGE"
-
-export workdir="$workdir"
-export MAC_PACKAGE_WORK_DIR="$workdir"
-export PYTORCH_ROOT="$workdir/pytorch"
-export BUILDER_ROOT="$workdir/builder"
-export MINICONDA_ROOT="$workdir/miniconda"
-export PYTORCH_FINAL_PACKAGE_DIR="$workdir/final_pkgs"
-
-export CIRCLE_TAG="${CIRCLE_TAG:-}"
-export CIRCLE_SHA1="$CIRCLE_SHA1"
-export CIRCLE_PR_NUMBER="${CIRCLE_PR_NUMBER:-}"
-export CIRCLE_BRANCH="$CIRCLE_BRANCH"
-# =================== The above code will be executed inside Docker container ===================
-EOL
-
-echo 'retry () {' >> "$envfile"
-echo '    $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)' >> "$envfile"
-echo '}' >> "$envfile"
-echo 'export -f retry' >> "$envfile"
-
-cat "$envfile"

.circleci/scripts/binary_run_in_docker.sh

@@ -1,48 +0,0 @@
-#!/bin/bash
-
-# This section is used in the binary_test and smoke_test jobs. It expects
-# 'binary_populate_env' to have populated /home/circleci/project/env and it
-# expects another section to populate /home/circleci/project/ci_test_script.sh
-# with the code to run in the docker
-
-# Expect all needed environment variables to be written to this file
-source /home/circleci/project/env
-echo "Running the following code in Docker"
-cat /home/circleci/project/ci_test_script.sh
-echo
-echo
-set -eux -o pipefail
-
-# Expect actual code to be written to this file
-chmod +x /home/circleci/project/ci_test_script.sh
-
-# Run the docker
-if [ -n "${USE_CUDA_DOCKER_RUNTIME:-}" ]; then
-  export id=$(docker run --runtime=nvidia -t -d "${DOCKER_IMAGE}")
-else
-  export id=$(docker run -t -d "${DOCKER_IMAGE}")
-fi
-
-# Copy the envfile and script with all the code to run into the docker.
-docker cp /home/circleci/project/. "$id:/circleci_stuff"
-
-# Copy built packages into the docker to test. This should only exist on the
-# binary test jobs. The package should've been created from a binary build job,
-# whhich persisted the package to a CircleCI workspace, which this job then
-# copies into a GPU enabled docker for testing
-if [[ -d "/home/circleci/project/final_pkgs" ]]; then
-  docker cp /home/circleci/project/final_pkgs "$id:/final_pkgs"
-fi
-
-# Copy the needed repos into the docker. These do not exist in the smoke test
-# jobs, since the smoke test jobs do not need the Pytorch source code.
-if [[ -d "$PYTORCH_ROOT" ]]; then
-  docker cp "$PYTORCH_ROOT" "$id:/pytorch"
-fi
-if [[ -d "$BUILDER_ROOT" ]]; then
-  docker cp "$BUILDER_ROOT" "$id:/builder"
-fi
-
-# Execute the test script that was populated by an earlier section
-export COMMAND='((echo "source /circleci_stuff/env && /circleci_stuff/ci_test_script.sh") | docker exec -i "$id" bash) 2>&1'
-echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts

.circleci/scripts/cpp_doc_push_script.sh

@@ -1,127 +0,0 @@
-# =================== The following code **should** be executed inside Docker container ===================
-
-# Install dependencies
-sudo apt-get -y update
-sudo apt-get -y install expect-dev
-
-# This is where the local pytorch install in the docker image is located
-pt_checkout="/var/lib/jenkins/workspace"
-
-# Since we're cat-ing this file, we need to escape all $'s
-echo "cpp_doc_push_script.sh: Invoked with $*"
-
-# Argument 1: Where to copy the built documentation for Python API to
-# (pytorch.github.io/$install_path)
-install_path="$1"
-if [ -z "$install_path" ]; then
-echo "error: cpp_doc_push_script.sh: install_path (arg1) not specified"
-  exit 1
-fi
-
-# Argument 2: What version of the Python API docs we are building.
-version="$2"
-if [ -z "$version" ]; then
-echo "error: cpp_doc_push_script.sh: version (arg2) not specified"
-  exit 1
-fi
-
-is_master_doc=false
-if [ "$version" == "master" ]; then
-  is_master_doc=true
-fi
-
-# Argument 3: (optional) If present, we will NOT do any pushing. Used for testing.
-dry_run=false
-if [ "$3" != "" ]; then
-  dry_run=true
-fi
-
-echo "install_path: $install_path  version: $version  dry_run: $dry_run"
-
-# ======================== Building PyTorch C++ API Docs ========================
-
-echo "Building PyTorch C++ API docs..."
-
-# Clone the cppdocs repo
-rm -rf cppdocs
-git clone https://github.com/pytorch/cppdocs
-
-set -ex
-
-sudo apt-get -y install doxygen
-
-# Generate ATen files
-pushd "${pt_checkout}"
-pip install -r requirements.txt
-time GEN_TO_SOURCE=1 python aten/src/ATen/gen.py \
-  -s aten/src/ATen \
-  -d build/aten/src/ATen \
-  aten/src/ATen/Declarations.cwrap \
-  aten/src/THNN/generic/THNN.h \
-  aten/src/THCUNN/generic/THCUNN.h \
-  aten/src/ATen/nn.yaml \
-  aten/src/ATen/native/native_functions.yaml
-
-# Copy some required files
-cp aten/src/ATen/common_with_cwrap.py tools/shared/cwrap_common.py
-cp torch/_utils_internal.py tools/shared
-
-# Generate PyTorch files
-time python tools/setup_helpers/generate_code.py \
-  --declarations-path build/aten/src/ATen/Declarations.yaml \
-  --nn-path aten/src/
-
-# Build the docs
-pushd docs/cpp
-pip install breathe==4.11.1 bs4 lxml six
-pip install --no-cache-dir -e "git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme"
-pip install exhale>=0.2.1
-pip install sphinx==1.8.5
-# Uncomment once it is fixed
-# pip install -r requirements.txt
-time make VERBOSE=1 html -j
-
-popd
-popd
-
-pushd cppdocs
-
-# Purge everything with some exceptions
-mkdir /tmp/cppdocs-sync
-mv _config.yml README.md /tmp/cppdocs-sync/
-rm -rf *
-
-# Copy over all the newly generated HTML
-cp -r "${pt_checkout}"/docs/cpp/build/html/* .
-
-# Copy back _config.yml
-rm -rf _config.yml
-mv /tmp/cppdocs-sync/* .
-
-# Make a new commit
-git add . || true
-git status
-git config user.email "soumith+bot@pytorch.org"
-git config user.name "pytorchbot"
-# If there aren't changes, don't make a commit; push is no-op
-git commit -m "Automatic sync on $(date)" || true
-git status
-
-if [ "$dry_run" = false ]; then
-  echo "Pushing to https://github.com/pytorch/cppdocs"
-  set +x
-/usr/bin/expect <<DONE
-  spawn git push -u origin master
-  expect "Username*"
-  send "pytorchbot\n"
-  expect "Password*"
-  send "$::env(GITHUB_PYTORCHBOT_TOKEN)\n"
-  expect eof
-DONE
-  set -x
-else
-  echo "Skipping push due to dry_run"
-fi
-
-popd
-# =================== The above code **should** be executed inside Docker container ===================

.circleci/scripts/python_doc_push_script.sh

@@ -1,118 +0,0 @@
-# =================== The following code **should** be executed inside Docker container ===================
-
-# Install dependencies
-sudo apt-get -y update
-sudo apt-get -y install expect-dev
-
-# This is where the local pytorch install in the docker image is located
-pt_checkout="/var/lib/jenkins/workspace"
-
-echo "python_doc_push_script.sh: Invoked with $*"
-
-set -ex
-
-# Argument 1: Where to copy the built documentation to
-# (pytorch.github.io/$install_path)
-install_path="$1"
-if [ -z "$install_path" ]; then
-echo "error: python_doc_push_script.sh: install_path (arg1) not specified"
-  exit 1
-fi
-
-# Argument 2: What version of the docs we are building.
-version="$2"
-if [ -z "$version" ]; then
-echo "error: python_doc_push_script.sh: version (arg2) not specified"
-  exit 1
-fi
-
-is_master_doc=false
-if [ "$version" == "master" ]; then
-  is_master_doc=true
-fi
-
-# Argument 3: The branch to push to. Usually is "site"
-branch="$3"
-if [ -z "$branch" ]; then
-echo "error: python_doc_push_script.sh: branch (arg3) not specified"
-  exit 1
-fi
-
-# Argument 4: (optional) If present, we will NOT do any pushing. Used for testing.
-dry_run=false
-if [ "$4" != "" ]; then
-  dry_run=true
-fi
-
-echo "install_path: $install_path  version: $version  dry_run: $dry_run"
-
-git clone https://github.com/pytorch/pytorch.github.io -b $branch
-pushd pytorch.github.io
-
-export LC_ALL=C
-export PATH=/opt/conda/bin:$PATH
-
-rm -rf pytorch || true
-
-# Install TensorBoard in python 3 so torch.utils.tensorboard classes render
-pip install -q https://s3.amazonaws.com/ossci-linux/wheels/tensorboard-1.14.0a0-py3-none-any.whl
-
-# Get all the documentation sources, put them in one place
-pushd "$pt_checkout"
-git clone https://github.com/pytorch/vision
-pushd vision
-conda install -q pillow
-time python setup.py install
-popd
-pushd docs
-rm -rf source/torchvision
-cp -a ../vision/docs/source source/torchvision
-
-# Build the docs
-pip -q install -r requirements.txt || true
-if [ "$is_master_doc" = true ]; then
-  make html
-else
-  make html-stable
-fi
-
-# Move them into the docs repo
-popd
-popd
-git rm -rf "$install_path" || true
-mv "$pt_checkout/docs/build/html" "$install_path"
-
-# Add the version handler by search and replace.
-# XXX: Consider moving this to the docs Makefile or site build
-if [ "$is_master_doc" = true ]; then
-  find "$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.[A-Fa-f0-9]+\+[A-Fa-f0-9]+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>\1 \&#x25BC</a>@g"
-else
-  find "$install_path" -name "*.html" -print0 | xargs -0 perl -pi -w -e "s@master\s+\((\d\.\d\.[A-Fa-f0-9]+\+[A-Fa-f0-9]+)\s+\)@<a href='http://pytorch.org/docs/versions.html'>$version \&#x25BC</a>@g"
-fi
-
-git add "$install_path" || true
-git status
-git config user.email "soumith+bot@pytorch.org"
-git config user.name "pytorchbot"
-# If there aren't changes, don't make a commit; push is no-op
-git commit -m "auto-generating sphinx docs" || true
-git status
-
-if [ "$dry_run" = false ]; then
-  echo "Pushing to pytorch.github.io:$branch"
-  set +x
-/usr/bin/expect <<DONE
-  spawn git push origin $branch
-  expect "Username*"
-  send "pytorchbot\n"
-  expect "Password*"
-  send "$::env(GITHUB_PYTORCHBOT_TOKEN)\n"
-  expect eof
-DONE
-  set -x
-else
-  echo "Skipping push due to dry_run"
-fi
-
-popd
-# =================== The above code **should** be executed inside Docker container ===================

.circleci/scripts/setup_ci_environment.sh

@@ -1,87 +0,0 @@
-#!/usr/bin/env bash
-set -ex -o pipefail
-
-# Set up NVIDIA docker repo
-curl -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
-echo "deb https://nvidia.github.io/libnvidia-container/ubuntu16.04/amd64 /" | sudo tee -a /etc/apt/sources.list.d/nvidia-docker.list
-echo "deb https://nvidia.github.io/nvidia-container-runtime/ubuntu16.04/amd64 /" | sudo tee -a /etc/apt/sources.list.d/nvidia-docker.list
-echo "deb https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64 /" | sudo tee -a /etc/apt/sources.list.d/nvidia-docker.list
-
-# Remove unnecessary sources
-sudo rm -f /etc/apt/sources.list.d/google-chrome.list
-sudo rm -f /etc/apt/heroku.list
-sudo rm -f /etc/apt/openjdk-r-ubuntu-ppa-xenial.list
-sudo rm -f /etc/apt/partner.list
-
-sudo apt-get -y update
-sudo apt-get -y remove linux-image-generic linux-headers-generic linux-generic docker-ce
-# WARNING: Docker version is hardcoded here; you must update the
-# version number below for docker-ce and nvidia-docker2 to get newer
-# versions of Docker.  We hardcode these numbers because we kept
-# getting broken CI when Docker would update their docker version,
-# and nvidia-docker2 would be out of date for a day until they
-# released a newer version of their package.
-#
-# How to figure out what the correct versions of these packages are?
-# My preferred method is to start a Docker instance of the correct
-# Ubuntu version (e.g., docker run -it ubuntu:16.04) and then ask
-# apt what the packages you need are.  Note that the CircleCI image
-# comes with Docker.
-sudo apt-get -y install \
-  linux-headers-$(uname -r) \
-  linux-image-generic \
-  moreutils \
-  docker-ce=5:18.09.4~3-0~ubuntu-xenial \
-  nvidia-container-runtime=2.0.0+docker18.09.4-1 \
-  nvidia-docker2=2.0.3+docker18.09.4-1 \
-  expect-dev
-
-sudo pkill -SIGHUP dockerd
-
-retry () {
-    $*  || $* || $* || $* || $*
-}
-
-retry sudo pip -q install awscli==1.16.35
-
-if [ -n "${USE_CUDA_DOCKER_RUNTIME:-}" ]; then
-  DRIVER_FN="NVIDIA-Linux-x86_64-410.104.run"
-  wget "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"
-  sudo /bin/bash "$DRIVER_FN" -s --no-drm || (sudo cat /var/log/nvidia-installer.log && false)
-  nvidia-smi
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *-build ]]; then
-  echo "declare -x IN_CIRCLECI=1" > /home/circleci/project/env
-  echo "declare -x COMMIT_SOURCE=${CIRCLE_BRANCH:-}" >> /home/circleci/project/env
-  echo "declare -x PYTHON_VERSION=${PYTHON_VERSION:-}" >> /home/circleci/project/env
-  echo "declare -x SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2" >> /home/circleci/project/env
-  if [ -n "${USE_CUDA_DOCKER_RUNTIME:-}" ]; then
-    echo "declare -x TORCH_CUDA_ARCH_LIST=5.2" >> /home/circleci/project/env
-  fi
-  export SCCACHE_MAX_JOBS=`expr $(nproc) - 1`
-  export MEMORY_LIMIT_MAX_JOBS=8  # the "large" resource class on CircleCI has 32 CPU cores, if we use all of them we'll OOM
-  export MAX_JOBS=$(( ${SCCACHE_MAX_JOBS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${SCCACHE_MAX_JOBS} ))
-  echo "declare -x MAX_JOBS=${MAX_JOBS}" >> /home/circleci/project/env
-
-  if [[ "${BUILD_ENVIRONMENT}" == *xla* ]]; then
-    # This IAM user allows write access to S3 bucket for sccache & bazels3cache
-    set +x
-    echo "declare -x AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_SCCACHE_AND_XLA_BAZEL_S3_BUCKET_V2:-}" >> /home/circleci/project/env
-    echo "declare -x AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_SCCACHE_AND_XLA_BAZEL_S3_BUCKET_V2:-}" >> /home/circleci/project/env
-    set -x
-  else
-    # This IAM user allows write access to S3 bucket for sccache
-    set +x
-    echo "declare -x AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_SCCACHE_S3_BUCKET_V4:-}" >> /home/circleci/project/env
-    echo "declare -x AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_SCCACHE_S3_BUCKET_V4:-}" >> /home/circleci/project/env
-    set -x
-  fi
-fi
-
-# This IAM user only allows read-write access to ECR
-set +x
-export AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_ECR_READ_WRITE_V4:-}
-export AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_ECR_READ_WRITE_V4:-}
-eval $(aws ecr get-login --region us-east-1 --no-include-email)
-set -x

.circleci/scripts/setup_linux_system_environment.sh

@@ -1,50 +0,0 @@
-#!/usr/bin/env bash
-set -eux -o pipefail
-
-# Set up CircleCI GPG keys for apt, if needed
-curl -L https://packagecloud.io/circleci/trusty/gpgkey | sudo apt-key add -
-
-# Stop background apt updates.  Hypothetically, the kill should not
-# be necessary, because stop is supposed to send a kill signal to
-# the process, but we've added it for good luck.  Also
-# hypothetically, it's supposed to be unnecessary to wait for
-# the process to block.  We also have that line for good luck.
-# If you like, try deleting them and seeing if it works.
-sudo systemctl stop apt-daily.service || true
-sudo systemctl kill --kill-who=all apt-daily.service || true
-
-sudo systemctl stop unattended-upgrades.service || true
-sudo systemctl kill --kill-who=all unattended-upgrades.service || true
-
-# wait until `apt-get update` has been killed
-while systemctl is-active --quiet apt-daily.service
-do
-    sleep 1;
-done
-while systemctl is-active --quiet unattended-upgrades.service
-do
-    sleep 1;
-done
-
-# See if we actually were successful
-systemctl list-units --all | cat
-
-# For good luck, try even harder to kill apt-get
-sudo pkill apt-get || true
-
-# For even better luck, purge unattended-upgrades
-sudo apt-get purge -y unattended-upgrades
-
-cat /etc/apt/sources.list
-
-# For the bestest luck, kill again now
-sudo pkill apt || true
-sudo pkill dpkg || true
-
-# Try to detect if apt/dpkg is stuck
-if ps auxfww | grep '[a]pt'; then
-  echo "WARNING: There are leftover apt processes; subsequent apt update will likely fail"
-fi
-if ps auxfww | grep '[d]pkg'; then
-  echo "WARNING: There are leftover dpkg processes; subsequent apt update will likely fail"
-fi

.circleci/scripts/should_run_job.py

@@ -1,96 +0,0 @@
-import argparse
-import re
-import sys
-
-# Modify this variable if you want to change the set of default jobs
-# which are run on all pull requests.
-#
-# WARNING: Actually, this is a lie; we're currently also controlling
-# the set of jobs to run via the Workflows filters in CircleCI config.
-
-default_set = [
-    # PyTorch CPU
-    # Selected oldest Python 2 version to ensure Python 2 coverage
-    'pytorch-linux-trusty-py2.7.9',
-    # PyTorch CUDA
-    'pytorch-linux-xenial-cuda9-cudnn7-py3',
-    # PyTorch ASAN
-    'pytorch-linux-xenial-py3-clang5-asan',
-    # PyTorch DEBUG
-    'pytorch-linux-trusty-py3.6-gcc5.4',
-
-    # Caffe2 CPU
-    'caffe2-py2-mkl-ubuntu16.04',
-    # Caffe2 CUDA
-    'caffe2-py2-cuda9.1-cudnn7-ubuntu16.04',
-    # Caffe2 ONNX
-    'caffe2-onnx-py2-gcc5-ubuntu16.04',
-    'caffe2-onnx-py3.6-clang7-ubuntu16.04',
-    # Caffe2 Clang
-    'caffe2-py2-clang7-ubuntu16.04',
-    # Caffe2 CMake
-    'caffe2-cmake-cuda9.0-cudnn7-ubuntu16.04',
-
-    # Binaries
-    'manywheel 2.7mu cpu devtoolset7',
-    'libtorch 2.7m cpu devtoolset7',
-
-    # Caffe2 Android
-    'caffe2-py2-android-ubuntu16.04',
-    # Caffe2 OSX
-    'caffe2-py2-system-macos10.13',
-    # PyTorch OSX
-    'pytorch-macos-10.13-cuda9.2-cudnn7-py3',
-    # PyTorch Android
-    'pytorch-linux-xenial-py3-clang5-android-ndk-r19c',
-
-    # XLA
-    'pytorch-xla-linux-trusty-py3.6-gcc5.4',
-
-    # Other checks
-    'pytorch-short-perf-test-gpu',
-    'pytorch-python-doc-push',
-    'pytorch-cpp-doc-push',
-]
-
-# Takes in commit message to analyze via stdin
-#
-# This script will query Git and attempt to determine if we should
-# run the current CI job under question
-#
-# NB: Try to avoid hard-coding names here, so there's less place to update when jobs
-# are updated/renamed
-#
-# Semantics in the presence of multiple tags:
-#   - Let D be the set of default builds
-#   - Let S be the set of explicitly specified builds
-#   - Run S \/ D
-
-parser = argparse.ArgumentParser()
-parser.add_argument('build_environment')
-args = parser.parse_args()
-
-commit_msg = sys.stdin.read()
-
-# Matches anything that looks like [foo ci] or [ci foo] or [foo test]
-# or [test foo]
-RE_MARKER = re.compile(r'\[(?:([^ \[\]]+) )?(?:ci|test)(?: ([^ \[\]]+))?\]')
-
-markers = RE_MARKER.finditer(commit_msg)
-
-for m in markers:
-    if m.group(1) and m.group(2):
-        print("Unrecognized marker: {}".format(m.group(0)))
-        continue
-    spec = m.group(1) or m.group(2)
-    if spec in args.build_environment or spec == 'all':
-        print("Accepting {} due to commit marker {}".format(args.build_environment, m.group(0)))
-        sys.exit(0)
-
-for spec in default_set:
-    if spec in args.build_environment:
-        print("Accepting {} as part of default set".format(args.build_environment))
-        sys.exit(0)
-
-print("Rejecting {}".format(args.build_environment))
-sys.exit(1)

.circleci/scripts/should_run_job.sh

@@ -1,29 +0,0 @@
-#!/usr/bin/env bash
-set -exu -o pipefail
-
-SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
-# Check if we should actually run
-echo "BUILD_ENVIRONMENT: ${BUILD_ENVIRONMENT:-}"
-echo "CIRCLE_PULL_REQUEST: ${CIRCLE_PULL_REQUEST:-}"
-if [ -z "${BUILD_ENVIRONMENT:-}" ]; then
-  echo "Cannot run should_run_job.sh if BUILD_ENVIRONMENT is not defined!"
-  echo "CircleCI scripts are probably misconfigured."
-  exit 1
-fi
-if ! [ -e "$SCRIPT_DIR/COMMIT_MSG" ]; then
-  echo "Cannot run should_run_job.sh if you don't have COMMIT_MSG"
-  echo "written out.  Are you perhaps running the wrong copy of this script?"
-  echo "You should be running the copy in ~/workspace; SCRIPT_DIR=$SCRIPT_DIR"
-  exit 1
-fi
-if [ -n "${CIRCLE_PULL_REQUEST:-}" ]; then
-  if [[ $CIRCLE_BRANCH != "ci-all/"* ]]; then
-    # Don't swallow "script doesn't exist
-    [ -e "$SCRIPT_DIR/should_run_job.py"  ]
-    if ! python "$SCRIPT_DIR/should_run_job.py" "${BUILD_ENVIRONMENT:-}" < "$SCRIPT_DIR/COMMIT_MSG" ; then
-      circleci step halt
-      exit
-    fi
-  fi
-fi

.circleci/verbatim-sources/binary-build-tests.yml

@@ -1,20 +0,0 @@
-
-# There is currently no testing for libtorch TODO
-#  binary_linux_libtorch_2.7m_cpu_test:
-#    environment:
-#      BUILD_ENVIRONMENT: "libtorch 2.7m cpu"
-#    resource_class: gpu.medium
-#    <<: *binary_linux_test
-#
-#  binary_linux_libtorch_2.7m_cu90_test:
-#    environment:
-#      BUILD_ENVIRONMENT: "libtorch 2.7m cu90"
-#    resource_class: gpu.medium
-#    <<: *binary_linux_test
-#
-#  binary_linux_libtorch_2.7m_cu100_test:
-#    environment:
-#      BUILD_ENVIRONMENT: "libtorch 2.7m cu100"
-#    resource_class: gpu.medium
-#    <<: *binary_linux_test
-

.circleci/verbatim-sources/binary_update_htmls.yml

@@ -1,98 +0,0 @@
-  
-  # update_s3_htmls job
-  # These jobs create html files for every cpu/cu## folder in s3. The html
-  # files just store the names of all the files in that folder (which are
-  # binary files (.whl files)). This is to allow pip installs of the latest
-  # version in a folder without having to know the latest date. Pip has a flag
-  # -f that you can pass an html file listing a bunch of packages, and pip will
-  # then install the one with the most recent version.
-  update_s3_htmls: &update_s3_htmls
-    machine:
-      image: ubuntu-1604:201903-01
-    steps:
-    - attach_workspace:
-        at: ~/workspace
-    - run:
-        <<: *setup_linux_system_environment
-    - run:
-        <<: *binary_checkout
-    # N.B. we do not run binary_populate_env. The only variable we need is
-    # PIP_UPLOAD_FOLDER (which is 'nightly/' for the nightlies and '' for
-    # releases, and sometimes other things for special cases). Instead we
-    # expect PIP_UPLOAD_FOLDER to be passed directly in the env. This is
-    # because, unlike all the other binary jobs, these jobs only get run once,
-    # in a separate workflow. They are not a step in other binary jobs like
-    # build, test, upload.
-    #
-    # You could attach this to every job, or include it in the upload step if
-    # you wanted. You would need to add binary_populate_env in this case to
-    # make sure it has the same upload folder as the job it's attached to. This
-    # function is idempotent, so it won't hurt anything; it's just a little
-    # unnescessary"
-    - run:
-        name: Update s3 htmls
-        no_output_timeout: "1h"
-        command: |
-          set +x
-          echo "declare -x \"AWS_ACCESS_KEY_ID=${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}\"" >> /home/circleci/project/env
-          echo "declare -x \"AWS_SECRET_ACCESS_KEY=${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}\"" >> /home/circleci/project/env
-          source /home/circleci/project/env
-          set -eux -o pipefail
-          retry () {
-              $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-          }
-          retry pip install awscli==1.6
-          "/home/circleci/project/builder/cron/update_s3_htmls.sh"
-
-  # Update s3 htmls for the nightlies
-  update_s3_htmls_for_nightlies:
-    environment:
-      PIP_UPLOAD_FOLDER: "nightly/"
-    <<: *update_s3_htmls
-
-  # Update s3 htmls for the nightlies for devtoolset7
-  update_s3_htmls_for_nightlies_devtoolset7:
-    environment:
-      PIP_UPLOAD_FOLDER: "nightly/devtoolset7/"
-    <<: *update_s3_htmls
-
-
-  # upload_binary_logs job
-  # The builder hud at pytorch.org/builder shows the sizes of all the binaries
-  # over time. It gets this info from html files stored in S3, which this job
-  # populates every day.
-  upload_binary_sizes: &upload_binary_sizes
-    machine:
-      image: ubuntu-1604:201903-01
-    steps:
-    - attach_workspace:
-        at: ~/workspace
-    - run:
-        <<: *setup_linux_system_environment
-    - run:
-        <<: *binary_checkout
-    - run:
-        <<: *binary_install_miniconda
-    - run:
-        name: Upload binary sizes
-        no_output_timeout: "1h"
-        command: |
-          set +x
-          echo "declare -x \"AWS_ACCESS_KEY_ID=${PYTORCH_BINARY_AWS_ACCESS_KEY_ID}\"" > /home/circleci/project/env
-          echo "declare -x \"AWS_SECRET_ACCESS_KEY=${PYTORCH_BINARY_AWS_SECRET_ACCESS_KEY}\"" >> /home/circleci/project/env
-          export DATE="$(date -u +%Y_%m_%d)"
-          retry () {
-              $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-          }
-          source /home/circleci/project/env
-          set -eux -o pipefail
-
-          # This is hardcoded to match binary_install_miniconda.sh
-          export PATH="/home/circleci/project/miniconda/bin:$PATH"
-          # Not any awscli will work. Most won't. This one will work
-          retry conda create -qyn aws36 python=3.6
-          source activate aws36
-          pip install awscli==1.16.46
-
-          "/home/circleci/project/builder/cron/upload_binary_sizes.sh"
-

.circleci/verbatim-sources/header-section.yml

@@ -1,63 +0,0 @@
-# WARNING: DO NOT EDIT THIS FILE DIRECTLY!!!
-# See the README.md in this directory.
-
-# IMPORTANT: To update Docker image version, please first update
-# https://github.com/pytorch/ossci-job-dsl/blob/master/src/main/groovy/ossci/pytorch/DockerVersion.groovy and
-# https://github.com/pytorch/ossci-job-dsl/blob/master/src/main/groovy/ossci/caffe2/DockerVersion.groovy,
-# and then update DOCKER_IMAGE_VERSION at the top of the following files:
-# * cimodel/data/pytorch_build_definitions.py
-# * cimodel/data/caffe2_build_definitions.py
-# And the inline copies of the variable in
-# * verbatim-sources/job-specs-custom.yml
-#   (grep for DOCKER_IMAGE)
-
-docker_config_defaults: &docker_config_defaults
-  user: jenkins
-  aws_auth:
-    # This IAM user only allows read-write access to ECR
-    aws_access_key_id: ${CIRCLECI_AWS_ACCESS_KEY_FOR_ECR_READ_WRITE_V4}
-    aws_secret_access_key: ${CIRCLECI_AWS_SECRET_KEY_FOR_ECR_READ_WRITE_V4}
-
-# This system setup script is meant to run before the CI-related scripts, e.g.,
-# installing Git client, checking out code, setting up CI env, and
-# building/testing.
-setup_linux_system_environment: &setup_linux_system_environment
-  name: Set Up System Environment
-  no_output_timeout: "1h"
-  command: ~/workspace/.circleci/scripts/setup_linux_system_environment.sh
-
-# NB: This (and the command below) must be run after attaching
-# ~/workspace.  This is NOT the default working directory (that's
-# ~/project); this workspace is generated by the setup job.
-should_run_job: &should_run_job
-  name: Should Run Job After attach_workspace
-  no_output_timeout: "2m"
-  command: ~/workspace/.circleci/scripts/should_run_job.sh
-
-setup_ci_environment: &setup_ci_environment
-  name: Set Up CI Environment After attach_workspace
-  no_output_timeout: "1h"
-  command: ~/workspace/.circleci/scripts/setup_ci_environment.sh
-
-# Installs expect and moreutils so that we can call `unbuffer` and `ts`.
-# Also installs OpenMP
-# !!!!NOTE!!!! this is copied into a binary_macos_brew_update job which is the
-# same but does not install libomp. If you are changing this, consider if you
-# need to change that step as well.
-macos_brew_update: &macos_brew_update
-  name: Brew update and install moreutils, expect and libomp
-  no_output_timeout: "1h"
-  command: |
-    set -ex
-    # See https://discourse.brew.sh/t/fetching-homebrew-repos-is-slow/5374/3
-    brew untap caskroom/homebrew-cask
-    # moreutils installs a `parallel` executable by default, which conflicts
-    # with the executable from the GNU `parallel`, so we must unlink GNU
-    # `parallel` first, and relink it afterwards
-    brew update
-    brew unlink parallel
-    brew install moreutils
-    brew link parallel --overwrite
-    brew install expect
-    brew install libomp
-

.circleci/verbatim-sources/job-specs-custom.yml

@@ -1,267 +0,0 @@
-  pytorch_short_perf_test_gpu:
-    environment:
-      BUILD_ENVIRONMENT: pytorch-short-perf-test-gpu
-      DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda9-cudnn7-py3:327"
-      PYTHON_VERSION: "3.6"
-      USE_CUDA_DOCKER_RUNTIME: "1"
-    resource_class: gpu.medium
-    machine:
-      image: ubuntu-1604:201903-01
-    steps:
-    # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-    - attach_workspace:
-        at: ~/workspace
-    - run:
-        <<: *should_run_job
-    - run:
-        <<: *setup_linux_system_environment
-    - run:
-        <<: *setup_ci_environment
-    - run:
-        name: Perf Test
-        no_output_timeout: "1h"
-        command: |
-          set -e
-          export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-          echo "DOCKER_IMAGE: "${COMMIT_DOCKER_IMAGE}
-          docker pull ${COMMIT_DOCKER_IMAGE} >/dev/null
-          export id=$(docker run --runtime=nvidia -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-
-          docker cp $id:/var/lib/jenkins/workspace/env /home/circleci/project/env
-          # This IAM user allows write access to S3 bucket for perf test numbers
-          set +x
-          echo "declare -x AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_PERF_TEST_S3_BUCKET_V4}" >> /home/circleci/project/env
-          echo "declare -x AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_PERF_TEST_S3_BUCKET_V4}" >> /home/circleci/project/env
-          set -x
-          docker cp /home/circleci/project/env $id:/var/lib/jenkins/workspace/env
-
-          export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && .jenkins/pytorch/short-perf-test-gpu.sh") | docker exec -u jenkins -i "$id" bash) 2>&1'
-          echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-
-  pytorch_python_doc_push:
-    environment:
-      BUILD_ENVIRONMENT: pytorch-python-doc-push
-      # TODO: stop hardcoding this
-      DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda9-cudnn7-py3:327"
-    resource_class: large
-    machine:
-      image: ubuntu-1604:201903-01
-    steps:
-    # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-    - attach_workspace:
-        at: ~/workspace
-    - run:
-        <<: *should_run_job
-    - run:
-        <<: *setup_linux_system_environment
-    - run:
-        <<: *setup_ci_environment
-    - run:
-        name: Doc Build and Push
-        no_output_timeout: "1h"
-        command: |
-          set -ex
-          export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-          echo "DOCKER_IMAGE: "${COMMIT_DOCKER_IMAGE}
-          docker pull ${COMMIT_DOCKER_IMAGE} >/dev/null
-          export id=$(docker run -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-
-          # master branch docs push
-          if [[ "${CIRCLE_BRANCH}" == "master" ]]; then
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "export GITHUB_PYTORCHBOT_TOKEN=${GITHUB_PYTORCHBOT_TOKEN}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && . ./.circleci/scripts/python_doc_push_script.sh docs/master master site") | docker exec -u jenkins -i "$id" bash) 2>&1'
-
-          # stable release docs push. Due to some circleci limitations, we keep
-          # an eternal PR open for merging v1.2.0 -> master for this job.
-          # XXX: The following code is only run on the v1.2.0 branch, which might
-          # not be exactly the same as what you see here.
-          elif [[ "${CIRCLE_BRANCH}" == "v1.2.0" ]]; then
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "export GITHUB_PYTORCHBOT_TOKEN=${GITHUB_PYTORCHBOT_TOKEN}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && . ./.circleci/scripts/python_doc_push_script.sh docs/stable 1.2.0 site dry_run") | docker exec -u jenkins -i "$id" bash) 2>&1'
-
-          # For open PRs: Do a dry_run of the docs build, don't push build
-          else
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "export GITHUB_PYTORCHBOT_TOKEN=${GITHUB_PYTORCHBOT_TOKEN}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && . ./.circleci/scripts/python_doc_push_script.sh docs/master master site dry_run") | docker exec -u jenkins -i "$id" bash) 2>&1'
-          fi
-
-          echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-
-          # Save the docs build so we can debug any problems
-          export DEBUG_COMMIT_DOCKER_IMAGE=${COMMIT_DOCKER_IMAGE}-debug
-          docker commit "$id" ${DEBUG_COMMIT_DOCKER_IMAGE}
-          docker push ${DEBUG_COMMIT_DOCKER_IMAGE}
-
-  pytorch_cpp_doc_push:
-    environment:
-      BUILD_ENVIRONMENT: pytorch-cpp-doc-push
-      DOCKER_IMAGE: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda9-cudnn7-py3:327"
-    resource_class: large
-    machine:
-      image: ubuntu-1604:201903-01
-    steps:
-    # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-    - attach_workspace:
-        at: ~/workspace
-    - run:
-        <<: *should_run_job
-    - run:
-        <<: *setup_linux_system_environment
-    - run:
-        <<: *setup_ci_environment
-    - run:
-        name: Doc Build and Push
-        no_output_timeout: "1h"
-        command: |
-          set -ex
-          export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-          echo "DOCKER_IMAGE: "${COMMIT_DOCKER_IMAGE}
-          docker pull ${COMMIT_DOCKER_IMAGE} >/dev/null
-          export id=$(docker run -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-
-          # master branch docs push
-          if [[ "${CIRCLE_BRANCH}" == "master" ]]; then
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "export GITHUB_PYTORCHBOT_TOKEN=${GITHUB_PYTORCHBOT_TOKEN}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && . ./.circleci/scripts/cpp_doc_push_script.sh docs/master master") | docker exec -u jenkins -i "$id" bash) 2>&1'
-
-          # stable release docs push. Due to some circleci limitations, we keep
-          # an eternal PR open (#16502) for merging v1.0.1 -> master for this job.
-          # XXX: The following code is only run on the v1.0.1 branch, which might
-          # not be exactly the same as what you see here.
-          elif [[ "${CIRCLE_BRANCH}" == "v1.0.1" ]]; then
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "export GITHUB_PYTORCHBOT_TOKEN=${GITHUB_PYTORCHBOT_TOKEN}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && . ./.circleci/scripts/cpp_doc_push_script.sh docs/stable 1.0.1") | docker exec -u jenkins -i "$id" bash) 2>&1'
-
-          # For open PRs: Do a dry_run of the docs build, don't push build
-          else
-            export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo "export GITHUB_PYTORCHBOT_TOKEN=${GITHUB_PYTORCHBOT_TOKEN}" && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && . ./.circleci/scripts/cpp_doc_push_script.sh docs/master master dry_run") | docker exec -u jenkins -i "$id" bash) 2>&1'
-          fi
-
-          echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-
-          # Save the docs build so we can debug any problems
-          export DEBUG_COMMIT_DOCKER_IMAGE=${COMMIT_DOCKER_IMAGE}-debug
-          docker commit "$id" ${DEBUG_COMMIT_DOCKER_IMAGE}
-          docker push ${DEBUG_COMMIT_DOCKER_IMAGE}
-
-  pytorch_macos_10_13_py3_build:
-    environment:
-      BUILD_ENVIRONMENT: pytorch-macos-10.13-py3-build
-    macos:
-      xcode: "9.0"
-    steps:
-      # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-      - attach_workspace:
-          at: ~/workspace
-      - run:
-          <<: *should_run_job
-      - checkout
-      - run:
-          <<: *macos_brew_update
-      - run:
-          name: Build
-          no_output_timeout: "1h"
-          command: |
-            set -e
-
-            export IN_CIRCLECI=1
-
-            # Install sccache
-            sudo curl https://s3.amazonaws.com/ossci-macos/sccache --output /usr/local/bin/sccache
-            sudo chmod +x /usr/local/bin/sccache
-
-            export SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
-            # This IAM user allows write access to S3 bucket for sccache
-            set +x
-            export AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_SCCACHE_S3_BUCKET_V4}
-            export AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_SCCACHE_S3_BUCKET_V4}
-            set -x
-
-            chmod a+x .jenkins/pytorch/macos-build.sh
-            unbuffer .jenkins/pytorch/macos-build.sh 2>&1 | ts
-
-            mkdir -p /Users/distiller/pytorch-ci-env/workspace
-
-            # copy with -a to preserve relative structure (e.g., symlinks), and be recursive
-            cp -a /Users/distiller/project/. /Users/distiller/pytorch-ci-env/workspace
-      - persist_to_workspace:
-          root: /Users/distiller/pytorch-ci-env
-          paths:
-            - "*"
-
-  pytorch_macos_10_13_py3_test:
-    environment:
-      BUILD_ENVIRONMENT: pytorch-macos-10.13-py3-test
-    macos:
-      xcode: "9.0"
-    steps:
-      # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-      # This workspace also carries binaries from the build job
-      - attach_workspace:
-          at: ~/workspace
-      - run:
-          <<: *should_run_job
-      - run:
-          <<: *macos_brew_update
-      - run:
-          name: Test
-          no_output_timeout: "1h"
-          command: |
-            set -e
-            export IN_CIRCLECI=1
-
-            # copy with -a to preserve relative structure (e.g., symlinks), and be recursive
-            # TODO: I'm not sure why we can't just run our job in
-            # ~/workspace and call it a day
-            # NB: Yes, you need workspace twice
-            cp -a ~/workspace/workspace/. /Users/distiller/project
-
-            chmod a+x .jenkins/pytorch/macos-test.sh
-            unbuffer .jenkins/pytorch/macos-test.sh 2>&1 | ts
-
-  pytorch_macos_10_13_cuda9_2_cudnn7_py3_build:
-    environment:
-      BUILD_ENVIRONMENT: pytorch-macos-10.13-cuda9.2-cudnn7-py3-build
-    macos:
-      xcode: "9.0"
-    steps:
-      # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-      - attach_workspace:
-          at: ~/workspace
-      - run:
-          <<: *should_run_job
-      - checkout
-      - run:
-          <<: *macos_brew_update
-      - run:
-          name: Build
-          no_output_timeout: "1h"
-          command: |
-            set -e
-
-            export IN_CIRCLECI=1
-
-            # Install CUDA 9.2
-            sudo rm -rf ~/cuda_9.2.64_mac_installer.app || true
-            curl https://s3.amazonaws.com/ossci-macos/cuda_9.2.64_mac_installer.zip -o ~/cuda_9.2.64_mac_installer.zip
-            unzip ~/cuda_9.2.64_mac_installer.zip -d ~/
-            sudo ~/cuda_9.2.64_mac_installer.app/Contents/MacOS/CUDAMacOSXInstaller --accept-eula --no-window
-            sudo cp /usr/local/cuda/lib/libcuda.dylib /Developer/NVIDIA/CUDA-9.2/lib/libcuda.dylib
-            sudo rm -rf /usr/local/cuda || true
-
-            # Install cuDNN 7.1 for CUDA 9.2
-            curl https://s3.amazonaws.com/ossci-macos/cudnn-9.2-osx-x64-v7.1.tgz -o ~/cudnn-9.2-osx-x64-v7.1.tgz
-            rm -rf ~/cudnn-9.2-osx-x64-v7.1 && mkdir ~/cudnn-9.2-osx-x64-v7.1
-            tar -xzvf ~/cudnn-9.2-osx-x64-v7.1.tgz -C ~/cudnn-9.2-osx-x64-v7.1
-            sudo cp ~/cudnn-9.2-osx-x64-v7.1/cuda/include/cudnn.h /Developer/NVIDIA/CUDA-9.2/include/
-            sudo cp ~/cudnn-9.2-osx-x64-v7.1/cuda/lib/libcudnn* /Developer/NVIDIA/CUDA-9.2/lib/
-            sudo chmod a+r /Developer/NVIDIA/CUDA-9.2/include/cudnn.h /Developer/NVIDIA/CUDA-9.2/lib/libcudnn*
-
-            # Install sccache
-            sudo curl https://s3.amazonaws.com/ossci-macos/sccache --output /usr/local/bin/sccache
-            sudo chmod +x /usr/local/bin/sccache
-            export SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
-            # This IAM user allows write access to S3 bucket for sccache
-            set +x
-            export AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_SCCACHE_S3_BUCKET_V4}
-            export AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_SCCACHE_S3_BUCKET_V4}
-            set -x
-
-            git submodule sync && git submodule update -q --init --recursive
-            chmod a+x .jenkins/pytorch/macos-build.sh
-            unbuffer .jenkins/pytorch/macos-build.sh 2>&1 | ts

.circleci/verbatim-sources/job-specs-setup.yml

@@ -1,34 +0,0 @@
-  
-  setup:
-    docker:
-      - image: circleci/python:3.7.3
-    steps:
-      - checkout
-      - run:
-          name: Ensure config is up to date
-          command: ./ensure-consistency.py
-          working_directory: .circleci
-      - run:
-          name: Save commit message
-          command: git log --format='%B' -n 1 HEAD > .circleci/scripts/COMMIT_MSG
-      # Note [Workspace for CircleCI scripts]
-      # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-      # In the beginning, you wrote your CI scripts in a
-      # .circleci/config.yml file, and life was good.  Your CI
-      # configurations flourished and multiplied.
-      #
-      # Then one day, CircleCI cometh down high and say, "Your YAML file
-      # is too biggeth, it stresses our servers so."  And thus they
-      # asketh us to smite the scripts in the yml file.
-      #
-      # But you can't just put the scripts in the .circleci folder,
-      # because in some jobs, you don't ever actually checkout the
-      # source repository.  Where you gonna get the scripts from?
-      #
-      # Here's how you do it: you persist .circleci/scripts into a
-      # workspace, attach the workspace in your subjobs, and run all
-      # your scripts from there.
-      - persist_to_workspace:
-          root: .
-          paths: .circleci/scripts
-

.circleci/verbatim-sources/linux-binary-build-defaults.yml

@@ -1,101 +0,0 @@
-
-# binary linux build defaults
-##############################################################################
-binary_linux_build: &binary_linux_build
-  resource_class: 2xlarge+
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *binary_checkout
-  - run:
-      <<: *binary_populate_env
-  - run:
-      name: Install unbuffer and ts
-      command: |
-        set -eux -o pipefail
-        source /env
-        retry yum -q -y install epel-release
-        retry yum -q -y install expect moreutils
-  - run:
-      name: Update compiler to devtoolset7
-      command: |
-        set -eux -o pipefail
-        source /env
-        if [[ "$DESIRED_DEVTOOLSET" == 'devtoolset7' ]]; then
-          source "/builder/update_compiler.sh"
-
-          # Env variables are not persisted into the next step
-          echo "export PATH=$PATH" >> /env
-          echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH" >> /env
-        else
-          echo "Not updating compiler"
-        fi
-  - run:
-      name: Build
-      no_output_timeout: "1h"
-      command: |
-        source "/pytorch/.circleci/scripts/binary_linux_build.sh"
-  - persist_to_workspace:
-      root: /
-      paths: final_pkgs
-
-# This should really just be another step of the binary_linux_build job above.
-# This isn't possible right now b/c the build job uses the docker executor
-# (otherwise they'd be really really slow) but this one uses the macine
-# executor (b/c we have to run the docker with --runtime=nvidia and we can't do
-# that on the docker executor)
-binary_linux_test: &binary_linux_test
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  # TODO: We shouldn't attach the workspace multiple times
-  - attach_workspace:
-      at: /home/circleci/project
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *setup_linux_system_environment
-  - run:
-      <<: *setup_ci_environment
-  - run:
-      <<: *binary_checkout
-  - run:
-      <<: *binary_populate_env
-  - run:
-      name: Prepare test code
-      no_output_timeout: "1h"
-      command: ~/workspace/.circleci/scripts/binary_linux_test.sh
-  - run:
-      <<: *binary_run_in_docker
-
-binary_linux_upload: &binary_linux_upload
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *setup_linux_system_environment
-  - run:
-      <<: *setup_ci_environment
-  - attach_workspace:
-      at: /home/circleci/project
-  - run:
-      <<: *binary_populate_env
-  - run:
-      <<: *binary_install_miniconda
-  - run:
-      name: Upload
-      no_output_timeout: "1h"
-      command: ~/workspace/.circleci/scripts/binary_linux_upload.sh
-

.circleci/verbatim-sources/linux-build-defaults.yml

@@ -1,234 +0,0 @@
-
-
-##############################################################################
-# Linux build defaults
-##############################################################################
-
-pytorch_linux_build_defaults: &pytorch_linux_build_defaults
-  resource_class: large
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *setup_linux_system_environment
-  - checkout
-  - run:
-      <<: *setup_ci_environment
-  - run:
-      name: Build
-      no_output_timeout: "1h"
-      command: |
-        set -e
-        # Pull Docker image and run build
-        echo "DOCKER_IMAGE: "${DOCKER_IMAGE}
-        docker pull ${DOCKER_IMAGE} >/dev/null
-        export id=$(docker run -t -d -w /var/lib/jenkins ${DOCKER_IMAGE})
-
-        git submodule sync && git submodule update -q --init --recursive
-
-        docker cp /home/circleci/project/. $id:/var/lib/jenkins/workspace
-
-        if [[ ${BUILD_ENVIRONMENT} == *"namedtensor"* ]]; then
-          NAMED_FLAG="export BUILD_NAMEDTENSOR=1"
-        fi
-
-        export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo '"$NAMED_FLAG"' && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && .jenkins/pytorch/build.sh") | docker exec -u jenkins -i "$id" bash) 2>&1'
-        echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-
-        # Push intermediate Docker image for next phase to use
-        if [ -z "${BUILD_ONLY}" ]; then
-          # Note [Special build images]
-          # The namedtensor and xla builds use the same docker image as
-          # pytorch-linux-trusty-py3.6-gcc5.4-build. In the push step, we have to
-          # distinguish between them so the test can pick up the correct image.
-          output_image=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-          if [[ ${BUILD_ENVIRONMENT} == *"namedtensor"* ]]; then
-            export COMMIT_DOCKER_IMAGE=$output_image-namedtensor
-          elif [[ ${BUILD_ENVIRONMENT} == *"xla"* ]]; then
-            export COMMIT_DOCKER_IMAGE=$output_image-xla
-          else
-            export COMMIT_DOCKER_IMAGE=$output_image
-          fi
-          docker commit "$id" ${COMMIT_DOCKER_IMAGE}
-          docker push ${COMMIT_DOCKER_IMAGE}
-        fi
-
-pytorch_linux_test_defaults: &pytorch_linux_test_defaults
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *setup_linux_system_environment
-  - run:
-      <<: *setup_ci_environment
-  - run:
-      name: Test
-      no_output_timeout: "90m"
-      command: |
-        set -e
-        # See Note [Special build images]
-        output_image=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-        if [[ ${BUILD_ENVIRONMENT} == *"namedtensor"* ]]; then
-          export COMMIT_DOCKER_IMAGE=$output_image-namedtensor
-          NAMED_FLAG="export BUILD_NAMEDTENSOR=1"
-        elif [[ ${BUILD_ENVIRONMENT} == *"xla"* ]]; then
-          export COMMIT_DOCKER_IMAGE=$output_image-xla
-        else
-          export COMMIT_DOCKER_IMAGE=$output_image
-        fi
-        echo "DOCKER_IMAGE: "${COMMIT_DOCKER_IMAGE}
-        docker pull ${COMMIT_DOCKER_IMAGE} >/dev/null
-        if [ -n "${USE_CUDA_DOCKER_RUNTIME}" ]; then
-          export id=$(docker run --runtime=nvidia -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-        else
-          export id=$(docker run -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-        fi
-        if [ -n "${MULTI_GPU}" ]; then
-          export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo '"$NAMED_FLAG"' && echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && .jenkins/pytorch/multigpu-test.sh") | docker exec -u jenkins -i "$id" bash) 2>&1'
-        else
-          export COMMAND='((echo "export BUILD_ENVIRONMENT=${BUILD_ENVIRONMENT}" && echo '"$NAMED_FLAG"'&& echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && .jenkins/pytorch/test.sh") | docker exec -u jenkins -i "$id" bash) 2>&1'
-        fi
-        echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-
-caffe2_linux_build_defaults: &caffe2_linux_build_defaults
-  resource_class: large
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *setup_linux_system_environment
-  - checkout
-  - run:
-      <<: *setup_ci_environment
-  - run:
-      name: Build
-      no_output_timeout: "1h"
-      command: |
-        set -e
-        cat >/home/circleci/project/ci_build_script.sh <<EOL
-        # =================== The following code will be executed inside Docker container ===================
-        set -ex
-        export BUILD_ENVIRONMENT="$BUILD_ENVIRONMENT"
-
-        # Reinitialize submodules
-        git submodule sync && git submodule update -q --init --recursive
-
-        # conda must be added to the path for Anaconda builds (this location must be
-        # the same as that in install_anaconda.sh used to build the docker image)
-        if [[ "${BUILD_ENVIRONMENT}" == conda* ]]; then
-          export PATH=/opt/conda/bin:$PATH
-          sudo chown -R jenkins:jenkins '/opt/conda'
-        fi
-
-        # Build
-        ./.jenkins/caffe2/build.sh
-
-        # Show sccache stats if it is running
-        if pgrep sccache > /dev/null; then
-          sccache --show-stats
-        fi
-        # =================== The above code will be executed inside Docker container ===================
-        EOL
-        chmod +x /home/circleci/project/ci_build_script.sh
-
-        echo "DOCKER_IMAGE: "${DOCKER_IMAGE}
-        docker pull ${DOCKER_IMAGE} >/dev/null
-        export id=$(docker run -t -d -w /var/lib/jenkins ${DOCKER_IMAGE})
-        docker cp /home/circleci/project/. $id:/var/lib/jenkins/workspace
-
-        export COMMAND='((echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./ci_build_script.sh") | docker exec -u jenkins -i "$id" bash) 2>&1'
-        echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-
-        # Push intermediate Docker image for next phase to use
-        if [ -z "${BUILD_ONLY}" ]; then
-          if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-            export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-cmake-${CIRCLE_SHA1}
-          else
-            export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-          fi
-          docker commit "$id" ${COMMIT_DOCKER_IMAGE}
-          docker push ${COMMIT_DOCKER_IMAGE}
-        fi
-
-caffe2_linux_test_defaults: &caffe2_linux_test_defaults
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *setup_linux_system_environment
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *setup_ci_environment
-  - run:
-      name: Test
-      no_output_timeout: "1h"
-      command: |
-        set -e
-        # TODO: merge this into Caffe2 test.sh
-        cat >/home/circleci/project/ci_test_script.sh <<EOL
-        # =================== The following code will be executed inside Docker container ===================
-        set -ex
-
-        export BUILD_ENVIRONMENT="$BUILD_ENVIRONMENT"
-
-        # libdc1394 (dependency of OpenCV) expects /dev/raw1394 to exist...
-        sudo ln /dev/null /dev/raw1394
-
-        # conda must be added to the path for Anaconda builds (this location must be
-        # the same as that in install_anaconda.sh used to build the docker image)
-        if [[ "${BUILD_ENVIRONMENT}" == conda* ]]; then
-          export PATH=/opt/conda/bin:$PATH
-        fi
-
-        # Upgrade SSL module to avoid old SSL warnings
-        pip -q install --user --upgrade pyOpenSSL ndg-httpsclient pyasn1
-
-        pip -q install --user -b /tmp/pip_install_onnx "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
-
-        # Build
-        ./.jenkins/caffe2/test.sh
-
-        # Remove benign core dumps.
-        # These are tests for signal handling (including SIGABRT).
-        rm -f ./crash/core.fatal_signal_as.*
-        rm -f ./crash/core.logging_test.*
-        # =================== The above code will be executed inside Docker container ===================
-        EOL
-        chmod +x /home/circleci/project/ci_test_script.sh
-
-        if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-          export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-cmake-${CIRCLE_SHA1}
-        else
-          export COMMIT_DOCKER_IMAGE=${DOCKER_IMAGE}-${CIRCLE_SHA1}
-        fi
-        echo "DOCKER_IMAGE: "${COMMIT_DOCKER_IMAGE}
-        docker pull ${COMMIT_DOCKER_IMAGE} >/dev/null
-        if [ -n "${USE_CUDA_DOCKER_RUNTIME}" ]; then
-          export id=$(docker run --runtime=nvidia -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-        else
-          export id=$(docker run -t -d -w /var/lib/jenkins ${COMMIT_DOCKER_IMAGE})
-        fi
-        docker cp /home/circleci/project/. "$id:/var/lib/jenkins/workspace"
-
-        export COMMAND='((echo "source ./workspace/env" && echo "sudo chown -R jenkins workspace && cd workspace && ./ci_test_script.sh") | docker exec -u jenkins -i "$id" bash) 2>&1'
-        echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts
-

.circleci/verbatim-sources/macos-binary-build-defaults.yml

@@ -1,72 +0,0 @@
-
-##############################################################################
-# Macos binary build defaults
-# The root of everything is /Users/distiller/pytorch-ci-env/workspace
-##############################################################################
-binary_mac_build: &binary_mac_build
-  macos:
-    xcode: "9.0"
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *binary_checkout
-  - run:
-      <<: *binary_populate_env
-  - run:
-      <<: *binary_macos_brew_update
-  - run:
-      <<: *binary_install_miniconda
-
-  - run:
-      name: Build
-      no_output_timeout: "1h"
-      command: |
-        set -eux -o pipefail
-        script="/Users/distiller/project/pytorch/.circleci/scripts/binary_macos_build.sh"
-        cat "$script"
-        source "$script"
-
-  - run:
-      name: Test
-      no_output_timeout: "1h"
-      command: |
-        set -eux -o pipefail
-        script="/Users/distiller/project/pytorch/.circleci/scripts/binary_macos_test.sh"
-        cat "$script"
-        source "$script"
-
-  - persist_to_workspace:
-      root: /Users/distiller/project
-      paths: final_pkgs
-
-binary_mac_upload: &binary_mac_upload
-  macos:
-    xcode: "9.0"
-  steps:
-  # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-  - attach_workspace:
-      at: ~/workspace
-  - run:
-      <<: *should_run_job
-  - run:
-      <<: *binary_checkout
-  - run:
-      <<: *binary_populate_env
-  - run:
-      <<: *binary_macos_brew_update
-  - run:
-      <<: *binary_install_miniconda
-  - attach_workspace: # TODO - we can `cp` from ~/workspace
-      at: /Users/distiller/project
-  - run:
-      name: Upload
-      no_output_timeout: "10m"
-      command: |
-        script="/Users/distiller/project/pytorch/.circleci/scripts/binary_macos_upload.sh"
-        cat "$script"
-        source "$script"
-

.circleci/verbatim-sources/macos-build-defaults.yml

@@ -1,90 +0,0 @@
-
-##############################################################################
-# Macos build defaults
-##############################################################################
-
-caffe2_macos_build_defaults: &caffe2_macos_build_defaults
-  macos:
-    xcode: "9.0"
-  steps:
-    # See Note [Workspace for CircleCI scripts] in job-specs-setup.yml
-    - attach_workspace:
-        at: ~/workspace
-    - run:
-        <<: *should_run_job
-    - checkout
-    - run:
-        <<: *macos_brew_update
-    - run:
-        name: Build
-        no_output_timeout: "1h"
-        command: |
-          set -e
-
-          export IN_CIRCLECI=1
-
-          brew install cmake
-
-          # Reinitialize submodules
-          git submodule sync && git submodule update -q --init --recursive
-
-          # Reinitialize path (see man page for path_helper(8))
-          eval `/usr/libexec/path_helper -s`
-
-          # Use Homebrew Python if configured to do so
-          if [ "${PYTHON_INSTALLATION}" == "homebrew" ]; then
-            export PATH=/usr/local/opt/python/libexec/bin:/usr/local/bin:$PATH
-          fi
-
-          pip -q install numpy
-
-          # Install Anaconda if we need to
-          if [ -n "${CAFFE2_USE_ANACONDA}" ]; then
-            rm -rf ${TMPDIR}/anaconda
-            curl -o ${TMPDIR}/conda.sh https://repo.continuum.io/miniconda/Miniconda${ANACONDA_VERSION}-latest-MacOSX-x86_64.sh
-            chmod +x ${TMPDIR}/conda.sh
-            /bin/bash ${TMPDIR}/conda.sh -b -p ${TMPDIR}/anaconda
-            rm -f ${TMPDIR}/conda.sh
-            export PATH="${TMPDIR}/anaconda/bin:${PATH}"
-            source ${TMPDIR}/anaconda/bin/activate
-          fi
-
-          # Install sccache
-          sudo curl https://s3.amazonaws.com/ossci-macos/sccache --output /usr/local/bin/sccache
-          sudo chmod +x /usr/local/bin/sccache
-          export SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
-
-          # This IAM user allows write access to S3 bucket for sccache
-          set +x
-          export AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_SCCACHE_S3_BUCKET_V4}
-          export AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_SCCACHE_S3_BUCKET_V4}
-          set -x
-
-          export SCCACHE_BIN=${PWD}/sccache_bin
-          mkdir -p ${SCCACHE_BIN}
-          if which sccache > /dev/null; then
-            printf "#!/bin/sh\nexec sccache $(which clang++) \$*" > "${SCCACHE_BIN}/clang++"
-            chmod a+x "${SCCACHE_BIN}/clang++"
-
-            printf "#!/bin/sh\nexec sccache $(which clang) \$*" > "${SCCACHE_BIN}/clang"
-            chmod a+x "${SCCACHE_BIN}/clang"
-
-            export PATH="${SCCACHE_BIN}:$PATH"
-          fi
-
-          # Build
-          if [ "${BUILD_IOS:-0}" -eq 1 ]; then
-            unbuffer scripts/build_ios.sh 2>&1 | ts
-          elif [ -n "${CAFFE2_USE_ANACONDA}" ]; then
-            # All conda build logic should be in scripts/build_anaconda.sh
-            unbuffer scripts/build_anaconda.sh 2>&1 | ts
-          else
-            unbuffer scripts/build_local.sh 2>&1 | ts
-          fi
-
-          # Show sccache stats if it is running
-          if which sccache > /dev/null; then
-            sccache --show-stats
-          fi
-
-

.circleci/verbatim-sources/nightly-binary-build-defaults.yml

@@ -1,70 +0,0 @@
-
-##############################################################################
-# Binary build (nightlies nightly build) defaults
-# The binary builds use the docker executor b/c at time of writing the machine
-# executor is limited to only two cores and is painfully slow (4.5+ hours per
-# GPU build). But the docker executor cannot be run with --runtime=nvidia, and
-# so the binary test/upload jobs must run on a machine executor. The package
-# built in the build job is persisted to the workspace, which the test jobs
-# expect. The test jobs just run a few quick smoke tests (very similar to the
-# second-round-user-facing smoke tests above) and then upload the binaries to
-# their final locations. The upload part requires credentials that should only
-# be available to org-members.
-#
-# binary_checkout MUST be run before other commands here. This is because the
-# other commands are written in .circleci/scripts/*.sh , so the pytorch source
-# code must be downloaded on the machine before they can be run. We cannot
-# inline all the code into this file, since that would cause the yaml size to
-# explode past 4 MB (all the code in the command section is just copy-pasted to
-# everywhere in the .circleci/config.yml file where it appears).
-##############################################################################
-
-# Checks out the Pytorch and Builder repos (always both of them), and places
-# them in the right place depending on what executor we're running on. We curl
-# our .sh file from the interweb to avoid yaml size bloat. Note that many jobs
-# do not need both the pytorch and builder repos, so this is a little wasteful
-# (smoke tests and upload jobs do not need the pytorch repo).
-binary_checkout: &binary_checkout
-  name: Checkout pytorch/builder repo
-  command: ~/workspace/.circleci/scripts/binary_checkout.sh
-
-# Parses circleci arguments in a consistent way, essentially routing to the
-# correct pythonXgccXcudaXos build we want
-binary_populate_env: &binary_populate_env
-  name: Set up binary env variables
-  command: ~/workspace/.circleci/scripts/binary_populate_env.sh
-
-binary_install_miniconda: &binary_install_miniconda
-  name: Install miniconda
-  no_output_timeout: "1h"
-  command: ~/workspace/.circleci/scripts/binary_install_miniconda.sh
-
-# This section is used in the binary_test and smoke_test jobs. It expects
-# 'binary_populate_env' to have populated /home/circleci/project/env and it
-# expects another section to populate /home/circleci/project/ci_test_script.sh
-# with the code to run in the docker
-binary_run_in_docker: &binary_run_in_docker
-  name: Run in docker
-  # This step only runs on circleci linux machine executors that themselves
-  # need to start docker images
-  command: ~/workspace/.circleci/scripts/binary_run_in_docker.sh
-
-# This is copied almost verbatim from the macos_brew_update job
-# In version 2.1 and above we could make this a command and pass a parameter to
-# it, but in this version there is no way to pass a parameter to a step
-binary_macos_brew_update: &binary_macos_brew_update
-  name: Brew update and install moreutils and expect
-  no_output_timeout: "1h"
-  command: |
-    set -eux -o pipefail
-    # See https://discourse.brew.sh/t/fetching-homebrew-repos-is-slow/5374/3
-    brew untap caskroom/homebrew-cask
-    # moreutils installs a `parallel` executable by default, which conflicts
-    # with the executable from the GNU `parallel`, so we must unlink GNU
-    # `parallel` first, and relink it afterwards
-    brew update
-    brew unlink parallel
-    brew install moreutils
-    brew link parallel --overwrite
-    brew install expect
-

.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml

@@ -1,64 +0,0 @@
-
-# Nighlty build smoke tests defaults
-# These are the second-round smoke tests. These make sure that the binaries are
-# correct from a user perspective, testing that they exist from the cloud are
-# are runnable. Note that the pytorch repo is never cloned into these jobs
-##############################################################################
-smoke_linux_test: &smoke_linux_test
-  machine:
-    image: ubuntu-1604:201903-01
-  steps:
-  - attach_workspace:
-      at: ~/workspace
-  - attach_workspace:
-      at: /home/circleci/project
-  - run:
-      <<: *setup_linux_system_environment
-  - run:
-      <<: *setup_ci_environment
-  - run:
-      <<: *binary_checkout
-  - run:
-      <<: *binary_populate_env
-  - run:
-      name: Test
-      no_output_timeout: "1h"
-      command: |
-        set -ex
-        cat >/home/circleci/project/ci_test_script.sh <<EOL
-        # The following code will be executed inside Docker container
-        set -eux -o pipefail
-        /builder/smoke_test.sh
-        # The above code will be executed inside Docker container
-        EOL
-  - run:
-      <<: *binary_run_in_docker
-
-smoke_mac_test: &smoke_mac_test
-  macos:
-    xcode: "9.0"
-  steps:
-    - attach_workspace:
-        at: ~/workspace
-    - attach_workspace: # TODO - we can `cp` from ~/workspace
-        at: /Users/distiller/project
-    - run:
-        <<: *binary_checkout
-    - run:
-        <<: *binary_populate_env
-    - run:
-        <<: *binary_macos_brew_update
-    - run:
-        <<: *binary_install_miniconda
-    - run:
-        name: Build
-        no_output_timeout: "1h"
-        command: |
-          set -ex
-          source "/Users/distiller/project/env"
-          export "PATH=$workdir/miniconda/bin:$PATH"
-          # TODO unbuffer and ts this, but it breaks cause miniconda overwrites
-          # tclsh. But unbuffer and ts aren't that important so they're just
-          # disabled for now
-          ./builder/smoke_test.sh
-

.circleci/verbatim-sources/workflows-binary-build-header.yml

@@ -1,4 +0,0 @@
-
-##############################################################################
-# Daily binary build trigger
-##############################################################################

.circleci/verbatim-sources/workflows-binary-builds-smoke-subset.yml

@@ -1,51 +0,0 @@
-      # Binary builds (subset, to smoke test that they'll work)
-      #
-      # NB: If you modify this file, you need to also modify
-      # the binary_and_smoke_tests_on_pr variable in
-      # pytorch-ci-hud to adjust the list of whitelisted builds
-      # at https://github.com/ezyang/pytorch-ci-hud/blob/master/src/BuildHistoryDisplay.js
-
-      - binary_linux_manywheel_2.7mu_cpu_devtoolset7_build:
-          requires:
-            - setup
-      - binary_linux_manywheel_3.7m_cu100_devtoolset7_build:
-          requires:
-            - setup
-      - binary_linux_conda_2.7_cpu_devtoolset7_build:
-          requires:
-            - setup
-      # This binary build is currently broken, see https://github.com/pytorch/pytorch/issues/16710
-      # - binary_linux_conda_3.6_cu90_devtoolset7_build
-      - binary_linux_libtorch_2.7m_cpu_devtoolset7_static-without-deps_build:
-          requires:
-            - setup
-      # TODO we should test a libtorch cuda build, but they take too long
-      # - binary_linux_libtorch_2.7m_cu90_devtoolset7_static-without-deps_build
-      - binary_macos_wheel_3.6_cpu_build:
-          requires:
-            - setup
-      - binary_macos_conda_2.7_cpu_build:
-          requires:
-            - setup
-      - binary_macos_libtorch_2.7_cpu_build:
-          requires:
-            - setup
-
-      - binary_linux_manywheel_2.7mu_cpu_devtoolset7_test:
-          requires:
-            - setup
-            - binary_linux_manywheel_2.7mu_cpu_devtoolset7_build
-      - binary_linux_manywheel_3.7m_cu100_devtoolset7_test:
-          requires:
-            - setup
-            - binary_linux_manywheel_3.7m_cu100_devtoolset7_build
-      - binary_linux_conda_2.7_cpu_devtoolset7_test:
-          requires:
-            - setup
-            - binary_linux_conda_2.7_cpu_devtoolset7_build
-      # This binary build is currently broken, see https://github.com/pytorch/pytorch/issues/16710
-      # - binary_linux_conda_3.6_cu90_devtoolset7_test:
-      #     requires:
-      #       - setup
-      #       - binary_linux_conda_3.6_cu90_devtoolset7_build
-

.circleci/verbatim-sources/workflows-nightly-uploads-header.yml

@@ -1,11 +0,0 @@
-      #- binary_linux_libtorch_2.7m_cpu_test:
-      #    requires:
-      #      - binary_linux_libtorch_2.7m_cpu_build
-      #- binary_linux_libtorch_2.7m_cu90_test:
-      #    requires:
-      #      - binary_linux_libtorch_2.7m_cu90_build
-      #- binary_linux_libtorch_2.7m_cu100_test:
-      #    requires:
-      #      - binary_linux_libtorch_2.7m_cu100_build
-
-      # Nightly uploads

.circleci/verbatim-sources/workflows-pytorch-macos-builds.yml

@@ -1,19 +0,0 @@
-      # Warning: indentation here matters!
-
-      # Pytorch MacOS builds
-      - pytorch_macos_10_13_py3_build:
-          requires:
-            - setup
-          filters:
-            branches:
-              only: master
-      - pytorch_macos_10_13_py3_test:
-          requires:
-            - setup
-            - pytorch_macos_10_13_py3_build
-          filters:
-            branches:
-              only: master
-      - pytorch_macos_10_13_cuda9_2_cudnn7_py3_build:
-          requires:
-            - setup

.circleci/verbatim-sources/workflows-s3-html.yml

@@ -1,30 +0,0 @@
-
-  # Scheduled to run 4 hours after the binary jobs start
-  # These jobs need to run after all the binary jobs run, regardless of if the
-  # jobs failed or not. There's no way to do this in CircleCI right now, so we
-  # just schedule this to run after all the binary jobs should've finished.
-  # These jobs are all idempotent and very lightweight; they just upload html
-  # files that track what binaries are available and what their sizes are.
-  update_s3_htmls:
-    triggers:
-      - schedule:
-          cron: "0 9 * * *"
-          filters:
-            branches:
-              only:
-                - master
-    jobs:
-      - setup
-      - update_s3_htmls_for_nightlies:
-          context: org-member
-          requires:
-            - setup
-      - update_s3_htmls_for_nightlies_devtoolset7:
-          context: org-member
-          requires:
-            - setup
-      - upload_binary_sizes:
-          context: org-member
-          requires:
-            - setup
-

.circleci/verbatim-sources/workflows.yml

@@ -1,12 +0,0 @@
-
-##############################################################################
-##############################################################################
-# Workflows
-##############################################################################
-##############################################################################
-
-# PR jobs pr builds
-workflows:
-  version: 2
-  build:
-    jobs:

.clang-tidy

@@ -1,34 +1,43 @@
 ---
-# NOTE there must be no spaces before the '-', so put the comma first.
+# NOTE: there must be no spaces before the '-', so put the comma first.
 Checks: '
-  -*
-  ,bugprone-*
-  ,-bugprone-forward-declaration-namespace
-  ,-bugprone-macro-parentheses
-  ,cppcoreguidelines-*
-  ,-cppcoreguidelines-interfaces-global-init
+  *
+  ,modernize-*
+  ,-cert-err58-cpp
+  ,-cert-err60-cpp
+  ,-clang-diagnostic-*
   ,-cppcoreguidelines-owning-memory
   ,-cppcoreguidelines-pro-bounds-array-to-pointer-decay
   ,-cppcoreguidelines-pro-bounds-constant-array-index
-  ,-cppcoreguidelines-pro-bounds-pointer-arithmetic
-  ,-cppcoreguidelines-pro-type-cstyle-cast
-  ,-cppcoreguidelines-pro-type-reinterpret-cast
   ,-cppcoreguidelines-pro-type-static-cast-downcast
-  ,-cppcoreguidelines-pro-type-union-access
   ,-cppcoreguidelines-pro-type-vararg
   ,-cppcoreguidelines-special-member-functions
-  ,hicpp-exception-baseclass
-  ,hicpp-avoid-goto
-  ,modernize-*
-  ,-modernize-return-braced-init-list
-  ,-modernize-use-auto
+  ,-fuchsia-*
+  ,-google-build-using-namespace
+  ,-google-explicit-constructor
+  ,-google-readability-braces-around-statements
+  ,-google-readability-namespace-comments
+  ,-google-readability-todo
+  ,-google-runtime-references
+  ,-google-runtime-references
+  ,-hicpp-braces-around-statements
+  ,-hicpp-explicit-conversions
+  ,-hicpp-no-array-decay
+  ,-hicpp-special-member-functions
+  ,-hicpp-vararg
+  ,-llvm-header-guard
+  ,-llvm-namespace-comment
+  ,-misc-unused-parameters
+  ,-modernize-make-unique
   ,-modernize-use-default-member-init
-  ,-modernize-use-using
-  ,performance-*
-  ,-performance-noexcept-move-constructor
+  ,-performance-unnecessary-value-param
+  ,-readability-braces-around-statements
+  ,-readability-else-after-return
+  ,-readability-named-parameter
+  ,clang-analyzer-*
   '
-WarningsAsErrors: '*'
-HeaderFilterRegex: 'torch/csrc/.*'
+WarningsAsErrors: ''
+HeaderFilterRegex: 'torch/csrc/'
 AnalyzeTemporaryDtors: false
 CheckOptions:
 ...

.ctags.d/pytorch.ctags

@@ -1,2 +0,0 @@
---exclude=build/*
---exclude=include/*

.flake8

@@ -1,12 +0,0 @@
-[flake8]
-select = B,C,E,F,P,T4,W,B9
-max-line-length = 120
-# C408 ignored because we like the dict keyword argument syntax
-# E501 is not flexible enough, we're using B950 instead
-ignore =
-    E203,E305,E402,E501,E721,E741,F403,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,
-    # these ignores are from flake8-bugbear; please fix!
-    B007,B008,
-    # these ignores are from flake8-comprehensions; please fix!
-    C400,C401,C402,C403,C404,C405,C407,C411,
-exclude = docs/src,venv,third_party,caffe2,scripts,docs/caffe2,tools/amd_build/pyHIPIFY,torch/lib/include,torch/lib/tmp_install,build,torch/include,*.pyi

.gitattributes

@@ -1 +0,0 @@
-*.bat	text eol=crlf

.github/ISSUE_TEMPLATE/bug-report.md

@@ -1,49 +0,0 @@
----
-name: "\U0001F41B Bug Report"
-about: Submit a bug report to help us improve PyTorch
-
----
-
-## 🐛 Bug
-
-<!-- A clear and concise description of what the bug is. -->
-
-## To Reproduce
-
-Steps to reproduce the behavior:
-
-1.
-1.
-1.
-
-<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
-
-## Expected behavior
-
-<!-- A clear and concise description of what you expected to happen. -->
-
-## Environment
-
-Please copy and paste the output from our
-[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
-(or fill out the checklist below manually).
-
-You can get the script and run it with:
-```
-wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
-# For security purposes, please check the contents of collect_env.py before running it.
-python collect_env.py
-```
-
- - PyTorch Version (e.g., 1.0):
- - OS (e.g., Linux):
- - How you installed PyTorch (`conda`, `pip`, source):
- - Build command you used (if compiling from source):
- - Python version:
- - CUDA/cuDNN version:
- - GPU models and configuration:
- - Any other relevant information:
-
-## Additional context
-
-<!-- Add any other context about the problem here. -->

.github/ISSUE_TEMPLATE/documentation.md

@@ -1,9 +0,0 @@
----
-name: "\U0001F4DA Documentation"
-about: Report an issue related to https://pytorch.org/docs
-
----
-
-## 📚 Documentation
-
-<!-- A clear and concise description of what content in https://pytorch.org/docs is an issue. If this has to do with the general https://pytorch.org website, please file an issue at https://github.com/pytorch/pytorch.github.io/issues/new/choose instead. If this has to do with https://pytorch.org/tutorials, please file an issue at https://github.com/pytorch/tutorials/issues/new -->

.github/ISSUE_TEMPLATE/feature-request.md

@@ -1,24 +0,0 @@
----
-name: "\U0001F680Feature Request"
-about: Submit a proposal/request for a new PyTorch feature
-
----
-
-## 🚀 Feature
-<!-- A clear and concise description of the feature proposal -->
-
-## Motivation
-
-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
-
-## Pitch
-
-<!-- A clear and concise description of what you want to happen. -->
-
-## Alternatives
-
-<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
-
-## Additional context
-
-<!-- Add any other context or screenshots about the feature request here. -->

.github/ISSUE_TEMPLATE/questions-help-support.md

@@ -1,13 +0,0 @@
----
-name: "❓Questions/Help/Support"
-about: Do you need support? We have resources.
-
----
-
-## ❓ Questions and Help
-
-### Please note that this issue tracker is not a help form and this issue will be closed.
-
-We have a set of [listed resources available on the website](https://pytorch.org/resources). Our primary means of support is our discussion forum:
-
-- [Discussion Forum](https://discuss.pytorch.org/)

.gitignore

@@ -22,33 +22,24 @@
 aten/build/
 aten/src/ATen/Config.h
 aten/src/ATen/cuda/CUDAConfig.h
-caffe2/cpp_test/
+build/
 dist/
 docs/src/**/*
-docs/cpp/build
-docs/cpp/source/api
 test/.coverage
-test/.hypothesis/
 test/cpp/api/mnist
-test/custom_operator/model.pt
+test/data/gpu_tensors.pt
 test/data/legacy_modules.t7
-test/data/*.pt
-dropout_model.pt
-test/generated_type_hints_smoketest.py
+test/data/legacy_serialized.pt
+test/data/linear.pt
 test/htmlcov
-test/cpp_extensions/install/
 third_party/build/
 tools/shared/_utils_internal.py
 torch.egg-info/
-torch/__init__.pyi
-torch/nn/functional.pyi
-torch/nn/modules/*.pyi
 torch/csrc/autograd/generated/*
 torch/csrc/cudnn/cuDNN.cpp
 torch/csrc/generated
 torch/csrc/generic/TensorMethods.cpp
 torch/csrc/jit/generated/*
-torch/csrc/jit/fuser/config.h
 torch/csrc/nn/THCUNN.cpp
 torch/csrc/nn/THCUNN.cwrap
 torch/csrc/nn/THNN_generic.cpp
@@ -56,36 +47,20 @@ torch/csrc/nn/THNN_generic.cwrap
 torch/csrc/nn/THNN_generic.h
 torch/csrc/nn/THNN.cpp
 torch/csrc/nn/THNN.cwrap
-torch/bin/
-torch/cmake/
 torch/lib/*.a*
 torch/lib/*.dll*
-torch/lib/*.exe*
 torch/lib/*.dylib*
 torch/lib/*.h
 torch/lib/*.lib
 torch/lib/*.so*
-torch/lib/protobuf*.pc
 torch/lib/build
-torch/lib/caffe2/
 torch/lib/cmake
 torch/lib/include
 torch/lib/pkgconfig
 torch/lib/protoc
-torch/lib/protobuf/
 torch/lib/tmp_install
 torch/lib/torch_shm_manager
-torch/lib/site-packages/
-torch/lib/python*
-torch/lib64
-torch/include/
-torch/share/
-torch/test/
 torch/version.py
-# Root level file used in CI to specify certain env configs.
-# E.g., see .circleci/config.yaml
-env
-.circleci/scripts/COMMIT_MSG
 
 # IPython notebook checkpoints
 .ipynb_checkpoints
@@ -165,12 +140,13 @@ docs/source/scripts/activation_images/
 # PyCharm files
 .idea
 
+# Visual Studio Code files
+.vscode
+.vs
+
 # OSX dir files
 .DS_Store
 
-# GDB history
-.gdb_history
-
 ## Caffe2
 
 # build, distribute, and bins (+ python proto bindings)
@@ -205,6 +181,7 @@ docs/dev
 *.sst
 *.ldb
 LOCK
+LOG*
 CURRENT
 MANIFEST-*
 
@@ -217,26 +194,3 @@ caffe2.egg-info
 
 # Atom/Watchman required file
 .watchmanconfig
-
-# Files generated by CLion
-cmake-build-debug
-
-# Files generated by ctags
-CTAGS
-tags
-TAGS
-
-# BEGIN NOT-CLEAN-FILES (setup.py handles this marker. Do not change.)
-#
-# Below files are not deleted by "setup.py clean".
-
-# Visual Studio Code files
-.vscode
-.vs
-
-# YouCompleteMe config file
-.ycm_extra_conf.py
-
-# Files generated when a patch is rejected
-*.orig
-*.rej

.gitmodules

@@ -1,116 +1,81 @@
+[submodule "third_party/catch"]
+	path = third_party/catch
+	url = https://github.com/catchorg/Catch2.git
+[submodule "third_party/nanopb"]
+	path = third_party/nanopb
+	url = https://github.com/nanopb/nanopb.git
 [submodule "third_party/pybind11"]
-    ignore = dirty
-    path = third_party/pybind11
-    url = https://github.com/pybind/pybind11.git
+	path = third_party/pybind11
+	url = https://github.com/pybind/pybind11.git
 [submodule "third_party/cub"]
-    ignore = dirty
-    path = third_party/cub
-    url = https://github.com/NVlabs/cub.git
+	path = third_party/cub
+	url = https://github.com/NVlabs/cub.git
 [submodule "third_party/eigen"]
-    ignore = dirty
-    path = third_party/eigen
-    url = https://github.com/eigenteam/eigen-git-mirror.git
+	path = third_party/eigen
+	url = https://github.com/eigenteam/eigen-git-mirror.git
 [submodule "third_party/googletest"]
-    ignore = dirty
-    path = third_party/googletest
-    url = https://github.com/google/googletest.git
+	path = third_party/googletest
+	url = https://github.com/google/googletest.git
+[submodule "third_party/nervanagpu"]
+	path = third_party/nervanagpu
+	url = https://github.com/NervanaSystems/nervanagpu.git
 [submodule "third_party/benchmark"]
-    ignore = dirty
-    path = third_party/benchmark
-    url = https://github.com/google/benchmark.git
+	path = third_party/benchmark
+	url = https://github.com/google/benchmark.git
 [submodule "third_party/protobuf"]
-    ignore = dirty
-    path = third_party/protobuf
-    url = https://github.com/protocolbuffers/protobuf.git
+	path = third_party/protobuf
+	url = https://github.com/google/protobuf.git
 [submodule "third_party/ios-cmake"]
-    ignore = dirty
-    path = third_party/ios-cmake
-    url = https://github.com/Yangqing/ios-cmake.git
+	path = third_party/ios-cmake
+	url = https://github.com/Yangqing/ios-cmake.git
 [submodule "third_party/NNPACK"]
-    ignore = dirty
-    path = third_party/NNPACK
-    url = https://github.com/Maratyszcza/NNPACK.git
+	path = third_party/NNPACK
+	url = https://github.com/Maratyszcza/NNPACK.git
 [submodule "third_party/gloo"]
-    ignore = dirty
-    path = third_party/gloo
-    url = https://github.com/facebookincubator/gloo
+	path = third_party/gloo
+	url = https://github.com/facebookincubator/gloo
 [submodule "third_party/NNPACK_deps/pthreadpool"]
-    ignore = dirty
-    path = third_party/pthreadpool
-    url = https://github.com/Maratyszcza/pthreadpool.git
+	path = third_party/pthreadpool
+	url = https://github.com/Maratyszcza/pthreadpool.git
 [submodule "third_party/NNPACK_deps/FXdiv"]
-    ignore = dirty
-    path = third_party/FXdiv
-    url = https://github.com/Maratyszcza/FXdiv.git
+	path = third_party/FXdiv
+	url = https://github.com/Maratyszcza/FXdiv.git
 [submodule "third_party/NNPACK_deps/FP16"]
-    ignore = dirty
-    path = third_party/FP16
-    url = https://github.com/Maratyszcza/FP16.git
+	path = third_party/FP16
+	url = https://github.com/Maratyszcza/FP16.git
 [submodule "third_party/NNPACK_deps/psimd"]
-    ignore = dirty
-    path = third_party/psimd
-    url = https://github.com/Maratyszcza/psimd.git
+	path = third_party/psimd
+	url = https://github.com/Maratyszcza/psimd.git
 [submodule "third_party/zstd"]
-    ignore = dirty
-    path = third_party/zstd
-    url = https://github.com/facebook/zstd.git
+	path = third_party/zstd
+	url = https://github.com/facebook/zstd.git
 [submodule "third-party/cpuinfo"]
-    ignore = dirty
-    path = third_party/cpuinfo
-    url = https://github.com/pytorch/cpuinfo.git
+	path = third_party/cpuinfo
+	url = https://github.com/Maratyszcza/cpuinfo.git
 [submodule "third_party/python-enum"]
-    ignore = dirty
-    path = third_party/python-enum
-    url = https://github.com/PeachPy/enum34.git
+	path = third_party/python-enum
+	url = https://github.com/PeachPy/enum34.git
 [submodule "third_party/python-peachpy"]
-    ignore = dirty
-    path = third_party/python-peachpy
-    url = https://github.com/Maratyszcza/PeachPy.git
+	path = third_party/python-peachpy
+	url = https://github.com/Maratyszcza/PeachPy.git
 [submodule "third_party/python-six"]
-    ignore = dirty
-    path = third_party/python-six
-    url = https://github.com/benjaminp/six.git
+	path = third_party/python-six
+	url = https://github.com/benjaminp/six.git
+[submodule "third_party/ComputeLibrary"]
+	path = third_party/ComputeLibrary
+	url = https://github.com/ARM-software/ComputeLibrary.git
 [submodule "third_party/onnx"]
-    ignore = dirty
-    path = third_party/onnx
-    url = https://github.com/onnx/onnx.git
+	path = third_party/onnx
+	url = https://github.com/onnx/onnx.git
+[submodule "third_party/cereal"]
+	path = third_party/cereal
+	url = https://github.com/USCiLab/cereal
 [submodule "third_party/onnx-tensorrt"]
-    ignore = dirty
-    path = third_party/onnx-tensorrt
-    url = https://github.com/onnx/onnx-tensorrt
+	path = third_party/onnx-tensorrt
+	url = https://github.com/onnx/onnx-tensorrt
 [submodule "third_party/sleef"]
-    ignore = dirty
-    path = third_party/sleef
-    url = https://github.com/zdevito/sleef
+	path = third_party/sleef
+	url = https://github.com/shibatch/sleef
 [submodule "third_party/ideep"]
-    ignore = dirty
-    path = third_party/ideep
-    url = https://github.com/intel/ideep
-[submodule "third_party/nccl/nccl"]
-    ignore = dirty
-    path = third_party/nccl/nccl
-    url = https://github.com/NVIDIA/nccl
-[submodule "third_party/gemmlowp/gemmlowp"]
-    ignore = dirty
-    path = third_party/gemmlowp/gemmlowp
-    url = https://github.com/google/gemmlowp.git
-[submodule "third_party/QNNPACK"]
-    ignore = dirty
-    path = third_party/QNNPACK
-    url = https://github.com/pytorch/QNNPACK
-[submodule "third_party/neon2sse"]
-    ignore = dirty
-    path = third_party/neon2sse
-    url = https://github.com/intel/ARM_NEON_2_x86_SSE.git
-[submodule "third_party/fbgemm"]
-    ignore = dirty
-    path = third_party/fbgemm
-    url = https://github.com/pytorch/fbgemm
-[submodule "third_party/foxi"]
-    ignore = dirty
-    path = third_party/foxi
-    url = https://github.com/houseroad/foxi.git
-[submodule "third_party/tbb"]
-	path = third_party/tbb
-	url = https://github.com/01org/tbb
-	branch = tbb_2018
+	path = third_party/ideep
+	url = https://github.com/intel/ideep

.jenkins/caffe2/bench.sh

@@ -1,60 +0,0 @@
-#!/bin/bash
-
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-# Anywhere except $ROOT_DIR should work. This is so the python import doesn't
-# get confused by any 'caffe2' directory in cwd
-cd "$INSTALL_PREFIX"
-
-if [[ $BUILD_ENVIRONMENT == *-cuda* ]]; then
-    num_gpus=$(nvidia-smi -L | wc -l)
-elif [[ $BUILD_ENVIRONMENT == *-rocm* ]]; then
-    num_gpus=$(rocminfo | grep 'Device Type.*GPU' | wc -l)
-else
-    num_gpus=0
-fi
-
-caffe2_pypath="$(cd /usr && $PYTHON -c 'import os; import caffe2; print(os.path.dirname(os.path.realpath(caffe2.__file__)))')"
-# Resnet50
-if (( $num_gpus == 0 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 128 --epoch_size 12800 --num_epochs 2 --use_cpu
-fi
-if (( $num_gpus >= 1 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 128 --epoch_size 12800 --num_epochs 2 --num_gpus 1
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 256 --epoch_size 25600 --num_epochs 2 --num_gpus 1 --float16_compute --dtype float16
-fi
-if (( $num_gpus >= 2 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 256 --epoch_size 25600 --num_epochs 2 --num_gpus 2
-fi
-if (( $num_gpus >= 4 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 512 --epoch_size 51200 --num_epochs 2 --num_gpus 4
-fi
-
-# ResNext
-if (( $num_gpus == 0 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --resnext_num_groups 32 --resnext_width_per_group 4 --num_layers 101 --train_data null --batch_size 32 --epoch_size 3200 --num_epochs 2 --use_cpu
-fi
-if (( $num_gpus >= 1 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --resnext_num_groups 32 --resnext_width_per_group 4 --num_layers 101 --train_data null --batch_size 32 --epoch_size 3200 --num_epochs 2 --num_gpus 1
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --resnext_num_groups 32 --resnext_width_per_group 4 --num_layers 101 --train_data null --batch_size 64 --epoch_size 3200 --num_epochs 2 --num_gpus 1 --float16_compute --dtype float16
-fi
-if (( $num_gpus >= 2 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --resnext_num_groups 32 --resnext_width_per_group 4 --num_layers 101 --train_data null --batch_size 64 --epoch_size 6400 --num_epochs 2 --num_gpus 2
-fi
-if (( $num_gpus >= 4 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --resnext_num_groups 32 --resnext_width_per_group 4 --num_layers 101 --train_data null --batch_size 128 --epoch_size 12800 --num_epochs 2 --num_gpus 4
-fi
-
-# Shufflenet
-if (( $num_gpus == 0 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 32 --epoch_size 3200 --num_epochs 2 --use_cpu --model shufflenet
-fi
-if (( $num_gpus >= 1 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 32 --epoch_size 3200 --num_epochs 2 --num_gpus 1 --model shufflenet
-fi
-if (( $num_gpus >= 2 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 64 --epoch_size 6400 --num_epochs 2 --num_gpus 2 --model shufflenet
-fi
-if (( $num_gpus >= 4 )); then
-    "$PYTHON" "$caffe2_pypath/python/examples/imagenet_trainer.py" --train_data null --batch_size 128 --epoch_size 12800 --num_epochs 2 --num_gpus 4 --model shufflenet
-fi

.jenkins/caffe2/build.sh

@@ -2,72 +2,46 @@
 
 set -ex
 
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-# TODO: Migrate all centos jobs to use proper devtoolset
-if [[ "$BUILD_ENVIRONMENT" == *py2-cuda9.0-cudnn7-centos7* ]]; then
-  # There is a bug in pango packge on Centos7 that causes undefined
-  # symbols, upgrading glib2 to >=2.56.1 solves the issue. See
-  # https://bugs.centos.org/view.php?id=15495
-  sudo yum install -y -q glib2-2.56.1
-fi
-
-# CMAKE_ARGS are only passed to 'cmake' and the -Dfoo=bar does not work with
-# setup.py, so we build a list of foo=bars and then either convert it to
-# -Dfoo=bars or export them before running setup.py
-build_args=()
-build_to_cmake () {
-  cmake_args=()
-  for build_arg in $*; do
-    cmake_args+=("-D$build_arg")
-  done
-  echo ${cmake_args[@]}
-}
+# The INSTALL_PREFIX here must match up with test.sh
+INSTALL_PREFIX="/usr/local/caffe2"
+LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
+CMAKE_ARGS=()
 
 
-SCCACHE="$(which sccache)"
-if [ "$(which gcc)" != "/root/sccache/gcc" ]; then
-  # Setup SCCACHE
-  ###############################################################################
-  # Setup sccache if SCCACHE_BUCKET is set
-  if [ -n "${SCCACHE_BUCKET}" ]; then
-    mkdir -p ./sccache
+# Setup SCCACHE
+###############################################################################
+# Setup sccache if SCCACHE_BUCKET is set
+if [ -n "${SCCACHE_BUCKET}" ]; then
+  mkdir -p ./sccache
 
-    SCCACHE="$(which sccache)"
-    if [ -z "${SCCACHE}" ]; then
-      echo "Unable to find sccache..."
-      exit 1
-    fi
-
-    # Setup wrapper scripts
-    wrapped="cc c++ gcc g++ x86_64-linux-gnu-gcc"
-    if [[ "${BUILD_ENVIRONMENT}" == *-cuda* ]]; then
-        wrapped="$wrapped nvcc"
-    fi
-    for compiler in $wrapped; do
-      (
-        echo "#!/bin/sh"
-
-        # TODO: if/when sccache gains native support for an
-        # SCCACHE_DISABLE flag analogous to ccache's CCACHE_DISABLE,
-        # this can be removed. Alternatively, this can be removed when
-        # https://github.com/pytorch/pytorch/issues/13362 is fixed.
-        #
-        # NOTE: carefully quoted - we want `which compiler` to be
-        # resolved as we execute the script, but SCCACHE_DISABLE and
-        # $@ to be evaluated when we execute the script
-        echo 'test $SCCACHE_DISABLE && exec '"$(which $compiler)"' "$@"'
-
-        echo "exec $SCCACHE $(which $compiler) \"\$@\""
-      ) > "./sccache/$compiler"
-      chmod +x "./sccache/$compiler"
-    done
-
-    export CACHE_WRAPPER_DIR="$PWD/sccache"
-
-    # CMake must find these wrapper scripts
-    export PATH="$CACHE_WRAPPER_DIR:$PATH"
+  SCCACHE="$(which sccache)"
+  if [ -z "${SCCACHE}" ]; then
+    echo "Unable to find sccache..."
+    exit 1
   fi
+
+  # Setup wrapper scripts
+  for compiler in cc c++ gcc g++ x86_64-linux-gnu-gcc; do
+    (
+      echo "#!/bin/sh"
+      echo "exec $SCCACHE $(which $compiler) \"\$@\""
+    ) > "./sccache/$compiler"
+    chmod +x "./sccache/$compiler"
+  done
+
+  if [[ "${BUILD_ENVIRONMENT}" == *-cuda* ]]; then
+    (
+      echo "#!/bin/sh"
+      echo "exec $SCCACHE $(which nvcc) \"\$@\""
+    ) > "./sccache/nvcc"
+    chmod +x "./sccache/nvcc"
+  fi
+
+  export CACHE_WRAPPER_DIR="$PWD/sccache"
+
+  # CMake must find these wrapper scripts
+  export PATH="$CACHE_WRAPPER_DIR:$PATH"
 fi
 
 # Setup ccache if configured to use it (and not sccache)
@@ -85,15 +59,6 @@ if [ -z "${SCCACHE}" ] && which ccache > /dev/null; then
   export PATH="$CACHE_WRAPPER_DIR:$PATH"
 fi
 
-# sccache will fail for CUDA builds if all cores are used for compiling
-if [ -z "$MAX_JOBS" ]; then
-  if [[ "${BUILD_ENVIRONMENT}" == *-cuda* ]] && [ -n "${SCCACHE}" ]; then
-    MAX_JOBS=`expr $(nproc) - 1`
-  else
-    MAX_JOBS=$(nproc)
-  fi
-fi
-
 report_compile_cache_stats() {
   if [[ -n "${SCCACHE}" ]]; then
     "$SCCACHE" --show-stats
@@ -102,61 +67,73 @@ report_compile_cache_stats() {
   fi
 }
 
+###############################################################################
+# Explicitly set Python executable.
+###############################################################################
+# On Ubuntu 16.04 the default Python is still 2.7.
+PYTHON="$(which python)"
+if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
+  PYTHON=$(which "python${BASH_REMATCH[1]}")
+  CMAKE_ARGS+=("-DPYTHON_EXECUTABLE=${PYTHON}")
+fi
+
 
 ###############################################################################
-# Use special scripts for Android and setup builds
+# Use special scripts for Android, conda, and setup builds
 ###############################################################################
 if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
   export ANDROID_NDK=/opt/ndk
-  build_args+=("BUILD_BINARY=ON")
-  build_args+=("BUILD_TEST=ON")
-  build_args+=("USE_OBSERVERS=ON")
-  build_args+=("USE_ZSTD=ON")
-  "${ROOT_DIR}/scripts/build_android.sh" $(build_to_cmake ${build_args[@]}) "$@"
+  CMAKE_ARGS+=("-DBUILD_BINARY=ON")
+  CMAKE_ARGS+=("-DBUILD_TEST=ON")
+  CMAKE_ARGS+=("-DUSE_OBSERVERS=ON")
+  CMAKE_ARGS+=("-DUSE_ZSTD=ON")
+  "${ROOT_DIR}/scripts/build_android.sh" ${CMAKE_ARGS[*]} "$@"
+  exit 0
+elif [[ "${BUILD_ENVIRONMENT}" == conda* ]]; then
+  "${ROOT_DIR}/scripts/build_anaconda.sh" --skip-tests --install-locally "$@"
+  report_compile_cache_stats
+
+  # This build will be tested against onnx tests, which needs onnx installed.
+  # At this point the visible protbuf installation will be in conda, since one
+  # of Caffe2's dependencies uses conda, so the correct protobuf include
+  # headers are those in conda as well
+  # This path comes from install_anaconda.sh which installs Anaconda into the
+  # docker image
+  PROTOBUF_INCDIR=/opt/conda/include pip install -b /tmp/pip_install_onnx "file://${ROOT_DIR}/third_party/onnx#egg=onnx"
+  report_compile_cache_stats
+  exit 0
+elif [[ $BUILD_ENVIRONMENT == *setup* ]]; then
+  rm -rf $INSTALL_PREFIX && mkdir $INSTALL_PREFIX
+  PYTHONPATH=$INSTALL_PREFIX $PYTHON setup_caffe2.py develop --install-dir $INSTALL_PREFIX
   exit 0
 fi
 
+
 ###############################################################################
-# Set parameters
+# Set cmake args
 ###############################################################################
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  build_args+=("BUILD_PYTHON=OFF")
-else
-  build_args+=("BUILD_PYTHON=ON")
-  build_args+=("PYTHON_EXECUTABLE=${PYTHON}")
+CMAKE_ARGS+=("-DBUILD_BINARY=ON")
+CMAKE_ARGS+=("-DBUILD_TEST=ON")
+CMAKE_ARGS+=("-DINSTALL_TEST=ON")
+CMAKE_ARGS+=("-DUSE_OBSERVERS=ON")
+CMAKE_ARGS+=("-DUSE_ZSTD=ON")
+CMAKE_ARGS+=("-DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX}")
+
+if [[ $BUILD_ENVIRONMENT == *-aten-* ]]; then
+  if [[ CMAKE_ARGS != *USE_ATEN* ]] && [[ CMAKE_ARGS != *BUILD_ATEN* ]]; then
+    CMAKE_ARGS+=("-DBUILD_ATEN=ON")
+  fi
 fi
 if [[ $BUILD_ENVIRONMENT == *mkl* ]]; then
-  build_args+=("BLAS=MKL")
-  build_args+=("USE_MKLDNN=ON")
+  CMAKE_ARGS+=("-DBLAS=MKL")
 fi
-build_args+=("BUILD_BINARY=ON")
-build_args+=("BUILD_TEST=ON")
-build_args+=("INSTALL_TEST=ON")
-build_args+=("USE_ZSTD=ON")
-
-if [[ $BUILD_ENVIRONMENT == *py2-cuda9.0-cudnn7-ubuntu16.04* ]]; then
-  # removing http:// duplicate in favor of nvidia-ml.list
-  # which is https:// version of the same repo
-  sudo rm -f /etc/apt/sources.list.d/nvidia-machine-learning.list
-  curl -o ./nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb
-  sudo dpkg -i ./nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb
-  sudo apt-key add /var/nvinfer-runtime-trt-repo-5.0.2-ga-cuda9.0/7fa2af80.pub
-  sudo apt-get -qq update
-  sudo apt-get install -y --no-install-recommends libnvinfer5=5.0.2-1+cuda9.0 libnvinfer-dev=5.0.2-1+cuda9.0
-  rm ./nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb
-
-  build_args+=("USE_TENSORRT=ON")
-fi
-
 if [[ $BUILD_ENVIRONMENT == *cuda* ]]; then
-  build_args+=("USE_CUDA=ON")
-  build_args+=("USE_NNPACK=OFF")
-
-  # Target only our CI GPU machine's CUDA arch to speed up the build
-  build_args+=("TORCH_CUDA_ARCH_LIST=Maxwell")
+  CMAKE_ARGS+=("-DUSE_CUDA=ON")
+  CMAKE_ARGS+=("-DCUDA_ARCH_NAME=Maxwell")
+  CMAKE_ARGS+=("-DUSE_NNPACK=OFF")
 
   # Explicitly set path to NVCC such that the symlink to ccache or sccache is used
-  build_args+=("CUDA_NVCC_EXECUTABLE=${CACHE_WRAPPER_DIR}/nvcc")
+  CMAKE_ARGS+=("-DCUDA_NVCC_EXECUTABLE=${CACHE_WRAPPER_DIR}/nvcc")
 
   # Ensure FindCUDA.cmake can infer the right path to the CUDA toolkit.
   # Setting PATH to resolve to the right nvcc alone isn't enough.
@@ -167,103 +144,81 @@ if [[ $BUILD_ENVIRONMENT == *cuda* ]]; then
   export PATH="/usr/local/cuda/bin:$PATH"
 fi
 if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
-  build_args+=("USE_ROCM=ON")
-  # This is needed to enable ImageInput operator in resnet50_trainer
-  build_args+=("USE_OPENCV=ON")
-  # This is needed to read datasets from https://download.caffe2.ai/databases/resnet_trainer.zip
-  build_args+=("USE_LMDB=ON")
-  # When hcc runs out of memory, it silently exits without stopping
-  # the build process, leaving undefined symbols in the shared lib
-  # which will cause undefined symbol errors when later running
-  # tests. Setting MAX_JOBS to smaller number to make CI less flaky.
-  export MAX_JOBS=4
+  # TODO: This is patching the official FindHip to properly handly
+  # cmake generator expression. A PR is opened in the upstream repo here:
+  # https://github.com/ROCm-Developer-Tools/HIP/pull/516
+  # remove this hack once it's merged.
+  if [[ -f /opt/rocm/hip/cmake/FindHIP.cmake ]]; then
+    sudo sed -i 's/\ -I${dir}/\ $<$<BOOL:${dir}>:-I${dir}>/' /opt/rocm/hip/cmake/FindHIP.cmake
+  fi
 
-  ########## HIPIFY Caffe2 operators
-  ${PYTHON} "${ROOT_DIR}/tools/amd_build/build_amd.py"
-fi
-
-# building bundled nccl in this config triggers a bug in nvlink. For
-# more, see https://github.com/pytorch/pytorch/issues/14486
-if [[ "${BUILD_ENVIRONMENT}" == *-cuda8*-cudnn7* ]]; then
-    build_args+=("USE_SYSTEM_NCCL=ON")
+  export LANG=C.UTF-8
+  export LC_ALL=C.UTF-8
+  export HCC_AMDGPU_TARGET=gfx900
 fi
 
 # Try to include Redis support for Linux builds
 if [ "$(uname)" == "Linux" ]; then
-  build_args+=("USE_REDIS=ON")
+  CMAKE_ARGS+=("-DUSE_REDIS=ON")
+fi
+
+# Currently, on Jenkins mac os, we will use custom protobuf. Mac OS
+# contbuild at the moment is minimal dependency - it doesn't use glog
+# or gflags either.
+if [ "$(uname)" == "Darwin" ]; then
+  CMAKE_ARGS+=("-DBUILD_CUSTOM_PROTOBUF=ON")
 fi
 
 # Use a speciallized onnx namespace in CI to catch hardcoded onnx namespace
-build_args+=("ONNX_NAMESPACE=ONNX_NAMESPACE_FOR_C2_CI")
+CMAKE_ARGS+=("-DONNX_NAMESPACE=ONNX_NAMESPACE_FOR_C2_CI")
+
+if [[ -n "$INTEGRATED" ]]; then
+    # TODO: This is a temporary hack to work around the issue that both
+    # caffe2 and pytorch have libcaffe2.so and crossfire at runtime.
+    CMAKE_ARGS+=("-DBUILD_SHARED_LIBS=OFF")
+    CMAKE_ARGS+=("-DBUILD_CUSTOM_PROTOBUF=OFF")
+    CMAKE_ARGS+=("-DCAFFE2_LINK_LOCAL_PROTOBUF=OFF")
+fi
+
+# We test the presence of cmake3 (for platforms like Centos and Ubuntu 14.04)
+# and use that if so.
+if [[ -x "$(command -v cmake3)" ]]; then
+    CMAKE_BINARY=cmake3
+else
+    CMAKE_BINARY=cmake
+fi
+# sccache will fail for CUDA builds if all cores are used for compiling
+if [[ "${BUILD_ENVIRONMENT}" == *-cuda* ]] && [ -n "${SCCACHE}" ]; then
+  MAX_JOBS=`expr $(nproc) - 1`
+else
+  MAX_JOBS=$(nproc)
+fi
+
 
 ###############################################################################
 # Configure and make
 ###############################################################################
+# Run cmake from ./build_caffe2 directory so it doesn't conflict with
+# standard PyTorch build directory. Eventually these won't need to
+# be separate.
+rm -rf build_caffe2
+mkdir build_caffe2
+cd ./build_caffe2
 
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  # cmake-only non-setup.py build, to test cpp only bits. This installs into
-  # /usr/local/caffe2 and installs no Python tests
-  build_args+=("CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX}")
-
-  # Run cmake from ./build_caffe2 directory so it doesn't conflict with
-  # standard PyTorch build directory. Eventually these won't need to
-  # be separate.
-  rm -rf build_caffe2
-  mkdir build_caffe2
-  cd ./build_caffe2
-
-  # We test the presence of cmake3 (for platforms like Centos and Ubuntu 14.04)
-  # and use that if so.
-  if [[ -x "$(command -v cmake3)" ]]; then
-      CMAKE_BINARY=cmake3
-  else
-      CMAKE_BINARY=cmake
-  fi
-
-  # Configure
-  ${CMAKE_BINARY} "${ROOT_DIR}" $(build_to_cmake ${build_args[@]}) "$@"
-
-  # Build
-  if [ "$(uname)" == "Linux" ]; then
-    make "-j${MAX_JOBS}" install
-  else
-    echo "Don't know how to build on $(uname)"
-    exit 1
-  fi
-
-  # This is to save test binaries for testing
-  mv "$INSTALL_PREFIX/test/" "$INSTALL_PREFIX/cpp_test/"
-
-  ls -lah $INSTALL_PREFIX
+# Configure
+${CMAKE_BINARY} "${ROOT_DIR}" ${CMAKE_ARGS[*]} "$@"
 
+# Build
+if [ "$(uname)" == "Linux" ]; then
+  make "-j${MAX_JOBS}" install
 else
-  # Python build. Uses setup.py to install into site-packages
-  build_args+=("USE_LEVELDB=ON")
-  build_args+=("USE_LMDB=ON")
-  build_args+=("USE_OPENCV=ON")
-  build_args+=("BUILD_TEST=ON")
-  # These flags preserve the flags that were used before this refactor (blame
-  # me)
-  build_args+=("USE_GLOG=ON")
-  build_args+=("USE_GFLAGS=ON")
-  build_args+=("USE_FBGEMM=OFF")
-  build_args+=("USE_MKLDNN=OFF")
-  build_args+=("USE_DISTRIBUTED=ON")
-  for build_arg in "${build_args[@]}"; do
-    export $build_arg
-  done
-
-  # sccache will be stuck if  all cores are used for compiling
-  # see https://github.com/pytorch/pytorch/pull/7361
-  if [[ -n "${SCCACHE}" && $BUILD_ENVIRONMENT != *rocm* ]]; then
-    export MAX_JOBS=`expr $(nproc) - 1`
-  fi
-
-  $PYTHON setup.py install --user
-
-  report_compile_cache_stats
+  echo "Don't know how to build on $(uname)"
+  exit 1
 fi
 
+report_compile_cache_stats
+
+
 ###############################################################################
 # Install ONNX
 ###############################################################################
@@ -271,14 +226,48 @@ fi
 # Install ONNX into a local directory
 pip install --user -b /tmp/pip_install_onnx "file://${ROOT_DIR}/third_party/onnx#egg=onnx"
 
-if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
-  ORIG_COMP=/opt/rocm/hcc/bin/clang-*_original
-  if [ -e $ORIG_COMP ]; then
-    # runtime compilation of MIOpen kernels manages to crash sccache - hence undo the wrapping
-    # note that the wrapping always names the compiler "clang-7.0_original"
-    WRAPPED=/opt/rocm/hcc/bin/clang-[0-99]
-    sudo mv $ORIG_COMP $WRAPPED
+report_compile_cache_stats
+
+if [[ -n "$INTEGRATED" ]]; then
+  # sccache will be stuck if  all cores are used for compiling
+  # see https://github.com/pytorch/pytorch/pull/7361
+  if [[ -n "${SCCACHE}" ]]; then
+    export MAX_JOBS=`expr $(nproc) - 1`
   fi
+  pip install --user -v -b /tmp/pip_install_torch "file://${ROOT_DIR}#egg=torch"
 fi
 
 report_compile_cache_stats
+
+# Symlink the caffe2 base python path into the system python path,
+# so that we can import caffe2 without having to change $PYTHONPATH.
+# Run in a subshell to contain environment set by /etc/os-release.
+#
+# This is only done when running on Jenkins!  We don't want to pollute
+# the user environment with Python symlinks and ld.so.conf.d hacks.
+#
+if [ -n "${JENKINS_URL}" ]; then
+  (
+    source /etc/os-release
+
+    function python_version() {
+      "$PYTHON" -c 'import sys; print("python%d.%d" % sys.version_info[0:2])'
+    }
+
+    # Debian/Ubuntu
+    if [[ "$ID_LIKE" == *debian* ]]; then
+      python_path="/usr/local/lib/$(python_version)/dist-packages"
+      sudo ln -sf "${INSTALL_PREFIX}/caffe2" "${python_path}"
+    fi
+
+    # RHEL/CentOS
+    if [[ "$ID_LIKE" == *rhel* ]]; then
+      python_path="/usr/lib64/$(python_version)/site-packages/"
+      sudo ln -sf "${INSTALL_PREFIX}/caffe2" "${python_path}"
+    fi
+
+    # /etc/ld.so.conf.d is used on both Debian and RHEL
+    echo "${INSTALL_PREFIX}/lib" | sudo tee /etc/ld.so.conf.d/caffe2.conf
+    sudo ldconfig
+  )
+fi

.jenkins/caffe2/common.sh

@@ -1,22 +0,0 @@
-set -ex
-
-LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
-ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
-TEST_DIR="$ROOT_DIR/caffe2_tests"
-gtest_reports_dir="${TEST_DIR}/cpp"
-pytest_reports_dir="${TEST_DIR}/python"
-
-# Figure out which Python to use
-PYTHON="$(which python)"
-if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
-  PYTHON=$(which "python${BASH_REMATCH[1]}")
-fi
-
-# /usr/local/caffe2 is where the cpp bits are installed to in in cmake-only
-# builds. In +python builds the cpp tests are copied to /usr/local/caffe2 so
-# that the test code in .jenkins/test.sh is the same
-INSTALL_PREFIX="/usr/local/caffe2"
-
-mkdir -p "$gtest_reports_dir" || true
-mkdir -p "$pytest_reports_dir" || true
-mkdir -p "$INSTALL_PREFIX" || true

.jenkins/caffe2/test.sh

@@ -1,6 +1,31 @@
 #!/bin/bash
 
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
+set -ex
+
+LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
+TEST_DIR=$ROOT_DIR/caffe2_tests
+
+# Figure out which Python to use
+PYTHON="python"
+if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
+  PYTHON="python${BASH_REMATCH[1]}"
+fi
+
+# The prefix must mirror the setting from build.sh
+INSTALL_PREFIX="/usr/local/caffe2"
+
+# Anaconda builds have a special install prefix and python
+if [[ "$BUILD_ENVIRONMENT" == conda* ]]; then
+  # This path comes from install_anaconda.sh which installs Anaconda into the
+  # docker image
+  PYTHON="/opt/conda/bin/python"
+  INSTALL_PREFIX="/opt/conda/"
+fi
+
+# Add the site-packages in the caffe2 install prefix to the PYTHONPATH
+SITE_DIR=$($PYTHON -c "from distutils import sysconfig; print(sysconfig.get_python_lib(prefix=''))")
+INSTALL_SITE_DIR="${INSTALL_PREFIX}/${SITE_DIR}"
 
 # Skip tests in environments where they are not built/applicable
 if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
@@ -8,129 +33,83 @@ if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
   exit 0
 fi
 
-# Find where cpp tests and Caffe2 itself are installed
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  # For cmake only build we install everything into /usr/local
-  cpp_test_dir="$INSTALL_PREFIX/cpp_test"
-  ld_library_path="$INSTALL_PREFIX/lib"
-else
-  # For Python builds we install into python
-  # cd to /usr first so the python import doesn't get confused by any 'caffe2'
-  # directory in cwd
-  python_installation="$(dirname $(dirname $(cd /usr && $PYTHON -c 'import os; import caffe2; print(os.path.realpath(caffe2.__file__))')))"
-  caffe2_pypath="$python_installation/caffe2"
-  cpp_test_dir="$python_installation/torch/test"
-  ld_library_path="$python_installation/torch/lib"
+# Set PYTHONPATH and LD_LIBRARY_PATH so that python can find the installed
+# Caffe2. This shouldn't be done on Anaconda, as Anaconda should handle this.
+if [[ "$BUILD_ENVIRONMENT" != conda* ]]; then
+  export PYTHONPATH="${PYTHONPATH}:$INSTALL_SITE_DIR"
+  export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${INSTALL_PREFIX}/lib"
 fi
 
-################################################################################
-# C++ tests #
-################################################################################
+cd "$ROOT_DIR"
+
+if [ -d $TEST_DIR ]; then
+  echo "Directory $TEST_DIR already exists; please remove it..."
+  exit 1
+fi
+
+mkdir -p $TEST_DIR/{cpp,python}
+
+cd ${INSTALL_PREFIX}
+
+# C++ tests
 echo "Running C++ tests.."
-for test in $(find "$cpp_test_dir" -executable -type f); do
+gtest_reports_dir="${TEST_DIR}/cpp"
+junit_reports_dir="${TEST_DIR}/junit_reports"
+mkdir -p "$gtest_reports_dir" "$junit_reports_dir"
+for test in $(find "${INSTALL_PREFIX}/test" -executable -type f); do
   case "$test" in
     # skip tests we know are hanging or bad
     */mkl_utils_test|*/aten/integer_divider_test)
       continue
       ;;
-    */scalar_tensor_test|*/basic|*/native_test)
-      if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-        continue
-      else
-        LD_LIBRARY_PATH="$ld_library_path" "$test"
-      fi
+    */aten/*)
+      # ATen uses test framework Catch2
+      "$test" -r=xml -o "${junit_reports_dir}/$(basename $test).xml"
       ;;
     *)
-      # Currently, we use a mixture of gtest (caffe2) and Catch2 (ATen). While
-      # planning to migrate to gtest as the common PyTorch c++ test suite, we
-      # currently do NOT use the xml test reporter, because Catch doesn't
-      # support multiple reporters
-      # c.f. https://github.com/catchorg/Catch2/blob/master/docs/release-notes.md#223
-      # which means that enabling XML output means you lose useful stdout
-      # output for Jenkins.  It's more important to have useful console
-      # output than it is to have XML output for Jenkins.
-      # Note: in the future, if we want to use xml test reporter once we switch
-      # to all gtest, one can simply do:
-      LD_LIBRARY_PATH="$ld_library_path" \
-          "$test" --gtest_output=xml:"$gtest_reports_dir/$(basename $test).xml"
+      "$test" --gtest_output=xml:"$gtest_reports_dir/$(basename $test).xml"
       ;;
   esac
 done
 
-################################################################################
-# Python tests #
-################################################################################
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  exit 0
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *ubuntu14.04* ]]; then
-  # Hotfix, use hypothesis 3.44.6 on Ubuntu 14.04
-  # See comments on
-  # https://github.com/HypothesisWorks/hypothesis-python/commit/eadd62e467d6cee6216e71b391951ec25b4f5830
-  sudo pip -q uninstall -y hypothesis
-  # "pip install hypothesis==3.44.6" from official server is unreliable on
-  # CircleCI, so we host a copy on S3 instead
-  sudo pip -q install attrs==18.1.0 -f https://s3.amazonaws.com/ossci-linux/wheels/attrs-18.1.0-py2.py3-none-any.whl
-  sudo pip -q install coverage==4.5.1 -f https://s3.amazonaws.com/ossci-linux/wheels/coverage-4.5.1-cp36-cp36m-macosx_10_12_x86_64.whl
-  sudo pip -q install hypothesis==3.44.6 -f https://s3.amazonaws.com/ossci-linux/wheels/hypothesis-3.44.6-py3-none-any.whl
-else
-  pip install --user --no-cache-dir hypothesis==3.59.0
-fi
+# Get the relative path to where the caffe2 python module was installed
+CAFFE2_PYPATH="$INSTALL_SITE_DIR/caffe2"
 
 # Collect additional tests to run (outside caffe2/python)
 EXTRA_TESTS=()
 
 # CUDA builds always include NCCL support
 if [[ "$BUILD_ENVIRONMENT" == *-cuda* ]]; then
-  EXTRA_TESTS+=("$caffe2_pypath/contrib/nccl")
+  EXTRA_TESTS+=("$CAFFE2_PYPATH/contrib/nccl")
 fi
 
-rocm_ignore_test=()
-if [[ $BUILD_ENVIRONMENT == *-rocm* ]]; then
-  # Currently these tests are failing on ROCM platform:
-
-  # Unknown reasons, need to debug
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/operator_test/piecewise_linear_transform_test.py")
-
-  # On ROCm, RCCL (distributed) development isn't complete.
-  # https://github.com/ROCmSoftwarePlatform/rccl
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/data_parallel_model_test.py")
+conda_ignore_test=()
+if [[ $BUILD_ENVIRONMENT == conda* ]]; then
+  # These tests both assume Caffe2 was built with leveldb, which is not the case
+  conda_ignore_test+=("--ignore $CAFFE2_PYPATH/python/dataio_test.py")
+  conda_ignore_test+=("--ignore $CAFFE2_PYPATH/python/operator_test/checkpoint_test.py")
 fi
 
-# NB: Warnings are disabled because they make it harder to see what
-# the actual erroring test is
-echo "Running Python tests.."
-if [[ "$BUILD_ENVIRONMENT" == *py3* ]]; then
-  # locale setting is required by click package with py3
-  export LC_ALL=C.UTF-8
-  export LANG=C.UTF-8
-fi
-pip install --user pytest-sugar
-"$PYTHON" \
-  -m pytest \
-  -x \
-  -v \
-  --disable-warnings \
-  --junit-xml="$pytest_reports_dir/result.xml" \
-  --ignore "$caffe2_pypath/python/test/executor_test.py" \
-  --ignore "$caffe2_pypath/python/operator_test/matmul_op_test.py" \
-  --ignore "$caffe2_pypath/python/operator_test/pack_ops_test.py" \
-  --ignore "$caffe2_pypath/python/mkl/mkl_sbn_speed_test.py" \
-  ${rocm_ignore_test[@]} \
-  "$caffe2_pypath/python" \
-  "${EXTRA_TESTS[@]}"
 
-#####################
-# torchvision tests #
-#####################
-if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
-  pip install -q --user git+https://github.com/pytorch/vision.git
-  pip install -q --user ninja
-  # JIT C++ extensions require ninja, so put it into PATH.
-  export PATH="/var/lib/jenkins/.local/bin:$PATH"
-  if [[ "$BUILD_ENVIRONMENT" == *py3* ]]; then
-    pip install -q --user onnxruntime==0.4.0
-  fi
-  "$ROOT_DIR/scripts/onnx/test.sh"
+# TODO: re-enable this for rocm CI jobs once we have more rocm workers
+if [[ $BUILD_ENVIRONMENT != *rocm* ]]; then
+  # Python tests
+  echo "Running Python tests.."
+  "$PYTHON" \
+    -m pytest \
+    -x \
+    -v \
+    --junit-xml="$TEST_DIR/python/result.xml" \
+    --ignore "$CAFFE2_PYPATH/python/test/executor_test.py" \
+    --ignore "$CAFFE2_PYPATH/python/operator_test/matmul_op_test.py" \
+    --ignore "$CAFFE2_PYPATH/python/operator_test/pack_ops_test.py" \
+    --ignore "$CAFFE2_PYPATH/python/mkl/mkl_sbn_speed_test.py" \
+    ${conda_ignore_test[@]} \
+    "$CAFFE2_PYPATH/python" \
+    "${EXTRA_TESTS[@]}"
+fi
+
+if [[ -n "$INTEGRATED" ]]; then
+  pip install --user pytest-xdist torchvision
+  "$ROOT_DIR/scripts/onnx/test.sh" -p
 fi

.jenkins/pytorch/build-asan.sh

@@ -4,9 +4,7 @@
 # (This is set by default in the Docker images we build, so you don't
 # need to set it yourself.
 
-# shellcheck disable=SC2034
-COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}"
-
+COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}-build"
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 echo "Clang version:"
@@ -16,24 +14,8 @@ clang --version
 # symbolize=1: Gives us much better errors when things go wrong
 export ASAN_OPTIONS=detect_leaks=0:symbolize=1
 
-# FIXME: Remove the hardcoded "-pthread" option.
-# With asan build, the cmake thread CMAKE_HAVE_LIBC_CREATE[1] checking will
-# succeed because "pthread_create" is in libasan.so. However, libasan doesn't
-# have the full pthread implementation. Other advanced pthread functions doesn't
-# exist in libasan.so[2]. If we need some pthread advanced functions, we still
-# need to link the pthread library.
-# This issue is already fixed in cmake 3.13[3]. If we use the newer cmake, we
-# could remove this hardcoded option.
-#
-# [1] https://github.com/Kitware/CMake/blob/8cabaaf054a16ea9c8332ce8e9291bd026b38c62/Modules/FindThreads.cmake#L135
-# [2] https://wiki.gentoo.org/wiki/AddressSanitizer/Problems
-# [3] https://github.com/Kitware/CMake/commit/e9a1ddc594de6e6251bf06d732775dae2cabe4c8
-#
-# TODO: Make the ASAN flags a centralized env var and unify with USE_ASAN option
+# TODO: Make the ASAN flags a more unified env var
 CC="clang" CXX="clang++" LDSHARED="clang --shared" \
-  CFLAGS="-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -shared-libasan -pthread" \
-  CXX_FLAGS="-pthread" \
-  USE_ASAN=1 USE_CUDA=0 USE_MKLDNN=0 \
+  CFLAGS="-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -shared-libasan" \
+  NO_CUDA=1 DEBUG=1 \
   python setup.py install
-
-assert_git_not_dirty

.jenkins/pytorch/build.sh

@@ -1,41 +1,22 @@
 #!/bin/bash
 
+if [[ "$BUILD_ENVIRONMENT" == "pytorch-linux-xenial-py3-clang5-asan" ]]; then
+  exec "$(dirname "${BASH_SOURCE[0]}")/build-asan.sh" $*
+fi
+
+# TODO: move this to Docker
+# TODO: add both NCCL and MPI in CI test by fixing these test first
+# sudo apt-get update
+# sudo apt-get install libnccl-dev libnccl2
+# sudo apt-get install openmpi-bin libopenmpi-dev
+
 # Required environment variable: $BUILD_ENVIRONMENT
 # (This is set by default in the Docker images we build, so you don't
 # need to set it yourself.
 
-# shellcheck disable=SC2034
-COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}"
-
+COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}-build"
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
-# For distributed, four environmental configs:
-# (1) build with only NCCL
-# (2) build with NCCL and MPI
-# (3) build with only MPI
-# (4) build with neither
-if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-* ]]; then
-  # TODO: move this to Docker
-  sudo apt-get -qq update
-  sudo apt-get -qq install --allow-downgrades --allow-change-held-packages libnccl-dev=2.2.13-1+cuda9.0 libnccl2=2.2.13-1+cuda9.0
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9*gcc7* ]] || [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-* ]] || [[ "$BUILD_ENVIRONMENT" == *-trusty-py2.7.9* ]]; then
-  # TODO: move this to Docker
-  sudo apt-get -qq update
-  if [[ "$BUILD_ENVIRONMENT" == *-trusty-py2.7.9* ]]; then
-    sudo apt-get -qq install openmpi-bin libopenmpi-dev
-  else
-    sudo apt-get -qq install --allow-downgrades --allow-change-held-packages openmpi-bin libopenmpi-dev
-  fi
-  sudo apt-get -qq install --no-install-recommends openssh-client openssh-server
-  sudo mkdir -p /var/run/sshd
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *-linux-xenial-py3-clang5-asan* ]]; then
-  exec "$(dirname "${BASH_SOURCE[0]}")/build-asan.sh" "$@"
-fi
-
 echo "Python version:"
 python --version
 
@@ -46,141 +27,67 @@ echo "CMake version:"
 cmake --version
 
 # TODO: Don't run this...
-pip_install -r requirements.txt || true
+pip install -r requirements.txt || true
+
+if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
+  export HCC_AMDGPU_TARGET=gfx900
+  export LANG=C.UTF-8
+  export LC_ALL=C.UTF-8
+
+  sudo chown -R jenkins:jenkins /usr/local
+  rm -rf "$(dirname "${BASH_SOURCE[0]}")/../../../pytorch_amd/" || true
+  python "$(dirname "${BASH_SOURCE[0]}")/../../tools/amd_build/build_pytorch_amd.py"
+  USE_ROCM=1 python setup.py install
+  exit
+fi
 
 # TODO: Don't install this here
 if ! which conda; then
-  # In ROCm CIs, we are doing cross compilation on build machines with
-  # intel cpu and later run tests on machines with amd cpu.
-  # Also leave out two builds to make sure non-mkldnn builds still work.
-  if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *-trusty-py3.5-* && "$BUILD_ENVIRONMENT" != *-xenial-cuda9-cudnn7-py3-* ]]; then
-    pip_install mkl mkl-devel
-    export USE_MKLDNN=1
-  else
-    export USE_MKLDNN=0
-  fi
-fi
-
-# Use special scripts for Android builds
-if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
-  export ANDROID_NDK=/opt/ndk
-  build_args=()
-  build_args+=("-DBUILD_CAFFE2_MOBILE=OFF")
-  build_args+=("-DCMAKE_PREFIX_PATH=$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')")
-  build_args+=("-DPYTHON_EXECUTABLE=$(python -c 'import sys; print(sys.executable)')")
-  exec ./scripts/build_android.sh "${build_args[@]}" "$@"
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-  # When hcc runs out of memory, it silently exits without stopping
-  # the build process, leaving undefined symbols in the shared lib
-  # which will cause undefined symbol errors when later running
-  # tests. Setting MAX_JOBS to smaller number to make CI less flaky.
-  export MAX_JOBS=4
-
-  # ROCm CI is using Caffe2 docker images, which needs these wrapper
-  # scripts to correctly use sccache.
-  if [ -n "${SCCACHE_BUCKET}" ]; then
-    mkdir -p ./sccache
-
-    SCCACHE="$(which sccache)"
-    if [ -z "${SCCACHE}" ]; then
-      echo "Unable to find sccache..."
-      exit 1
-    fi
-
-    # Setup wrapper scripts
-    for compiler in cc c++ gcc g++; do
-      (
-        echo "#!/bin/sh"
-        echo "exec $SCCACHE $(which $compiler) \"\$@\""
-      ) > "./sccache/$compiler"
-      chmod +x "./sccache/$compiler"
-    done
-
-    export CACHE_WRAPPER_DIR="$PWD/sccache"
-
-    # CMake must find these wrapper scripts
-    export PATH="$CACHE_WRAPPER_DIR:$PATH"
-  fi
-
-  python tools/amd_build/build_amd.py
-  # OPENCV is needed to enable ImageInput operator in caffe2 resnet5_trainer
-  # LMDB is needed to read datasets from https://download.caffe2.ai/databases/resnet_trainer.zip
-  USE_ROCM=1 USE_LMDB=1 USE_OPENCV=1 python setup.py install --user
-
-  ORIG_COMP=/opt/rocm/hcc/bin/clang-*_original
-  if [ -e $ORIG_COMP ]; then
-    # runtime compilation of MIOpen kernels manages to crash sccache - hence undo the wrapping
-    # note that the wrapping always names the compiler "clang-7.0_original"
-    WRAPPED=/opt/rocm/hcc/bin/clang-[0-99]
-    sudo mv $ORIG_COMP $WRAPPED
-
-  fi
-  exit 0
+  pip install mkl mkl-devel
 fi
 
 # sccache will fail for CUDA builds if all cores are used for compiling
 # gcc 7 with sccache seems to have intermittent OOM issue if all cores are used
-if [ -z "$MAX_JOBS" ]; then
-  if ([[ "$BUILD_ENVIRONMENT" == *cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *gcc7* ]]) && which sccache > /dev/null; then
-    export MAX_JOBS=$(($(nproc) - 1))
-  fi
+if ([[ "$BUILD_ENVIRONMENT" == *cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *gcc7* ]]) && which sccache > /dev/null; then
+  export MAX_JOBS=`expr $(nproc) - 1`
 fi
 
 # Target only our CI GPU machine's CUDA arch to speed up the build
-export TORCH_CUDA_ARCH_LIST="5.2"
-
-if [[ "$BUILD_ENVIRONMENT" == *ppc64le* ]]; then
-  export TORCH_CUDA_ARCH_LIST="6.0"
-fi
+export TORCH_CUDA_ARCH_LIST=5.2
 
 if [[ "$BUILD_ENVIRONMENT" == *trusty-py3.6-gcc5.4* ]]; then
   export DEBUG=1
 fi
 
-# Patch required to build xla
-if [[ "${BUILD_ENVIRONMENT}" == *xla* ]]; then
-  git clone --recursive https://github.com/pytorch/xla.git
-  ./xla/scripts/apply_patches.sh
+WERROR=1 python setup.py install
+
+# Add the test binaries so that they won't be git clean'ed away
+git add -f build/bin
+
+# Testing ATen install
+if [[ "$BUILD_ENVIRONMENT" != *cuda* ]]; then
+  echo "Testing ATen install"
+  time tools/test_aten_install.sh
 fi
 
-
-# check that setup.py would fail with bad arguments
-echo "The next three invocations are expected to fail with invalid command error messages."
-( ! get_exit_code python setup.py bad_argument )
-( ! get_exit_code python setup.py clean] )
-( ! get_exit_code python setup.py clean bad_argument )
-
-# ppc64le build fails when WERROR=1
-# set only when building other architectures
-# only use for "python setup.py install" line
-if [[ "$BUILD_ENVIRONMENT" != *ppc64le* ]]; then
-  WERROR=1 python setup.py install
-else
-  python setup.py install
+# Test C FFI plugins
+# cffi install doesn't work for Python 3.7
+if [[ "$BUILD_ENVIRONMENT" != *pynightly* ]]; then
+  # TODO: Don't run this here
+  pip install cffi
+  git clone https://github.com/pytorch/extension-ffi.git
+  pushd extension-ffi/script
+  python build.py
+  popd
 fi
 
-assert_git_not_dirty
-
 # Test documentation build
-if [[ "$BUILD_ENVIRONMENT" == *xenial-cuda9-cudnn7-py3* ]]; then
+if [[ "$BUILD_ENVIRONMENT" == *xenial-cuda8-cudnn6-py3* ]]; then
   pushd docs
   # TODO: Don't run this here
-  pip_install -r requirements.txt || true
-  LC_ALL=C make html
+  pip install -r requirements.txt || true
+  make html
   popd
-  assert_git_not_dirty
-fi
-
-# Test standalone c10 build
-if [[ "$BUILD_ENVIRONMENT" == *xenial-cuda9-cudnn7-py3* ]]; then
-  mkdir -p c10/build
-  pushd c10/build
-  cmake ..
-  make -j
-  popd
-  assert_git_not_dirty
 fi
 
 # Test no-Python build
@@ -188,69 +95,5 @@ if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
   echo "Building libtorch"
   # NB: Install outside of source directory (at the same level as the root
   # pytorch folder) so that it doesn't get cleaned away prior to docker push.
-  BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-  mkdir -p ../cpp-build/caffe2
-  pushd ../cpp-build/caffe2
-  WERROR=1 VERBOSE=1 DEBUG=1 python $BUILD_LIBTORCH_PY
-  popd
-
-  # Build custom operator tests.
-  CUSTOM_OP_BUILD="$PWD/../custom-op-build"
-  CUSTOM_OP_TEST="$PWD/test/custom_operator"
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  mkdir "$CUSTOM_OP_BUILD"
-  pushd "$CUSTOM_OP_BUILD"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" cmake "$CUSTOM_OP_TEST"
-  make VERBOSE=1
-  popd
-  assert_git_not_dirty
-fi
-
-# Test XLA build
-if [[ "${BUILD_ENVIRONMENT}" == *xla* ]]; then
-  # TODO: Move this to Dockerfile.
-
-  pip_install lark-parser
-
-  # Bazel doesn't work with sccache gcc. https://github.com/bazelbuild/bazel/issues/3642
-  sudo add-apt-repository "deb http://apt.llvm.org/trusty/ llvm-toolchain-trusty-7 main"
-  wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key|sudo apt-key add -
-  sudo apt-get -qq update
-
-  # Install clang-7 clang++-7 for xla
-  sudo apt-get -qq install clang-7 clang++-7
-
-  # Bazel dependencies
-  sudo apt-get -qq install pkg-config zip zlib1g-dev unzip
-  # XLA build requires Bazel
-  wget https://github.com/bazelbuild/bazel/releases/download/0.24.1/bazel-0.24.1-installer-linux-x86_64.sh
-  chmod +x bazel-*.sh
-  sudo ./bazel-*.sh
-  BAZEL="$(which bazel)"
-  if [ -z "${BAZEL}" ]; then
-    echo "Unable to find bazel..."
-    exit 1
-  fi
-
-  # Install bazels3cache for cloud cache
-  sudo apt-get -qq install npm
-  npm config set strict-ssl false
-  curl -sL https://deb.nodesource.com/setup_6.x | sudo -E bash -
-  sudo apt-get install -qq nodejs
-  sudo npm install -g bazels3cache
-  BAZELS3CACHE="$(which bazels3cache)"
-  if [ -z "${BAZELS3CACHE}" ]; then
-    echo "Unable to find bazels3cache..."
-    exit 1
-  fi
-
-  bazels3cache --bucket=ossci-compiler-cache-circleci-xla --maxEntrySizeBytes=0
-  pushd xla
-  export CC=clang-7 CXX=clang++-7
-  # Use cloud cache to build when available.
-  sed -i '/bazel build/ a --remote_http_cache=http://localhost:7777 \\' build_torch_xla_libs.sh
-
-  python setup.py install
-  popd
-  assert_git_not_dirty
+  WERROR=1 VERBOSE=1 tools/cpp_build/build_all.sh "$PWD/../cpp-build"
 fi

.jenkins/pytorch/common.sh

@@ -17,28 +17,14 @@ function cleanup {
 
 set -ex
 
-# Save the SCRIPT_DIR absolute path in case later we chdir (as occurs in the gpu perf test)
-SCRIPT_DIR="$( cd "$(dirname "${BASH_SOURCE[0]}")" ; pwd -P )"
-
 # Required environment variables:
 #   $BUILD_ENVIRONMENT (should be set by your Docker image)
 
-# Figure out which Python to use for ROCm
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]] && [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
-  PYTHON=$(which "python${BASH_REMATCH[1]}")
-  # non-interactive bashs do not expand aliases by default
-  shopt -s expand_aliases
-  export PYTORCH_TEST_WITH_ROCM=1
-  alias python="$PYTHON"
-fi
-
 # This token is used by a parser on Jenkins logs for determining
 # if a failure is a legitimate problem, or a problem with the build
 # system; to find out more, grep for this string in ossci-job-dsl.
 echo "ENTERED_USER_LAND"
 
-export IS_PYTORCH_CI=1
-
 # compositional trap taken from https://stackoverflow.com/a/7287873/23845
 
 # note: printf is used instead of echo to avoid backslash
@@ -75,33 +61,17 @@ declare -f -t trap_add
 
 trap_add cleanup EXIT
 
-function assert_git_not_dirty() {
-    # TODO: we should add an option to `build_amd.py` that reverts the repo to
-    #       an unmodified state.
-    if ([[ "$BUILD_ENVIRONMENT" != *rocm* ]] && [[ "$BUILD_ENVIRONMENT" != *xla* ]]) ; then
-        git_status=$(git status --porcelain)
-        if [[ $git_status ]]; then
-            echo "Build left local git repository checkout dirty"
-            echo "git status --porcelain:"
-            echo "${git_status}"
-            exit 1
-        fi
-    fi
-}
-
 if which sccache > /dev/null; then
   # Save sccache logs to file
   sccache --stop-server || true
   rm ~/sccache_error.log || true
-  # increasing SCCACHE_IDLE_TIMEOUT so that extension_backend_test.cpp can build after this PR:
-  # https://github.com/pytorch/pytorch/pull/16645
-  SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=1200 RUST_LOG=sccache::server=error sccache --start-server
+  SCCACHE_ERROR_LOG=~/sccache_error.log RUST_LOG=sccache::server=error sccache --start-server
 
   # Report sccache stats for easier debugging
   sccache --zero-stats
   function sccache_epilogue() {
     echo '=================== sccache compilation log ==================='
-    python "$SCRIPT_DIR/print_sccache_log.py" ~/sccache_error.log 2>/dev/null
+    python $(dirname "${BASH_SOURCE[0]}")/print_sccache_log.py ~/sccache_error.log
     echo '=========== If your build fails, please take a look at the log above for possible reasons ==========='
     sccache --show-stats
     sccache --stop-server || true
@@ -128,9 +98,22 @@ if [ -z "$COMPACT_JOB_NAME" ]; then
   exit 1
 fi
 
-if [[ "$BUILD_ENVIRONMENT" == *pytorch-linux-xenial-cuda9-cudnn7-py3* ]] || \
-   [[ "$BUILD_ENVIRONMENT" == *pytorch-linux-trusty-py3.6-gcc7* ]] || \
-   [[ "$BUILD_ENVIRONMENT" == *pytorch_macos* ]]; then
+if grep --line-regexp -q "$COMPACT_JOB_NAME" "$(dirname "${BASH_SOURCE[0]}")/disabled-configs.txt"; then
+  echo "Job is explicitly disabled, SKIPPING"
+  exit 0
+else
+  echo "Job is not disabled, proceeding"
+fi
+
+if grep --line-regexp -q "$COMPACT_JOB_NAME" "$(dirname "${BASH_SOURCE[0]}")/enabled-configs.txt"; then
+  echo "Job is enabled, proceeding"
+else
+  echo "Job is not enabled, FAILING now (revert changes to enabled-configs.txt to fix this)"
+  exit 1
+fi
+
+if [[ "$BUILD_ENVIRONMENT" == *pytorch-linux-xenial-cuda9-cudnn7-py3 ]] || \
+   [[ "$BUILD_ENVIRONMENT" == *pytorch-linux-trusty-py3.6-gcc7* ]]; then
   BUILD_TEST_LIBTORCH=1
 else
   BUILD_TEST_LIBTORCH=0
@@ -139,7 +122,7 @@ fi
 # Use conda cmake in some CI build. Conda cmake will be newer than our supported
 # min version 3.5, so we only do it in two builds that we know should use conda.
 if [[ "$BUILD_ENVIRONMENT" == *pytorch-linux-xenial-cuda* ]]; then
-  if [[ "$BUILD_ENVIRONMENT" == *cuda9-cudnn7-py2* ]] || \
+  if [[ "$BUILD_ENVIRONMENT" == *cuda8-cudnn6-py2* ]] || \
      [[ "$BUILD_ENVIRONMENT" == *cuda9-cudnn7-py3* ]]; then
     if ! which conda; then
       echo "Expected ${BUILD_ENVIRONMENT} to use conda, but 'which conda' returns empty"
@@ -155,16 +138,3 @@ if [[ "$BUILD_ENVIRONMENT" == *pytorch-linux-xenial-cuda* ]]; then
     fi
   fi
 fi
-
-function pip_install() {
-  # retry 3 times
-  pip install --progress-bar off "$@" || pip install --progress-bar off "$@" || pip install --progress-bar off "$@"
-}
-
-function get_exit_code() {
-  set +e
-  "$@"
-  retcode=$?
-  set -e
-  return $retcode
-}

.jenkins/pytorch/disabled-configs.txt

@@ -0,0 +1,5 @@
+# This file contains a list of disabled configurations.  Disabled
+# configurations are skipped and not considered a failure if they
+# fail.  You can use this to temporarily reserve a test name to
+# turn on CI side before PyTorch repository supports it.  This
+# file has the same format as .jenkins/enabled-configs.txt

.jenkins/pytorch/docker-build-test.sh

@@ -1,8 +1,6 @@
 #!/bin/bash
 
-# shellcheck disable=SC2034
 COMPACT_JOB_NAME="docker-build-test"
-
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 docker build -t pytorch .

.jenkins/pytorch/enabled-configs.txt

@@ -0,0 +1,43 @@
+# This file contains a list of enabled configurations
+# to perform tests on.  If you want to run tests on CI on
+# a limited set of tests before enabling the full test suite,
+# you can delete lines from this file.  Any test that is not
+# in this file will report a failure (so you don't forget to
+# reenable the tests on merge ;)
+
+pytorch-linux-xenial-cuda8-cudnn6-py3-build
+pytorch-linux-xenial-cuda8-cudnn6-py3-test
+pytorch-linux-xenial-cuda8-cudnn6-py3-multigpu-test
+pytorch-linux-xenial-cuda9-cudnn7-py2-build
+pytorch-linux-xenial-cuda9-cudnn7-py2-test
+pytorch-linux-xenial-cuda9-cudnn7-py3-build
+pytorch-linux-xenial-cuda9-cudnn7-py3-test
+pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc7-build
+pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc7-test
+pytorch-linux-xenial-py3-clang5-asan-build
+pytorch-linux-xenial-py3-clang5-asan-test
+pytorch-linux-trusty-py2.7.9-build
+pytorch-linux-trusty-py2.7.9-test
+pytorch-linux-trusty-py2.7-build
+pytorch-linux-trusty-py2.7-test
+pytorch-linux-trusty-py3.5-build
+pytorch-linux-trusty-py3.5-test
+pytorch-linux-trusty-py3.6-gcc4.8-build
+pytorch-linux-trusty-py3.6-gcc4.8-test
+pytorch-linux-trusty-py3.6-gcc5.4-build
+pytorch-linux-trusty-py3.6-gcc5.4-test
+pytorch-linux-trusty-py3.6-gcc7.2-build
+pytorch-linux-trusty-py3.6-gcc7.2-test
+pytorch-linux-trusty-py3.6-gcc7-build
+pytorch-linux-trusty-py3.6-gcc7-test
+pytorch-linux-trusty-pynightly-build
+pytorch-linux-trusty-pynightly-test
+pytorch-win-ws2016-cuda9-cudnn7-py3-build
+pytorch-win-ws2016-cuda9-cudnn7-py3-test
+pytorch-macos-10.13-py3-build
+pytorch-macos-10.13-py3-test
+pytorch-macos-10.13-cuda9.2-cudnn7-py3-build
+pytorch-docker-build-test
+short-perf-test-cpu
+short-perf-test-gpu
+py2-clang3.8-rocmnightly-ubuntu16.04-build

.jenkins/pytorch/macos-build-test.sh

@@ -1,9 +1,9 @@
 #!/bin/bash
 
-if [ -z "${BUILD_ENVIRONMENT}" ] || [[ "${BUILD_ENVIRONMENT}" == *-build* ]]; then
+if [ -z "${JOB_BASE_NAME}" ] || [[ "${JOB_BASE_NAME}" == *-build* ]]; then
   source "$(dirname "${BASH_SOURCE[0]}")/macos-build.sh"
 fi
 
-if [ -z "${BUILD_ENVIRONMENT}" ] || [[ "${BUILD_ENVIRONMENT}" == *-test* ]]; then
+if [ -z "${JOB_BASE_NAME}" ] || [[ "${JOB_BASE_NAME}" == *-test* ]]; then
   source "$(dirname "${BASH_SOURCE[0]}")/macos-test.sh"
 fi

.jenkins/pytorch/macos-build.sh

@@ -1,8 +1,6 @@
 #!/bin/bash
 
-# shellcheck disable=SC2034
-COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}"
-
+COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}-build"
 export PATH="/usr/local/bin:$PATH"
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
@@ -19,28 +17,23 @@ source ${PYTORCH_ENV_DIR}/miniconda3/bin/activate
 conda install -y mkl mkl-include numpy pyyaml setuptools cmake cffi ninja
 rm -rf ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
 
-git submodule sync --recursive
 git submodule update --init --recursive
 export CMAKE_PREFIX_PATH=${PYTORCH_ENV_DIR}/miniconda3/
 
 # Build PyTorch
-if [[ "${BUILD_ENVIRONMENT}" == *cuda9.2* ]]; then
+if [[ "${JOB_BASE_NAME}" == *cuda9.2* ]]; then
   export CUDA_VERSION=9.2
   export TORCH_CUDA_ARCH_LIST=5.2
   export PATH=/Developer/NVIDIA/CUDA-${CUDA_VERSION}/bin${PATH:+:${PATH}}
   export DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-${CUDA_VERSION}/lib${DYLD_LIBRARY_PATH:+:${DYLD_LIBRARY_PATH}}
   export CUDA_HOME=/Developer/NVIDIA/CUDA-${CUDA_VERSION}
-  export USE_CUDA=1
+  export NO_CUDA=0
 
-  if [ -z "${IN_CIRCLECI}" ]; then
-    # Eigen gives "explicit specialization of class must precede its first use" error
-    # when compiling with Xcode 9.1 toolchain, so we have to use Xcode 8.2 toolchain instead.
-    export DEVELOPER_DIR=/Library/Developer/CommandLineTools
-  fi
+  # Eigen gives "explicit specialization of class must precede its first use" error
+  # when compiling with Xcode 9.1 toolchain, so we have to use Xcode 8.2 toolchain instead.
+  export DEVELOPER_DIR=/Library/Developer/CommandLineTools
 else
-  if [ -z "${IN_CIRCLECI}" ]; then
-    export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
-  fi
+  export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
 fi
 
 export MACOSX_DEPLOYMENT_TARGET=10.9
@@ -53,7 +46,7 @@ if which sccache > /dev/null; then
   printf "#!/bin/sh\nexec sccache $(which clang) \$*" > "${PYTORCH_ENV_DIR}/clang"
   chmod a+x "${PYTORCH_ENV_DIR}/clang"
 
-  if [[ "${BUILD_ENVIRONMENT}" == *cuda* ]]; then
+  if [[ "${JOB_BASE_NAME}" == *cuda* ]]; then
     printf "#!/bin/sh\nexec sccache $(which nvcc) \$*" > "${PYTORCH_ENV_DIR}/nvcc"
     chmod a+x "${PYTORCH_ENV_DIR}/nvcc"
     export CUDA_NVCC_EXECUTABLE="${PYTORCH_ENV_DIR}/nvcc"
@@ -68,10 +61,6 @@ export IMAGE_COMMIT_TAG=${BUILD_ENVIRONMENT}-${IMAGE_COMMIT_ID}
 
 python setup.py install
 
-assert_git_not_dirty
-
 # Upload torch binaries when the build job is finished
-if [ -z "${IN_CIRCLECI}" ]; then
-  7z a ${IMAGE_COMMIT_TAG}.7z ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
-  aws s3 cp ${IMAGE_COMMIT_TAG}.7z s3://ossci-macos-build/pytorch/${IMAGE_COMMIT_TAG}.7z --acl public-read
-fi
+7z a ${IMAGE_COMMIT_TAG}.7z ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
+aws s3 cp ${IMAGE_COMMIT_TAG}.7z s3://ossci-macos-build/pytorch/${IMAGE_COMMIT_TAG}.7z --acl public-read

.jenkins/pytorch/macos-test.sh

@@ -1,14 +1,12 @@
 #!/bin/bash
 
-# shellcheck disable=SC2034
-COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}"
-
+COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}-test"
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 export PATH="/usr/local/bin:$PATH"
 
 # Set up conda environment
-export PYTORCH_ENV_DIR="${HOME}/workspace"
+export PYTORCH_ENV_DIR="${HOME}/pytorch-ci-env"
 # If a local installation of conda doesn't exist, we download and install conda
 if [ ! -d "${PYTORCH_ENV_DIR}/miniconda3" ]; then
   mkdir -p ${PYTORCH_ENV_DIR}
@@ -17,31 +15,19 @@ if [ ! -d "${PYTORCH_ENV_DIR}/miniconda3" ]; then
 fi
 export PATH="${PYTORCH_ENV_DIR}/miniconda3/bin:$PATH"
 source ${PYTORCH_ENV_DIR}/miniconda3/bin/activate
-conda install -y mkl mkl-include numpy pyyaml setuptools cmake cffi ninja six
-pip install -q hypothesis "librosa>=0.6.2" psutil
+conda install -y mkl mkl-include numpy pyyaml setuptools cmake cffi ninja
+rm -rf ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
 
-# faulthandler become built-in since 3.3
-if [[ ! $(python -c "import sys; print(int(sys.version_info >= (3, 3)))") == "1" ]]; then
-  pip install -q faulthandler
-fi
-
-if [ -z "${IN_CIRCLECI}" ]; then
-  rm -rf ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
-fi
-
-git submodule sync --recursive
 git submodule update --init --recursive
 export CMAKE_PREFIX_PATH=${PYTORCH_ENV_DIR}/miniconda3/
 
 # Test PyTorch
-if [ -z "${IN_CIRCLECI}" ]; then
-  if [[ "${BUILD_ENVIRONMENT}" == *cuda9.2* ]]; then
-    # Eigen gives "explicit specialization of class must precede its first use" error
-    # when compiling with Xcode 9.1 toolchain, so we have to use Xcode 8.2 toolchain instead.
-    export DEVELOPER_DIR=/Library/Developer/CommandLineTools
-  else
-    export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
-  fi
+if [[ "${JOB_BASE_NAME}" == *cuda9.2* ]]; then
+  # Eigen gives "explicit specialization of class must precede its first use" error
+  # when compiling with Xcode 9.1 toolchain, so we have to use Xcode 8.2 toolchain instead.
+  export DEVELOPER_DIR=/Library/Developer/CommandLineTools
+else
+  export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
 fi
 export MACOSX_DEPLOYMENT_TARGET=10.9
 export CXX=clang++
@@ -52,88 +38,43 @@ export MAX_JOBS=2
 export IMAGE_COMMIT_TAG=${BUILD_ENVIRONMENT}-${IMAGE_COMMIT_ID}
 
 # Download torch binaries in the test jobs
-if [ -z "${IN_CIRCLECI}" ]; then
-  rm -rf ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
-  aws s3 cp s3://ossci-macos-build/pytorch/${IMAGE_COMMIT_TAG}.7z ${IMAGE_COMMIT_TAG}.7z
-  7z x ${IMAGE_COMMIT_TAG}.7z -o"${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages"
-fi
-
-# Test that OpenMP is enabled
-pushd test
-if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available()))") == "1" ]]; then
-  echo "Build should have OpenMP enabled, but torch.backends.openmp.is_available() is False"
-  exit 1
-fi
-popd
+rm -rf ${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages/torch*
+aws s3 cp s3://ossci-macos-build/pytorch/${IMAGE_COMMIT_TAG}.7z ${IMAGE_COMMIT_TAG}.7z
+7z x ${IMAGE_COMMIT_TAG}.7z -o"${PYTORCH_ENV_DIR}/miniconda3/lib/python3.6/site-packages"
 
 test_python_all() {
   echo "Ninja version: $(ninja --version)"
   python test/run_test.py --verbose
-  assert_git_not_dirty
 }
 
-test_libtorch() {
+test_cpp_api() {
   # C++ API
 
-  if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
-    # NB: Install outside of source directory (at the same level as the root
-    # pytorch folder) so that it doesn't get cleaned away prior to docker push.
-    # But still clean it before we perform our own build.
+  # NB: Install outside of source directory (at the same level as the root
+  # pytorch folder) so that it doesn't get cleaned away prior to docker push.
+  # But still clean it before we perform our own build.
+  #
+  CPP_BUILD="$PWD/../cpp-build"
+  rm -rf $CPP_BUILD
+  mkdir -p $CPP_BUILD
+  WERROR=1 VERBOSE=1 tools/cpp_build/build_all.sh "$CPP_BUILD"
 
-    echo "Testing libtorch"
+  python tools/download_mnist.py --quiet -d test/cpp/api/mnist
 
-    CPP_BUILD="$PWD/../cpp-build"
-    rm -rf $CPP_BUILD
-    mkdir -p $CPP_BUILD/caffe2
-
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    pushd $CPP_BUILD/caffe2
-    VERBOSE=1 DEBUG=1 python $BUILD_LIBTORCH_PY
-    popd
-
-    python tools/download_mnist.py --quiet -d test/cpp/api/mnist
-
-    # Unfortunately it seems like the test can't load from miniconda3
-    # without these paths being set
-    export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" "$CPP_BUILD"/caffe2/bin/test_api
-
-    assert_git_not_dirty
-  fi
+  # Unfortunately it seems like the test can't load from miniconda3
+  # without these paths being set
+  export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$PWD/miniconda3/lib"
+  export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/miniconda3/lib"
+  "$CPP_BUILD"/libtorch/bin/test_api
 }
 
-test_custom_script_ops() {
-  echo "Testing custom script operators"
-  pushd test/custom_operator
-  # Build the custom operator library.
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" cmake ..
-  make VERBOSE=1
-  popd
-
-  # Run tests Python-side and export a script module.
-  python test_custom_ops.py -v
-  python test_custom_classes.py -v
-  python model.py --export-script-module=model.pt
-  # Run tests C++-side and load the exported script module.
-  build/test_custom_ops ./model.pt
-  popd
-  assert_git_not_dirty
-}
-
-
-if [ -z "${BUILD_ENVIRONMENT}" ] || [[ "${BUILD_ENVIRONMENT}" == *-test ]]; then
+if [ -z "${JOB_BASE_NAME}" ] || [[ "${JOB_BASE_NAME}" == *-test ]]; then
   test_python_all
-  test_libtorch
-  test_custom_script_ops
+  test_cpp_api
 else
-  if [[ "${BUILD_ENVIRONMENT}" == *-test1 ]]; then
+  if [[ "${JOB_BASE_NAME}" == *-test1 ]]; then
     test_python_all
-  elif [[ "${BUILD_ENVIRONMENT}" == *-test2 ]]; then
-    test_libtorch
-    test_custom_script_ops
+  elif [[ "${JOB_BASE_NAME}" == *-test2 ]]; then
+    test_cpp_api
   fi
 fi

.jenkins/pytorch/multigpu-test.sh

@@ -4,32 +4,8 @@
 # (This is set by default in the Docker images we build, so you don't
 # need to set it yourself.
 
-# shellcheck disable=SC2034
-COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}"
-
+COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}-multigpu-test"
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 echo "Testing pytorch (distributed only)"
-
-if [ -n "${IN_CIRCLECI}" ]; then
-  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-* ]]; then
-    # TODO: move this to Docker
-    sudo apt-get update
-    sudo apt-get install -y --allow-downgrades --allow-change-held-packages libnccl-dev=2.2.13-1+cuda9.0 libnccl2=2.2.13-1+cuda9.0
-  fi
-
-  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda8-* ]] || [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-cudnn7-py2* ]]; then
-    # TODO: move this to Docker
-    sudo apt-get update
-    sudo apt-get install -y --allow-downgrades --allow-change-held-packages openmpi-bin libopenmpi-dev
-    sudo apt-get install -y --no-install-recommends openssh-client openssh-server
-    sudo mkdir -p /var/run/sshd
-  fi
-fi
-
-python tools/download_mnist.py --quiet -d test/cpp/api/mnist
-OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" "$PWD/../cpp-build"/caffe2/build/bin/test_api
 time python test/run_test.py --verbose -i distributed
-time python test/run_test.py --verbose -i c10d
-time python test/run_test.py --verbose -i c10d_spawn
-assert_git_not_dirty

.jenkins/pytorch/perf_test/common.sh

@@ -10,7 +10,7 @@ get_runtime_of_command () {
   TIMEFORMAT=%R
 
   # runtime=$( { time ($@ &> /dev/null); } 2>&1 1>/dev/null)
-  runtime=$( { time "$@"; } 2>&1 1>/dev/null)
+  runtime=$( { time $@; } 2>&1 1>/dev/null)
   if [[ $runtime == *"Error"* ]]; then
     exit 1
   fi

.jenkins/pytorch/perf_test/compare_with_baseline.py

@@ -1,6 +1,6 @@
 import sys
 import json
-import math
+import numpy
 import argparse
 
 parser = argparse.ArgumentParser()
@@ -35,25 +35,14 @@ else:
 print("population mean: ", mean)
 print("population sigma: ", sigma)
 
-# Let the test pass if baseline number is NaN (which happened in
-# the past when we didn't have logic for catching NaN numbers)
-if math.isnan(mean) or math.isnan(sigma):
-    mean = sys.maxsize
-    sigma = 0.001
-
 sample_stats_data = json.loads(args.sample_stats)
 
-sample_mean = float(sample_stats_data['mean'])
-sample_sigma = float(sample_stats_data['sigma'])
+sample_mean = sample_stats_data['mean']
+sample_sigma = sample_stats_data['sigma']
 
 print("sample mean: ", sample_mean)
 print("sample sigma: ", sample_sigma)
 
-if math.isnan(sample_mean):
-    raise Exception('''Error: sample mean is NaN''')
-elif math.isnan(sample_sigma):
-    raise Exception('''Error: sample sigma is NaN''')
-
 z_value = (sample_mean - mean) / sigma
 
 print("z-value: ", z_value)
@@ -61,10 +50,8 @@ print("z-value: ", z_value)
 if z_value >= 3:
     raise Exception('''\n
 z-value >= 3, there is high chance of perf regression.\n
-To reproduce this regression, run
-`cd .jenkins/pytorch/perf_test/ && bash {}.sh` on your local machine
-and compare the runtime before/after your code change.
-'''.format(test_name))
+To reproduce this regression, run `cd .jenkins/pytorch/perf_test/ && bash ''' + test_name + '''.sh` on your local machine and compare the runtime before/after your code change.
+''')
 else:
     print("z-value < 3, no perf regression detected.")
     if args.update:

.jenkins/pytorch/perf_test/test_cpu_speed_mini_sequence_labeler.sh

@@ -19,14 +19,14 @@ test_cpu_speed_mini_sequence_labeler () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python main.py)
     SAMPLE_ARRAY+=(${runtime})
   done
 
   cd ../../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/perf_test/test_cpu_speed_mnist.sh

@@ -12,7 +12,7 @@ test_cpu_speed_mnist () {
 
   cd examples/mnist
 
-  conda install -c pytorch torchvision-cpu
+  pip install -r requirements.txt
 
   # Download data
   python main.py --epochs 0
@@ -20,7 +20,7 @@ test_cpu_speed_mnist () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python main.py --epochs 1 --no-log)
     echo $runtime
     SAMPLE_ARRAY+=(${runtime})
@@ -28,7 +28,7 @@ test_cpu_speed_mnist () {
 
   cd ../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/perf_test/test_cpu_speed_torch.sh

@@ -1,5 +1,3 @@
-#!/bin/bash
-
 . ./common.sh
 
 test_cpu_speed_torch () {
@@ -19,7 +17,7 @@ test_cpu_speed_torch () {
   fi
 
   if ! python perf-tests/modules/test_cpu_torch.py ${ARGS}; then
-    echo "To reproduce this regression, run \`cd .jenkins/pytorch/perf_test/ && bash ${FUNCNAME[0]}.sh\` on your local machine and compare the runtime before/after your code change."
+    echo "To reproduce this regression, run \`cd .jenkins/pytorch/perf_test/ && bash "${FUNCNAME[0]}".sh\` on your local machine and compare the runtime before/after your code change."
     exit 1
   fi
 }

.jenkins/pytorch/perf_test/test_cpu_speed_torch_tensor.sh

@@ -1,5 +1,3 @@
-#!/bin/bash
-
 . ./common.sh
 
 test_cpu_speed_torch_tensor () {
@@ -19,7 +17,7 @@ test_cpu_speed_torch_tensor () {
   fi
 
   if ! python perf-tests/modules/test_cpu_torch_tensor.py ${ARGS}; then
-    echo "To reproduce this regression, run \`cd .jenkins/pytorch/perf_test/ && bash ${FUNCNAME[0]}.sh\` on your local machine and compare the runtime before/after your code change."
+    echo "To reproduce this regression, run \`cd .jenkins/pytorch/perf_test/ && bash "${FUNCNAME[0]}".sh\` on your local machine and compare the runtime before/after your code change."
     exit 1
   fi
 }

.jenkins/pytorch/perf_test/test_gpu_speed_cudnn_lstm.sh

@@ -19,7 +19,7 @@ test_gpu_speed_cudnn_lstm () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python cudnn_lstm.py --skip-cpu-governor-check)
     echo $runtime
     SAMPLE_ARRAY+=(${runtime})
@@ -27,7 +27,7 @@ test_gpu_speed_cudnn_lstm () {
 
   cd ../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/perf_test/test_gpu_speed_lstm.sh

@@ -19,7 +19,7 @@ test_gpu_speed_lstm () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python lstm.py --skip-cpu-governor-check)
     echo $runtime
     SAMPLE_ARRAY+=(${runtime})
@@ -27,7 +27,7 @@ test_gpu_speed_lstm () {
 
   cd ../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/perf_test/test_gpu_speed_mlstm.sh

@@ -19,7 +19,7 @@ test_gpu_speed_mlstm () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python mlstm.py --skip-cpu-governor-check)
     echo $runtime
     SAMPLE_ARRAY+=(${runtime})
@@ -27,7 +27,7 @@ test_gpu_speed_mlstm () {
 
   cd ../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/perf_test/test_gpu_speed_mnist.sh

@@ -12,7 +12,7 @@ test_gpu_speed_mnist () {
 
   cd examples/mnist
 
-  conda install -c pytorch torchvision
+  pip install -r requirements.txt
 
   # Download data
   python main.py --epochs 0
@@ -20,10 +20,7 @@ test_gpu_speed_mnist () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  # Needs warm up to get accurate number
-  python main.py --epochs 1 --no-log
-
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python main.py --epochs 1 --no-log)
     echo $runtime
     SAMPLE_ARRAY+=(${runtime})
@@ -31,7 +28,7 @@ test_gpu_speed_mnist () {
 
   cd ../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/perf_test/test_gpu_speed_word_language_model.sh

@@ -28,7 +28,7 @@ test_gpu_speed_word_language_model () {
   SAMPLE_ARRAY=()
   NUM_RUNS=$1
 
-  for (( i=1; i<=NUM_RUNS; i++ )) do
+  for (( i=1; i<=$NUM_RUNS; i++ )) do
     runtime=$(get_runtime_of_command python main.py --cuda --epochs 1)
     echo $runtime
     SAMPLE_ARRAY+=(${runtime})
@@ -36,7 +36,7 @@ test_gpu_speed_word_language_model () {
 
   cd ../..
 
-  stats=$(python ../get_stats.py "${SAMPLE_ARRAY[@]}")
+  stats=$(python ../get_stats.py ${SAMPLE_ARRAY[@]})
   echo "Runtime stats in seconds:"
   echo $stats
 

.jenkins/pytorch/short-perf-test-cpu.sh

@@ -1,18 +1,13 @@
 #!/bin/bash
 
-# shellcheck disable=SC2034
 COMPACT_JOB_NAME="short-perf-test-cpu"
-
-SCRIPT_PARENT_DIR=$(dirname "${BASH_SOURCE[0]}")
-
-# shellcheck source=.jenkins/pytorch/common.sh
-source "$SCRIPT_PARENT_DIR/common.sh"
+source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 cd .jenkins/pytorch/perf_test
 
 echo "Running CPU perf test for PyTorch..."
 
-pip install -q awscli
+pip install awscli
 
 # Set multipart_threshold to be sufficiently high, so that `aws s3 cp` is not a multipart read
 # More info at https://github.com/aws/aws-cli/issues/2321

.jenkins/pytorch/short-perf-test-gpu.sh

@@ -1,17 +1,13 @@
 #!/bin/bash
 
-# shellcheck disable=SC2034
 COMPACT_JOB_NAME="short-perf-test-gpu"
-
 source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
 pushd .jenkins/pytorch/perf_test
 
 echo "Running GPU perf test for PyTorch..."
 
-# Trying to uninstall PyYAML can cause problem. Workaround according to:
-# https://github.com/pypa/pip/issues/5247#issuecomment-415571153
-pip install -q awscli --ignore-installed PyYAML
+pip install awscli
 
 # Set multipart_threshold to be sufficiently high, so that `aws s3 cp` is not a multipart read
 # More info at https://github.com/aws/aws-cli/issues/2321

.jenkins/pytorch/test.sh

@@ -1,82 +1,32 @@
 #!/bin/bash
 
+COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}-test"
+source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
+
 # Required environment variable: $BUILD_ENVIRONMENT
 # (This is set by default in the Docker images we build, so you don't
 # need to set it yourself.
 
-# shellcheck disable=SC2034
-COMPACT_JOB_NAME="${BUILD_ENVIRONMENT}"
-
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
 echo "Testing pytorch"
 
-if [ -n "${IN_CIRCLECI}" ]; then
-  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-* ]]; then
-    # TODO: move this to Docker
-    sudo apt-get -qq update
-    sudo apt-get -qq install --allow-downgrades --allow-change-held-packages libnccl-dev=2.2.13-1+cuda9.0 libnccl2=2.2.13-1+cuda9.0
-  fi
-
-  if [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda8-* ]] || [[ "$BUILD_ENVIRONMENT" == *-xenial-cuda9-cudnn7-py2* ]]; then
-    # TODO: move this to Docker
-    sudo apt-get -qq update
-    sudo apt-get -qq install --allow-downgrades --allow-change-held-packages openmpi-bin libopenmpi-dev
-    sudo apt-get -qq install --no-install-recommends openssh-client openssh-server
-    sudo mkdir -p /var/run/sshd
-  fi
-
-  if [[ "$BUILD_ENVIRONMENT" == *-slow-* ]]; then
-    export PYTORCH_TEST_WITH_SLOW=1
-    export PYTORCH_TEST_SKIP_FAST=1
-  fi
+if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
+  echo "Skipping ROCm tests for now"
+  exit 0
 fi
 
-# --user breaks ppc64le builds and these packages are already in ppc64le docker
-if [[ "$BUILD_ENVIRONMENT" != *ppc64le* ]]; then
-  # JIT C++ extensions require ninja.
-  pip_install --user ninja
-  # ninja is installed in /var/lib/jenkins/.local/bin
-  export PATH="/var/lib/jenkins/.local/bin:$PATH"
-
-  # TODO: move this to Docker
-  pip_install --user hypothesis
-
-  # TODO: move this to Docker
-  PYTHON_VERSION=$(python -c 'import platform; print(platform.python_version())'|cut -c1)
-  echo $PYTHON_VERSION
-  # if [[ $PYTHON_VERSION == "2" ]]; then
-  #   pip_install --user https://s3.amazonaws.com/ossci-linux/wheels/tensorboard-1.14.0a0-py2-none-any.whl
-  # else
-  #   pip_install --user https://s3.amazonaws.com/ossci-linux/wheels/tensorboard-1.14.0a0-py3-none-any.whl
-  # fi
-  pip_install --user tb-nightly
-  # mypy will fail to install on Python <3.4.  In that case,
-  # we just won't run these tests.
-  pip_install --user mypy || true
-fi
-
-# faulthandler become built-in since 3.3
-if [[ ! $(python -c "import sys; print(int(sys.version_info >= (3, 3)))") == "1" ]]; then
-  pip_install --user faulthandler
-fi
+# JIT C++ extensions require ninja.
+git clone https://github.com/ninja-build/ninja --quiet
+pushd ninja
+python ./configure.py --bootstrap
+export PATH="$PWD:$PATH"
+popd
 
 # DANGER WILL ROBINSON.  The LD_PRELOAD here could cause you problems
 # if you're not careful.  Check this if you made some changes and the
 # ASAN test is not working
 if [[ "$BUILD_ENVIRONMENT" == *asan* ]]; then
-    export ASAN_OPTIONS=detect_leaks=0:symbolize=1:strict_init_order=true
-    # We suppress the vptr volation, since we have separate copies of
-    # libprotobuf in both libtorch.so and libcaffe2.so, and it causes
-    # the following problem:
-    #    test_cse (__main__.TestJit) ... torch/csrc/jit/export.cpp:622:38:
-    #        runtime error: member call on address ... which does not point
-    #        to an object of type 'google::protobuf::MessageLite'
-    #        ...: note: object is of type 'onnx_torch::ModelProto'
-    #
-    # This problem should be solved when libtorch.so and libcaffe2.so are
-    # merged.
-    export UBSAN_OPTIONS=print_stacktrace=1:suppressions=$PWD/ubsan.supp
+    export ASAN_OPTIONS=detect_leaks=0:symbolize=1
+    export UBSAN_OPTIONS=print_stacktrace=1
     export PYTORCH_TEST_WITH_ASAN=1
     export PYTORCH_TEST_WITH_UBSAN=1
     # TODO: Figure out how to avoid hard-coding these paths
@@ -85,6 +35,13 @@ if [[ "$BUILD_ENVIRONMENT" == *asan* ]]; then
     # Increase stack size, because ASAN red zones use more stack
     ulimit -s 81920
 
+    function get_exit_code() {
+      set +e
+      "$@"
+      retcode=$?
+      set -e
+      return $retcode
+    }
     (cd test && python -c "import torch")
     echo "The next three invocations are expected to crash; if they don't that means ASAN/UBSAN is misconfigured"
     (cd test && ! get_exit_code python -c "import torch; torch._C._crash_if_csrc_asan(3)")
@@ -92,114 +49,83 @@ if [[ "$BUILD_ENVIRONMENT" == *asan* ]]; then
     (cd test && ! get_exit_code python -c "import torch; torch._C._crash_if_aten_asan(3)")
 fi
 
-if [[ "${BUILD_ENVIRONMENT}" == *-NO_AVX-* ]]; then
-  export ATEN_CPU_CAPABILITY=default
-elif [[ "${BUILD_ENVIRONMENT}" == *-NO_AVX2-* ]]; then
-  export ATEN_CPU_CAPABILITY=avx
+export ATEN_DISABLE_AVX=
+export ATEN_DISABLE_AVX2=
+if [[ "${JOB_BASE_NAME}" == *-NO_AVX-* ]]; then
+  export ATEN_DISABLE_AVX=1
+fi
+if [[ "${JOB_BASE_NAME}" == *-NO_AVX2-* ]]; then
+  export ATEN_DISABLE_AVX2=1
 fi
 
 test_python_nn() {
   time python test/run_test.py --include nn --verbose
-  assert_git_not_dirty
 }
 
 test_python_all_except_nn() {
   time python test/run_test.py --exclude nn --verbose
-  assert_git_not_dirty
 }
 
 test_aten() {
   # Test ATen
-  # The following test(s) of ATen have already been skipped by caffe2 in rocm environment:
-  # scalar_tensor_test, basic, native_test
-  if ([[ "$BUILD_ENVIRONMENT" != *asan* ]] && [[ "$BUILD_ENVIRONMENT" != *rocm* ]]); then
+  if [[ "$BUILD_ENVIRONMENT" != *asan* ]]; then
     echo "Running ATen tests with pytorch lib"
     TORCH_LIB_PATH=$(python -c "import site; print(site.getsitepackages()[0])")/torch/lib
     # NB: the ATen test binaries don't have RPATH set, so it's necessary to
     # put the dynamic libraries somewhere were the dynamic linker can find them.
     # This is a bit of a hack.
-    if [[ "$BUILD_ENVIRONMENT" == *ppc64le* ]]; then
-      SUDO=sudo
-    fi
-
-    ${SUDO} ln -s "$TORCH_LIB_PATH"/libc10* build/bin
-    ${SUDO} ln -s "$TORCH_LIB_PATH"/libcaffe2* build/bin
-    ${SUDO} ln -s "$TORCH_LIB_PATH"/libmkldnn* build/bin
-    ${SUDO} ln -s "$TORCH_LIB_PATH"/libnccl* build/bin
-
+    ln -s "$TORCH_LIB_PATH"/libcaffe2* build/bin
+    ln -s "$TORCH_LIB_PATH"/libnccl* build/bin
     ls build/bin
     aten/tools/run_tests.sh build/bin
-    assert_git_not_dirty
   fi
 }
 
 test_torchvision() {
-  pip_install --user git+https://github.com/pytorch/vision.git@2b73a4846773a670632b29fb2fc2ac57df7bce5d
+  rm -rf ninja
+
+  echo "Installing torchvision at branch master"
+  rm -rf vision
+  # TODO: This git clone is bad, it means pushes to torchvision can break
+  # PyTorch CI
+  git clone https://github.com/pytorch/vision --quiet
+  pushd vision
+  # python setup.py install with a tqdm dependency is broken in the
+  # Travis Python nightly (but not in latest Python nightlies, so
+  # this should be a transient requirement...)
+  # See https://github.com/pytorch/pytorch/issues/7525
+  #time python setup.py install
+  pip install .
+  popd
 }
 
 test_libtorch() {
   if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
-    echo "Testing libtorch"
-    python test/cpp/jit/tests_setup.py setup
-    CPP_BUILD="$PWD/../cpp-build"
-    if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-      "$CPP_BUILD"/caffe2/build/bin/test_jit
-    else
-      "$CPP_BUILD"/caffe2/build/bin/test_jit "[cpu]"
-    fi
-    python test/cpp/jit/tests_setup.py shutdown
-    python tools/download_mnist.py --quiet -d test/cpp/api/mnist
-    OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" "$CPP_BUILD"/caffe2/build/bin/test_api
-    assert_git_not_dirty
+     echo "Testing libtorch"
+     CPP_BUILD="$PWD/../cpp-build"
+     if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
+       "$CPP_BUILD"/libtorch/bin/test_jit
+     else
+       "$CPP_BUILD"/libtorch/bin/test_jit "[cpu]"
+     fi
+     python tools/download_mnist.py --quiet -d test/cpp/api/mnist
+     OMP_NUM_THREADS=2 "$CPP_BUILD"/libtorch/bin/test_api
   fi
 }
 
-test_custom_script_ops() {
-  if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
-    echo "Testing custom script operators"
-    CUSTOM_OP_BUILD="$PWD/../custom-op-build"
-    pushd test/custom_operator
-    cp -a "$CUSTOM_OP_BUILD" build
-    # Run tests Python-side and export a script module.
-    python test_custom_ops.py -v
-    python test_custom_classes.py -v
-    python model.py --export-script-module=model.pt
-    # Run tests C++-side and load the exported script module.
-    build/test_custom_ops ./model.pt
-    popd
-    assert_git_not_dirty
-  fi
-}
-
-test_xla() {
-  export XLA_USE_XRT=1 XRT_DEVICE_MAP="CPU:0;/job:localservice/replica:0/task:0/device:XLA_CPU:0"
-  export XRT_WORKERS="localservice:0;grpc://localhost:40934"
-  pushd xla
-  python test/test_operations.py
-  python test/test_train_mnist.py --tidy
-  popd
-  assert_git_not_dirty
-}
-
-(cd test && python -c "import torch; print(torch.__config__.show())")
-(cd test && python -c "import torch; print(torch.__config__.parallel_info())")
-
-if [[ "${BUILD_ENVIRONMENT}" == *xla* ]]; then
-  test_torchvision
-  test_xla
-elif [[ "${BUILD_ENVIRONMENT}" == *-test1 ]]; then
-  test_torchvision
+if [ -z "${JOB_BASE_NAME}" ] || [[ "${JOB_BASE_NAME}" == *-test ]]; then
   test_python_nn
-elif [[ "${BUILD_ENVIRONMENT}" == *-test2 ]]; then
   test_python_all_except_nn
   test_aten
+  test_torchvision
   test_libtorch
-  test_custom_script_ops
 else
-  test_torchvision
-  test_python_nn
-  test_python_all_except_nn
-  test_aten
-  test_libtorch
-  test_custom_script_ops
+  if [[ "${JOB_BASE_NAME}" == *-test1 ]]; then
+    test_python_nn
+  elif [[ "${JOB_BASE_NAME}" == *-test2 ]]; then
+    test_python_all_except_nn
+    test_aten
+    test_torchvision
+    test_libtorch
+  fi
 fi

.jenkins/pytorch/win-build.sh

@@ -9,29 +9,147 @@ if [ ! -f setup.py ]; then
   exit 1
 fi
 
-# shellcheck disable=SC2034
 COMPACT_JOB_NAME=pytorch-win-ws2016-cuda9-cudnn7-py3-build
+source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
 
-SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
-source "$SCRIPT_PARENT_DIR/common.sh"
-
-export IMAGE_COMMIT_ID=`git rev-parse HEAD`
 export IMAGE_COMMIT_TAG=${BUILD_ENVIRONMENT}-${IMAGE_COMMIT_ID}
 if [[ ${JOB_NAME} == *"develop"* ]]; then
   export IMAGE_COMMIT_TAG=develop-${IMAGE_COMMIT_TAG}
 fi
 
-export TMP_DIR="${PWD}/build/win_tmp"
-export TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
+mkdir -p ci_scripts/
 
-export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
+cat >ci_scripts/upload_image.py << EOL
 
+import os
+import sys
+import boto3
 
-$SCRIPT_HELPERS_DIR/build_pytorch.bat
+IMAGE_COMMIT_TAG = os.getenv('IMAGE_COMMIT_TAG')
 
-assert_git_not_dirty
+session = boto3.session.Session()
+s3 = session.resource('s3')
+data = open(sys.argv[1], 'rb')
+s3.Bucket('ossci-windows-build').put_object(Key='pytorch/'+IMAGE_COMMIT_TAG+'.7z', Body=data)
+object_acl = s3.ObjectAcl('ossci-windows-build','pytorch/'+IMAGE_COMMIT_TAG+'.7z')
+response = object_acl.put(ACL='public-read')
 
-if [ ! -f ${TMP_DIR}/${IMAGE_COMMIT_TAG}.7z ] && [ ! ${BUILD_ENVIRONMENT} == "" ]; then
+EOL
+
+cat >ci_scripts/build_pytorch.bat <<EOL
+
+set PATH=C:\\Program Files\\CMake\\bin;C:\\Program Files\\7-Zip;C:\\curl-7.57.0-win64-mingw\\bin;C:\\Program Files\\Git\\cmd;C:\\Program Files\\Amazon\\AWSCLI;%PATH%
+
+:: Install MKL
+if "%REBUILD%"=="" (
+  if "%BUILD_ENVIRONMENT%"=="" (
+    curl -k https://s3.amazonaws.com/ossci-windows/mkl_2018.2.185.7z --output mkl.7z
+  ) else (
+    aws s3 cp s3://ossci-windows/mkl_2018.2.185.7z mkl.7z --quiet
+  )
+  7z x -aoa mkl.7z -omkl
+)
+set CMAKE_INCLUDE_PATH=%cd%\\mkl\\include
+set LIB=%cd%\\mkl\\lib;%LIB
+
+:: Install MAGMA
+if "%REBUILD%"=="" (
+  if "%BUILD_ENVIRONMENT%"=="" (
+    curl -k https://s3.amazonaws.com/ossci-windows/magma_cuda90_release_mkl_2018.2.185.7z --output magma_cuda90_release_mkl_2018.2.185.7z
+  ) else (
+    aws s3 cp s3://ossci-windows/magma_cuda90_release_mkl_2018.2.185.7z magma_cuda90_release_mkl_2018.2.185.7z --quiet
+  )
+  7z x -aoa magma_cuda90_release_mkl_2018.2.185.7z -omagma
+)
+set MAGMA_HOME=%cd%\\magma
+
+:: Install sccache
+mkdir %CD%\\tmp_bin
+if "%REBUILD%"=="" (
+  :check_sccache
+  %CD%\\tmp_bin\\sccache.exe --show-stats || (
+    taskkill /im sccache.exe /f /t || ver > nul
+    del %CD%\\tmp_bin\\sccache.exe
+    if "%BUILD_ENVIRONMENT%"=="" (
+      curl -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output %CD%\\tmp_bin\\sccache.exe
+    ) else (
+      aws s3 cp s3://ossci-windows/sccache.exe %CD%\\tmp_bin\\sccache.exe
+    )
+    goto :check_sccache
+  )
+)
+
+:: Install Miniconda3
+if "%REBUILD%"=="" (
+  IF EXIST C:\\Jenkins\\Miniconda3 ( rd /s /q C:\\Jenkins\\Miniconda3 )
+  curl -k https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe -O
+  .\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=C:\\Jenkins\\Miniconda3
+)
+call C:\\Jenkins\\Miniconda3\\Scripts\\activate.bat C:\\Jenkins\\Miniconda3
+if "%REBUILD%"=="" ( call conda install -y -q numpy cffi pyyaml boto3 )
+
+:: Install ninja
+if "%REBUILD%"=="" ( pip install ninja )
+
+call "C:\\Program Files (x86)\\Microsoft Visual Studio\\2017\\Community\\VC\\Auxiliary\\Build\\vcvarsall.bat" x86_amd64
+
+git submodule update --init --recursive
+
+set PATH=%CD%\\tmp_bin;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\bin;C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\libnvvp;%PATH%
+set CUDA_PATH=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0
+set CUDA_PATH_V9_0=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0
+set NVTOOLSEXT_PATH=C:\\Program Files\\NVIDIA Corporation\\NvToolsExt
+set CUDNN_LIB_DIR=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\lib\\x64
+set CUDA_TOOLKIT_ROOT_DIR=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0
+set CUDNN_ROOT_DIR=C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0
+
+:: Target only our CI GPU machine's CUDA arch to speed up the build
+set TORCH_CUDA_ARCH_LIST=5.2
+
+sccache --stop-server
+sccache --start-server
+sccache --zero-stats
+set CC=sccache cl
+set CXX=sccache cl
+
+set DISTUTILS_USE_SDK=1
+
+set CMAKE_GENERATOR=Ninja
+
+if not "%USE_CUDA%"=="1" (
+  if "%REBUILD%"=="" (
+    set NO_CUDA=1
+    python setup.py install
+  )
+  if errorlevel 1 exit /b 1
+  if not errorlevel 0 exit /b 1
+)
+
+if not "%USE_CUDA%"=="0" (
+  if "%REBUILD%"=="" (
+    sccache --show-stats
+    sccache --zero-stats
+    rd /s /q C:\\Jenkins\\Miniconda3\\Lib\\site-packages\\torch
+    copy %CD%\\tmp_bin\\sccache.exe tmp_bin\\nvcc.exe
+  )
+
+  set CUDA_NVCC_EXECUTABLE=%CD%\\tmp_bin\\nvcc
+
+  if "%REBUILD%"=="" set NO_CUDA=0
+
+  python setup.py install && sccache --show-stats && (
+    if "%BUILD_ENVIRONMENT%"=="" (
+      echo "NOTE: To run \`import torch\`, please make sure to activate the conda environment by running \`call C:\\Jenkins\\Miniconda3\\Scripts\\activate.bat C:\\Jenkins\\Miniconda3\` in Command Prompt before running Git Bash."
+    ) else (
+      7z a %IMAGE_COMMIT_TAG%.7z C:\\Jenkins\\Miniconda3\\Lib\\site-packages\\torch && python ci_scripts\\upload_image.py %IMAGE_COMMIT_TAG%.7z
+    )
+  )
+)
+
+EOL
+
+ci_scripts/build_pytorch.bat
+if [ ! -f $IMAGE_COMMIT_TAG.7z ] && [ ! ${BUILD_ENVIRONMENT} == "" ]; then
     exit 1
 fi
 echo "BUILD PASSED"

.jenkins/pytorch/win-test-helpers/build_pytorch.bat

@@ -1,116 +0,0 @@
-if "%DEBUG%" == "1" (
-  set BUILD_TYPE=debug
-) ELSE (
-  set BUILD_TYPE=release
-)
-
-set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;%PATH%
-
-
-set INSTALLER_DIR=%SCRIPT_HELPERS_DIR%\installation-helpers
-
-call %INSTALLER_DIR%\install_mkl.bat
-call %INSTALLER_DIR%\install_magma.bat
-call %INSTALLER_DIR%\install_sccache.bat
-call %INSTALLER_DIR%\install_miniconda3.bat
-
-
-:: Install ninja
-if "%REBUILD%"=="" ( pip install -q ninja )
-
-git submodule sync --recursive
-git submodule update --init --recursive
-
-if "%CUDA_VERSION%" == "9" goto cuda_build_9
-if "%CUDA_VERSION%" == "10" goto cuda_build_10
-goto cuda_build_end
-
-:cuda_build_9
-
-:: Override VS env here
-pushd .
-call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvarsall.bat" x64
-@echo on
-popd
-set DISTUTILS_USE_SDK=1
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0
-set CUDA_PATH_V9_0=%CUDA_PATH%
-
-goto cuda_build_common
-
-:cuda_build_10
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
-set CUDA_PATH_V10_1=%CUDA_PATH%
-
-goto cuda_build_common
-
-:cuda_build_common
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-
-:cuda_build_end
-
-set PATH=%TMP_DIR_WIN%\bin;%PATH%
-
-:: Target only our CI GPU machine's CUDA arch to speed up the build
-set TORCH_CUDA_ARCH_LIST=5.2
-
-sccache --stop-server
-sccache --start-server
-sccache --zero-stats
-set CC=sccache cl
-set CXX=sccache cl
-
-set CMAKE_GENERATOR=Ninja
-
-:: The following code will try to build PyTorch twice if USE_CUDA is neither 0
-:: nor 1. It is intended so that both builds can be folded into 1 CI run.
-
-if not "%USE_CUDA%"=="1" (
-  if "%REBUILD%"=="" (
-    :: Must save and restore the original value of USE_CUDA, otherwise the
-    :: `if not "%USE_CUDA%"=="0"` line can be messed up.
-    set OLD_USE_CUDA=%USE_CUDA%
-    set USE_CUDA=0
-    python setup.py install
-    set USE_CUDA=%OLD_USE_CUDA%
-  )
-  if errorlevel 1 exit /b 1
-  if not errorlevel 0 exit /b 1
-)
-
-if not "%USE_CUDA%"=="0" (
-  :: sccache will fail for CUDA builds if all cores are used for compiling
-  if not defined MAX_JOBS set /A MAX_JOBS=%NUMBER_OF_PROCESSORS%-1
-
-  if "%REBUILD%"=="" (
-    sccache --show-stats
-    sccache --zero-stats
-    rd /s /q %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch
-    for /f "delims=" %%i in ('where /R caffe2\proto *.py') do (
-      IF NOT "%%i" == "%CD%\caffe2\proto\__init__.py" (
-        del /S /Q %%i
-      )
-    )
-    copy %TMP_DIR_WIN%\bin\sccache.exe %TMP_DIR_WIN%\bin\nvcc.exe
-  )
-
-  set CUDA_NVCC_EXECUTABLE=%TMP_DIR_WIN%\bin\nvcc
-
-  if "%REBUILD%"=="" set USE_CUDA=1
-
-  python setup.py install --cmake && sccache --show-stats && (
-    if "%BUILD_ENVIRONMENT%"=="" (
-      echo NOTE: To run `import torch`, please make sure to activate the conda environment by running `call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3` in Command Prompt before running Git Bash.
-    ) else (
-      7z a %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\caffe2 && python %SCRIPT_HELPERS_DIR%\upload_image.py %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z
-    )
-  )
-)
-

.jenkins/pytorch/win-test-helpers/download_image.py

@@ -1,21 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import sys
-import boto3
-import botocore
-
-IMAGE_COMMIT_TAG = os.getenv('IMAGE_COMMIT_TAG')
-
-session = boto3.session.Session()
-s3 = session.resource('s3')
-BUCKET_NAME = 'ossci-windows-build'
-KEY = 'pytorch/' + IMAGE_COMMIT_TAG + '.7z'
-LOCAL_FILE_PATH = sys.argv[1]
-try:
-    s3.Bucket(BUCKET_NAME).download_file(KEY, LOCAL_FILE_PATH)
-except botocore.exceptions.ClientError as e:
-    if e.response['Error']['Code'] == "404":
-        print("The object does not exist.")
-    else:
-        raise

.jenkins/pytorch/win-test-helpers/installation-helpers/install_magma.bat

@@ -1,17 +0,0 @@
-if "%CUDA_VERSION%" == "9" set CUDA_SUFFIX=cuda90
-if "%CUDA_VERSION%" == "10" set CUDA_SUFFIX=cuda101
-
-if "%CUDA_SUFFIX%" == "" (
-  echo unknown CUDA version, please set `CUDA_VERSION` to 9 or 10.
-  exit /b 1
-)
-
-if "%REBUILD%"=="" (
-  if "%BUILD_ENVIRONMENT%"=="" (
-    curl -k https://s3.amazonaws.com/ossci-windows/magma_2.5.0_%CUDA_SUFFIX%_%BUILD_TYPE%.7z --output %TMP_DIR_WIN%\magma_2.5.0_%CUDA_SUFFIX%_%BUILD_TYPE%.7z
-  ) else (
-    aws s3 cp s3://ossci-windows/magma_2.5.0_%CUDA_SUFFIX%_%BUILD_TYPE%.7z %TMP_DIR_WIN%\magma_2.5.0_%CUDA_SUFFIX%_%BUILD_TYPE%.7z --quiet
-  )
-  7z x -aoa %TMP_DIR_WIN%\magma_2.5.0_%CUDA_SUFFIX%_%BUILD_TYPE%.7z -o%TMP_DIR_WIN%\magma
-)
-set MAGMA_HOME=%TMP_DIR_WIN%\magma

.jenkins/pytorch/win-test-helpers/installation-helpers/install_miniconda3.bat

@@ -1,15 +0,0 @@
-if "%BUILD_ENVIRONMENT%"=="" (
-  set CONDA_PARENT_DIR=%CD%
-) else (
-  set CONDA_PARENT_DIR=C:\Jenkins
-)
-if "%REBUILD%"=="" (
-  IF EXIST %CONDA_PARENT_DIR%\Miniconda3 ( rd /s /q %CONDA_PARENT_DIR%\Miniconda3 )
-  curl -k https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
-  %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
-)
-call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3
-if "%REBUILD%"=="" (
-  :: We have to pin Python version to 3.6.7, until mkl supports Python 3.7
-  call conda install -y -q python=3.6.7 numpy cffi pyyaml boto3
-)

.jenkins/pytorch/win-test-helpers/installation-helpers/install_mkl.bat

@@ -1,10 +0,0 @@
-if "%REBUILD%"=="" (
-  if "%BUILD_ENVIRONMENT%"=="" (
-    curl -k https://s3.amazonaws.com/ossci-windows/mkl_2019.4.245.7z --output %TMP_DIR_WIN%\mkl.7z
-  ) else (
-    aws s3 cp s3://ossci-windows/mkl_2019.4.245.7z %TMP_DIR_WIN%\mkl.7z --quiet
-  )
-  7z x -aoa %TMP_DIR_WIN%\mkl.7z -o%TMP_DIR_WIN%\mkl
-)
-set CMAKE_INCLUDE_PATH=%TMP_DIR_WIN%\mkl\include
-set LIB=%TMP_DIR_WIN%\mkl\lib;%LIB

.jenkins/pytorch/win-test-helpers/installation-helpers/install_sccache.bat

@@ -1,15 +0,0 @@
-mkdir %TMP_DIR_WIN%\bin
-
-if "%REBUILD%"=="" (
-  :check_sccache
-  %TMP_DIR_WIN%\bin\sccache.exe --show-stats || (
-    taskkill /im sccache.exe /f /t || ver > nul
-    del %TMP_DIR_WIN%\bin\sccache.exe
-    if "%BUILD_ENVIRONMENT%"=="" (
-      curl -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output %TMP_DIR_WIN%\bin\sccache.exe
-    ) else (
-      aws s3 cp s3://ossci-windows/sccache.exe %TMP_DIR_WIN%\bin\sccache.exe
-    )
-    goto :check_sccache
-  )
-)

.jenkins/pytorch/win-test-helpers/run_python_nn_smoketests.py

@@ -1,39 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import print_function
-
-import subprocess
-
-
-TESTS = [
-    (
-        "Checking that caffe2.python is available",
-        "from caffe2.python import core",
-    ),
-    (
-        "Checking that MKL is available",
-        "import torch; exit(0 if torch.backends.mkl.is_available() else 1)",
-    ),
-    (
-        "Checking that CUDA archs are setup correctly",
-        "import torch; torch.randn([3,5]).cuda()",
-    ),
-    (
-        "Checking that magma is available",
-        "import torch; torch.rand(1).cuda(); exit(0 if torch.cuda.has_magma else 1)",
-    ),
-    (
-        "Checking that CuDNN is available",
-        "import torch; exit(0 if torch.backends.cudnn.is_available() else 1)",
-    ),
-]
-
-
-if __name__ == "__main__":
-
-    for description, python_commands in TESTS:
-        print(description)
-        command_args = ["python", "-c", python_commands]
-        command_string = " ".join(command_args)
-        print("Command:", command_string)
-        subprocess.check_call(command_args)

.jenkins/pytorch/win-test-helpers/setup_pytorch_env.bat

@@ -1,85 +0,0 @@
-if exist "%TMP_DIR%/ci_scripts/pytorch_env_restore.bat" (
-    call %TMP_DIR%/ci_scripts/pytorch_env_restore.bat
-    exit /b 0
-)
-
-set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;%PATH%
-
-:: Install Miniconda3
-if "%BUILD_ENVIRONMENT%"=="" (
-    set CONDA_PARENT_DIR=%CD%
-) else (
-    set CONDA_PARENT_DIR=C:\Jenkins
-)
-if NOT "%BUILD_ENVIRONMENT%"=="" (
-    IF EXIST %CONDA_PARENT_DIR%\Miniconda3 ( rd /s /q %CONDA_PARENT_DIR%\Miniconda3 )
-    curl https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
-    %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
-)
-call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3
-if NOT "%BUILD_ENVIRONMENT%"=="" (
-    :: We have to pin Python version to 3.6.7, until mkl supports Python 3.7
-    :: Numba is pinned to 0.44.0 to avoid https://github.com/numba/numba/issues/4352
-    call conda install -y -q python=3.6.7 numpy mkl cffi pyyaml boto3 protobuf numba==0.44.0
-)
-pip install -q ninja future hypothesis "librosa>=0.6.2" psutil pillow
-:: No need to install faulthandler since we only test Python >= 3.6 on Windows
-:: faulthandler is builtin since Python 3.3
-
-if "%CUDA_VERSION%" == "9" goto cuda_build_9
-if "%CUDA_VERSION%" == "10" goto cuda_build_10
-goto cuda_build_end
-
-:cuda_build_9
-
-pushd .
-call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvarsall.bat" x64
-@echo on
-popd
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0
-set CUDA_PATH_V9_0=%CUDA_PATH%
-
-goto cuda_build_common
-
-:cuda_build_10
-
-pushd .
-call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64
-@echo on
-popd
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
-set CUDA_PATH_V10_1=%CUDA_PATH%
-
-goto cuda_build_common
-
-:cuda_build_common
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-set NUMBAPRO_CUDALIB=%CUDA_PATH%\bin
-set NUMBAPRO_LIBDEVICE=%CUDA_PATH%\nvvm\libdevice
-set NUMBAPRO_NVVM=%CUDA_PATH%\nvvm\bin\nvvm64_32_0.dll
-
-:cuda_build_end
-
-set PYTHONPATH=%TMP_DIR_WIN%\build;%PYTHONPATH%
-
-if NOT "%BUILD_ENVIRONMENT%"=="" (
-    pushd %TMP_DIR_WIN%\build
-    python %SCRIPT_HELPERS_DIR%\download_image.py %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z
-    :: 7z: -aos skips if exists because this .bat can be called multiple times
-    7z x %TMP_DIR_WIN%\%IMAGE_COMMIT_TAG%.7z -aos
-    popd
-) else (
-    xcopy /s %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
-)
-
-@echo off
-echo @echo off >> %TMP_DIR%/ci_scripts/pytorch_env_restore.bat
-for /f "usebackq tokens=*" %%i in (`set`) do echo set "%%i" >> %TMP_DIR%/ci_scripts/pytorch_env_restore.bat
-@echo on

.jenkins/pytorch/win-test-helpers/test_custom_script_ops.bat

@@ -1,32 +0,0 @@
-call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
-
-git submodule update --init --recursive third_party/pybind11
-cd test\custom_operator
-
-:: Build the custom operator library.
-mkdir build
-pushd build
-
-echo "Executing CMake for custom_operator test..."
-
-:: Note: Caffe2 does not support MSVC + CUDA + Debug mode (has to be Release mode)
-cmake -DCMAKE_PREFIX_PATH=%TMP_DIR_WIN%\build\torch -DCMAKE_BUILD_TYPE=Release -GNinja ..
-if ERRORLEVEL 1 exit /b 1
-
-echo "Executing Ninja for custom_operator test..."
-
-ninja -v
-if ERRORLEVEL 1 exit /b 1
-
-echo "Ninja succeeded for custom_operator test."
-
-popd
-
-:: Run tests Python-side and export a script module.
-python test_custom_ops.py -v
-python test_custom_classes.py -v
-python model.py --export-script-module="build/model.pt"
-:: Run tests C++-side and load the exported script module.
-cd build
-set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
-test_custom_ops.exe model.pt