Add files using upload-large-folder tool

wemm/lib/python3.10/site-packages/accelerate-1.2.1.dist-info/LICENSE

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wemm/lib/python3.10/site-packages/accelerate-1.2.1.dist-info/RECORD

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wemm/lib/python3.10/site-packages/accelerate-1.2.1.dist-info/WHEEL

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wemm/lib/python3.10/site-packages/accelerate-1.2.1.dist-info/entry_points.txt

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+[console_scripts]
+accelerate = accelerate.commands.accelerate_cli:main
+accelerate-config = accelerate.commands.config:main
+accelerate-estimate-memory = accelerate.commands.estimate:main
+accelerate-launch = accelerate.commands.launch:main
+accelerate-merge-weights = accelerate.commands.merge:main

wemm/lib/python3.10/site-packages/boto3-1.26.118.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: boto3
+Version: 1.26.118
+Summary: The AWS SDK for Python
+Home-page: https://github.com/boto/boto3
+Author: Amazon Web Services
+License: Apache License 2.0
+Project-URL: Documentation, https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
+Project-URL: Source, https://github.com/boto/boto3
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Natural Language :: English
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Requires-Python: >= 3.7
+License-File: LICENSE
+License-File: NOTICE
+Requires-Dist: botocore (<1.30.0,>=1.29.118)
+Requires-Dist: jmespath (<2.0.0,>=0.7.1)
+Requires-Dist: s3transfer (<0.7.0,>=0.6.0)
+Provides-Extra: crt
+Requires-Dist: botocore[crt] (<2.0a0,>=1.21.0) ; extra == 'crt'
+
+===============================
+Boto3 - The AWS SDK for Python
+===============================
+
+|Version| |Python| |License|
+
+Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for
+Python, which allows Python developers to write software that makes use
+of services like Amazon S3 and Amazon EC2. You can find the latest, most
+up to date, documentation at our `doc site`_, including a list of
+services that are supported.
+
+Boto3 is maintained and published by `Amazon Web Services`_.
+
+Boto (pronounced boh-toh) was named after the fresh water dolphin native to the Amazon river. The name was chosen by the author of the original Boto library, Mitch Garnaat, as a reference to the company.
+
+Notices
+-------
+
+On 2021-01-15, deprecation for Python 2.7 was announced and support was dropped
+on 2021-07-15. To avoid disruption, customers using Boto3 on Python 2.7 may
+need to upgrade their version of Python or pin the version of Boto3. For
+more information, see this `blog post <https://aws.amazon.com/blogs/developer/announcing-end-of-support-for-python-2-7-in-aws-sdk-for-python-and-aws-cli-v1/>`__.
+
+On 2022-05-30, support for Python 3.6 was ended. This follows the
+Python Software Foundation `end of support <https://www.python.org/dev/peps/pep-0494/#lifespan>`__
+for the runtime which occurred on 2021-12-23.
+For more information, see this `blog post <https://aws.amazon.com/blogs/developer/python-support-policy-updates-for-aws-sdks-and-tools/>`__.
+
+.. _boto: https://docs.pythonboto.org/
+.. _`doc site`: https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
+.. _`Amazon Web Services`: https://aws.amazon.com/what-is-aws/
+.. |Python| image:: https://img.shields.io/pypi/pyversions/boto3.svg?style=flat
+    :target: https://pypi.python.org/pypi/boto3/
+    :alt: Python Versions
+.. |Version| image:: http://img.shields.io/pypi/v/boto3.svg?style=flat
+    :target: https://pypi.python.org/pypi/boto3/
+    :alt: Package Version
+.. |License| image:: http://img.shields.io/pypi/l/boto3.svg?style=flat
+    :target: https://github.com/boto/boto3/blob/develop/LICENSE
+    :alt: License
+
+Getting Started
+---------------
+Assuming that you have a supported version of Python installed, you can first
+set up your environment with:
+
+.. code-block:: sh
+
+    $ python -m venv .venv
+    ...
+    $ . .venv/bin/activate
+
+Then, you can install boto3 from PyPI with:
+
+.. code-block:: sh
+
+    $ python -m pip install boto3
+
+or install from source with:
+
+.. code-block:: sh
+
+    $ git clone https://github.com/boto/boto3.git
+    $ cd boto3
+    $ python -m pip install -r requirements.txt
+    $ python -m pip install -e .
+
+
+Using Boto3
+~~~~~~~~~~~~~~
+After installing boto3
+
+Next, set up credentials (in e.g. ``~/.aws/credentials``):
+
+.. code-block:: ini
+
+    [default]
+    aws_access_key_id = YOUR_KEY
+    aws_secret_access_key = YOUR_SECRET
+
+Then, set up a default region (in e.g. ``~/.aws/config``):
+
+.. code-block:: ini
+
+   [default]
+   region=us-east-1
+
+Other credentials configuration method can be found `here <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`__
+
+Then, from a Python interpreter:
+
+.. code-block:: python
+
+    >>> import boto3
+    >>> s3 = boto3.resource('s3')
+    >>> for bucket in s3.buckets.all():
+            print(bucket.name)
+
+Running Tests
+~~~~~~~~~~~~~
+You can run tests in all supported Python versions using ``tox``. By default,
+it will run all of the unit and functional tests, but you can also specify your own
+``pytest`` options. Note that this requires that you have all supported
+versions of Python installed, otherwise you must pass ``-e`` or run the
+``pytest`` command directly:
+
+.. code-block:: sh
+
+    $ tox
+    $ tox -- unit/test_session.py
+    $ tox -e py26,py33 -- integration/
+
+You can also run individual tests with your default Python version:
+
+.. code-block:: sh
+
+    $ pytest tests/unit
+
+
+Getting Help
+------------
+
+We use GitHub issues for tracking bugs and feature requests and have limited
+bandwidth to address them. Please use these community resources for getting
+help:
+
+* Ask a question on `Stack Overflow <https://stackoverflow.com/>`__ and tag it with `boto3 <https://stackoverflow.com/questions/tagged/boto3>`__
+* Open a support ticket with `AWS Support <https://console.aws.amazon.com/support/home#/>`__
+* If it turns out that you may have found a bug, please `open an issue <https://github.com/boto/boto3/issues/new>`__
+
+
+Contributing
+------------
+
+We value feedback and contributions from our community. Whether it's a bug report, new feature, correction, or additional documentation, we welcome your issues and pull requests. Please read through this `CONTRIBUTING <https://github.com/boto/boto3/blob/develop/CONTRIBUTING.rst>`__ document before submitting any issues or pull requests to ensure we have all the necessary information to effectively respond to your contribution.
+
+
+Maintenance and Support for SDK Major Versions
+----------------------------------------------
+
+Boto3 was made generally available on 06/22/2015 and is currently in the full support phase of the availability life cycle.
+
+For information about maintenance and support for SDK major versions and their underlying dependencies, see the following in the AWS SDKs and Tools Shared Configuration and Credentials Reference Guide:
+
+* `AWS SDKs and Tools Maintenance Policy <https://docs.aws.amazon.com/sdkref/latest/guide/maint-policy.html>`__
+* `AWS SDKs and Tools Version Support Matrix <https://docs.aws.amazon.com/sdkref/latest/guide/version-support-matrix.html>`__
+
+
+More Resources
+--------------
+
+* `NOTICE <https://github.com/boto/boto3/blob/develop/NOTICE>`__
+* `Changelog <https://github.com/boto/boto3/blob/develop/CHANGELOG.rst>`__
+* `License <https://github.com/boto/boto3/blob/develop/LICENSE>`__
+
+

wemm/lib/python3.10/site-packages/boto3-1.26.118.dist-info/WHEEL

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wemm/lib/python3.10/site-packages/jinja2-3.1.5.dist-info/LICENSE.txt

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+Copyright 2007 Pallets
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1.  Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

wemm/lib/python3.10/site-packages/jinja2-3.1.5.dist-info/METADATA

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+Metadata-Version: 2.3
+Name: Jinja2
+Version: 3.1.5
+Summary: A very fast and expressive template engine.
+Maintainer-email: Pallets <contact@palletsprojects.com>
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Classifier: Topic :: Text Processing :: Markup :: HTML
+Classifier: Typing :: Typed
+Requires-Dist: MarkupSafe>=2.0
+Requires-Dist: Babel>=2.7 ; extra == "i18n"
+Project-URL: Changes, https://jinja.palletsprojects.com/changes/
+Project-URL: Chat, https://discord.gg/pallets
+Project-URL: Documentation, https://jinja.palletsprojects.com/
+Project-URL: Donate, https://palletsprojects.com/donate
+Project-URL: Source, https://github.com/pallets/jinja/
+Provides-Extra: i18n
+
+# Jinja
+
+Jinja is a fast, expressive, extensible templating engine. Special
+placeholders in the template allow writing code similar to Python
+syntax. Then the template is passed data to render the final document.
+
+It includes:
+
+-   Template inheritance and inclusion.
+-   Define and import macros within templates.
+-   HTML templates can use autoescaping to prevent XSS from untrusted
+    user input.
+-   A sandboxed environment can safely render untrusted templates.
+-   AsyncIO support for generating templates and calling async
+    functions.
+-   I18N support with Babel.
+-   Templates are compiled to optimized Python code just-in-time and
+    cached, or can be compiled ahead-of-time.
+-   Exceptions point to the correct line in templates to make debugging
+    easier.
+-   Extensible filters, tests, functions, and even syntax.
+
+Jinja's philosophy is that while application logic belongs in Python if
+possible, it shouldn't make the template designer's job difficult by
+restricting functionality too much.
+
+
+## In A Nutshell
+
+```jinja
+{% extends "base.html" %}
+{% block title %}Members{% endblock %}
+{% block content %}
+  <ul>
+  {% for user in users %}
+    <li><a href="{{ user.url }}">{{ user.username }}</a></li>
+  {% endfor %}
+  </ul>
+{% endblock %}
+```
+
+## Donate
+
+The Pallets organization develops and supports Jinja and other popular
+packages. In order to grow the community of contributors and users, and
+allow the maintainers to devote more time to the projects, [please
+donate today][].
+
+[please donate today]: https://palletsprojects.com/donate
+

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+jinja2/parser.py,sha256=lLOFy3sEmHc5IaEHRiH1sQVnId2moUQzhyeJZTtdY30,40383
+jinja2/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+jinja2/runtime.py,sha256=gDk-GvdriJXqgsGbHgrcKTP0Yp6zPXzhzrIpCFH3jAU,34249
+jinja2/sandbox.py,sha256=Mw2aitlY2I8la7FYhcX2YG9BtUYcLnD0Gh3d29cDWrY,15009
+jinja2/tests.py,sha256=VLsBhVFnWg-PxSBz1MhRnNWgP1ovXk3neO1FLQMeC9Q,5926
+jinja2/utils.py,sha256=rRp3o9e7ZKS4fyrWRbELyLcpuGVTFcnooaOa1qx_FIk,24129
+jinja2/visitor.py,sha256=EcnL1PIwf_4RVCOMxsRNuR8AXHbS1qfAdMOE2ngKJz4,3557

wemm/lib/python3.10/site-packages/jinja2-3.1.5.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.10.1
+Root-Is-Purelib: true
+Tag: py3-none-any

wemm/lib/python3.10/site-packages/jinja2-3.1.5.dist-info/entry_points.txt

@@ -0,0 +1,3 @@
+[babel.extractors]
+jinja2=jinja2.ext:babel_extract[i18n]
+

wemm/lib/python3.10/site-packages/lit-18.1.8.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.41.2)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

wemm/lib/python3.10/site-packages/pillow-11.1.0.dist-info/METADATA

@@ -0,0 +1,176 @@
+Metadata-Version: 2.1
+Name: pillow
+Version: 11.1.0
+Summary: Python Imaging Library (Fork)
+Author-email: "Jeffrey A. Clark" <aclark@aclark.net>
+License: MIT-CMU
+Project-URL: Changelog, https://github.com/python-pillow/Pillow/releases
+Project-URL: Documentation, https://pillow.readthedocs.io
+Project-URL: Funding, https://tidelift.com/subscription/pkg/pypi-pillow?utm_source=pypi-pillow&utm_medium=pypi
+Project-URL: Homepage, https://python-pillow.github.io
+Project-URL: Mastodon, https://fosstodon.org/@pillow
+Project-URL: Release notes, https://pillow.readthedocs.io/en/stable/releasenotes/index.html
+Project-URL: Source, https://github.com/python-pillow/Pillow
+Keywords: Imaging
+Classifier: Development Status :: 6 - Mature
+Classifier: License :: OSI Approved :: CMU License (MIT-CMU)
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Multimedia :: Graphics
+Classifier: Topic :: Multimedia :: Graphics :: Capture :: Digital Camera
+Classifier: Topic :: Multimedia :: Graphics :: Capture :: Screen Capture
+Classifier: Topic :: Multimedia :: Graphics :: Graphics Conversion
+Classifier: Topic :: Multimedia :: Graphics :: Viewers
+Classifier: Typing :: Typed
+Requires-Python: >=3.9
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Provides-Extra: docs
+Requires-Dist: furo; extra == "docs"
+Requires-Dist: olefile; extra == "docs"
+Requires-Dist: sphinx>=8.1; extra == "docs"
+Requires-Dist: sphinx-copybutton; extra == "docs"
+Requires-Dist: sphinx-inline-tabs; extra == "docs"
+Requires-Dist: sphinxext-opengraph; extra == "docs"
+Provides-Extra: fpx
+Requires-Dist: olefile; extra == "fpx"
+Provides-Extra: mic
+Requires-Dist: olefile; extra == "mic"
+Provides-Extra: tests
+Requires-Dist: check-manifest; extra == "tests"
+Requires-Dist: coverage>=7.4.2; extra == "tests"
+Requires-Dist: defusedxml; extra == "tests"
+Requires-Dist: markdown2; extra == "tests"
+Requires-Dist: olefile; extra == "tests"
+Requires-Dist: packaging; extra == "tests"
+Requires-Dist: pyroma; extra == "tests"
+Requires-Dist: pytest; extra == "tests"
+Requires-Dist: pytest-cov; extra == "tests"
+Requires-Dist: pytest-timeout; extra == "tests"
+Requires-Dist: trove-classifiers>=2024.10.12; extra == "tests"
+Provides-Extra: typing
+Requires-Dist: typing-extensions; python_version < "3.10" and extra == "typing"
+Provides-Extra: xmp
+Requires-Dist: defusedxml; extra == "xmp"
+
+<p align="center">
+    <img width="248" height="250" src="https://raw.githubusercontent.com/python-pillow/pillow-logo/main/pillow-logo-248x250.png" alt="Pillow logo">
+</p>
+
+# Pillow
+
+## Python Imaging Library (Fork)
+
+Pillow is the friendly PIL fork by [Jeffrey A. Clark and
+contributors](https://github.com/python-pillow/Pillow/graphs/contributors).
+PIL is the Python Imaging Library by Fredrik Lundh and contributors.
+As of 2019, Pillow development is
+[supported by Tidelift](https://tidelift.com/subscription/pkg/pypi-pillow?utm_source=pypi-pillow&utm_medium=readme&utm_campaign=enterprise).
+
+<table>
+    <tr>
+        <th>docs</th>
+        <td>
+            <a href="https://pillow.readthedocs.io/?badge=latest"><img
+                alt="Documentation Status"
+                src="https://readthedocs.org/projects/pillow/badge/?version=latest"></a>
+        </td>
+    </tr>
+    <tr>
+        <th>tests</th>
+        <td>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/lint.yml"><img
+                alt="GitHub Actions build status (Lint)"
+                src="https://github.com/python-pillow/Pillow/workflows/Lint/badge.svg"></a>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test.yml"><img
+                alt="GitHub Actions build status (Test Linux and macOS)"
+                src="https://github.com/python-pillow/Pillow/workflows/Test/badge.svg"></a>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-windows.yml"><img
+                alt="GitHub Actions build status (Test Windows)"
+                src="https://github.com/python-pillow/Pillow/workflows/Test%20Windows/badge.svg"></a>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-mingw.yml"><img
+                alt="GitHub Actions build status (Test MinGW)"
+                src="https://github.com/python-pillow/Pillow/workflows/Test%20MinGW/badge.svg"></a>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-cygwin.yml"><img
+                alt="GitHub Actions build status (Test Cygwin)"
+                src="https://github.com/python-pillow/Pillow/workflows/Test%20Cygwin/badge.svg"></a>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/test-docker.yml"><img
+                alt="GitHub Actions build status (Test Docker)"
+                src="https://github.com/python-pillow/Pillow/workflows/Test%20Docker/badge.svg"></a>
+            <a href="https://ci.appveyor.com/project/python-pillow/Pillow"><img
+                alt="AppVeyor CI build status (Windows)"
+                src="https://img.shields.io/appveyor/build/python-pillow/Pillow/main.svg?label=Windows%20build"></a>
+            <a href="https://github.com/python-pillow/Pillow/actions/workflows/wheels.yml"><img
+                alt="GitHub Actions build status (Wheels)"
+                src="https://github.com/python-pillow/Pillow/workflows/Wheels/badge.svg"></a>
+            <a href="https://app.codecov.io/gh/python-pillow/Pillow"><img
+                alt="Code coverage"
+                src="https://codecov.io/gh/python-pillow/Pillow/branch/main/graph/badge.svg"></a>
+            <a href="https://issues.oss-fuzz.com/issues?q=title:pillow"><img
+                alt="Fuzzing Status"
+                src="https://oss-fuzz-build-logs.storage.googleapis.com/badges/pillow.svg"></a>
+        </td>
+    </tr>
+    <tr>
+        <th>package</th>
+        <td>
+            <a href="https://zenodo.org/badge/latestdoi/17549/python-pillow/Pillow"><img
+                alt="Zenodo"
+                src="https://zenodo.org/badge/17549/python-pillow/Pillow.svg"></a>
+            <a href="https://tidelift.com/subscription/pkg/pypi-pillow?utm_source=pypi-pillow&utm_medium=badge"><img
+                alt="Tidelift"
+                src="https://tidelift.com/badges/package/pypi/pillow?style=flat"></a>
+            <a href="https://pypi.org/project/pillow/"><img
+                alt="Newest PyPI version"
+                src="https://img.shields.io/pypi/v/pillow.svg"></a>
+            <a href="https://pypi.org/project/pillow/"><img
+                alt="Number of PyPI downloads"
+                src="https://img.shields.io/pypi/dm/pillow.svg"></a>
+            <a href="https://www.bestpractices.dev/projects/6331"><img
+                alt="OpenSSF Best Practices"
+                src="https://www.bestpractices.dev/projects/6331/badge"></a>
+        </td>
+    </tr>
+    <tr>
+        <th>social</th>
+        <td>
+            <a href="https://gitter.im/python-pillow/Pillow?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge"><img
+                alt="Join the chat at https://gitter.im/python-pillow/Pillow"
+                src="https://badges.gitter.im/python-pillow/Pillow.svg"></a>
+            <a href="https://fosstodon.org/@pillow"><img
+                alt="Follow on https://fosstodon.org/@pillow"
+                src="https://img.shields.io/badge/publish-on%20Mastodon-595aff.svg"
+                rel="me"></a>
+        </td>
+    </tr>
+</table>
+
+## Overview
+
+The Python Imaging Library adds image processing capabilities to your Python interpreter.
+
+This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities.
+
+The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.
+
+## More Information
+
+- [Documentation](https://pillow.readthedocs.io/)
+  - [Installation](https://pillow.readthedocs.io/en/latest/installation/basic-installation.html)
+  - [Handbook](https://pillow.readthedocs.io/en/latest/handbook/index.html)
+- [Contribute](https://github.com/python-pillow/Pillow/blob/main/.github/CONTRIBUTING.md)
+  - [Issues](https://github.com/python-pillow/Pillow/issues)
+  - [Pull requests](https://github.com/python-pillow/Pillow/pulls)
+- [Release notes](https://pillow.readthedocs.io/en/stable/releasenotes/index.html)
+- [Changelog](https://github.com/python-pillow/Pillow/releases)
+  - [Pre-fork](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst#pre-fork)
+
+## Report a Vulnerability
+
+To report a security vulnerability, please follow the procedure described in the [Tidelift security policy](https://tidelift.com/docs/security).

wemm/lib/python3.10/site-packages/qcloud_cos/__init__.py

@@ -0,0 +1,19 @@
+from .cos_client import CosS3Client
+from .cos_client import CosConfig
+from .cos_exception import CosServiceError
+from .cos_exception import CosClientError
+from .cos_auth import CosS3Auth
+from .cos_comm import get_date
+from .meta_insight import MetaInsightClient
+from .ai_recognition import AIRecognitionClient
+
+import logging
+
+try:
+    from logging import NullHandler
+except ImportError:
+    class NullHandler(logging.Handler):
+        def emit(self, record):
+            pass
+
+logging.getLogger(__name__).addHandler(NullHandler())

wemm/lib/python3.10/site-packages/qcloud_cos/cos_exception.py

@@ -0,0 +1,101 @@
+# -*- coding=utf-8
+
+import xml.dom.minidom
+
+
+class CosException(Exception):
+    def __init__(self, message):
+        self._message = message
+
+    def __str__(self):
+        return str(self._message)
+
+
+def digest_xml(data):
+    msg = dict()
+    try:
+        tree = xml.dom.minidom.parseString(data)
+        root = tree.documentElement
+
+        result = root.getElementsByTagName('Code')
+        msg['code'] = result[0].childNodes[0].nodeValue
+
+        result = root.getElementsByTagName('Message')
+        msg['message'] = result[0].childNodes[0].nodeValue
+
+        result = root.getElementsByTagName('Resource')
+        msg['resource'] = result[0].childNodes[0].nodeValue
+
+        result = root.getElementsByTagName('RequestId')
+        msg['requestid'] = result[0].childNodes[0].nodeValue
+
+        result = root.getElementsByTagName('TraceId')
+        if result:
+            msg['traceid'] = result[0].childNodes[0].nodeValue
+        else:
+            msg['traceid'] = 'Unknown'
+        return msg
+    except Exception as e:
+        return "Response Error Msg Is INVALID"
+
+
+class CosClientError(CosException):
+    """Client端错误，如timeout"""
+
+    def __init__(self, message):
+        CosException.__init__(self, message)
+
+
+class CosServiceError(CosException):
+    """COS Server端错误，可以获取特定的错误信息"""
+
+    def __init__(self, method, message, status_code):
+        CosException.__init__(self, message)
+        if isinstance(message, dict):
+            self._origin_msg = ''
+            self._digest_msg = message
+        else:
+            self._origin_msg = message
+            self._digest_msg = digest_xml(message)
+        self._status_code = status_code
+
+    def __str__(self):
+        return str(self._digest_msg)
+
+    def get_origin_msg(self):
+        """获取原始的XML格式错误信息"""
+        return self._origin_msg
+
+    def get_digest_msg(self):
+        """获取经过处理的dict格式的错误信息"""
+        return self._digest_msg
+
+    def get_status_code(self):
+        """获取http error code"""
+        return self._status_code
+
+    def get_error_code(self):
+        """获取COS定义的错误码描述,服务器返回错误信息格式出错时，返回空 """
+        if isinstance(self._digest_msg, dict):
+            return self._digest_msg['code']
+        return "Unknown"
+
+    def get_error_msg(self):
+        if isinstance(self._digest_msg, dict):
+            return self._digest_msg['message']
+        return "Unknown"
+
+    def get_resource_location(self):
+        if isinstance(self._digest_msg, dict):
+            return self._digest_msg['resource']
+        return "Unknown"
+
+    def get_trace_id(self):
+        if isinstance(self._digest_msg, dict):
+            return self._digest_msg['traceid']
+        return "Unknown"
+
+    def get_request_id(self):
+        if isinstance(self._digest_msg, dict):
+            return self._digest_msg['requestid']
+        return "Unknown"

wemm/lib/python3.10/site-packages/qcloud_cos/cos_threadpool.py

@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+
+from threading import Thread
+from logging import getLogger
+from six.moves.queue import Queue
+from threading import Lock
+import gc
+
+logger = getLogger(__name__)
+
+
+class WorkerThread(Thread):
+    def __init__(self, task_queue, *args, **kwargs):
+        super(WorkerThread, self).__init__(*args, **kwargs)
+
+        self._task_queue = task_queue
+        self._succ_task_num = 0
+        self._fail_task_num = 0
+        self._ret = list()
+
+    def run(self):
+        while True:
+            func, args, kwargs = self._task_queue.get()
+            # 判断线程是否需要退出
+            if func is None:
+                return
+            try:
+                ret = func(*args, **kwargs)
+                self._succ_task_num += 1
+                self._ret.append(ret)
+
+            except Exception as e:
+                logger.error(str(e))
+                self._fail_task_num += 1
+                if hasattr(e, '_message') and e._message:
+                    self._ret.append(e._message)
+                elif hasattr(e, 'message') and e.message:
+                    self._ret.append(e.message)
+                else:
+                    self._ret.append('meet some exception')
+            finally:
+                self._task_queue.task_done()
+
+    def get_result(self):
+        return self._succ_task_num, self._fail_task_num, self._ret
+
+
+class SimpleThreadPool:
+
+    def __init__(self, num_threads=5, num_queue=0):
+        self._num_threads = num_threads
+        self._queue = Queue(num_queue)
+        self._lock = Lock()
+        self._active = False
+        self._workers = list()
+        self._finished = False
+
+    def add_task(self, func, *args, **kwargs):
+        if not self._active:
+            with self._lock:
+                if not self._active:
+                    self._workers = []
+                    self._active = True
+
+                    for i in range(self._num_threads):
+                        w = WorkerThread(self._queue)
+                        self._workers.append(w)
+                        w.start()
+
+        self._queue.put((func, args, kwargs))
+
+    def wait_completion(self):
+        self._queue.join()
+        self._finished = True
+        # 已经结束的任务, 需要将线程都退出, 防止卡死
+        for i in range(self._num_threads):
+            self._queue.put((None, None, None))
+
+        self._active = False
+
+    def get_result(self):
+        assert self._finished
+        detail = [worker.get_result() for worker in self._workers]
+        succ_all = all([tp[1] == 0 for tp in detail])
+        return {'success_all': succ_all, 'detail': detail}
+
+
+if __name__ == '__main__':
+    pass
+
+    # pool = SimpleThreadPool(2)
+
+    # def task_sleep(x):
+    #     from time import sleep
+    #     sleep(x)
+    #     return 'hello, sleep %d seconds' % x
+
+    # def raise_exception():
+    #     raise ValueError("Pa! Exception!")
+
+    # for i in range(1000):
+    #     pool.add_task(task_sleep, 0.001)
+    #     print(i)
+    # pool.add_task(task_sleep, 0)
+    # pool.add_task(task_sleep, 0)
+    # pool.add_task(raise_exception)
+    # pool.add_task(raise_exception)
+
+    # pool.wait_completion()
+    # print(pool.get_result())
+    # [(1, 0, ['hello, sleep 5 seconds']), (2, 1, ['hello, sleep 2 seconds', 'hello, sleep 3 seconds', ValueError('Pa! Exception!',)])]

wemm/lib/python3.10/site-packages/qcloud_cos/meta_insight.py

@@ -0,0 +1,908 @@
+# -*- coding=utf-8
+import json
+
+from qcloud_cos import CosS3Auth
+from qcloud_cos.cos_client import logger, CosS3Client
+from .cos_comm import *
+
+
+class MetaInsightClient(CosS3Client):
+
+    def ci_create_dataset(self, Body, **kwargs):
+        """ 创建数据集 https://cloud.tencent.com/document/product/460/106020
+
+        :param Body:(dict) 创建数据集配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  创建数据集
+            response, data = client.ci_create_dataset(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "dataset"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_create_dataset result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='POST',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_create_dataset_binding(self, Body, **kwargs):
+        """ 绑定存储桶与数据集 https://cloud.tencent.com/document/product/460/106159
+
+        :param Body:(dict) 绑定存储桶与数据集配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  绑定存储桶与数据集
+            response, data = client.ci_create_dataset_binding(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "datasetbinding"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_create_dataset_binding result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='POST',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_create_file_meta_index(self, Body, **kwargs):
+        """ 创建元数据索引 https://cloud.tencent.com/document/product/460/106022
+
+        :param Body:(dict) 创建元数据索引配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  创建元数据索引
+            response, data = client.ci_create_file_meta_index(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "filemeta"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_create_file_meta_index result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='POST',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_dataset_face_search(self, Body, **kwargs):
+        """ 人脸搜索 https://cloud.tencent.com/document/product/460/106166
+
+        :param Body:(dict) 人脸搜索配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  人脸搜索
+            response, data = client.ci_dataset_face_search(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "datasetquery" + "/" + "facesearch"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_dataset_face_search result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='POST',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_dataset_simple_query(self, Body, **kwargs):
+        """ 简单查询 https://cloud.tencent.com/document/product/460/106375
+
+        :param Body:(dict) 简单查询配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  简单查询
+            response, data = client.ci_dataset_simple_query(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "datasetquery" + "/" + "simple"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_dataset_simple_query result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='POST',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_delete_dataset(self, Body, **kwargs):
+        """ 删除数据集 https://cloud.tencent.com/document/product/460/106157
+
+        :param Body:(dict) 删除数据集配置信息.
+        :param kwargs:(dict) 设置上传���headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  删除数据集
+            response, data = client.ci_delete_dataset(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "dataset"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_delete_dataset result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='DELETE',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_delete_dataset_binding(self, Body, **kwargs):
+        """ 解绑存储桶与数据集 https://cloud.tencent.com/document/product/460/106160
+
+        :param Body:(dict) 解绑存储桶与数据集配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  解绑存储桶与数据集
+            response, data = client.ci_delete_dataset_binding(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "datasetbinding"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_delete_dataset_binding result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='DELETE',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_delete_file_meta_index(self, Body, **kwargs):
+        """ 删除元数据索引 https://cloud.tencent.com/document/product/460/106163
+
+        :param Body:(dict) 删除元数据索引配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  删除元数据索引
+            response, data = client.ci_delete_file_meta_index(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "filemeta"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_delete_file_meta_index result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='DELETE',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_describe_dataset(self, DatasetName, Statistics=False, **kwargs):
+        """ 查询数据集 https://cloud.tencent.com/document/product/460/106155
+
+        :param DatasetName:(string) 数据集名称，同一个账户下唯一。.
+        :param Statistics:(bool) 是否需要实时统计数据集中文件相关信息。有效值： false：不统计，返回的文件的总大小、数量信息可能不正确也可能都为0。 true：需要统计，返回数据集中当前的文件的总大小、数量信息。 默认值为false。.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  查询数据集
+            response, data = client.ci_describe_dataset(
+                Datasetname='',
+                Statistics=''
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+        params["datasetname"] = DatasetName
+        params["statistics"] = Statistics
+
+        params = format_values(params)
+
+        path = "/" + "dataset"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_describe_dataset result, url=:{url} ,headers=:{headers}, params=:{params}".format(
+            url=url,
+            headers=headers,
+            params=params))
+        rt = self.send_request(
+            method='GET',
+            url=url,
+            appid=self._conf._appid,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_describe_dataset_binding(self, DatasetName, Uri, **kwargs):
+        """ 查询数据集与存储桶的绑定关系 https://cloud.tencent.com/document/product/460/106485
+
+        :param DatasetName:(string) 数据集名称，同一个账户下唯一。.
+        :param Uri:(string) 资源标识字段，表示需要与数据集绑定的资源，当前仅支持COS存储桶，字段规则：cos://，其中BucketName表示COS存储桶名称，例如（需要进行urlencode）：cos%3A%2F%2Fexample-125000.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  查询数据集与存储桶的绑定关系
+            response, data = client.ci_describe_dataset_binding(
+                DatasetName='',
+                Uri=''
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+        params["datasetname"] = DatasetName
+        params["uri"] = Uri
+
+        params = format_values(params)
+
+        path = "/" + "datasetbinding"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_describe_dataset_binding result, url=:{url} ,headers=:{headers}, params=:{params}".format(
+            url=url,
+            headers=headers,
+            params=params))
+        rt = self.send_request(
+            method='GET',
+            url=url,
+            appid=self._conf._appid,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_describe_dataset_bindings(self, DatasetName, NextToken=None, MaxResults=100, **kwargs):
+        """ 查询绑定关系列表 https://cloud.tencent.com/document/product/460/106161
+
+        :param DatasetName:(string) 数据集名称，同一个账户下唯一。.
+        :param MaxResults:(int) 返回绑定关系的最大个数，取值范围为0~200。不设置此参数或者设置为0时，则默认值为100。.
+        :param NextToken:(string) 当绑定关系总数大于设置的MaxResults时，用于翻页的token。从NextToken开始按字典序返回绑定关系信息列表。第一次调用此接口时，设置为空。.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  查询绑定关系列表
+            response, data = client.ci_describe_dataset_bindings(
+                DatasetName='',
+                MaxResults='',
+                NextToken=''
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+        params["datasetname"] = DatasetName
+        if NextToken is not None:
+            params["nexttoken"] = NextToken
+        params["maxresults"] = MaxResults
+
+        params = format_values(params)
+
+        path = "/" + "datasetbindings"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_describe_dataset_bindings result, url=:{url} ,headers=:{headers}, params=:{params}".format(
+            url=url,
+            headers=headers,
+            params=params))
+        rt = self.send_request(
+            method='GET',
+            url=url,
+            appid=self._conf._appid,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_describe_datasets(self, NextToken=None, Prefix=None, MaxResults=100, **kwargs):
+        """ 列出数据集 https://cloud.tencent.com/document/product/460/106158
+
+        :param MaxResults:(int) 本次返回数据集的最大个数，取值范围为0~200。不设置此参数或者设置为0时，则默认值为100。.
+        :param NextToken:(string) 翻页标记。当文件总数大于设置的MaxResults时，用于翻页的Token。从NextToken开始按字典序返回文件信息列表。填写上次查询返回的值，首次使用时填写为空。.
+        :param Prefix:(string) 数据集名称前缀。.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  列出数据集
+            response, data = client.ci_describe_datasets(
+                MaxResults='',
+                NextToken='',
+                Prefix=''
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+        if NextToken is not None:
+            params["nexttoken"] = NextToken
+        if Prefix is not None:
+            params["prefix"] = Prefix
+        params["maxresults"] = MaxResults
+
+        params = format_values(params)
+
+        path = "/" + "datasets"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_describe_datasets result, url=:{url} ,headers=:{headers}, params=:{params}".format(
+            url=url,
+            headers=headers,
+            params=params))
+        rt = self.send_request(
+            method='GET',
+            url=url,
+            appid=self._conf._appid,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_describe_file_meta_index(self, DatasetName, Uri, **kwargs):
+        """ 查询元数据索引 https://cloud.tencent.com/document/product/460/106164
+
+        :param DatasetName:(string) 数据集名称，同一个账户下唯一。.
+        :param Uri:(string) 资源标识字段，表示需要建立索引的文件地址，当前仅支持 COS 上的文件，字段规则：cos://<BucketName>/<ObjectKey>，其中BucketName表示 COS 存储桶名称，ObjectKey 表示文件完整路径，例如：cos://examplebucket-1250000000/test1/img.jpg。 注意： 仅支持本账号内的 COS 文件 不支持 HTTP 开头的地址 需 UrlEncode.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  查询元数据索引
+            response, data = client.ci_describe_file_meta_index(
+                Datasetname='',
+                Uri=''
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+        params["datasetname"] = DatasetName
+        params["uri"] = Uri
+
+        params = format_values(params)
+
+        path = "/" + "filemeta"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_describe_file_meta_index result, url=:{url} ,headers=:{headers}, params=:{params}".format(
+            url=url,
+            headers=headers,
+            params=params))
+        rt = self.send_request(
+            method='GET',
+            url=url,
+            appid=self._conf._appid,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_search_image(self, Body, **kwargs):
+        """ 图像检索 https://cloud.tencent.com/document/product/460/106376
+
+        :param Body:(dict) 图像检索配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  图像检索
+            response, data = client.ci_search_image(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "datasetquery" + "/" + "imagesearch"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_search_image result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='POST',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_update_dataset(self, Body, **kwargs):
+        """ 更新数据集 https://cloud.tencent.com/document/product/460/106156
+
+        :param Body:(dict) 更新数据集配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  更新数据集
+            response, data = client.ci_update_dataset(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "dataset"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_update_dataset result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='PUT',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data
+
+    def ci_update_file_meta_index(self, Body, **kwargs):
+        """ 更新元数据索引 https://cloud.tencent.com/document/product/460/106162
+
+        :param Body:(dict) 更新元数据索引配置信息.
+        :param kwargs:(dict) 设置上传的headers.
+        :return(dict): response header.
+        :return(dict): 请求成功返回的结果,dict类型.
+
+        .. code-block:: python
+
+            config = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token)  # 获取配置对象
+            client = CosS3Client(config)
+            #  更新元数据索引
+            response, data = client.ci_update_file_meta_index(
+                Body={}
+            )
+            print data
+            print response
+        """
+        headers = mapped(kwargs)
+        final_headers = {}
+        params = {}
+        for key in headers:
+            if key.startswith("response"):
+                params[key] = headers[key]
+            else:
+                final_headers[key] = headers[key]
+        headers = final_headers
+
+        params = format_values(params)
+        body = json.dumps(Body)
+        path = "/" + "filemeta"
+        url = self._conf.uri(path=path, endpoint=self._conf._endpoint_ci, useAppid=True)
+
+        logger.info("ci_update_file_meta_index result, url=:{url} ,headers=:{headers}, params=:{params},body=:{body}".format(
+            url=url,
+            headers=headers,
+            params=params,
+            body=body))
+        rt = self.send_request(
+            method='PUT',
+            url=url,
+            appid=self._conf._appid,
+            data=body,
+            auth=CosS3Auth(self._conf, path, params=params),
+            params=params,
+            headers=headers,
+            ci_request=True)
+
+        data = xml_to_dict(rt.content)
+        format_dict(data, ['Response'])
+
+        response = dict(**rt.headers)
+        return response, data

wemm/lib/python3.10/site-packages/qcloud_cos/resumable_downloader.py

@@ -0,0 +1,226 @@
+# -*- coding: utf-8 -*-
+
+import json
+import os
+import sys
+import threading
+import logging
+import uuid
+import hashlib
+import crcmod
+from .cos_comm import *
+from .streambody import StreamBody
+from .cos_threadpool import SimpleThreadPool
+
+logger = logging.getLogger(__name__)
+
+
+class ResumableDownLoader(object):
+    def __init__(self, cos_client, bucket, key, dest_filename, object_info, part_size=20, max_thread=5,
+                 enable_crc=False, progress_callback=None, dump_record_dir=None, key_simplify_check=True, **kwargs):
+        self.__cos_client = cos_client
+        self.__bucket = bucket
+        self.__key = key
+        self.__dest_file_path = os.path.abspath(dest_filename)
+        self.__object_info = object_info
+        self.__max_thread = max_thread
+        self.__enable_crc = enable_crc
+        self.__progress_callback = progress_callback
+        self.__headers = kwargs
+        self.__key_simplify_check = key_simplify_check
+
+        self.__max_part_count = 100  # 取决于服务端是否对并发有限制
+        self.__min_part_size = 1024 * 1024  # 1M
+        self.__part_size = self.__determine_part_size_internal(int(object_info['Content-Length']), part_size)
+        self.__finished_parts = []
+        self.__lock = threading.Lock()
+        self.__record = None  # 记录当前的上下文
+        if not dump_record_dir:
+            self.__dump_record_dir = os.path.join(os.path.expanduser('~'), '.cos_download_tmp_file')
+        else:
+            self.__dump_record_dir = dump_record_dir
+
+        record_filename = self.__get_record_filename(bucket, key, self.__dest_file_path)
+        self.__record_filepath = os.path.join(self.__dump_record_dir, record_filename)
+        self.__tmp_file = None
+
+        if not os.path.exists(self.__dump_record_dir):
+            os.makedirs(self.__dump_record_dir)
+        logger.debug('resumale downloader init finish, bucket: {0}, key: {1}'.format(bucket, key))
+
+    def start(self):
+        logger.debug('start resumable downloade, bucket: {0}, key: {1}'.format(self.__bucket, self.__key))
+        self.__load_record()  # 从record文件中恢复读取上下文
+
+        assert self.__tmp_file
+        open(self.__tmp_file, 'a').close()
+
+        # 已完成分块先设置下载进度
+        if self.__progress_callback:
+            for finished_part in self.__finished_parts:
+                self.__progress_callback.report(finished_part.length)
+
+        parts_need_to_download = self.__get_parts_need_to_download()
+        logger.debug('parts_need_to_download: {0}'.format(parts_need_to_download))
+        pool = SimpleThreadPool(self.__max_thread)
+        for part in parts_need_to_download:
+            part_range = "bytes=" + str(part.start) + "-" + str(part.start + part.length - 1)
+            headers = dict.copy(self.__headers)
+            headers["Range"] = part_range
+            pool.add_task(self.__download_part, part, headers)
+
+        pool.wait_completion()
+        result = pool.get_result()
+        if not result['success_all']:
+            raise CosClientError('some download_part fail after max_retry, please downloade_file again')
+
+        if os.path.exists(self.__dest_file_path):
+            os.remove(self.__dest_file_path)
+        os.rename(self.__tmp_file, self.__dest_file_path)
+
+        if self.__enable_crc:
+            self.__check_crc()
+
+        self.__del_record()
+        logger.debug('download success, bucket: {0}, key: {1}'.format(self.__bucket, self.__key))
+
+    def __get_record_filename(self, bucket, key, dest_file_path):
+        dest_file_path_md5 = hashlib.md5(dest_file_path.encode("utf-8")).hexdigest()
+        key_md5 = hashlib.md5(key.encode("utf-8")).hexdigest()
+        return '{0}_{1}.{2}'.format(bucket, key_md5, dest_file_path_md5)
+
+    def __determine_part_size_internal(self, file_size, part_size):
+        real_part_size = part_size * 1024 * 1024  # MB
+        if real_part_size < self.__min_part_size:
+            real_part_size = self.__min_part_size
+
+        while real_part_size * self.__max_part_count < file_size:
+            real_part_size = real_part_size * 2
+        logger.debug('finish to determine part size, file_size: {0}, part_size: {1}'.format(file_size, real_part_size))
+        return real_part_size
+
+    def __splite_to_parts(self):
+        parts = []
+        file_size = int(self.__object_info['Content-Length'])
+        num_parts = int((file_size + self.__part_size - 1) / self.__part_size)
+        for i in range(num_parts):
+            start = i * self.__part_size
+            if i == num_parts - 1:
+                length = file_size - start
+            else:
+                length = self.__part_size
+
+            parts.append(PartInfo(i + 1, start, length))
+        return parts
+
+    def __get_parts_need_to_download(self):
+        all_set = set(self.__splite_to_parts())
+        logger.debug('all_set: {0}'.format(len(all_set)))
+        finished_set = set(self.__finished_parts)
+        logger.debug('finished_set: {0}'.format(len(finished_set)))
+        return list(all_set - finished_set)
+
+    def __download_part(self, part, headers):
+        with open(self.__tmp_file, 'rb+') as f:
+            f.seek(part.start, 0)
+            range = None
+            traffic_limit = None
+            if 'Range' in headers:
+                range = headers['Range']
+
+            if 'TrafficLimit' in headers:
+                traffic_limit = headers['TrafficLimit']
+            logger.debug("part_id: {0}, part_range: {1}, traffic_limit:{2}".format(part.part_id, range, traffic_limit))
+            result = self.__cos_client.get_object(Bucket=self.__bucket, Key=self.__key, KeySimplifyCheck=self.__key_simplify_check, **headers)
+            result["Body"].pget_stream_to_file(f, part.start, part.length)
+
+        self.__finish_part(part)
+
+        if self.__progress_callback:
+            self.__progress_callback.report(part.length)
+
+    def __finish_part(self, part):
+        logger.debug('download part finished,bucket: {0}, key: {1}, part_id: {2}'.
+                     format(self.__bucket, self.__key, part.part_id))
+        with self.__lock:
+            self.__finished_parts.append(part)
+            self.__record['parts'].append({'part_id': part.part_id, 'start': part.start, 'length': part.length})
+            self.__dump_record(self.__record)
+
+    def __dump_record(self, record):
+        record_filepath = self.__record_filepath
+        if os.path.exists(self.__record_filepath):
+            record_filepath += '.tmp'
+        with open(record_filepath, 'w') as f:
+            json.dump(record, f)
+            logger.debug(
+                'dump record to {0}, bucket: {1}, key: {2}'.format(record_filepath, self.__bucket, self.__key))
+        if record_filepath != self.__record_filepath:
+            os.remove(self.__record_filepath)
+            os.rename(record_filepath, self.__record_filepath)
+
+    def __load_record(self):
+        record = None
+
+        if os.path.exists(self.__record_filepath):
+            with open(self.__record_filepath, 'r') as f:
+                record = json.load(f)
+            ret = self.__check_record(record)
+            # record记录是否跟head object的一致，不一致则删除
+            if not ret:
+                self.__del_record()
+                record = None
+            else:
+                self.__part_size = record['part_size']
+                self.__tmp_file = record['tmp_filename']
+                if not os.path.exists(self.__tmp_file):
+                    record = None
+                    self.__tmp_file = None
+                    self.__del_record()
+                else:
+                    self.__finished_parts = list(
+                        PartInfo(p['part_id'], p['start'], p['length']) for p in record['parts'])
+                    logger.debug('load record: finished parts nums: {0}'.format(len(self.__finished_parts)))
+                    self.__record = record
+
+        if not record:
+            self.__tmp_file = "{file_name}_{uuid}".format(file_name=self.__dest_file_path, uuid=uuid.uuid4().hex)
+            record = {'bucket': self.__bucket, 'key': self.__key, 'tmp_filename': self.__tmp_file,
+                      'mtime': self.__object_info['Last-Modified'], 'etag': self.__object_info['ETag'],
+                      'file_size': self.__object_info['Content-Length'], 'part_size': self.__part_size, 'parts': []}
+            self.__record = record
+            self.__dump_record(record)
+
+    def __check_record(self, record):
+        return record['etag'] == self.__object_info['ETag'] and \
+               record['mtime'] == self.__object_info['Last-Modified'] and \
+               record['file_size'] == self.__object_info['Content-Length']
+
+    def __del_record(self):
+        os.remove(self.__record_filepath)
+        logger.debug('ResumableDownLoader delete record_file, path: {0}'.format(self.__record_filepath))
+
+    def __check_crc(self):
+        logger.debug('start to check crc')
+        c64 = crcmod.mkCrcFun(0x142F0E1EBA9EA3693, initCrc=0, xorOut=0xffffffffffffffff, rev=True)
+        with open(self.__dest_file_path, 'rb') as f:
+            local_crc64 = str(c64(f.read()))
+        object_crc64 = self.__object_info['x-cos-hash-crc64ecma']
+        if local_crc64 is not None and object_crc64 is not None and local_crc64 != object_crc64:
+            raise CosClientError('crc of client: {0} is mismatch with cos: {1}'.format(local_crc64, object_crc64))
+
+
+class PartInfo(object):
+    def __init__(self, part_id, start, length):
+        self.part_id = part_id
+        self.start = start
+        self.length = length
+
+    def __eq__(self, other):
+        return self.__key() == other.__key()
+
+    def __hash__(self):
+        return hash(self.__key())
+
+    def __key(self):
+        return self.part_id, self.start, self.length

wemm/lib/python3.10/site-packages/qcloud_cos/streambody.py

@@ -0,0 +1,83 @@
+# -*- coding=utf-8
+import os
+import uuid
+
+
+class StreamBody(object):
+    def __init__(self, rt):
+        self._rt = rt
+        self._read_len = 0
+        self._content_len = 0
+        self._use_chunked = False
+        self._use_encoding = False
+        if 'Content-Length' in self._rt.headers:
+            self._content_len = int(self._rt.headers['Content-Length'])
+        elif 'Transfer-Encoding' in self._rt.headers and self._rt.headers['Transfer-Encoding'] == "chunked":
+            self._use_chunked = True
+        else:
+            raise IOError("create StreamBody failed without Content-Length header or Transfer-Encoding header")
+
+        if 'Content-Encoding' in self._rt.headers:
+            self._use_encoding = True
+
+    def __iter__(self):
+        """提供一个默认的迭代器"""
+        return self._rt.iter_content(1024)
+
+    def __len__(self):
+        return self._content_len
+
+    def get_raw_stream(self):
+        """提供原始流"""
+        return self._rt.raw
+
+    def get_stream(self, chunk_size=1024):
+        """提供一个chunk可变的迭代器"""
+        return self._rt.iter_content(chunk_size=chunk_size)
+
+    def read(self, chunk_size=1024, auto_decompress=False):
+        chunk = None
+        if self._use_encoding and not auto_decompress:
+            chunk = self._rt.raw.read(chunk_size)
+        else:
+            try:
+                chunk = next(self._rt.iter_content(chunk_size))
+            except StopIteration:
+                return ''
+        return chunk
+
+    def get_stream_to_file(self, file_name, auto_decompress=False):
+        """保存流到本地文件"""
+        self._read_len = 0
+        tmp_file_name = "{file_name}_{uuid}".format(file_name=file_name, uuid=uuid.uuid4().hex)
+        with open(tmp_file_name, 'wb') as fp:
+            while 1:
+                chunk = self.read(1024, auto_decompress)
+                if not chunk:
+                    break
+                self._read_len += len(chunk)
+                fp.write(chunk)
+
+        if not self._use_chunked and not (
+                self._use_encoding and auto_decompress) and self._read_len != self._content_len:
+            if os.path.exists(tmp_file_name):
+                os.remove(tmp_file_name)
+            raise IOError("download failed with incomplete file")
+        if os.path.exists(file_name):
+            os.remove(file_name)
+        os.rename(tmp_file_name, file_name)
+
+    def pget_stream_to_file(self, fdst, offset, expected_len, auto_decompress=False):
+        """保存流到本地文件的offset偏移"""
+        self._read_len = 0
+        fdst.seek(offset, 0)
+        chunk_size = 1024 * 1024
+        while 1:
+            chunk = self.read(chunk_size, auto_decompress)
+            if not chunk:
+                break
+            self._read_len += len(chunk)
+            fdst.write(chunk)
+
+        if not self._use_chunked and not (self._use_encoding and auto_decompress) and self._read_len != expected_len:
+            raise IOError("download failed with incomplete file")

wemm/lib/python3.10/site-packages/qcloud_cos/version.py

@@ -0,0 +1 @@
+__version__ = '5.1.9.33'

wemm/lib/python3.10/site-packages/torchgen/api/autograd.py

@@ -0,0 +1,663 @@
+import copy
+import re
+from dataclasses import dataclass
+from typing import Dict, List, Match, Optional, Sequence, Set, Tuple
+
+from torchgen.api import cpp
+from torchgen.api.types import BaseCType, Binding, NamedCType, tensorListT
+from torchgen.model import (
+    FunctionSchema,
+    NativeFunction,
+    NativeFunctionsViewGroup,
+    SchemaKind,
+    Type,
+)
+from torchgen.utils import IDENT_REGEX
+
+# Represents a saved attribute involved in backward calculation.
+# Note that it can be a derived property of an input argument, e.g.:
+# we could save `other.scalar_type()` instead of the entire `other` tensor.
+@dataclass(frozen=True)
+class SavedAttribute:
+    # The NamedCType holds the updated name and cpp type of the attribute
+    # for the name, Suffix is appended if it's derived property, e.g.: `other_scalar_type`
+    nctype: NamedCType
+
+    # The expression to read the derived property at save time, e.g.:
+    # `other.scalar_type()`.
+    expr: str
+
+
+# Represents a backward formula that calculates derivatives for one
+# or more tensors.
+@dataclass(frozen=True)
+class Derivative:
+    # The formula string (legit C++ expression).
+    # Note that expressions against input arguments have been replaced with the
+    # corresponding saved attributes.
+    # E.g.:
+    #  raw formula: `mul_tensor_backward(grad, self, other.scalar_type())`
+    #         here: `mul_tensor_backward(grad, self, other_scalar_type)`
+    formula: str
+
+    # The formula string before input argument replacement
+    original_formula: str
+
+    # Names of the arguments for which this formula calculates derivatives.
+    var_names: Tuple[str, ...]
+
+    # Saved inputs that are referenced by the formula.
+    saved_inputs: Tuple[SavedAttribute, ...]
+
+    # Saved outputs that are referenced by the formula.
+    saved_outputs: Tuple[SavedAttribute, ...]
+
+    # Gradients that are referenced by name in the formula.
+    named_gradients: Set[str]
+
+
+# Represents a forward formula that calculates forward derivatives
+# for one tensor.
+@dataclass(frozen=True)
+class ForwardDerivative:
+    # The formula string (legit C++ expression).
+    # Note that special keywords such as "linear" or "element_wise" have been
+    # replaced by the automatically generated formula.
+    formula: str
+
+    # Name of the output arguments for which this formula calculates forward
+    # derivatives
+    var_names: Tuple[str, ...]
+
+    # Type of the output arguments for which this formula calculates forward
+    # derivatives
+    var_types: Tuple[Type, ...]
+
+    # Inputs for which the forward derivatives are required for this formula
+    required_inputs_fw_grad: Optional[Tuple[str, ...]]
+
+    # Inputs for which the primal is required for this formula
+    required_inputs_primal: Optional[Tuple[str, ...]]
+
+    # Flag to specify if this formula requires the original value of self
+    # This is only used by inplace operations
+    required_original_self_value: bool
+
+    # If this formula is specified in derivatives.yaml or if we are re-using the
+    # out of place formula for inplace
+    is_reusing_outplace_formula: bool
+
+
+# Represents differentiability info for a NativeFunction.
+@dataclass(frozen=True)
+class DifferentiabilityInfo:
+    # The base name read from derivatives.yaml.
+    name: str
+
+    # The matching native function.
+    #
+    # There can be multiple NativeFunction having the same base name:
+    #  - different overloads with different types of input arguments;
+    #  - in-place/out/functional variants of the same function;
+    #
+    # We first use the schema string (under the 'name' key) in derivatives.yaml
+    # to find the NativeFunction having the same schema string.
+    # Then we find the in-place/out/functional variants of the matching function.
+    # Among these variants, we choose the one having the same name as the
+    # derivatives.yaml entry. If there is no exact match, then we choose the
+    # in-place variant.
+    # TODO: maybe the logic to search for all variants is no longer necessary?
+    func: NativeFunction
+
+    # The name of the generated autograd function.
+    # It's set only if we will calculate a derivative, i.e.
+    # 'args_with_derivatives' is not empty.
+    op: Optional[str]
+
+    # The derivatives formulae for this function.
+    # Note that the length of this sequence is the number of differentiable inputs
+    derivatives: Sequence[Derivative]
+
+    # The forward derivatives formulae for this function.
+    # Note that the length of this sequence is the number of differentiable outputs
+    forward_derivatives: Sequence[ForwardDerivative]
+
+    # The union of 'saved_inputs' of all 'derivatives'.
+    all_saved_inputs: Sequence[SavedAttribute]
+
+    # The union of 'saved_outputs' of all 'derivatives'.
+    all_saved_outputs: Sequence[SavedAttribute]
+
+    # All named gradients that are available for use, in the same
+    # order as in the grads vector.
+    available_named_gradients: Sequence[str]
+
+    # The named gradients that are used in any of the derivatives.
+    # Invariant: all(name in available_named_gradients for name in used_named_gradients)
+    used_named_gradients: Set[str]
+
+    # The function's input arguments for which it calculates derivatives.
+    # It's the union of 'var_names' of all 'derivatives', sorted by the
+    # argument order in the function schema.
+    args_with_derivatives: Sequence[Binding]
+
+    # Names of arguments whose derivative formula is 'non_differentiable'.
+    non_differentiable_arg_names: Sequence[str]
+
+    # Raw data read from derivatives.yaml.
+    output_differentiability: Optional[List[bool]]
+
+    # output_differentiability in derivatives.yaml can be a list of
+    # conditions that express if the output is differentiable. In this case,
+    # the number of conditions must match the number of outputs
+    # (NB: we only support one condition right now).
+    # output_differentiability gets populated with True for each condition,
+    # while output_differentiability_conditions gets populated with the conditions
+    output_differentiability_conditions: Optional[List[str]]
+
+    @property
+    def has_derivatives(self) -> bool:
+        return len(self.args_with_derivatives) > 0
+
+    # Generates a new DifferentiabilityInfo using the exact same set of derivative information,
+    # but with a new operator name.
+    # This is used when generating "copy" variants of view ops,
+    # which are able to use the exact same derivative formula as the original view op
+    # See Note [Codegen'd {view}_copy Operators]
+    def create_view_copy_from_view_derivative(
+        self, g: NativeFunctionsViewGroup
+    ) -> Optional["DifferentiabilityInfo"]:
+        if g.view_copy is None:
+            return None
+        f = g.view_copy
+
+        name_split_by_period = self.name.split(".", maxsplit=2)
+        # Append a "_copy" to the base name of the operator (but keep the overload name the same)
+        view_copy_name = f"{name_split_by_period[0]}_copy." + ".".join(
+            name_split_by_period[1:]
+        )
+        view_copy_op_name = None if self.op is None else f"{self.op}_copy"
+
+        return DifferentiabilityInfo(
+            # Use the "_copy" version of name/func/op
+            name=view_copy_name,
+            func=f,
+            op=view_copy_op_name,
+            # But keep all derivative info the same
+            derivatives=self.derivatives,
+            forward_derivatives=self.forward_derivatives,
+            all_saved_inputs=self.all_saved_inputs,
+            all_saved_outputs=self.all_saved_outputs,
+            available_named_gradients=self.available_named_gradients,
+            used_named_gradients=self.used_named_gradients,
+            args_with_derivatives=self.args_with_derivatives,
+            non_differentiable_arg_names=self.non_differentiable_arg_names,
+            output_differentiability=self.output_differentiability,
+            output_differentiability_conditions=self.output_differentiability_conditions,
+        )
+
+
+def uses_ident(info: Optional[DifferentiabilityInfo], ident: str) -> bool:
+    if info is None:
+        return False
+    for derivative in info.derivatives:
+        formula = derivative.formula
+        if re.search(IDENT_REGEX.format(ident), formula):
+            return True
+    return False
+
+
+def uses_retain_variables(info: Optional[DifferentiabilityInfo]) -> bool:
+    return uses_ident(info, "retain_variables")
+
+
+def uses_single_grad(info: Optional[DifferentiabilityInfo]) -> bool:
+    return uses_ident(info, "grad")
+
+
+# Represents a differentiable `Argument`.
+# How is it different from the `Argument` type?
+# - It's processed Arguments which are differentiable and only used in the
+#   context of the autograd codegen;
+# - It can represent SelfArgument or regular Argument but not TensorOptionsArgument;
+@dataclass(frozen=True)
+class DifferentiableInput:
+    name: str
+    type: Type
+
+    # TODO: only to keep it byte-for-byte compatible with the old codegen, should remove.
+    cpp_type: str
+
+
+# Represents a differentiable `Return`.
+# How it it different from the `Return` type?
+# - The name in `Return` is optional. Here it is always populated using the same
+#   `cpp.return_names()` method.
+#   TODO: some cpp naming logic (e.g. resolving name conflict) might be irrelevant?
+# - It's processed Returns which are differentiable, in compliance with the
+#   `output_differentiability` field defined in derivatives.yaml (if specified),
+#   and are only used in the context of the autograd codegen;
+@dataclass(frozen=True)
+class DifferentiableOutput:
+    name: str
+    type: Type
+
+    # TODO: only to keep it byte-for-byte compatible with the old codegen, should remove.
+    cpp_type: str
+
+
+@dataclass(frozen=True)
+class NativeFunctionWithDifferentiabilityInfo:
+    func: NativeFunction
+    info: Optional[Dict[str, DifferentiabilityInfo]]
+    fw_derivatives: Optional[Dict[str, Sequence[ForwardDerivative]]]
+
+
+# TODO: Update comment below since it is out of date.
+def dispatch_strategy(fn: NativeFunctionWithDifferentiabilityInfo) -> str:
+    """How are we going to call the underlying implementation of a
+    declaration?  There are two strategies:
+        - use_derived: we want to call the implementation on CPUDoubleType
+          (or a similar, derived Type instance).  Because these derived
+          instances deal in Tensors, not Variables (it's a completely different
+          object, so it doesn't dispatch back to VariableType), code on
+          this dispatch path needs to wrap/unwrap tensors.  If the
+          derived implementation takes and returns tensors, the
+          implementation is usually differentiable (although we also use
+          the derived dispatch path for non-differentiable functions
+          that we still want to dispatch on the derived Type instance;
+          e.g., size())
+        - use_type: we want to call the implementation on Type, because
+          it is implemented concretely, and the functions it invokes will
+          get dispatched back to VariableType (which will ensure that they
+          are differentiable.)
+    """
+    # fn is derived as long as any of its per-key differentiability infos
+    # has_derivatives. dispatch_strategy() is used to guard generation of fns in VariableType
+    # and ADInplaceOrViewType. We want to generate these functions as long as a
+    # derivative is defined for ANY dispatch key.
+    if fn.func.is_abstract or (
+        fn.info is not None and any(info.has_derivatives for info in fn.info.values())
+    ):
+        # If the function is abstract (not implemented on at::Type), we must
+        # call the implementation on the derived type with unpacked tensors.
+
+        # If the function has a derivative specified and is concrete, we could
+        # call either implementation. We prefer the calling the derived
+        # type's implementation with unpacked tensors because it is more
+        # performant in some cases: any internal calls to other ATen functions
+        # won't have the history tracked.
+
+        # If the function has a type dispatched argument (i.e. is a factory),
+        # we prefer calling the derived type's implementation both because it is
+        # more performant and to ensure factory functions return tensors with _version
+        # of 0 (probably not strictly necessary, but nice to have to keeps versions simple
+        # to understand.
+
+        return "use_derived"
+    else:
+        # If the function is concrete (we don't have to override it) and we
+        # didn't declare it in derivatives.yaml, we'll assume that it is
+        # actually implemented out of differentiable functions. (This
+        # assumption might not hold, but then you'll see gradcheck fail.)
+        return "use_type"
+
+
+def match_differentiability_info(
+    native_functions: List[NativeFunction],
+    differentiability_infos: Dict[FunctionSchema, Dict[str, DifferentiabilityInfo]],
+) -> List[NativeFunctionWithDifferentiabilityInfo]:
+    """Sets the "derivative" key on declarations to matching autograd function
+    In-place functions will use the out-of-place derivative definition if there
+    is no in-place specific derivative.
+    """
+
+    functional_info_by_signature = {
+        schema.signature(strip_default=True): info_dict
+        for schema, info_dict in differentiability_infos.items()
+        if schema.kind() == SchemaKind.functional
+    }
+    non_functional_info_by_signature = {
+        schema.signature(strip_default=True): info_dict
+        for schema, info_dict in differentiability_infos.items()
+        if schema.kind() != SchemaKind.functional
+    }
+
+    def find_info(
+        f: NativeFunction,
+    ) -> Tuple[Optional[Dict[str, DifferentiabilityInfo]], bool]:
+        # Don't bother matching info to generated out= variants
+        if "generated" in f.tags and f.func.kind() == SchemaKind.out:
+            return None, False
+
+        # (1) Check for an exact match
+        if f.func in differentiability_infos:
+            return differentiability_infos[f.func], True
+
+        # (2) If no exact match, check if the out-of-place variant
+        # of this operator has a match.
+        # i.e mul() for mul_() or mul_out()
+        f_sig = f.func.signature(strip_default=True)
+        if f_sig in functional_info_by_signature:
+            return functional_info_by_signature[f_sig], False
+
+        # (3) Some operators have a derivative explicitly defined for the mutable
+        # variant, but get a code-generated out-of-place variant which does *not*
+        # come with a derivative formula.
+        # For the generated out-of-place variant, use the mutable variant's formula
+        # if it exists.
+        if "generated" in f.tags and f_sig in non_functional_info_by_signature:
+            info_dict = non_functional_info_by_signature[f_sig]
+            # See https://github.com/pytorch/pytorch/pull/76320/files#r874816389
+            assert not any(
+                any("self" in str(inpt.nctype.name) for inpt in info.all_saved_inputs)
+                for info in info_dict.values()
+            ), f"""\
+Attempted to convert a derivative formula for a mutable operator
+ to be used by automatically by its functional variant ("{str(f.func)}").
+ this is not currently supported (we'd need to fix up the formula in the codegen)."""
+            return info_dict, False
+
+        # (4) Generate derivative information of unary foreach functions if none is defined in `derivatives.yaml`
+        base_op_name = f.func.name.name
+        if (
+            base_op_name.base.startswith("_foreach")
+            and not base_op_name.inplace
+            and len(f.func.arguments.post_self_positional) == 0
+        ):
+            ref_native_op_name = base_op_name.base.split("_foreach_")[-1]
+            for function_schema in functional_info_by_signature:
+                if (
+                    function_schema.name.name.base == ref_native_op_name
+                    and not function_schema.name.name.inplace
+                ):
+                    all_saved_inputs = []
+                    all_saved_outputs = []
+                    diff_info_dict = copy.deepcopy(
+                        differentiability_infos[function_schema]
+                    )
+                    diff_info = diff_info_dict["Default"]
+                    modified_derivative_formulas = []
+                    for derivative in diff_info.derivatives:
+                        saved_inputs = []
+                        saved_outputs = []
+                        modified_formula = (
+                            derivative.formula.replace("grad", "grads[i]")
+                            .replace("self", "self[i]")
+                            .replace("result", "result[i]")
+                        )
+                        if "self" in modified_formula:
+                            saved_inputs.append(
+                                SavedAttribute(
+                                    nctype=NamedCType(
+                                        name="self", type=BaseCType(tensorListT)
+                                    ),
+                                    expr="self",
+                                )
+                            )
+                            all_saved_inputs.append(saved_inputs[-1])
+                        if "result" in modified_formula:
+                            saved_outputs.append(
+                                SavedAttribute(
+                                    nctype=NamedCType(
+                                        name="result", type=BaseCType(tensorListT)
+                                    ),
+                                    expr="result",
+                                )
+                            )
+                            all_saved_outputs.append(saved_outputs[-1])
+                        modified_derivative = Derivative(
+                            formula=modified_formula,
+                            original_formula=derivative.original_formula,
+                            var_names=("self",),
+                            saved_inputs=tuple(saved_inputs),
+                            saved_outputs=tuple(saved_outputs),
+                            named_gradients=set(),
+                        )
+                        modified_derivative_formulas.append(modified_derivative)
+                    assert f.func.arguments.self_arg is not None
+                    diff_info = DifferentiabilityInfo(
+                        name=base_op_name.base,
+                        func=f,
+                        op=f"Foreach{diff_info.op}",
+                        derivatives=modified_derivative_formulas,
+                        forward_derivatives=[],
+                        all_saved_inputs=tuple(set(all_saved_inputs)),
+                        all_saved_outputs=tuple(set(all_saved_outputs)),
+                        available_named_gradients=(),
+                        used_named_gradients=set(),
+                        args_with_derivatives=[
+                            Binding(
+                                name="self",
+                                nctype=NamedCType(
+                                    name="self", type=BaseCType(tensorListT)
+                                ),
+                                argument=f.func.arguments.self_arg.argument,
+                                default=None,
+                            )
+                        ],
+                        non_differentiable_arg_names=[],
+                        output_differentiability=None,
+                        output_differentiability_conditions=None,
+                    )
+                    diff_info_dict["Default"] = diff_info
+                    if f.func not in differentiability_infos:
+                        differentiability_infos[f.func] = diff_info_dict
+                        functional_info_by_signature[f.func] = diff_info_dict
+                    return diff_info_dict, True
+
+        return None, False
+
+    result: List[NativeFunctionWithDifferentiabilityInfo] = []
+    for f in native_functions:
+        info_dict, is_exact_match = find_info(f)
+
+        # Currently, the '.strides()' to 'strides_or_error' replacement does not support
+        # 'self' derivatives of an inplace function, so we must check for this case.
+        if f.func.kind() == SchemaKind.inplace and (info_dict is not None):
+            for info in info_dict.values():
+                for derivative in info.derivatives:
+                    if "self" in derivative.var_names:
+                        for saved_input in derivative.saved_inputs:
+                            assert "strides_or_error" not in saved_input.expr, (
+                                "Calling '.strides()' in the 'self' derivative formula of an "
+                                f"in-place function is not supported: {f.func}"
+                            )
+
+        if not info_dict:
+            result.append(
+                NativeFunctionWithDifferentiabilityInfo(
+                    func=f, info=None, fw_derivatives=None
+                )
+            )
+            continue
+
+        fw_derivative_dict: Dict[str, Sequence[ForwardDerivative]] = {}
+        for key, info in info_dict.items():
+            if not info.forward_derivatives:
+                fw_derivative_dict[key] = []
+                continue
+
+            forward_derivatives = info.forward_derivatives
+
+            # For functions that have a single def for out-of-place and inplace (like abs())
+            if f.func.kind() == SchemaKind.inplace:
+                # For inplace functions there is a little bit of work to do:
+                #  1) Validate the formula and make sure the input that is modified in not used:
+                #    - If there is a formula for the inplace variant of the function (is_exact_match == True) then
+                #      we make sure that the original value of the input that is being modified inplace (self_p) is
+                #      not used in the formula. Note that the formula can use "original_self_p" here and that would
+                #      trigger a clone of the original input.
+                #    - If we are re-using the out of place formula (is_exact_match == False) then we replace every
+                #      occurrence of self_p and self_t by original_self_p and original_self_t. These will be
+                #      populated by cloned version of the original input (either the clone done by the backward AD
+                #      logic if self is also used in a backward formula or a special clone that we add).
+                #  2) At this point, there cannot be a self_p in the formula.
+                #  3) Change "result" into "self_p" as by design, in the inplace function codegen, the result is
+                #     simply called self (as it is modified inplace).
+                #  4) Update the required primals data in case it used to contain "result" but should now contain
+                #     "self"
+                #  5) If it is not an exact match, the user formula is not modifying the existing forward grad
+                #     inplace as it should. So add some code that makes sure that we do so if the forward grad
+                #     already exists.
+
+                assert (
+                    len(info.forward_derivatives) == 1
+                )  # Only single output inplace should exist
+                fw_info = info.forward_derivatives[0]
+                formula = fw_info.formula
+
+                def replace_self_with_original_self(formula: str, postfix: str) -> str:
+                    def repl(m: Match[str]) -> str:
+                        return f"{m.group(1)}original_self{postfix}{m.group(2)}"
+
+                    return re.sub(IDENT_REGEX.format(f"self{postfix}"), repl, formula)
+
+                if re.search(IDENT_REGEX.format("self_p"), formula):
+                    if is_exact_match:
+                        # For manually defined formulas, don't allow the original value to be used
+                        raise RuntimeError(
+                            f'The formula for "{f.func.name}" is using the original value of self '
+                            "that is being modified inplace. This would lead to wrong forward gradients. "
+                            'Please use "result" in the formula only.'
+                        )
+                    else:
+                        # When the original formula is out of place, we save a clone of the primal
+                        # value to be able to access this value if needed
+                        # replace "self_p"/"self_t" from the formula by "original_self_p"/"original_self_t"
+                        formula = replace_self_with_original_self(formula, "_p")
+                        formula = replace_self_with_original_self(formula, "_t")
+
+                # replace "result" from the formula by "self_p"
+                def repl(m: Match[str]) -> str:
+                    return f"{m.group(1)}self_p{m.group(2)}"
+
+                formula = re.sub(IDENT_REGEX.format("result"), repl, formula)
+
+                required_primals = fw_info.required_inputs_primal
+                if re.search(IDENT_REGEX.format("self_p"), formula):
+                    required_primals = (
+                        required_primals + ("self",) if required_primals else ("self",)
+                    )
+
+                if not is_exact_match:
+                    # NOTE [In-place forward AD formula Optimization]
+                    #
+                    # This optimization transforms the formula to directly do inplace, i.e.
+                    # instead of self_t.copy_(self_t.op()) we do self_t.op_() when the following are met:
+                    #
+                    # 1) the formula satisfies the pattern: "self_t.op(*args)"
+                    # 2) "op" in (1) needs to be the same as the op the derivative is for
+                    #
+                    # (2) may seem too strict, but currently the only ops that satisfy (1) also satisfy (2)
+                    # If there is a need, we can relax (2) to allow any op that has an in-place variant
+                    is_single_method_on_self_t = False
+                    directly_do_inplace = False
+                    op_name: Optional[str] = None
+                    between_parens: Optional[str] = None
+                    match = re.fullmatch(r"self_t.([\w]*)\((.*)\)", formula)
+                    if match:
+                        op_name, between_parens = match.group(1), match.group(2)
+
+                        # We want to...
+                        #   Match: self_t.op1(other_p.op2(arg))
+                        #   Avoid: self_t.op1(args) + self_t.op2(args)
+                        #   Avoid: self_t.op1(other_p.op2(arg)) + self_t.op2(args)
+                        def check_parens_nest_level_gt_zero(s: str) -> bool:
+                            level = 1
+                            for ch in s:
+                                if ch == ")":
+                                    level -= 1
+                                    if level == 0:
+                                        return False
+                                if ch == "(":
+                                    level += 1
+                            return True
+
+                        is_single_method_on_self_t = check_parens_nest_level_gt_zero(
+                            between_parens
+                        )
+                        directly_do_inplace = (
+                            is_single_method_on_self_t and op_name == info.name
+                        )
+
+                    if directly_do_inplace:
+                        assert op_name is not None
+                        assert between_parens is not None
+                        formula = f"self_t_raw.defined() ? self_t_raw.{op_name}_({between_parens}) : {formula}"
+                    else:
+                        # Make sure that the forward grad is modified inplace when the original formula
+                        # is out of place
+                        formula = f"self_t_raw.defined() ? self_t_raw.copy_({formula}) : {formula}"
+
+                required_original_self_value = bool(
+                    re.search(IDENT_REGEX.format("original_self_p"), formula)
+                ) or bool(re.search(IDENT_REGEX.format("original_self_t"), formula))
+
+                forward_derivatives = [
+                    ForwardDerivative(
+                        formula=formula,
+                        var_names=("self",),
+                        var_types=fw_info.var_types,
+                        required_inputs_fw_grad=fw_info.required_inputs_fw_grad,
+                        required_inputs_primal=required_primals,
+                        required_original_self_value=required_original_self_value,
+                        is_reusing_outplace_formula=not is_exact_match,
+                    ),
+                ]
+
+            fw_derivative_dict[key] = forward_derivatives
+
+        result.append(
+            NativeFunctionWithDifferentiabilityInfo(
+                func=f, info=info_dict, fw_derivatives=fw_derivative_dict
+            )
+        )
+
+    return result
+
+
+def is_differentiable(
+    name: str, type: Type, info: Optional[DifferentiabilityInfo]
+) -> bool:
+    return type.is_tensor_like() and (
+        info is None or name not in info.non_differentiable_arg_names
+    )
+
+
+def gen_differentiable_outputs(
+    fn: NativeFunctionWithDifferentiabilityInfo, key: str = "Default"
+) -> List[DifferentiableOutput]:
+    f = fn.func
+    info = fn.info[key] if fn.info else None
+    outputs: List[DifferentiableOutput] = [
+        DifferentiableOutput(
+            name=name,
+            type=ret.type,
+            cpp_type=cpp.return_type(ret, symint=True).cpp_type(),
+        )
+        for name, ret in zip(cpp.return_names(f), f.func.returns)
+    ]
+    output_differentiability = info.output_differentiability if info else None
+    if output_differentiability is not None:
+        if len(output_differentiability) != len(outputs):
+            raise RuntimeError(
+                f"The length of output_differentiability ({len(output_differentiability)}), "
+                f"does not match the number of outputs ({len(outputs)})."
+            )
+        differentiable_outputs: List[DifferentiableOutput] = []
+        if False in output_differentiability and f.func.kind() == SchemaKind.inplace:
+            raise RuntimeError(
+                "output_differentiability=False for inplace operation (version_counter won't get updated)"
+            )
+        for differentiable, output in zip(output_differentiability, outputs):
+            if differentiable:
+                differentiable_outputs.append(output)
+        return differentiable_outputs
+    candidate_differentiable_outputs = list(
+        filter(lambda r: is_differentiable(r.name, r.type, info), outputs)
+    )
+    if uses_single_grad(info):
+        return candidate_differentiable_outputs[:1]
+    else:
+        return candidate_differentiable_outputs

wemm/lib/python3.10/site-packages/torchgen/api/cpp.py

@@ -0,0 +1,460 @@
+from typing import List, Optional, Sequence, Set, Union
+
+from torchgen import local
+from torchgen.api.types import (
+    ArgName,
+    ArrayCType,
+    ArrayRefCType,
+    BaseCType,
+    BaseTypeToCppMapping,
+    Binding,
+    boolT,
+    ConstRefCType,
+    CType,
+    dimnameListT,
+    intArrayRefT,
+    iTensorListRefT,
+    ListCType,
+    longT,
+    MutRefCType,
+    NamedCType,
+    OptionalCType,
+    optionalIntArrayRefT,
+    optionalSymIntArrayRefT,
+    scalarT,
+    SpecialArgName,
+    symIntArrayRefT,
+    SymIntT,
+    tensorListT,
+    tensorOptionsT,
+    tensorT,
+    TupleCType,
+    VectorCType,
+    voidT,
+)
+from torchgen.model import (
+    Argument,
+    Arguments,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    OptionalType,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+
+# This file describes the translation of JIT schema to the public C++
+# API, which is what people use when they call functions like at::add.
+#
+# Prominent characteristics of the C++ API:
+#
+#   - dtype, layout, device and pin_memory are collected into
+#     a single C++ type TensorOptions  (the native functions API
+#     also has this, but tensor options is really most relevant
+#     for the C++ API; it makes calling kwarg factory functions
+#     pleasant)
+#
+#   - defaulting lives here (in fact, the dispatcher is completely
+#     oblivious of defaults!)
+#
+# BTW: policy on name collisions: we try not to have types with
+# collisions, but functions are fair game to collide
+
+
+def name(
+    func: FunctionSchema,
+    *,
+    faithful_name_for_out_overloads: bool = False,
+    symint_overload: bool = False,
+) -> str:
+    name = str(func.name.name)
+    if symint_overload:
+        name += "_symint"
+    if func.is_out_fn():
+        if faithful_name_for_out_overloads:
+            name += "_outf"
+        else:
+            name += "_out"
+
+    return name
+
+
+# Translation of "value types" in JIT schema to C++ API type.  Value
+# types look the same no matter if they are argument types or return
+# types.  Returns None if the type in question is not a value type.
+def valuetype_type(
+    t: Type,
+    *,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = False,
+) -> Optional[NamedCType]:
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor or t.name == BaseTy.Scalar:
+            return None
+        elif str(t) == "SymInt":
+            if symint:
+                return NamedCType(binds, BaseCType(SymIntT))
+            else:
+                return NamedCType(binds, BaseCType(longT))
+        if remove_non_owning_ref_types:
+            if t.name == BaseTy.str:
+                raise AssertionError(
+                    "string ref->value conversion: not implemented yet"
+                )
+        # All other BaseType currently map directly to BaseCppTypes.
+        return NamedCType(binds, BaseCType(BaseTypeToCppMapping[t.name]))
+    elif isinstance(t, OptionalType):
+        elem = valuetype_type(t.elem, binds=binds, symint=symint)
+        if elem is None:
+            return None
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        if str(t.elem) == "bool":
+            assert t.size is not None
+            return NamedCType(binds, ArrayCType(BaseCType(boolT), t.size))
+        else:
+            return None
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+# Translation of types occuring in JIT arguments to a C++ argument type.
+# If remove_non_owning_ref_types is set, we'll guarantee that the outputed CType is not a non-owning reference type.
+# For example, we'll return std::vector<int> instead of IntArrayRef.
+# See Note [translation from C++ reference to value types]
+def argumenttype_type(
+    t: Type,
+    *,
+    mutable: bool,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = False,
+) -> NamedCType:
+    # If it's a value type, do the value type translation
+    r = valuetype_type(
+        t,
+        binds=binds,
+        symint=symint,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+    )
+    if r is not None:
+        return r
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            if mutable and not local.use_const_ref_for_mutable_tensors():
+                return NamedCType(binds, MutRefCType(BaseCType(tensorT)))
+            else:
+                return NamedCType(binds, ConstRefCType(BaseCType(tensorT)))
+        elif t.name == BaseTy.Scalar:
+            return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+        else:
+            raise AssertionError(f"base type should have been value type {t}")
+    elif isinstance(t, OptionalType):
+        if str(t.elem) == "Tensor":
+            if mutable and not local.use_const_ref_for_mutable_tensors():
+                return NamedCType(
+                    binds, MutRefCType(BaseCType(tensorT))
+                )  # TODO: fix this discrepancy
+            else:
+                return NamedCType(
+                    binds, ConstRefCType(OptionalCType(BaseCType(tensorT)))
+                )
+        elif str(t.elem) == "Scalar":
+            return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
+        elif isinstance(t.elem, ListType) and str(t.elem.elem) == "int":
+            return NamedCType(binds, BaseCType(optionalIntArrayRefT))
+        elif isinstance(t.elem, ListType) and str(t.elem.elem) == "SymInt":
+            if symint:
+                return NamedCType(binds, BaseCType(optionalSymIntArrayRefT))
+            else:
+                return NamedCType(binds, BaseCType(optionalIntArrayRefT))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds, symint=symint)
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        # TODO: remove these special cases, ArrayRef fallthrough works fine
+        if str(t.elem) == "int":
+            if remove_non_owning_ref_types:
+                return NamedCType(binds, VectorCType(BaseCType(longT)))
+            else:
+                return NamedCType(binds, BaseCType(intArrayRefT))
+        if str(t.elem) == "SymInt":
+            if remove_non_owning_ref_types:
+                if symint:
+                    return NamedCType(binds, VectorCType(BaseCType(SymIntT)))
+                else:
+                    return NamedCType(binds, VectorCType(BaseCType(longT)))
+            else:
+                if symint:
+                    return NamedCType(binds, BaseCType(symIntArrayRefT))
+                else:
+                    return NamedCType(binds, BaseCType(intArrayRefT))
+        if str(t.elem) == "Tensor":
+            if local.use_ilistref_for_tensor_lists():
+                return NamedCType(binds, ConstRefCType(BaseCType(iTensorListRefT)))
+            else:
+                return NamedCType(binds, BaseCType(tensorListT))
+        elif str(t.elem) == "Scalar":
+            return NamedCType(binds, ArrayRefCType(BaseCType(scalarT)))
+        elif str(t.elem) == "Dimname":
+            return NamedCType(binds, BaseCType(dimnameListT))
+        elif str(t.elem) == "Tensor?":
+            return NamedCType(
+                binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT))))
+            )
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds, symint=symint)
+        return NamedCType(binds, ArrayRefCType(elem.type))
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+# Translate a JIT argument into its C++ type
+def argument_type(a: Argument, *, binds: ArgName, symint: bool = False) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, symint=symint, binds=binds)
+
+
+# Translation of a (non-multi) return type from JIT to C++
+# N.B: returntype_type returns a CType, not a NamedCType.
+# This is mostly because of the mismatch between return types and return names.
+# e.g. a function with a return type of 'void' has 0 return names,
+# and a function with a return type of 'std::tuple' has >1 return name.
+def returntype_type(t: Type, *, mutable: bool, symint: bool = False) -> CType:
+    # placeholder is ignored
+    r = valuetype_type(t, binds="__placeholder__", symint=symint)
+    if r is not None:
+        return r.type
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            if mutable:
+                if local.use_const_ref_for_mutable_tensors():
+                    return ConstRefCType(BaseCType(tensorT))
+                else:
+                    return MutRefCType(BaseCType(tensorT))
+            else:
+                # Note [Tensor Copy Returns]
+                # Currently, we use "Argument.is_write" to determine
+                # whether or not Tensor return types should be copies or references.
+                # If that ever changes, take a look at other locations of this note!
+                return BaseCType(tensorT)
+        elif t.name == BaseTy.Scalar:
+            return BaseCType(scalarT)
+    elif isinstance(t, ListType):
+        assert (
+            not mutable
+        ), "Native functions should never return a mutable tensor list. They should return void."
+        elem = returntype_type(t.elem, mutable=False, symint=symint)
+        assert t.size is None, f"fixed size list returns not supported: {t}"
+        return VectorCType(elem)
+
+    raise AssertionError(f"unrecognized return type {t}")
+
+
+# Translation of a single return to its C++ type
+def return_type(r: Return, *, symint: bool = False) -> CType:
+    return returntype_type(r.type, mutable=r.is_write, symint=symint)
+
+
+# Translation of a full (possibly multi) return from JIT to its C++ type
+def returns_type(rs: Sequence[Return], *, symint: bool = False) -> CType:
+    if len(rs) == 0:
+        return BaseCType(voidT)
+    elif len(rs) == 1:
+        return return_type(rs[0], symint=symint)
+    else:
+        return TupleCType([return_type(r, symint=symint) for r in rs])
+
+
+def return_names(f: NativeFunction, *, fallback_name: str = "result") -> Sequence[str]:
+    returns: List[str] = []
+    for i, r in enumerate(f.func.returns):
+        # If we have an inplace function, the return argument is
+        # implicitly named self.
+        # TODO: Consider incorporating this into the data model
+        if f.func.name.name.inplace:
+            assert i == 0, "illegal inplace function with multiple returns"
+            name = "self"
+        # If we are out function, the name is the name of the
+        # corresponding output function (r.name will get recorded
+        # in field_name later.)
+        elif f.func.is_out_fn():
+            name = f.func.arguments.out[i].name
+        # If the return argument is explicitly named...
+        elif r.name:
+            name_conflict = any(
+                r.name == a.name for a in f.func.schema_order_arguments()
+            )
+            if name_conflict and not f.func.is_out_fn():
+                name = f"{r.name}_return"
+            else:
+                name = r.name
+        # If there is no explicit name and no fallback name was passed in, we just name the output result,
+        # unless it's a multi-return, in which case it's result0,
+        # result1, etc (zero-indexed)
+        else:
+            name = fallback_name if len(f.func.returns) == 1 else f"{fallback_name}{i}"
+        returns.append(name)
+    return returns
+
+
+JIT_TO_CPP_DEFAULT = {
+    "False": "false",
+    "True": "true",
+    "None": "c10::nullopt",  # UGH this one is type directed
+    "Mean": "at::Reduction::Mean",
+    "[]": "{}",
+    "contiguous_format": "MemoryFormat::Contiguous",
+    "long": "at::kLong",
+}
+
+# Convert a JIT default into C++ expression representing the default
+def default_expr(d: str, t: Type, *, symint: bool) -> str:
+    if d == "None" and str(t) == "Tensor?":
+        return "{}"
+    if isinstance(t, BaseType) and t.name is BaseTy.str:
+        # Schema allows single quotes but C++ needs double
+        if len(d) >= 2 and d[0] == "'" and d[-1] == "'":
+            s = ""
+            i = 1
+            while i + 1 < len(d):
+                if d[i] != "\\":
+                    if d[i] == '"':
+                        s += '\\"'
+                    else:
+                        s += d[i]
+                    i += 1
+                else:
+                    if d[i + 1] == "'":
+                        s += "'"
+                    else:
+                        s += d[i : i + 2]
+                    i += 2
+
+            return f'"{s}"'
+
+    if isinstance(t, OptionalType):
+        if d == "None":
+            return "c10::nullopt"
+
+        return default_expr(d, t.elem, symint=symint)
+
+    if isinstance(t, ListType):
+        if d.startswith("[") and d.endswith("]"):
+            return "{" + d[1:-1] + "}"
+        elif symint and d.isdigit() and str(t.elem) == "SymInt":
+            return f"c10::SymInt({d})"
+        elif t.size is None:
+            # NOTE: Sized lists can have scalar defaults
+            raise ValueError(f"Expected a list default '[...]' but found: '{d}'")
+
+    return JIT_TO_CPP_DEFAULT.get(d, d)
+
+
+# Convert an argument into its C++ API form
+
+
+def argument(
+    a: Union[Argument, TensorOptionsArguments, SelfArgument],
+    *,
+    cpp_no_default_args: Set[str],
+    method: bool,
+    faithful: bool,
+    symint: bool = False,
+    has_tensor_options: bool,
+) -> List[Binding]:
+    def sub_argument(
+        a: Union[Argument, TensorOptionsArguments, SelfArgument]
+    ) -> List[Binding]:
+        return argument(
+            a,
+            cpp_no_default_args=cpp_no_default_args,
+            method=method,
+            faithful=faithful,
+            symint=symint,
+            has_tensor_options=has_tensor_options,
+        )
+
+    if isinstance(a, Argument):
+        binds: ArgName
+        if a.name == "memory_format" and has_tensor_options:
+            binds = SpecialArgName.possibly_redundant_memory_format
+        else:
+            binds = a.name
+        default: Optional[str] = None
+        if a.name not in cpp_no_default_args and a.default is not None:
+            default = default_expr(a.default, a.type, symint=symint)
+        return [
+            Binding(
+                nctype=argument_type(a, binds=binds, symint=symint),
+                name=a.name,
+                default=default,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, TensorOptionsArguments):
+        if faithful:
+            return (
+                sub_argument(a.dtype)
+                + sub_argument(a.layout)
+                + sub_argument(a.device)
+                + sub_argument(a.pin_memory)
+            )
+        else:
+            default = None
+            # Enforced by NativeFunction.__post_init__
+            assert "options" not in cpp_no_default_args
+            if all(x.default == "None" for x in a.all()):
+                default = "{}"
+            elif a.dtype.default == "long":
+                default = "at::kLong"  # TODO: this is wrong
+            return [
+                Binding(
+                    nctype=NamedCType("options", BaseCType(tensorOptionsT)),
+                    name="options",
+                    default=default,
+                    argument=a,
+                )
+            ]
+    elif isinstance(a, SelfArgument):
+        if method:
+            # Caller is responsible for installing implicit this in context!
+            return []
+        else:
+            return sub_argument(a.argument)
+    else:
+        assert_never(a)
+
+
+def arguments(
+    arguments: Arguments,
+    *,
+    faithful: bool,
+    symint: bool = False,
+    method: bool,
+    cpp_no_default_args: Set[str],
+) -> List[Binding]:
+    args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
+    if faithful:
+        args.extend(arguments.non_out)
+        args.extend(arguments.out)
+    else:
+        args.extend(arguments.out)
+        args.extend(arguments.non_out)
+    return [
+        r.no_default() if faithful else r
+        for a in args
+        for r in argument(
+            a,
+            faithful=faithful,
+            symint=symint,
+            method=method,
+            has_tensor_options=arguments.tensor_options is not None,
+            cpp_no_default_args=cpp_no_default_args,
+        )
+    ]

wemm/lib/python3.10/site-packages/torchgen/api/dispatcher.py

@@ -0,0 +1,118 @@
+import itertools
+from typing import List, Sequence, Union
+
+from torchgen.api import cpp
+
+from torchgen.api.types import ArgName, Binding, CType, NamedCType
+from torchgen.model import (
+    Argument,
+    FunctionSchema,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never, concatMap
+
+# This file describes the translation of JIT schema to the dispatcher
+# API, the *unboxed* calling convention by which invocations through
+# the dispatcher are made.  Historically, the dispatcher API matched
+# the C++ API, but with the establishment of the boxed API, we've
+# made changes to the dispatcher API to so that the unboxed API
+# better aligns with the boxed API.  The dispatcher API hooks heavily
+# into our template based boxing/unboxing machinery, so changes
+# to this convention will usually need template updates too.
+#
+# Prominent characteristics of the dispatcher API:
+#
+#   - dtype, layout, device and pin_memory are represented as separate
+#     arguments.
+#
+
+
+def name(func: FunctionSchema) -> str:
+    return cpp.name(func)
+
+
+def argumenttype_type(
+    t: Type,
+    *,
+    mutable: bool,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = True,
+) -> NamedCType:
+    # This is a faux amis.  If it makes sense in the future to add
+    # more special cases here, or invert things so cpp.argument_type
+    # calls this, or just completely inline the function, please do
+    # it.
+    return cpp.argumenttype_type(
+        t,
+        mutable=mutable,
+        binds=binds,
+        symint=symint,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+    )
+
+
+def argument_type(
+    a: Argument,
+    *,
+    binds: ArgName,
+    remove_non_owning_ref_types: bool = False,
+    symint: bool = True,
+) -> NamedCType:
+    return argumenttype_type(
+        a.type,
+        mutable=a.is_write,
+        binds=binds,
+        remove_non_owning_ref_types=remove_non_owning_ref_types,
+        symint=symint,
+    )
+
+
+def returns_type(rs: Sequence[Return], *, symint: bool = True) -> CType:
+    # At present, there is no difference. But there could be!
+    return cpp.returns_type(rs, symint=symint)
+
+
+def jit_arguments(func: FunctionSchema) -> List[Argument]:
+    def to_argument(
+        a: Union[Argument, TensorOptionsArguments, SelfArgument]
+    ) -> List[Argument]:
+        if isinstance(a, Argument):
+            return [a]
+        elif isinstance(a, SelfArgument):
+            return [a.argument]
+        elif isinstance(a, TensorOptionsArguments):
+            return [a.dtype, a.layout, a.device, a.pin_memory]
+        else:
+            assert_never(a)
+
+    return list(
+        concatMap(
+            to_argument,
+            itertools.chain(
+                func.arguments.positional, func.arguments.kwarg_only, func.arguments.out
+            ),
+        )
+    )
+
+
+def argument(
+    a: Argument, *, remove_non_owning_ref_types: bool = False, symint: bool = True
+) -> Binding:
+    return Binding(
+        nctype=argument_type(
+            a,
+            binds=a.name,
+            remove_non_owning_ref_types=remove_non_owning_ref_types,
+            symint=symint,
+        ),
+        name=a.name,
+        argument=a,
+    )
+
+
+def arguments(func: FunctionSchema, *, symint: bool = True) -> List[Binding]:
+    return [argument(a, symint=symint) for a in jit_arguments(func)]

wemm/lib/python3.10/site-packages/torchgen/api/native.py

@@ -0,0 +1,153 @@
+from typing import List, Optional, Sequence, Union
+
+from torchgen import local
+from torchgen.api import cpp
+
+from torchgen.api.types import (
+    ArgName,
+    BaseCType,
+    Binding,
+    boolT,
+    ConstRefCType,
+    CType,
+    deviceT,
+    layoutT,
+    ListCType,
+    MutRefCType,
+    NamedCType,
+    OptionalCType,
+    scalarT,
+    scalarTypeT,
+    tensorT,
+)
+from torchgen.model import (
+    Argument,
+    FunctionSchema,
+    Return,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+
+# This file describes the translation of JIT schema to the native functions API.
+# This looks a lot like the C++ API (which makes historical sense, because the
+# idea was you wrote native functions to implement functions in the C++ API),
+# but over time we have evolved the C++ API without actually changing our
+# native:: kernels.  The intention is to make native API and dispatcher API
+# line up as closely as possible, since this results in the least overhead
+# (no translation is needed from dispatcher API to native API).
+#
+# NB: this is symint aware, you will get the non-SymInt variant for some
+# dispatch entries and SymInt for others.
+
+
+def name(func: FunctionSchema) -> str:
+    name = str(func.name.name)
+    # TODO: delete this!
+    if func.is_out_fn():
+        name += "_out"
+    if func.name.overload_name:
+        name += f"_{func.name.overload_name}"
+    return name
+
+
+def argumenttype_type(
+    t: Type, *, mutable: bool, binds: ArgName, symint: bool
+) -> NamedCType:
+    if str(t) == "Tensor?":
+        tensor_type: OptionalCType = OptionalCType(BaseCType(tensorT))
+        if mutable and not local.use_const_ref_for_mutable_tensors():
+            return NamedCType(binds, MutRefCType(tensor_type))
+        else:
+            return NamedCType(binds, ConstRefCType(tensor_type))
+    elif str(t) == "Tensor?[]":
+        return NamedCType(
+            binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT))))
+        )
+    elif str(t) == "Scalar":
+        return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+    elif str(t) == "Scalar?":
+        return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
+    return cpp.argumenttype_type(t, mutable=mutable, binds=binds, symint=symint)
+
+
+def returns_type(rs: Sequence[Return], *, symint: bool) -> CType:
+    return cpp.returns_type(rs, symint=symint)
+
+
+def argument_type(a: Argument, *, binds: ArgName, symint: bool) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, binds=binds, symint=symint)
+
+
+def argument(
+    a: Union[Argument, SelfArgument, TensorOptionsArguments],
+    *,
+    is_out: bool,
+    symint: bool,
+) -> List[Binding]:
+    # Ideally, we NEVER default native functions.  However, there are a number
+    # of functions that call native:: directly and rely on the defaulting
+    # existing.  So for BC, we generate defaults for non-out variants (but not
+    # for out variants, where it is impossible to generate an appropriate
+    # default)
+    should_default = not is_out
+    if isinstance(a, Argument):
+        default: Optional[str] = None
+        if should_default and a.default is not None:
+            default = cpp.default_expr(a.default, a.type, symint=symint)
+        return [
+            Binding(
+                nctype=argument_type(a, binds=a.name, symint=symint),
+                name=a.name,
+                default=default,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, SelfArgument):
+        # Erase SelfArgument from the distinction
+        return argument(a.argument, is_out=is_out, symint=symint)
+    elif isinstance(a, TensorOptionsArguments):
+        default = None
+        if should_default:
+            default = "{}"
+        # TODO: Not sure why the arguments assigned here are for
+        # TensorOptionsArguments and not the constituent pieces.  It seems
+        # to matter
+        return [
+            Binding(
+                nctype=NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))),
+                name="dtype",
+                default=default,
+                argument=a,
+            ),
+            Binding(
+                nctype=NamedCType("layout", OptionalCType(BaseCType(layoutT))),
+                name="layout",
+                default=default,
+                argument=a,
+            ),
+            Binding(
+                nctype=NamedCType("device", OptionalCType(BaseCType(deviceT))),
+                name="device",
+                default=default,
+                argument=a,
+            ),
+            Binding(
+                nctype=NamedCType("pin_memory", OptionalCType(BaseCType(boolT))),
+                name="pin_memory",
+                default=default,
+                argument=a,
+            ),
+        ]
+    else:
+        assert_never(a)
+
+
+def arguments(func: FunctionSchema, *, symint: bool) -> List[Binding]:
+    args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
+    args.extend(func.arguments.non_out)
+    args.extend(func.arguments.out)
+    return [
+        r for arg in args for r in argument(arg, symint=symint, is_out=func.is_out_fn())
+    ]

wemm/lib/python3.10/site-packages/torchgen/api/structured.py

@@ -0,0 +1,156 @@
+from typing import List, Union
+
+from torchgen.api import cpp
+
+from torchgen.api.types import (
+    ArgName,
+    ArrayRefCType,
+    BaseCType,
+    Binding,
+    ConstRefCType,
+    dimnameListT,
+    intArrayRefT,
+    iOptTensorListRefT,
+    iTensorListRefT,
+    NamedCType,
+    OptionalCType,
+    optionalIntArrayRefT,
+    optionalScalarRefT,
+    optionalTensorRefT,
+    scalarT,
+    tensorT,
+)
+from torchgen.model import (
+    Argument,
+    BaseTy,
+    BaseType,
+    ListType,
+    NativeFunctionsGroup,
+    OptionalType,
+    SelfArgument,
+    TensorOptionsArguments,
+    Type,
+)
+from torchgen.utils import assert_never
+
+# This file describes the translation of JIT schema to the structured functions API.
+# This is similar to native API, but a number of historical problems with native
+# API have been fixed.
+
+# Translation of types occuring in JIT arguments to a C++ argument type.
+# NB: For now, mutable doesn't do anything; but it could if we make
+# some more nominal types
+def argumenttype_type(t: Type, *, mutable: bool, binds: ArgName) -> NamedCType:
+    # If it's a value type, do the value type translation
+    # NB: structured kernels ALWAYS have symint off, since they involve actual
+    # kernels that require real ints.  The one exception is the
+    # CompositeExplicitAutograd and the meta function (which could
+    # hypothetically be SymInt), but for simplicity we plan for these to just
+    # be handled in Python
+    r = cpp.valuetype_type(t, symint=False, binds=binds)
+    if r is not None:
+        return r
+
+    if isinstance(t, BaseType):
+        if t.name == BaseTy.Tensor:
+            return NamedCType(binds, ConstRefCType(BaseCType(tensorT)))
+        elif t.name == BaseTy.Scalar:
+            return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
+        else:
+            raise AssertionError(f"base type should have been value type {t}")
+    elif isinstance(t, OptionalType):
+        if t.elem == BaseType(BaseTy.Tensor):
+            return NamedCType(binds, BaseCType(optionalTensorRefT))
+        elif t.elem == BaseType(BaseTy.Scalar):
+            return NamedCType(binds, BaseCType(optionalScalarRefT))
+        elif isinstance(t.elem, ListType) and str(t.elem.elem) == "int":
+            return NamedCType(binds, BaseCType(optionalIntArrayRefT))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
+        return NamedCType(binds, OptionalCType(elem.type))
+    elif isinstance(t, ListType):
+        if t.elem == BaseType(BaseTy.Tensor):
+            return NamedCType(binds, ConstRefCType(BaseCType(iTensorListRefT)))
+        elif t.elem == OptionalType(BaseType(BaseTy.Tensor)):
+            return NamedCType(binds, BaseCType(iOptTensorListRefT))
+        # TODO: delete these special cases; see torchgen.api.cpp--these
+        # must be changed in tandem, but there are problems; see
+        # https://github.com/pytorch/pytorch/pull/51485
+        elif str(t.elem) == "int":
+            return NamedCType(binds, BaseCType(intArrayRefT))
+        elif str(t.elem) == "Dimname":
+            return NamedCType(binds, BaseCType(dimnameListT))
+        elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
+        return NamedCType(binds, ArrayRefCType(elem.type))
+    else:
+        raise AssertionError(f"unrecognized type {repr(t)}")
+
+
+def argument_type(a: Argument, *, binds: ArgName) -> NamedCType:
+    return argumenttype_type(a.type, mutable=a.is_write, binds=binds)
+
+
+# returns_type intentionally omitted, because structured kernels never "return";
+# instead, they always indirectly report their outputs (in the case of a meta
+# function, by calling set_output; in the case of an impl function, by writing
+# directly into the provided out argument).
+
+# Structured kernels are never defaulted
+def argument(a: Union[Argument, SelfArgument, TensorOptionsArguments]) -> List[Binding]:
+    if isinstance(a, Argument):
+        return [
+            Binding(
+                nctype=argument_type(a, binds=a.name),
+                name=a.name,
+                default=None,
+                argument=a,
+            )
+        ]
+    elif isinstance(a, SelfArgument):
+        return argument(a.argument)
+    elif isinstance(a, TensorOptionsArguments):
+        raise AssertionError("structured kernels don't support TensorOptions yet")
+    else:
+        assert_never(a)
+
+
+def impl_arguments(g: NativeFunctionsGroup) -> List[Binding]:
+    args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
+
+    if g.out.precomputed:
+        # A list of parameters for the impl function with
+        # certain parameters replaced with precomputed counterparts
+        # as specified in native_functions.yaml.
+        non_out_args_replaced: List[
+            Union[Argument, TensorOptionsArguments, SelfArgument]
+        ] = []
+        for a in g.out.func.arguments.non_out:
+            if isinstance(a, Argument) and a.name in g.out.precomputed.replace:
+                # If a is in precompute.replace, append the parameters
+                # that should replace it onto non_out_args_replaced.
+                for replacement in g.out.precomputed.replace[a.name]:
+                    non_out_args_replaced.append(replacement)
+            else:
+                # If not, push a as it is.
+                non_out_args_replaced.append(a)
+
+        args.extend(non_out_args_replaced)
+        # g.out.precomputed.add is the list of parameters that are added
+        # without replacement after the non out args and just before the out args
+        args.extend(g.out.precomputed.add)
+    else:
+        args.extend(g.out.func.arguments.non_out)
+
+    args.extend(g.out.func.arguments.out)
+    return [r for arg in args for r in argument(arg)]
+
+
+def meta_arguments(g: NativeFunctionsGroup) -> List[Binding]:
+    args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
+    args.extend(g.functional.func.arguments.non_out)
+    return [r for arg in args for r in argument(arg)]
+
+
+def out_arguments(g: NativeFunctionsGroup) -> List[Binding]:
+    args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
+    args.extend(g.out.func.arguments.out)
+    return [r for arg in args for r in argument(arg)]

wemm/lib/python3.10/site-packages/torchgen/api/translate.py

@@ -0,0 +1,431 @@
+from typing import Dict, List, NoReturn, Sequence, Union
+
+from torchgen.api.types import (
+    ArrayRefCType,
+    BaseCType,
+    Binding,
+    boolT,
+    ConstRefCType,
+    deviceT,
+    Expr,
+    intArrayRefT,
+    iOptTensorListRefT,
+    layoutT,
+    ListCType,
+    longT,
+    memoryFormatT,
+    MutRefCType,
+    NamedCType,
+    opmath_t,
+    OptionalCType,
+    optionalIntArrayRefT,
+    optionalScalarRefT,
+    optionalSymIntArrayRefT,
+    optionalTensorRefT,
+    scalar_t,
+    scalarT,
+    scalarTypeT,
+    SpecialArgName,
+    symIntArrayRefT,
+    SymIntT,
+    tensorOptionsT,
+    tensorT,
+    VectorCType,
+)
+
+# This file implements a small program synthesis engine that implements
+# conversions between one API to another.
+#
+# The key data type in this file in NamedCType, short for Named C++ semantic type.  A NamedCType
+# represents a C++ type, plus semantic information about what it represents.
+# For example, consider the argument "bool pin_memory"; its normal C++ type is
+# "bool", but its C++ semantic type also keeps track that this represents a
+# "pin_memory"; you can't just use a random other boolean in a context where you
+# need a "pin_memory"!
+#
+# The translator takes a list of needed NamedCTypes, and then figures out how
+# to construct expressions with these NamedCTypes from the given bindings.  Many
+# of these expressions are trivial (I need a Tensor other; there's a Tensor
+# other scope); others are more nontrivial and may require packing/unpacking.
+# Some examples of non-trivial action:
+#
+#   - Need the "dtype" binding?  Well, maybe "dtype" isn't available
+#     in the context, instead, "options" is, and you need to extract
+#     it from there.  (Gather)
+#
+#   - Need the "context" binding?  Well, maybe "context" isn't available
+#     in the context, and you need to construct it from "dtype", "device",
+#     etc.  (Scatter)
+#
+#   - Need the "memory_format" binding?  Well, actually, it's available
+#     from both "memory_format" and "options", so you had better make sure
+#     they are consistent.  (Join)
+
+options_ctype = NamedCType("options", ConstRefCType(BaseCType(tensorOptionsT)))
+
+out_tensor_ctype = NamedCType("out", ConstRefCType(BaseCType(tensorT)))
+
+longVec_ctype = VectorCType(BaseCType(longT))
+longSymVec_ctype = VectorCType(BaseCType(SymIntT))
+optionalLongVec_ctype = OptionalCType(VectorCType(BaseCType(longT)))
+optionalScalar_ctype = OptionalCType(BaseCType(scalarT))
+optionalTensor_ctype = OptionalCType(BaseCType(tensorT))
+
+
+class UnsatError(RuntimeError):
+    pass
+
+
+# Given a set of in-scope bindings and a set of target bindings, synthesize
+# a list of expressions that uses only the in-scope bindings (bindings) that
+# have all of the types of goals.  You may want to use this function if
+# you're generating code for a function like:
+#
+#   void f({args}) {
+#     g({exprs}); // g is a different API
+#   }
+#
+# and you need to generate "exprs".
+#
+# Typically, a list of Bindings is convenient to get (you usually call something
+# like arguments() to get them); but technically you only need less information:
+# for 'bindings' an (un-ordered) list of Exprs is sufficient; similarly, for
+# 'goals', an (ordered) list of NamedCType goals is sufficient.  If you are doing
+# something more complicated, e.g., tracking the set of bindings in a context,
+# you may find using these smaller types more convenient.
+def translate(
+    bindings: Sequence[Union[Expr, Binding]],
+    goals: Sequence[Union[NamedCType, Binding]],
+    *,
+    method: bool = False,
+    allow_expensive_conversions: bool = False,
+) -> List[Expr]:
+
+    binding_exprs: List[Expr] = []
+    for b in bindings:
+        if isinstance(b, Binding):
+            binding_exprs.append(
+                Expr(
+                    expr=b.name,
+                    type=b.nctype,
+                )
+            )
+        else:
+            binding_exprs.append(b)
+
+    goal_ctypes: List[NamedCType] = []
+    for g in goals:
+        if isinstance(g, Binding):
+            goal_ctypes.append(g.nctype)
+        else:
+            goal_ctypes.append(g)
+
+    # Add all the bindings to the context
+    ctx: Dict[NamedCType, str] = {}
+    for b in binding_exprs:
+        ctx[b.type] = b.expr
+
+        # While we're at it, do some simple forward inference, looking through
+        # constructors.
+        #
+        # NB: When should you do forward inference versus backward inference?
+        # The general idea:
+        #
+        #   - Backward inference WHEN the goal gets smaller
+        #   - Forward inference WHEN the hypothesis gets smaller
+        #
+        # This helps ensure termination: backward inference starts with a goal
+        # and tries to make it simpler and simpler until it's trivial; if the
+        # goal can grow in size, we blow up to a really huge goal size.
+        # Similarly, with forward inference we take hypotheses and decompose
+        # them into simpler hypotheses; if hypotheses could expand in size,
+        # we also have potential nontermination.  (In the code below, forward
+        # inference is only ever carried out at a single step, but you could
+        # imagine repeated application of forward inference being profitable.)
+        #
+        # A good starting point in the literature for exploring more about proof
+        # search are these lecture notes
+        # https://www.cs.cmu.edu/~fp/courses/oregon-m10/04-focusing.pdf
+        #
+        # TODO: My kingdom for a pattern matcher
+        # https://www.python.org/dev/peps/pep-0634/
+        #
+        # TODO: This could get us in recomputation trouble if b.expr is nontrivial.
+        # Fix this by implementing some sort of sharing so that if multiple
+        # goals share the same expression, we only compute it once.  This seems
+        # to matter in practice as compiler is often unwilling to CSE nontrivial
+        # expressions like scalar.to<scalar_t>()
+        t = b.type
+        if (
+            isinstance(t, ConstRefCType)
+            and isinstance(t.elem, OptionalCType)
+            and isinstance(t.elem.elem, BaseCType)
+            and str(t.elem.elem.type) == "at::Tensor"
+        ):
+            ctx[
+                NamedCType(t.elem.elem.name, ConstRefCType(BaseCType(tensorT)))
+            ] = f"({b.expr}.has_value() ? *{b.expr} : at::Tensor())"
+
+        if t.type == ConstRefCType(OptionalCType(BaseCType(tensorT))):
+            ctx[
+                NamedCType(t.name, BaseCType(optionalTensorRefT))
+            ] = f"(({b.expr}.has_value() && (*{b.expr}).defined()) ? at::OptionalTensorRef(*{b.expr}) : at::OptionalTensorRef())"
+
+        if t.type == ConstRefCType(BaseCType(scalarT)):
+            ctx[NamedCType(t.name, BaseCType(opmath_t))] = f"({b.expr}).to<opmath_t>()"
+
+        if t.type == ConstRefCType(OptionalCType(BaseCType(scalarT))):
+            ctx[
+                NamedCType(t.name, BaseCType(optionalScalarRefT))
+            ] = f"({b.expr}.has_value() ? at::OptionalScalarRef(&({b.expr}.value())) : at::OptionalScalarRef())"
+
+        if t.type == BaseCType(scalar_t):
+            ctx[
+                NamedCType(t.name, BaseCType(opmath_t))
+            ] = f"static_cast<opmath_t>({b.expr})"
+
+        # [Note: IOptTensorListRef]
+        if t.type == ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT)))):
+            ctx[
+                NamedCType(t.name, BaseCType(iOptTensorListRefT))
+            ] = f"at::IOptTensorListRef({b.expr})"
+
+    # Add implicit bindings if the generated code is inside a Tensor method
+    if method:
+        ctx[
+            NamedCType("self", MutRefCType(BaseCType(tensorT)))
+        ] = "const_cast<Tensor&>(*this)"
+        ctx[
+            NamedCType("self", ConstRefCType(BaseCType(tensorT)))
+        ] = "const_cast<Tensor&>(*this)"
+        # This is better!  Byte-for-byte compat
+        # ctx[NamedCType("self", ConstRefCType(BaseCType(tensorT)))] = "*this"
+
+    def unsat(goal: NamedCType) -> NoReturn:
+        ctx_desc = "\n".join(
+            f"  {t.cpp_type()} {t.name}; // {e}" for t, e in ctx.items()
+        )
+        raise UnsatError(
+            f"""
+Failed to synthesize the expression "{goal.cpp_type()} {goal.name}".
+When I failed, the following bindings were available in the context:
+
+{ctx_desc}
+
+This probably means there is a missing rule in the rules of torchgen.api.translate.
+Check this module for more information.
+"""
+        )
+
+    # A shitty backtracking search implementation.  It's shitty because it
+    # does backtracking via stack (bad idea!) and for the most part tries to
+    # avoid backtracking.  In particular, if
+    # direct=True, we won't try to do any fancy synthesis, just trivial
+    # conversions (e.g., "T a" is OK for "const T& a").  So all of the
+    # existing rules in this function simply try to solve immediately,
+    # and bail if things don't work out.
+    def solve(goal: NamedCType, *, direct: bool) -> str:
+        def direct_solve(goal: NamedCType) -> str:
+            return solve(goal, direct=True)
+
+        if goal in ctx:
+            # Trivial
+            return ctx[goal]
+
+        # const & is satisfied with mutable &
+        if isinstance(goal.type, ConstRefCType):
+            try:
+                # WARNING: not strictly decreasing; be careful not
+                # to add a direct conversion that goes satisfies
+                # mutable& with const&
+                return solve(
+                    NamedCType(goal.name, MutRefCType(goal.type.elem)), direct=direct
+                )
+            except UnsatError:
+                pass
+
+        # mutable & is satisfied with value
+        if isinstance(goal.type, MutRefCType):
+            try:
+                return solve(NamedCType(goal.name, goal.type.elem), direct=direct)
+            except UnsatError:
+                pass
+
+        # TODO: These are referentially equal, shouldn't have to do this;
+        # ensuring we don't use type synonym IntArrayRef in codegen would
+        # help
+        if goal.type == ArrayRefCType(BaseCType(longT)):
+            return solve(NamedCType(goal.name, BaseCType(intArrayRefT)), direct=direct)
+
+        if direct:
+            unsat(goal)
+
+        # For now, all of these rules are mutually exclusive.
+        if goal == NamedCType("memory_format", OptionalCType(BaseCType(memoryFormatT))):
+            memory_format = direct_solve(
+                NamedCType(
+                    SpecialArgName.possibly_redundant_memory_format,
+                    OptionalCType(BaseCType(memoryFormatT)),
+                )
+            )
+            # No need to join "memory_format" and "options" if the target API takes "options" directly.
+            # Otherwise it will cause the redundant memory_format error.
+            if options_ctype in goal_ctypes:
+                return memory_format
+            try:
+                options = direct_solve(options_ctype)
+                return f"c10::impl::check_tensor_options_and_extract_memory_format({options}, {memory_format})"
+            except UnsatError:
+                return memory_format
+        elif goal == NamedCType("options", BaseCType(tensorOptionsT)):
+            dtype = direct_solve(
+                NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT)))
+            )
+            pin_memory = direct_solve(
+                NamedCType("pin_memory", OptionalCType(BaseCType(boolT)))
+            )
+            device = direct_solve(
+                NamedCType("device", OptionalCType(BaseCType(deviceT)))
+            )
+            layout = direct_solve(
+                NamedCType("layout", OptionalCType(BaseCType(layoutT)))
+            )
+            return f"TensorOptions().dtype({dtype}).layout({layout}).device({device}).pinned_memory({pin_memory})"
+
+        elif goal == NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"optTypeMetaToScalarType({options}.dtype_opt())"
+            except UnsatError:
+                out_tensor = direct_solve(out_tensor_ctype)
+                return f"{out_tensor}.scalar_type()"
+
+        elif goal == NamedCType("layout", OptionalCType(BaseCType(layoutT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"{options}.layout_opt()"
+            except UnsatError:
+                out_tensor = direct_solve(out_tensor_ctype)
+                return f"{out_tensor}.layout()"
+
+        elif goal == NamedCType("device", OptionalCType(BaseCType(deviceT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"{options}.device_opt()"
+            except UnsatError:
+                out_tensor = direct_solve(out_tensor_ctype)
+                return f"{out_tensor}.device()"
+
+        elif goal == NamedCType("pin_memory", OptionalCType(BaseCType(boolT))):
+            try:
+                options = direct_solve(options_ctype)
+                return f"{options}.pinned_memory_opt()"
+            except UnsatError:
+                # If we're calling a factory op from its out= variant,
+                # We don't actually care about the value of pin_memory.
+                out_tensor = direct_solve(out_tensor_ctype)
+                return "c10::nullopt"
+
+        # We can always do translations from value types to reference types, like vector<int> -> IntArrayRef
+        elif goal.type == BaseCType(intArrayRefT):
+            try:
+                return direct_solve(NamedCType(goal.name, longVec_ctype))
+            except UnsatError:
+                # We can also go SymIntArrayRef -> IntArrayRef
+                symIntArrayRef_type = direct_solve(
+                    NamedCType(goal.name, BaseCType(symIntArrayRefT))
+                )
+                return f"C10_AS_INTARRAYREF_SLOW({symIntArrayRef_type})"
+        elif goal.type == BaseCType(symIntArrayRefT):
+            try:
+                r = direct_solve(NamedCType(goal.name, BaseCType(intArrayRefT)))
+                return f"c10::fromIntArrayRefSlow({r})"
+            except UnsatError:
+                return direct_solve(NamedCType(goal.name, longSymVec_ctype))
+        elif goal.type == BaseCType(SymIntT):
+            return direct_solve(NamedCType(goal.name, BaseCType(longT)))
+        elif goal.type == OptionalCType(BaseCType(SymIntT)):
+            argname = direct_solve(
+                NamedCType(goal.name, OptionalCType(BaseCType(longT)))
+            )
+            return f"{argname}.has_value() ? c10::make_optional(c10::SymInt(*{argname})) : c10::nullopt"
+        elif goal.type == BaseCType(longT):
+            symInt_type = direct_solve(NamedCType(goal.name, BaseCType(SymIntT)))
+            return f"{symInt_type}.expect_int()"
+        elif goal.type == OptionalCType(BaseCType(longT)):
+            argname = direct_solve(
+                NamedCType(goal.name, OptionalCType(BaseCType(SymIntT)))
+            )
+            return f"{argname}.has_value() ? c10::make_optional({argname}->expect_int()) : c10::nullopt"
+        elif goal.type == BaseCType(optionalIntArrayRefT):
+            try:
+                return direct_solve(NamedCType(goal.name, optionalLongVec_ctype))
+            except UnsatError:
+                argname = direct_solve(
+                    NamedCType(goal.name, BaseCType(optionalSymIntArrayRefT))
+                )
+                return f"{argname}.has_value() ? c10::make_optional(C10_AS_INTARRAYREF_SLOW(*{argname})) : c10::nullopt"
+        elif goal.type == BaseCType(optionalSymIntArrayRefT):
+            # TODO: You might also want to solve this from longSymVec_ctype or
+            # an optional version of it
+            argname = direct_solve(
+                NamedCType(goal.name, BaseCType(optionalIntArrayRefT))
+            )
+            return f"{argname}.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*{argname})) : c10::nullopt"
+        elif goal.type == BaseCType(optionalScalarRefT):
+            return direct_solve(NamedCType(goal.name, optionalScalar_ctype))
+        elif goal.type == BaseCType(optionalTensorRefT):
+            return direct_solve(NamedCType(goal.name, optionalTensor_ctype))
+
+        # Note [translation from C++ reference to value types]
+        # The below cases are all for when we have an argument with a reference type,
+        # and a corresponding goal with a value type.
+        # These are needed when we populate the inputs to a lambda capture and we need
+        # to guarantee the lifetime of each captured argument.
+        # We guard it with an explicit kwarg because converting to a value type is expensive
+        # (O(n)) to convert from IntArrayRef to vector<int>),
+        # so the caller of translate() should be explicit that they need it.
+        if allow_expensive_conversions:
+            if goal.type == VectorCType(BaseCType(longT)):
+                intArrayRef_ctype = NamedCType(goal.name, BaseCType(intArrayRefT))
+                argname = direct_solve(intArrayRef_ctype)
+                return f"{argname}.vec()"
+            if goal.type == VectorCType(BaseCType(SymIntT)):
+                symIntArrayRef_ctype = NamedCType(goal.name, BaseCType(symIntArrayRefT))
+                argname = direct_solve(symIntArrayRef_ctype)
+                return f"{argname}.vec()"
+            elif goal.type == OptionalCType(VectorCType(BaseCType(longT))):
+                optionalIntArrayRef_ctype = NamedCType(
+                    goal.name, BaseCType(optionalIntArrayRefT)
+                )
+                argname = direct_solve(optionalIntArrayRef_ctype)
+                return f"{argname}.has_value() ? c10::make_optional({argname}->vec()) : c10::nullopt"
+            elif goal.type == OptionalCType(BaseCType(scalarT)):
+                optionalScalarRef_ctype = NamedCType(
+                    goal.name, BaseCType(optionalScalarRefT)
+                )
+                argname = direct_solve(optionalScalarRef_ctype)
+                return f"{argname}.has_value() ? c10::make_optional({argname}) : c10::nullopt"
+            elif goal.type == OptionalCType(BaseCType(scalarT)):
+                optionalTensorRef_ctype = NamedCType(
+                    goal.name, BaseCType(optionalTensorRefT)
+                )
+                argname = direct_solve(optionalTensorRef_ctype)
+                return f"{argname}.has_value() ? c10::make_optional({argname}) : c10::nullopt"
+            # Technically, we also need to handle cases of C++ containers holding reference types.
+            # But there currently aren't any ops that require lambda capture codegen
+            # With arguments like std::vector<IntArrayRef>.
+            # If that changes, we'll have to add the translation here.
+
+        # We allow const casting on tensors, since const-correctness is a bit broken for at::Tensor.
+        # We could probably generalize this to non-tensor types too.
+        if goal.type == MutRefCType(BaseCType(tensorT)):
+            const_ref_tensor_ctype = NamedCType(
+                goal.name, ConstRefCType(BaseCType(tensorT))
+            )
+            argname = direct_solve(const_ref_tensor_ctype)
+            return f"const_cast<Tensor&>({argname})"
+
+        unsat(goal)
+
+    return [Expr(solve(g, direct=False), g) for g in goal_ctypes]

wemm/lib/python3.10/site-packages/torchgen/api/types/types.py

@@ -0,0 +1,182 @@
+"""
+Where should I add a new type? `types_base.py` vs `types.py`
+
+This file defines data model classes for torchgen typing system, as well as some base types such as int32_t.
+
+`types.py` defines ATen Tensor type and some c10 types, along with signatures that use these types.
+
+The difference between these two files, is `types_base.py` should be implementation-agnostic, meaning it shouldn't
+contain any type definition that is tight to a specific C++ library (e.g., ATen), so that it can be easily reused
+if we want to generate code for another C++ library.
+
+Add new types to `types.py` if these types are ATen/c10 related.
+Add new types to `types_base.py` if they are basic and not attached to ATen/c10.
+"""
+from dataclasses import dataclass
+from typing import Dict, TypeVar
+
+from torchgen.model import BaseTy, ScalarType
+
+from .types_base import (
+    BaseCppType,
+    BaseCType,
+    boolT,
+    byteT,
+    charT,
+    CType,
+    doubleT,
+    floatT,
+    int32T,
+    longT,
+    shortT,
+)
+
+_T = TypeVar("_T")
+
+TENSOR_LIST_LIKE_CTYPES = [
+    "at::TensorList",
+    "const c10::List<c10::optional<at::Tensor>> &",
+    "const at::ITensorListRef &",
+]
+
+
+halfT = BaseCppType("at", "Half")
+complexHalfT = BaseCppType(
+    "c10", "complex<c10::Half>"
+)  # stuffing template param here is an abuse
+complexFloatT = BaseCppType("c10", "complex<float>")
+complexDoubleT = BaseCppType("c10", "complex<double>")
+bfloat16T = BaseCppType("at", "BFloat16")
+stringT = BaseCppType("c10", "string_view")
+generatorT = BaseCppType("at", "Generator")
+scalarTypeT = BaseCppType("at", "ScalarType")
+tensorT = BaseCppType("at", "Tensor")
+optionalTensorRefT = BaseCppType("at", "OptionalTensorRef")
+tensorListT = BaseCppType("at", "TensorList")
+iTensorListRefT = BaseCppType("at", "ITensorListRef")
+iOptTensorListRefT = BaseCppType("at", "IOptTensorListRef")
+dimnameT = BaseCppType("at", "Dimname")
+dimnameListT = BaseCppType("at", "DimnameList")
+dimVectorT = BaseCppType("at", "DimVector")
+layoutT = BaseCppType("at", "Layout")
+deviceT = BaseCppType("at", "Device")
+scalarT = BaseCppType("at", "Scalar")
+optionalScalarRefT = BaseCppType("at", "OptionalScalarRef")
+memoryFormatT = BaseCppType("at", "MemoryFormat")
+qschemeT = BaseCppType("at", "QScheme")
+storageT = BaseCppType("at", "Storage")
+streamT = BaseCppType("at", "Stream")
+intArrayRefT = BaseCppType("at", "IntArrayRef")
+optionalIntArrayRefT = BaseCppType("at", "OptionalIntArrayRef")
+optionalSymIntArrayRefT = BaseCppType("at", "OptionalSymIntArrayRef")
+tensorOptionsT = BaseCppType("at", "TensorOptions")
+typeAndSizeT = BaseCppType("torch::autograd::generated", "TypeAndSize")
+tensorGeometryT = BaseCppType("at", "TensorGeometry")
+SymIntT = BaseCppType("c10", "SymInt")
+symIntArrayRefT = BaseCppType("c10", "SymIntArrayRef")
+
+# Types representing template parameters.  Technically, we probably shouldn't
+# represent them this way in codegen, but it was pretty convenient.
+scalar_t = BaseCppType("", "scalar_t")
+opmath_t = BaseCppType("", "opmath_t")
+
+ScalarTypeToCppMapping: Dict[ScalarType, BaseCppType] = {
+    ScalarType.Byte: byteT,
+    ScalarType.Char: charT,
+    ScalarType.Short: shortT,
+    ScalarType.Int: int32T,
+    ScalarType.Long: longT,
+    ScalarType.Half: halfT,
+    ScalarType.Float: floatT,
+    ScalarType.Double: doubleT,
+    ScalarType.ComplexHalf: complexHalfT,
+    ScalarType.ComplexFloat: complexFloatT,
+    ScalarType.ComplexDouble: complexDoubleT,
+    ScalarType.Bool: boolT,
+    ScalarType.BFloat16: bfloat16T,
+}
+
+BaseTypeToCppMapping: Dict[BaseTy, BaseCppType] = {
+    BaseTy.int: longT,
+    BaseTy.float: doubleT,
+    BaseTy.bool: boolT,
+    BaseTy.str: stringT,
+    BaseTy.Generator: generatorT,
+    BaseTy.ScalarType: scalarTypeT,
+    BaseTy.Tensor: tensorT,
+    BaseTy.Dimname: dimnameT,
+    BaseTy.DimVector: dimVectorT,
+    BaseTy.Layout: layoutT,
+    BaseTy.Device: deviceT,
+    BaseTy.Scalar: scalarT,
+    BaseTy.MemoryFormat: memoryFormatT,
+    BaseTy.QScheme: qschemeT,
+    BaseTy.Storage: storageT,
+    BaseTy.Stream: streamT,
+    BaseTy.SymInt: SymIntT,
+}
+
+# CTypes encode C++ type structure as needed for translation.
+
+
+@dataclass(frozen=True)
+class OptionalCType(CType):
+    elem: "CType"
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        # Do not pass `strip_ref` recursively.
+        return f"c10::optional<{self.elem.cpp_type()}>"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"c10::optional<{self.elem.cpp_type_registration_declarations()}>"
+
+    def remove_const_ref(self) -> "CType":
+        return OptionalCType(self.elem.remove_const_ref())
+
+
+@dataclass(frozen=True)
+class ListCType(CType):
+    elem: "CType"
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        # Do not pass `strip_ref` recursively.
+        return f"c10::List<{self.elem.cpp_type()}>"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"c10::List<{self.elem.cpp_type_registration_declarations()}>"
+
+    def remove_const_ref(self) -> "CType":
+        return ListCType(self.elem.remove_const_ref())
+
+
+@dataclass(frozen=True)
+class ArrayRefCType(CType):
+    elem: "CType"
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        # Do not pass `strip_ref` recursively.
+        return f"at::ArrayRef<{self.elem.cpp_type()}>"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"ArrayRef<{self.elem.cpp_type_registration_declarations()}>"
+
+    def remove_const_ref(self) -> "CType":
+        return ArrayRefCType(self.elem.remove_const_ref())
+
+
+@dataclass(frozen=True)
+class VectorizedCType(CType):
+    # This template is explicitly specialized, so the only valid
+    # elems are those we have specializations for (e.g., float, double, ...)
+    # scalar_t is also a common argument here (when we are codegen in
+    # a templated context)
+    elem: BaseCType
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        return f"at::vec::Vectorized<{self.elem.cpp_type()}>"
+
+    def cpp_type_registration_declarations(self) -> str:
+        raise NotImplementedError
+
+    def remove_const_ref(self) -> "CType":
+        return self

wemm/lib/python3.10/site-packages/torchgen/api/types/types_base.py

@@ -0,0 +1,267 @@
+"""
+Where should I add a new type? `types_base.py` vs `types.py`
+
+This file defines data model classes for torchgen typing system, as well as some base types such as int32_t.
+
+`types.py` defines ATen Tensor type and some c10 types, along with signatures that use these types.
+
+The difference between these two files, is `types_base.py` should be implementation-agnostic, meaning it shouldn't
+contain any type definition that is tight to a specific C++ library (e.g., ATen), so that it can be easily reused
+if we want to generate code for another C++ library.
+
+Add new types to `types.py` if these types are ATen/c10 related.
+Add new types to `types_base.py` if they are basic and not attached to ATen/c10.
+"""
+from abc import ABC
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import List, Optional, Union
+
+from torchgen.model import Argument, SelfArgument, TensorOptionsArguments
+
+# An ArgName is just the str name of the argument in schema;
+# but in some special circumstances, we may add a little extra
+# context.  The Enum SpecialArgName covers all of these cases;
+# grep for their construction sites to see when they can occr.
+
+
+class SpecialArgName(Enum):
+    possibly_redundant_memory_format = auto()
+
+
+ArgName = Union[str, SpecialArgName]
+
+
+# This class shouldn't be created directly; instead, use/create one of the singletons below.
+@dataclass(frozen=True)
+class BaseCppType:
+    ns: Optional[str]
+    name: str
+
+    def __str__(self) -> str:
+        if self.ns is None or self.ns == "":
+            return self.name
+        return f"{self.ns}::{self.name}"
+
+
+# The set of all non-templated, valid, fully-qualified names of C++ types that are used in the codegen.
+# Templated types get their own dataclass, mainly to make namespace parsing easier.
+byteT = BaseCppType("", "uint8_t")
+charT = BaseCppType("", "int8_t")
+shortT = BaseCppType("", "int16_t")
+# It would be more symmetric for this to be called intT, but it easy to mix
+# this up with JIT int (which is int64_t in C++), so we intentionally don't
+# define intT to make it obvious when you've stuffed it up
+int32T = BaseCppType("", "int32_t")
+longT = BaseCppType("", "int64_t")
+doubleT = BaseCppType("", "double")
+floatT = BaseCppType("", "float")
+boolT = BaseCppType("", "bool")
+voidT = BaseCppType("", "void")
+
+
+class CType(ABC):
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        raise NotImplementedError
+
+    def cpp_type_registration_declarations(self) -> str:
+        raise NotImplementedError
+
+    def remove_const_ref(self) -> "CType":
+        return self
+
+
+@dataclass(frozen=True)
+class BaseCType(CType):
+    type: BaseCppType
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        return str(self.type)
+
+    # For BC reasons, we don't want to introduce at:: namespaces to RegistrationDeclarations.yaml
+    # TODO: Kill this when we eventually remove it!
+    def cpp_type_registration_declarations(self) -> str:
+        return str(self.type).replace("at::", "")
+
+    def remove_const_ref(self) -> "CType":
+        return self
+
+
+@dataclass(frozen=True)
+class ConstRefCType(CType):
+    elem: "CType"
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        if strip_ref:
+            return self.elem.cpp_type(strip_ref=strip_ref)
+        return f"const {self.elem.cpp_type()} &"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"const {self.elem.cpp_type_registration_declarations()} &"
+
+    def remove_const_ref(self) -> "CType":
+        return self.elem.remove_const_ref()
+
+
+@dataclass(frozen=True)
+class VectorCType(CType):
+    elem: "CType"
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        # Do not pass `strip_ref` recursively.
+        return f"::std::vector<{self.elem.cpp_type()}>"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"::std::vector<{self.elem.cpp_type_registration_declarations()}>"
+
+    def remove_const_ref(self) -> "CType":
+        return VectorCType(self.elem.remove_const_ref())
+
+
+@dataclass(frozen=True)
+class ArrayCType(CType):
+    elem: "CType"
+    size: int
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        # Do not pass `strip_ref` recursively.
+        return f"::std::array<{self.elem.cpp_type()},{self.size}>"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"::std::array<{self.elem.cpp_type_registration_declarations()},{self.size}>"
+
+    def remove_const_ref(self) -> "CType":
+        return ArrayCType(self.elem.remove_const_ref(), self.size)
+
+
+@dataclass(frozen=True)
+class TupleCType(CType):
+    elems: List["CType"]
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        # Do not pass `strip_ref` recursively.
+        return f'::std::tuple<{",".join([e.cpp_type() for e in self.elems])}>'
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f'::std::tuple<{",".join([e.cpp_type_registration_declarations() for e in self.elems])}>'
+
+    def remove_const_ref(self) -> "CType":
+        return TupleCType([e.remove_const_ref() for e in self.elems])
+
+
+@dataclass(frozen=True)
+class MutRefCType(CType):
+    elem: "CType"
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        if strip_ref:
+            return self.elem.cpp_type(strip_ref=strip_ref)
+        return f"{self.elem.cpp_type()} &"
+
+    def cpp_type_registration_declarations(self) -> str:
+        return f"{self.elem.cpp_type_registration_declarations()} &"
+
+    def remove_const_ref(self) -> "CType":
+        return self.elem.remove_const_ref()
+
+
+# A NamedCType is short for Named C++ semantic type.  A NamedCType represents a C++ type, plus
+# semantic information about what it represents.  For example, consider the
+# argument "bool pin_memory"; its normal C++ type is "bool", but its C++
+# semantic type also keeps track that this represents a "pin_memory"; you can't
+# just use a random other boolean in a context where you need a "pin_memory"!
+#
+
+
+@dataclass(frozen=True)
+class NamedCType:
+    name: ArgName
+    type: CType
+
+    def cpp_type(self, *, strip_ref: bool = False) -> str:
+        return self.type.cpp_type(strip_ref=strip_ref)
+
+    # For BC reasons, we don't want to introduce at:: namespaces to RegistrationDeclarations.yaml
+    # TODO: Kill this when we eventually remove it!
+    def cpp_type_registration_declarations(self) -> str:
+        return self.type.cpp_type_registration_declarations()
+
+    def remove_const_ref(self) -> "NamedCType":
+        return NamedCType(self.name, self.type.remove_const_ref())
+
+    def with_name(self, name: str) -> "NamedCType":
+        return NamedCType(name, self.type)
+
+
+# A binding represents any C++ binding site for a formal parameter.
+# We don't distinguish between binding sites for different APIs;
+# instead, all of the important distinctions are encoded in CType,
+# which you can use to figure out if a given Binding is appropriate
+# for use in another context.  (See torchgen.api.translate)
+
+
+@dataclass(frozen=True)
+class Binding:
+    name: str
+    nctype: NamedCType
+    argument: Union[Argument, TensorOptionsArguments, SelfArgument]
+    # TODO: maybe don't represent default here
+    default: Optional[str] = None
+
+    def rename(self, name: str) -> "Binding":
+        return Binding(
+            name=name,
+            nctype=self.nctype,
+            argument=self.argument,
+            default=self.default,
+        )
+
+    @property
+    def type(self) -> str:
+        return self.nctype.cpp_type()
+
+    def no_default(self) -> "Binding":
+        return Binding(
+            name=self.name,
+            nctype=self.nctype,
+            default=None,
+            argument=self.argument,
+        )
+
+    def decl(self, *, func_ptr_cast: bool = False) -> str:
+        mb_default = ""
+        if self.default is not None:
+            mb_default = f"={self.default}"
+
+        # casting only needs to know the type
+        if func_ptr_cast:
+            return f"{self.type}"
+        else:
+            return f"{self.type} {self.name}{mb_default}"
+
+    # For BC reasons, we don't want to introduce at:: namespaces to RegistrationDeclarations.yaml
+    # TODO: Kill this when we eventually remove it!
+    def decl_registration_declarations(self) -> str:
+        type_s = self.nctype.cpp_type_registration_declarations()
+        mb_default = ""
+        if self.default is not None:
+            mb_default = f"={self.default}"
+        return f"{type_s} {self.name}{mb_default}"
+
+    def defn(self) -> str:
+        return f"{self.type} {self.name}"
+
+    def with_name(self, name: str) -> "Binding":
+        return Binding(
+            name=name, nctype=self.nctype, argument=self.argument, default=self.default
+        )
+
+
+# An Expr is a C++ expression.  It has a C++ string representing its syntax,
+# as well as a CType saying what it provides.
+
+
+@dataclass(frozen=True)
+class Expr:
+    expr: str
+    type: NamedCType

wemm/lib/python3.10/site-packages/torchgen/dest/lazy_ir.py

@@ -0,0 +1,710 @@
+import itertools
+from abc import ABC
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torchgen.api.dispatcher as dispatcher
+from torchgen.api.lazy import (
+    getValueT,
+    isValueType,
+    LazyArgument,
+    LazyIrProperties,
+    LazyIrSchema,
+    tensorListValueT,
+)
+from torchgen.api.translate import translate
+from torchgen.api.types import (
+    BaseCType,
+    Binding,
+    deviceT,
+    DispatcherSignature,
+    kernel_signature,
+    NativeSignature,
+    OptionalCType,
+    VectorCType,
+)
+from torchgen.context import method_with_native_function
+from torchgen.dest.lazy_ts_lowering import ts_lowering_body
+from torchgen.model import (
+    Argument,
+    BackendIndex,
+    BackendMetadata,
+    BaseTy,
+    BaseType,
+    FunctionSchema,
+    ListType,
+    NativeFunction,
+    NativeFunctionsGroup,
+)
+
+
+def node_ctor_arg_rvalue_string(arg: LazyArgument) -> str:
+    """
+    Given a LazyArgument,
+    generate a c++ string for materializing an rvalue of that arg for passing into
+    a lazy Node constructor.
+    """
+
+    # TODO: Matching on CType seems wrong; should be matching on Type
+    if isValueType(arg.lazy_type):
+        if isinstance(arg.lazy_type, BaseCType):
+            if arg.is_wrapped_scalar:
+                return f"node_{arg.name}"
+            elif arg.lazy_type.type is tensorListValueT:
+                return f"lazy_{arg.name}_tensorlist"
+            elif arg.is_symint_or_list:
+                return f"GetSymIntValue({arg.name})"
+            return f"lazy_{arg.name}->GetIrValue()"
+        elif isinstance(arg.lazy_type, OptionalCType):
+            if arg.is_symint_or_list:
+                # TODO: I don't understand when you should put lazy_ in the name
+                # or not
+                return f"{arg.name} ? c10::make_optional(GetSymIntValue(*{arg.name})) : c10::nullopt"
+            elif arg.is_wrapped_scalar:
+                return f"node_{arg.name}"
+            return (
+                f"lazy_{arg.name} ? "
+                f"c10::make_optional(lazy_{arg.name}->GetIrValue()) : "
+                "c10::nullopt"
+            )
+        else:
+            raise AssertionError(
+                f"TODO not sure if there are other valid types to handle here ({arg.lazy_type})"
+            )
+    else:
+        # NB: this is here because right now we aren't treating SymInt[] as a
+        # value type; when we do this needs to move above
+        # NB: we cannot test arg.lazy_type as we've already specified it is an
+        # int64_t and so we cannot distinguish between SymInt and int64_t
+        if isinstance(arg.orig_type, ListType) and arg.orig_type.elem == BaseType(
+            BaseTy.SymInt
+        ):
+            if arg.symint:
+                return f"GetSymIntArrayRefValue({arg.name})"
+            else:
+                return f"std::vector<int64_t>({arg.name}.begin(), {arg.name}.end())"
+        elif isinstance(arg.lazy_type, VectorCType) and isinstance(
+            arg.lazy_type.elem, BaseCType
+        ):
+            return f"std::vector<{arg.lazy_type.elem.type}>({arg.name}.begin(), {arg.name}.end())"
+        elif (
+            isinstance(arg.lazy_type, OptionalCType)
+            and isinstance(arg.lazy_type.elem, VectorCType)
+            and isinstance(arg.lazy_type.elem.elem, BaseCType)
+        ):
+            return f"torch::lazy::ToOptionalVector<{arg.lazy_type.elem.elem.type}>({arg.name})"
+        else:
+            return f"{arg.name}"
+
+
+def node_ctor_inputs(schema: LazyIrSchema) -> str:
+    """
+    Produce a formatted string with the arguments as passed into the constructor of a node class.
+    """
+    node_ctor_values = [
+        node_ctor_arg_rvalue_string(arg) for arg in schema.filtered_args()
+    ]
+    return ", ".join(node_ctor_values)
+
+
+def gen_fallback_code(
+    schema: LazyIrSchema,
+    sig: Union[DispatcherSignature, NativeSignature],
+    overload_name: str,
+) -> str:
+    """
+    Generate code that falls back to eager conditioned on a predicate
+    """
+    dispatcher_sig = DispatcherSignature.from_schema(schema.func)
+    exprs = translate(sig.arguments(), dispatcher_sig.arguments())
+    fallback_args = ",\n                ".join([a.expr for a in exprs])
+    if len(overload_name):
+        aten_op_str = f"ATEN_OP2({schema.aten_name}, {overload_name})"
+    else:
+        aten_op_str = f"ATEN_OP({schema.aten_name})"
+    or_has_generator = ""
+    if schema.generator_arg:
+        # generators are always optional and there is never more than one, at least currently
+        or_has_generator = f" || ({schema.generator_arg.name}.has_value() && {schema.generator_arg.name}->defined())"
+    return f"""
+        if (force_eager_fallback({aten_symbol(schema)}){or_has_generator}) {{
+            return at::native::call_fallback_fn_symint<&ltc_eager_fallback, {aten_op_str}>::call(
+                {fallback_args}
+            );
+        }}
+"""
+
+
+def aten_symbol(schema: LazyIrSchema) -> str:
+    missing_interned_strings = {
+        "sigmoid_backward",
+    }
+    if schema.aten_name in missing_interned_strings:
+        return f'c10::Symbol::fromQualString("aten::{schema.aten_name}")'
+
+    if not schema.aten_name.startswith("at::"):
+        return f"at::aten::{schema.aten_name}"
+    else:
+        return schema.aten_name
+
+
+# converts  all tensor-like arguments to meta tensors. Returns:
+# (1) a string containing all of the logic that does the conversions.
+# (2) a context, to be used by translate(), with all of the relevant bindings.
+def convert_to_meta_tensors(sig: DispatcherSignature) -> Tuple[str, List[Binding]]:
+    context: List[Binding] = []
+    unwrapped_tensor_args: List[str] = []
+    for arg in sig.arguments():
+        if isinstance(arg.argument, Argument) and arg.argument.type.is_tensor_like():
+            unwrapped_name = f"{arg.name}_meta"
+            unwrapped_tensor_args.append(
+                f"auto {unwrapped_name} = to_meta({arg.name});"
+            )
+            context.append(arg.with_name(unwrapped_name))
+        else:
+            context.append(arg)
+    unwrap_tensor_args_str = "\n        ".join(unwrapped_tensor_args)
+    return unwrap_tensor_args_str, context
+
+
+@dataclass(frozen=True)
+class GenLazyIR(ABC):
+    backend_index: BackendIndex
+    backend_name: str
+    node_base: str
+    use_lazy_shape: bool
+
+    @method_with_native_function
+    def __call__(self, f: Union[NativeFunctionsGroup, NativeFunction]) -> List[str]:
+        func = f.functional.func if isinstance(f, NativeFunctionsGroup) else f.func
+        metadata = self.backend_index.get_kernel(
+            f.functional if isinstance(f, NativeFunctionsGroup) else f
+        )
+        schema = LazyIrSchema(
+            func, symint=metadata is not None and metadata.supports_symint()
+        )
+        return self.gen(schema)
+
+    # there is no lowering functionality generated unless this IR base class is subclassed and
+    # implemented as a backend-specific node
+    def lowering_function(self, schema: LazyIrSchema) -> str:
+        return ""
+
+    def create_function(self, schema: LazyIrSchema, node_ctor_args: str) -> str:
+        return ""
+
+    def can_be_reused_function(self, schema: LazyIrSchema, node_ctor_args: str) -> str:
+        return f"""bool CanBeReused({node_ctor_args}) const {{
+    return false;
+    }}"""
+
+    def node_base_ctor_call(self, schema: LazyIrSchema) -> str:
+        value_args = schema.filtered_args(values=True, scalars=False)
+        # backends can customize the way the node base class constructor is called,
+        # as long as all of its arguments can be generated from information available from the schema
+        base_ctor_value_args_list = []
+        for arg in value_args:
+            if isinstance(arg.lazy_type, BaseCType) or isinstance(
+                arg.lazy_type, VectorCType
+            ):
+                base_ctor_value_args_list.append(f"{arg.name}")
+            elif isinstance(arg.lazy_type, OptionalCType):
+                base_ctor_value_args_list.append(f"{arg.name}.value_or(kNullValue)")
+            else:
+                raise AssertionError(
+                    f"Unsupported type ({arg.lazy_type}) - add support if necessary"
+                )
+        base_ctor_value_args = ", ".join(base_ctor_value_args_list)
+
+        scalar_args = schema.filtered_args(values=False, scalars=True)
+
+        # Shape constuction.
+        # Conditionally build shape depending on specified shape property
+        if schema.properties.ShapePrecompute:
+            shape_ctor_arg = "std::move(shapes),"
+        elif schema.properties.ShapeCompute:
+            shape_args = [a.name for a in value_args]
+            shape_args.extend(a.name for a in scalar_args)
+            shape_ctor_arg = f"compute_shape_{schema.name}({', '.join(shape_args)}),"
+        elif schema.properties.ShapeCache:
+            shape_args = [f"operand({i})" for i in range(len(value_args))]
+            shape_args.extend(a.name for a in scalar_args)
+            shape_ctor_arg = f"[&](){{ return compute_shape_{schema.name}({', '.join(shape_args)})[0]; }},"
+        else:
+            shape_ctor_arg = ""
+
+        scalar_hashes = ", ".join(f"{a.name}" for a in scalar_args)
+
+        return f"""{self.node_base}(
+              {schema.node_name}::ClassOpKind(),
+              OpList{{{base_ctor_value_args}}},
+              {shape_ctor_arg}
+              /* num_outputs */ {len(schema.returns)},
+              torch::lazy::MHash({scalar_hashes}))"""
+
+    def gen(self, schema: LazyIrSchema) -> List[str]:
+        opkind = schema.opkind or aten_symbol(schema)
+
+        # for now, we just want one IR class decl and soon after also the method defs
+        # and we use the functional version not out/inplace.
+        all_args = schema.filtered_args()
+        value_args = schema.filtered_args(values=True, scalars=False)
+        scalar_args = schema.filtered_args(values=False, scalars=True)
+
+        ctor_args = [f"const {i.lazy_type.cpp_type()}& {i.name}" for i in all_args]
+        reuse_ctor_args = ", ".join(ctor_args)
+        if self.use_lazy_shape and schema.properties.ShapePrecompute:
+            ctor_args.append("std::vector<torch::lazy::Shape>&& shapes")
+        node_ctor_args = ", ".join(ctor_args)
+
+        scalar_initializers = ",\n        ".join(
+            [
+                # This code is just special casing the mapping from string_view -> strings
+                f"{a.name}({a.name}.has_value() ? c10::make_optional(std::string(*{a.name})) : c10::nullopt)"
+                if a.lazy_type.cpp_type() == "c10::optional<c10::string_view>"
+                else f"{a.name}({a.name})"
+                for a in scalar_args
+            ]
+        )
+        if len(scalar_initializers):
+            scalar_initializers = f",\n        {scalar_initializers}"
+        scalar_decls = "\n  ".join(
+            [
+                f"std::string {a.name};"
+                if a.lazy_type.cpp_type() == "c10::string_view"
+                else f"c10::optional<std::string> {a.name};"
+                if a.lazy_type.cpp_type() == "c10::optional<c10::string_view>"
+                else f"{a.lazy_type.cpp_type()} {a.name};"
+                for a in scalar_args
+            ]
+        )
+        optional_values = [
+            arg.name
+            for arg in schema.filtered_args(values=True, scalars=False)
+            if isinstance(arg.lazy_type, OptionalCType)
+        ]
+        has_optional_decls = "\n  ".join(
+            [f"bool has_{value}: 1;" for value in optional_values]
+        )
+        has_optional_defs = "\n    ".join(
+            [f"has_{value} = !!{value};" for value in optional_values]
+        )
+        members_to_string = []
+        for arg in scalar_args:
+            if isinstance(arg.lazy_type, OptionalCType):
+                members_to_string.append(
+                    f"""if ({arg.name}.has_value()) {{
+      ss << ", {arg.name}=" << {arg.name}.value();
+    }} else {{
+      ss << ", {arg.name}=null";
+    }}"""
+                )
+            else:
+                members_to_string.append(f'ss << ", {arg.name}=" << {arg.name};')
+        members_to_string_str = "\n    ".join(members_to_string)
+
+        return [
+            f"""\
+class {schema.node_name} : public {self.node_base} {{
+ public:
+  static torch::lazy::OpKind ClassOpKind() {{
+    return torch::lazy::OpKind({opkind});
+  }}
+
+  {schema.node_name}({node_ctor_args})
+      : {self.node_base_ctor_call(schema)}{scalar_initializers}
+  {{
+    {has_optional_defs}
+  }}
+
+  std::string ToString() const override {{
+    std::stringstream ss;
+    ss << {self.node_base}::ToString();
+    {members_to_string_str}
+    return ss.str();
+  }}
+
+  {self.create_function(schema, reuse_ctor_args)}
+
+  {self.can_be_reused_function(schema, reuse_ctor_args)}
+
+  {self.lowering_function(schema)}
+
+  {scalar_decls}
+  {has_optional_decls}
+
+}};
+
+""",
+        ]
+
+
+@dataclass(frozen=True)
+class GenTSLazyIR(GenLazyIR):
+    def lowering_function(self, schema: LazyIrSchema) -> str:
+        signature = """
+  torch::lazy::TSOpVector Lower(
+      std::shared_ptr<torch::jit::GraphFunction> function,
+      torch::lazy::TSLoweringContext* loctx) const override"""
+
+        if schema.properties.LowerDeclOnly:
+            return f"{signature};"
+        elif schema.properties.Lower:
+            return f"""{signature} {{
+    {ts_lowering_body(schema)}
+  }}
+            """
+        else:
+            return ""
+
+    def create_function(self, schema: LazyIrSchema, node_ctor_args: str) -> str:
+        signature = f"static NodePtr Create({node_ctor_args})"
+        if schema.properties.CreateFnDeclOnly:
+            return f"{signature};"
+        elif not schema.properties.CreateFn:
+            return ""
+        return f"""{signature} {{
+    return ReuseOrMakeNode<{schema.node_name}>(data);
+  }}"""
+
+    def can_be_reused_function(self, schema: LazyIrSchema, node_ctor_args: str) -> str:
+        signature = f"bool CanBeReused({node_ctor_args}) const"
+        if schema.properties.CanBeReusedDeclOnly:
+            return f"{signature};"
+        elif not schema.properties.CanBeReused:
+            return ""
+        value_comparison = []
+        for arg in itertools.chain(schema.positional_values, schema.keyword_values):
+            if isinstance(arg.lazy_type, OptionalCType):
+                value_comparison.append(
+                    f"nullable_operand(i++) == {arg.name}.value_or(kNullValue)"
+                )
+            else:
+                value_comparison.append(f"operand(i++) == {arg.name}")
+        for arg in itertools.chain(schema.positional_scalars, schema.keyword_scalars):
+            if isinstance(arg.lazy_type, OptionalCType):
+                value_comparison.append(
+                    f"((!this->{arg.name}&&!{arg.name}) || (this->{arg.name}&&{arg.name} && *(this->{arg.name}) == *{arg.name}))"
+                )
+            else:
+                value_comparison.append(f"this->{arg.name} == {arg.name}")
+        value_comparison_str = " &&\n        ".join(value_comparison)
+
+        return f"""{signature} {{
+    size_t i = 0;
+    return ({value_comparison_str});
+  }}"""
+
+
+@dataclass(frozen=True)
+class GenLazyNativeFuncDefinition:
+    class_method_name: str
+    backend_index: BackendIndex
+    tensor_class: str
+    gen_forced_fallback_code: bool
+    backend_namespace: str
+    get_tensorlist: str
+    get_tensor_or_wrap_number: str
+    try_get_tensor: str
+    metrics_counter: str
+    create_tensor: str
+    create_from_first_tensor: bool
+    create_aten_from_ltc_tensor: str
+    tuple_aten_from_ltc_tensors: str
+    lazy_tensor_ptr: str
+    get_device_fn: str
+
+    def lazy_tensor_decls(self, func: NativeFunction, schema: LazyIrSchema) -> str:
+        value_args = schema.filtered_args(values=True, scalars=False)
+        # Generates lazy_{name} variables for LazyTensors wrapping input tensors
+        lazy_tensor_decls: List[str] = []
+        for arg in value_args:
+            if arg.is_wrapped_scalar:
+                if isinstance(arg.lazy_type, OptionalCType):
+                    lazy_tensor_decls.append(
+                        f"""auto node_{arg.name} = {arg.name} ?
+                c10::make_optional(torch::lazy::LazyGraphExecutor::Get()->
+                    GetIrValueForScalarFromCodegen(*{arg.name}, *common_device)):
+                c10::nullopt;"""
+                    )
+                else:
+                    lazy_tensor_decls.append(
+                        f"""auto node_{arg.name} = torch::lazy::LazyGraphExecutor::Get()->
+                            GetIrValueForScalarFromCodegen({arg.name}, *common_device);"""
+                    )
+            elif arg.is_symint_or_list:
+                continue  # values are extracted in isValueType
+            elif isinstance(arg.lazy_type, BaseCType):
+                if arg.lazy_type.type is tensorListValueT:
+                    lazy_tensor_decls.append(
+                        f"auto lazy_{arg.name}_tensorlist = "
+                        f"{self.backend_namespace}::{self.get_tensorlist}({arg.name});"
+                    )
+                else:
+                    lazy_tensor_decls.append(
+                        f"{self.lazy_tensor_ptr} lazy_{arg.name} = "
+                        f"{self.backend_namespace}::{self.get_tensor_or_wrap_number}({arg.name}, *common_device);"
+                    )
+            elif isinstance(arg.lazy_type, OptionalCType):
+                assert arg.lazy_type.elem == BaseCType(getValueT()), arg.lazy_type.elem
+                # TODO(alanwaketan): Maybe we want to apply GetLtcTensorOrCreateForWrappedNumber here, but hold it
+                # until we encounter a real world example.
+                lazy_tensor_decls.append(
+                    f"{self.lazy_tensor_ptr} lazy_{arg.name} = "
+                    f"{self.backend_namespace}::{self.try_get_tensor}({arg.name}.value_or(at::Tensor()));"
+                )
+            else:
+                raise AssertionError(
+                    f"TODO not sure if there are other valid types to handle here ({arg.lazy_type})"
+                )
+        return ("\n        ").join(lazy_tensor_decls)
+
+    def force_eager_fallback(
+        self,
+        func: NativeFunction,
+        schema: LazyIrSchema,
+        metadata: BackendMetadata,
+        sig: Union[DispatcherSignature, NativeSignature],
+    ) -> str:
+        if self.gen_forced_fallback_code:
+            return gen_fallback_code(
+                schema, sig, overload_name=func.func.name.overload_name
+            )
+        return ""
+
+    def metrics(self, func: NativeFunction, schema: LazyIrSchema) -> str:
+        return f"{self.metrics_counter};"
+
+    def get_device(self, func: NativeFunction, schema: LazyIrSchema) -> str:
+        value_args = schema.filtered_args(values=True, scalars=False)
+        scalar_args = schema.filtered_args(values=False, scalars=True)
+        value_types_names = [f"{a.name}" for a in value_args if not a.is_wrapped_scalar]
+        optional_device = OptionalCType(BaseCType(deviceT))
+        optional_devices = [
+            a.name for a in scalar_args if a.lazy_type == optional_device
+        ]
+        assert (
+            len(value_types_names) > 0 or len(optional_devices) > 0
+        ), "Expected at least one Value or Device type"
+        get_device_str = (
+            f"{self.get_device_fn}({', '.join(value_types_names + optional_devices)})"
+        )
+        return f"""auto common_device = {get_device_str};
+        TORCH_INTERNAL_ASSERT(common_device);
+        """
+
+    def shape_inference(self, func: NativeFunction, schema: LazyIrSchema) -> str:
+        metadata = self.backend_index.get_kernel(func)
+        assert metadata is not None
+        all_args = schema.filtered_args()
+        returns_length = len(schema.returns)
+        # call the meta kernel if it exists, to compute output shape/dtype for our IR
+        # Note [Generated LTC Shape Functions]
+        # LTC uses meta tensors from core to do shape inference when possible, and otherwise
+        # we generate a shape function declaration that needs to be manually implemented.
+        # How do we detect which ops are eligible to use meta tensors?
+        # In general we should be able to use meta tensors not just on structured operators,
+        # but also on composite operators that are implemented in terms of structured kernels.
+        # We don't currently have a way of knowing at codegen time which ops are implemented that way.
+        # This is the case for all view and view_copy operators however, so we're going to
+        # use them specifically for all of the view_copy ops (instead of manually writing shape rules for all of them).
+        is_view_copy_op = "view_copy" in func.tags
+        is_structured = func.structured or func.structured_delegate is not None
+        if is_structured or is_view_copy_op:
+            meta_out = """
+std::vector<torch::lazy::Shape> shapes{torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};"""
+            if returns_length > 1:
+
+                def this_shape(i: int) -> str:
+                    return f"torch::lazy::Shape(std::get<{i}>(out_meta).scalar_type(), std::get<{i}>(out_meta).sizes().vec())"
+
+                shapes_str = ",".join([this_shape(i) for i in range(returns_length)])
+                meta_out = "std::vector<torch::lazy::Shape> shapes{" + shapes_str + "};"
+
+            # Convert tensor args to the meta device and call it.
+            # (We can't pass in the input tensors directly, because they are "functional wrappers".
+            # If any of the meta kernels call a tensor op and redispatch, we don't want to hit the functionalize kernels.)
+            # Even at::meta:: functions might redispatch, e.g. if they call into view ops.
+            dispatcher_sig = DispatcherSignature.from_schema(func.func)
+            meta_conversion_str, meta_call_ctx = convert_to_meta_tensors(dispatcher_sig)
+            meta_call_args = [
+                e.expr
+                for e in translate(
+                    meta_call_ctx, dispatcher_sig.arguments(), method=False
+                )
+            ]
+            if is_view_copy_op:
+                # view_copy ops always have a CompositeExplicitAutogradNonFunctional kernel
+                assert func.has_composite_explicit_autograd_non_functional_kernel
+                dispatch_ns = "compositeexplicitautogradnonfunctional"
+            else:
+                dispatch_ns = "meta"
+            aten_name = schema.aten_name
+            # TODO: this is trolling
+            if func.func.has_symint() and metadata.supports_symint():
+                aten_name += "_symint"
+            shape_str = f"""\
+        {meta_conversion_str}
+        auto out_meta = at::{dispatch_ns}::{aten_name}({', '.join(meta_call_args)});
+        {meta_out}"""
+        else:
+            shape_sig = ComputeShapeSignature(
+                metadata.kernel, func, symint=metadata.supports_symint()
+            )
+            shape_str = f"""
+            auto shapes = {shape_sig.shape_call};"""
+
+        shape_str += f"""
+            TORCH_INTERNAL_ASSERT(shapes.size() == {returns_length});"""
+
+        # Calculating which dimensions are symbolic
+        func_schema_str = "aten::" + str(func.func)
+        shape_str += f"""
+            if(torch::lazy::symbolicShapeEnabled()){{
+                std::vector<torch::jit::IValue> inputs = {{ {', '.join(str(a.name) for a in all_args)} }};
+                const char* schema_str = "{func_schema_str}";
+                applySymbolicShapesOnLT(schema_str, inputs, shapes);
+            }}
+        """
+        return shape_str
+
+    def build_ir_node(self, func: NativeFunction, schema: LazyIrSchema) -> str:
+        node_ctor_input_str = node_ctor_inputs(schema)
+        return f"""torch::lazy::NodePtr node = torch::lazy::ReuseNode<{schema.node_name}>({node_ctor_input_str});
+        if (!node) {{
+            {self.shape_inference(func, schema)}
+            node = torch::lazy::MakeNode<{schema.node_name}>({node_ctor_input_str}, std::move(shapes));
+            CacheNode(node);
+        }}
+        """
+
+    def create_lazy_tensor(self, first_tensor_name: Optional[str] = None) -> str:
+        # xla uses an instance method for tensor creation, for the time being
+        if self.create_from_first_tensor:
+            # TODO(whc) remove this if XLA switches to using static method for creation
+            assert (
+                first_tensor_name is not None
+            ), "Requires first tensor to create lazy tensor"
+            return f"{first_tensor_name}.{self.create_tensor}"
+        return f"{self.backend_namespace}::{self.create_tensor}"
+
+    def return_aten_tensor(self, func: NativeFunction, schema: LazyIrSchema) -> str:
+        returns_length = len(schema.returns)
+        value_args = schema.filtered_args(values=True, scalars=False)
+        value_types_names = [f"{a.name}" for a in value_args if not a.is_wrapped_scalar]
+        first_tensor_name = value_types_names[0] if len(value_types_names) > 0 else None
+        bridge_str = f"""auto result = {self.create_aten_from_ltc_tensor}(
+                {self.create_lazy_tensor(first_tensor_name)}(std::move(node), *common_device));"""
+
+        if returns_length > 1:
+            assert (
+                len(value_types_names) > 0
+            ), "Code below assumes there is at least one tensor arg"
+            bridge_str = f"""std::vector<{self.lazy_tensor_ptr}> lazy_tensors;
+        for (int i = 0; i < {returns_length}; i++) {{
+            lazy_tensors.push_back({self.create_lazy_tensor(first_tensor_name)}({getValueT()}(node, i), *common_device));
+        }}
+        auto result = {self.tuple_aten_from_ltc_tensors}<{returns_length}>(lazy_tensors);"""
+
+        if schema.name.name.inplace or func.func.is_out_fn():
+            assert returns_length == 1, (
+                "We assumed there was no such case where an op is an in-place variant "
+                f"and has tuple outputs, but got tuple of len {returns_length}."
+            )
+            bridge_str = f"""lazy_{first_tensor_name}->SetInPlaceIrValue(node);
+        auto& result = {first_tensor_name};"""
+
+        bridge_str += """
+        return result;"""
+        return bridge_str
+
+    @method_with_native_function
+    def __call__(self, func: NativeFunction) -> List[str]:
+        sig = kernel_signature(func, self.backend_index)
+        metadata = self.backend_index.get_kernel(func)
+        assert metadata is not None
+        schema = LazyIrSchema(func.func, symint=metadata.supports_symint())
+        return [
+            f"""\
+    {sig.decl(name=f"{self.class_method_name}::{metadata.kernel}")} {{
+        {self.force_eager_fallback(func, schema, metadata, sig)}
+        {self.metrics(func, schema)}
+        {self.get_device(func, schema)}
+        {self.lazy_tensor_decls(func, schema)}
+        {self.build_ir_node(func, schema)}
+        {self.return_aten_tensor(func, schema)}
+    }}\n
+    """
+        ]
+
+
+class ComputeShapeSignature:
+    """
+    Here we use the base name as the suffix of the signature to avoid generating for in-place variants.
+    """
+
+    def __init__(self, kernel_name: str, f: NativeFunction, *, symint: bool):
+        self.__schema = LazyIrSchema(f.func, symint=symint)
+        self.__dispatch_args = ", ".join(
+            [a.decl() for a in dispatcher.arguments(f.func, symint=symint)]
+        )
+        self.__call_args = ", ".join(
+            [f"{arg.name}" for arg in self.__schema.filtered_args(generator=True)]
+        )
+        self.__kernel_name = kernel_name
+
+    def __decl_suffix(self) -> str:
+        return f"{self.__kernel_name}({self.__dispatch_args})"
+
+    def __call_suffix(self) -> str:
+        return f"{self.__kernel_name}({self.__call_args})"
+
+    @property
+    def shape_decl(self) -> str:
+        return f"TORCH_API std::vector<torch::lazy::Shape> compute_shape_{self.__decl_suffix()}"
+
+    @property
+    def shape_call(self) -> str:
+        return f"torch::lazy::compute_shape_{self.__call_suffix()}"
+
+
+@dataclass(frozen=True)
+class GenLazyShapeInferenceDefinition:
+    backend_index: BackendIndex
+    tensor_class: str
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> List[str]:
+        sig = kernel_signature(f, self.backend_index)
+        metadata = self.backend_index.get_kernel(f)
+        assert metadata is not None
+
+        # See Note [Generated LTC Shape Functions]
+        is_view_copy_op = "view_copy" in f.tags
+        is_structured = f.structured or f.structured_delegate is not None
+        if is_structured or is_view_copy_op:
+            return []
+        else:
+            shape_sig = ComputeShapeSignature(
+                metadata.kernel, f, symint=metadata.supports_symint()
+            )
+            return ["\n".join([f"{shape_sig.shape_decl};"])]
+
+
+def generate_non_native_lazy_ir_nodes(
+    non_native: List[Dict[str, Any]], gen_lazy_ir: GenLazyIR
+) -> List[str]:
+    """Generate the non-native lazy IR node classes"""
+    nodes = []
+    for op in non_native:
+        # Set default properties for Non-Native IRs
+        properties = LazyIrProperties("ShapeCache", "CanBeReused", "LowerDeclOnly")
+        for p in op.get("properties", []):
+            setattr(properties, p, True)
+
+        # non-native is assumed to want symint bindings if you wrote symint
+        schema = LazyIrSchema(FunctionSchema.parse(op["func"]), properties, symint=True)
+        schema.opkind = op.get("opkind")
+        nodes.append(gen_lazy_ir.gen(schema)[0])
+
+    return nodes

wemm/lib/python3.10/site-packages/torchgen/dest/register_dispatch_key.py

@@ -0,0 +1,983 @@
+import itertools
+import textwrap
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+from typing_extensions import Literal  # Python 3.8+
+
+import torchgen.api.cpp as cpp
+import torchgen.api.meta as meta
+import torchgen.api.structured as structured
+from torchgen.api.translate import translate
+from torchgen.api.types import (
+    BaseCType,
+    Binding,
+    ConstRefCType,
+    CppSignature,
+    CppSignatureGroup,
+    DispatcherSignature,
+    Expr,
+    kernel_signature,
+    MutRefCType,
+    NamedCType,
+    NativeSignature,
+    tensorT,
+)
+
+from torchgen.context import method_with_native_function, native_function_manager
+from torchgen.model import (
+    Argument,
+    BackendIndex,
+    DeviceCheckType,
+    DispatchKey,
+    gets_generated_out_inplace_wrapper,
+    is_cuda_dispatch_key,
+    NativeFunction,
+    NativeFunctionsGroup,
+    SchemaKind,
+    TensorOptionsArguments,
+)
+from torchgen.selective_build.selector import SelectiveBuilder
+from torchgen.utils import assert_never, mapMaybe, Target
+
+
+def gen_registration_headers(
+    backend_index: BackendIndex,
+    per_operator_headers: bool,
+    rocm: bool,
+) -> List[str]:
+    if per_operator_headers:
+        headers = ["#include <ATen/ops/as_strided_native.h>"]
+    else:
+        headers = ["#include <ATen/NativeFunctions.h>"]
+
+    if backend_index.dispatch_key in (DispatchKey.CPU, DispatchKey.Meta):
+        headers.append("#include <ATen/EmptyTensor.h>")
+    elif backend_index.dispatch_key == DispatchKey.CUDA:
+        if rocm:
+            headers.append("#include <ATen/hip/EmptyTensor.h>")
+        else:
+            headers.append("#include <ATen/cuda/EmptyTensor.h>")
+    elif backend_index.dispatch_key == DispatchKey.MPS:
+        headers.append("#include <ATen/mps/EmptyTensor.h>")
+    elif per_operator_headers:
+        headers += [
+            "#include <ATen/ops/empty.h>",
+            "#include <ATen/ops/empty_strided.h>",
+            "#include <ATen/ops/_copy_from_and_resize.h>",
+            "#include <ATen/ops/_copy_from.h>",
+        ]
+    else:
+        headers.append("#include <ATen/Functions.h>")
+
+    return headers
+
+
+def gen_empty_impl_names(
+    backend_index: BackendIndex,
+) -> Tuple[Optional[str], Optional[str]]:
+    empty_impl = None
+    empty_strided_impl = None
+
+    if backend_index.dispatch_key in (
+        DispatchKey.Meta,
+        DispatchKey.CPU,
+        DispatchKey.CUDA,
+        DispatchKey.MPS,
+    ):
+        dispatch = str(backend_index.dispatch_key).lower()
+        empty_impl = f"at::detail::empty_{dispatch}"
+        empty_strided_impl = f"at::detail::empty_strided_{dispatch}"
+    elif backend_index.dispatch_key in (
+        DispatchKey.CompositeExplicitAutogradNonFunctional,
+        DispatchKey.QuantizedCPU,
+        DispatchKey.QuantizedCUDA,
+    ):
+        empty_impl = "at::empty"
+        empty_strided_impl = "at::empty_strided"
+
+    return empty_impl, empty_strided_impl
+
+
+def gen_create_out_helper(backend_index: BackendIndex) -> List[str]:
+    if backend_index.dispatch_key == DispatchKey.Meta:
+        empty_options = "options.device(at::kMeta)"
+    else:
+        empty_options = "options"
+
+    empty_impl, empty_strided_impl = gen_empty_impl_names(backend_index)
+    if empty_impl is None:
+        return []
+
+    return [
+        f"""
+Tensor create_out(IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) {{
+  if (strides.empty()) {{
+      return {empty_impl}(sizes, {empty_options});
+  }} else {{
+      return {empty_strided_impl}(sizes, strides, {empty_options});
+  }}
+}}
+"""
+    ]
+
+
+def gen_maybe_create_proxy_helper(backend_index: BackendIndex) -> List[str]:
+    _, empty_strided_impl = gen_empty_impl_names(backend_index)
+    return (
+        []
+        if empty_strided_impl is None
+        else [
+            f"""
+c10::optional<Tensor> maybe_create_proxy(const Tensor &out, IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) {{
+  if (out.strides() != strides) {{
+    return {empty_strided_impl}(sizes, strides, options);
+  }}
+  return c10::nullopt;
+}}
+"""
+        ]
+    )
+
+
+def gen_resize_out_helper(backend_index: BackendIndex) -> List[str]:
+    if backend_index.dispatch_key == DispatchKey.CompositeExplicitAutogradNonFunctional:
+        # The function isn't used by this key (since only functional ops have a kernel for this key),
+        # so we need to not include it to avoid a defined-but-not-used error.
+        return []
+    return [
+        """
+void resize_out(const Tensor &out, IntArrayRef sizes, IntArrayRef strides, const TensorOptions &options) {
+  TORCH_CHECK(options.dtype() == out.dtype(),
+      "Expected out tensor to have dtype ", options.dtype(), ", but got ", out.dtype(), " instead");
+  TORCH_CHECK(options.device() == out.device(),
+      "Expected out tensor to have device ", options.device(), ", but got ", out.device(), " instead");
+  const bool resized = at::native::resize_output(out, sizes);
+  // Only restride if a resize occurred; otherwise we ignore the (advisory)
+  // strides from the meta function and directly use the output tensor's
+  // preexisting strides
+  if (resized) {
+    if (!strides.empty()) {
+      TORCH_INTERNAL_ASSERT(!options.memory_format_opt().has_value());
+      // TODO: avoid the redispatch here
+      out.as_strided_(sizes, strides);
+    } else if (options.memory_format_opt().has_value()) {
+      out.unsafeGetTensorImpl()->empty_tensor_restride(*options.memory_format_opt());
+    }
+  }
+}
+"""
+    ]
+
+
+def gen_check_inplace_helper(backend_index: BackendIndex) -> List[str]:
+    return [
+        """
+void check_inplace(const Tensor &self, IntArrayRef sizes, const TensorOptions &options) {
+  // These checks are needed on those operators that:
+  //   1) don't use 'TensorIterator' (e.g. 'addmm' and 'baddbmm')
+  //   2) have particular typing rules (e.g. 'cumsum' and 'cumprod')
+  // For other operators (e.g. 'add'), 'TensorIterator' already checks
+  // these things separately.
+  TORCH_CHECK(options.dtype() == self.dtype(),
+      "Bad in-place call: ",
+      "input tensor dtype ", self.dtype(), " and output tensor dtype ", options.dtype(), " should match");
+  TORCH_CHECK(options.device() == self.device(),
+      "Bad in-place call: ",
+      "input tensor device ", self.device(), " and output tensor device ", options.device(), " should match");
+  TORCH_CHECK(sizes == self.sizes(),
+      "Bad in-place call: ",
+      "input tensor size ", self.sizes(), " and output tensor size ", sizes, " should match");
+}
+"""
+    ]
+
+
+def gen_registration_helpers(backend_index: BackendIndex) -> List[str]:
+    return [
+        *gen_create_out_helper(backend_index),
+        *gen_resize_out_helper(backend_index),
+        *gen_check_inplace_helper(backend_index),
+        *gen_maybe_create_proxy_helper(backend_index),
+    ]
+
+
+# Generates Register{dispatch}.cpp (e.g., RegisterCPU.cpp).
+#
+#   - The primary function of this file is to register all of the
+#     implementations for the given dispatch key to the dispatcher,
+#     so they are available for use in PyTorch.  If dispatch is
+#     None, we generate schema (def) registrations and catchall
+#     registrations.
+#   - The secondary function of this file is to generate a wrapper
+#     around functions.  In CPUType these wrappers do nothing
+#     (and should be removed), but in other cases they handle
+#     DeviceGuard. A small extra benefit of wrappers is they
+#     are not overloaded, so they can be used in the registration
+#     API without having to disambiguate which overload you want
+#     (as would be the case if you directly registered native::
+#     functions).
+#   - The tertiary function of this file is to generate *static*
+#     cpp API bindings which can be used to bypass dispatcher
+#     directly to kernels, but with user-friendly cpp-style API
+@dataclass(frozen=True)
+class RegisterDispatchKey:
+    backend_index: BackendIndex
+
+    target: Union[
+        Literal[Target.ANONYMOUS_DEFINITION],
+        Literal[Target.NAMESPACED_DEFINITION],
+        Literal[Target.NAMESPACED_DECLARATION],
+        Literal[Target.REGISTRATION],
+    ]
+
+    # Selector object to determine which operators to generate
+    # registration code for.
+    selector: SelectiveBuilder
+
+    # Whether or not we are actually code-genning for ROCm
+    rocm: bool
+
+    # Whether or not to generate symint registrations or not.  External users
+    # of codegen who don't care about symints can set this to false to get
+    # non-SymInt codegen
+    symint: bool
+
+    # The class that all unstructured native functions live under. This is used to improve
+    # compiler error messages when a kernel writer adds a native function with the wrong signature.
+    # This is only used in unstructured kernels, since structured kernels already live in a class.
+    # Finally, this field is currently Optional because it is only used by external backends.
+    # It would be nice if we can add the same logic to in-tree kernels too, but that requires updating
+    # all of the existing kernel signatures scattered across aten/src/ATen/native.
+    class_method_name: Optional[str]
+
+    # Only set to true in lightweight dispatch. If lightweight dispatch is enabled we are registering
+    # operators into JIT op registry, thus we need to avoid generating code to register into the dispatcher.
+    skip_dispatcher_op_registration: bool
+
+    @staticmethod
+    def gen_device_check(
+        type: DeviceCheckType, args: List[Argument], method_name: str
+    ) -> str:
+        if type == DeviceCheckType.NoCheck:
+            return "  // No device check\n"
+
+        device_check = "c10::optional<Device> common_device = nullopt;\n"
+        device_check += "(void)common_device; // Suppress unused variable warning\n"
+        for arg in args:
+            # Only tensor like arguments are eligible
+            if arg.type.is_tensor_like():
+                device_check += f"""
+  c10::impl::check_and_update_common_device(common_device, {arg.name}, "{method_name}", "{arg.name}");"""
+        return device_check
+
+    @method_with_native_function
+    def __call__(self, f: Union[NativeFunctionsGroup, NativeFunction]) -> List[str]:
+        if isinstance(f, NativeFunctionsGroup):
+            g: NativeFunctionsGroup = f
+            # Note: We call gen_structured() if the operator is marked structured, regardless of the backend.
+            # gen_structured() has special logic to handle auto-generated kernels.
+            if g.structured:
+                return self.gen_structured(g)
+            else:
+                return list(
+                    mapMaybe(lambda f: self.gen_unstructured(f, g), g.functions())
+                )
+        elif isinstance(f, NativeFunction):
+            r = self.gen_unstructured(f)
+            return [] if r is None else [r]
+        else:
+            assert_never(f)
+
+    def wrapper_kernel_sig(
+        self, f: NativeFunction
+    ) -> Union[NativeSignature, DispatcherSignature]:
+        # The prefix is just to ensure uniqueness. The Dispatcher API doesn't guarantee unique kernel names.
+        return DispatcherSignature.from_schema(
+            f.func,
+            prefix=f"wrapper_{self.backend_index.dispatch_key}_{f.func.name.overload_name}_",
+            symint=self.symint,
+        )
+
+    def gen_out_inplace_wrapper(
+        self, f: NativeFunction, g: Optional[NativeFunctionsGroup]
+    ) -> Optional[str]:
+        if g is None:
+            return None
+        k = f.func.kind()
+        if k is SchemaKind.inplace:
+            copy_op = "at::_copy_from"
+        elif k is SchemaKind.out:
+            copy_op = "at::_copy_from_and_resize"
+        else:
+            raise AssertionError("gen_out_inplace_wrapper called on a functional op")
+
+        sig = self.wrapper_kernel_sig(f)
+        name = sig.name()
+
+        func_res = f"{name}_tmp"
+        return_names = cpp.return_names(f)
+        if len(return_names) > 1:
+            updates = "\n  ".join(
+                f"{copy_op}(std::get<{i}>({func_res}), {ret_name});"
+                for i, ret_name in enumerate(return_names)
+            )
+            returns = f'{sig.returns_type().cpp_type()}({", ".join(return_names)})'
+        else:
+            ret_name = return_names[0]
+            updates = f"{copy_op}({func_res}, {ret_name});"
+            returns = ret_name
+
+        functional_sig = self.wrapper_kernel_sig(g.functional)
+        wrapper_name = sig.name()
+
+        return f"""\
+{sig.defn(name=wrapper_name)} {{
+  auto {func_res} = {functional_sig.name()}({", ".join(e.expr for e in translate(sig.arguments(), functional_sig.arguments()))});
+  {updates}
+  return {returns};
+}}
+"""
+
+    def gen_structured(self, g: NativeFunctionsGroup) -> List[str]:
+        metadata = self.backend_index.get_kernel(g)
+        if self.backend_index.dispatch_key == DispatchKey.Meta:
+            assert not self.backend_index.has_kernel(g.out), (
+                "Do not explicitly specify Meta dispatch key on structured "
+                "functions, they will be automatically generated for you"
+            )
+        elif (
+            self.backend_index.dispatch_key
+            == DispatchKey.CompositeExplicitAutogradNonFunctional
+        ):
+            assert not self.backend_index.has_kernel(g.out), (
+                "Do not explicitly specify CompositeExplicitAutograd dispatch key on structured "
+                "functions, they will be automatically generated for you"
+            )
+        elif metadata is None or not metadata.structured:
+            return list(mapMaybe(lambda f: self.gen_unstructured(f, g), g.functions()))
+        structured_gen = StructuredRegisterDispatchKey(
+            self.backend_index,
+            self.target,
+            self.selector,
+            self.rocm,
+            self.symint,
+            self.class_method_name,
+            self.skip_dispatcher_op_registration,
+            g,
+        )
+        return list(mapMaybe(structured_gen.gen_one, g.functions()))
+
+    def gen_unstructured(
+        self, f: NativeFunction, g: Optional[NativeFunctionsGroup] = None
+    ) -> Optional[str]:
+        with native_function_manager(f):
+            inplace_meta = False
+            gets_out_inplace_wrapper = False
+            if not self.backend_index.has_kernel(f):
+                if (
+                    self.backend_index.dispatch_key == DispatchKey.Meta
+                    and f.func.kind() is SchemaKind.inplace
+                    and
+                    # Defer to composites for meta implementation
+                    not f.has_composite_kernel
+                    and
+                    # Inplace list operations are not supported
+                    len(f.func.returns) == 1
+                ):
+                    inplace_meta = True
+                elif (
+                    not self.backend_index.use_out_as_primary
+                    and g is not None
+                    and gets_generated_out_inplace_wrapper(f, g, self.backend_index)
+                ):
+                    # We want to generate inplace/out wrappers, that don't have a kernel for the backend.
+                    gets_out_inplace_wrapper = True
+                else:
+                    return None
+            if f.manual_kernel_registration:
+                return None
+
+            if (
+                self.target is Target.REGISTRATION
+                and not self.selector.is_native_function_selected(f)
+            ):
+                return None
+
+            sig = self.wrapper_kernel_sig(f)
+
+            name = sig.name()
+            returns_type = sig.returns_type().cpp_type()
+            args = sig.arguments()
+            args_str = ", ".join(a.defn() for a in args)
+
+            # See Note [Direct dispatch bindings]
+            cpp_sig_group = CppSignatureGroup.from_native_function(
+                f, method=False, fallback_binding=False
+            )
+
+            # TODO: dedupe this with the structured codegen
+            if self.target is Target.NAMESPACED_DECLARATION:
+                result = ""
+                for cpp_sig in cpp_sig_group.signatures(symint=self.symint):
+                    result += f"TORCH_API {cpp_sig.decl()};\n"
+                return result
+            elif self.target is Target.NAMESPACED_DEFINITION:
+
+                def generate_defn(cpp_sig: CppSignature) -> str:
+                    return f"""
+{cpp_sig.defn()} {{
+return {sig.name()}({', '.join(e.expr for e in translate(cpp_sig.arguments(), sig.arguments()))});
+}}
+"""
+
+                result = ""
+                for cpp_sig in cpp_sig_group.signatures(symint=self.symint):
+                    result += generate_defn(cpp_sig)
+                return result
+
+            elif self.target is Target.ANONYMOUS_DEFINITION:
+                # short circuit for inplace_meta
+                if inplace_meta:
+                    assert f.func.arguments.self_arg is not None
+                    self_arg_name = f.func.arguments.self_arg.argument.name
+                    # TODO: handle in place on tensor list
+                    return f"""
+{returns_type} {name}({args_str}) {{
+  TORCH_CHECK_NOT_IMPLEMENTED({self_arg_name}.is_meta(),
+    "Cannot inplace into non-meta tensor with meta tensor argument");
+  return {self_arg_name};
+}}
+"""
+
+                # short circuit for generated inplace/out wrappers
+                if gets_out_inplace_wrapper:
+                    return self.gen_out_inplace_wrapper(f, g)
+
+                metadata = self.backend_index.get_kernel(f)
+                if metadata is None:
+                    return None
+                if self.class_method_name is None:
+                    impl_name = f"{metadata.cpp_namespace}::{metadata.kernel}"
+                else:
+                    impl_name = f"{metadata.cpp_namespace}::{self.class_method_name}::{metadata.kernel}"
+
+                kernel_sig = kernel_signature(f, self.backend_index)
+
+                args_exprs_str = ", ".join(
+                    e.expr
+                    for e in translate(
+                        sig.arguments(), kernel_sig.arguments(), method=False
+                    )
+                )
+
+                device_check = "  // No device check\n"
+                # Backends that require device guards presumably also require device checks.
+                if self.backend_index.device_guard:
+                    device_check_args = itertools.chain(
+                        f.func.arguments.out, f.func.arguments.flat_positional
+                    )
+                    device_check = RegisterDispatchKey.gen_device_check(
+                        f.device_check, list(device_check_args), name
+                    )
+
+                device_guard = "// DeviceGuard omitted"  # default
+                if f.device_guard and self.backend_index.device_guard:
+                    has_tensor_options = any(
+                        isinstance(a, TensorOptionsArguments)
+                        for a in f.func.arguments.non_out
+                    )
+                    if has_tensor_options:
+                        # kernel is creating a tensor
+                        device_guard = """
+  const DeviceGuard device_guard(device_or_default(device));"""
+
+                        # CUDA requires special handling
+                        if is_cuda_dispatch_key(self.backend_index.dispatch_key):
+                            device_guard = (
+                                f"globalContext().lazyInitCUDA();\n{device_guard}"
+                            )
+                    else:
+                        # kernel is operating on existing tensors
+
+                        # There is precedence for which argument we use to do
+                        # device guard.  This describes the precedence order.
+                        self_arg = (
+                            [f.func.arguments.self_arg.argument]
+                            if f.func.arguments.self_arg is not None
+                            else []
+                        )
+                        candidate_args = itertools.chain(
+                            self_arg,
+                            f.func.arguments.out,
+                            f.func.arguments.flat_positional,
+                        )
+
+                        # Only tensor like arguments are eligible
+                        device_of = next(
+                            (
+                                f"{a.name}"
+                                for a in candidate_args
+                                if a.type.is_tensor_like()
+                            ),
+                            None,
+                        )
+                        if device_of is not None:
+                            device_guard = f"const OptionalDeviceGuard device_guard(device_of({device_of}));"
+
+                return f"""\
+namespace {{
+
+{returns_type} {name}({args_str}) {{
+  {device_check}
+
+  {device_guard}
+  return {impl_name}({args_exprs_str});
+}}
+
+}} // anonymous namespace
+"""
+
+            elif self.target is Target.REGISTRATION:
+                if f.manual_kernel_registration or self.skip_dispatcher_op_registration:
+                    return None
+                else:
+                    payload = f"TORCH_FN({name})"
+                    return f'm.impl("{f.func.name}",\n{payload});\n'
+            else:
+                assert_never(self.target)
+
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+#
+#                           STRUCTURED
+#
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
+
+
+@dataclass(frozen=True)
+class StructuredRegisterDispatchKey(RegisterDispatchKey):
+    g: NativeFunctionsGroup
+
+    def gen_class_set_output_functions(
+        self, k: SchemaKind, parent_class: str, generate_super: bool
+    ) -> str:
+        if generate_super:
+            set_output_super = f"{parent_class}::set_output_raw_strided(output_idx, sizes, strides, options, names);"
+        else:
+            set_output_super = ""
+
+        def gen_set_output_function(name: str, maybe_create_proxy: bool) -> str:
+            maybe_star = "*" if k is SchemaKind.functional else ""
+            return f"""
+void set_output_{name}(
+    int64_t output_idx, IntArrayRef sizes, IntArrayRef strides,
+    TensorOptions options, DimnameList names
+) override {{
+{textwrap.indent(self.gen_class_set_output_body(k, maybe_create_proxy), "    ")}
+    if (!names.empty()) {{
+      namedinference::propagate_names({maybe_star}outputs_[output_idx], names);
+    }}
+    // super must happen after, so that downstream can use maybe_get_output
+    // to retrieve the output
+{textwrap.indent(set_output_super, "    ")}
+}}
+"""
+
+        return f"""
+{gen_set_output_function("strided", maybe_create_proxy=True)}
+{gen_set_output_function("raw_strided", maybe_create_proxy=False)}
+"""
+
+    def gen_class_set_output_body(self, k: SchemaKind, maybe_create_proxy: bool) -> str:
+        if self.backend_index.dispatch_key in [
+            DispatchKey.CUDA,
+            DispatchKey.MPS,
+            DispatchKey.CompositeExplicitAutogradNonFunctional,
+        ]:
+            maybe_set_guard = """
+auto current_device = guard_.current_device();
+if (C10_UNLIKELY(current_device.has_value())) {
+  TORCH_INTERNAL_ASSERT(*current_device == options.device(),
+    "structured kernels don't support multi-device outputs");
+} else {
+  guard_.reset_device(options.device());
+}
+"""
+            maybe_set_guard_line = maybe_set_guard + "\n"
+        else:
+            maybe_set_guard_line = maybe_set_guard = ""
+
+        if maybe_create_proxy:
+            create_proxy = """
+auto maybe_proxy = maybe_create_proxy(out, sizes, strides, options);
+if (C10_UNLIKELY(maybe_proxy.has_value())) {
+    proxy_outputs_[output_idx] = c10::ExclusivelyOwned<Tensor>(std::move(maybe_proxy).value());
+}
+"""
+        else:
+            create_proxy = ""
+
+        if k is SchemaKind.functional:
+            assert self.backend_index.dispatch_key in (
+                DispatchKey.Meta,
+                DispatchKey.CPU,
+                DispatchKey.CUDA,
+                DispatchKey.MPS,
+                DispatchKey.CompositeExplicitAutogradNonFunctional,
+            )
+            return f"""{maybe_set_guard_line}
+outputs_[output_idx] = create_out(sizes, strides, options);"""
+        elif k is SchemaKind.inplace:
+            return f"""{maybe_set_guard_line}
+const auto& out = outputs_[output_idx].get();
+check_inplace(out, sizes, options);
+{create_proxy}"""
+        elif k is SchemaKind.out:
+            return f"""{maybe_set_guard_line}
+const auto& out = outputs_[output_idx].get();
+resize_out(out, sizes, strides, options);
+{create_proxy}"""
+        elif k is SchemaKind.mutable or k is SchemaKind.scratch:
+            raise AssertionError(
+                f"{k} structured operators are currently not supported"
+            )
+        else:
+            assert_never(k)
+
+    # returns the definition of a ctor, as well as how to construct
+    # this class to a variable named op
+    def gen_class_ctor(self, k: SchemaKind, class_name: str, returns: int) -> str:
+        if k is SchemaKind.functional:
+            return ""
+        elif k is SchemaKind.inplace:
+            # TODO: Make sure out argument is guaranteed to be self
+            return f"{class_name}(Tensor& self) : outputs_{{std::ref(self)}} {{}}"
+        elif k is SchemaKind.out:
+            out_args = ", ".join(f"Tensor& out{i}" for i in range(returns))
+            out_refs = ", ".join(f"std::ref(out{i})" for i in range(returns))
+            return f"{class_name}({out_args}) : outputs_{{ {out_refs} }} {{}}"
+        elif k is SchemaKind.mutable or k is SchemaKind.scratch:
+            raise AssertionError(
+                f"{k} structured operators are currently not supported"
+            )
+        else:
+            assert_never(k)
+
+    def gen_class(
+        self,
+        f: NativeFunction,
+        k: SchemaKind,
+        *,
+        class_name: str,
+        parent_class: str,
+        generate_super: bool,
+    ) -> str:
+        if k is SchemaKind.functional:
+            output_type = "c10::ExclusivelyOwned<Tensor>"
+            output_value = "*outputs_[output_idx]"
+            proxy_field = ""
+        elif k is SchemaKind.inplace:
+            output_type = "std::reference_wrapper<Tensor>"
+            output_value = "proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get()"
+            proxy_field = f"std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, {len(f.func.returns)}> proxy_outputs_;"
+        elif k is SchemaKind.out:
+            output_type = "std::reference_wrapper<Tensor>"
+            output_value = "proxy_outputs_[output_idx].has_value() ? **proxy_outputs_[output_idx] : outputs_[output_idx].get()"
+            proxy_field = f"std::array<c10::optional<c10::ExclusivelyOwned<Tensor>>, {len(f.func.returns)}> proxy_outputs_;"
+
+        if self.backend_index.dispatch_key == DispatchKey.CUDA:
+            if self.rocm:
+                guard_field = "c10::hip::OptionalHIPGuardMasqueradingAsCUDA guard_;"
+            else:
+                guard_field = "c10::cuda::OptionalCUDAGuard guard_;"
+        elif (
+            self.backend_index.dispatch_key
+            == DispatchKey.CompositeExplicitAutogradNonFunctional
+        ):
+            guard_field = "c10::OptionalDeviceGuard guard_;"
+        elif self.backend_index.dispatch_key == DispatchKey.MPS:
+            # TODO: Move to OptionalMPSGuard.
+            guard_field = "c10::OptionalDeviceGuard guard_;"
+        else:
+            guard_field = ""
+
+        indent = " " * 4
+        class_ctor_str = self.gen_class_ctor(k, class_name, len(f.func.returns))
+        lines = (
+            f"struct {class_name} final : public {parent_class} {{",
+            f"{textwrap.indent(class_ctor_str, indent)}",
+            f"{textwrap.indent(self.gen_class_set_output_functions(k, parent_class, generate_super), indent)}",
+            "    const Tensor& maybe_get_output(int64_t output_idx) override {",
+            f"      return {output_value};\n",
+            "    }",
+            f"    std::array<{output_type}, {len(f.func.returns)}> outputs_;",
+            f"{textwrap.indent(proxy_field, indent)}",
+            f"{textwrap.indent(guard_field, indent)}",
+            "};",
+        )
+        return "\n".join(line for line in lines if line)
+
+    @method_with_native_function
+    def gen_one(self, f: NativeFunction) -> Optional[str]:
+        assert not f.manual_kernel_registration
+
+        if (
+            self.target is Target.REGISTRATION
+            and not self.selector.is_native_function_selected(f)
+        ):
+            return None
+
+        # TODO: Now, there is something interesting going on here.  In the code below,
+        # we generate CompositeExplicitAutogradNonFunctional implementations of functional and inplace
+        # based on the out implementation.  But in fact, out is definable by
+        # functional too (just not very efficiently), and this is honestly the
+        # MORE likely situation for a backend implementor.  How do we pick?
+        # Well, taking a page from Haskell type classes and default methods,
+        # we could conceivably register a circular definition (out in terms
+        # of functional, and functional in terms of out) and just require
+        # someone to implement one or the other.  We'd have to do a little bit
+        # of work to not register one of these "weak" definitions unless there
+        # is a strong definition somewhere in the DAG!  So it's not implemented yet.
+        if (
+            self.backend_index.dispatch_key
+            == DispatchKey.CompositeExplicitAutogradNonFunctional
+            and f.func.kind() is SchemaKind.out
+        ):
+            # Never generate a default implementation for out, that's what you
+            # have to define as a backend implementor
+            return None
+
+        # Note [Direct dispatch bindings]
+        # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+        # Signature of the non-dispatched function we'll expose in a header
+        # (e.g., at::cpu::add).  We don't generate methods (TODO: do this
+        # when CPUTensor class is a thing); nor do we generate fallback
+        # bindings for manual_cpp_binding functions.
+        cpp_sig_group = CppSignatureGroup.from_native_function(
+            f, method=False, fallback_binding=False
+        )
+
+        # Signature of the wrapper function we'll register to the dispatcher
+        kern = self.backend_index.get_kernel(f)
+        sig = NativeSignature(
+            f.func,
+            prefix=f"wrapper_{self.backend_index.dispatch_key}_",
+            symint=kern is not None and kern.supports_symint(),
+        )
+
+        if self.target is Target.NAMESPACED_DECLARATION:
+            result = ""
+            for cpp_sig in cpp_sig_group.signatures(symint=self.symint):
+                result += f"TORCH_API {cpp_sig.decl()};\n"
+            return result
+
+        elif self.target is Target.NAMESPACED_DEFINITION:
+
+            def generate_defn(cpp_sig: CppSignature) -> str:
+                return f"""
+{cpp_sig.defn()} {{
+return {sig.name()}({', '.join(e.expr for e in translate(cpp_sig.arguments(), sig.arguments()))});
+}}
+"""
+
+            result = ""
+            for cpp_sig in cpp_sig_group.signatures(symint=self.symint):
+                result += generate_defn(cpp_sig)
+            return result
+
+        elif self.target is Target.ANONYMOUS_DEFINITION:
+
+            k = f.func.kind()
+
+            # Construct the body of the wrapper function with signature sig
+            sig_body = []
+            # We'll use context to keep track of any variables we've brought
+            # into scope while generating code
+            context: List[Union[Binding, Expr]] = list(sig.arguments())
+
+            # Initialize the class corresponding to this structured
+            # operator; feeding it the output argument(s) if it is known
+            if self.backend_index.dispatch_key is DispatchKey.Meta:
+                class_name = f"structured_{meta.name(self.g)}_meta_{k.name}"
+                parent_class = f"at::meta::structured_{meta.name(self.g)}"
+            elif (
+                self.backend_index.dispatch_key
+                is DispatchKey.CompositeExplicitAutogradNonFunctional
+            ):
+                # TODO: dedup this branch
+                class_name = f"structured_{meta.name(self.g)}_default_backend_{k.name}"
+                parent_class = f"at::meta::structured_{meta.name(self.g)}"
+            else:
+                metadata = self.backend_index.get_kernel(self.g)
+                assert metadata is not None
+                class_name = f"structured_{metadata.kernel}_{k.name}"
+                parent_class = f"{metadata.cpp_namespace}::structured_{metadata.kernel}"
+
+            if self.backend_index.device_guard:
+                device_check_args = itertools.chain(
+                    f.func.arguments.out, f.func.arguments.flat_positional
+                )
+                sig_body.append(
+                    RegisterDispatchKey.gen_device_check(
+                        f.device_check, list(device_check_args), sig.name()
+                    )
+                )
+
+            if k is SchemaKind.functional:
+                sig_body.append(f"{class_name} op;")
+            elif k is SchemaKind.inplace:
+                sig_body.append(f"{class_name} op(self);")
+            elif k is SchemaKind.out:
+                out_args_str = ", ".join(a.name for a in f.func.arguments.out)
+                sig_body.append(f"{class_name} op({out_args_str});")
+
+            # Translate the input native arguments into structured
+            # arguments for the meta call
+            meta_exprs = ", ".join(
+                e.expr
+                for e in translate(
+                    context, structured.meta_arguments(self.g), method=False
+                )
+            )
+
+            if self.g.out.precomputed:
+                # If this function group has precomputed elements, the meta function
+                # returns a struct containing them which must be saved so that it
+                # can be unpacked when generating code to call the impl.
+                sig_body.append(f"auto precompute = op.meta({meta_exprs});")
+
+                # Put all of the contents of the precompute struct into the context
+                # so that translate will be able to return the correct args for the
+                # call to the impl.
+                precomputed_values = [
+                    *self.g.out.precomputed.replace.values(),
+                    self.g.out.precomputed.add,
+                ]
+                for precomputed_elems in precomputed_values:
+                    for arg in precomputed_elems:
+                        context.append(
+                            Expr(
+                                expr=f"precompute.{arg.name}",
+                                type=structured.argument_type(arg, binds=arg.name),
+                            )
+                        )
+
+                # Add a use of the precompute struct so FB internal compilers don't
+                # complain that there is an unused variable.
+                sig_body.append("(void)precompute;")
+            else:
+                sig_body.append(f"op.meta({meta_exprs});")
+
+            # After running meta, op.outputs_ is guaranteed to be valid;
+            # add it to the context
+            out_args = structured.out_arguments(self.g)
+            for i, out_arg in enumerate(out_args):
+                assert ConstRefCType(BaseCType(tensorT)) == out_arg.nctype.type
+
+                if k is SchemaKind.out:
+                    expr = f"op.maybe_get_output({i})"
+                else:
+                    maybe_star = "*" if k is SchemaKind.functional else ""
+                    expr = f"{maybe_star}op.outputs_[{i}]"
+
+                context.append(
+                    Expr(
+                        expr=expr,
+                        # TODO: Stop hardcoding that the output type is a Tensor.  Note
+                        # that for the codegen here this is fine because outputs_ is
+                        # hardcoded to be tensor already
+                        type=NamedCType(
+                            out_arg.nctype.name, MutRefCType(BaseCType(tensorT))
+                        ),
+                    )
+                )
+
+            # With the expanded context, do the impl call (if not a meta
+            # function)
+            if (
+                self.backend_index.dispatch_key
+                == DispatchKey.CompositeExplicitAutogradNonFunctional
+            ):
+                # TODO: https://github.com/pytorch/pytorch/issues/53023
+                out_sig_group = CppSignatureGroup.from_native_function(
+                    self.g.out, method=False, fallback_binding=f.manual_cpp_binding
+                )
+                out_sig = out_sig_group.most_faithful_signature()
+                api_name = out_sig.name()
+                out_exprs = ", ".join(
+                    e.expr
+                    for e in translate(context, out_sig.arguments(), method=False)
+                )
+                # TODO: I think this means structured won't work with method
+                # only functions (but maybe you're saved by faithful? iunno.)
+                # NB: Originally I wrote this as an at::redispatch call, but
+                # I got in trouble because that meant I needed a DispatchKeySet
+                # in the wrapper function, which meant I needed a DispatchKeySet
+                # in the DispatchKeyFunctions declarations, but the defined API
+                # there does NOT permit a dispatch key set.  I think you can
+                # probably unwind this by calling some function to do the TLS
+                # fetch and get the DispatchKeySet when you don't have it, but
+                # I didn't do it for this version
+                sig_body.append(f"at::{api_name}({out_exprs});")
+            elif self.backend_index.dispatch_key != DispatchKey.Meta:
+                impl_exprs = ", ".join(
+                    e.expr
+                    for e in translate(
+                        context, structured.impl_arguments(self.g), method=False
+                    )
+                )
+                sig_body.append(f"op.impl({impl_exprs});")
+
+            # Go over each output, and check if there is a proxy created for it.
+            # If so, copy it over to the original output.
+            if k is SchemaKind.out or k is SchemaKind.inplace:
+                for i in range(len(f.func.returns)):
+                    sig_body.append(
+                        f"if (op.proxy_outputs_[{i}].has_value()) op.outputs_[{i}].get().copy_(**op.proxy_outputs_[{i}]);"
+                    )
+
+            # Destructively return the final tensors
+            # TODO: Do this in translate instead
+            if k is SchemaKind.functional:
+                if len(f.func.returns) == 1:
+                    ret_expr = "std::move(op.outputs_[0]).take()"  # small optimization
+                else:
+                    moved = ", ".join(
+                        f"std::move(op.outputs_[{i}]).take()"
+                        for i in range(len(f.func.returns))
+                    )
+                    ret_expr = f"std::make_tuple({moved})"
+            elif k is SchemaKind.inplace:
+                ret_expr = "self"
+            elif k is SchemaKind.out:
+                if len(f.func.returns) == 1:
+                    ret_expr = f.func.arguments.out[0].name
+                else:
+                    refs = ", ".join(a.name for a in f.func.arguments.out)
+                    ret_expr = f"std::forward_as_tuple({refs})"
+            sig_body.append(f"return {ret_expr};")
+
+            sig_body_str = "\n".join(sig_body)
+
+            # For an overview of what this template code looks like, see
+            # https://github.com/pytorch/rfcs/pull/9
+            return f"""\
+{self.gen_class(
+f, k,
+class_name=class_name,
+parent_class=parent_class,
+generate_super=self.g.out.structured_inherits is not None
+)}
+
+{sig.defn()} {{
+{sig_body_str}
+}}
+"""
+
+        elif self.target is Target.REGISTRATION:
+            return f'm.impl("{f.func.name}", TORCH_FN({sig.name()}));'
+        else:
+            assert_never(self.target)
+            # Silence mypy's "Missing return statement" error
+            return None

wemm/lib/python3.10/site-packages/torchgen/packaged/ATen/templates/FunctionalInverses.h

@@ -0,0 +1,16 @@
+#pragma once
+
+// ${generated_comment}
+
+#include <ATen/Tensor.h>
+
+namespace at {
+namespace functionalization {
+
+struct FunctionalInverses {
+
+${view_inverse_declarations}
+
+};
+}
+}

wemm/lib/python3.10/site-packages/torchgen/packaged/ATen/templates/MethodOperators.h

@@ -0,0 +1,24 @@
+#pragma once
+
+// ${generated_comment}
+
+#ifdef TORCH_ASSERT_NO_OPERATORS
+#error This change adds a dependency on native_functions.yaml,             \
+  meaning the file will need to be re-compiled every time an operator      \
+  is changed or added. Consider if your change would be better placed in   \
+  another file, or if a more specific header might achieve the same goal.  \
+  See NOTE: [Tensor vs. TensorBase]
+#endif
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+${MethodOperators_includes}
+
+namespace at {
+namespace _ops {
+${MethodOperators_declarations}
+} // namespace _ops
+} // namespace at

wemm/lib/python3.10/site-packages/torchgen/packaged/ATen/templates/Operators.cpp

@@ -0,0 +1,19 @@
+#include <ATen/Tensor.h>
+#include <ATen/core/dispatch/Dispatcher.h>
+
+// ${generated_comment}
+// NOTE See [Sharded File] comment in VariableType
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Operators.h>
+#else
+${operator_headers}
+#endif
+
+${static_dispatch_extra_headers}
+
+namespace at { namespace _ops {
+
+${definitions}
+
+}} // namespace at::_ops

wemm/lib/python3.10/site-packages/torchgen/packaged/ATen/templates/RegisterBackendSelect.cpp

@@ -0,0 +1,49 @@
+// We register ops with a higher priority dispatch key (BackendSelect) than the usual backend-specific keys (e.g. CPU)
+// which makes calls to the factory functions dispatch to here.
+// We then 'manually' compute a lower-priority to re-dispatch to (e.g. CPU) to get to the eventually correct backend.
+// ${generated_comment}
+
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+#include <ATen/core/Tensor.h>
+#include <ATen/core/dispatch/DispatchKeyExtractor.h>
+#include <torch/library.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Operators.h>
+#else
+#include <ATen/ops/is_pinned_ops.h>
+#include <ATen/ops/_pin_memory_ops.h>
+
+${ops_headers}
+#endif
+
+namespace at {
+
+namespace {
+
+${backend_select_method_definitions}
+
+bool is_pinned(const Tensor& self, c10::optional<at::Device> device) {
+  // Only CPU tensors can be pinned
+  if (!self.is_cpu()) {
+    return false;
+  }
+  // TODO: fetch scalar type from Tensor? But it doesn't really matter...
+  DispatchKeySet _dk = c10::DispatchKeySet(c10::computeDispatchKey(c10::nullopt, self.layout(), device.value_or(at::kCUDA)));
+  return at::_ops::is_pinned::redispatch(_dk, self, device);
+}
+
+at::Tensor _pin_memory(const Tensor& self, c10::optional<at::Device> device) {
+  TORCH_CHECK(self.device().is_cpu(), "cannot pin '", self.toString(), "' only dense CPU tensors can be pinned");
+  DispatchKeySet _dk = c10::DispatchKeySet(c10::computeDispatchKey(c10::nullopt, self.layout(), device.value_or(at::kCUDA)));
+  return at::_ops::_pin_memory::redispatch(_dk, self, device);
+}
+
+TORCH_LIBRARY_IMPL(aten, BackendSelect, m) {
+  ${backend_select_function_registrations};
+  m.impl(TORCH_SELECTIVE_NAME("aten::is_pinned"), TORCH_FN(is_pinned));
+  m.impl(TORCH_SELECTIVE_NAME("aten::_pin_memory"), TORCH_FN(_pin_memory));
+}
+
+} // namespace
+} // at

wemm/lib/python3.10/site-packages/torchgen/packaged/ATen/templates/UnboxingFunctions.cpp

@@ -0,0 +1,35 @@
+#include <ATen/UnboxingFunctions.h>
+#include <ATen/Functions.h>
+
+#include <ATen/Tensor.h>
+#include <ATen/core/functional.h>
+#include <ATen/core/interned_strings.h>
+#include <ATen/core/ivalue.h>
+#include <ATen/core/stack.h>
+
+#include <algorithm>
+#include <array>
+#include <cstddef>
+#include <cstring>
+#include <sstream>
+#include <stdexcept>
+#include <tuple>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+namespace at {
+namespace unboxing {
+
+using ::c10::fmap;
+using ::c10::filter;
+using torch::jit::peek;
+using torch::jit::drop;
+using torch::jit::pack;
+using torch::jit::pop;
+
+// Generated function declaration
+${definitions}
+
+} // namespace unboxing
+} // namespace at

wemm/lib/python3.10/site-packages/torchgen/static_runtime/config.py

@@ -0,0 +1,388 @@
+from typing import Dict, Union
+
+from torchgen.model import NativeFunctionsGroup, NativeFunctionsViewGroup
+
+
+def func_name_base_str(g: Union[NativeFunctionsGroup, NativeFunctionsViewGroup]) -> str:
+    if isinstance(g, NativeFunctionsGroup):
+        return str(g.functional.func.name.name.base)
+    else:
+        return str(g.view.root_name)
+
+
+is_hand_written_ops_ = frozenset(
+    (
+        "abs",
+        "add",
+        "addmm",
+        "all",
+        "any",
+        "argmin",
+        "bmm",
+        "clamp",
+        "clamp_min",
+        "cumsum",
+        "div",
+        "fmod",
+        "index_select",
+        "leaky_relu",
+        "linear",
+        "log",
+        "matmul",
+        "mul",
+        "narrow_copy",
+        "nonzero",
+        "pow",
+        "remainder",
+        "sigmoid",
+        "sign",
+        "sub",
+        "tanh",
+        "detach",
+        "expand_as",
+        "flatten",
+        "narrow",
+        "reshape_as",
+        "select",
+        "slice",
+        "softmax",
+        "split",
+        "squeeze",
+        "transpose",
+        "view",
+        "where",
+    )
+)
+
+
+def is_hand_written(g: Union[NativeFunctionsGroup, NativeFunctionsViewGroup]) -> bool:
+    name_base = func_name_base_str(g)
+    return name_base in is_hand_written_ops_
+
+
+def override_test_values(arg_map: Dict[str, str], op_name: str, index: int) -> None:
+    assert index == 0 or index == 1
+    if op_name == "addr":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["vec1"] = "at::rand({6})"
+            arg_map["vec2"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["vec1"] = "at::rand({22})"
+            arg_map["vec2"] = "at::rand({22})"
+        return
+    if op_name == "mv":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["vec"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["vec"] = "at::rand({22})"
+        return
+    if op_name == "addbmm":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+        return
+    if op_name == "cross":
+        if index == 0:
+            arg_map["self"] = "at::rand({3, 3, 3})"
+            arg_map["other"] = "at::rand({3, 3, 3})"
+        else:
+            arg_map["self"] = "at::rand({22, 3, 22})"
+            arg_map["other"] = "at::rand({22, 3, 22})"
+        return
+    if op_name == "take":
+        if index == 0:
+            arg_map["index"] = "at::randint(0, 216, {20}, torch::kInt64)"
+        else:
+            arg_map["index"] = "at::randint(0, 1000, {100}, torch::kInt64)"
+        return
+    if op_name == "take_along_dim":
+        if index == 0:
+            arg_map["indices"] = "at::argsort(self0, 1, true)"
+        else:
+            arg_map["indices"] = "at::argsort(self1, 1, true)"
+        return
+    if op_name == "masked_select":
+        if index == 0:
+            arg_map["mask"] = "at::randn({6, 6, 6}) > 0.5"
+        else:
+            arg_map["mask"] = "at::rand({22, 22, 22}) > 0.5"
+        return
+    if op_name == "orgqr":
+        if index == 0:
+            arg_map["input2"] = "at::rand({6, 6})"
+        else:
+            arg_map["input2"] = "at::rand({22, 22})"
+        return
+    if op_name == "ormqr":
+        if index == 0:
+            arg_map["input2"] = "at::rand({6, 6})"
+        else:
+            arg_map["input2"] = "at::rand({22, 22})"
+        return
+    if op_name == "quantile":
+        if index == 0:
+            arg_map["q"] = "at::rand({6})"
+            arg_map["interpolation"] = '"linear"'
+        else:
+            arg_map["q"] = "at::rand({22})"
+            arg_map["interpolation"] = '"linear"'
+        return
+    if op_name == "nanquantile":
+        if index == 0:
+            arg_map["q"] = "at::rand({6})"
+            arg_map["interpolation"] = '"linear"'
+        else:
+            arg_map["q"] = "at::rand({22})"
+            arg_map["interpolation"] = '"linear"'
+        return
+    if op_name == "multi_margin_loss":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({22})"
+        return
+    if op_name == "multilabel_margin_loss":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["target"] = "at::randint(6, {6, 6}, torch::kInt64)"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["target"] = "at::randint(22, {22, 22}, torch::kInt64)"
+        return
+    if op_name == "nll_loss":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6})"
+            arg_map["target"] = "at::randint(6, {6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22})"
+            arg_map["target"] = "at::randint(22, {22}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({22})"
+        return
+    if op_name == "nll_loss2d":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6, 6, 6})"
+            arg_map["target"] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+        else:
+            arg_map["self"] = "at::rand({22, 22, 22, 22})"
+            arg_map["target"] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({22})"
+        return
+    if op_name in (
+        "fft_fft",
+        "fft_ifft",
+        "fft_rfft",
+        "fft_irfft",
+        "fft_hfft",
+        "fft_ihfft",
+    ):
+        arg_map["norm"] = '"forward"'
+        return
+    if op_name == "linalg_tensorinv":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6, 6, 6})"
+            arg_map["ind"] = "2"
+        else:
+            arg_map["self"] = "at::rand({22, 22, 22, 22})"
+            arg_map["ind"] = "2"
+        return
+    if op_name == "addmv":
+        if index == 0:
+            arg_map["self"] = "at::rand({2})"
+            arg_map["mat"] = "at::rand({2, 2})"
+            arg_map["vec"] = "at::rand({2})"
+        else:
+            arg_map["self"] = "at::rand({35})"
+            arg_map["mat"] = "at::rand({35, 35})"
+            arg_map["vec"] = "at::rand({35})"
+        return
+    if op_name == "acosh":
+        if index == 0:
+            arg_map["self"] = "at::rand({2, 2, 2}) + at::ones({2, 2, 2})"
+        else:
+            arg_map["self"] = "at::rand({5, 5, 5}) + at::ones({5, 5, 5})"
+        return
+    if op_name == "adaptive_max_pool2d_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({2, 2, 2}, at::kFloat)"
+            arg_map["self"] = "at::rand({2, 2, 2}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {2, 2, 2}, at::kLong)"
+        else:
+            arg_map["grad_output"] = "at::rand({3, 3, 3}, at::kFloat)"
+            arg_map["self"] = "at::rand({3, 3, 3}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {3, 3, 3}, at::kLong)"
+        return
+    if op_name == "adaptive_max_pool3d_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
+            arg_map["self"] = "at::rand({2, 2, 2, 2}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {2, 2, 2, 2}, at::kLong)"
+        else:
+            arg_map["grad_output"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
+            arg_map["self"] = "at::rand({3, 3, 3, 3}, at::kFloat)"
+            arg_map["indices"] = "at::randint(0, 1, {3, 3, 3, 3}, at::kLong)"
+        return
+    if op_name == "bitwise_left_shift":
+        if index == 0:
+            arg_map["self"] = "at::randint(1, 1 << 4, {6, 6, 6}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 26, {6, 6, 6}, at::kInt)"
+        else:
+            arg_map["self"] = "at::randint(1, 1 << 4, {22, 22, 22}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 26, {22, 22, 22}, at::kInt)"
+        return
+    if op_name == "bitwise_right_shift":
+        if index == 0:
+            arg_map["self"] = "at::randint(1 << 21, 1 << 30, {6, 6, 6}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 22, {6, 6, 6}, at::kInt)"
+        else:
+            arg_map["self"] = "at::randint(1 << 21, 1 << 30, {22, 22, 22}, at::kInt)"
+            arg_map["other"] = "at::randint(1, 22, {22, 22, 22}, at::kInt)"
+        return
+    if op_name == "gather":
+        if index == 0:
+            arg_map["self"] = "at::randint(1, 100, {2,2,2}, at::kInt)"
+            arg_map["dim"] = "1"
+            arg_map["index"] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
+            arg_map["sparse_grad"] = "false"
+        else:
+            arg_map["self"] = "at::randint(1, 100, {5,5,5}, at::kInt)"
+            arg_map["dim"] = "1"
+            arg_map["index"] = "at::randint(0, 4, {5,5,5}, torch::kInt64)"
+            arg_map["sparse_grad"] = "false"
+        return
+    if op_name == "gelu":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 6, 6})"
+            arg_map["approximate"] = '"tanh"'
+        else:
+            arg_map["self"] = "at::rand({22, 22, 22})"
+            arg_map["approximate"] = '"tanh"'
+        return
+    if op_name == "gelu_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({6, 6, 6})"
+            arg_map["self"] = "at::rand({6, 6, 6})"
+            arg_map["approximate"] = '"tanh"'
+        else:
+            arg_map["grad_output"] = "at::rand({22, 22, 22})"
+            arg_map["self"] = "at::rand({22, 22, 22})"
+            arg_map["approximate"] = '"tanh"'
+        return
+    if op_name == "index_add":
+        if index == 0:
+            arg_map["self"] = "at::rand({2})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 1, {2}, at::kInt)"
+            arg_map["source"] = "at::rand({2})"
+            arg_map["alpha"] = "2"
+        else:
+            arg_map["self"] = "at::rand({16})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 10, {16}, at::kInt)"
+            arg_map["source"] = "at::rand({16})"
+            arg_map["alpha"] = "2"
+        return
+    if op_name == "index_copy":
+        if index == 0:
+            arg_map["self"] = "at::rand({2})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 1, {2}, at::kLong)"
+            arg_map["source"] = "at::rand({2})"
+        else:
+            arg_map["self"] = "at::rand({32})"
+            arg_map["dim"] = "0"
+            arg_map["index"] = "at::randint(0, 10, {32}, at::kLong)"
+            arg_map["source"] = "at::rand({32})"
+        return
+    if op_name == "linalg_cross":
+        if index == 0:
+            arg_map["self"] = "at::rand({6, 3, 6})"
+            arg_map["other"] = "at::rand({6, 3, 6})"
+            arg_map["dim"] = "1"
+        else:
+            arg_map["self"] = "at::rand({22, 3, 22})"
+            arg_map["other"] = "at::rand({22, 3, 22})"
+            arg_map["dim"] = "1"
+        return
+    if op_name == "nll_loss_backward":
+        if index == 0:
+            arg_map["grad_output"] = "at::rand({})"
+            arg_map["self"] = "at::rand({6})"
+            arg_map["target"] = "at::randint(0, 5, {6}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({6})"
+            arg_map["reduction"] = "1"
+            arg_map["ignore_index"] = "1"
+            arg_map["total_weight"] = "at::rand({})"
+        else:
+            arg_map["grad_output"] = "at::rand({})"
+            arg_map["self"] = "at::rand({36})"
+            arg_map["target"] = "at::randint(0, 11, {36}, torch::kInt64)"
+            arg_map["weight"] = "at::rand({36})"
+            arg_map["reduction"] = "1"
+            arg_map["ignore_index"] = "1"
+            arg_map["total_weight"] = "at::rand({})"
+        return
+    if op_name in ["scatter", "scatter_add", "_scatter_reduce"]:
+        if index == 0:
+            arg_map["self"] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
+            arg_map["index"] = "at::randint(0, 1, {2,2,2}, torch::kInt64)"
+            arg_map["src"] = "at::randint(1, 100, {2,2,2}, torch::kInt64)"
+        else:
+            arg_map["self"] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
+            arg_map["index"] = "at::randint(0, 1, {5,5,5}, torch::kInt64)"
+            arg_map["src"] = "at::randint(1, 100, {5,5,5}, torch::kInt64)"
+        if "reduce" in arg_map:
+            arg_map["reduce"] = '"sum"' if op_name == "_scatter_reduce" else '"add"'
+        return
+    if op_name == "scatter_reduce":
+        arg_map["reduce"] = '"mean"'
+        if index == 0:
+            arg_map["index"] = "at::randint(6, {6, 6, 6}, torch::kInt64)"
+        else:
+            arg_map["index"] = "at::randint(22, {22, 22, 22}, torch::kInt64)"
+        return
+    if op_name == "special_zeta":
+        if index == 0:
+            arg_map["self"] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
+            arg_map["other"] = "at::rand({2,2,2}, at::kDouble) + at::ones({2,2,2})"
+        else:
+            arg_map["self"] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
+            arg_map["other"] = "at::rand({5,5,5}, at::kDouble) + at::ones({5,5,5})"
+        return
+    if op_name == "_convert_indices_from_csr_to_coo":
+        if index == 0:
+            arg_map["crow_indices"] = "torch::tensor({1}, torch::kInt32)"
+            arg_map["col_indices"] = "torch::tensor({0, 1, 0}, torch::kInt32)"
+            arg_map["out_int32"] = "false"
+        else:
+            arg_map["crow_indices"] = "torch::tensor({0}, torch::kInt32)"
+            arg_map[
+                "col_indices"
+            ] = "torch::tensor({0, 1, 0, 2, 1, 2, 0, 1, 0, 2, 1, 2}, torch::kInt32)"
+            arg_map["out_int32"] = "false"
+        return
+    if op_name == "_convert_indices_from_coo_to_csr":
+        if index == 0:
+            arg_map["self"] = "at::randint(0, 3, {2}, at::kInt)"
+            arg_map["size"] = "10"
+            arg_map["out_int32"] = "false"
+        else:
+            arg_map["self"] = "at::randint(0, 3, {12}, at::kInt)"
+            arg_map["size"] = "24"
+            arg_map["out_int32"] = "false"
+        return
+    if op_name in ("diagonal", "linalg_diagonal"):
+        arg_map["offset"] = "0"
+        arg_map["dim0"] = "1"
+        arg_map["dim1"] = "2"
+        return