diff --git a/.gitattributes b/.gitattributes index 8c634229e88db7cd9374672ae758950dc7c924a6..c04ed5e9e0825a1447957f9ac4d7c27ffa80f9c9 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1603,3 +1603,4 @@ evalkit_cambrian/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc-bui evalkit_internvl/lib/python3.10/site-packages/transformers/models/perceiver/__pycache__/modeling_perceiver.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text evalkit_internvl/lib/python3.10/site-packages/transformers/models/speecht5/__pycache__/modeling_speecht5.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text evalkit_internvl/lib/python3.10/site-packages/transformers/models/seamless_m4t_v2/__pycache__/modeling_seamless_m4t_v2.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text +evalkit_internvl/lib/python3.10/site-packages/transformers/__pycache__/tokenization_utils_base.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text diff --git a/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/INSTALLER b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/LICENSE b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..8dada3edaf50dbc082c9a125058f25def75e625a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Start of user-guide + +The ``asttokens`` module annotates Python abstract syntax trees (ASTs) with the positions of tokens +and text in the source code that generated them. + +It makes it possible for tools that work with logical AST nodes to find the particular text that +resulted in those nodes, for example for automated refactoring or highlighting. + +Installation +------------ +asttokens is available on PyPI: https://pypi.python.org/pypi/asttokens/:: + + pip install asttokens + +The code is on GitHub: https://github.com/gristlabs/asttokens. + +The API Reference is here: http://asttokens.readthedocs.io/en/latest/api-index.html. + +Usage +----- +ASTTokens works with both Python2 and Python3. + +ASTTokens can annotate both trees built by `ast `_, +AND those built by `astroid `_. + +Here's an example: + +.. code-block:: python + + import asttokens, ast + source = "Robot('blue').walk(steps=10*n)" + atok = asttokens.ASTTokens(source, parse=True) + +Once the tree has been marked, nodes get ``.first_token``, ``.last_token`` attributes, and +the ``ASTTokens`` object offers helpful methods: + +.. code-block:: python + + attr_node = next(n for n in ast.walk(atok.tree) if isinstance(n, ast.Attribute)) + print(atok.get_text(attr_node)) + start, end = attr_node.last_token.startpos, attr_node.last_token.endpos + print(atok.text[:start] + 'RUN' + atok.text[end:]) + +Which produces this output: + +.. code-block:: text + + Robot('blue').walk + Robot('blue').RUN(steps=10*n) + +The ``ASTTokens`` object also offers methods to walk and search the list of tokens that make up +the code (or a particular AST node), which is more useful and powerful than dealing with the text +directly. + + +Contribute +---------- + +To contribute: + +1. Fork this repository, and clone your fork. +2. Install the package with test dependencies (ideally in a virtualenv) with:: + + pip install -e '.[test]' + +3. Run tests in your current interpreter with the command ``pytest`` or ``python -m pytest``. +4. Run tests across all supported interpreters with the ``tox`` command. You will need to have the interpreters installed separately. We recommend ``pyenv`` for that. Use ``tox -p auto`` to run the tests in parallel. +5. By default certain tests which take a very long time to run are skipped, but they are run on travis CI. To run them locally, set the environment variable ``ASTTOKENS_SLOW_TESTS``. For example run ``ASTTOKENS_SLOW_TESTS=1 tox`` to run the full suite of tests. diff --git a/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/RECORD b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..eb6a6b928272171d79c6ade369c0fa522a3c8c9b --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/RECORD @@ -0,0 +1,22 @@ +asttokens-2.4.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +asttokens-2.4.1.dist-info/LICENSE,sha256=tAkwu8-AdEyGxGoSvJ2gVmQdcicWw3j1ZZueVV74M-E,11357 +asttokens-2.4.1.dist-info/METADATA,sha256=NVktxMNmzWSV0jf8-LgkKQZ2w7HmHI_4ZHcuLTg6y-A,5197 +asttokens-2.4.1.dist-info/RECORD,, +asttokens-2.4.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +asttokens-2.4.1.dist-info/WHEEL,sha256=iYlv5fX357PQyRT2o6tw1bN-YcKFFHKqB_LwHO5wP-g,110 +asttokens-2.4.1.dist-info/top_level.txt,sha256=nJDweSD7_NBhOlR3c8bkKJMKM-pxlAS8Kyh8GcCT2dk,10 +asttokens/__init__.py,sha256=8eONA3X-9s93-v-2gEoz4649fDUpvzBthFB5Ld7dHAg,962 +asttokens/__pycache__/__init__.cpython-310.pyc,, +asttokens/__pycache__/astroid_compat.cpython-310.pyc,, +asttokens/__pycache__/asttokens.cpython-310.pyc,, +asttokens/__pycache__/line_numbers.cpython-310.pyc,, +asttokens/__pycache__/mark_tokens.cpython-310.pyc,, +asttokens/__pycache__/util.cpython-310.pyc,, +asttokens/__pycache__/version.cpython-310.pyc,, +asttokens/astroid_compat.py,sha256=ilaVBRWcHpQ3ZLBSBs9usUwnLW3Orfn6sM89cMN8zNI,586 +asttokens/asttokens.py,sha256=WIExmOOKNK4OMzCwgmFKK7pJSvp90a40zf27_Ht03W4,18867 +asttokens/line_numbers.py,sha256=z3E38XvQaocXm_5MW8-jimFr-In5iMExFkmLPHBxenY,2842 +asttokens/mark_tokens.py,sha256=Yw9sNJ8BgQ7BVohzKjCAuSowj2fT4tVrEnby9D4g0gA,22956 +asttokens/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +asttokens/util.py,sha256=VzwdnLd_ZLc89mt6BBPGkDhdWKhNRfuFTJnFOVzC5_Q,17889 +asttokens/version.py,sha256=LgDSW5laOqA_7i2VW0cZ9QumZREigUxs3ZCBzJ1EG0o,22 diff --git a/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/REQUESTED b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/WHEEL b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..c34f1162ef9a50c355df1261ef6194ffc1b39975 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.41.2) +Root-Is-Purelib: true +Tag: py2-none-any +Tag: py3-none-any + diff --git a/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/top_level.txt b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..7adf4c51fd2d7b06ec051d95245af6cb8c5931ed --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/asttokens-2.4.1.dist-info/top_level.txt @@ -0,0 +1 @@ +asttokens diff --git a/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/INSTALLER b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/LICENSE b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..67da58ecd7a5500dc5baa751043f092b580022af --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/LICENSE @@ -0,0 +1,20 @@ +The MIT License (MIT) + +Copyright (c) 2014-2024 Thomas Kemmer + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/METADATA b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..b3ff29d8995730c23114dd0cea0110b13329bd72 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/METADATA @@ -0,0 +1,150 @@ +Metadata-Version: 2.1 +Name: cachetools +Version: 5.5.0 +Summary: Extensible memoizing collections and decorators +Home-page: https://github.com/tkem/cachetools/ +Author: Thomas Kemmer +Author-email: tkemmer@computer.org +License: MIT +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Other Environment +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +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 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.7 +License-File: LICENSE + +cachetools +======================================================================== + +.. image:: https://img.shields.io/pypi/v/cachetools + :target: https://pypi.org/project/cachetools/ + :alt: Latest PyPI version + +.. image:: https://img.shields.io/github/actions/workflow/status/tkem/cachetools/ci.yml + :target: https://github.com/tkem/cachetools/actions/workflows/ci.yml + :alt: CI build status + +.. image:: https://img.shields.io/readthedocs/cachetools + :target: https://cachetools.readthedocs.io/ + :alt: Documentation build status + +.. image:: https://img.shields.io/codecov/c/github/tkem/cachetools/master.svg + :target: https://codecov.io/gh/tkem/cachetools + :alt: Test coverage + +.. image:: https://img.shields.io/librariesio/sourcerank/pypi/cachetools + :target: https://libraries.io/pypi/cachetools + :alt: Libraries.io SourceRank + +.. image:: https://img.shields.io/github/license/tkem/cachetools + :target: https://raw.github.com/tkem/cachetools/master/LICENSE + :alt: License + +.. image:: https://img.shields.io/badge/code%20style-black-000000.svg + :target: https://github.com/psf/black + :alt: Code style: black + + +This module provides various memoizing collections and decorators, +including variants of the Python Standard Library's `@lru_cache`_ +function decorator. + +.. code-block:: python + + from cachetools import cached, LRUCache, TTLCache + + # speed up calculating Fibonacci numbers with dynamic programming + @cached(cache={}) + def fib(n): + return n if n < 2 else fib(n - 1) + fib(n - 2) + + # cache least recently used Python Enhancement Proposals + @cached(cache=LRUCache(maxsize=32)) + def get_pep(num): + url = 'http://www.python.org/dev/peps/pep-%04d/' % num + with urllib.request.urlopen(url) as s: + return s.read() + + # cache weather data for no longer than ten minutes + @cached(cache=TTLCache(maxsize=1024, ttl=600)) + def get_weather(place): + return owm.weather_at_place(place).get_weather() + +For the purpose of this module, a *cache* is a mutable_ mapping_ of a +fixed maximum size. When the cache is full, i.e. by adding another +item the cache would exceed its maximum size, the cache must choose +which item(s) to discard based on a suitable `cache algorithm`_. + +This module provides multiple cache classes based on different cache +algorithms, as well as decorators for easily memoizing function and +method calls. + + +Installation +------------------------------------------------------------------------ + +cachetools is available from PyPI_ and can be installed by running:: + + pip install cachetools + +Typing stubs for this package are provided by typeshed_ and can be +installed by running:: + + pip install types-cachetools + + +Project Resources +------------------------------------------------------------------------ + +- `Documentation`_ +- `Issue tracker`_ +- `Source code`_ +- `Change log`_ + + +Related Projects +------------------------------------------------------------------------ + +- asyncache_: Helpers to use cachetools with async functions +- cacheing_: Pure Python Cacheing Library +- CacheToolsUtils_: Cachetools Utilities +- kids.cache_: Kids caching library +- shelved-cache_: Persistent cache for Python cachetools + + +License +------------------------------------------------------------------------ + +Copyright (c) 2014-2024 Thomas Kemmer. + +Licensed under the `MIT License`_. + + +.. _@lru_cache: https://docs.python.org/3/library/functools.html#functools.lru_cache +.. _mutable: https://docs.python.org/dev/glossary.html#term-mutable +.. _mapping: https://docs.python.org/dev/glossary.html#term-mapping +.. _cache algorithm: https://en.wikipedia.org/wiki/Cache_algorithms + +.. _PyPI: https://pypi.org/project/cachetools/ +.. _typeshed: https://github.com/python/typeshed/ +.. _Documentation: https://cachetools.readthedocs.io/ +.. _Issue tracker: https://github.com/tkem/cachetools/issues/ +.. _Source code: https://github.com/tkem/cachetools/ +.. _Change log: https://github.com/tkem/cachetools/blob/master/CHANGELOG.rst +.. _MIT License: https://raw.github.com/tkem/cachetools/master/LICENSE + +.. _asyncache: https://pypi.org/project/asyncache/ +.. _cacheing: https://github.com/breid48/cacheing +.. _CacheToolsUtils: https://pypi.org/project/CacheToolsUtils/ +.. _kids.cache: https://pypi.org/project/kids.cache/ +.. _shelved-cache: https://pypi.org/project/shelved-cache/ diff --git a/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/RECORD b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..e98da12d65619d05cc36cac70624f4f6b34467a9 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/cachetools-5.5.0.dist-info/RECORD @@ -0,0 +1,13 @@ +cachetools-5.5.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +cachetools-5.5.0.dist-info/LICENSE,sha256=L00v8F8Fxdo4efQCkrdgAzLXddx-0yDUPdQvPNfZLJs,1085 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+cachetools diff --git a/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/INSTALLER b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/License.txt b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/License.txt new file mode 100644 index 0000000000000000000000000000000000000000..e318c666958b8bd82e56ef2c4314f67c3ba80b48 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/License.txt @@ -0,0 +1,31 @@ + + Copyright (c) 2015-2019, NVIDIA CORPORATION. All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * 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. + * Neither the name of NVIDIA CORPORATION, Lawrence Berkeley National + Laboratory, the U.S. Department of Energy, nor the names of their + 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 ``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 OWNER 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. + + The U.S. Department of Energy funded the development of this software + under subcontract 7078610 with Lawrence Berkeley National Laboratory. + diff --git a/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/METADATA b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..8af280f92f34f140f83cf80a3b1a033b0dcb7a62 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/METADATA @@ -0,0 +1,35 @@ +Metadata-Version: 2.1 +Name: nvidia-nccl-cu11 +Version: 2.19.3 +Summary: NVIDIA Collective Communication Library (NCCL) Runtime +Home-page: https://developer.nvidia.com/cuda-zone +Author: Nvidia CUDA Installer Team +Author-email: cuda_installer@nvidia.com +License: NVIDIA Proprietary Software +Keywords: cuda,nvidia,runtime,machine learning,deep learning +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: Other/Proprietary License +Classifier: Natural Language :: English +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +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 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Software Development +Classifier: Topic :: Software Development :: Libraries +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: POSIX :: Linux +Requires-Python: >=3 +License-File: License.txt + +NCCL (pronounced "Nickel") is a stand-alone library of standard collective communication routines for GPUs, implementing all-reduce, all-gather, reduce, broadcast, and reduce-scatter. It has been optimized to achieve high bandwidth on any platform using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. diff --git a/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/RECORD b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..39f58aceb8b9cb1afb860f9e54befde37577853a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/RECORD @@ -0,0 +1,17 @@ +nvidia/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +nvidia/__pycache__/__init__.cpython-310.pyc,, +nvidia/nccl/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +nvidia/nccl/__pycache__/__init__.cpython-310.pyc,, +nvidia/nccl/include/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +nvidia/nccl/include/__pycache__/__init__.cpython-310.pyc,, +nvidia/nccl/include/nccl.h,sha256=r5ktDhEQdKl4Jo6fQzuUNAmhq6jm3NMiEHvvGF6wAQ0,18641 +nvidia/nccl/include/nccl_net.h,sha256=MDno5IdD4TfRBCFA5Xzh5bOyrpgMyv3pps5zWmhsW0k,18463 +nvidia/nccl/lib/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +nvidia/nccl/lib/__pycache__/__init__.cpython-310.pyc,, +nvidia/nccl/lib/libnccl.so.2,sha256=ZIZ4O2rd6lNjYd8x0qFrRzObSibwswhZQ3r5cV8UKmo,176493424 +nvidia_nccl_cu11-2.19.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +nvidia_nccl_cu11-2.19.3.dist-info/License.txt,sha256=92n6LTYyE_WZNm2kbiqNZQyG6q6EWuxNRLL1_QHU7Fk,1735 +nvidia_nccl_cu11-2.19.3.dist-info/METADATA,sha256=yDTVrPtUix-jZOcrTGJAzSG53_1RnzNN5PbNokCmgfY,1834 +nvidia_nccl_cu11-2.19.3.dist-info/RECORD,, +nvidia_nccl_cu11-2.19.3.dist-info/WHEEL,sha256=-kQi_VMfvRQozZJT7HUPMfY-5vLo0LVTmAylNJ3Ft98,106 +nvidia_nccl_cu11-2.19.3.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7 diff --git a/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/WHEEL b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..06e355fe0e3ed7077903f119ae6928a17da8eb6f --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.37.1) +Root-Is-Purelib: true +Tag: py3-none-manylinux1_x86_64 + diff --git a/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/top_level.txt b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..862f7abf232cdfbb928609856247292e81c9decb --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/nvidia_nccl_cu11-2.19.3.dist-info/top_level.txt @@ -0,0 +1 @@ +nvidia diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/INSTALLER b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/LICENSE b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..67589cbb8600ecbd6589f3374ccb724320c82617 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/LICENSE @@ -0,0 +1,13 @@ +Copyright 2011 Sybren A. Stüvel + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/METADATA b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..926968149bf44093ead38e5b403598063eac407a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/METADATA @@ -0,0 +1,106 @@ +Metadata-Version: 2.1 +Name: rsa +Version: 4.9 +Summary: Pure-Python RSA implementation +Home-page: https://stuvel.eu/rsa +License: Apache-2.0 +Author: Sybren A. Stüvel +Author-email: sybren@stuvel.eu +Requires-Python: >=3.6,<4 +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Information Technology +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Security :: Cryptography +Requires-Dist: pyasn1 (>=0.1.3) +Project-URL: Repository, https://github.com/sybrenstuvel/python-rsa +Description-Content-Type: text/markdown + +# Pure Python RSA implementation + +[![PyPI](https://img.shields.io/pypi/v/rsa.svg)](https://pypi.org/project/rsa/) +[![Build Status](https://travis-ci.org/sybrenstuvel/python-rsa.svg?branch=master)](https://travis-ci.org/sybrenstuvel/python-rsa) +[![Coverage Status](https://coveralls.io/repos/github/sybrenstuvel/python-rsa/badge.svg?branch=master)](https://coveralls.io/github/sybrenstuvel/python-rsa?branch=master) +[![Code Climate](https://api.codeclimate.com/v1/badges/a99a88d28ad37a79dbf6/maintainability)](https://codeclimate.com/github/codeclimate/codeclimate/maintainability) + +[Python-RSA](https://stuvel.eu/rsa) is a pure-Python RSA implementation. It supports +encryption and decryption, signing and verifying signatures, and key +generation according to PKCS#1 version 1.5. It can be used as a Python +library as well as on the commandline. The code was mostly written by +Sybren A. Stüvel. + +Documentation can be found at the [Python-RSA homepage](https://stuvel.eu/rsa). For all changes, check [the changelog](https://github.com/sybrenstuvel/python-rsa/blob/master/CHANGELOG.md). + +Download and install using: + + pip install rsa + +or download it from the [Python Package Index](https://pypi.org/project/rsa/). + +The source code is maintained at [GitHub](https://github.com/sybrenstuvel/python-rsa/) and is +licensed under the [Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0) + +## Security + +Because of how Python internally stores numbers, it is very hard (if not impossible) to make a pure-Python program secure against timing attacks. This library is no exception, so use it with care. See https://securitypitfalls.wordpress.com/2018/08/03/constant-time-compare-in-python/ for more info. + +## Setup of Development Environment + +``` +python3 -m venv .venv +. ./.venv/bin/activate +pip install poetry +poetry install +``` + +## Publishing a New Release + +Since this project is considered critical on the Python Package Index, +two-factor authentication is required. For uploading packages to PyPi, an API +key is required; username+password will not work. + +First, generate an API token at https://pypi.org/manage/account/token/. Then, +use this token when publishing instead of your username and password. + +As username, use `__token__`. +As password, use the token itself, including the `pypi-` prefix. + +See https://pypi.org/help/#apitoken for help using API tokens to publish. This +is what I have in `~/.pypirc`: + +``` +[distutils] +index-servers = + rsa + +# Use `twine upload -r rsa` to upload with this token. +[rsa] + repository = https://upload.pypi.org/legacy/ + username = __token__ + password = pypi-token +``` + +``` +. ./.venv/bin/activate +pip install twine + +poetry build +twine check dist/rsa-4.9.tar.gz dist/rsa-4.9-*.whl +twine upload -r rsa dist/rsa-4.9.tar.gz dist/rsa-4.9-*.whl +``` + +The `pip install twine` is necessary as Python-RSA requires Python >= 3.6, and +Twine requires at least version 3.7. This means Poetry refuses to add it as +dependency. + diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/RECORD b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..c1714a3799b63b75f6d65f03c011edc34f901ba7 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/RECORD @@ -0,0 +1,42 @@ +../../../bin/pyrsa-decrypt,sha256=S6LDCj0lGDDdZp5xk_EGzp87_NES_oaSKJSQYR0V_nM,234 +../../../bin/pyrsa-encrypt,sha256=rEta2Ny25qATspyHYwhLmeJzNMG3Cea-TWnAk_rAms4,234 +../../../bin/pyrsa-keygen,sha256=ZGsNTjZwSSIXo0M4kJ3W-yD_T7tqMGWLRw789QM08b8,232 +../../../bin/pyrsa-priv2pub,sha256=RdsdjkkUp-hUMjbEoFNFpHEp7XtLNGb1jI59AjflqKI,255 +../../../bin/pyrsa-sign,sha256=fip0vUeZqd8lwVJbI3SRUfwlu7mIOhKG7r7sAAUM5mM,228 +../../../bin/pyrsa-verify,sha256=nYf48kJi0gdoqAsx7Dak_1AQhPtXqNC6ERcEjfGLmMg,232 +rsa-4.9.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +rsa-4.9.dist-info/LICENSE,sha256=Bz8ot9OJyP509gfhfCf4HqpazmntxDqITyP0G0HFxyY,577 +rsa-4.9.dist-info/METADATA,sha256=-540qZBdoxQdUSuhxWlXTnY-oMNVz3EML49u9IfmmQ4,4173 +rsa-4.9.dist-info/RECORD,, +rsa-4.9.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +rsa-4.9.dist-info/WHEEL,sha256=y3eDiaFVSNTPbgzfNn0nYn5tEn1cX6WrdetDlQM4xWw,83 +rsa-4.9.dist-info/entry_points.txt,sha256=p0nVsezmPSjm5x4GDMD4a9Sshc9ukdfw1kkmOmpaAu0,201 +rsa/__init__.py,sha256=5bc5rkBB8vxWEtVYwoMQxM8df3O1Ak2_zEXqnkK9oes,1605 +rsa/__pycache__/__init__.cpython-310.pyc,, +rsa/__pycache__/asn1.cpython-310.pyc,, +rsa/__pycache__/cli.cpython-310.pyc,, +rsa/__pycache__/common.cpython-310.pyc,, +rsa/__pycache__/core.cpython-310.pyc,, +rsa/__pycache__/key.cpython-310.pyc,, +rsa/__pycache__/parallel.cpython-310.pyc,, +rsa/__pycache__/pem.cpython-310.pyc,, +rsa/__pycache__/pkcs1.cpython-310.pyc,, +rsa/__pycache__/pkcs1_v2.cpython-310.pyc,, +rsa/__pycache__/prime.cpython-310.pyc,, +rsa/__pycache__/randnum.cpython-310.pyc,, +rsa/__pycache__/transform.cpython-310.pyc,, +rsa/__pycache__/util.cpython-310.pyc,, +rsa/asn1.py,sha256=WL2bhDg-q7riT8P8cBMpydsh020i6Ejl6vcQIuA0VXA,1792 +rsa/cli.py,sha256=DOE66cB0-0SjUhs-PX2gbxiSma5-CT1lEAdcCYrTXwE,10183 +rsa/common.py,sha256=DAWwAuOSv1X67CBHzBvH-1wOsRe9np6eVsL_ZLrBWcg,4863 +rsa/core.py,sha256=Rf33atg4-pI7U-mTdoosmn8gTeTyX5xP7yv0iqWyogc,1714 +rsa/key.py,sha256=3_xv7B-AZZ5jIIz-vpnpfJtStS415e8fNr2iTYOu5CM,28285 +rsa/parallel.py,sha256=NcL1QjNWJxH9zL2OAOYKgr-HbAeEEmdckdxC6KMhkmM,2405 +rsa/pem.py,sha256=lzFulzgLHyqhimeo3T4GeBXuGRClfkTMYYZbgmYYmQk,4123 +rsa/pkcs1.py,sha256=wN9SWn1_zFJvHDNLGPeGZxoDA5T7ipVy9DntNcCYBpU,16690 +rsa/pkcs1_v2.py,sha256=d5A27EcOgbgJeikuLZkzANOzBQh4nVX-Bom5DUXgXHw,3549 +rsa/prime.py,sha256=Kij81g-VneGw20Cq6LRaCVT3b9tX4gWIzkWV-3h4qMg,5304 +rsa/py.typed,sha256=TfYjsEjlfDcVNGFibSYzbCf81u37bSXWmv4oTYf0zY8,64 +rsa/randnum.py,sha256=AwhXEZAT6spbUUPjhwQXGXKOTlG8FPHOI3gmTAcQ0pk,2752 +rsa/transform.py,sha256=i-nVC7JcPZkYz1W-d-qg0n0PQS17kKeXhfd9IkDehj4,2272 +rsa/util.py,sha256=9PuWg2jQfV8FHdE9hpGHDCi2iGM8Z-r4tIQXRVFmqYY,3090 diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/REQUESTED b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/WHEEL b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..376465212164654cc5cf792aeaf69d01141f0343 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: poetry 1.0.7 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/entry_points.txt b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf058e3ebda1b5bda76eefdf3e7c188ae20bb911 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/rsa-4.9.dist-info/entry_points.txt @@ -0,0 +1,8 @@ +[console_scripts] +pyrsa-decrypt=rsa.cli:decrypt +pyrsa-encrypt=rsa.cli:encrypt +pyrsa-keygen=rsa.cli:keygen +pyrsa-priv2pub=rsa.util:private_to_public +pyrsa-sign=rsa.cli:sign +pyrsa-verify=rsa.cli:verify + diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/__init__.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c9a5d2ca92b5248ce798a19f8e14c3492992cae1 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__init__.py @@ -0,0 +1,9 @@ +# Re-export this +from ._safetensors_rust import ( # noqa: F401 + SafetensorError, + __version__, + deserialize, + safe_open, + serialize, + serialize_file, +) diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/__init__.pyi b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..a125e8de683c424db86d8edd9a301ced30d56296 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__init__.pyi @@ -0,0 +1,73 @@ +# Generated content DO NOT EDIT +@staticmethod +def deserialize(bytes): + """ + Opens a safetensors lazily and returns tensors as asked + + Args: + data (:obj:`bytes`): + The byte content of a file + + Returns: + (:obj:`List[str, Dict[str, Dict[str, any]]]`): + The deserialized content is like: + [("tensor_name", {"shape": [2, 3], "dtype": "F32", "data": b"\0\0.." }), (...)] + """ + pass + +@staticmethod +def serialize(tensor_dict, metadata=None): + """ + Serializes raw data. + + Args: + tensor_dict (:obj:`Dict[str, Dict[Any]]`): + The tensor dict is like: + {"tensor_name": {"dtype": "F32", "shape": [2, 3], "data": b"\0\0"}} + metadata (:obj:`Dict[str, str]`, *optional*): + The optional purely text annotations + + Returns: + (:obj:`bytes`): + The serialized content. + """ + pass + +@staticmethod +def serialize_file(tensor_dict, filename, metadata=None): + """ + Serializes raw data. + + Args: + tensor_dict (:obj:`Dict[str, Dict[Any]]`): + The tensor dict is like: + {"tensor_name": {"dtype": "F32", "shape": [2, 3], "data": b"\0\0"}} + filename (:obj:`str`): + The name of the file to write into. + metadata (:obj:`Dict[str, str]`, *optional*): + The optional purely text annotations + + Returns: + (:obj:`bytes`): + The serialized content. + """ + pass + +class safe_open: + """ + Opens a safetensors lazily and returns tensors as asked + + Args: + filename (:obj:`str`): + The filename to open + + framework (:obj:`str`): + The framework you want you tensors in. Supported values: + `pt`, `tf`, `flax`, `numpy`. + + device (:obj:`str`, defaults to :obj:`"cpu"`): + The device on which you want the tensors. + """ + + def __init__(self, filename, framework, device="cpu"): + pass diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/flax.cpython-310.pyc b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/flax.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ccb076b538da814abcd05a2caccd96a33d83d87a Binary files /dev/null and b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/flax.cpython-310.pyc differ diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/numpy.cpython-310.pyc b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/numpy.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a8dbaf5939782a5570c9cad90bfaf35c4f017bf7 Binary files /dev/null and b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/numpy.cpython-310.pyc differ diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/paddle.cpython-310.pyc b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/paddle.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..093b858b9beed4f377fd4c53b695bfe0ed0d52af Binary files /dev/null and b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/paddle.cpython-310.pyc differ diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/tensorflow.cpython-310.pyc b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/tensorflow.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f56bd12cdea3b2cdbda8e84406a3a2af264ab0f5 Binary files /dev/null and b/evalkit_internvl/lib/python3.10/site-packages/safetensors/__pycache__/tensorflow.cpython-310.pyc differ diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/flax.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/flax.py new file mode 100644 index 0000000000000000000000000000000000000000..d0b8375e038eff487af33fcfaa4a597aacb5743f --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/flax.py @@ -0,0 +1,138 @@ +import os +from typing import Dict, Optional, Union + +import numpy as np + +import jax.numpy as jnp +from jax import Array +from safetensors import numpy, safe_open + + +def save(tensors: Dict[str, Array], metadata: Optional[Dict[str, str]] = None) -> bytes: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, Array]`): + The incoming tensors. Tensors need to be contiguous and dense. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `bytes`: The raw bytes representing the format + + Example: + + ```python + from safetensors.flax import save + from jax import numpy as jnp + + tensors = {"embedding": jnp.zeros((512, 1024)), "attention": jnp.zeros((256, 256))} + byte_data = save(tensors) + ``` + """ + np_tensors = _jnp2np(tensors) + return numpy.save(np_tensors, metadata=metadata) + + +def save_file( + tensors: Dict[str, Array], + filename: Union[str, os.PathLike], + metadata: Optional[Dict[str, str]] = None, +) -> None: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, Array]`): + The incoming tensors. Tensors need to be contiguous and dense. + filename (`str`, or `os.PathLike`)): + The filename we're saving into. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `None` + + Example: + + ```python + from safetensors.flax import save_file + from jax import numpy as jnp + + tensors = {"embedding": jnp.zeros((512, 1024)), "attention": jnp.zeros((256, 256))} + save_file(tensors, "model.safetensors") + ``` + """ + np_tensors = _jnp2np(tensors) + return numpy.save_file(np_tensors, filename, metadata=metadata) + + +def load(data: bytes) -> Dict[str, Array]: + """ + Loads a safetensors file into flax format from pure bytes. + + Args: + data (`bytes`): + The content of a safetensors file + + Returns: + `Dict[str, Array]`: dictionary that contains name as key, value as `Array` on cpu + + Example: + + ```python + from safetensors.flax import load + + file_path = "./my_folder/bert.safetensors" + with open(file_path, "rb") as f: + data = f.read() + + loaded = load(data) + ``` + """ + flat = numpy.load(data) + return _np2jnp(flat) + + +def load_file(filename: Union[str, os.PathLike]) -> Dict[str, Array]: + """ + Loads a safetensors file into flax format. + + Args: + filename (`str`, or `os.PathLike`)): + The name of the file which contains the tensors + + Returns: + `Dict[str, Array]`: dictionary that contains name as key, value as `Array` + + Example: + + ```python + from safetensors.flax import load_file + + file_path = "./my_folder/bert.safetensors" + loaded = load_file(file_path) + ``` + """ + result = {} + with safe_open(filename, framework="flax") as f: + for k in f.keys(): + result[k] = f.get_tensor(k) + return result + + +def _np2jnp(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, Array]: + for k, v in numpy_dict.items(): + numpy_dict[k] = jnp.array(v) + return numpy_dict + + +def _jnp2np(jnp_dict: Dict[str, Array]) -> Dict[str, np.array]: + for k, v in jnp_dict.items(): + jnp_dict[k] = np.asarray(v) + return jnp_dict diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/mlx.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/mlx.py new file mode 100644 index 0000000000000000000000000000000000000000..cf9fe37519c817e4d9db87e8ce53c2dc8b85254f --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/mlx.py @@ -0,0 +1,138 @@ +import os +from typing import Dict, Optional, Union + +import numpy as np + +import mlx.core as mx +from safetensors import numpy, safe_open + + +def save(tensors: Dict[str, mx.array], metadata: Optional[Dict[str, str]] = None) -> bytes: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, mx.array]`): + The incoming tensors. Tensors need to be contiguous and dense. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `bytes`: The raw bytes representing the format + + Example: + + ```python + from safetensors.mlx import save + import mlx.core as mx + + tensors = {"embedding": mx.zeros((512, 1024)), "attention": mx.zeros((256, 256))} + byte_data = save(tensors) + ``` + """ + np_tensors = _mx2np(tensors) + return numpy.save(np_tensors, metadata=metadata) + + +def save_file( + tensors: Dict[str, mx.array], + filename: Union[str, os.PathLike], + metadata: Optional[Dict[str, str]] = None, +) -> None: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, mx.array]`): + The incoming tensors. Tensors need to be contiguous and dense. + filename (`str`, or `os.PathLike`)): + The filename we're saving into. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `None` + + Example: + + ```python + from safetensors.mlx import save_file + import mlx.core as mx + + tensors = {"embedding": mx.zeros((512, 1024)), "attention": mx.zeros((256, 256))} + save_file(tensors, "model.safetensors") + ``` + """ + np_tensors = _mx2np(tensors) + return numpy.save_file(np_tensors, filename, metadata=metadata) + + +def load(data: bytes) -> Dict[str, mx.array]: + """ + Loads a safetensors file into MLX format from pure bytes. + + Args: + data (`bytes`): + The content of a safetensors file + + Returns: + `Dict[str, mx.array]`: dictionary that contains name as key, value as `mx.array` + + Example: + + ```python + from safetensors.mlx import load + + file_path = "./my_folder/bert.safetensors" + with open(file_path, "rb") as f: + data = f.read() + + loaded = load(data) + ``` + """ + flat = numpy.load(data) + return _np2mx(flat) + + +def load_file(filename: Union[str, os.PathLike]) -> Dict[str, mx.array]: + """ + Loads a safetensors file into MLX format. + + Args: + filename (`str`, or `os.PathLike`)): + The name of the file which contains the tensors + + Returns: + `Dict[str, mx.array]`: dictionary that contains name as key, value as `mx.array` + + Example: + + ```python + from safetensors.flax import load_file + + file_path = "./my_folder/bert.safetensors" + loaded = load_file(file_path) + ``` + """ + result = {} + with safe_open(filename, framework="mlx") as f: + for k in f.keys(): + result[k] = f.get_tensor(k) + return result + + +def _np2mx(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, mx.array]: + for k, v in numpy_dict.items(): + numpy_dict[k] = mx.array(v) + return numpy_dict + + +def _mx2np(mx_dict: Dict[str, mx.array]) -> Dict[str, np.array]: + new_dict = {} + for k, v in mx_dict.items(): + new_dict[k] = np.asarray(v) + return new_dict diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/numpy.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..0b245f12c1c949456c9b2edb45a11343e6a8099a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/numpy.py @@ -0,0 +1,176 @@ +import os +import sys +from typing import Dict, Optional, Union + +import numpy as np + +from safetensors import deserialize, safe_open, serialize, serialize_file + + +def _tobytes(tensor: np.ndarray) -> bytes: + if not _is_little_endian(tensor): + tensor = tensor.byteswap(inplace=False) + return tensor.tobytes() + + +def save(tensor_dict: Dict[str, np.ndarray], metadata: Optional[Dict[str, str]] = None) -> bytes: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensor_dict (`Dict[str, np.ndarray]`): + The incoming tensors. Tensors need to be contiguous and dense. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `bytes`: The raw bytes representing the format + + Example: + + ```python + from safetensors.numpy import save + import numpy as np + + tensors = {"embedding": np.zeros((512, 1024)), "attention": np.zeros((256, 256))} + byte_data = save(tensors) + ``` + """ + flattened = {k: {"dtype": v.dtype.name, "shape": v.shape, "data": _tobytes(v)} for k, v in tensor_dict.items()} + serialized = serialize(flattened, metadata=metadata) + result = bytes(serialized) + return result + + +def save_file( + tensor_dict: Dict[str, np.ndarray], filename: Union[str, os.PathLike], metadata: Optional[Dict[str, str]] = None +) -> None: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensor_dict (`Dict[str, np.ndarray]`): + The incoming tensors. Tensors need to be contiguous and dense. + filename (`str`, or `os.PathLike`)): + The filename we're saving into. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `None` + + Example: + + ```python + from safetensors.numpy import save_file + import numpy as np + + tensors = {"embedding": np.zeros((512, 1024)), "attention": np.zeros((256, 256))} + save_file(tensors, "model.safetensors") + ``` + """ + flattened = {k: {"dtype": v.dtype.name, "shape": v.shape, "data": _tobytes(v)} for k, v in tensor_dict.items()} + serialize_file(flattened, filename, metadata=metadata) + + +def load(data: bytes) -> Dict[str, np.ndarray]: + """ + Loads a safetensors file into numpy format from pure bytes. + + Args: + data (`bytes`): + The content of a safetensors file + + Returns: + `Dict[str, np.ndarray]`: dictionary that contains name as key, value as `np.ndarray` on cpu + + Example: + + ```python + from safetensors.numpy import load + + file_path = "./my_folder/bert.safetensors" + with open(file_path, "rb") as f: + data = f.read() + + loaded = load(data) + ``` + """ + flat = deserialize(data) + return _view2np(flat) + + +def load_file(filename: Union[str, os.PathLike]) -> Dict[str, np.ndarray]: + """ + Loads a safetensors file into numpy format. + + Args: + filename (`str`, or `os.PathLike`)): + The name of the file which contains the tensors + + Returns: + `Dict[str, np.ndarray]`: dictionary that contains name as key, value as `np.ndarray` + + Example: + + ```python + from safetensors.numpy import load_file + + file_path = "./my_folder/bert.safetensors" + loaded = load_file(file_path) + ``` + """ + result = {} + with safe_open(filename, framework="np") as f: + for k in f.keys(): + result[k] = f.get_tensor(k) + return result + + +_TYPES = { + "F64": np.float64, + "F32": np.float32, + "F16": np.float16, + "I64": np.int64, + "U64": np.uint64, + "I32": np.int32, + "U32": np.uint32, + "I16": np.int16, + "U16": np.uint16, + "I8": np.int8, + "U8": np.uint8, + "BOOL": bool, +} + + +def _getdtype(dtype_str: str) -> np.dtype: + return _TYPES[dtype_str] + + +def _view2np(safeview) -> Dict[str, np.ndarray]: + result = {} + for k, v in safeview: + dtype = _getdtype(v["dtype"]) + arr = np.frombuffer(v["data"], dtype=dtype).reshape(v["shape"]) + result[k] = arr + return result + + +def _is_little_endian(tensor: np.ndarray) -> bool: + byteorder = tensor.dtype.byteorder + if byteorder == "=": + if sys.byteorder == "little": + return True + else: + return False + elif byteorder == "|": + return True + elif byteorder == "<": + return True + elif byteorder == ">": + return False + raise ValueError(f"Unexpected byte order {byteorder}") diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/paddle.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/paddle.py new file mode 100644 index 0000000000000000000000000000000000000000..cec368665de31d17757c0c6621df5dc4926bfab1 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/paddle.py @@ -0,0 +1,138 @@ +import os +from typing import Dict, Optional, Union + +import numpy as np + +import paddle +from safetensors import numpy + + +def save(tensors: Dict[str, paddle.Tensor], metadata: Optional[Dict[str, str]] = None) -> bytes: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, paddle.Tensor]`): + The incoming tensors. Tensors need to be contiguous and dense. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `bytes`: The raw bytes representing the format + + Example: + + ```python + from safetensors.paddle import save + import paddle + + tensors = {"embedding": paddle.zeros((512, 1024)), "attention": paddle.zeros((256, 256))} + byte_data = save(tensors) + ``` + """ + np_tensors = _paddle2np(tensors) + return numpy.save(np_tensors, metadata=metadata) + + +def save_file( + tensors: Dict[str, paddle.Tensor], + filename: Union[str, os.PathLike], + metadata: Optional[Dict[str, str]] = None, +) -> None: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, paddle.Tensor]`): + The incoming tensors. Tensors need to be contiguous and dense. + filename (`str`, or `os.PathLike`)): + The filename we're saving into. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `None` + + Example: + + ```python + from safetensors.paddle import save_file + import paddle + + tensors = {"embedding": paddle.zeros((512, 1024)), "attention": paddle.zeros((256, 256))} + save_file(tensors, "model.safetensors") + ``` + """ + np_tensors = _paddle2np(tensors) + return numpy.save_file(np_tensors, filename, metadata=metadata) + + +def load(data: bytes, device: str = "cpu") -> Dict[str, paddle.Tensor]: + """ + Loads a safetensors file into paddle format from pure bytes. + + Args: + data (`bytes`): + The content of a safetensors file + + Returns: + `Dict[str, paddle.Tensor]`: dictionary that contains name as key, value as `paddle.Tensor` on cpu + + Example: + + ```python + from safetensors.paddle import load + + file_path = "./my_folder/bert.safetensors" + with open(file_path, "rb") as f: + data = f.read() + + loaded = load(data) + ``` + """ + flat = numpy.load(data) + return _np2paddle(flat, device) + + +def load_file(filename: Union[str, os.PathLike], device="cpu") -> Dict[str, paddle.Tensor]: + """ + Loads a safetensors file into paddle format. + + Args: + filename (`str`, or `os.PathLike`)): + The name of the file which contains the tensors + device (`Union[Dict[str, any], str]`, *optional*, defaults to `cpu`): + The device where the tensors need to be located after load. + available options are all regular paddle device locations + + Returns: + `Dict[str, paddle.Tensor]`: dictionary that contains name as key, value as `paddle.Tensor` + + Example: + + ```python + from safetensors.paddle import load_file + + file_path = "./my_folder/bert.safetensors" + loaded = load_file(file_path) + ``` + """ + flat = numpy.load_file(filename) + output = _np2paddle(flat, device) + return output + + +def _np2paddle(numpy_dict: Dict[str, np.ndarray], device: str = "cpu") -> Dict[str, paddle.Tensor]: + for k, v in numpy_dict.items(): + numpy_dict[k] = paddle.to_tensor(v, place=device) + return numpy_dict + + +def _paddle2np(paddle_dict: Dict[str, paddle.Tensor]) -> Dict[str, np.array]: + for k, v in paddle_dict.items(): + paddle_dict[k] = v.detach().cpu().numpy() + return paddle_dict diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/py.typed b/evalkit_internvl/lib/python3.10/site-packages/safetensors/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/tensorflow.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/tensorflow.py new file mode 100644 index 0000000000000000000000000000000000000000..e2d74b0522698b3748a7da93753e065f4053beea --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/tensorflow.py @@ -0,0 +1,137 @@ +import os +from typing import Dict, Optional, Union + +import numpy as np +import tensorflow as tf + +from safetensors import numpy, safe_open + + +def save(tensors: Dict[str, tf.Tensor], metadata: Optional[Dict[str, str]] = None) -> bytes: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, tf.Tensor]`): + The incoming tensors. Tensors need to be contiguous and dense. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `bytes`: The raw bytes representing the format + + Example: + + ```python + from safetensors.tensorflow import save + import tensorflow as tf + + tensors = {"embedding": tf.zeros((512, 1024)), "attention": tf.zeros((256, 256))} + byte_data = save(tensors) + ``` + """ + np_tensors = _tf2np(tensors) + return numpy.save(np_tensors, metadata=metadata) + + +def save_file( + tensors: Dict[str, tf.Tensor], + filename: Union[str, os.PathLike], + metadata: Optional[Dict[str, str]] = None, +) -> None: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, tf.Tensor]`): + The incoming tensors. Tensors need to be contiguous and dense. + filename (`str`, or `os.PathLike`)): + The filename we're saving into. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `None` + + Example: + + ```python + from safetensors.tensorflow import save_file + import tensorflow as tf + + tensors = {"embedding": tf.zeros((512, 1024)), "attention": tf.zeros((256, 256))} + save_file(tensors, "model.safetensors") + ``` + """ + np_tensors = _tf2np(tensors) + return numpy.save_file(np_tensors, filename, metadata=metadata) + + +def load(data: bytes) -> Dict[str, tf.Tensor]: + """ + Loads a safetensors file into tensorflow format from pure bytes. + + Args: + data (`bytes`): + The content of a safetensors file + + Returns: + `Dict[str, tf.Tensor]`: dictionary that contains name as key, value as `tf.Tensor` on cpu + + Example: + + ```python + from safetensors.tensorflow import load + + file_path = "./my_folder/bert.safetensors" + with open(file_path, "rb") as f: + data = f.read() + + loaded = load(data) + ``` + """ + flat = numpy.load(data) + return _np2tf(flat) + + +def load_file(filename: Union[str, os.PathLike]) -> Dict[str, tf.Tensor]: + """ + Loads a safetensors file into tensorflow format. + + Args: + filename (`str`, or `os.PathLike`)): + The name of the file which contains the tensors + + Returns: + `Dict[str, tf.Tensor]`: dictionary that contains name as key, value as `tf.Tensor` + + Example: + + ```python + from safetensors.tensorflow import load_file + + file_path = "./my_folder/bert.safetensors" + loaded = load_file(file_path) + ``` + """ + result = {} + with safe_open(filename, framework="tf") as f: + for k in f.keys(): + result[k] = f.get_tensor(k) + return result + + +def _np2tf(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, tf.Tensor]: + for k, v in numpy_dict.items(): + numpy_dict[k] = tf.convert_to_tensor(v) + return numpy_dict + + +def _tf2np(tf_dict: Dict[str, tf.Tensor]) -> Dict[str, np.array]: + for k, v in tf_dict.items(): + tf_dict[k] = v.numpy() + return tf_dict diff --git a/evalkit_internvl/lib/python3.10/site-packages/safetensors/torch.py b/evalkit_internvl/lib/python3.10/site-packages/safetensors/torch.py new file mode 100644 index 0000000000000000000000000000000000000000..48532ea5996cd807510b97458a0451f092ea0f35 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/safetensors/torch.py @@ -0,0 +1,503 @@ +import os +import sys +from collections import defaultdict +from typing import Any, Dict, List, Optional, Set, Tuple, Union + +import torch + +from safetensors import deserialize, safe_open, serialize, serialize_file + + +def storage_ptr(tensor: torch.Tensor) -> int: + try: + return tensor.untyped_storage().data_ptr() + except Exception: + # Fallback for torch==1.10 + try: + return tensor.storage().data_ptr() + except NotImplementedError: + # Fallback for meta storage + return 0 + + +def _end_ptr(tensor: torch.Tensor) -> int: + if tensor.nelement(): + stop = tensor.view(-1)[-1].data_ptr() + _SIZE[tensor.dtype] + else: + stop = tensor.data_ptr() + return stop + + +def storage_size(tensor: torch.Tensor) -> int: + try: + return tensor.untyped_storage().nbytes() + except AttributeError: + # Fallback for torch==1.10 + try: + return tensor.storage().size() * _SIZE[tensor.dtype] + except NotImplementedError: + # Fallback for meta storage + # On torch >=2.0 this is the tensor size + return tensor.nelement() * _SIZE[tensor.dtype] + + +def _filter_shared_not_shared(tensors: List[Set[str]], state_dict: Dict[str, torch.Tensor]) -> List[Set[str]]: + filtered_tensors = [] + for shared in tensors: + if len(shared) < 2: + filtered_tensors.append(shared) + continue + + areas = [] + for name in shared: + tensor = state_dict[name] + areas.append((tensor.data_ptr(), _end_ptr(tensor), name)) + areas.sort() + + _, last_stop, last_name = areas[0] + filtered_tensors.append({last_name}) + for start, stop, name in areas[1:]: + if start >= last_stop: + filtered_tensors.append({name}) + else: + filtered_tensors[-1].add(name) + last_stop = stop + + return filtered_tensors + + +def _find_shared_tensors(state_dict: Dict[str, torch.Tensor]) -> List[Set[str]]: + tensors = defaultdict(set) + for k, v in state_dict.items(): + if v.device != torch.device("meta") and storage_ptr(v) != 0 and storage_size(v) != 0: + # Need to add device as key because of multiple GPU. + tensors[(v.device, storage_ptr(v), storage_size(v))].add(k) + tensors = list(sorted(tensors.values())) + tensors = _filter_shared_not_shared(tensors, state_dict) + return tensors + + +def _is_complete(tensor: torch.Tensor) -> bool: + return tensor.data_ptr() == storage_ptr(tensor) and tensor.nelement() * _SIZE[tensor.dtype] == storage_size(tensor) + + +def _remove_duplicate_names( + state_dict: Dict[str, torch.Tensor], + *, + preferred_names: Optional[List[str]] = None, + discard_names: Optional[List[str]] = None, +) -> Dict[str, List[str]]: + if preferred_names is None: + preferred_names = [] + preferred_names = set(preferred_names) + if discard_names is None: + discard_names = [] + discard_names = set(discard_names) + + shareds = _find_shared_tensors(state_dict) + to_remove = defaultdict(list) + for shared in shareds: + complete_names = set([name for name in shared if _is_complete(state_dict[name])]) + if not complete_names: + raise RuntimeError( + "Error while trying to find names to remove to save state dict, but found no suitable name to keep" + f" for saving amongst: {shared}. None is covering the entire storage.Refusing to save/load the model" + " since you could be storing much more memory than needed. Please refer to" + " https://huggingface.co/docs/safetensors/torch_shared_tensors for more information. Or open an" + " issue." + ) + + keep_name = sorted(list(complete_names))[0] + + # Mechanism to preferentially select keys to keep + # coming from the on-disk file to allow + # loading models saved with a different choice + # of keep_name + preferred = complete_names.difference(discard_names) + if preferred: + keep_name = sorted(list(preferred))[0] + + if preferred_names: + preferred = preferred_names.intersection(complete_names) + if preferred: + keep_name = sorted(list(preferred))[0] + for name in sorted(shared): + if name != keep_name: + to_remove[keep_name].append(name) + return to_remove + + +def save_model( + model: torch.nn.Module, filename: str, metadata: Optional[Dict[str, str]] = None, force_contiguous: bool = True +): + """ + Saves a given torch model to specified filename. + This method exists specifically to avoid tensor sharing issues which are + not allowed in `safetensors`. [More information on tensor sharing](../torch_shared_tensors) + + Args: + model (`torch.nn.Module`): + The model to save on disk. + filename (`str`): + The filename location to save the file + metadata (`Dict[str, str]`, *optional*): + Extra information to save along with the file. + Some metadata will be added for each dropped tensors. + This information will not be enough to recover the entire + shared structure but might help understanding things + force_contiguous (`boolean`, *optional*, defaults to True): + Forcing the state_dict to be saved as contiguous tensors. + This has no effect on the correctness of the model, but it + could potentially change performance if the layout of the tensor + was chosen specifically for that reason. + """ + state_dict = model.state_dict() + to_removes = _remove_duplicate_names(state_dict) + + for kept_name, to_remove_group in to_removes.items(): + for to_remove in to_remove_group: + if metadata is None: + metadata = {} + + if to_remove not in metadata: + # Do not override user data + metadata[to_remove] = kept_name + del state_dict[to_remove] + if force_contiguous: + state_dict = {k: v.contiguous() for k, v in state_dict.items()} + try: + save_file(state_dict, filename, metadata=metadata) + except ValueError as e: + msg = str(e) + msg += " Or use save_model(..., force_contiguous=True), read the docs for potential caveats." + raise ValueError(msg) + + +def load_model( + model: torch.nn.Module, filename: Union[str, os.PathLike], strict: bool = True, device: Union[str, int] = "cpu" +) -> Tuple[List[str], List[str]]: + """ + Loads a given filename onto a torch model. + This method exists specifically to avoid tensor sharing issues which are + not allowed in `safetensors`. [More information on tensor sharing](../torch_shared_tensors) + + Args: + model (`torch.nn.Module`): + The model to load onto. + filename (`str`, or `os.PathLike`): + The filename location to load the file from. + strict (`bool`, *optional*, defaults to True): + Whether to fail if you're missing keys or having unexpected ones. + When false, the function simply returns missing and unexpected names. + device (`Union[str, int]`, *optional*, defaults to `cpu`): + The device where the tensors need to be located after load. + available options are all regular torch device locations. + + Returns: + `(missing, unexpected): (List[str], List[str])` + `missing` are names in the model which were not modified during loading + `unexpected` are names that are on the file, but weren't used during + the load. + """ + state_dict = load_file(filename, device=device) + model_state_dict = model.state_dict() + to_removes = _remove_duplicate_names(model_state_dict, preferred_names=state_dict.keys()) + missing, unexpected = model.load_state_dict(state_dict, strict=False) + missing = set(missing) + for to_remove_group in to_removes.values(): + for to_remove in to_remove_group: + if to_remove not in missing: + unexpected.append(to_remove) + else: + missing.remove(to_remove) + if strict and (missing or unexpected): + missing_keys = ", ".join([f'"{k}"' for k in sorted(missing)]) + unexpected_keys = ", ".join([f'"{k}"' for k in sorted(unexpected)]) + error = f"Error(s) in loading state_dict for {model.__class__.__name__}:" + if missing: + error += f"\n Missing key(s) in state_dict: {missing_keys}" + if unexpected: + error += f"\n Unexpected key(s) in state_dict: {unexpected_keys}" + raise RuntimeError(error) + return missing, unexpected + + +def save(tensors: Dict[str, torch.Tensor], metadata: Optional[Dict[str, str]] = None) -> bytes: + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, torch.Tensor]`): + The incoming tensors. Tensors need to be contiguous and dense. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `bytes`: The raw bytes representing the format + + Example: + + ```python + from safetensors.torch import save + import torch + + tensors = {"embedding": torch.zeros((512, 1024)), "attention": torch.zeros((256, 256))} + byte_data = save(tensors) + ``` + """ + serialized = serialize(_flatten(tensors), metadata=metadata) + result = bytes(serialized) + return result + + +def save_file( + tensors: Dict[str, torch.Tensor], + filename: Union[str, os.PathLike], + metadata: Optional[Dict[str, str]] = None, +): + """ + Saves a dictionary of tensors into raw bytes in safetensors format. + + Args: + tensors (`Dict[str, torch.Tensor]`): + The incoming tensors. Tensors need to be contiguous and dense. + filename (`str`, or `os.PathLike`)): + The filename we're saving into. + metadata (`Dict[str, str]`, *optional*, defaults to `None`): + Optional text only metadata you might want to save in your header. + For instance it can be useful to specify more about the underlying + tensors. This is purely informative and does not affect tensor loading. + + Returns: + `None` + + Example: + + ```python + from safetensors.torch import save_file + import torch + + tensors = {"embedding": torch.zeros((512, 1024)), "attention": torch.zeros((256, 256))} + save_file(tensors, "model.safetensors") + ``` + """ + serialize_file(_flatten(tensors), filename, metadata=metadata) + + +def load_file(filename: Union[str, os.PathLike], device: Union[str, int] = "cpu") -> Dict[str, torch.Tensor]: + """ + Loads a safetensors file into torch format. + + Args: + filename (`str`, or `os.PathLike`): + The name of the file which contains the tensors + device (`Union[str, int]`, *optional*, defaults to `cpu`): + The device where the tensors need to be located after load. + available options are all regular torch device locations. + + Returns: + `Dict[str, torch.Tensor]`: dictionary that contains name as key, value as `torch.Tensor` + + Example: + + ```python + from safetensors.torch import load_file + + file_path = "./my_folder/bert.safetensors" + loaded = load_file(file_path) + ``` + """ + result = {} + with safe_open(filename, framework="pt", device=device) as f: + for k in f.keys(): + result[k] = f.get_tensor(k) + return result + + +def load(data: bytes) -> Dict[str, torch.Tensor]: + """ + Loads a safetensors file into torch format from pure bytes. + + Args: + data (`bytes`): + The content of a safetensors file + + Returns: + `Dict[str, torch.Tensor]`: dictionary that contains name as key, value as `torch.Tensor` on cpu + + Example: + + ```python + from safetensors.torch import load + + file_path = "./my_folder/bert.safetensors" + with open(file_path, "rb") as f: + data = f.read() + + loaded = load(data) + ``` + """ + flat = deserialize(data) + return _view2torch(flat) + + +# torch.float8 formats require 2.1; we do not support these dtypes on earlier versions +_float8_e4m3fn = getattr(torch, "float8_e4m3fn", None) +_float8_e5m2 = getattr(torch, "float8_e5m2", None) + +_SIZE = { + torch.int64: 8, + torch.float32: 4, + torch.int32: 4, + torch.bfloat16: 2, + torch.float16: 2, + torch.int16: 2, + torch.uint8: 1, + torch.int8: 1, + torch.bool: 1, + torch.float64: 8, + _float8_e4m3fn: 1, + _float8_e5m2: 1, +} + +_TYPES = { + "F64": torch.float64, + "F32": torch.float32, + "F16": torch.float16, + "BF16": torch.bfloat16, + "I64": torch.int64, + # "U64": torch.uint64, + "I32": torch.int32, + # "U32": torch.uint32, + "I16": torch.int16, + # "U16": torch.uint16, + "I8": torch.int8, + "U8": torch.uint8, + "BOOL": torch.bool, + "F8_E4M3": _float8_e4m3fn, + "F8_E5M2": _float8_e5m2, +} + + +def _getdtype(dtype_str: str) -> torch.dtype: + return _TYPES[dtype_str] + + +def _view2torch(safeview) -> Dict[str, torch.Tensor]: + result = {} + for k, v in safeview: + dtype = _getdtype(v["dtype"]) + if len(v["data"]) == 0: + # Workaround because frombuffer doesn't accept zero-size tensors + assert any(x == 0 for x in v["shape"]) + arr = torch.empty(v["shape"], dtype=dtype) + else: + arr = torch.frombuffer(v["data"], dtype=dtype).reshape(v["shape"]) + if sys.byteorder == "big": + arr = torch.from_numpy(arr.numpy().byteswap(inplace=False)) + result[k] = arr + + return result + + +def _tobytes(tensor: torch.Tensor, name: str) -> bytes: + if tensor.layout != torch.strided: + raise ValueError( + f"You are trying to save a sparse tensor: `{name}` which this library does not support." + " You can make it a dense tensor before saving with `.to_dense()` but be aware this might" + " make a much larger file than needed." + ) + + if not tensor.is_contiguous(): + raise ValueError( + f"You are trying to save a non contiguous tensor: `{name}` which is not allowed. It either means you" + " are trying to save tensors which are reference of each other in which case it's recommended to save" + " only the full tensors, and reslice at load time, or simply call `.contiguous()` on your tensor to" + " pack it before saving." + ) + if tensor.device.type != "cpu": + # Moving tensor to cpu before saving + tensor = tensor.to("cpu") + + import ctypes + + import numpy as np + + # When shape is empty (scalar), np.prod returns a float + # we need a int for the following calculations + length = int(np.prod(tensor.shape).item()) + bytes_per_item = _SIZE[tensor.dtype] + + total_bytes = length * bytes_per_item + + ptr = tensor.data_ptr() + if ptr == 0: + return b"" + newptr = ctypes.cast(ptr, ctypes.POINTER(ctypes.c_ubyte)) + data = np.ctypeslib.as_array(newptr, (total_bytes,)) # no internal copy + if sys.byteorder == "big": + NPDTYPES = { + torch.int64: np.int64, + torch.float32: np.float32, + torch.int32: np.int32, + # XXX: This is ok because both have the same width + torch.bfloat16: np.float16, + torch.float16: np.float16, + torch.int16: np.int16, + torch.uint8: np.uint8, + torch.int8: np.int8, + torch.bool: bool, + torch.float64: np.float64, + # XXX: This is ok because both have the same width and byteswap is a no-op anyway + _float8_e4m3fn: np.uint8, + _float8_e5m2: np.uint8, + } + npdtype = NPDTYPES[tensor.dtype] + # Not in place as that would potentially modify a live running model + data = data.view(npdtype).byteswap(inplace=False) + return data.tobytes() + + +def _flatten(tensors: Dict[str, torch.Tensor]) -> Dict[str, Dict[str, Any]]: + if not isinstance(tensors, dict): + raise ValueError(f"Expected a dict of [str, torch.Tensor] but received {type(tensors)}") + + invalid_tensors = [] + for k, v in tensors.items(): + if not isinstance(v, torch.Tensor): + raise ValueError(f"Key `{k}` is invalid, expected torch.Tensor but received {type(v)}") + + if v.layout != torch.strided: + invalid_tensors.append(k) + if invalid_tensors: + raise ValueError( + f"You are trying to save a sparse tensors: `{invalid_tensors}` which this library does not support." + " You can make it a dense tensor before saving with `.to_dense()` but be aware this might" + " make a much larger file than needed." + ) + + shared_pointers = _find_shared_tensors(tensors) + failing = [] + for names in shared_pointers: + if len(names) > 1: + failing.append(names) + + if failing: + raise RuntimeError( + f""" + Some tensors share memory, this will lead to duplicate memory on disk and potential differences when loading them again: {failing}. + A potential way to correctly save your model is to use `save_model`. + More information at https://huggingface.co/docs/safetensors/torch_shared_tensors + """ + ) + + return { + k: { + "dtype": str(v.dtype).split(".")[-1], + "shape": v.shape, + "data": _tobytes(v, k), + } + for k, v in tensors.items() + } diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/__pycache__/tokenization_utils_base.cpython-310.pyc b/evalkit_internvl/lib/python3.10/site-packages/transformers/__pycache__/tokenization_utils_base.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..baea9c6f936d1af5090de97c2de3055966c3b947 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/__pycache__/tokenization_utils_base.cpython-310.pyc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e32cb992ae5b1f858d09f7b912fe455fe9ae85fa11c75c97d1a44a1eff547a95 +size 145200 diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_kernel.cu b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..87ed89052873813153786bd416a981d3e5279af9 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_kernel.cu @@ -0,0 +1,383 @@ +#include "cuda_kernel.h" + +////////////////////////////////////////////////////////////////////////////////////////////////// +////////////////////////////////////////////////////////////////////////////////////////////////// + +__global__ void index_max_cuda_kernel( + float *index_vals, // [batch_size, 32, num_block] + int *indices, // [batch_size, num_block] + float *max_vals, // [batch_size, A_num_block * 32] + float *max_vals_scatter, // [batch_size, 32, num_block] + long batch_size, + long A_num_block, + long B_num_block, + long num_block +) { + + long batch_idx = blockIdx.x; + + long thread_idx = threadIdx.x; + long num_thread = blockDim.x; + + extern __shared__ float buffer[]; + int *max_buffer = (int*)buffer; + + for (int i = 0; i < A_num_block * 32; i = i + num_thread) { + int idx = i + thread_idx; + if (idx < A_num_block * 32) { + max_buffer[idx] = -1e8; + } + } + __syncthreads(); + + int *indices_pt = &indices[batch_idx * num_block]; + float *index_vals_pt = &index_vals[batch_idx * num_block * 32]; + + for (int idx_start = 0; idx_start < 32 * num_block; idx_start = idx_start + num_thread) { + int idx = idx_start + thread_idx; + int A_block_idx = indices_pt[idx % num_block] / B_num_block; + atomicMax(&max_buffer[A_block_idx * 32 + idx / num_block], (int)(index_vals_pt[idx] * 1000)); + } + __syncthreads(); + + float *max_vals_pt = &max_vals[batch_idx * A_num_block * 32]; + for (int i = 0; i < A_num_block * 32; i = i + num_thread) { + int idx = i + thread_idx; + if (idx < A_num_block * 32) { + max_vals_pt[idx] = (float)max_buffer[idx] / 1000.; + } + } + + float *max_vals_scatter_pt = &max_vals_scatter[batch_idx * num_block * 32]; + for (int idx_start = 0; idx_start < 32 * num_block; idx_start = idx_start + num_thread) { + int idx = idx_start + thread_idx; + int A_block_idx = indices_pt[idx % num_block] / B_num_block; + max_vals_scatter_pt[idx] = (float)max_buffer[A_block_idx * 32 + idx / num_block] / 1000.; + } + +} + +__global__ void mm_to_sparse_cuda_kernel( + float *dense_A, // [batch_size, A_num_block, dim, 32] + float *dense_B, // [batch_size, B_num_block, dim, 32] + int *indices, // [batch_size, num_block] + float *sparse_C, // [batch_size, num_block, 32, 32] + long batch_size, + long A_num_block, + long B_num_block, + long dim, + long num_block +) { + + long batch_idx = blockIdx.y; + long block_idx = blockIdx.x * blockDim.y + threadIdx.y; + + long thread_idx = threadIdx.x; + + __shared__ float buffer[4096]; + float *A_buffer = &buffer[threadIdx.y * 1024]; // [2, 8, 32] + float *B_buffer = &buffer[threadIdx.y * 1024 + 512]; // [2, 8, 32] + + long batch_idx__block_idx = batch_idx * num_block + block_idx; + + long AB_block_idx = indices[batch_idx__block_idx]; + float *dense_A_pt = &dense_A[(batch_idx * A_num_block + AB_block_idx / B_num_block) * dim * 32]; + float *dense_B_pt = &dense_B[(batch_idx * B_num_block + AB_block_idx % B_num_block) * dim * 32]; + + int reg_1_idx = thread_idx / 8; // [0000000011111111222222223333333344444444555555556666666677777777] + int reg_2_idx = thread_idx % 8; // [0123456701234567012345670123456701234567012345670123456701234567] + + float reg_1[8]; + float reg_2[8]; + + float reg_array[16] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + + #pragma unroll + for (int i = 0; i < 4; i++) { + A_buffer[i * 64 + thread_idx] = dense_A_pt[i * 64 + thread_idx]; + B_buffer[i * 64 + thread_idx] = dense_B_pt[i * 64 + thread_idx]; + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < 4; i++) { + reg_1[i] = A_buffer[reg_1_idx * 4 + i]; + reg_2[i] = B_buffer[reg_2_idx * 4 + i]; + } + + for (int dim_stride = 1; dim_stride < (dim / 8); dim_stride++) { + + #pragma unroll + for (int i = 0; i < 4; i++) { + A_buffer[(dim_stride % 2) * 256 + i * 64 + thread_idx] = dense_A_pt[dim_stride * 256 + i * 64 + thread_idx]; + B_buffer[(dim_stride % 2) * 256 + i * 64 + thread_idx] = dense_B_pt[dim_stride * 256 + i * 64 + thread_idx]; + } + + #pragma unroll + for (int mini_dim_idx = 1; mini_dim_idx < 8; mini_dim_idx++) { + #pragma unroll + for (int i = 0; i < 4; i++) { + reg_1[(mini_dim_idx % 2) * 4 + i] = A_buffer[((dim_stride - 1) % 2) * 256 + mini_dim_idx * 32 + reg_1_idx * 4 + i]; + reg_2[(mini_dim_idx % 2) * 4 + i] = B_buffer[((dim_stride - 1) % 2) * 256 + mini_dim_idx * 32 + reg_2_idx * 4 + i]; + } + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + reg_array[i * 4 + j] += reg_1[((mini_dim_idx - 1) % 2) * 4 + i] * reg_2[((mini_dim_idx - 1) % 2) * 4 + j]; + } + } + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < 4; i++) { + reg_1[i] = A_buffer[(dim_stride % 2) * 256 + reg_1_idx * 4 + i]; + reg_2[i] = B_buffer[(dim_stride % 2) * 256 + reg_2_idx * 4 + i]; + } + + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + reg_array[i * 4 + j] += reg_1[4 + i] * reg_2[4 + j]; + } + } + + } + + #pragma unroll + for (int mini_dim_idx = 1; mini_dim_idx < 8; mini_dim_idx++) { + #pragma unroll + for (int i = 0; i < 4; i++) { + reg_1[(mini_dim_idx % 2) * 4 + i] = A_buffer[256 + mini_dim_idx * 32 + reg_1_idx * 4 + i]; + reg_2[(mini_dim_idx % 2) * 4 + i] = B_buffer[256 + mini_dim_idx * 32 + reg_2_idx * 4 + i]; + } + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + reg_array[i * 4 + j] += reg_1[((mini_dim_idx - 1) % 2) * 4 + i] * reg_2[((mini_dim_idx - 1) % 2) * 4 + j]; + } + } + } + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + reg_array[i * 4 + j] += reg_1[4 + i] * reg_2[4 + j]; + } + } + __syncthreads(); + + float *C_buffer = &buffer[threadIdx.y * 1024]; // [32, 32] + + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + C_buffer[(reg_2_idx * 4 + j) * 32 + reg_1_idx * 4 + i] = reg_array[i * 4 + j]; + } + } + __syncthreads(); + + float *sparse_C_pt = &sparse_C[batch_idx__block_idx * 1024]; + + #pragma unroll + for (int i = 0; i < 16; i++) { + sparse_C_pt[i * 64 + thread_idx] = C_buffer[i * 64 + thread_idx]; + } + +} + +__global__ void sparse_dense_mm_cuda_kernel( + float *sparse_A, // [batch_size, num_block, 32, 32] + int *indices, // [batch_size, num_block] + float *dense_B, // [batch_size, B_num_block, dim, 32] + float *dense_C, // [batch_size, A_num_block, dim, 32] + long batch_size, + long A_num_block, + long B_num_block, + long dim, + long num_block +) { + + long batch_idx = blockIdx.y; + long block_idx = blockIdx.x * blockDim.y + threadIdx.y; + + long thread_idx = threadIdx.x; + + __shared__ float buffer[6144]; + float *A_buffer = &buffer[threadIdx.y * 3072]; // [32, 32] + float *B_buffer = &buffer[threadIdx.y * 3072 + 1024]; // [32, 64] + + long batch_idx__block_idx = batch_idx * num_block + block_idx; + + float *sparse_A_pt = &sparse_A[batch_idx__block_idx * 1024]; + #pragma unroll + for (int i = 0; i < 8; i++) { + A_buffer[i * 128 + thread_idx] = sparse_A_pt[i * 128 + thread_idx]; + } + + long AB_block_idx = indices[batch_idx__block_idx]; + float *dense_B_pt = &dense_B[(batch_idx * B_num_block + AB_block_idx % B_num_block) * 32 * dim]; + float *dense_C_pt = &dense_C[(batch_idx * A_num_block + AB_block_idx / B_num_block) * 32 * dim]; + + // [0000000011111111222222223333333344444444555555556666666677777777] + // [0123456701234567012345670123456701234567012345670123456701234567] + int reg_1_idx = thread_idx / 8; + int reg_2_idx = thread_idx % 8; + + float reg_1[8]; + float reg_2[8]; + + float reg_array[16]; + + for (int dim_stride = 0; dim_stride < dim; dim_stride = dim_stride + 64) { + + #pragma unroll + for (int i = 0; i < 16; i++) { + B_buffer[i * 128 + thread_idx] = dense_B_pt[dim_stride * 32 + i * 128 + thread_idx]; + } + + #pragma unroll + for (int i = 0; i < 16; i++) { + reg_array[i] = 0; + } + + __syncthreads(); + + #pragma unroll + for (int i = 0; i < 4; i++) { + reg_1[i] = B_buffer[(reg_1_idx * 4 + i) * 32]; + reg_2[i] = A_buffer[reg_2_idx * 4 + i]; + } + + #pragma unroll + for (int mini_dim_idx = 1; mini_dim_idx < 32; mini_dim_idx++) { + #pragma unroll + for (int i = 0; i < 4; i++) { + reg_1[(mini_dim_idx % 2) * 4 + i] = B_buffer[(reg_1_idx * 4 + i) * 32 + mini_dim_idx]; + reg_2[(mini_dim_idx % 2) * 4 + i] = A_buffer[mini_dim_idx * 32 + reg_2_idx * 4 + i]; + } + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + reg_array[i * 4 + j] += reg_1[((mini_dim_idx - 1) % 2) * 4 + i] * reg_2[((mini_dim_idx - 1) % 2) * 4 + j]; + } + } + } + + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + reg_array[i * 4 + j] += reg_1[4 + i] * reg_2[4 + j]; + } + } + + __syncthreads(); + + float *C_buffer = &buffer[threadIdx.y * 3072 + 1024]; // [64, 32] + + #pragma unroll + for (int i = 0; i < 4; i++) { + #pragma unroll + for (int j = 0; j < 4; j++) { + C_buffer[(reg_1_idx * 4 + i) * 32 + reg_2_idx * 4 + j] = reg_array[i * 4 + j]; + } + } + __syncthreads(); + + #pragma unroll + for (int i = 0; i < 16; i++) { + atomicAdd(&dense_C_pt[dim_stride * 32 + i * 128 + thread_idx], C_buffer[i * 128 + thread_idx]); + } + __syncthreads(); + + } + +} + + +__global__ void reduce_sum_cuda_kernel( + float *sparse_A, // [batch_size, num_block, 32, 32] + int *indices, // [batch_size, num_block] + float *dense_C, // [batch_size, A_num_block, 32] + long batch_size, + long A_num_block, + long B_num_block, + long num_block +) { + + long batch_idx = blockIdx.y; + long block_idx = blockIdx.x * blockDim.y + threadIdx.y; + + long thread_idx = threadIdx.x; + + long batch_idx__block_idx = batch_idx * num_block + block_idx; + + long AB_block_idx = indices[batch_idx__block_idx]; + float *sparse_A_pt = &sparse_A[batch_idx__block_idx * 1024]; + + float reg_array[16]; + float value = 0; + + #pragma unroll + for (int i = 0; i < 8; i++) { + reg_array[i] = sparse_A_pt[i * 32 + thread_idx]; + } + #pragma unroll + for (int stride = 8; stride < 32; stride = stride + 8) { + #pragma unroll + for (int i = 0; i < 8; i++) { + reg_array[(stride + i) % 16] = sparse_A_pt[(stride + i) * 32 + thread_idx]; + } + #pragma unroll + for (int i = 0; i < 8; i++) { + value = value + reg_array[(stride - 8 + i) % 16]; + } + } + #pragma unroll + for (int i = 0; i < 8; i++) { + value = value + reg_array[8 + i]; + } + + float *dense_C_pt = &dense_C[(batch_idx * A_num_block + AB_block_idx / B_num_block) * 32]; + + atomicAdd(&dense_C_pt[thread_idx], value); + +} + +__global__ void scatter_cuda_kernel( + float *dense_A, // [batch_size, A_num_block, 32] + int *indices, // [batch_size, num_block] + float *sparse_C, // [batch_size, num_block, 32, 32] + long batch_size, + long A_num_block, + long B_num_block, + long num_block +) { + + long batch_idx = blockIdx.y; + long block_idx = blockIdx.x * blockDim.y + threadIdx.y; + + long thread_idx = threadIdx.x; + + long batch_idx__block_idx = batch_idx * num_block + block_idx; + + long AB_block_idx = indices[batch_idx__block_idx]; + float *dense_A_pt = &dense_A[(batch_idx * A_num_block + AB_block_idx / B_num_block) * 32]; + float *sparse_C_pt = &sparse_C[(batch_idx * num_block + block_idx) * 1024]; + + float value = dense_A_pt[thread_idx]; + + #pragma unroll + for (int i = 0; i < 32; i++) { + sparse_C_pt[i * 32 + thread_idx] = value; + } + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_kernel.h b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_kernel.h new file mode 100644 index 0000000000000000000000000000000000000000..a95b46f7d159b11851143710034cf80c20aa6bf8 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_kernel.h @@ -0,0 +1,59 @@ + +#define WARP_SIZE 32 +#define FULL_MASK 0xffffffff +#define OPTIMAL_THREADS 256 + +__global__ void index_max_cuda_kernel( + float *index_vals, // [batch_size, 32, num_block] + int *indices, // [batch_size, num_block] + float *max_vals, // [batch_size, A_num_block * 32] + float *max_vals_scatter, // [batch_size, 32, num_block] + long batch_size, + long A_num_block, + long B_num_block, + long num_block +); + +__global__ void mm_to_sparse_cuda_kernel( + float *dense_A, // [batch_size, A_num_block, dim, 32] + float *dense_B, // [batch_size, B_num_block, dim, 32] + int *indices, // [batch_size, num_block] + float *sparse_C, // [batch_size, num_block, 32, 32] + long batch_size, + long A_num_block, + long B_num_block, + long dim, + long num_block +); + +__global__ void sparse_dense_mm_cuda_kernel( + float *sparse_A, // [batch_size, num_block, 32, 32] + int *indices, // [batch_size, num_block] + float *dense_B, // [batch_size, B_num_block, dim, 32] + float *dense_C, // [batch_size, A_num_block, dim, 32] + long batch_size, + long A_num_block, + long B_num_block, + long dim, + long num_block +); + +__global__ void reduce_sum_cuda_kernel( + float *sparse_A, // [batch_size, num_block, 32, 32] + int *indices, // [batch_size, num_block] + float *dense_C, // [batch_size, A_num_block, 32] + long batch_size, + long A_num_block, + long B_num_block, + long num_block +); + +__global__ void scatter_cuda_kernel( + float *dense_A, // [batch_size, A_num_block, 32] + int *indices, // [batch_size, num_block] + float *sparse_C, // [batch_size, num_block, 32, 32] + long batch_size, + long A_num_block, + long B_num_block, + long num_block +); diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_launch.cu b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_launch.cu new file mode 100644 index 0000000000000000000000000000000000000000..ba2a0cacfe614e75e06d2dde80dc77a6e8a4ec1a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_launch.cu @@ -0,0 +1,154 @@ +#include +#include +#include "cuda_launch.h" +#include "cuda_kernel.h" +#include + +////////////////////////////////////////////////////////////////////////////////////////////////// +////////////////////////////////////////////////////////////////////////////////////////////////// + +std::vector index_max_kernel( + at::Tensor index_vals, // [batch_size, 32, num_block] + at::Tensor indices, // [batch_size, num_block], + int A_num_block, + int B_num_block +) { + int batch_size = indices.size(0); + int num_block = indices.size(1); + + at::Tensor max_vals = at::zeros({batch_size, A_num_block * 32}, index_vals.options()); + at::Tensor max_vals_scatter = at::zeros({batch_size, 32, num_block}, index_vals.options()); + + dim3 threads(256); + dim3 blocks(batch_size); + int shared_mem = A_num_block * 32 * sizeof(float); + + index_max_cuda_kernel<<>>( + index_vals.data_ptr(), + indices.data_ptr(), + max_vals.data_ptr(), + max_vals_scatter.data_ptr(), + batch_size, + A_num_block, + B_num_block, + num_block + ); + + return {max_vals, max_vals_scatter}; +} + +at::Tensor mm_to_sparse_kernel( + at::Tensor dense_A, // [batch_size, A_num_block, dim, 32] + at::Tensor dense_B, // [batch_size, B_num_block, dim, 32] + at::Tensor indices // [batch_size, num_block] +) { + int batch_size = dense_A.size(0); + int A_num_block = dense_A.size(1); + int B_num_block = dense_B.size(1); + int dim = dense_A.size(2); + int num_block = indices.size(1); + + at::Tensor sparse_C = at::zeros({batch_size, num_block, 32, 32}, dense_A.options()); + + dim3 threads(64, 4); + dim3 blocks(num_block / 4, batch_size); + + mm_to_sparse_cuda_kernel<<>>( + dense_A.data_ptr(), + dense_B.data_ptr(), + indices.data_ptr(), + sparse_C.data_ptr(), + batch_size, + A_num_block, + B_num_block, + dim, + num_block + ); + + return sparse_C; +} + +at::Tensor sparse_dense_mm_kernel( + at::Tensor sparse_A, // [batch_size, num_block, 32, 32] + at::Tensor indices, // [batch_size, num_block] + at::Tensor dense_B, // [batch_size, B_num_block, dim, 32] + int A_num_block +) { + int batch_size = sparse_A.size(0); + int num_block = sparse_A.size(1); + int B_num_block = dense_B.size(1); + int dim = dense_B.size(2); + + at::Tensor dense_C = at::zeros({batch_size, A_num_block, dim, 32}, dense_B.options()); + + dim3 threads(128, 2); + dim3 blocks(num_block / 2, batch_size); + + sparse_dense_mm_cuda_kernel<<>>( + sparse_A.data_ptr(), + indices.data_ptr(), + dense_B.data_ptr(), + dense_C.data_ptr(), + batch_size, + A_num_block, + B_num_block, + dim, + num_block + ); + + return dense_C; +} + +at::Tensor reduce_sum_kernel( + at::Tensor sparse_A, // [batch_size, num_block, 32, 32] + at::Tensor indices, // [batch_size, num_block] + int A_num_block, + int B_num_block +) { + int batch_size = sparse_A.size(0); + int num_block = sparse_A.size(1); + + at::Tensor dense_C = at::zeros({batch_size, A_num_block, 32}, sparse_A.options()); + + dim3 threads(32, 4); + dim3 blocks(num_block / 4, batch_size); + + reduce_sum_cuda_kernel<<>>( + sparse_A.data_ptr(), + indices.data_ptr(), + dense_C.data_ptr(), + batch_size, + A_num_block, + B_num_block, + num_block + ); + + return dense_C; +} + +at::Tensor scatter_kernel( + at::Tensor dense_A, // [batch_size, A_num_block, 32] + at::Tensor indices, // [batch_size, num_block] + int B_num_block +) { + int batch_size = dense_A.size(0); + int A_num_block = dense_A.size(1); + int num_block = indices.size(1); + + at::Tensor sparse_C = at::zeros({batch_size, num_block, 32, 32}, dense_A.options()); + + dim3 threads(32, 4); + dim3 blocks(num_block / 4, batch_size); + + scatter_cuda_kernel<<>>( + dense_A.data_ptr(), + indices.data_ptr(), + sparse_C.data_ptr(), + batch_size, + A_num_block, + B_num_block, + num_block + ); + + return sparse_C; +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_launch.h b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_launch.h new file mode 100644 index 0000000000000000000000000000000000000000..0200140ee337b8c5d9583767bbad1e842e9d4677 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/cuda_launch.h @@ -0,0 +1,39 @@ +#include +#include +#include + +#define min(a, b) ((a)<(b)?(a):(b)) +#define max(a, b) ((a)>(b)?(a):(b)) + +std::vector index_max_kernel( + at::Tensor index_vals, + at::Tensor indices, + int A_num_block, + int B_num_block +); + +at::Tensor mm_to_sparse_kernel( + at::Tensor dense_A, + at::Tensor dense_B, + at::Tensor indices +); + +at::Tensor sparse_dense_mm_kernel( + at::Tensor sparse_A, + at::Tensor indices, + at::Tensor dense_B, + int A_num_block +); + +at::Tensor reduce_sum_kernel( + at::Tensor sparse_A, + at::Tensor indices, + int A_num_block, + int B_num_block +); + +at::Tensor scatter_kernel( + at::Tensor dense_A, + at::Tensor indices, + int B_num_block +); diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/torch_extension.cpp b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/torch_extension.cpp new file mode 100644 index 0000000000000000000000000000000000000000..60c9262b779270a6e95ae54f53a67daa6d740a9e --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/mra/torch_extension.cpp @@ -0,0 +1,78 @@ +#include +#include +#include "cuda_launch.h" +#include + +std::vector index_max( + at::Tensor index_vals, + at::Tensor indices, + int A_num_block, + int B_num_block +) { + return index_max_kernel( + index_vals, + indices, + A_num_block, + B_num_block + ); +} + +at::Tensor mm_to_sparse( + at::Tensor dense_A, + at::Tensor dense_B, + at::Tensor indices +) { + return mm_to_sparse_kernel( + dense_A, + dense_B, + indices + ); +} + +at::Tensor sparse_dense_mm( + at::Tensor sparse_A, + at::Tensor indices, + at::Tensor dense_B, + int A_num_block +) { + return sparse_dense_mm_kernel( + sparse_A, + indices, + dense_B, + A_num_block + ); +} + +at::Tensor reduce_sum( + at::Tensor sparse_A, + at::Tensor indices, + int A_num_block, + int B_num_block +) { + return reduce_sum_kernel( + sparse_A, + indices, + A_num_block, + B_num_block + ); +} + +at::Tensor scatter( + at::Tensor dense_A, + at::Tensor indices, + int B_num_block +) { + return scatter_kernel( + dense_A, + indices, + B_num_block + ); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("index_max", &index_max, "index_max (CUDA)"); + m.def("mm_to_sparse", &mm_to_sparse, "mm_to_sparse (CUDA)"); + m.def("sparse_dense_mm", &sparse_dense_mm, "sparse_dense_mm (CUDA)"); + m.def("reduce_sum", &reduce_sum, "reduce_sum (CUDA)"); + m.def("scatter", &scatter, "scatter (CUDA)"); +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/common_cuda.h b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/common_cuda.h new file mode 100644 index 0000000000000000000000000000000000000000..97030870649a2fdac58cb26cf966e8f5c8cc7909 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/common_cuda.h @@ -0,0 +1,9 @@ + +#define MAX_THREADS_PER_BLOCK 1024 +#define OPTIMAL_THREADS_PER_BLOCK 256 +#define WARP_SIZE 32 +#define MAX_NUM_BLOCK_X 2147483647 +#define MAX_NUM_BLOCK_Y 65535 +#define MAX_NUM_BLOCK_Z 65535 +#define MAX_SHARED_MEM_PER_BLOCK 48000 +#define FULL_MASK 0xffffffff diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/common_cuda_device.h b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/common_cuda_device.h new file mode 100644 index 0000000000000000000000000000000000000000..6674f93afdc25ab35c5d83881d00028bcf2989fc --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/common_cuda_device.h @@ -0,0 +1,79 @@ + +#include "common.h" + +template +__device__ int set_insert(T *set, int set_size, T value) { + int slot = value % set_size; + int start_slot = slot; + while (true) { + T prev = atomicCAS(&set[slot], EMPTY_VALUE, value); + if (prev == EMPTY_VALUE || prev == value) { + return slot; + } + slot = (slot + 1) % set_size; + if (slot == start_slot) { + return -1; + } + } + return -1; +} + +template +__device__ int set_lookup(T *set, int set_size, T value) { + int slot = value % set_size; + int start_slot = slot; + while (true) { + if (set[slot] == value) { + return slot; + } + slot = (slot + 1) % set_size; + if (slot == start_slot) { + return -1; + } + } + return -1; +} + +template +__device__ void init_buffer(T init_value, T *buffer, int buffer_size, int num_threads, int thread_id) { + __syncthreads(); + for (int i = 0; i < buffer_size; i = i + num_threads) { + int offset_idx = i + thread_id; + if (offset_idx < buffer_size) { + buffer[offset_idx] = init_value; + } + } + __syncthreads(); +} + +template +__device__ void copy_data(T *src_pt, T *dist_pt, int data_length, int num_threads, int thread_id) { + __syncthreads(); + for (int i = 0; i < data_length; i = i + num_threads) { + int offset_idx = i + thread_id; + if (offset_idx < data_length) { + dist_pt[offset_idx] = src_pt[offset_idx]; + } + } + __syncthreads(); +} + +template +__device__ void init_buffer_nonblocking(T init_value, T *buffer, int buffer_size, int num_threads, int thread_id) { + for (int i = 0; i < buffer_size; i = i + num_threads) { + int offset_idx = i + thread_id; + if (offset_idx < buffer_size) { + buffer[offset_idx] = init_value; + } + } +} + +template +__device__ void copy_data_nonblocking(T *src_pt, T *dist_pt, int data_length, int num_threads, int thread_id) { + for (int i = 0; i < data_length; i = i + num_threads) { + int offset_idx = i + thread_id; + if (offset_idx < data_length) { + dist_pt[offset_idx] = src_pt[offset_idx]; + } + } +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation.cu b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation.cu new file mode 100644 index 0000000000000000000000000000000000000000..c6b13e6cb5f53c9c62e51d2c399a14d14dab7037 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation.cu @@ -0,0 +1,588 @@ +// File from https://github.com/mlpen/YOSO/blob/main/encoders/backbones/efficient_attentions/yoso/yoso_v1/cuda/fast_lsh_cumulation.cu + +#include +#include +#include "fast_lsh_cumulation.h" +#include "fast_lsh_cumulation_cuda.h" +#include "common_cuda.h" +#include "common.h" +#include +////////////////////////////////////////////////////////////////////////////////////////////////// +////////////////////////////////////////////////////////////////////////////////////////////////// + +std::vector fast_hash_ver1_kernel( + at::Tensor query_mask, + at::Tensor query_vector, + at::Tensor key_mask, + at::Tensor key_vector, + int num_hash_f, + int hash_code_len, + bool use_cuda +) { + + int batch_size = query_vector.size(0); + int num_query = query_vector.size(1); + int num_key = key_vector.size(1); + int vector_dim = query_vector.size(2); + + int num_hash_per_part = vector_dim / hash_code_len; + int num_part = max(1, ceil_divide(num_hash_f, num_hash_per_part)); + + at::Tensor Dmat = 2 * at::randint(0, 2, {batch_size, 3, num_part, vector_dim}, query_mask.options()) - 1; + at::Tensor query_hash_code = at::zeros({batch_size, num_query, num_hash_f}, query_mask.options()); + at::Tensor key_hash_code = at::zeros({batch_size, num_key, num_hash_f}, key_mask.options()); + + int *query_mask_ptr = query_mask.data_ptr(); + float *query_vector_ptr = query_vector.data_ptr(); + int *key_mask_ptr = key_mask.data_ptr(); + float *key_vector_ptr = key_vector.data_ptr(); + + int *Dmat_ptr = Dmat.data_ptr(); + + int *query_hash_code_ptr = query_hash_code.data_ptr(); + int *key_hash_code_ptr = key_hash_code.data_ptr(); + + if (use_cuda) { + { + dim3 threads(vector_dim); + dim3 blocks(num_part, num_query, batch_size); + int shared_mem = vector_dim * sizeof(float); + fast_hash_ver1_cuda_kernel<<>>( + query_mask_ptr, + query_vector_ptr, + Dmat_ptr, + query_hash_code_ptr, + batch_size, + num_query, + vector_dim, + num_part, + num_hash_f, + hash_code_len + ); + } + { + dim3 threads(vector_dim); + dim3 blocks(num_part, num_key, batch_size); + int shared_mem = vector_dim * sizeof(float); + fast_hash_ver1_cuda_kernel<<>>( + key_mask_ptr, + key_vector_ptr, + Dmat_ptr, + key_hash_code_ptr, + batch_size, + num_key, + vector_dim, + num_part, + num_hash_f, + hash_code_len + ); + } + } + + return {query_hash_code, key_hash_code}; + +} + +at::Tensor lsh_cumulation_ver1_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +) { + + int batch_size = query_hash_code.size(0); + int num_hash_f = query_hash_code.size(2); + + int num_query = query_hash_code.size(1); + int num_key = key_hash_code.size(1); + int value_dim = value.size(2); + + at::Tensor hashtable_value = at::empty({batch_size, num_hash_f, hashtable_capacity, WARP_SIZE}, value.options()); + at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); + + if (use_cuda) { + int threads_x = WARP_SIZE; + int threads_y = OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE; + int block_x_step1 = num_key / threads_y; + int block_x_step2 = num_query / threads_y; + int block_y = batch_size; + + dim3 threads(threads_x, threads_y); + dim3 blocks_step1(block_x_step1, block_y); + dim3 blocks_step2(block_x_step2, block_y); + + int *query_mask_ptr = query_mask.data_ptr(); + int *query_hash_code_ptr = query_hash_code.data_ptr(); + int *key_mask_ptr = key_mask.data_ptr(); + int *key_hash_code_ptr = key_hash_code.data_ptr(); + float *value_ptr = value.data_ptr(); + float *hashtable_value_ptr = hashtable_value.data_ptr(); + float *cumulation_value_ptr = cumulation_value.data_ptr(); + + for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { + + cudaMemset(hashtable_value_ptr, 0, (batch_size * num_hash_f * hashtable_capacity * WARP_SIZE) * sizeof(float)); + + lsh_cumulation_ver1_step1_cuda_kernel<<>>( + key_mask_ptr, + key_hash_code_ptr, + value_ptr, + hashtable_value_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_key, + value_dim, + value_offset + ); + + lsh_cumulation_ver1_step2_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + hashtable_value_ptr, + cumulation_value_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query, + value_dim, + value_offset + ); + } + + } + + return cumulation_value; + +} + +at::Tensor lsh_weighted_cumulation_ver1_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +) { + + int batch_size = query_hash_code.size(0); + int num_hash_f = query_hash_code.size(2); + + int num_query = query_hash_code.size(1); + int num_key = key_hash_code.size(1); + int value_dim = value.size(2); + int weight_dim = query_weight.size(2); + + at::Tensor hashtable_value = at::zeros({batch_size, num_hash_f, hashtable_capacity, WARP_SIZE}, value.options()); + at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); + + if (use_cuda) { + int threads_x = WARP_SIZE; + int threads_y = OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE; + int block_x_step1 = num_key / threads_y; + int block_x_step2 = num_query / threads_y; + int block_y = batch_size; + + dim3 threads(threads_x, threads_y); + dim3 blocks_step1(block_x_step1, block_y); + dim3 blocks_step2(block_x_step2, block_y); + + int *query_mask_ptr = query_mask.data_ptr(); + int *query_hash_code_ptr = query_hash_code.data_ptr(); + float *query_weight_ptr = query_weight.data_ptr(); + int *key_mask_ptr = key_mask.data_ptr(); + int *key_hash_code_ptr = key_hash_code.data_ptr(); + float *key_weight_ptr = key_weight.data_ptr(); + float *value_ptr = value.data_ptr(); + float *hashtable_value_ptr = hashtable_value.data_ptr(); + float *cumulation_value_ptr = cumulation_value.data_ptr(); + + for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { + for (int weight_idx = 0; weight_idx < weight_dim; weight_idx++) { + + cudaMemset(hashtable_value_ptr, 0, (batch_size * num_hash_f * hashtable_capacity * WARP_SIZE) * sizeof(float)); + + lsh_weighted_cumulation_ver1_step1_cuda_kernel<<>>( + key_mask_ptr, + key_hash_code_ptr, + key_weight_ptr, + value_ptr, + hashtable_value_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_key, + value_dim, + weight_dim, + value_offset, + weight_idx + ); + + lsh_weighted_cumulation_ver1_step2_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + query_weight_ptr, + hashtable_value_ptr, + cumulation_value_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query, + value_dim, + weight_dim, + value_offset, + weight_idx + ); + } + } + + } + + return cumulation_value; + +} + +at::Tensor lsh_weighted_cumulation_ver2_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +) { + + int batch_size = query_hash_code.size(0); + int num_hash_f = query_hash_code.size(2); + + int num_query = query_hash_code.size(1); + int num_key = key_hash_code.size(1); + int value_dim = value.size(2); + int weight_dim = query_weight.size(2); + + at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options()); + at::Tensor key_sorted_idxes = at::zeros({batch_size, num_hash_f, num_key}, query_hash_code.options()); + at::Tensor query_info = at::zeros({batch_size, num_query, 2, num_hash_f}, query_hash_code.options()); + at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); + + if (use_cuda) { + + int *query_mask_ptr = query_mask.data_ptr(); + int *query_hash_code_ptr = query_hash_code.data_ptr(); + float *query_weight_ptr = query_weight.data_ptr(); + int *key_mask_ptr = key_mask.data_ptr(); + int *key_hash_code_ptr = key_hash_code.data_ptr(); + float *key_weight_ptr = key_weight.data_ptr(); + float *value_ptr = value.data_ptr(); + + int *count_sort_table_ptr = count_sort_table.data_ptr(); + int *key_sorted_idxes_ptr = key_sorted_idxes.data_ptr(); + int *query_info_ptr = query_info.data_ptr(); + + float *cumulation_value_ptr = cumulation_value.data_ptr(); + + { + dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); + dim3 blocks_step13(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); + dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK)); + dim3 blocks_step2(num_hash_f, batch_size); + int shared_mem = hashtable_capacity * sizeof(float); + count_sort_step1_cuda_kernel<<>>( + key_mask_ptr, + key_hash_code_ptr, + count_sort_table_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_key + ); + count_sort_step2_cuda_kernel<<>>( + count_sort_table_ptr, + batch_size, + num_hash_f, + hashtable_capacity + ); + count_sort_step3_cuda_kernel<<>>( + key_mask_ptr, + key_hash_code_ptr, + count_sort_table_ptr, + key_sorted_idxes_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_key + ); + } + { + dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); + dim3 blocks(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); + extract_query_info_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + count_sort_table_ptr, + query_info_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query + ); + } + { + dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE); + dim3 blocks(num_query, num_hash_f, batch_size); + int shared_mem = (weight_dim + WARP_SIZE) * sizeof(float); + lsh_weighted_cumulation_ver2_step2_cuda_kernel<<>>( + query_mask_ptr, + query_info_ptr, + key_sorted_idxes_ptr, + query_weight_ptr, + key_weight_ptr, + value_ptr, + cumulation_value_ptr, + batch_size, + num_hash_f, + num_query, + num_key, + value_dim, + weight_dim + ); + } + } + + return cumulation_value; + +} + +at::Tensor lsh_weighted_cumulation_ver3_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +) { + + int batch_size = query_hash_code.size(0); + int num_hash_f = query_hash_code.size(2); + + int num_query = query_hash_code.size(1); + int num_key = key_hash_code.size(1); + int value_dim = value.size(2); + int weight_dim = query_weight.size(2); + + at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options()); + at::Tensor query_sorted_idxes = at::zeros({batch_size, num_hash_f, num_query}, query_hash_code.options()); + at::Tensor key_info = at::zeros({batch_size, num_key, 2, num_hash_f}, query_hash_code.options()); + at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); + + if (use_cuda) { + + int *query_mask_ptr = query_mask.data_ptr(); + int *query_hash_code_ptr = query_hash_code.data_ptr(); + float *query_weight_ptr = query_weight.data_ptr(); + int *key_mask_ptr = key_mask.data_ptr(); + int *key_hash_code_ptr = key_hash_code.data_ptr(); + float *key_weight_ptr = key_weight.data_ptr(); + float *value_ptr = value.data_ptr(); + + int *count_sort_table_ptr = count_sort_table.data_ptr(); + int *query_sorted_idxes_ptr = query_sorted_idxes.data_ptr(); + int *key_info_ptr = key_info.data_ptr(); + + float *cumulation_value_ptr = cumulation_value.data_ptr(); + + { + dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); + dim3 blocks_step13(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); + dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK)); + dim3 blocks_step2(num_hash_f, batch_size); + int shared_mem = hashtable_capacity * sizeof(float); + count_sort_step1_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + count_sort_table_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query + ); + count_sort_step2_cuda_kernel<<>>( + count_sort_table_ptr, + batch_size, + num_hash_f, + hashtable_capacity + ); + count_sort_step3_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + count_sort_table_ptr, + query_sorted_idxes_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query + ); + } + { + dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); + dim3 blocks(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); + extract_query_info_cuda_kernel<<>>( + key_mask_ptr, + key_hash_code_ptr, + count_sort_table_ptr, + key_info_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_key + ); + } + { + dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE); + dim3 blocks(num_key, num_hash_f, batch_size); + int shared_mem = (weight_dim + value_dim + WARP_SIZE) * sizeof(float); + lsh_weighted_cumulation_ver3_step2_cuda_kernel<<>>( + query_sorted_idxes_ptr, + key_mask_ptr, + key_info_ptr, + query_weight_ptr, + key_weight_ptr, + value_ptr, + cumulation_value_ptr, + batch_size, + num_hash_f, + num_query, + num_key, + value_dim, + weight_dim + ); + } + } + + return cumulation_value; + +} + +at::Tensor lsh_weighted_cumulation_ver4_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +) { + + int batch_size = query_hash_code.size(0); + int num_hash_f = query_hash_code.size(2); + + int num_query = query_hash_code.size(1); + int num_key = key_hash_code.size(1); + int value_dim = value.size(2); + int weight_dim = query_weight.size(2); + + at::Tensor count_sort_table = at::zeros({batch_size, num_hash_f, hashtable_capacity}, query_hash_code.options()); + at::Tensor query_sorted_idxes = at::zeros({batch_size, num_hash_f, num_query}, query_hash_code.options()); + at::Tensor key_info = at::zeros({batch_size, num_key, 2, num_hash_f}, query_hash_code.options()); + at::Tensor cumulation_value = at::zeros({batch_size, num_query, value_dim}, value.options()); + + if (use_cuda) { + + int *query_mask_ptr = query_mask.data_ptr(); + int *query_hash_code_ptr = query_hash_code.data_ptr(); + float *query_weight_ptr = query_weight.data_ptr(); + int *key_mask_ptr = key_mask.data_ptr(); + int *key_hash_code_ptr = key_hash_code.data_ptr(); + float *key_weight_ptr = key_weight.data_ptr(); + float *value_ptr = value.data_ptr(); + + int *count_sort_table_ptr = count_sort_table.data_ptr(); + int *query_sorted_idxes_ptr = query_sorted_idxes.data_ptr(); + int *key_info_ptr = key_info.data_ptr(); + + float *cumulation_value_ptr = cumulation_value.data_ptr(); + + { + dim3 threads_step13(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); + dim3 blocks_step13(num_query / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); + dim3 threads_step2(min(hashtable_capacity, OPTIMAL_THREADS_PER_BLOCK)); + dim3 blocks_step2(num_hash_f, batch_size); + int shared_mem = hashtable_capacity * sizeof(float); + count_sort_step1_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + count_sort_table_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query + ); + count_sort_step2_cuda_kernel<<>>( + count_sort_table_ptr, + batch_size, + num_hash_f, + hashtable_capacity + ); + count_sort_step3_cuda_kernel<<>>( + query_mask_ptr, + query_hash_code_ptr, + count_sort_table_ptr, + query_sorted_idxes_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_query + ); + } + { + dim3 threads(num_hash_f, max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f)); + dim3 blocks(num_key / max(1, OPTIMAL_THREADS_PER_BLOCK / num_hash_f), batch_size); + extract_query_info_cuda_kernel<<>>( + key_mask_ptr, + key_hash_code_ptr, + count_sort_table_ptr, + key_info_ptr, + batch_size, + num_hash_f, + hashtable_capacity, + num_key + ); + } + { + dim3 threads(WARP_SIZE, OPTIMAL_THREADS_PER_BLOCK / WARP_SIZE); + dim3 blocks(num_key, batch_size); + int shared_mem = (weight_dim + value_dim + 2 * num_hash_f) * sizeof(float); + lsh_weighted_cumulation_ver4_step2_cuda_kernel<<>>( + query_sorted_idxes_ptr, + key_mask_ptr, + key_info_ptr, + query_weight_ptr, + key_weight_ptr, + value_ptr, + cumulation_value_ptr, + batch_size, + num_hash_f, + num_query, + num_key, + value_dim, + weight_dim + ); + } + } + + return cumulation_value; + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation.h b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation.h new file mode 100644 index 0000000000000000000000000000000000000000..dd48de0ed159f49ee3afe93b12aaae719fe87688 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation.h @@ -0,0 +1,71 @@ +#include +#include +#include + +std::vector fast_hash_ver1_kernel( + at::Tensor query_mask, + at::Tensor query_vector, + at::Tensor key_mask, + at::Tensor key_vector, + int num_hash_f, + int hash_code_len, + bool use_cuda +); + +at::Tensor lsh_cumulation_ver1_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +); + +at::Tensor lsh_weighted_cumulation_ver1_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +); + +at::Tensor lsh_weighted_cumulation_ver2_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +); + +at::Tensor lsh_weighted_cumulation_ver3_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +); + +at::Tensor lsh_weighted_cumulation_ver4_kernel( + at::Tensor query_mask, + at::Tensor query_hash_code, + at::Tensor query_weight, + at::Tensor key_mask, + at::Tensor key_hash_code, + at::Tensor key_weight, + at::Tensor value, + int hashtable_capacity, + bool use_cuda +); diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_cuda.cu b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_cuda.cu new file mode 100644 index 0000000000000000000000000000000000000000..ebc6260dd6db3ecaf8cb7b35c3c1a6e1ab3851dc --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_cuda.cu @@ -0,0 +1,825 @@ +// File from https://github.com/mlpen/YOSO/blob/main/encoders/backbones/efficient_attentions/yoso/yoso_v1/cuda/fast_lsh_cumulation_cuda.cu + +#include "fast_lsh_cumulation_cuda.h" +#include "common_cuda_device.h" +#include "common_cuda.h" +#include "common.h" +#include +////////////////////////////////////////////////////////////////////////////////////////////////// +////////////////////////////////////////////////////////////////////////////////////////////////// + +inline __device__ void fast_hadamard_transform(float *vector_buffer, int vector_dim, int dim_idx) { + int stride = vector_dim / 2; + while (stride > (WARP_SIZE / 2)) { + __syncthreads(); + int sign = 1 - ((dim_idx / stride) % 2) * 2; + float val1 = vector_buffer[dim_idx]; + float val2 = vector_buffer[dim_idx + sign * stride]; + __syncthreads(); + vector_buffer[dim_idx] = float(sign) * val1 + val2; + stride = stride / 2; + } + + float val = vector_buffer[dim_idx]; + #pragma unroll + for (stride = (WARP_SIZE / 2); stride > 0; stride = stride / 2) { + int sign = 1 - ((dim_idx / stride) % 2) * 2; + val = float(sign) * val + __shfl_xor_sync(FULL_MASK, val, stride); + } + vector_buffer[dim_idx] = val; +} + +__global__ void fast_hash_ver1_cuda_kernel( + int *mask, // [batch_size, num_vector] + float *vector, // [batch_size, num_vector, vector_dim] + int *Dmat, // [batch_size, 3, num_part, vector_dim] + int *hash_code, // [batch_size, num_vector, num_hash_f] + int batch_size, + int num_vector, + int vector_dim, + int num_part, + int num_hash_f, + int hash_code_len +) { + + int batch_idx = blockIdx.z; + int vector_idx = blockIdx.y; + int part_idx = blockIdx.x; + + int dim_idx = threadIdx.x; + + int batch_idx__vector_idx = batch_idx * num_vector + vector_idx; + if (mask[batch_idx__vector_idx] == 0) { + return; + } + + extern __shared__ float buffer[]; + float *vector_buffer = buffer; + + vector_buffer[dim_idx] = vector[batch_idx__vector_idx * vector_dim + dim_idx]; + + vector_buffer[dim_idx] = vector_buffer[dim_idx] * (float)Dmat[((batch_idx * 3 + 0) * num_part + part_idx) * vector_dim + dim_idx]; + fast_hadamard_transform(vector_buffer, vector_dim, dim_idx); + vector_buffer[dim_idx] = vector_buffer[dim_idx] * (float)Dmat[((batch_idx * 3 + 1) * num_part + part_idx) * vector_dim + dim_idx]; + fast_hadamard_transform(vector_buffer, vector_dim, dim_idx); + vector_buffer[dim_idx] = vector_buffer[dim_idx] * (float)Dmat[((batch_idx * 3 + 2) * num_part + part_idx) * vector_dim + dim_idx]; + fast_hadamard_transform(vector_buffer, vector_dim, dim_idx); + + int num_hash_per_part = vector_dim / hash_code_len; + if (hash_code_len == 8 || hash_code_len == 16) { + int code = select(vector_buffer[dim_idx] > 0, 1 << (dim_idx % hash_code_len), 0); + for (int offset = 1; offset < hash_code_len; offset = offset * 2) { + code += __shfl_xor_sync(FULL_MASK, code, offset); + } + if (dim_idx % hash_code_len == 0) { + int hash_f_idx = part_idx * num_hash_per_part + dim_idx / hash_code_len; + if (hash_f_idx < num_hash_f) { + hash_code[batch_idx__vector_idx * num_hash_f + hash_f_idx] = code; + } + } + } else { + vector_buffer[dim_idx] = select(vector_buffer[dim_idx] > 0, 1 << (dim_idx % hash_code_len), 0); + __syncthreads(); + if (dim_idx < num_hash_per_part) { + int code = 0; + for (int i = 0; i < hash_code_len; i++) { + code += vector_buffer[dim_idx * hash_code_len + i]; + } + int hash_f_idx = part_idx * num_hash_per_part + dim_idx; + if (hash_f_idx < num_hash_f) { + hash_code[batch_idx__vector_idx * num_hash_f + hash_f_idx] = code; + } + } + } +} + +__global__ void lsh_cumulation_ver1_step1_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + float *value, // [batch_size, num_key, value_dim] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key, + int value_dim, + int offset_warp +) { + + int warp_thread_idx = threadIdx.x; + + int batch_idx = blockIdx.y; + int key_idx = blockIdx.x * blockDim.y + threadIdx.y; + + int batch_idx__key_idx = batch_idx * num_key + key_idx; + if (key_mask[batch_idx__key_idx] == 0) { + return; + } + + if (num_hash_f > WARP_SIZE) { + float warp_value = value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx]; + for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) { + int warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_start + warp_thread_idx]; + #pragma unroll + for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset); + int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode; + atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value); + } + } + } else { + float warp_value = value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx]; + int warp_hashcode = 0; + if (warp_thread_idx < num_hash_f) { + warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + warp_thread_idx]; + } + for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx); + int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode; + atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value); + } + } + +} + +__global__ void lsh_cumulation_ver1_step2_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_hash_code, // [batch_size, num_query, num_hash_f] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_query, + int value_dim, + int offset_warp +) { + + int warp_thread_idx = threadIdx.x; + + int batch_idx = blockIdx.y; + int query_idx = blockIdx.x * blockDim.y + threadIdx.y; + + int batch_idx__query_idx = batch_idx * num_query + query_idx; + if (query_mask[batch_idx__query_idx] == 0) { + return; + } + + if (num_hash_f > WARP_SIZE) { + float warp_value = 0; + for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) { + int warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + hash_f_start + warp_thread_idx]; + #pragma unroll + for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset); + int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode; + warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx]; + } + } + cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] = warp_value / float(num_hash_f); + } else { + float warp_value = 0; + int warp_hashcode = 0; + if (warp_thread_idx < num_hash_f) { + warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + warp_thread_idx]; + } + for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx); + int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode; + warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx]; + } + cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] = warp_value / float(num_hash_f); + } + +} + +__global__ void lsh_weighted_cumulation_ver1_step1_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key, + int value_dim, + int weight_dim, + int offset_warp, + int weight_idx +) { + + int warp_thread_idx = threadIdx.x; + + int batch_idx = blockIdx.y; + int key_idx = blockIdx.x * blockDim.y + threadIdx.y; + + int batch_idx__key_idx = batch_idx * num_key + key_idx; + if (key_mask[batch_idx__key_idx] == 0) { + return; + } + + if (num_hash_f > WARP_SIZE) { + float warp_value = key_weight[batch_idx__key_idx * weight_dim + weight_idx] * value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx]; + for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) { + int warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_start + warp_thread_idx]; + #pragma unroll + for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset); + int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode; + atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value); + } + } + } else { + float warp_value = key_weight[batch_idx__key_idx * weight_dim + weight_idx] * value[batch_idx__key_idx * value_dim + offset_warp + warp_thread_idx]; + int warp_hashcode = 0; + if (warp_thread_idx < num_hash_f) { + warp_hashcode = key_hash_code[batch_idx__key_idx * num_hash_f + warp_thread_idx]; + } + for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx); + int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode; + atomicAdd(&hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx], warp_value); + } + } + +} + +__global__ void lsh_weighted_cumulation_ver1_step2_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_hash_code, // [batch_size, num_query, num_hash_f] + float *query_weight, // [batch_size, num_query, weight_dim] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_query, + int value_dim, + int weight_dim, + int offset_warp, + int weight_idx +) { + + int warp_thread_idx = threadIdx.x; + + int batch_idx = blockIdx.y; + int query_idx = blockIdx.x * blockDim.y + threadIdx.y; + + int batch_idx__query_idx = batch_idx * num_query + query_idx; + if (query_mask[batch_idx__query_idx] == 0) { + return; + } + + if (num_hash_f > WARP_SIZE) { + float warp_value = 0; + for (int hash_f_start = 0; hash_f_start < num_hash_f; hash_f_start = hash_f_start + WARP_SIZE) { + int warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + hash_f_start + warp_thread_idx]; + #pragma unroll + for (int hash_f_offset = 0; hash_f_offset < WARP_SIZE; hash_f_offset++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_offset); + int hashtable_idx = (batch_idx * num_hash_f + (hash_f_start + hash_f_offset)) * hashtable_capacity + current_hashcode; + warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx]; + } + } + float warp_weight = query_weight[batch_idx__query_idx * weight_dim + weight_idx]; + cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] += warp_weight * warp_value / float(num_hash_f); + } else { + float warp_value = 0; + int warp_hashcode = 0; + if (warp_thread_idx < num_hash_f) { + warp_hashcode = query_hash_code[batch_idx__query_idx * num_hash_f + warp_thread_idx]; + } + for (int hash_f_idx = 0; hash_f_idx < num_hash_f; hash_f_idx++) { + int current_hashcode = warp_hashcode; + current_hashcode = __shfl_sync(FULL_MASK, current_hashcode, hash_f_idx); + int hashtable_idx = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + current_hashcode; + warp_value = warp_value + hashtable_value[hashtable_idx * WARP_SIZE + warp_thread_idx]; + } + float warp_weight = query_weight[batch_idx__query_idx * weight_dim + weight_idx]; + cumulation_value[batch_idx__query_idx * value_dim + offset_warp + warp_thread_idx] += warp_weight * warp_value / float(num_hash_f); + } + +} + +__global__ void count_sort_step1_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key +) { + + int batch_idx = blockIdx.y; + int key_idx = blockIdx.x * blockDim.y + threadIdx.y; + int hash_f_idx = threadIdx.x; + + int batch_idx__key_idx = batch_idx * num_key + key_idx; + if (key_mask[batch_idx__key_idx] == 0) { + return; + } + + int hash_code = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_idx]; + atomicAdd(&count_sort_table[(batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + hash_code], 1); + +} + +__global__ void count_sort_step2_cuda_kernel( + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int batch_size, + int num_hash_f, + int hashtable_capacity +) { + + int batch_idx = blockIdx.y; + int hash_f_idx = blockIdx.x; + + int num_threads = blockDim.x; + int thread_id = threadIdx.x; + + int batch_idx__hash_f_idx = batch_idx * num_hash_f + hash_f_idx; + + extern __shared__ float buffer[]; + int *table_buffer = (int*)buffer; + + if (thread_id == 0) { + table_buffer[0] = 0; + } + copy_data(&count_sort_table[batch_idx__hash_f_idx * hashtable_capacity], &table_buffer[1], hashtable_capacity - 1, num_threads, thread_id); + + for (int table_idx_start = 0; table_idx_start < hashtable_capacity; table_idx_start = table_idx_start + num_threads) { + int thread_value = table_buffer[table_idx_start + thread_id]; + int next_thread_value = 0; + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + next_thread_value = __shfl_up_sync(FULL_MASK, thread_value, offset); + if (thread_id % WARP_SIZE >= offset) { + thread_value = thread_value + next_thread_value; + } + } + table_buffer[table_idx_start + thread_id] = thread_value; + } + __syncthreads(); + + if (hashtable_capacity > WARP_SIZE) { + if (thread_id < WARP_SIZE) { + for (int table_idx_start = WARP_SIZE; table_idx_start < hashtable_capacity; table_idx_start = table_idx_start + WARP_SIZE) { + table_buffer[table_idx_start + thread_id] += table_buffer[table_idx_start - 1]; + } + } + } + + copy_data(table_buffer, &count_sort_table[batch_idx__hash_f_idx * hashtable_capacity], hashtable_capacity, num_threads, thread_id); + +} + + +__global__ void count_sort_step3_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int *key_sorted_idxes, // [batch_size, num_hash_f, num_key] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key +) { + + int batch_idx = blockIdx.y; + int key_idx = blockIdx.x * blockDim.y + threadIdx.y; + int hash_f_idx = threadIdx.x; + + int batch_idx__key_idx = batch_idx * num_key + key_idx; + if (key_mask[batch_idx__key_idx] == 0) { + return; + } + + int batch_idx__hash_f_idx = batch_idx * num_hash_f + hash_f_idx; + + int hash_code = key_hash_code[batch_idx__key_idx * num_hash_f + hash_f_idx]; + int sort_idx = atomicAdd(&count_sort_table[batch_idx__hash_f_idx * hashtable_capacity + hash_code], 1); + key_sorted_idxes[batch_idx__hash_f_idx * num_key + sort_idx] = key_idx; + +} + +__global__ void extract_query_info_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_hash_code, // [batch_size, num_query, num_hash_f] + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int *query_info, // [batch_size, num_query, 2, num_hash_f] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_query +) { + + int batch_idx = blockIdx.y; + int query_idx = blockIdx.x * blockDim.y + threadIdx.y; + int hash_f_idx = threadIdx.x; + + int batch_idx__query_idx = batch_idx * num_query + query_idx; + if (query_mask[batch_idx__query_idx] == 0) { + return; + } + + int hash_code = query_hash_code[batch_idx__query_idx * num_hash_f + hash_f_idx]; + int batch_idx__hash_f_idx__hash_code = (batch_idx * num_hash_f + hash_f_idx) * hashtable_capacity + hash_code; + + int key_offset = select(hash_code == 0, 0, count_sort_table[batch_idx__hash_f_idx__hash_code - 1]); + int key_count = count_sort_table[batch_idx__hash_f_idx__hash_code] - key_offset; + + query_info[batch_idx__query_idx * 2 * num_hash_f + hash_f_idx] = key_offset; + query_info[(batch_idx__query_idx * 2 + 1) * num_hash_f + hash_f_idx] = key_count; + +} + +__global__ void lsh_weighted_cumulation_ver2_step2_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_info, // [batch_size, num_query, 2, num_hash_f] + int *key_sorted_idxes, // [batch_size, num_hash_f, num_key] + float *query_weight, // [batch_size, num_query, weight_dim] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int num_query, + int num_key, + int value_dim, + int weight_dim +) { + + int batch_idx = blockIdx.z; + int hash_f_idx = blockIdx.y; + int query_idx = blockIdx.x; + + int num_threads = blockDim.y * blockDim.x; + int thread_id = threadIdx.y * blockDim.x + threadIdx.x; + + int num_warps = blockDim.y; + int warp_idx = threadIdx.y; + int warp_thread_idx = threadIdx.x; + + int batch_idx__query_idx = batch_idx * num_query + query_idx; + if (query_mask[batch_idx__query_idx] == 0) { + return; + } + + int key_offset = query_info[batch_idx__query_idx * 2 * num_hash_f + hash_f_idx]; + int key_count = query_info[(batch_idx__query_idx * 2 + 1) * num_hash_f + hash_f_idx]; + + if (key_count == 0) { + return; + } + + extern __shared__ float buffer[]; + + if (key_count == 1) { + if (warp_idx == 0) { + int key_idx = key_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_key + key_offset]; + int batch_idx__key_idx = batch_idx * num_key + key_idx; + float weight = 0; + for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) { + int weight_dim_idx = weight_offset + warp_thread_idx; + float val = query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx] * key_weight[batch_idx__key_idx * weight_dim + weight_dim_idx]; + #pragma unroll + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + val += __shfl_xor_sync(FULL_MASK, val, offset); + } + weight = weight + val; + } + weight = weight / float(num_hash_f); + for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { + int value_dim_idx = value_offset + warp_thread_idx; + float val = value[batch_idx__key_idx * value_dim + value_dim_idx]; + atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val); + } + } + } else { + float *weight_buffer = buffer; + int *key_idxes_buffer = (int*)&buffer[weight_dim]; + + copy_data_nonblocking(&query_weight[batch_idx__query_idx * weight_dim], weight_buffer, weight_dim, num_threads, thread_id); + + while (key_count > 0) { + int work_size = min(WARP_SIZE, key_count); + copy_data_nonblocking(&key_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_key + key_offset], key_idxes_buffer, work_size, num_threads, thread_id); + __syncthreads(); + for (int work_offset = 0; work_offset < WARP_SIZE; work_offset = work_offset + num_warps) { + int work_idx = work_offset + warp_idx; + if (work_idx < key_count) { + int key_idx = key_idxes_buffer[work_idx]; + int batch_idx__key_idx = batch_idx * num_key + key_idx; + float weight = 0; + for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) { + int weight_dim_idx = weight_offset + warp_thread_idx; + float val = weight_buffer[weight_dim_idx] * key_weight[batch_idx__key_idx * weight_dim + weight_dim_idx]; + #pragma unroll + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + val += __shfl_xor_sync(FULL_MASK, val, offset); + } + weight = weight + val; + } + weight = weight / float(num_hash_f); + for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { + int value_dim_idx = value_offset + warp_thread_idx; + float val = value[batch_idx__key_idx * value_dim + value_dim_idx]; + atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val); + } + } + } + key_count = key_count - work_size; + key_offset = key_offset + work_size; + } + } + +} + +__global__ void lsh_weighted_cumulation_ver3_step2_cuda_kernel( + int *query_sorted_idxes, // [batch_size, num_hash_f, num_query] + int *key_mask, // [batch_size, num_key] + int *key_info, // [batch_size, num_key, 2, num_hash_f] + float *query_weight, // [batch_size, num_query, weight_dim] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int num_query, + int num_key, + int value_dim, + int weight_dim +) { + + int batch_idx = blockIdx.z; + int hash_f_idx = blockIdx.y; + int key_idx = blockIdx.x; + + int num_threads = blockDim.y * blockDim.x; + int thread_id = threadIdx.y * blockDim.x + threadIdx.x; + + int num_warps = blockDim.y; + int warp_idx = threadIdx.y; + int warp_thread_idx = threadIdx.x; + + int batch_idx__key_idx = batch_idx * num_key + key_idx; + if (key_mask[batch_idx__key_idx] == 0) { + return; + } + + int query_offset = key_info[batch_idx__key_idx * 2 * num_hash_f + hash_f_idx]; + int query_count = key_info[(batch_idx__key_idx * 2 + 1) * num_hash_f + hash_f_idx]; + + if (query_count == 0) { + return; + } + + extern __shared__ float buffer[]; + + if (query_count == 1) { + if (warp_idx == 0) { + int query_idx = query_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_query + query_offset]; + int batch_idx__query_idx = batch_idx * num_query + query_idx; + float weight = 0; + for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) { + int weight_dim_idx = weight_offset + warp_thread_idx; + float val = key_weight[batch_idx__key_idx * weight_dim + weight_dim_idx] * query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx]; + #pragma unroll + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + val += __shfl_xor_sync(FULL_MASK, val, offset); + } + weight = weight + val; + } + weight = weight / float(num_hash_f); + for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { + int value_dim_idx = value_offset + warp_thread_idx; + float val = value[batch_idx__key_idx * value_dim + value_dim_idx]; + atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val); + } + } + } else { + float *weight_buffer = buffer; + float *value_buffer = &buffer[weight_dim]; + int *query_idxes_buffer = (int*)&buffer[weight_dim + value_dim]; + + copy_data_nonblocking(&key_weight[batch_idx__key_idx * weight_dim], weight_buffer, weight_dim, num_threads, thread_id); + copy_data_nonblocking(&value[batch_idx__key_idx * value_dim], value_buffer, value_dim, num_threads, thread_id); + + while (query_count > 0) { + int work_size = min(WARP_SIZE, query_count); + copy_data_nonblocking(&query_sorted_idxes[(batch_idx * num_hash_f + hash_f_idx) * num_query + query_offset], query_idxes_buffer, work_size, num_threads, thread_id); + __syncthreads(); + for (int work_offset = 0; work_offset < WARP_SIZE; work_offset = work_offset + num_warps) { + int work_idx = work_offset + warp_idx; + if (work_idx < query_count) { + int query_idx = query_idxes_buffer[work_idx]; + int batch_idx__query_idx = batch_idx * num_query + query_idx; + float weight = 0; + for (int weight_offset = 0; weight_offset < weight_dim; weight_offset = weight_offset + WARP_SIZE) { + int weight_dim_idx = weight_offset + warp_thread_idx; + float val = weight_buffer[weight_dim_idx] * query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx]; + #pragma unroll + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + val += __shfl_xor_sync(FULL_MASK, val, offset); + } + weight = weight + val; + } + weight = weight / float(num_hash_f); + for (int value_offset = 0; value_offset < value_dim; value_offset = value_offset + WARP_SIZE) { + int value_dim_idx = value_offset + warp_thread_idx; + float val = value_buffer[value_dim_idx]; + atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val); + } + } + } + query_count = query_count - work_size; + query_offset = query_offset + work_size; + } + } + +} + +__global__ void lsh_weighted_cumulation_ver4_step2_cuda_kernel( + int *query_sorted_idxes, // [batch_size, num_hash_f, num_query] + int *key_mask, // [batch_size, num_key] + int *key_info, // [batch_size, num_key, 2, num_hash_f] + float *query_weight, // [batch_size, num_query, weight_dim] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int num_query, + int num_key, + int value_dim, + int weight_dim +) { + + int batch_idx = blockIdx.y; + int key_idx = blockIdx.x; + + int num_threads = blockDim.y * blockDim.x; + int thread_id = threadIdx.y * blockDim.x + threadIdx.x; + + int num_warps = blockDim.y; + int warp_idx = threadIdx.y; + int warp_thread_idx = threadIdx.x; + + int batch_idx__key_idx = batch_idx * num_key + key_idx; + if (key_mask[batch_idx__key_idx] == 0) { + return; + } + + extern __shared__ float buffer[]; + float *weight_buffer = buffer; + float *value_buffer = &buffer[weight_dim]; + int *key_info_buffer = (int*)&buffer[weight_dim + value_dim]; + + copy_data_nonblocking(&key_weight[batch_idx__key_idx * weight_dim], weight_buffer, weight_dim, num_threads, thread_id); + copy_data_nonblocking(&value[batch_idx__key_idx * value_dim], value_buffer, value_dim, num_threads, thread_id); + copy_data_nonblocking(&key_info[batch_idx__key_idx * 2 * num_hash_f], key_info_buffer, 2 * num_hash_f, num_threads, thread_id); + + int *query_offset_buffer = key_info_buffer; + int *query_count_buffer = &key_info_buffer[num_hash_f]; + + const int hashtable_size = 1024 + OPTIMAL_THREADS_PER_BLOCK; + __shared__ int hashtable_query[hashtable_size]; + __shared__ int hashtable_count[hashtable_size]; + __shared__ int inserted_query[hashtable_size]; + __shared__ int query_counter[1]; + + int hash_f_idx_base = 0; + + while (true) { + + init_buffer_nonblocking(EMPTY_VALUE, hashtable_query, hashtable_size, num_threads, thread_id); + init_buffer_nonblocking(0, hashtable_count, hashtable_size, num_threads, thread_id); + init_buffer_nonblocking(EMPTY_VALUE, inserted_query, hashtable_size, num_threads, thread_id); + init_buffer_nonblocking(0, query_counter, 1, num_threads, thread_id); + __syncthreads(); + + while (hash_f_idx_base < num_hash_f) { + + int hash_f_idx = hash_f_idx_base + warp_idx; + int batch_idx__hash_f_idx = batch_idx * num_hash_f + hash_f_idx; + + int stop_flag = 0; + + int query_offset = query_offset_buffer[hash_f_idx]; + int query_count = query_count_buffer[hash_f_idx]; + + while (query_count > 0) { + + int work_size = min(query_count, WARP_SIZE); + + // try inserting query to set and check whether the query is new + int found_new_query = 0; + int query_idx = -1; + if (warp_thread_idx < work_size) { + query_idx = query_sorted_idxes[batch_idx__hash_f_idx * num_query + query_offset + warp_thread_idx]; + int slot = set_insert(hashtable_query, hashtable_size, query_idx); + if (slot >= 0) { + found_new_query = atomicAdd(&hashtable_count[slot], 1) == 0; + } + } + + // compute cumulative offset + int position_offset = found_new_query; + int next_position_offset = 0; + #pragma unroll + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + next_position_offset = __shfl_up_sync(FULL_MASK, position_offset, offset); + if (thread_id % WARP_SIZE >= offset) { + position_offset = position_offset + next_position_offset; + } + } + + // get the inserted query list end index + int inserted_query_base = 0; + if (thread_id % WARP_SIZE == WARP_SIZE - 1) { + inserted_query_base = atomicAdd(query_counter, position_offset); + } + inserted_query_base = __shfl_sync(FULL_MASK, inserted_query_base, WARP_SIZE - 1); + + // insert new queries to list + int insert_idx = inserted_query_base + position_offset - 1; + if (found_new_query) { + inserted_query[insert_idx] = query_idx; + } + + // remove inserted queries from list + query_offset_buffer[hash_f_idx] += work_size; + query_count_buffer[hash_f_idx] -= work_size; + query_offset += work_size; + query_count -= work_size; + + // if list is almost full, stop inserting + if (inserted_query_base + OPTIMAL_THREADS_PER_BLOCK > hashtable_size) { + stop_flag = 1; + break; + } + + } + + if (stop_flag) { + break; + } + + hash_f_idx_base = hash_f_idx_base + num_warps; + + } + + __syncthreads(); + + int num_distint_query = query_counter[0]; + + if (num_distint_query > 0) { + for (int idx_base = 0; idx_base < num_distint_query; idx_base = idx_base + num_warps) { + int idx = idx_base + warp_idx; + if (idx < num_distint_query) { + int query_idx = inserted_query[idx]; + int batch_idx__query_idx = batch_idx * num_query + query_idx; + + int slot = set_lookup(hashtable_query, hashtable_size, query_idx); + int duplicate_count = hashtable_count[slot]; + + float weight = 0; + for (int weight_idx_base = 0; weight_idx_base < weight_dim; weight_idx_base = weight_idx_base + WARP_SIZE) { + int weight_dim_idx = weight_idx_base + warp_thread_idx; + float val = weight_buffer[weight_dim_idx] * query_weight[batch_idx__query_idx * weight_dim + weight_dim_idx]; + #pragma unroll + for (int offset = 1; offset < WARP_SIZE; offset = offset << 1) { + val += __shfl_xor_sync(FULL_MASK, val, offset); + } + weight = weight + val; + } + + weight = (float)duplicate_count * weight / float(num_hash_f); + + for (int value_idx_base = 0; value_idx_base < value_dim; value_idx_base = value_idx_base + WARP_SIZE) { + int value_dim_idx = value_idx_base + warp_thread_idx; + float val = value_buffer[value_dim_idx]; + atomicAdd(&cumulation_value[batch_idx__query_idx * value_dim + value_dim_idx], weight * val); + } + } + } + } else { + + // all computation is completed if num_distint_query == 0 + break; + + } + + __syncthreads(); + + } + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_cuda.h b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_cuda.h new file mode 100644 index 0000000000000000000000000000000000000000..b2adc0f735358d0fcb6a056e7d19ba745977e129 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_cuda.h @@ -0,0 +1,157 @@ +__global__ void fast_hash_ver1_cuda_kernel( + int *mask, // [batch_size, num_vector] + float *vector, // [batch_size, num_vector, vector_dim] + int *Dmat, // [3, num_part, vector_dim] + int *hash_code, // [batch_size, num_vector, num_hash_f] + int batch_size, + int num_vector, + int vector_dim, + int num_part, + int num_hash_f, + int hash_code_len +); + +__global__ void lsh_cumulation_ver1_step1_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + float *value, // [batch_size, num_key, value_dim] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, value_dim] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key, + int value_dim, + int offset_warp +); + +__global__ void lsh_cumulation_ver1_step2_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_hash_code, // [batch_size, num_query, num_hash_f] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_query, + int value_dim, + int offset_warp +); + +__global__ void lsh_weighted_cumulation_ver1_step1_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key, + int value_dim, + int weight_dim, + int offset_warp, + int weight_idx +); + +__global__ void lsh_weighted_cumulation_ver1_step2_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_hash_code, // [batch_size, num_query, num_hash_f] + float *query_weight, // [batch_size, num_query, weight_dim] + float *hashtable_value, // [batch_size, num_hash_f, hashtable_capacity, WARP_SIZE] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_query, + int value_dim, + int weight_dim, + int offset_warp, + int weight_idx +); + +__global__ void count_sort_step1_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key +); + +__global__ void count_sort_step2_cuda_kernel( + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int batch_size, + int num_hash_f, + int hashtable_capacity +); + +__global__ void count_sort_step3_cuda_kernel( + int *key_mask, // [batch_size, num_key] + int *key_hash_code, // [batch_size, num_key, num_hash_f] + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int *key_sorted_idxes, // [batch_size, num_hash_f, num_key] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_key +); + +__global__ void extract_query_info_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_hash_code, // [batch_size, num_query, num_hash_f] + int *count_sort_table, // [batch_size, num_hash_f, hashtable_capacity] + int *query_info, // [batch_size, num_query, 2, num_hash_f] + int batch_size, + int num_hash_f, + int hashtable_capacity, + int num_query +); + +__global__ void lsh_weighted_cumulation_ver2_step2_cuda_kernel( + int *query_mask, // [batch_size, num_query] + int *query_info, // [batch_size, num_query, 2, num_hash_f] + int *key_sorted_idxes, // [batch_size, num_hash_f, num_key] + float *query_weight, // [batch_size, num_query, weight_dim] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int num_query, + int num_key, + int value_dim, + int weight_dim +); + +__global__ void lsh_weighted_cumulation_ver3_step2_cuda_kernel( + int *query_sorted_idxes, // [batch_size, num_hash_f, num_query] + int *key_mask, // [batch_size, num_key] + int *key_info, // [batch_size, num_key, 2, num_hash_f] + float *query_weight, // [batch_size, num_query, weight_dim] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int num_query, + int num_key, + int value_dim, + int weight_dim +); + +__global__ void lsh_weighted_cumulation_ver4_step2_cuda_kernel( + int *query_sorted_idxes, // [batch_size, num_hash_f, num_query] + int *key_mask, // [batch_size, num_key] + int *key_info, // [batch_size, num_key, 2, num_hash_f] + float *query_weight, // [batch_size, num_query, weight_dim] + float *key_weight, // [batch_size, num_key, weight_dim] + float *value, // [batch_size, num_key, value_dim] + float *cumulation_value, // [batch_size, num_query, value_dim] + int batch_size, + int num_hash_f, + int num_query, + int num_key, + int value_dim, + int weight_dim +); diff --git a/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_torch.cpp b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_torch.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e150a2be604b28f600ab345a8cc9e97819cca416 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/transformers/kernels/yoso/fast_lsh_cumulation_torch.cpp @@ -0,0 +1,128 @@ +#include +#include +#include "fast_lsh_cumulation.h" +#include "common_cuda.h" +#include + +std::vector fast_hash( + at::Tensor query_mask, + at::Tensor query_vector, + at::Tensor key_mask, + at::Tensor key_vector, + int num_hash_f, + int hash_code_len, + bool use_cuda, + int version +) { + return fast_hash_ver1_kernel( + query_mask, + query_vector, + key_mask, + key_vector, + num_hash_f, + hash_code_len, + use_cuda + ); +} + +at::Tensor lsh_cumulation( + at::Tensor query_mask, // [batch_size, num_query] + at::Tensor query_hash_code, // [batch_size, num_query, num_hash_f] + at::Tensor key_mask, // [batch_size, num_key] + at::Tensor key_hash_code, // [batch_size, num_key, num_hash_f] + at::Tensor value, // [batch_size, num_key, value_dim] + int hashtable_capacity, + bool use_cuda, + int version +) { + return lsh_cumulation_ver1_kernel( + query_mask, + query_hash_code, + key_mask, + key_hash_code, + value, + hashtable_capacity, + use_cuda + ); +} + +at::Tensor lsh_weighted_cumulation( + at::Tensor query_mask, // [batch_size, num_query] + at::Tensor query_hash_code, // [batch_size, num_query, num_hash_f] + at::Tensor query_weight, // [batch_size, num_query, weight_dim] + at::Tensor key_mask, // [batch_size, num_key] + at::Tensor key_hash_code, // [batch_size, num_key, num_hash_f] + at::Tensor key_weight, // [batch_size, num_key, weight_dim] + at::Tensor value, // [batch_size, num_key, value_dim] + int hashtable_capacity, + bool use_cuda, + int version +) { + if (version == 1) { + return lsh_weighted_cumulation_ver1_kernel( + query_mask, + query_hash_code, + query_weight, + key_mask, + key_hash_code, + key_weight, + value, + hashtable_capacity, + use_cuda + ); + } else if (version == 2) { + return lsh_weighted_cumulation_ver2_kernel( + query_mask, + query_hash_code, + query_weight, + key_mask, + key_hash_code, + key_weight, + value, + hashtable_capacity, + use_cuda + ); + } else if (version == 3) { + return lsh_weighted_cumulation_ver3_kernel( + query_mask, + query_hash_code, + query_weight, + key_mask, + key_hash_code, + key_weight, + value, + hashtable_capacity, + use_cuda + ); + } else if (version == 4) { + return lsh_weighted_cumulation_ver4_kernel( + query_mask, + query_hash_code, + query_weight, + key_mask, + key_hash_code, + key_weight, + value, + hashtable_capacity, + use_cuda + ); + } else { + return lsh_weighted_cumulation_ver3_kernel( + query_mask, + query_hash_code, + query_weight, + key_mask, + key_hash_code, + key_weight, + value, + hashtable_capacity, + use_cuda + ); + } +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fast_hash", &fast_hash, "Fast Hash (CUDA)"); + m.def("lsh_cumulation", &lsh_cumulation, "LSH Cumulation (CUDA)"); + m.def("lsh_weighted_cumulation", &lsh_weighted_cumulation, "LSH Weighted Cumulation (CUDA)"); +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/INSTALLER b/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/METADATA b/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..0b7645966bf6aab57748b20bea01180902e7eab7 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/METADATA @@ -0,0 +1,155 @@ +Metadata-Version: 2.3 +Name: urllib3 +Version: 2.2.3 +Summary: HTTP library with thread-safe connection pooling, file post, and more. +Project-URL: Changelog, https://github.com/urllib3/urllib3/blob/main/CHANGES.rst +Project-URL: Documentation, https://urllib3.readthedocs.io +Project-URL: Code, https://github.com/urllib3/urllib3 +Project-URL: Issue tracker, https://github.com/urllib3/urllib3/issues +Author-email: Andrey Petrov +Maintainer-email: Seth Michael Larson , Quentin Pradet , Illia Volochii +License-File: LICENSE.txt +Keywords: filepost,http,httplib,https,pooling,ssl,threadsafe,urllib +Classifier: Environment :: Web Environment +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.8 +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 :: Internet :: WWW/HTTP +Classifier: Topic :: Software Development :: Libraries +Requires-Python: >=3.8 +Provides-Extra: brotli +Requires-Dist: brotli>=1.0.9; (platform_python_implementation == 'CPython') and extra == 'brotli' +Requires-Dist: brotlicffi>=0.8.0; (platform_python_implementation != 'CPython') and extra == 'brotli' +Provides-Extra: h2 +Requires-Dist: h2<5,>=4; extra == 'h2' +Provides-Extra: socks +Requires-Dist: pysocks!=1.5.7,<2.0,>=1.5.6; extra == 'socks' +Provides-Extra: zstd +Requires-Dist: zstandard>=0.18.0; extra == 'zstd' +Description-Content-Type: text/markdown + +

+ +![urllib3](https://github.com/urllib3/urllib3/raw/main/docs/_static/banner_github.svg) + +

+ +

+ PyPI Version + Python Versions + Join our Discord + Coverage Status + Build Status on GitHub + Documentation Status
+ OpenSSF Scorecard + SLSA 3 + CII Best Practices +

+ +urllib3 is a powerful, *user-friendly* HTTP client for Python. Much of the +Python ecosystem already uses urllib3 and you should too. +urllib3 brings many critical features that are missing from the Python +standard libraries: + +- Thread safety. +- Connection pooling. +- Client-side SSL/TLS verification. +- File uploads with multipart encoding. +- Helpers for retrying requests and dealing with HTTP redirects. +- Support for gzip, deflate, brotli, and zstd encoding. +- Proxy support for HTTP and SOCKS. +- 100% test coverage. + +urllib3 is powerful and easy to use: + +```python3 +>>> import urllib3 +>>> resp = urllib3.request("GET", "http://httpbin.org/robots.txt") +>>> resp.status +200 +>>> resp.data +b"User-agent: *\nDisallow: /deny\n" +``` + +## Installing + +urllib3 can be installed with [pip](https://pip.pypa.io): + +```bash +$ python -m pip install urllib3 +``` + +Alternatively, you can grab the latest source code from [GitHub](https://github.com/urllib3/urllib3): + +```bash +$ git clone https://github.com/urllib3/urllib3.git +$ cd urllib3 +$ pip install . +``` + + +## Documentation + +urllib3 has usage and reference documentation at [urllib3.readthedocs.io](https://urllib3.readthedocs.io). + + +## Community + +urllib3 has a [community Discord channel](https://discord.gg/urllib3) for asking questions and +collaborating with other contributors. Drop by and say hello 👋 + + +## Contributing + +urllib3 happily accepts contributions. Please see our +[contributing documentation](https://urllib3.readthedocs.io/en/latest/contributing.html) +for some tips on getting started. + + +## Security Disclosures + +To report a security vulnerability, please use the +[Tidelift security contact](https://tidelift.com/security). +Tidelift will coordinate the fix and disclosure with maintainers. + + +## Maintainers + +- [@sethmlarson](https://github.com/sethmlarson) (Seth M. Larson) +- [@pquentin](https://github.com/pquentin) (Quentin Pradet) +- [@illia-v](https://github.com/illia-v) (Illia Volochii) +- [@theacodes](https://github.com/theacodes) (Thea Flowers) +- [@haikuginger](https://github.com/haikuginger) (Jess Shapiro) +- [@lukasa](https://github.com/lukasa) (Cory Benfield) +- [@sigmavirus24](https://github.com/sigmavirus24) (Ian Stapleton Cordasco) +- [@shazow](https://github.com/shazow) (Andrey Petrov) + +👋 + + +## Sponsorship + +If your company benefits from this library, please consider [sponsoring its +development](https://urllib3.readthedocs.io/en/latest/sponsors.html). + + +## For Enterprise + +Professional support for urllib3 is available as part of the [Tidelift +Subscription][1]. Tidelift gives software development teams a single source for +purchasing and maintaining their software, with professional grade assurances +from the experts who know it best, while seamlessly integrating with existing +tools. + +[1]: https://tidelift.com/subscription/pkg/pypi-urllib3?utm_source=pypi-urllib3&utm_medium=referral&utm_campaign=readme diff --git a/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/RECORD b/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..24c353b3330008fc5d28a07307ffe1bc199acd3e --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/urllib3-2.2.3.dist-info/RECORD @@ -0,0 +1,80 @@ +urllib3-2.2.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +urllib3-2.2.3.dist-info/METADATA,sha256=NpyLMYu-QoAmQK6pmmgoZRuq0HPt-lf_J9zIuCGMRNY,6485 +urllib3-2.2.3.dist-info/RECORD,, 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associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/paint_by_example/__pycache__/pipeline_paint_by_example.cpython-310.pyc b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/paint_by_example/__pycache__/pipeline_paint_by_example.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..81711e34411020aa64be098ddf5bed18689b71df Binary files /dev/null and b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/paint_by_example/__pycache__/pipeline_paint_by_example.cpython-310.pyc differ diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/__pycache__/__init__.cpython-310.pyc b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9c2e0fbd80f9b7b2ec13e58fc5180c46ea174f09 Binary files /dev/null and b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/__pycache__/__init__.cpython-310.pyc differ diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/__pycache__/pipeline_pia.cpython-310.pyc b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/__pycache__/pipeline_pia.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40ed55b64df875bf1d32f9ce47d382368875c06f Binary files /dev/null and b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/__pycache__/pipeline_pia.cpython-310.pyc differ diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/pipeline_pia.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/pipeline_pia.py new file mode 100644 index 0000000000000000000000000000000000000000..507088991a5e07361f210237089e25fd263ef205 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/pia/pipeline_pia.py @@ -0,0 +1,1034 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +import math +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import PIL +import torch +import torch.fft as fft +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection + +from ...image_processor import PipelineImageInput, VaeImageProcessor +from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin +from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel +from ...models.lora import adjust_lora_scale_text_encoder +from ...models.unets.unet_motion_model import MotionAdapter +from ...schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from ...utils import ( + USE_PEFT_BACKEND, + BaseOutput, + logging, + replace_example_docstring, + scale_lora_layers, + unscale_lora_layers, +) +from ...utils.torch_utils import randn_tensor +from ..free_init_utils import FreeInitMixin +from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import ( + ... EulerDiscreteScheduler, + ... MotionAdapter, + ... PIAPipeline, + ... ) + >>> from diffusers.utils import export_to_gif, load_image + >>> adapter = MotionAdapter.from_pretrained("../checkpoints/pia-diffusers") + >>> pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter) + >>> pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) + >>> image = load_image( + ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" + ... ) + >>> image = image.resize((512, 512)) + >>> prompt = "cat in a hat" + >>> negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality, deformed, distorted, disfigured, bad eyes, wrong lips,weird mouth, bad teeth, mutated hands and fingers, bad anatomy,wrong anatomy, amputation, extra limb, missing limb, floating,limbs, disconnected limbs, mutation, ugly, disgusting, bad_pictures, negative_hand-neg" + >>> generator = torch.Generator("cpu").manual_seed(0) + >>> output = pipe(image=image, prompt=prompt, negative_prompt=negative_prompt, generator=generator) + >>> frames = output.frames[0] + >>> export_to_gif(frames, "pia-animation.gif") + ``` +""" + +RANGE_LIST = [ + [1.0, 0.9, 0.85, 0.85, 0.85, 0.8], # 0 Small Motion + [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75], # Moderate Motion + [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5], # Large Motion + [1.0, 0.9, 0.85, 0.85, 0.85, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.85, 0.85, 0.9, 1.0], # Loop + [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78, 0.79, 0.8, 0.8, 1.0], # Loop + [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5, 0.6, 0.7, 0.7, 0.7, 0.7, 0.8, 1.0], # Loop + [0.5, 0.4, 0.4, 0.4, 0.35, 0.3], # Style Transfer Candidate Small Motion + [0.5, 0.4, 0.4, 0.4, 0.35, 0.35, 0.3, 0.25, 0.2], # Style Transfer Moderate Motion + [0.5, 0.2], # Style Transfer Large Motion +] + + +# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid +def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"): + batch_size, channels, num_frames, height, width = video.shape + outputs = [] + for batch_idx in range(batch_size): + batch_vid = video[batch_idx].permute(1, 0, 2, 3) + batch_output = processor.postprocess(batch_vid, output_type) + + outputs.append(batch_output) + + if output_type == "np": + outputs = np.stack(outputs) + + elif output_type == "pt": + outputs = torch.stack(outputs) + + elif not output_type == "pil": + raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']") + + return outputs + + +def prepare_mask_coef_by_statistics(num_frames: int, cond_frame: int, motion_scale: int): + assert num_frames > 0, "video_length should be greater than 0" + + assert num_frames > cond_frame, "video_length should be greater than cond_frame" + + range_list = RANGE_LIST + + assert motion_scale < len(range_list), f"motion_scale type{motion_scale} not implemented" + + coef = range_list[motion_scale] + coef = coef + ([coef[-1]] * (num_frames - len(coef))) + + order = [abs(i - cond_frame) for i in range(num_frames)] + coef = [coef[order[i]] for i in range(num_frames)] + + return coef + + +def _get_freeinit_freq_filter( + shape: Tuple[int, ...], + device: Union[str, torch.dtype], + filter_type: str, + order: float, + spatial_stop_frequency: float, + temporal_stop_frequency: float, +) -> torch.Tensor: + r"""Returns the FreeInit filter based on filter type and other input conditions.""" + + time, height, width = shape[-3], shape[-2], shape[-1] + mask = torch.zeros(shape) + + if spatial_stop_frequency == 0 or temporal_stop_frequency == 0: + return mask + + if filter_type == "butterworth": + + def retrieve_mask(x): + return 1 / (1 + (x / spatial_stop_frequency**2) ** order) + elif filter_type == "gaussian": + + def retrieve_mask(x): + return math.exp(-1 / (2 * spatial_stop_frequency**2) * x) + elif filter_type == "ideal": + + def retrieve_mask(x): + return 1 if x <= spatial_stop_frequency * 2 else 0 + else: + raise NotImplementedError("`filter_type` must be one of gaussian, butterworth or ideal") + + for t in range(time): + for h in range(height): + for w in range(width): + d_square = ( + ((spatial_stop_frequency / temporal_stop_frequency) * (2 * t / time - 1)) ** 2 + + (2 * h / height - 1) ** 2 + + (2 * w / width - 1) ** 2 + ) + mask[..., t, h, w] = retrieve_mask(d_square) + + return mask.to(device) + + +def _freq_mix_3d(x: torch.Tensor, noise: torch.Tensor, LPF: torch.Tensor) -> torch.Tensor: + r"""Noise reinitialization.""" + # FFT + x_freq = fft.fftn(x, dim=(-3, -2, -1)) + x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1)) + noise_freq = fft.fftn(noise, dim=(-3, -2, -1)) + noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1)) + + # frequency mix + HPF = 1 - LPF + x_freq_low = x_freq * LPF + noise_freq_high = noise_freq * HPF + x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain + + # IFFT + x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1)) + x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real + + return x_mixed + + +@dataclass +class PIAPipelineOutput(BaseOutput): + r""" + Output class for PIAPipeline. + + Args: + frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): + Nested list of length `batch_size` with denoised PIL image sequences of length `num_frames`, + NumPy array of shape `(batch_size, num_frames, channels, height, width, + Torch tensor of shape `(batch_size, num_frames, channels, height, width)`. + """ + + frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]] + + +class PIAPipeline( + DiffusionPipeline, + StableDiffusionMixin, + TextualInversionLoaderMixin, + IPAdapterMixin, + LoraLoaderMixin, + FromSingleFileMixin, + FreeInitMixin, +): + r""" + Pipeline for text-to-video generation. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + The pipeline also inherits the following loading methods: + - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings + - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights + - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights + - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). + tokenizer (`CLIPTokenizer`): + A [`~transformers.CLIPTokenizer`] to tokenize text. + unet ([`UNet2DConditionModel`]): + A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents. + motion_adapter ([`MotionAdapter`]): + A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + """ + + model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae" + _optional_components = ["feature_extractor", "image_encoder", "motion_adapter"] + _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: Union[UNet2DConditionModel, UNetMotionModel], + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + motion_adapter: Optional[MotionAdapter] = None, + feature_extractor: CLIPImageProcessor = None, + image_encoder: CLIPVisionModelWithProjection = None, + ): + super().__init__() + if isinstance(unet, UNet2DConditionModel): + unet = UNetMotionModel.from_unet2d(unet, motion_adapter) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + motion_adapter=motion_adapter, + scheduler=scheduler, + feature_extractor=feature_extractor, + image_encoder=image_encoder, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt + def encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + clip_skip: Optional[int] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + lora_scale (`float`, *optional*): + A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + """ + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(self.text_encoder, lora_scale) + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + # textual inversion: process multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + if clip_skip is None: + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) + prompt_embeds = prompt_embeds[0] + else: + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True + ) + # Access the `hidden_states` first, that contains a tuple of + # all the hidden states from the encoder layers. Then index into + # the tuple to access the hidden states from the desired layer. + prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] + # We also need to apply the final LayerNorm here to not mess with the + # representations. The `last_hidden_states` that we typically use for + # obtaining the final prompt representations passes through the LayerNorm + # layer. + prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) + + if self.text_encoder is not None: + prompt_embeds_dtype = self.text_encoder.dtype + elif self.unet is not None: + prompt_embeds_dtype = self.unet.dtype + else: + prompt_embeds_dtype = prompt_embeds.dtype + + prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + # textual inversion: process multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + return prompt_embeds, negative_prompt_embeds + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image + def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): + dtype = next(self.image_encoder.parameters()).dtype + + if not isinstance(image, torch.Tensor): + image = self.feature_extractor(image, return_tensors="pt").pixel_values + + image = image.to(device=device, dtype=dtype) + if output_hidden_states: + image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] + image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) + uncond_image_enc_hidden_states = self.image_encoder( + torch.zeros_like(image), output_hidden_states=True + ).hidden_states[-2] + uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( + num_images_per_prompt, dim=0 + ) + return image_enc_hidden_states, uncond_image_enc_hidden_states + else: + image_embeds = self.image_encoder(image).image_embeds + image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) + uncond_image_embeds = torch.zeros_like(image_embeds) + + return image_embeds, uncond_image_embeds + + # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents + def decode_latents(self, latents): + latents = 1 / self.vae.config.scaling_factor * latents + + batch_size, channels, num_frames, height, width = latents.shape + latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width) + + image = self.vae.decode(latents).sample + video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + video = video.float() + return video + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ip_adapter_image=None, + ip_adapter_image_embeds=None, + callback_on_step_end_tensor_inputs=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + if ip_adapter_image is not None and ip_adapter_image_embeds is not None: + raise ValueError( + "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." + ) + + if ip_adapter_image_embeds is not None: + if not isinstance(ip_adapter_image_embeds, list): + raise ValueError( + f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" + ) + elif ip_adapter_image_embeds[0].ndim not in [3, 4]: + raise ValueError( + f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds + def prepare_ip_adapter_image_embeds( + self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance + ): + if ip_adapter_image_embeds is None: + if not isinstance(ip_adapter_image, list): + ip_adapter_image = [ip_adapter_image] + + if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): + raise ValueError( + f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." + ) + + image_embeds = [] + for single_ip_adapter_image, image_proj_layer in zip( + ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers + ): + output_hidden_state = not isinstance(image_proj_layer, ImageProjection) + single_image_embeds, single_negative_image_embeds = self.encode_image( + single_ip_adapter_image, device, 1, output_hidden_state + ) + single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0) + single_negative_image_embeds = torch.stack( + [single_negative_image_embeds] * num_images_per_prompt, dim=0 + ) + + if do_classifier_free_guidance: + single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) + single_image_embeds = single_image_embeds.to(device) + + image_embeds.append(single_image_embeds) + else: + repeat_dims = [1] + image_embeds = [] + for single_image_embeds in ip_adapter_image_embeds: + if do_classifier_free_guidance: + single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) + single_image_embeds = single_image_embeds.repeat( + num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) + ) + single_negative_image_embeds = single_negative_image_embeds.repeat( + num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) + ) + single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) + else: + single_image_embeds = single_image_embeds.repeat( + num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) + ) + image_embeds.append(single_image_embeds) + + return image_embeds + + # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents + def prepare_latents( + self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None + ): + shape = ( + batch_size, + num_channels_latents, + num_frames, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_masked_condition( + self, + image, + batch_size, + num_channels_latents, + num_frames, + height, + width, + dtype, + device, + generator, + motion_scale=0, + ): + shape = ( + batch_size, + num_channels_latents, + num_frames, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + _, _, _, scaled_height, scaled_width = shape + + image = self.image_processor.preprocess(image) + image = image.to(device, dtype) + + if isinstance(generator, list): + image_latent = [ + self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size) + ] + image_latent = torch.cat(image_latent, dim=0) + else: + image_latent = self.vae.encode(image).latent_dist.sample(generator) + + image_latent = image_latent.to(device=device, dtype=dtype) + image_latent = torch.nn.functional.interpolate(image_latent, size=[scaled_height, scaled_width]) + image_latent_padding = image_latent.clone() * self.vae.config.scaling_factor + + mask = torch.zeros((batch_size, 1, num_frames, scaled_height, scaled_width)).to(device=device, dtype=dtype) + mask_coef = prepare_mask_coef_by_statistics(num_frames, 0, motion_scale) + masked_image = torch.zeros(batch_size, 4, num_frames, scaled_height, scaled_width).to( + device=device, dtype=self.unet.dtype + ) + for f in range(num_frames): + mask[:, :, f, :, :] = mask_coef[f] + masked_image[:, :, f, :, :] = image_latent_padding.clone() + + mask = torch.cat([mask] * 2) if self.do_classifier_free_guidance else mask + masked_image = torch.cat([masked_image] * 2) if self.do_classifier_free_guidance else masked_image + + return mask, masked_image + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps + def get_timesteps(self, num_inference_steps, strength, device): + # get the original timestep using init_timestep + init_timestep = min(int(num_inference_steps * strength), num_inference_steps) + + t_start = max(num_inference_steps - init_timestep, 0) + timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] + if hasattr(self.scheduler, "set_begin_index"): + self.scheduler.set_begin_index(t_start * self.scheduler.order) + + return timesteps, num_inference_steps - t_start + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def clip_skip(self): + return self._clip_skip + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 + + @property + def cross_attention_kwargs(self): + return self._cross_attention_kwargs + + @property + def num_timesteps(self): + return self._num_timesteps + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + image: PipelineImageInput, + prompt: Union[str, List[str]] = None, + strength: float = 1.0, + num_frames: Optional[int] = 16, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_videos_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + ip_adapter_image: Optional[PipelineImageInput] = None, + ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, + motion_scale: int = 0, + output_type: Optional[str] = "pil", + return_dict: bool = True, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + ): + r""" + The call function to the pipeline for generation. + + Args: + image (`PipelineImageInput`): + The input image to be used for video generation. + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. + strength (`float`, *optional*, defaults to 1.0): Indicates extent to transform the reference `image`. Must be between 0 and 1. + height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The height in pixels of the generated video. + width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The width in pixels of the generated video. + num_frames (`int`, *optional*, defaults to 16): + The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds + amounts to 2 seconds of video. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality videos at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + A higher guidance scale value encourages the model to generate images closely linked to the text + `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide what to not include in image generation. If not defined, you need to + pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies + to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. Latents should be of shape + `(batch_size, num_channel, num_frames, height, width)`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If + not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. + ip_adapter_image: (`PipelineImageInput`, *optional*): + Optional image input to work with IP Adapters. + ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): + Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. + Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding + if `do_classifier_free_guidance` is set to `True`. + If not provided, embeddings are computed from the `ip_adapter_image` input argument. + motion_scale: (`int`, *optional*, defaults to 0): + Parameter that controls the amount and type of motion that is added to the image. Increasing the value increases the amount of motion, while specific + ranges of values control the type of motion that is added. Must be between 0 and 8. + Set between 0-2 to only increase the amount of motion. + Set between 3-5 to create looping motion. + Set between 6-8 to perform motion with image style transfer. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or + `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead + of a plain tuple. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in + [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeine class. + + Examples: + + Returns: + [`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] is + returned, otherwise a `tuple` is returned where the first element is a list with the generated frames. + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + num_videos_per_prompt = 1 + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + height, + width, + negative_prompt, + prompt_embeds, + negative_prompt_embeds, + ip_adapter_image, + ip_adapter_image_embeds, + callback_on_step_end_tensor_inputs, + ) + + self._guidance_scale = guidance_scale + self._clip_skip = clip_skip + self._cross_attention_kwargs = cross_attention_kwargs + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # 3. Encode input prompt + text_encoder_lora_scale = ( + self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None + ) + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt, + device, + num_videos_per_prompt, + self.do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + lora_scale=text_encoder_lora_scale, + clip_skip=self.clip_skip, + ) + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + if self.do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + if ip_adapter_image is not None or ip_adapter_image_embeds is not None: + image_embeds = self.prepare_ip_adapter_image_embeds( + ip_adapter_image, + ip_adapter_image_embeds, + device, + batch_size * num_videos_per_prompt, + self.do_classifier_free_guidance, + ) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) + latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt) + self._num_timesteps = len(timesteps) + + # 5. Prepare latent variables + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + 4, + num_frames, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents=latents, + ) + mask, masked_image = self.prepare_masked_condition( + image, + batch_size * num_videos_per_prompt, + 4, + num_frames=num_frames, + height=height, + width=width, + dtype=self.unet.dtype, + device=device, + generator=generator, + motion_scale=motion_scale, + ) + if strength < 1.0: + noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype) + latents = self.scheduler.add_noise(masked_image[0], noise, latent_timestep) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Add image embeds for IP-Adapter + added_cond_kwargs = ( + {"image_embeds": image_embeds} + if ip_adapter_image is not None or ip_adapter_image_embeds is not None + else None + ) + + # 8. Denoising loop + num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1 + for free_init_iter in range(num_free_init_iters): + if self.free_init_enabled: + latents, timesteps = self._apply_free_init( + latents, free_init_iter, num_inference_steps, device, latents.dtype, generator + ) + + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = torch.cat([latent_model_input, mask, masked_image], dim=1) + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + ).sample + + # perform guidance + if self.do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + # 9. Post processing + if output_type == "latent": + video = latents + else: + video_tensor = self.decode_latents(latents) + video = tensor2vid(video_tensor, self.image_processor, output_type=output_type) + + # 10. Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video,) + + return PIAPipelineOutput(frames=video) diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/__init__.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8088fbcfceba205b9b908613f4ca3fdc579120e8 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/__init__.py @@ -0,0 +1,76 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_flax_available, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_additional_imports = {} +_import_structure = {"pipeline_output": ["StableDiffusionXLPipelineOutput"]} + +if is_transformers_available() and is_flax_available(): + _import_structure["pipeline_output"].extend(["FlaxStableDiffusionXLPipelineOutput"]) +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_stable_diffusion_xl"] = ["StableDiffusionXLPipeline"] + _import_structure["pipeline_stable_diffusion_xl_img2img"] = ["StableDiffusionXLImg2ImgPipeline"] + _import_structure["pipeline_stable_diffusion_xl_inpaint"] = ["StableDiffusionXLInpaintPipeline"] + _import_structure["pipeline_stable_diffusion_xl_instruct_pix2pix"] = ["StableDiffusionXLInstructPix2PixPipeline"] + +if is_transformers_available() and is_flax_available(): + from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState + + _additional_imports.update({"PNDMSchedulerState": PNDMSchedulerState}) + _import_structure["pipeline_flax_stable_diffusion_xl"] = ["FlaxStableDiffusionXLPipeline"] + + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 + else: + from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline + from .pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipeline + from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline + from .pipeline_stable_diffusion_xl_instruct_pix2pix import StableDiffusionXLInstructPix2PixPipeline + + try: + if not (is_transformers_available() and is_flax_available()): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + from ...utils.dummy_flax_objects import * + else: + from .pipeline_flax_stable_diffusion_xl import ( + FlaxStableDiffusionXLPipeline, + ) + from .pipeline_output import FlaxStableDiffusionXLPipelineOutput + +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) + for name, value in _additional_imports.items(): + setattr(sys.modules[__name__], name, value) diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py new file mode 100644 index 0000000000000000000000000000000000000000..0783f44486ee1448bd15529f745af381ee7fa69f --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_output.py @@ -0,0 +1,37 @@ +from dataclasses import dataclass +from typing import List, Union + +import numpy as np +import PIL.Image + +from ...utils import BaseOutput, is_flax_available + + +@dataclass +class StableDiffusionXLPipelineOutput(BaseOutput): + """ + Output class for Stable Diffusion pipelines. + + Args: + images (`List[PIL.Image.Image]` or `np.ndarray`) + List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, + num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. + """ + + images: Union[List[PIL.Image.Image], np.ndarray] + + +if is_flax_available(): + import flax + + @flax.struct.dataclass + class FlaxStableDiffusionXLPipelineOutput(BaseOutput): + """ + Output class for Flax Stable Diffusion XL pipelines. + + Args: + images (`np.ndarray`) + Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline. + """ + + images: np.ndarray diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py new file mode 100644 index 0000000000000000000000000000000000000000..51e413d4b5ea409cffa0ab1524f5bb1284c584da --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py @@ -0,0 +1,976 @@ +# Copyright 2024 Harutatsu Akiyama and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import PIL.Image +import torch +from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer + +from ...image_processor import PipelineImageInput, VaeImageProcessor +from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin +from ...models import AutoencoderKL, UNet2DConditionModel +from ...models.attention_processor import ( + AttnProcessor2_0, + FusedAttnProcessor2_0, + LoRAAttnProcessor2_0, + LoRAXFormersAttnProcessor, + XFormersAttnProcessor, +) +from ...models.lora import adjust_lora_scale_text_encoder +from ...schedulers import KarrasDiffusionSchedulers +from ...utils import ( + USE_PEFT_BACKEND, + deprecate, + is_invisible_watermark_available, + is_torch_xla_available, + logging, + replace_example_docstring, + scale_lora_layers, +) +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin +from .pipeline_output import StableDiffusionXLPipelineOutput + + +if is_invisible_watermark_available(): + from .watermark import StableDiffusionXLWatermarker + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import StableDiffusionXLInstructPix2PixPipeline + >>> from diffusers.utils import load_image + + >>> resolution = 768 + >>> image = load_image( + ... "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" + ... ).resize((resolution, resolution)) + >>> edit_instruction = "Turn sky into a cloudy one" + + >>> pipe = StableDiffusionXLInstructPix2PixPipeline.from_pretrained( + ... "diffusers/sdxl-instructpix2pix-768", torch_dtype=torch.float16 + ... ).to("cuda") + + >>> edited_image = pipe( + ... prompt=edit_instruction, + ... image=image, + ... height=resolution, + ... width=resolution, + ... guidance_scale=3.0, + ... image_guidance_scale=1.5, + ... num_inference_steps=30, + ... ).images[0] + >>> edited_image + ``` +""" + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents +def retrieve_latents( + encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" +): + if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": + return encoder_output.latent_dist.sample(generator) + elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": + return encoder_output.latent_dist.mode() + elif hasattr(encoder_output, "latents"): + return encoder_output.latents + else: + raise AttributeError("Could not access latents of provided encoder_output") + + +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg + + +class StableDiffusionXLInstructPix2PixPipeline( + DiffusionPipeline, + StableDiffusionMixin, + TextualInversionLoaderMixin, + FromSingleFileMixin, + StableDiffusionXLLoraLoaderMixin, +): + r""" + Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + The pipeline also inherits the following loading methods: + - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings + - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files + - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights + - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion XL uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + text_encoder_2 ([` CLIPTextModelWithProjection`]): + Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), + specifically the + [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) + variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + tokenizer_2 (`CLIPTokenizer`): + Second Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`): + Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config + of `stabilityai/stable-diffusion-xl-refiner-1-0`. + force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): + Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of + `stabilityai/stable-diffusion-xl-base-1-0`. + add_watermarker (`bool`, *optional*): + Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to + watermark output images. If not defined, it will default to True if the package is installed, otherwise no + watermarker will be used. + """ + + model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" + _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + text_encoder_2: CLIPTextModelWithProjection, + tokenizer: CLIPTokenizer, + tokenizer_2: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + force_zeros_for_empty_prompt: bool = True, + add_watermarker: Optional[bool] = None, + ): + super().__init__() + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + tokenizer=tokenizer, + tokenizer_2=tokenizer_2, + unet=unet, + scheduler=scheduler, + ) + self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.default_sample_size = self.unet.config.sample_size + + add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() + + if add_watermarker: + self.watermark = StableDiffusionXLWatermarker() + else: + self.watermark = None + + def encode_prompt( + self, + prompt: str, + prompt_2: Optional[str] = None, + device: Optional[torch.device] = None, + num_images_per_prompt: int = 1, + do_classifier_free_guidance: bool = True, + negative_prompt: Optional[str] = None, + negative_prompt_2: Optional[str] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + used in both text-encoders + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + negative_prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and + `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` + input argument. + lora_scale (`float`, *optional*): + A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + """ + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if self.text_encoder is not None: + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(self.text_encoder, lora_scale) + + if self.text_encoder_2 is not None: + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) + else: + scale_lora_layers(self.text_encoder_2, lora_scale) + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # Define tokenizers and text encoders + tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] + text_encoders = ( + [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] + ) + + if prompt_embeds is None: + prompt_2 = prompt_2 or prompt + # textual inversion: process multi-vector tokens if necessary + prompt_embeds_list = [] + prompts = [prompt, prompt_2] + for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, tokenizer) + + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + text_input_ids = text_inputs.input_ids + untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {tokenizer.model_max_length} tokens: {removed_text}" + ) + + prompt_embeds = text_encoder( + text_input_ids.to(device), + output_hidden_states=True, + ) + + # We are only ALWAYS interested in the pooled output of the final text encoder + pooled_prompt_embeds = prompt_embeds[0] + prompt_embeds = prompt_embeds.hidden_states[-2] + + prompt_embeds_list.append(prompt_embeds) + + prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) + + # get unconditional embeddings for classifier free guidance + zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt + if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: + negative_prompt_embeds = torch.zeros_like(prompt_embeds) + negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) + elif do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt_2 = negative_prompt_2 or negative_prompt + + uncond_tokens: List[str] + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt, negative_prompt_2] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = [negative_prompt, negative_prompt_2] + + negative_prompt_embeds_list = [] + for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = tokenizer( + negative_prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + negative_prompt_embeds = text_encoder( + uncond_input.input_ids.to(device), + output_hidden_states=True, + ) + # We are only ALWAYS interested in the pooled output of the final text encoder + negative_pooled_prompt_embeds = negative_prompt_embeds[0] + negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] + + negative_prompt_embeds_list.append(negative_prompt_embeds) + + negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) + + prompt_embeds_dtype = self.text_encoder_2.dtype if self.text_encoder_2 is not None else self.unet.dtype + prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + if do_classifier_free_guidance: + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + + return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.check_inputs + def check_inputs( + self, + prompt, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + callback_on_step_end_tensor_inputs=None, + ): + if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_image_latents( + self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None + ): + if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): + raise ValueError( + f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" + ) + + image = image.to(device=device, dtype=dtype) + + batch_size = batch_size * num_images_per_prompt + + if image.shape[1] == 4: + image_latents = image + else: + # make sure the VAE is in float32 mode, as it overflows in float16 + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + if needs_upcasting: + self.upcast_vae() + image = image.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax") + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + + if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: + # expand image_latents for batch_size + deprecation_message = ( + f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" + " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" + " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" + " your script to pass as many initial images as text prompts to suppress this warning." + ) + deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) + additional_image_per_prompt = batch_size // image_latents.shape[0] + image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) + elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." + ) + else: + image_latents = torch.cat([image_latents], dim=0) + + if do_classifier_free_guidance: + uncond_image_latents = torch.zeros_like(image_latents) + image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0) + + if image_latents.dtype != self.vae.dtype: + image_latents = image_latents.to(dtype=self.vae.dtype) + + return image_latents + + # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids + def _get_add_time_ids( + self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None + ): + add_time_ids = list(original_size + crops_coords_top_left + target_size) + + passed_add_embed_dim = ( + self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim + ) + expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features + + if expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + return add_time_ids + + # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae + def upcast_vae(self): + dtype = self.vae.dtype + self.vae.to(dtype=torch.float32) + use_torch_2_0_or_xformers = isinstance( + self.vae.decoder.mid_block.attentions[0].processor, + ( + AttnProcessor2_0, + XFormersAttnProcessor, + LoRAXFormersAttnProcessor, + LoRAAttnProcessor2_0, + FusedAttnProcessor2_0, + ), + ) + # if xformers or torch_2_0 is used attention block does not need + # to be in float32 which can save lots of memory + if use_torch_2_0_or_xformers: + self.vae.post_quant_conv.to(dtype) + self.vae.decoder.conv_in.to(dtype) + self.vae.decoder.mid_block.to(dtype) + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + prompt_2: Optional[Union[str, List[str]]] = None, + image: PipelineImageInput = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 100, + denoising_end: Optional[float] = None, + guidance_scale: float = 5.0, + image_guidance_scale: float = 1.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + negative_prompt_2: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + original_size: Tuple[int, int] = None, + crops_coords_top_left: Tuple[int, int] = (0, 0), + target_size: Tuple[int, int] = None, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + used in both text-encoders + image (`torch.FloatTensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.FloatTensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`): + The image(s) to modify with the pipeline. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + denoising_end (`float`, *optional*): + When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be + completed before it is intentionally prematurely terminated. As a result, the returned sample will + still retain a substantial amount of noise as determined by the discrete timesteps selected by the + scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a + "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image + Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) + guidance_scale (`float`, *optional*, defaults to 5.0): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + image_guidance_scale (`float`, *optional*, defaults to 1.5): + Image guidance scale is to push the generated image towards the inital image `image`. Image guidance + scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to + generate images that are closely linked to the source image `image`, usually at the expense of lower + image quality. This pipeline requires a value of at least `1`. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + negative_prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and + `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` + input argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + guidance_rescale (`float`, *optional*, defaults to 0.0): + Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are + Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of + [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). + Guidance rescale factor should fix overexposure when using zero terminal SNR. + original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): + If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. + `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as + explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): + `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position + `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting + `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): + For most cases, `target_size` should be set to the desired height and width of the generated image. If + not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in + section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + aesthetic_score (`float`, *optional*, defaults to 6.0): + Used to simulate an aesthetic score of the generated image by influencing the positive text condition. + Part of SDXL's micro-conditioning as explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + negative_aesthetic_score (`float`, *optional*, defaults to 2.5): + Part of SDXL's micro-conditioning as explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to + simulate an aesthetic score of the generated image by influencing the negative text condition. + + Examples: + + Returns: + [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a + `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + # 0. Default height and width to unet + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_sample_size * self.vae_scale_factor + + original_size = original_size or (height, width) + target_size = target_size or (height, width) + + # 1. Check inputs. Raise error if not correct + self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) + + if image is None: + raise ValueError("`image` input cannot be undefined.") + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0 + + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + ( + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) = self.encode_prompt( + prompt=prompt, + prompt_2=prompt_2, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + negative_prompt_2=negative_prompt_2, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, + lora_scale=text_encoder_lora_scale, + ) + + # 4. Preprocess image + image = self.image_processor.preprocess(image, height=height, width=width).to(device) + + # 5. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 6. Prepare Image latents + image_latents = self.prepare_image_latents( + image, + batch_size, + num_images_per_prompt, + prompt_embeds.dtype, + device, + do_classifier_free_guidance, + ) + + # 7. Prepare latent variables + num_channels_latents = self.vae.config.latent_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 8. Check that shapes of latents and image match the UNet channels + num_channels_image = image_latents.shape[1] + if num_channels_latents + num_channels_image != self.unet.config.in_channels: + raise ValueError( + f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" + f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" + f" `num_channels_image`: {num_channels_image} " + f" = {num_channels_latents + num_channels_image}. Please verify the config of" + " `pipeline.unet` or your `image` input." + ) + + # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 10. Prepare added time ids & embeddings + add_text_embeds = pooled_prompt_embeds + if self.text_encoder_2 is None: + text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) + else: + text_encoder_projection_dim = self.text_encoder_2.config.projection_dim + + add_time_ids = self._get_add_time_ids( + original_size, + crops_coords_top_left, + target_size, + dtype=prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + + if do_classifier_free_guidance: + # The extra concat similar to how it's done in SD InstructPix2Pix. + prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds], dim=0) + add_text_embeds = torch.cat( + [add_text_embeds, negative_pooled_prompt_embeds, negative_pooled_prompt_embeds], dim=0 + ) + add_time_ids = torch.cat([add_time_ids, add_time_ids, add_time_ids], dim=0) + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + + # 11. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # Expand the latents if we are doing classifier free guidance. + # The latents are expanded 3 times because for pix2pix the guidance + # is applied for both the text and the input image. + latent_model_input = torch.cat([latents] * 3) if do_classifier_free_guidance else latents + + # concat latents, image_latents in the channel dimension + scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1) + + # predict the noise residual + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + noise_pred = self.unet( + scaled_latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # perform guidance + if do_classifier_free_guidance: + noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond + + guidance_scale * (noise_pred_text - noise_pred_image) + + image_guidance_scale * (noise_pred_image - noise_pred_uncond) + ) + + if do_classifier_free_guidance and guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + if XLA_AVAILABLE: + xm.mark_step() + + if not output_type == "latent": + # make sure the VAE is in float32 mode, as it overflows in float16 + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + + if needs_upcasting: + self.upcast_vae() + latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + # unscale/denormalize the latents + # denormalize with the mean and std if available and not None + has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None + has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None + if has_latents_mean and has_latents_std: + latents_mean = ( + torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype) + ) + latents_std = ( + torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype) + ) + latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean + else: + latents = latents / self.vae.config.scaling_factor + + image = self.vae.decode(latents, return_dict=False)[0] + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + else: + return StableDiffusionXLPipelineOutput(images=latents) + + # apply watermark if available + if self.watermark is not None: + image = self.watermark.apply_watermark(image) + + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return StableDiffusionXLPipelineOutput(images=image) diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/watermark.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/watermark.py new file mode 100644 index 0000000000000000000000000000000000000000..5b6e36d9f44756da494cee0b996b1871721872e7 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/watermark.py @@ -0,0 +1,36 @@ +import numpy as np +import torch + +from ...utils import is_invisible_watermark_available + + +if is_invisible_watermark_available(): + from imwatermark import WatermarkEncoder + + +# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66 +WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110 +# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1 +WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]] + + +class StableDiffusionXLWatermarker: + def __init__(self): + self.watermark = WATERMARK_BITS + self.encoder = WatermarkEncoder() + + self.encoder.set_watermark("bits", self.watermark) + + def apply_watermark(self, images: torch.FloatTensor): + # can't encode images that are smaller than 256 + if images.shape[-1] < 256: + return images + + images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy() + + images = [self.encoder.encode(image, "dwtDct") for image in images] + + images = torch.from_numpy(np.array(images)).permute(0, 3, 1, 2) + + images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0) + return images diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unclip/__pycache__/__init__.cpython-310.pyc b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unclip/__pycache__/__init__.cpython-310.pyc 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OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import ( + ImageTextPipelineOutput, + UniDiffuserPipeline, + ) + + _dummy_objects.update( + {"ImageTextPipelineOutput": ImageTextPipelineOutput, "UniDiffuserPipeline": UniDiffuserPipeline} + ) +else: + _import_structure["modeling_text_decoder"] = ["UniDiffuserTextDecoder"] + _import_structure["modeling_uvit"] = ["UniDiffuserModel", "UTransformer2DModel"] + _import_structure["pipeline_unidiffuser"] = ["ImageTextPipelineOutput", "UniDiffuserPipeline"] + + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import ( + ImageTextPipelineOutput, + UniDiffuserPipeline, + ) + else: + from .modeling_text_decoder import UniDiffuserTextDecoder + from .modeling_uvit import UniDiffuserModel, UTransformer2DModel + from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline + +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/__init__.cpython-310.pyc b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1e56a676b789ed7be27c17544103073cb8cbd84b Binary files /dev/null and b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/__init__.cpython-310.pyc differ diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/modeling_text_decoder.cpython-310.pyc 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a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/pipeline_unidiffuser.cpython-310.pyc b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/pipeline_unidiffuser.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c18ff28e63e986134550c8b9d461e6945504700b Binary files /dev/null and b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/__pycache__/pipeline_unidiffuser.cpython-310.pyc differ diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/modeling_text_decoder.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/modeling_text_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..bf0a4eb475c0e8cda0a62a8b4bb83a8a02733903 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/modeling_text_decoder.py @@ -0,0 +1,296 @@ +from typing import Optional + +import numpy as np +import torch +from torch import nn +from transformers import GPT2Config, GPT2LMHeadModel +from transformers.modeling_utils import ModuleUtilsMixin + +from ...configuration_utils import ConfigMixin, register_to_config +from ...models import ModelMixin + + +# Modified from ClipCaptionModel in https://github.com/thu-ml/unidiffuser/blob/main/libs/caption_decoder.py +class UniDiffuserTextDecoder(ModelMixin, ConfigMixin, ModuleUtilsMixin): + """ + Text decoder model for a image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model. This is used to + generate text from the UniDiffuser image-text embedding. + + Parameters: + prefix_length (`int`): + Max number of prefix tokens that will be supplied to the model. + prefix_inner_dim (`int`): + The hidden size of the incoming prefix embeddings. For UniDiffuser, this would be the hidden dim of the + CLIP text encoder. + prefix_hidden_dim (`int`, *optional*): + Hidden dim of the MLP if we encode the prefix. + vocab_size (`int`, *optional*, defaults to 50257): + Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`]. + n_positions (`int`, *optional*, defaults to 1024): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + n_embd (`int`, *optional*, defaults to 768): + Dimensionality of the embeddings and hidden states. + n_layer (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + n_head (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + n_inner (`int`, *optional*, defaults to None): + Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd + activation_function (`str`, *optional*, defaults to `"gelu"`): + Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. + resid_pdrop (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + embd_pdrop (`float`, *optional*, defaults to 0.1): + The dropout ratio for the embeddings. + attn_pdrop (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention. + layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): + The epsilon to use in the layer normalization layers. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + scale_attn_weights (`bool`, *optional*, defaults to `True`): + Scale attention weights by dividing by sqrt(hidden_size).. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). + scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): + Whether to additionally scale attention weights by `1 / layer_idx + 1`. + reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): + Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention + dot-product/softmax to float() when training with mixed precision. + """ + + _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"] + + @register_to_config + def __init__( + self, + prefix_length: int, + prefix_inner_dim: int, + prefix_hidden_dim: Optional[int] = None, + vocab_size: int = 50257, # Start of GPT2 config args + n_positions: int = 1024, + n_embd: int = 768, + n_layer: int = 12, + n_head: int = 12, + n_inner: Optional[int] = None, + activation_function: str = "gelu_new", + resid_pdrop: float = 0.1, + embd_pdrop: float = 0.1, + attn_pdrop: float = 0.1, + layer_norm_epsilon: float = 1e-5, + initializer_range: float = 0.02, + scale_attn_weights: bool = True, + use_cache: bool = True, + scale_attn_by_inverse_layer_idx: bool = False, + reorder_and_upcast_attn: bool = False, + ): + super().__init__() + + self.prefix_length = prefix_length + + if prefix_inner_dim != n_embd and prefix_hidden_dim is None: + raise ValueError( + f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and" + f" `n_embd`: {n_embd} are not equal." + ) + + self.prefix_inner_dim = prefix_inner_dim + self.prefix_hidden_dim = prefix_hidden_dim + + self.encode_prefix = ( + nn.Linear(self.prefix_inner_dim, self.prefix_hidden_dim) + if self.prefix_hidden_dim is not None + else nn.Identity() + ) + self.decode_prefix = ( + nn.Linear(self.prefix_hidden_dim, n_embd) if self.prefix_hidden_dim is not None else nn.Identity() + ) + + gpt_config = GPT2Config( + vocab_size=vocab_size, + n_positions=n_positions, + n_embd=n_embd, + n_layer=n_layer, + n_head=n_head, + n_inner=n_inner, + activation_function=activation_function, + resid_pdrop=resid_pdrop, + embd_pdrop=embd_pdrop, + attn_pdrop=attn_pdrop, + layer_norm_epsilon=layer_norm_epsilon, + initializer_range=initializer_range, + scale_attn_weights=scale_attn_weights, + use_cache=use_cache, + scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, + reorder_and_upcast_attn=reorder_and_upcast_attn, + ) + self.transformer = GPT2LMHeadModel(gpt_config) + + def forward( + self, + input_ids: torch.Tensor, + prefix_embeds: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + ): + """ + Args: + input_ids (`torch.Tensor` of shape `(N, max_seq_len)`): + Text tokens to use for inference. + prefix_embeds (`torch.Tensor` of shape `(N, prefix_length, 768)`): + Prefix embedding to preprend to the embedded tokens. + attention_mask (`torch.Tensor` of shape `(N, prefix_length + max_seq_len, 768)`, *optional*): + Attention mask for the prefix embedding. + labels (`torch.Tensor`, *optional*): + Labels to use for language modeling. + """ + embedding_text = self.transformer.transformer.wte(input_ids) + hidden = self.encode_prefix(prefix_embeds) + prefix_embeds = self.decode_prefix(hidden) + embedding_cat = torch.cat((prefix_embeds, embedding_text), dim=1) + + if labels is not None: + dummy_token = self.get_dummy_token(input_ids.shape[0], input_ids.device) + labels = torch.cat((dummy_token, input_ids), dim=1) + out = self.transformer(inputs_embeds=embedding_cat, labels=labels, attention_mask=attention_mask) + if self.prefix_hidden_dim is not None: + return out, hidden + else: + return out + + def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor: + return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device) + + def encode(self, prefix): + return self.encode_prefix(prefix) + + @torch.no_grad() + def generate_captions(self, features, eos_token_id, device): + """ + Generate captions given text embedding features. Returns list[L]. + + Args: + features (`torch.Tensor` of shape `(B, L, D)`): + Text embedding features to generate captions from. + eos_token_id (`int`): + The token ID of the EOS token for the text decoder model. + device: + Device to perform text generation on. + + Returns: + `List[str]`: A list of strings generated from the decoder model. + """ + + features = torch.split(features, 1, dim=0) + generated_tokens = [] + generated_seq_lengths = [] + for feature in features: + feature = self.decode_prefix(feature.to(device)) # back to the clip feature + # Only support beam search for now + output_tokens, seq_lengths = self.generate_beam( + input_embeds=feature, device=device, eos_token_id=eos_token_id + ) + generated_tokens.append(output_tokens[0]) + generated_seq_lengths.append(seq_lengths[0]) + generated_tokens = torch.stack(generated_tokens) + generated_seq_lengths = torch.stack(generated_seq_lengths) + return generated_tokens, generated_seq_lengths + + @torch.no_grad() + def generate_beam( + self, + input_ids=None, + input_embeds=None, + device=None, + beam_size: int = 5, + entry_length: int = 67, + temperature: float = 1.0, + eos_token_id: Optional[int] = None, + ): + """ + Generates text using the given tokenizer and text prompt or token embedding via beam search. This + implementation is based on the beam search implementation from the [original UniDiffuser + code](https://github.com/thu-ml/unidiffuser/blob/main/libs/caption_decoder.py#L89). + + Args: + eos_token_id (`int`, *optional*): + The token ID of the EOS token for the text decoder model. + input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): + Tokenizer indices of input sequence tokens in the vocabulary. One of `input_ids` and `input_embeds` + must be supplied. + input_embeds (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*): + An embedded representation to directly pass to the transformer as a prefix for beam search. One of + `input_ids` and `input_embeds` must be supplied. + device: + The device to perform beam search on. + beam_size (`int`, *optional*, defaults to `5`): + The number of best states to store during beam search. + entry_length (`int`, *optional*, defaults to `67`): + The number of iterations to run beam search. + temperature (`float`, *optional*, defaults to 1.0): + The temperature to use when performing the softmax over logits from the decoding model. + + Returns: + `Tuple(torch.Tensor, torch.Tensor)`: A tuple of tensors where the first element is a tensor of generated + token sequences sorted by score in descending order, and the second element is the sequence lengths + corresponding to those sequences. + """ + # Generates text until stop_token is reached using beam search with the desired beam size. + stop_token_index = eos_token_id + tokens = None + scores = None + seq_lengths = torch.ones(beam_size, device=device, dtype=torch.int) + is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool) + + if input_embeds is not None: + generated = input_embeds + else: + generated = self.transformer.transformer.wte(input_ids) + + for i in range(entry_length): + outputs = self.transformer(inputs_embeds=generated) + logits = outputs.logits + logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0) + logits = logits.softmax(-1).log() + + if scores is None: + scores, next_tokens = logits.topk(beam_size, -1) + generated = generated.expand(beam_size, *generated.shape[1:]) + next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0) + if tokens is None: + tokens = next_tokens + else: + tokens = tokens.expand(beam_size, *tokens.shape[1:]) + tokens = torch.cat((tokens, next_tokens), dim=1) + else: + logits[is_stopped] = -float(np.inf) + logits[is_stopped, 0] = 0 + scores_sum = scores[:, None] + logits + seq_lengths[~is_stopped] += 1 + scores_sum_average = scores_sum / seq_lengths[:, None] + scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1) + next_tokens_source = next_tokens // scores_sum.shape[1] + seq_lengths = seq_lengths[next_tokens_source] + next_tokens = next_tokens % scores_sum.shape[1] + next_tokens = next_tokens.unsqueeze(1) + tokens = tokens[next_tokens_source] + tokens = torch.cat((tokens, next_tokens), dim=1) + generated = generated[next_tokens_source] + scores = scores_sum_average * seq_lengths + is_stopped = is_stopped[next_tokens_source] + + next_token_embed = self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1) + generated = torch.cat((generated, next_token_embed), dim=1) + is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze() + if is_stopped.all(): + break + + scores = scores / seq_lengths + order = scores.argsort(descending=True) + # tokens tensors are already padded to max_seq_length + output_texts = [tokens[i] for i in order] + output_texts = torch.stack(output_texts, dim=0) + seq_lengths = torch.tensor([seq_lengths[i] for i in order], dtype=seq_lengths.dtype) + return output_texts, seq_lengths diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/modeling_uvit.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/modeling_uvit.py new file mode 100644 index 0000000000000000000000000000000000000000..c074b9916301f1b673580d2974f5f3bc0f802535 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/modeling_uvit.py @@ -0,0 +1,1197 @@ +import math +from typing import Optional, Union + +import torch +from torch import nn + +from ...configuration_utils import ConfigMixin, register_to_config +from ...models import ModelMixin +from ...models.attention import FeedForward +from ...models.attention_processor import Attention +from ...models.embeddings import TimestepEmbedding, Timesteps, get_2d_sincos_pos_embed +from ...models.normalization import AdaLayerNorm +from ...models.transformers.transformer_2d import Transformer2DModelOutput +from ...utils import logging + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + logger.warning( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect." + ) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): + # type: (torch.Tensor, float, float, float, float) -> torch.Tensor + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, + \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for + generating the random values works best when :math:`a \leq \text{mean} \leq b`. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding""" + + def __init__( + self, + height=224, + width=224, + patch_size=16, + in_channels=3, + embed_dim=768, + layer_norm=False, + flatten=True, + bias=True, + use_pos_embed=True, + ): + super().__init__() + + num_patches = (height // patch_size) * (width // patch_size) + self.flatten = flatten + self.layer_norm = layer_norm + + self.proj = nn.Conv2d( + in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias + ) + if layer_norm: + self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6) + else: + self.norm = None + + self.use_pos_embed = use_pos_embed + if self.use_pos_embed: + pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5)) + self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False) + + def forward(self, latent): + latent = self.proj(latent) + if self.flatten: + latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC + if self.layer_norm: + latent = self.norm(latent) + if self.use_pos_embed: + return latent + self.pos_embed + else: + return latent + + +class SkipBlock(nn.Module): + def __init__(self, dim: int): + super().__init__() + + self.skip_linear = nn.Linear(2 * dim, dim) + + # Use torch.nn.LayerNorm for now, following the original code + self.norm = nn.LayerNorm(dim) + + def forward(self, x, skip): + x = self.skip_linear(torch.cat([x, skip], dim=-1)) + x = self.norm(x) + + return x + + +# Modified to support both pre-LayerNorm and post-LayerNorm configurations +# Don't support AdaLayerNormZero for now +# Modified from diffusers.models.attention.BasicTransformerBlock +class UTransformerBlock(nn.Module): + r""" + A modification of BasicTransformerBlock which supports pre-LayerNorm and post-LayerNorm configurations. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): + Activation function to be used in feed-forward. + num_embeds_ada_norm (:obj: `int`, *optional*): + The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (:obj: `bool`, *optional*, defaults to `False`): + Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the query and key to float32 when performing the attention calculation. + norm_elementwise_affine (`bool`, *optional*): + Whether to use learnable per-element affine parameters during layer normalization. + norm_type (`str`, defaults to `"layer_norm"`): + The layer norm implementation to use. + pre_layer_norm (`bool`, *optional*): + Whether to perform layer normalization before the attention and feedforward operations ("pre-LayerNorm"), + as opposed to after ("post-LayerNorm"). Note that `BasicTransformerBlock` uses pre-LayerNorm, e.g. + `pre_layer_norm = True`. + final_dropout (`bool`, *optional*): + Whether to use a final Dropout layer after the feedforward network. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + pre_layer_norm: bool = True, + final_dropout: bool = False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + self.pre_layer_norm = pre_layer_norm + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # 1. Self-Attn + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.attn2 = None + + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + else: + self.norm2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + def forward( + self, + hidden_states, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + timestep=None, + cross_attention_kwargs=None, + class_labels=None, + ): + # Pre-LayerNorm + if self.pre_layer_norm: + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + else: + norm_hidden_states = self.norm1(hidden_states) + else: + norm_hidden_states = hidden_states + + # 1. Self-Attention + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + # Post-LayerNorm + if not self.pre_layer_norm: + if self.use_ada_layer_norm: + attn_output = self.norm1(attn_output, timestep) + else: + attn_output = self.norm1(attn_output) + + hidden_states = attn_output + hidden_states + + if self.attn2 is not None: + # Pre-LayerNorm + if self.pre_layer_norm: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + else: + norm_hidden_states = hidden_states + # TODO (Birch-San): Here we should prepare the encoder_attention mask correctly + # prepare attention mask here + + # 2. Cross-Attention + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + + # Post-LayerNorm + if not self.pre_layer_norm: + attn_output = self.norm2(attn_output, timestep) if self.use_ada_layer_norm else self.norm2(attn_output) + + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + # Pre-LayerNorm + if self.pre_layer_norm: + norm_hidden_states = self.norm3(hidden_states) + else: + norm_hidden_states = hidden_states + + ff_output = self.ff(norm_hidden_states) + + # Post-LayerNorm + if not self.pre_layer_norm: + ff_output = self.norm3(ff_output) + + hidden_states = ff_output + hidden_states + + return hidden_states + + +# Like UTransformerBlock except with LayerNorms on the residual backbone of the block +# Modified from diffusers.models.attention.BasicTransformerBlock +class UniDiffuserBlock(nn.Module): + r""" + A modification of BasicTransformerBlock which supports pre-LayerNorm and post-LayerNorm configurations and puts the + LayerNorms on the residual backbone of the block. This matches the transformer block in the [original UniDiffuser + implementation](https://github.com/thu-ml/unidiffuser/blob/main/libs/uvit_multi_post_ln_v1.py#L104). + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + activation_fn (`str`, *optional*, defaults to `"geglu"`): + Activation function to be used in feed-forward. + num_embeds_ada_norm (:obj: `int`, *optional*): + The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (:obj: `bool`, *optional*, defaults to `False`): + Configure if the attentions should contain a bias parameter. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + upcast_attention (`bool`, *optional*): + Whether to upcast the query and key to float() when performing the attention calculation. + norm_elementwise_affine (`bool`, *optional*): + Whether to use learnable per-element affine parameters during layer normalization. + norm_type (`str`, defaults to `"layer_norm"`): + The layer norm implementation to use. + pre_layer_norm (`bool`, *optional*): + Whether to perform layer normalization before the attention and feedforward operations ("pre-LayerNorm"), + as opposed to after ("post-LayerNorm"). The original UniDiffuser implementation is post-LayerNorm + (`pre_layer_norm = False`). + final_dropout (`bool`, *optional*): + Whether to use a final Dropout layer after the feedforward network. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + pre_layer_norm: bool = False, + final_dropout: bool = True, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + self.pre_layer_norm = pre_layer_norm + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # 1. Self-Attn + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) # is self-attn if encoder_hidden_states is none + else: + self.attn2 = None + + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + else: + self.norm2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + def forward( + self, + hidden_states, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + timestep=None, + cross_attention_kwargs=None, + class_labels=None, + ): + # Following the diffusers transformer block implementation, put the LayerNorm on the + # residual backbone + # Pre-LayerNorm + if self.pre_layer_norm: + if self.use_ada_layer_norm: + hidden_states = self.norm1(hidden_states, timestep) + else: + hidden_states = self.norm1(hidden_states) + + # 1. Self-Attention + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + attn_output = self.attn1( + hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + hidden_states = attn_output + hidden_states + + # Following the diffusers transformer block implementation, put the LayerNorm on the + # residual backbone + # Post-LayerNorm + if not self.pre_layer_norm: + if self.use_ada_layer_norm: + hidden_states = self.norm1(hidden_states, timestep) + else: + hidden_states = self.norm1(hidden_states) + + if self.attn2 is not None: + # Pre-LayerNorm + if self.pre_layer_norm: + hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + # TODO (Birch-San): Here we should prepare the encoder_attention mask correctly + # prepare attention mask here + + # 2. Cross-Attention + attn_output = self.attn2( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + + hidden_states = attn_output + hidden_states + + # Post-LayerNorm + if not self.pre_layer_norm: + hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + # 3. Feed-forward + # Pre-LayerNorm + if self.pre_layer_norm: + hidden_states = self.norm3(hidden_states) + + ff_output = self.ff(hidden_states) + + hidden_states = ff_output + hidden_states + + # Post-LayerNorm + if not self.pre_layer_norm: + hidden_states = self.norm3(hidden_states) + + return hidden_states + + +# Modified from diffusers.models.transformer_2d.Transformer2DModel +# Modify the transformer block structure to be U-Net like following U-ViT +# Only supports patch-style input and torch.nn.LayerNorm currently +# https://github.com/baofff/U-ViT +class UTransformer2DModel(ModelMixin, ConfigMixin): + """ + Transformer model based on the [U-ViT](https://github.com/baofff/U-ViT) architecture for image-like data. Compared + to [`Transformer2DModel`], this model has skip connections between transformer blocks in a "U"-shaped fashion, + similar to a U-Net. Supports only continuous (actual embeddings) inputs, which are embedded via a [`PatchEmbed`] + layer and then reshaped to (b, t, d). + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + Pass if the input is continuous. The number of channels in the input. + out_channels (`int`, *optional*): + The number of output channels; if `None`, defaults to `in_channels`. + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + norm_num_groups (`int`, *optional*, defaults to `32`): + The number of groups to use when performing Group Normalization. + cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use. + attention_bias (`bool`, *optional*): + Configure if the TransformerBlocks' attention should contain a bias parameter. + sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images. + Note that this is fixed at training time as it is used for learning a number of position embeddings. See + `ImagePositionalEmbeddings`. + num_vector_embeds (`int`, *optional*): + Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels. + Includes the class for the masked latent pixel. + patch_size (`int`, *optional*, defaults to 2): + The patch size to use in the patch embedding. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`. + The number of diffusion steps used during training. Note that this is fixed at training time as it is used + to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for + up to but not more than steps than `num_embeds_ada_norm`. + use_linear_projection (int, *optional*): TODO: Not used + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used in each + transformer block. + upcast_attention (`bool`, *optional*): + Whether to upcast the query and key to float() when performing the attention calculation. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The Layer Normalization implementation to use. Defaults to `torch.nn.LayerNorm`. + block_type (`str`, *optional*, defaults to `"unidiffuser"`): + The transformer block implementation to use. If `"unidiffuser"`, has the LayerNorms on the residual + backbone of each transformer block; otherwise has them in the attention/feedforward branches (the standard + behavior in `diffusers`.) + pre_layer_norm (`bool`, *optional*): + Whether to perform layer normalization before the attention and feedforward operations ("pre-LayerNorm"), + as opposed to after ("post-LayerNorm"). The original UniDiffuser implementation is post-LayerNorm + (`pre_layer_norm = False`). + norm_elementwise_affine (`bool`, *optional*): + Whether to use learnable per-element affine parameters during layer normalization. + use_patch_pos_embed (`bool`, *optional*): + Whether to use position embeddings inside the patch embedding layer (`PatchEmbed`). + final_dropout (`bool`, *optional*): + Whether to use a final Dropout layer after the feedforward network. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = 2, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + block_type: str = "unidiffuser", + pre_layer_norm: bool = False, + norm_elementwise_affine: bool = True, + use_patch_pos_embed=False, + ff_final_dropout: bool = False, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # 1. Input + # Only support patch input of shape (batch_size, num_channels, height, width) for now + assert in_channels is not None and patch_size is not None, "Patch input requires in_channels and patch_size." + + assert sample_size is not None, "UTransformer2DModel over patched input must provide sample_size" + + # 2. Define input layers + self.height = sample_size + self.width = sample_size + + self.patch_size = patch_size + self.pos_embed = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=inner_dim, + use_pos_embed=use_patch_pos_embed, + ) + + # 3. Define transformers blocks + # Modify this to have in_blocks ("downsample" blocks, even though we don't actually downsample), a mid_block, + # and out_blocks ("upsample" blocks). Like a U-Net, there are skip connections from in_blocks to out_blocks in + # a "U"-shaped fashion (e.g. first in_block to last out_block, etc.). + # Quick hack to make the transformer block type configurable + if block_type == "unidiffuser": + block_cls = UniDiffuserBlock + else: + block_cls = UTransformerBlock + self.transformer_in_blocks = nn.ModuleList( + [ + block_cls( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + pre_layer_norm=pre_layer_norm, + norm_elementwise_affine=norm_elementwise_affine, + final_dropout=ff_final_dropout, + ) + for d in range(num_layers // 2) + ] + ) + + self.transformer_mid_block = block_cls( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + pre_layer_norm=pre_layer_norm, + norm_elementwise_affine=norm_elementwise_affine, + final_dropout=ff_final_dropout, + ) + + # For each skip connection, we use a SkipBlock (concatenation + Linear + LayerNorm) to process the inputs + # before each transformer out_block. + self.transformer_out_blocks = nn.ModuleList( + [ + nn.ModuleDict( + { + "skip": SkipBlock( + inner_dim, + ), + "block": block_cls( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + pre_layer_norm=pre_layer_norm, + norm_elementwise_affine=norm_elementwise_affine, + final_dropout=ff_final_dropout, + ), + } + ) + for d in range(num_layers // 2) + ] + ) + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + + # Following the UniDiffuser U-ViT implementation, we process the transformer output with + # a LayerNorm layer with per-element affine params + self.norm_out = nn.LayerNorm(inner_dim) + + def forward( + self, + hidden_states, + encoder_hidden_states=None, + timestep=None, + class_labels=None, + cross_attention_kwargs=None, + return_dict: bool = True, + hidden_states_is_embedding: bool = False, + unpatchify: bool = True, + ): + """ + Args: + hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`. + When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input + hidden_states + encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.long`, *optional*): + Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels + conditioning. + cross_attention_kwargs (*optional*): + Keyword arguments to supply to the cross attention layers, if used. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + hidden_states_is_embedding (`bool`, *optional*, defaults to `False`): + Whether or not hidden_states is an embedding directly usable by the transformer. In this case we will + ignore input handling (e.g. continuous, vectorized, etc.) and directly feed hidden_states into the + transformer blocks. + unpatchify (`bool`, *optional*, defaults to `True`): + Whether to unpatchify the transformer output. + + Returns: + [`~models.transformer_2d.Transformer2DModelOutput`] or `tuple`: + [`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # 0. Check inputs + + if not unpatchify and return_dict: + raise ValueError( + f"Cannot both define `unpatchify`: {unpatchify} and `return_dict`: {return_dict} since when" + f" `unpatchify` is {unpatchify} the returned output is of shape (batch_size, seq_len, hidden_dim)" + " rather than (batch_size, num_channels, height, width)." + ) + + # 1. Input + if not hidden_states_is_embedding: + hidden_states = self.pos_embed(hidden_states) + + # 2. Blocks + + # In ("downsample") blocks + skips = [] + for in_block in self.transformer_in_blocks: + hidden_states = in_block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + skips.append(hidden_states) + + # Mid block + hidden_states = self.transformer_mid_block(hidden_states) + + # Out ("upsample") blocks + for out_block in self.transformer_out_blocks: + hidden_states = out_block["skip"](hidden_states, skips.pop()) + hidden_states = out_block["block"]( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + # Don't support AdaLayerNorm for now, so no conditioning/scale/shift logic + hidden_states = self.norm_out(hidden_states) + # hidden_states = self.proj_out(hidden_states) + + if unpatchify: + # unpatchify + height = width = int(hidden_states.shape[1] ** 0.5) + hidden_states = hidden_states.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + else: + output = hidden_states + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + +class UniDiffuserModel(ModelMixin, ConfigMixin): + """ + Transformer model for a image-text [UniDiffuser](https://arxiv.org/pdf/2303.06555.pdf) model. This is a + modification of [`UTransformer2DModel`] with input and output heads for the VAE-embedded latent image, the + CLIP-embedded image, and the CLIP-embedded prompt (see paper for more details). + + Parameters: + text_dim (`int`): The hidden dimension of the CLIP text model used to embed images. + clip_img_dim (`int`): The hidden dimension of the CLIP vision model used to embed prompts. + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + Pass if the input is continuous. The number of channels in the input. + out_channels (`int`, *optional*): + The number of output channels; if `None`, defaults to `in_channels`. + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + norm_num_groups (`int`, *optional*, defaults to `32`): + The number of groups to use when performing Group Normalization. + cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use. + attention_bias (`bool`, *optional*): + Configure if the TransformerBlocks' attention should contain a bias parameter. + sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images. + Note that this is fixed at training time as it is used for learning a number of position embeddings. See + `ImagePositionalEmbeddings`. + num_vector_embeds (`int`, *optional*): + Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels. + Includes the class for the masked latent pixel. + patch_size (`int`, *optional*, defaults to 2): + The patch size to use in the patch embedding. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`. + The number of diffusion steps used during training. Note that this is fixed at training time as it is used + to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for + up to but not more than steps than `num_embeds_ada_norm`. + use_linear_projection (int, *optional*): TODO: Not used + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used in each + transformer block. + upcast_attention (`bool`, *optional*): + Whether to upcast the query and key to float32 when performing the attention calculation. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The Layer Normalization implementation to use. Defaults to `torch.nn.LayerNorm`. + block_type (`str`, *optional*, defaults to `"unidiffuser"`): + The transformer block implementation to use. If `"unidiffuser"`, has the LayerNorms on the residual + backbone of each transformer block; otherwise has them in the attention/feedforward branches (the standard + behavior in `diffusers`.) + pre_layer_norm (`bool`, *optional*): + Whether to perform layer normalization before the attention and feedforward operations ("pre-LayerNorm"), + as opposed to after ("post-LayerNorm"). The original UniDiffuser implementation is post-LayerNorm + (`pre_layer_norm = False`). + norm_elementwise_affine (`bool`, *optional*): + Whether to use learnable per-element affine parameters during layer normalization. + use_patch_pos_embed (`bool`, *optional*): + Whether to use position embeddings inside the patch embedding layer (`PatchEmbed`). + ff_final_dropout (`bool`, *optional*): + Whether to use a final Dropout layer after the feedforward network. + use_data_type_embedding (`bool`, *optional*): + Whether to use a data type embedding. This is only relevant for UniDiffuser-v1 style models; UniDiffuser-v1 + is continue-trained from UniDiffuser-v0 on non-publically-available data and accepts a `data_type` + argument, which can either be `1` to use the weights trained on non-publically-available data or `0` + otherwise. This argument is subsequently embedded by the data type embedding, if used. + """ + + @register_to_config + def __init__( + self, + text_dim: int = 768, + clip_img_dim: int = 512, + num_text_tokens: int = 77, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + num_vector_embeds: Optional[int] = None, + patch_size: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + norm_type: str = "layer_norm", + block_type: str = "unidiffuser", + pre_layer_norm: bool = False, + use_timestep_embedding=False, + norm_elementwise_affine: bool = True, + use_patch_pos_embed=False, + ff_final_dropout: bool = True, + use_data_type_embedding: bool = False, + ): + super().__init__() + + # 0. Handle dimensions + self.inner_dim = num_attention_heads * attention_head_dim + + assert sample_size is not None, "UniDiffuserModel over patched input must provide sample_size" + self.sample_size = sample_size + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + + self.patch_size = patch_size + # Assume image is square... + self.num_patches = (self.sample_size // patch_size) * (self.sample_size // patch_size) + + # 1. Define input layers + # 1.1 Input layers for text and image input + # For now, only support patch input for VAE latent image input + self.vae_img_in = PatchEmbed( + height=sample_size, + width=sample_size, + patch_size=patch_size, + in_channels=in_channels, + embed_dim=self.inner_dim, + use_pos_embed=use_patch_pos_embed, + ) + self.clip_img_in = nn.Linear(clip_img_dim, self.inner_dim) + self.text_in = nn.Linear(text_dim, self.inner_dim) + + # 1.2. Timestep embeddings for t_img, t_text + self.timestep_img_proj = Timesteps( + self.inner_dim, + flip_sin_to_cos=True, + downscale_freq_shift=0, + ) + self.timestep_img_embed = ( + TimestepEmbedding( + self.inner_dim, + 4 * self.inner_dim, + out_dim=self.inner_dim, + ) + if use_timestep_embedding + else nn.Identity() + ) + + self.timestep_text_proj = Timesteps( + self.inner_dim, + flip_sin_to_cos=True, + downscale_freq_shift=0, + ) + self.timestep_text_embed = ( + TimestepEmbedding( + self.inner_dim, + 4 * self.inner_dim, + out_dim=self.inner_dim, + ) + if use_timestep_embedding + else nn.Identity() + ) + + # 1.3. Positional embedding + self.num_text_tokens = num_text_tokens + self.num_tokens = 1 + 1 + num_text_tokens + 1 + self.num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, self.inner_dim)) + self.pos_embed_drop = nn.Dropout(p=dropout) + trunc_normal_(self.pos_embed, std=0.02) + + # 1.4. Handle data type token embeddings for UniDiffuser-V1, if necessary + self.use_data_type_embedding = use_data_type_embedding + if self.use_data_type_embedding: + self.data_type_token_embedding = nn.Embedding(2, self.inner_dim) + self.data_type_pos_embed_token = nn.Parameter(torch.zeros(1, 1, self.inner_dim)) + + # 2. Define transformer blocks + self.transformer = UTransformer2DModel( + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + in_channels=in_channels, + out_channels=out_channels, + num_layers=num_layers, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attention_bias=attention_bias, + sample_size=sample_size, + num_vector_embeds=num_vector_embeds, + patch_size=patch_size, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + norm_type=norm_type, + block_type=block_type, + pre_layer_norm=pre_layer_norm, + norm_elementwise_affine=norm_elementwise_affine, + use_patch_pos_embed=use_patch_pos_embed, + ff_final_dropout=ff_final_dropout, + ) + + # 3. Define output layers + patch_dim = (patch_size**2) * out_channels + self.vae_img_out = nn.Linear(self.inner_dim, patch_dim) + self.clip_img_out = nn.Linear(self.inner_dim, clip_img_dim) + self.text_out = nn.Linear(self.inner_dim, text_dim) + + @torch.jit.ignore + def no_weight_decay(self): + return {"pos_embed"} + + def forward( + self, + latent_image_embeds: torch.FloatTensor, + image_embeds: torch.FloatTensor, + prompt_embeds: torch.FloatTensor, + timestep_img: Union[torch.Tensor, float, int], + timestep_text: Union[torch.Tensor, float, int], + data_type: Optional[Union[torch.Tensor, float, int]] = 1, + encoder_hidden_states=None, + cross_attention_kwargs=None, + ): + """ + Args: + latent_image_embeds (`torch.FloatTensor` of shape `(batch size, latent channels, height, width)`): + Latent image representation from the VAE encoder. + image_embeds (`torch.FloatTensor` of shape `(batch size, 1, clip_img_dim)`): + CLIP-embedded image representation (unsqueezed in the first dimension). + prompt_embeds (`torch.FloatTensor` of shape `(batch size, seq_len, text_dim)`): + CLIP-embedded text representation. + timestep_img (`torch.long` or `float` or `int`): + Current denoising step for the image. + timestep_text (`torch.long` or `float` or `int`): + Current denoising step for the text. + data_type: (`torch.int` or `float` or `int`, *optional*, defaults to `1`): + Only used in UniDiffuser-v1-style models. Can be either `1`, to use weights trained on nonpublic data, + or `0` otherwise. + encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + cross_attention_kwargs (*optional*): + Keyword arguments to supply to the cross attention layers, if used. + + + Returns: + `tuple`: Returns relevant parts of the model's noise prediction: the first element of the tuple is tbe VAE + image embedding, the second element is the CLIP image embedding, and the third element is the CLIP text + embedding. + """ + batch_size = latent_image_embeds.shape[0] + + # 1. Input + # 1.1. Map inputs to shape (B, N, inner_dim) + vae_hidden_states = self.vae_img_in(latent_image_embeds) + clip_hidden_states = self.clip_img_in(image_embeds) + text_hidden_states = self.text_in(prompt_embeds) + + num_text_tokens, num_img_tokens = text_hidden_states.size(1), vae_hidden_states.size(1) + + # 1.2. Encode image timesteps to single token (B, 1, inner_dim) + if not torch.is_tensor(timestep_img): + timestep_img = torch.tensor([timestep_img], dtype=torch.long, device=vae_hidden_states.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep_img = timestep_img * torch.ones(batch_size, dtype=timestep_img.dtype, device=timestep_img.device) + + timestep_img_token = self.timestep_img_proj(timestep_img) + # t_img_token does not contain any weights and will always return f32 tensors + # but time_embedding might be fp16, so we need to cast here. + timestep_img_token = timestep_img_token.to(dtype=self.dtype) + timestep_img_token = self.timestep_img_embed(timestep_img_token) + timestep_img_token = timestep_img_token.unsqueeze(dim=1) + + # 1.3. Encode text timesteps to single token (B, 1, inner_dim) + if not torch.is_tensor(timestep_text): + timestep_text = torch.tensor([timestep_text], dtype=torch.long, device=vae_hidden_states.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep_text = timestep_text * torch.ones(batch_size, dtype=timestep_text.dtype, device=timestep_text.device) + + timestep_text_token = self.timestep_text_proj(timestep_text) + # t_text_token does not contain any weights and will always return f32 tensors + # but time_embedding might be fp16, so we need to cast here. + timestep_text_token = timestep_text_token.to(dtype=self.dtype) + timestep_text_token = self.timestep_text_embed(timestep_text_token) + timestep_text_token = timestep_text_token.unsqueeze(dim=1) + + # 1.4. Concatenate all of the embeddings together. + if self.use_data_type_embedding: + assert data_type is not None, "data_type must be supplied if the model uses a data type embedding" + if not torch.is_tensor(data_type): + data_type = torch.tensor([data_type], dtype=torch.int, device=vae_hidden_states.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + data_type = data_type * torch.ones(batch_size, dtype=data_type.dtype, device=data_type.device) + + data_type_token = self.data_type_token_embedding(data_type).unsqueeze(dim=1) + hidden_states = torch.cat( + [ + timestep_img_token, + timestep_text_token, + data_type_token, + text_hidden_states, + clip_hidden_states, + vae_hidden_states, + ], + dim=1, + ) + else: + hidden_states = torch.cat( + [timestep_img_token, timestep_text_token, text_hidden_states, clip_hidden_states, vae_hidden_states], + dim=1, + ) + + # 1.5. Prepare the positional embeddings and add to hidden states + # Note: I think img_vae should always have the proper shape, so there's no need to interpolate + # the position embeddings. + if self.use_data_type_embedding: + pos_embed = torch.cat( + [self.pos_embed[:, : 1 + 1, :], self.data_type_pos_embed_token, self.pos_embed[:, 1 + 1 :, :]], dim=1 + ) + else: + pos_embed = self.pos_embed + hidden_states = hidden_states + pos_embed + hidden_states = self.pos_embed_drop(hidden_states) + + # 2. Blocks + hidden_states = self.transformer( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=None, + class_labels=None, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + hidden_states_is_embedding=True, + unpatchify=False, + )[0] + + # 3. Output + # Split out the predicted noise representation. + if self.use_data_type_embedding: + ( + t_img_token_out, + t_text_token_out, + data_type_token_out, + text_out, + img_clip_out, + img_vae_out, + ) = hidden_states.split((1, 1, 1, num_text_tokens, 1, num_img_tokens), dim=1) + else: + t_img_token_out, t_text_token_out, text_out, img_clip_out, img_vae_out = hidden_states.split( + (1, 1, num_text_tokens, 1, num_img_tokens), dim=1 + ) + + img_vae_out = self.vae_img_out(img_vae_out) + + # unpatchify + height = width = int(img_vae_out.shape[1] ** 0.5) + img_vae_out = img_vae_out.reshape( + shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) + ) + img_vae_out = torch.einsum("nhwpqc->nchpwq", img_vae_out) + img_vae_out = img_vae_out.reshape( + shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) + ) + + img_clip_out = self.clip_img_out(img_clip_out) + + text_out = self.text_out(text_out) + + return img_vae_out, img_clip_out, text_out diff --git a/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py new file mode 100644 index 0000000000000000000000000000000000000000..5d61b1054e1caaada47ee87ef414723c014c5db2 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py @@ -0,0 +1,1419 @@ +import inspect +from dataclasses import dataclass +from typing import Callable, List, Optional, Union + +import numpy as np +import PIL.Image +import torch +from transformers import ( + CLIPImageProcessor, + CLIPTextModel, + CLIPTokenizer, + CLIPVisionModelWithProjection, + GPT2Tokenizer, +) + +from ...image_processor import VaeImageProcessor +from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin +from ...models import AutoencoderKL +from ...models.lora import adjust_lora_scale_text_encoder +from ...schedulers import KarrasDiffusionSchedulers +from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers +from ...utils.outputs import BaseOutput +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline +from .modeling_text_decoder import UniDiffuserTextDecoder +from .modeling_uvit import UniDiffuserModel + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# New BaseOutput child class for joint image-text output +@dataclass +class ImageTextPipelineOutput(BaseOutput): + """ + Output class for joint image-text pipelines. + + Args: + images (`List[PIL.Image.Image]` or `np.ndarray`) + List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, + num_channels)`. + text (`List[str]` or `List[List[str]]`) + List of generated text strings of length `batch_size` or a list of list of strings whose outer list has + length `batch_size`. + """ + + images: Optional[Union[List[PIL.Image.Image], np.ndarray]] + text: Optional[Union[List[str], List[List[str]]]] + + +class UniDiffuserPipeline(DiffusionPipeline): + r""" + Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned + image generation, image-conditioned text generation, and joint image-text generation. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. This + is part of the UniDiffuser image representation along with the CLIP vision encoding. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). + image_encoder ([`CLIPVisionModel`]): + A [`~transformers.CLIPVisionModel`] to encode images as part of its image representation along with the VAE + latent representation. + image_processor ([`CLIPImageProcessor`]): + [`~transformers.CLIPImageProcessor`] to preprocess an image before CLIP encoding it with `image_encoder`. + clip_tokenizer ([`CLIPTokenizer`]): + A [`~transformers.CLIPTokenizer`] to tokenize the prompt before encoding it with `text_encoder`. + text_decoder ([`UniDiffuserTextDecoder`]): + Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser + embedding. + text_tokenizer ([`GPT2Tokenizer`]): + A [`~transformers.GPT2Tokenizer`] to decode text for text generation; used along with the `text_decoder`. + unet ([`UniDiffuserModel`]): + A [U-ViT](https://github.com/baofff/U-ViT) model with UNNet-style skip connections between transformer + layers to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image and/or text latents. The + original UniDiffuser paper uses the [`DPMSolverMultistepScheduler`] scheduler. + """ + + # TODO: support for moving submodules for components with enable_model_cpu_offload + model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae->text_decoder" + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + image_encoder: CLIPVisionModelWithProjection, + clip_image_processor: CLIPImageProcessor, + clip_tokenizer: CLIPTokenizer, + text_decoder: UniDiffuserTextDecoder, + text_tokenizer: GPT2Tokenizer, + unet: UniDiffuserModel, + scheduler: KarrasDiffusionSchedulers, + ): + super().__init__() + + if text_encoder.config.hidden_size != text_decoder.prefix_inner_dim: + raise ValueError( + f"The text encoder hidden size and text decoder prefix inner dim must be the same, but" + f" `text_encoder.config.hidden_size`: {text_encoder.config.hidden_size} and `text_decoder.prefix_inner_dim`: {text_decoder.prefix_inner_dim}" + ) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + image_encoder=image_encoder, + clip_image_processor=clip_image_processor, + clip_tokenizer=clip_tokenizer, + text_decoder=text_decoder, + text_tokenizer=text_tokenizer, + unet=unet, + scheduler=scheduler, + ) + + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + + self.num_channels_latents = vae.config.latent_channels + self.text_encoder_seq_len = text_encoder.config.max_position_embeddings + self.text_encoder_hidden_size = text_encoder.config.hidden_size + self.image_encoder_projection_dim = image_encoder.config.projection_dim + self.unet_resolution = unet.config.sample_size + + self.text_intermediate_dim = self.text_encoder_hidden_size + if self.text_decoder.prefix_hidden_dim is not None: + self.text_intermediate_dim = self.text_decoder.prefix_hidden_dim + + self.mode = None + + # TODO: handle safety checking? + self.safety_checker = None + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def _infer_mode(self, prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents): + r""" + Infer the generation task ('mode') from the inputs to `__call__`. If the mode has been manually set, the set + mode will be used. + """ + prompt_available = (prompt is not None) or (prompt_embeds is not None) + image_available = image is not None + input_available = prompt_available or image_available + + prompt_latents_available = prompt_latents is not None + vae_latents_available = vae_latents is not None + clip_latents_available = clip_latents is not None + full_latents_available = latents is not None + image_latents_available = vae_latents_available and clip_latents_available + all_indv_latents_available = prompt_latents_available and image_latents_available + + if self.mode is not None: + # Preferentially use the mode set by the user + mode = self.mode + elif prompt_available: + mode = "text2img" + elif image_available: + mode = "img2text" + else: + # Neither prompt nor image supplied, infer based on availability of latents + if full_latents_available or all_indv_latents_available: + mode = "joint" + elif prompt_latents_available: + mode = "text" + elif image_latents_available: + mode = "img" + else: + # No inputs or latents available + mode = "joint" + + # Give warnings for ambiguous cases + if self.mode is None and prompt_available and image_available: + logger.warning( + f"You have supplied both a text prompt and image to the pipeline and mode has not been set manually," + f" defaulting to mode '{mode}'." + ) + + if self.mode is None and not input_available: + if vae_latents_available != clip_latents_available: + # Exactly one of vae_latents and clip_latents is supplied + logger.warning( + f"You have supplied exactly one of `vae_latents` and `clip_latents`, whereas either both or none" + f" are expected to be supplied. Defaulting to mode '{mode}'." + ) + elif not prompt_latents_available and not vae_latents_available and not clip_latents_available: + # No inputs or latents supplied + logger.warning( + f"No inputs or latents have been supplied, and mode has not been manually set," + f" defaulting to mode '{mode}'." + ) + + return mode + + # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_tiling + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger images. + """ + self.vae.enable_tiling() + + # Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_tiling + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + # Functions to manually set the mode + def set_text_mode(self): + r"""Manually set the generation mode to unconditional ("marginal") text generation.""" + self.mode = "text" + + def set_image_mode(self): + r"""Manually set the generation mode to unconditional ("marginal") image generation.""" + self.mode = "img" + + def set_text_to_image_mode(self): + r"""Manually set the generation mode to text-conditioned image generation.""" + self.mode = "text2img" + + def set_image_to_text_mode(self): + r"""Manually set the generation mode to image-conditioned text generation.""" + self.mode = "img2text" + + def set_joint_mode(self): + r"""Manually set the generation mode to unconditional joint image-text generation.""" + self.mode = "joint" + + def reset_mode(self): + r"""Removes a manually set mode; after calling this, the pipeline will infer the mode from inputs.""" + self.mode = None + + def _infer_batch_size( + self, + mode, + prompt, + prompt_embeds, + image, + num_images_per_prompt, + num_prompts_per_image, + latents, + prompt_latents, + vae_latents, + clip_latents, + ): + r"""Infers the batch size and multiplier depending on mode and supplied arguments to `__call__`.""" + if num_images_per_prompt is None: + num_images_per_prompt = 1 + if num_prompts_per_image is None: + num_prompts_per_image = 1 + + assert num_images_per_prompt > 0, "num_images_per_prompt must be a positive integer" + assert num_prompts_per_image > 0, "num_prompts_per_image must be a positive integer" + + if mode in ["text2img"]: + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + # Either prompt or prompt_embeds must be present for text2img. + batch_size = prompt_embeds.shape[0] + multiplier = num_images_per_prompt + elif mode in ["img2text"]: + if isinstance(image, PIL.Image.Image): + batch_size = 1 + else: + # Image must be available and type either PIL.Image.Image or torch.FloatTensor. + # Not currently supporting something like image_embeds. + batch_size = image.shape[0] + multiplier = num_prompts_per_image + elif mode in ["img"]: + if vae_latents is not None: + batch_size = vae_latents.shape[0] + elif clip_latents is not None: + batch_size = clip_latents.shape[0] + else: + batch_size = 1 + multiplier = num_images_per_prompt + elif mode in ["text"]: + if prompt_latents is not None: + batch_size = prompt_latents.shape[0] + else: + batch_size = 1 + multiplier = num_prompts_per_image + elif mode in ["joint"]: + if latents is not None: + batch_size = latents.shape[0] + elif prompt_latents is not None: + batch_size = prompt_latents.shape[0] + elif vae_latents is not None: + batch_size = vae_latents.shape[0] + elif clip_latents is not None: + batch_size = clip_latents.shape[0] + else: + batch_size = 1 + + if num_images_per_prompt == num_prompts_per_image: + multiplier = num_images_per_prompt + else: + multiplier = min(num_images_per_prompt, num_prompts_per_image) + logger.warning( + f"You are using mode `{mode}` and `num_images_per_prompt`: {num_images_per_prompt} and" + f" num_prompts_per_image: {num_prompts_per_image} are not equal. Using batch size equal to" + f" `min(num_images_per_prompt, num_prompts_per_image) = {batch_size}." + ) + return batch_size, multiplier + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt + def _encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + **kwargs, + ): + deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." + deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) + + prompt_embeds_tuple = self.encode_prompt( + prompt=prompt, + device=device, + num_images_per_prompt=num_images_per_prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + lora_scale=lora_scale, + **kwargs, + ) + + # concatenate for backwards comp + prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) + + return prompt_embeds + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with self.tokenizer->self.clip_tokenizer + def encode_prompt( + self, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + clip_skip: Optional[int] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + lora_scale (`float`, *optional*): + A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + """ + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(self.text_encoder, lora_scale) + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + # textual inversion: process multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.clip_tokenizer) + + text_inputs = self.clip_tokenizer( + prompt, + padding="max_length", + max_length=self.clip_tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.clip_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.clip_tokenizer.batch_decode( + untruncated_ids[:, self.clip_tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.clip_tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + if clip_skip is None: + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) + prompt_embeds = prompt_embeds[0] + else: + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True + ) + # Access the `hidden_states` first, that contains a tuple of + # all the hidden states from the encoder layers. Then index into + # the tuple to access the hidden states from the desired layer. + prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] + # We also need to apply the final LayerNorm here to not mess with the + # representations. The `last_hidden_states` that we typically use for + # obtaining the final prompt representations passes through the LayerNorm + # layer. + prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) + + if self.text_encoder is not None: + prompt_embeds_dtype = self.text_encoder.dtype + elif self.unet is not None: + prompt_embeds_dtype = self.unet.dtype + else: + prompt_embeds_dtype = prompt_embeds.dtype + + prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + # textual inversion: process multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.clip_tokenizer) + + max_length = prompt_embeds.shape[1] + uncond_input = self.clip_tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + return prompt_embeds, negative_prompt_embeds + + # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.prepare_image_latents + # Add num_prompts_per_image argument, sample from autoencoder moment distribution + def encode_image_vae_latents( + self, + image, + batch_size, + num_prompts_per_image, + dtype, + device, + do_classifier_free_guidance, + generator=None, + ): + if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): + raise ValueError( + f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" + ) + + image = image.to(device=device, dtype=dtype) + + batch_size = batch_size * num_prompts_per_image + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if isinstance(generator, list): + image_latents = [ + self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i]) + * self.vae.config.scaling_factor + for i in range(batch_size) + ] + image_latents = torch.cat(image_latents, dim=0) + else: + image_latents = self.vae.encode(image).latent_dist.sample(generator=generator) + # Scale image_latents by the VAE's scaling factor + image_latents = image_latents * self.vae.config.scaling_factor + + if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: + # expand image_latents for batch_size + deprecation_message = ( + f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" + " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" + " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" + " your script to pass as many initial images as text prompts to suppress this warning." + ) + deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) + additional_image_per_prompt = batch_size // image_latents.shape[0] + image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) + elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." + ) + else: + image_latents = torch.cat([image_latents], dim=0) + + if do_classifier_free_guidance: + uncond_image_latents = torch.zeros_like(image_latents) + image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0) + + return image_latents + + def encode_image_clip_latents( + self, + image, + batch_size, + num_prompts_per_image, + dtype, + device, + generator=None, + ): + # Map image to CLIP embedding. + if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): + raise ValueError( + f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" + ) + + preprocessed_image = self.clip_image_processor.preprocess( + image, + return_tensors="pt", + ) + preprocessed_image = preprocessed_image.to(device=device, dtype=dtype) + + batch_size = batch_size * num_prompts_per_image + if isinstance(generator, list): + image_latents = [ + self.image_encoder(**preprocessed_image[i : i + 1]).image_embeds for i in range(batch_size) + ] + image_latents = torch.cat(image_latents, dim=0) + else: + image_latents = self.image_encoder(**preprocessed_image).image_embeds + + if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: + # expand image_latents for batch_size + deprecation_message = ( + f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" + " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" + " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" + " your script to pass as many initial images as text prompts to suppress this warning." + ) + deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) + additional_image_per_prompt = batch_size // image_latents.shape[0] + image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) + elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." + ) + else: + image_latents = torch.cat([image_latents], dim=0) + + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + return image_latents + + def prepare_text_latents( + self, batch_size, num_images_per_prompt, seq_len, hidden_size, dtype, device, generator, latents=None + ): + # Prepare latents for the CLIP embedded prompt. + shape = (batch_size * num_images_per_prompt, seq_len, hidden_size) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + # latents is assumed to have shace (B, L, D) + latents = latents.repeat(num_images_per_prompt, 1, 1) + latents = latents.to(device=device, dtype=dtype) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + # Rename prepare_latents -> prepare_image_vae_latents and add num_prompts_per_image argument. + def prepare_image_vae_latents( + self, + batch_size, + num_prompts_per_image, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size * num_prompts_per_image, + num_channels_latents, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + # latents is assumed to have shape (B, C, H, W) + latents = latents.repeat(num_prompts_per_image, 1, 1, 1) + latents = latents.to(device=device, dtype=dtype) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_image_clip_latents( + self, batch_size, num_prompts_per_image, clip_img_dim, dtype, device, generator, latents=None + ): + # Prepare latents for the CLIP embedded image. + shape = (batch_size * num_prompts_per_image, 1, clip_img_dim) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + # latents is assumed to have shape (B, L, D) + latents = latents.repeat(num_prompts_per_image, 1, 1) + latents = latents.to(device=device, dtype=dtype) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def decode_text_latents(self, text_latents, device): + output_token_list, seq_lengths = self.text_decoder.generate_captions( + text_latents, self.text_tokenizer.eos_token_id, device=device + ) + output_list = output_token_list.cpu().numpy() + generated_text = [ + self.text_tokenizer.decode(output[: int(length)], skip_special_tokens=True) + for output, length in zip(output_list, seq_lengths) + ] + return generated_text + + def _split(self, x, height, width): + r""" + Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W) + and (B, 1, clip_img_dim) + """ + batch_size = x.shape[0] + latent_height = height // self.vae_scale_factor + latent_width = width // self.vae_scale_factor + img_vae_dim = self.num_channels_latents * latent_height * latent_width + + img_vae, img_clip = x.split([img_vae_dim, self.image_encoder_projection_dim], dim=1) + + img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width)) + img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim)) + return img_vae, img_clip + + def _combine(self, img_vae, img_clip): + r""" + Combines a latent iamge img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1, + clip_img_dim) into a single tensor of shape (B, C * H * W + clip_img_dim). + """ + img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1)) + img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1)) + return torch.concat([img_vae, img_clip], dim=-1) + + def _split_joint(self, x, height, width): + r""" + Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae, + img_clip, text) where img_vae is of shape (B, C, H, W), img_clip is of shape (B, 1, clip_img_dim), and text is + of shape (B, text_seq_len, text_dim). + """ + batch_size = x.shape[0] + latent_height = height // self.vae_scale_factor + latent_width = width // self.vae_scale_factor + img_vae_dim = self.num_channels_latents * latent_height * latent_width + text_dim = self.text_encoder_seq_len * self.text_intermediate_dim + + img_vae, img_clip, text = x.split([img_vae_dim, self.image_encoder_projection_dim, text_dim], dim=1) + + img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width)) + img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim)) + text = torch.reshape(text, (batch_size, self.text_encoder_seq_len, self.text_intermediate_dim)) + return img_vae, img_clip, text + + def _combine_joint(self, img_vae, img_clip, text): + r""" + Combines a latent image img_vae of shape (B, C, H, W), a CLIP-embedded image img_clip of shape (B, L_img, + clip_img_dim), and a text embedding text of shape (B, L_text, text_dim) into a single embedding x of shape (B, + C * H * W + L_img * clip_img_dim + L_text * text_dim). + """ + img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1)) + img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1)) + text = torch.reshape(text, (text.shape[0], -1)) + return torch.concat([img_vae, img_clip, text], dim=-1) + + def _get_noise_pred( + self, + mode, + latents, + t, + prompt_embeds, + img_vae, + img_clip, + max_timestep, + data_type, + guidance_scale, + generator, + device, + height, + width, + ): + r""" + Gets the noise prediction using the `unet` and performs classifier-free guidance, if necessary. + """ + if mode == "joint": + # Joint text-image generation + img_vae_latents, img_clip_latents, text_latents = self._split_joint(latents, height, width) + + img_vae_out, img_clip_out, text_out = self.unet( + img_vae_latents, img_clip_latents, text_latents, timestep_img=t, timestep_text=t, data_type=data_type + ) + + x_out = self._combine_joint(img_vae_out, img_clip_out, text_out) + + if guidance_scale <= 1.0: + return x_out + + # Classifier-free guidance + img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype) + img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype) + text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype) + + _, _, text_out_uncond = self.unet( + img_vae_T, img_clip_T, text_latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type + ) + + img_vae_out_uncond, img_clip_out_uncond, _ = self.unet( + img_vae_latents, + img_clip_latents, + text_T, + timestep_img=t, + timestep_text=max_timestep, + data_type=data_type, + ) + + x_out_uncond = self._combine_joint(img_vae_out_uncond, img_clip_out_uncond, text_out_uncond) + + return guidance_scale * x_out + (1.0 - guidance_scale) * x_out_uncond + elif mode == "text2img": + # Text-conditioned image generation + img_vae_latents, img_clip_latents = self._split(latents, height, width) + + img_vae_out, img_clip_out, text_out = self.unet( + img_vae_latents, img_clip_latents, prompt_embeds, timestep_img=t, timestep_text=0, data_type=data_type + ) + + img_out = self._combine(img_vae_out, img_clip_out) + + if guidance_scale <= 1.0: + return img_out + + # Classifier-free guidance + text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype) + + img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet( + img_vae_latents, + img_clip_latents, + text_T, + timestep_img=t, + timestep_text=max_timestep, + data_type=data_type, + ) + + img_out_uncond = self._combine(img_vae_out_uncond, img_clip_out_uncond) + + return guidance_scale * img_out + (1.0 - guidance_scale) * img_out_uncond + elif mode == "img2text": + # Image-conditioned text generation + img_vae_out, img_clip_out, text_out = self.unet( + img_vae, img_clip, latents, timestep_img=0, timestep_text=t, data_type=data_type + ) + + if guidance_scale <= 1.0: + return text_out + + # Classifier-free guidance + img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype) + img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype) + + img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet( + img_vae_T, img_clip_T, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type + ) + + return guidance_scale * text_out + (1.0 - guidance_scale) * text_out_uncond + elif mode == "text": + # Unconditional ("marginal") text generation (no CFG) + img_vae_out, img_clip_out, text_out = self.unet( + img_vae, img_clip, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type + ) + + return text_out + elif mode == "img": + # Unconditional ("marginal") image generation (no CFG) + img_vae_latents, img_clip_latents = self._split(latents, height, width) + + img_vae_out, img_clip_out, text_out = self.unet( + img_vae_latents, + img_clip_latents, + prompt_embeds, + timestep_img=t, + timestep_text=max_timestep, + data_type=data_type, + ) + + img_out = self._combine(img_vae_out, img_clip_out) + return img_out + + def check_latents_shape(self, latents_name, latents, expected_shape): + latents_shape = latents.shape + expected_num_dims = len(expected_shape) + 1 # expected dimensions plus the batch dimension + expected_shape_str = ", ".join(str(dim) for dim in expected_shape) + if len(latents_shape) != expected_num_dims: + raise ValueError( + f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape" + f" {latents_shape} has {len(latents_shape)} dimensions." + ) + for i in range(1, expected_num_dims): + if latents_shape[i] != expected_shape[i - 1]: + raise ValueError( + f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape" + f" {latents_shape} has {latents_shape[i]} != {expected_shape[i - 1]} at dimension {i}." + ) + + def check_inputs( + self, + mode, + prompt, + image, + height, + width, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + latents=None, + prompt_latents=None, + vae_latents=None, + clip_latents=None, + ): + # Check inputs before running the generative process. + if height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0: + raise ValueError( + f"`height` and `width` have to be divisible by {self.vae_scale_factor} but are {height} and {width}." + ) + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if mode == "text2img": + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + if mode == "img2text": + if image is None: + raise ValueError("`img2text` mode requires an image to be provided.") + + # Check provided latents + latent_height = height // self.vae_scale_factor + latent_width = width // self.vae_scale_factor + full_latents_available = latents is not None + prompt_latents_available = prompt_latents is not None + vae_latents_available = vae_latents is not None + clip_latents_available = clip_latents is not None + + if full_latents_available: + individual_latents_available = ( + prompt_latents is not None or vae_latents is not None or clip_latents is not None + ) + if individual_latents_available: + logger.warning( + "You have supplied both `latents` and at least one of `prompt_latents`, `vae_latents`, and" + " `clip_latents`. The value of `latents` will override the value of any individually supplied latents." + ) + # Check shape of full latents + img_vae_dim = self.num_channels_latents * latent_height * latent_width + text_dim = self.text_encoder_seq_len * self.text_encoder_hidden_size + latents_dim = img_vae_dim + self.image_encoder_projection_dim + text_dim + latents_expected_shape = (latents_dim,) + self.check_latents_shape("latents", latents, latents_expected_shape) + + # Check individual latent shapes, if present + if prompt_latents_available: + prompt_latents_expected_shape = (self.text_encoder_seq_len, self.text_encoder_hidden_size) + self.check_latents_shape("prompt_latents", prompt_latents, prompt_latents_expected_shape) + + if vae_latents_available: + vae_latents_expected_shape = (self.num_channels_latents, latent_height, latent_width) + self.check_latents_shape("vae_latents", vae_latents, vae_latents_expected_shape) + + if clip_latents_available: + clip_latents_expected_shape = (1, self.image_encoder_projection_dim) + self.check_latents_shape("clip_latents", clip_latents, clip_latents_expected_shape) + + if mode in ["text2img", "img"] and vae_latents_available and clip_latents_available: + if vae_latents.shape[0] != clip_latents.shape[0]: + raise ValueError( + f"Both `vae_latents` and `clip_latents` are supplied, but their batch dimensions are not equal:" + f" {vae_latents.shape[0]} != {clip_latents.shape[0]}." + ) + + if mode == "joint" and prompt_latents_available and vae_latents_available and clip_latents_available: + if prompt_latents.shape[0] != vae_latents.shape[0] or prompt_latents.shape[0] != clip_latents.shape[0]: + raise ValueError( + f"All of `prompt_latents`, `vae_latents`, and `clip_latents` are supplied, but their batch" + f" dimensions are not equal: {prompt_latents.shape[0]} != {vae_latents.shape[0]}" + f" != {clip_latents.shape[0]}." + ) + + @torch.no_grad() + def __call__( + self, + prompt: Optional[Union[str, List[str]]] = None, + image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + data_type: Optional[int] = 1, + num_inference_steps: int = 50, + guidance_scale: float = 8.0, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + num_prompts_per_image: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_latents: Optional[torch.FloatTensor] = None, + vae_latents: Optional[torch.FloatTensor] = None, + clip_latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + ): + r""" + The call function to the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. + Required for text-conditioned image generation (`text2img`) mode. + image (`torch.FloatTensor` or `PIL.Image.Image`, *optional*): + `Image` or tensor representing an image batch. Required for image-conditioned text generation + (`img2text`) mode. + height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The width in pixels of the generated image. + data_type (`int`, *optional*, defaults to 1): + The data type (either 0 or 1). Only used if you are loading a checkpoint which supports a data type + embedding; this is added for compatibility with the + [UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1) checkpoint. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 8.0): + A higher guidance scale value encourages the model to generate images closely linked to the text + `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide what to not include in image generation. If not defined, you need to + pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). Used in + text-conditioned image generation (`text2img`) mode. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. Used in `text2img` (text-conditioned image generation) and + `img` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are + supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated. + num_prompts_per_image (`int`, *optional*, defaults to 1): + The number of prompts to generate per image. Used in `img2text` (image-conditioned text generation) and + `text` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are + supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies + to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for joint + image-text generation. Can be used to tweak the same generation with different prompts. If not + provided, a latents tensor is generated by sampling using the supplied random `generator`. This assumes + a full set of VAE, CLIP, and text latents, if supplied, overrides the value of `prompt_latents`, + `vae_latents`, and `clip_latents`. + prompt_latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + vae_latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + clip_latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not + provided, text embeddings are generated from the `prompt` input argument. Used in text-conditioned + image generation (`text2img`) mode. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If + not provided, `negative_prompt_embeds` are be generated from the `negative_prompt` input argument. Used + in text-conditioned image generation (`text2img`) mode. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.ImageTextPipelineOutput`] instead of a plain tuple. + callback (`Callable`, *optional*): + A function that calls every `callback_steps` steps during inference. The function is called with the + following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function is called. If not specified, the callback is called at + every step. + + Returns: + [`~pipelines.unidiffuser.ImageTextPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.unidiffuser.ImageTextPipelineOutput`] is returned, otherwise a + `tuple` is returned where the first element is a list with the generated images and the second element + is a list of generated texts. + """ + + # 0. Default height and width to unet + height = height or self.unet_resolution * self.vae_scale_factor + width = width or self.unet_resolution * self.vae_scale_factor + + # 1. Check inputs + # Recalculate mode for each call to the pipeline. + mode = self._infer_mode(prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents) + self.check_inputs( + mode, + prompt, + image, + height, + width, + callback_steps, + negative_prompt, + prompt_embeds, + negative_prompt_embeds, + latents, + prompt_latents, + vae_latents, + clip_latents, + ) + + # 2. Define call parameters + batch_size, multiplier = self._infer_batch_size( + mode, + prompt, + prompt_embeds, + image, + num_images_per_prompt, + num_prompts_per_image, + latents, + prompt_latents, + vae_latents, + clip_latents, + ) + device = self._execution_device + reduce_text_emb_dim = self.text_intermediate_dim < self.text_encoder_hidden_size or self.mode != "text2img" + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + # Note that this differs from the formulation in the unidiffusers paper! + do_classifier_free_guidance = guidance_scale > 1.0 + + # check if scheduler is in sigmas space + # scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas") + + # 3. Encode input prompt, if available; otherwise prepare text latents + if latents is not None: + # Overwrite individual latents + vae_latents, clip_latents, prompt_latents = self._split_joint(latents, height, width) + + if mode in ["text2img"]: + # 3.1. Encode input prompt, if available + assert prompt is not None or prompt_embeds is not None + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt=prompt, + device=device, + num_images_per_prompt=multiplier, + do_classifier_free_guidance=do_classifier_free_guidance, + negative_prompt=negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + + # if do_classifier_free_guidance: + # prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + else: + # 3.2. Prepare text latent variables, if input not available + prompt_embeds = self.prepare_text_latents( + batch_size=batch_size, + num_images_per_prompt=multiplier, + seq_len=self.text_encoder_seq_len, + hidden_size=self.text_encoder_hidden_size, + dtype=self.text_encoder.dtype, # Should work with both full precision and mixed precision + device=device, + generator=generator, + latents=prompt_latents, + ) + + if reduce_text_emb_dim: + prompt_embeds = self.text_decoder.encode(prompt_embeds) + + # 4. Encode image, if available; otherwise prepare image latents + if mode in ["img2text"]: + # 4.1. Encode images, if available + assert image is not None, "`img2text` requires a conditioning image" + # Encode image using VAE + image_vae = self.image_processor.preprocess(image) + height, width = image_vae.shape[-2:] + image_vae_latents = self.encode_image_vae_latents( + image=image_vae, + batch_size=batch_size, + num_prompts_per_image=multiplier, + dtype=prompt_embeds.dtype, + device=device, + do_classifier_free_guidance=False, # Copied from InstructPix2Pix, don't use their version of CFG + generator=generator, + ) + + # Encode image using CLIP + image_clip_latents = self.encode_image_clip_latents( + image=image, + batch_size=batch_size, + num_prompts_per_image=multiplier, + dtype=prompt_embeds.dtype, + device=device, + generator=generator, + ) + # (batch_size, clip_hidden_size) => (batch_size, 1, clip_hidden_size) + image_clip_latents = image_clip_latents.unsqueeze(1) + else: + # 4.2. Prepare image latent variables, if input not available + # Prepare image VAE latents in latent space + image_vae_latents = self.prepare_image_vae_latents( + batch_size=batch_size, + num_prompts_per_image=multiplier, + num_channels_latents=self.num_channels_latents, + height=height, + width=width, + dtype=prompt_embeds.dtype, + device=device, + generator=generator, + latents=vae_latents, + ) + + # Prepare image CLIP latents + image_clip_latents = self.prepare_image_clip_latents( + batch_size=batch_size, + num_prompts_per_image=multiplier, + clip_img_dim=self.image_encoder_projection_dim, + dtype=prompt_embeds.dtype, + device=device, + generator=generator, + latents=clip_latents, + ) + + # 5. Set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + # max_timestep = timesteps[0] + max_timestep = self.scheduler.config.num_train_timesteps + + # 6. Prepare latent variables + if mode == "joint": + latents = self._combine_joint(image_vae_latents, image_clip_latents, prompt_embeds) + elif mode in ["text2img", "img"]: + latents = self._combine(image_vae_latents, image_clip_latents) + elif mode in ["img2text", "text"]: + latents = prompt_embeds + + # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + logger.debug(f"Scheduler extra step kwargs: {extra_step_kwargs}") + + # 8. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # predict the noise residual + # Also applies classifier-free guidance as described in the UniDiffuser paper + noise_pred = self._get_noise_pred( + mode, + latents, + t, + prompt_embeds, + image_vae_latents, + image_clip_latents, + max_timestep, + data_type, + guidance_scale, + generator, + device, + height, + width, + ) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + # 9. Post-processing + image = None + text = None + if mode == "joint": + image_vae_latents, image_clip_latents, text_latents = self._split_joint(latents, height, width) + + if not output_type == "latent": + # Map latent VAE image back to pixel space + image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0] + else: + image = image_vae_latents + + text = self.decode_text_latents(text_latents, device) + elif mode in ["text2img", "img"]: + image_vae_latents, image_clip_latents = self._split(latents, height, width) + + if not output_type == "latent": + # Map latent VAE image back to pixel space + image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0] + else: + image = image_vae_latents + elif mode in ["img2text", "text"]: + text_latents = latents + text = self.decode_text_latents(text_latents, device) + + self.maybe_free_model_hooks() + + # 10. Postprocess the image, if necessary + if image is not None: + do_denormalize = [True] * image.shape[0] + image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) + + # Offload last model to CPU + if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: + self.final_offload_hook.offload() + + if not return_dict: + return (image, text) + + return ImageTextPipelineOutput(images=image, text=text)