Add files using upload-large-folder tool

llmeval-env/lib/python3.10/site-packages/nltk-3.8.1.dist-info/AUTHORS.md

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+# Natural Language Toolkit (NLTK) Authors
+
+## Original Authors
+
+- Steven Bird <[email protected]>
+- Edward Loper <[email protected]>
+- Ewan Klein <[email protected]>
+
+## Contributors
+
+- Tom Aarsen
+- Rami Al-Rfou'
+- Mark Amery
+- Greg Aumann
+- Ivan Barria
+- Ingolf Becker
+- Yonatan Becker
+- Paul Bedaride
+- Steven Bethard
+- Robert Berwick
+- Dan Blanchard
+- Nathan Bodenstab
+- Alexander Böhm
+- Francis Bond
+- Paul Bone
+- Jordan Boyd-Graber
+- Daniel Blanchard
+- Phil Blunsom
+- Lars Buitinck
+- Cristian Capdevila
+- Steve Cassidy
+- Chen-Fu Chiang
+- Dmitry Chichkov
+- Jinyoung Choi
+- Andrew Clausen
+- Lucas Champollion
+- Graham Christensen
+- Trevor Cohn
+- David Coles
+- Tom Conroy <https://github.com/tconroy>
+- Claude Coulombe
+- Lucas Cooper
+- Robin Cooper
+- Chris Crowner
+- James Curran
+- Arthur Darcet
+- Dariel Dato-on
+- Selina Dennis
+- Leon Derczynski
+- Alexis Dimitriadis
+- Nikhil Dinesh
+- Liang Dong
+- David Doukhan
+- Rebecca Dridan
+- Pablo Duboue
+- Long Duong
+- Christian Federmann
+- Campion Fellin
+- Michelle Fullwood
+- Dan Garrette
+- Maciej Gawinecki
+- Jean Mark Gawron
+- Sumukh Ghodke
+- Yoav Goldberg
+- Michael Wayne Goodman
+- Dougal Graham
+- Brent Gray
+- Simon Greenhill
+- Clark Grubb
+- Eduardo Pereira Habkost
+- Masato Hagiwara
+- Lauri Hallila
+- Michael Hansen
+- Yurie Hara
+- Will Hardy
+- Tyler Hartley
+- Peter Hawkins
+- Saimadhav Heblikar
+- Fredrik Hedman
+- Helder
+- Michael Heilman
+- Ofer Helman
+- Christopher Hench
+- Bruce Hill
+- Amy Holland
+- Kristy Hollingshead
+- Marcus Huderle
+- Baden Hughes
+- Nancy Ide
+- Rebecca Ingram
+- Edward Ivanovic
+- Thomas Jakobsen
+- Nick Johnson
+- Eric Kafe
+- Piotr Kasprzyk
+- Angelos Katharopoulos
+- Sudharshan Kaushik
+- Chris Koenig
+- Mikhail Korobov
+- Denis Krusko
+- Ilia Kurenkov
+- Stefano Lattarini
+- Pierre-François Laquerre
+- Stefano Lattarini
+- Haejoong Lee
+- Jackson Lee
+- Max Leonov
+- Chris Liechti
+- Hyuckin David Lim
+- Tom Lippincott
+- Peter Ljunglöf
+- Alex Louden
+- Joseph Lynch
+- Nitin Madnani
+- Felipe Madrigal
+- Bjørn Mæland
+- Dean Malmgren
+- Christopher Maloof
+- Rob Malouf
+- Iker Manterola
+- Carl de Marcken
+- Mitch Marcus
+- Torsten Marek
+- Robert Marshall
+- Marius Mather
+- Duncan McGreggor
+- David McClosky
+- Xinfan Meng
+- Dmitrijs Milajevs
+- Margaret Mitchell
+- Tomonori Nagano
+- Jason Narad
+- Shari A’aidil Nasruddin
+- Lance Nathan
+- Morten Neergaard
+- David Nemeskey
+- Eric Nichols
+- Joel Nothman
+- Alireza Nourian
+- Alexander Oleynikov
+- Pierpaolo Pantone
+- Ted Pedersen
+- Jacob Perkins
+- Alberto Planas
+- Ondrej Platek
+- Alessandro Presta
+- Qi Liu
+- Martin Thorsen Ranang
+- Michael Recachinas
+- Brandon Rhodes
+- Joshua Ritterman
+- Will Roberts
+- Stuart Robinson
+- Carlos Rodriguez
+- Lorenzo Rubio
+- Alex Rudnick
+- Jussi Salmela
+- Geoffrey Sampson
+- Kepa Sarasola
+- Kevin Scannell
+- Nathan Schneider
+- Rico Sennrich
+- Thomas Skardal
+- Eric Smith
+- Lynn Soe
+- Rob Speer
+- Peter Spiller
+- Richard Sproat
+- Ceri Stagg
+- Peter Stahl
+- Oliver Steele
+- Thomas Stieglmaier
+- Jan Strunk
+- Liling Tan
+- Claire Taylor
+- Louis Tiao
+- Steven Tomcavage
+- Tiago Tresoldi
+- Marcus Uneson
+- Yu Usami
+- Petro Verkhogliad
+- Peter Wang
+- Zhe Wang
+- Charlotte Wilson
+- Chuck Wooters
+- Steven Xu
+- Beracah Yankama
+- Lei Ye (叶磊)
+- Patrick Ye
+- Geraldine Sim Wei Ying
+- Jason Yoder
+- Thomas Zieglier
+- 0ssifrage
+- ducki13
+- kiwipi
+- lade
+- isnowfy
+- onesandzeros
+- pquentin
+- wvanlint
+- Álvaro Justen <https://github.com/turicas>
+- bjut-hz
+- Sergio Oller
+- Will Monroe
+- Elijah Rippeth
+- Emil Manukyan
+- Casper Lehmann-Strøm
+- Andrew Giel
+- Tanin Na Nakorn
+- Linghao Zhang
+- Colin Carroll
+- Heguang Miao
+- Hannah Aizenman (story645)
+- George Berry
+- Adam Nelson
+- J Richard Snape
+- Alex Constantin <[email protected]>
+- Tsolak Ghukasyan
+- Prasasto Adi
+- Safwan Kamarrudin
+- Arthur Tilley
+- Vilhjalmur Thorsteinsson
+- Jaehoon Hwang <https://github.com/jaehoonhwang>
+- Chintan Shah <https://github.com/chintanshah24>
+- sbagan
+- Zicheng Xu
+- Albert Au Yeung <https://github.com/albertauyeung>
+- Shenjian Zhao
+- Deng Wang <https://github.com/lmatt-bit>
+- Ali Abdullah
+- Stoytcho Stoytchev
+- Lakhdar Benzahia
+- Kheireddine Abainia <https://github.com/xprogramer>
+- Yibin Lin <https://github.com/yibinlin>
+- Artiem Krinitsyn
+- Björn Mattsson
+- Oleg Chislov
+- Pavan Gururaj Joshi <https://github.com/PavanGJ>
+- Ethan Hill <https://github.com/hill1303>
+- Vivek Lakshmanan
+- Somnath Rakshit <https://github.com/somnathrakshit>
+- Anlan Du
+- Pulkit Maloo <https://github.com/pulkitmaloo>
+- Brandon M. Burroughs <https://github.com/brandonmburroughs>
+- John Stewart <https://github.com/free-variation>
+- Iaroslav Tymchenko <https://github.com/myproblemchild>
+- Aleš Tamchyna
+- Tim Gianitsos <https://github.com/timgianitsos>
+- Philippe Partarrieu <https://github.com/ppartarr>
+- Andrew Owen Martin
+- Adrian Ellis <https://github.com/adrianjellis>
+- Nat Quayle Nelson <https://github.com/nqnstudios>
+- Yanpeng Zhao <https://github.com/zhaoyanpeng>
+- Matan Rak <https://github.com/matanrak>
+- Nick Ulle <https://github.com/nick-ulle>
+- Uday Krishna <https://github.com/udaykrishna>
+- Osman Zubair <https://github.com/okz12>
+- Viresh Gupta <https://github.com/virresh>
+- Ondřej Cífka <https://github.com/cifkao>
+- Iris X. Zhou <https://github.com/irisxzhou>
+- Devashish Lal <https://github.com/BLaZeKiLL>
+- Gerhard Kremer <https://github.com/GerhardKa>
+- Nicolas Darr <https://github.com/ndarr>
+- Hervé Nicol <https://github.com/hervenicol>
+- Alexandre H. T. Dias <https://github.com/alexandredias3d>
+- Daksh Shah <https://github.com/Daksh>
+- Jacob Weightman <https://github.com/jacobdweightman>
+- Bonifacio de Oliveira <https://github.com/Bonifacio2>
+- Armins Bagrats Stepanjans <https://github.com/ab-10>
+- Vassilis Palassopoulos <https://github.com/palasso>
+- Ram Rachum <https://github.com/cool-RR>
+- Or Sharir <https://github.com/orsharir>
+- Denali Molitor <https://github.com/dmmolitor>
+- Jacob Moorman <https://github.com/jdmoorman>
+- Cory Nezin <https://github.com/corynezin>
+- Matt Chaput
+- Danny Sepler <https://github.com/dannysepler>
+- Akshita Bhagia <https://github.com/AkshitaB>
+- Pratap Yadav <https://github.com/prtpydv>
+- Hiroki Teranishi <https://github.com/chantera>
+- Ruben Cartuyvels <https://github.com/rubencart>
+- Dalton Pearson <https://github.com/daltonpearson>
+- Robby Horvath <https://github.com/robbyhorvath>
+- Gavish Poddar <https://github.com/gavishpoddar>
+- Saibo Geng <https://github.com/Saibo-creator>
+- Ahmet Yildirim <https://github.com/RnDevelover>
+- Yuta Nakamura <https://github.com/yutanakamura-tky>
+- Adam Hawley <https://github.com/adamjhawley>
+- Panagiotis Simakis <https://github.com/sp1thas>
+- Richard Wang <https://github.com/richarddwang>
+- Alexandre Perez-Lebel <https://github.com/aperezlebel>
+- Fernando Carranza <https://github.com/fernandocar86>
+- Martin Kondratzky <https://github.com/martinkondra>
+- Heungson Lee <https://github.com/heungson>
+- M.K. Pawelkiewicz <https://github.com/hamiltonianflow>
+- Steven Thomas Smith <https://github.com/essandess>
+- Jan Lennartz <https://github.com/Madnex>
+
+## Others whose work we've taken and included in NLTK, but who didn't directly contribute it:
+
+### Contributors to the Porter Stemmer
+
+- Martin Porter
+- Vivake Gupta
+- Barry Wilkins
+- Hiranmay Ghosh
+- Chris Emerson
+
+### Authors of snowball arabic stemmer algorithm
+
+- Assem Chelli
+- Abdelkrim Aries
+- Lakhdar Benzahia

llmeval-env/lib/python3.10/site-packages/nltk-3.8.1.dist-info/INSTALLER

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+pip

llmeval-env/lib/python3.10/site-packages/nltk-3.8.1.dist-info/LICENSE.txt

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llmeval-env/lib/python3.10/site-packages/nltk-3.8.1.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: nltk
+Version: 3.8.1
+Summary: Natural Language Toolkit
+Home-page: https://www.nltk.org/
+Author: NLTK Team
+Author-email: [email protected]
+Maintainer: NLTK Team
+Maintainer-email: [email protected]
+License: Apache License, Version 2.0
+Project-URL: Documentation, https://www.nltk.org/
+Project-URL: Source Code, https://github.com/nltk/nltk
+Project-URL: Issue Tracker, https://github.com/nltk/nltk/issues
+Keywords: NLP,CL,natural language processing,computational linguistics,parsing,tagging,tokenizing,syntax,linguistics,language,natural language,text analytics
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Information Technology
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+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: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Text Processing
+Classifier: Topic :: Text Processing :: Filters
+Classifier: Topic :: Text Processing :: General
+Classifier: Topic :: Text Processing :: Indexing
+Classifier: Topic :: Text Processing :: Linguistic
+Requires-Python: >=3.7
+Requires-Dist: click
+Requires-Dist: joblib
+Requires-Dist: regex (>=2021.8.3)
+Requires-Dist: tqdm
+Provides-Extra: all
+Requires-Dist: scikit-learn ; extra == 'all'
+Requires-Dist: python-crfsuite ; extra == 'all'
+Requires-Dist: requests ; extra == 'all'
+Requires-Dist: numpy ; extra == 'all'
+Requires-Dist: pyparsing ; extra == 'all'
+Requires-Dist: twython ; extra == 'all'
+Requires-Dist: scipy ; extra == 'all'
+Requires-Dist: matplotlib ; extra == 'all'
+Provides-Extra: corenlp
+Requires-Dist: requests ; extra == 'corenlp'
+Provides-Extra: machine_learning
+Requires-Dist: numpy ; extra == 'machine_learning'
+Requires-Dist: python-crfsuite ; extra == 'machine_learning'
+Requires-Dist: scikit-learn ; extra == 'machine_learning'
+Requires-Dist: scipy ; extra == 'machine_learning'
+Provides-Extra: plot
+Requires-Dist: matplotlib ; extra == 'plot'
+Provides-Extra: tgrep
+Requires-Dist: pyparsing ; extra == 'tgrep'
+Provides-Extra: twitter
+Requires-Dist: twython ; extra == 'twitter'
+
+The Natural Language Toolkit (NLTK) is a Python package for
+natural language processing.  NLTK requires Python 3.7, 3.8, 3.9, 3.10 or 3.11.
+

llmeval-env/lib/python3.10/site-packages/nltk-3.8.1.dist-info/README.md

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+# Natural Language Toolkit (NLTK)
+[![PyPI](https://img.shields.io/pypi/v/nltk.svg)](https://pypi.python.org/pypi/nltk)
+![CI](https://github.com/nltk/nltk/actions/workflows/ci.yaml/badge.svg?branch=develop)
+
+NLTK -- the Natural Language Toolkit -- is a suite of open source Python
+modules, data sets, and tutorials supporting research and development in Natural
+Language Processing. NLTK requires Python version 3.7, 3.8, 3.9, 3.10 or 3.11.
+
+For documentation, please visit [nltk.org](https://www.nltk.org/).
+
+
+## Contributing
+
+Do you want to contribute to NLTK development? Great!
+Please read [CONTRIBUTING.md](CONTRIBUTING.md) for more details.
+
+See also [how to contribute to NLTK](https://www.nltk.org/contribute.html).
+
+
+## Donate
+
+Have you found the toolkit helpful?  Please support NLTK development by donating
+to the project via PayPal, using the link on the NLTK homepage.
+
+
+## Citing
+
+If you publish work that uses NLTK, please cite the NLTK book, as follows:
+
+    Bird, Steven, Edward Loper and Ewan Klein (2009).
+    Natural Language Processing with Python.  O'Reilly Media Inc.
+
+
+## Copyright
+
+Copyright (C) 2001-2023 NLTK Project
+
+For license information, see [LICENSE.txt](LICENSE.txt).
+
+[AUTHORS.md](AUTHORS.md) contains a list of everyone who has contributed to NLTK.
+
+
+### Redistributing
+
+- NLTK source code is distributed under the Apache 2.0 License.
+- NLTK documentation is distributed under the Creative Commons
+  Attribution-Noncommercial-No Derivative Works 3.0 United States license.
+- NLTK corpora are provided under the terms given in the README file for each
+  corpus; all are redistributable and available for non-commercial use.
+- NLTK may be freely redistributed, subject to the provisions of these licenses.

llmeval-env/lib/python3.10/site-packages/nltk-3.8.1.dist-info/RECORD

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+nltk-3.8.1.dist-info/AUTHORS.md,sha256=lwegiKq14iCouEfpgu85VSAWadP2X1MkLhUsgYBfPOI,7628
+nltk-3.8.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+nltk-3.8.1.dist-info/LICENSE.txt,sha256=Pd-b5cKP4n2tFDpdx27qJSIq0d1ok0oEcGTlbtL6QMU,11560
+nltk-3.8.1.dist-info/METADATA,sha256=CUHc77qyEPWGmH6DiO6622SIsRU2fGENF_LCKUzGEOI,2847
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@@ -0,0 +1 @@
+nltk

llmeval-env/lib/python3.10/site-packages/peft/tuners/adaption_prompt/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+from .config import AdaptionPromptConfig
+from .layer import AdaptedAttention
+from .model import AdaptionPromptModel
+
+
+__all__ = ["AdaptionPromptConfig", "AdaptedAttention", "AdaptionPromptModel"]

llmeval-env/lib/python3.10/site-packages/peft/tuners/adaption_prompt/config.py

@@ -0,0 +1,80 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from collections import namedtuple
+from dataclasses import dataclass, field
+
+from peft.config import PeftConfig
+from peft.utils import PeftType
+
+from .utils import llama_compute_query_states
+
+
+@dataclass
+class AdaptionPromptConfig(PeftConfig):
+    """Stores the configuration of an [`AdaptionPromptModel`]."""
+
+    target_modules: str = field(
+        default=None, metadata={"help": "Name of the attention submodules to insert adaption prompts into."}
+    )
+    adapter_len: int = field(default=None, metadata={"help": "Number of adapter tokens to insert"})
+    adapter_layers: int = field(default=None, metadata={"help": "Number of adapter layers (from the top)"})
+
+    def __post_init__(self):
+        self.peft_type = PeftType.ADAPTION_PROMPT
+
+    @property
+    def is_adaption_prompt(self) -> bool:
+        """Return True if this is an adaption prompt config."""
+        return True
+
+
+# Contains the config that is specific to a transformers model type.
+ModelTypeConfig = namedtuple(
+    "ModelTypeConfig", ["compute_query_states", "target_modules", "k_proj_layer", "v_proj_layer", "o_proj_layer"]
+)
+
+# Mapping of transformers model types to their specific configuration.
+TRANSFORMERS_MODEL_CONFIG = {
+    "llama": ModelTypeConfig(
+        compute_query_states=llama_compute_query_states,
+        target_modules="self_attn",
+        k_proj_layer="k_proj",
+        v_proj_layer="v_proj",
+        o_proj_layer="o_proj",
+    ),
+    "mistral": ModelTypeConfig(  # same as llama,
+        compute_query_states=llama_compute_query_states,
+        target_modules="self_attn",
+        k_proj_layer="k_proj",
+        v_proj_layer="v_proj",
+        o_proj_layer="o_proj",
+    ),
+}
+
+
+def prepare_config(
+    peft_config: AdaptionPromptConfig,
+    model,
+) -> AdaptionPromptConfig:
+    """Prepare the config based on the llama model type."""
+    if model.config.model_type not in TRANSFORMERS_MODEL_CONFIG:
+        raise ValueError("Unsupported model type for adaption prompt: '{model.config.model_type}'.")
+
+    model_config = TRANSFORMERS_MODEL_CONFIG[model.config.model_type]
+
+    if peft_config.target_modules is None:
+        peft_config.target_modules = model_config.target_modules
+
+    return peft_config

llmeval-env/lib/python3.10/site-packages/peft/tuners/adaption_prompt/layer.py

@@ -0,0 +1,128 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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 math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .config import TRANSFORMERS_MODEL_CONFIG
+
+
+class AdaptedAttention(nn.Module):
+    """This module wraps a LLamaAttention module and injects adaption prompts."""
+
+    def __init__(self, model_type: str, adapter_len: int, model):
+        """
+        Initialize object.
+
+        Args:
+            model_type: The transformer model type. This is used to retrieve the right method to
+                compute query states.
+            adapter_len: The length of the adaption prompt to insert.
+            model: The original transformer attention module that is being wrapped.
+        """
+        assert not isinstance(model, AdaptedAttention)
+        super().__init__()
+        self.model_type = model_type
+        self.model = model
+        self.adapter_len = adapter_len
+        # Assume all parameters of the attention model we are wrapping are on the same device.
+        device = next(model.parameters()).device
+        # Don't think this was specified in the paper, but we follow the official repo which used an Embedding
+        # which initializes the tokens with standard normal values.
+        # https://github.com/ZrrSkywalker/LLaMA-Adapter/blob/41c3546fe1997ab8a65809dc8d8f9252b19d9faf/llama/model.py#L234
+        # (bsz, adapter_len, hidden_size)
+        target_dtype = (
+            model.q_proj.weight.dtype if model.q_proj.weight.dtype not in [torch.int8, torch.uint8] else torch.float32
+        )
+        self.adaption_prompt = nn.Parameter(
+            torch.empty(1, adapter_len, self.model.hidden_size, device=device, dtype=target_dtype).normal_()
+        )
+        # Initialize the gate to 0 as this is "zero-init".
+        self.adaption_gate = nn.Parameter(torch.zeros(1, device=device, dtype=target_dtype))
+
+    def forward(self, **kwargs):
+        """
+        Forward pass for the adapter which wraps the original LlamaAttention module.
+
+        "Official" paper implementation:
+        https://github.com/ZrrSkywalker/LLaMA-Adapter/blob/41c3546fe1997ab8a65809dc8d8f9252b19d9faf/llama/model.py#L141
+
+        Args:
+            kwargs: See the original LlamaAttention module.
+        """
+        if kwargs.get("output_attention", False):
+            raise NotImplementedError("output_attention is not currently supported.")
+
+        output, _, past_key_value = self.model(**kwargs)
+        bsz = output.shape[0]
+        q_len = output.shape[1]
+        embed_dim = output.shape[2]
+        k_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].k_proj_layer
+        v_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].v_proj_layer
+        o_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].o_proj_layer
+        factor = (
+            self.model.k_proj.in_features // self.model.k_proj.out_features
+        )  # Mistral has different input and output dimension for k_proj and v_proj layers
+
+        if k_proj_layer == v_proj_layer:
+            _, key, value = getattr(self.model, k_proj_layer)(self.adaption_prompt).split(embed_dim, dim=2)
+        else:
+            key = getattr(self.model, k_proj_layer)(self.adaption_prompt)
+            value = getattr(self.model, v_proj_layer)(self.adaption_prompt)
+
+        # (bsz, num_key_value_heads, adapter_len, head_dim)
+        adapter_k = (
+            key.view(1, self.adapter_len, (self.model.num_heads // factor), self.model.head_dim)
+            .repeat(bsz, 1, 1, 1)
+            .transpose(1, 2)
+        )
+        adapter_v = (
+            value.view(1, self.adapter_len, (self.model.num_heads // factor), self.model.head_dim)
+            .repeat(bsz, 1, 1, 1)
+            .transpose(1, 2)
+        )
+        # Below is taken from https://github.com/huggingface/transformers/blob/e547458c43dfdbbb8f6a7757237e234c44e20a8f/src/transformers/models/mistral/modeling_mistral.py#L181
+        # (bsz, num_heads, adapter_len, head_dim)
+        adapter_k = torch.repeat_interleave(adapter_k, repeats=factor, dim=1)
+        adapter_v = torch.repeat_interleave(adapter_v, repeats=factor, dim=1)
+        # Recompute query states.
+        compute_query_states = TRANSFORMERS_MODEL_CONFIG[self.model_type].compute_query_states
+        # (bsz, num_heads, q_len, head_dim)
+        query_states = compute_query_states(model=self.model, **kwargs)
+
+        previous_dtype = query_states.dtype
+
+        # (bsz, num_heads, q_len, adapter_len)
+        scores = torch.matmul(query_states, adapter_k.transpose(2, 3).to(previous_dtype)) / math.sqrt(
+            self.model.head_dim
+        )
+        # Upcast attention to fp32
+        # (bsz, num_heads, q_len, adapter_len)
+        scores = self.adaption_gate * F.softmax(scores, dim=-1, dtype=torch.float32).to(previous_dtype)
+        # (bsz, q_len, num_heads * head_dim)
+        adapter_output = torch.matmul(scores, adapter_v).transpose(1, 2).reshape(bsz, q_len, -1)
+
+        # (bsz, q_len, hidden_size)
+        if o_proj_layer is not None:
+            adapter_output = getattr(self.model, o_proj_layer)(adapter_output)
+
+        # Add adaption prompt output to original output.
+        output = output + adapter_output
+
+        # Restore original dtype.
+        output = output.to(previous_dtype)
+        return output, None, past_key_value

llmeval-env/lib/python3.10/site-packages/peft/tuners/adaption_prompt/model.py

@@ -0,0 +1,161 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from typing import Dict, List
+
+import torch.nn as nn
+
+from peft.utils import _freeze_adapter, _get_submodules
+
+from .config import AdaptionPromptConfig, prepare_config
+from .layer import AdaptedAttention
+from .utils import is_adaption_prompt_trainable
+
+
+class AdaptionPromptModel(nn.Module):
+    """
+    Implements adaption prompts as described in https://arxiv.org/pdf/2303.16199.pdf.
+
+    The top L attention modules are replaced with AdaptedAttention modules that wrap the original ones, but insert
+    trainable prompts with gates (for zero init).
+
+    Notes on the multi-adapter pattern:
+    - We store the states of different adapters by keeping a dictionary of AdaptedAttention modules indexed by adapter
+      name.
+    - Every time we switch adapters, we remove the modules of the currently active adapter from the model, store them
+      in the dictionary, and replace them with the modules of the new adapter.
+    - To avoid duplicated and potentially inconsistent state, the currently active adapter is always removed from the
+      dictionary.
+    - Disabling the adapter would also result in the modules being removed from the model.
+    """
+
+    def __init__(self, model, configs: Dict, adapter_name: str):
+        super().__init__()
+        self.model = model
+        # Store adapter configs by name.
+        self.peft_config: Dict[str, AdaptionPromptConfig] = {}
+        # Store lists of the parents of the affected attention modules by adapter name.
+        # We keep references to the parents so we can swap the adapters in-and-out of the model.
+        self._parents: Dict[str, List[nn.Module]] = {}
+        # Store lists of cached AdaptedAttention modules by name.
+        self._cached_adapters: Dict[str, List] = {}
+        # The name of the currently active adapter.
+        self._active_adapter = None
+        # Whether the adapter is enabled.
+        self._enabled = True
+        self.forward = self.model.forward
+        self.add_adapter(adapter_name, configs[adapter_name])
+        self._mark_only_adaption_prompts_as_trainable(self.model)
+
+    def add_adapter(self, adapter_name: str, config: AdaptionPromptConfig) -> None:
+        """Add an adapter with the given name and config."""
+        config = prepare_config(config, self.model)
+        if adapter_name in self.peft_config:
+            raise ValueError(f"Adapter with name '{adapter_name}' already exists.")
+
+        parents = []
+        for name, _ in self.model.named_modules():
+            if name.endswith(config.target_modules):
+                par, _, _ = _get_submodules(self.model, name)
+                parents.append(par)
+        if len(parents) < config.adapter_layers:
+            raise ValueError(
+                f"Config specifies more adapter layers '{config.adapter_layers}'"
+                f" than the model has '{len(parents)}'."
+            )
+        # Note that if the target modules are not in Sequential, ModuleList, or
+        # some other PyTorch ordered container, the behavior is undefined as we
+        # assume here that the order of the modules is the same as the order of
+        # the transformer decoder layers.
+        parents = parents[-config.adapter_layers :]
+        self._parents[adapter_name] = parents
+
+        # It is only None during initialization.
+        # If it is disabled, we don't have to remove the modules.
+        if self._active_adapter is not None and self._enabled:
+            self._remove_adapted_attentions(self._active_adapter)
+        self._active_adapter = adapter_name
+        self.peft_config[adapter_name] = config
+        self._create_adapted_attentions(config, parents)
+        if not self._enabled:
+            self._remove_adapted_attentions(self._active_adapter)
+
+        if config.inference_mode:
+            _freeze_adapter(self.model, adapter_name)
+
+    def set_adapter(self, adapter_name: str) -> None:
+        """Set the model to use the adapter with the given name."""
+        if self._active_adapter == adapter_name:
+            return
+        if adapter_name not in self.peft_config:
+            raise ValueError(f"Adapter with name '{adapter_name}' does not exist.")
+
+        if self._enabled:
+            self._remove_adapted_attentions(self._active_adapter)
+            self._set_adapted_attentions(adapter_name)
+
+        self._active_adapter = adapter_name
+
+    def enable_adapter_layers(self):
+        """Enable adapter layers by swapping in cached AdaptedAttention modules."""
+        self._enabled = True
+        self._set_adapted_attentions(self._active_adapter)
+
+    def disable_adapter_layers(self):
+        """Disable adapter layers by swapping out AdaptedAttention modules."""
+        self._enabled = False
+        self._remove_adapted_attentions(self._active_adapter)
+
+    def _create_adapted_attentions(self, config: AdaptionPromptConfig, parents: List[nn.Module]) -> None:
+        """Wrap LlamaAttention modules with newly created AdaptedAttention modules."""
+        for par in parents:
+            attn = AdaptedAttention(
+                model_type=self.model.config.model_type,
+                adapter_len=config.adapter_len,
+                model=getattr(par, config.target_modules),
+            )
+            setattr(par, config.target_modules, attn)
+
+    def _set_adapted_attentions(self, adapter_name: str) -> None:
+        """Replace LlamaAttention modules with cached AdaptedAttention modules."""
+        cached = self._cached_adapters[adapter_name]
+        del self._cached_adapters[adapter_name]
+        config = self.peft_config[adapter_name]
+        for i, par in enumerate(self._parents[adapter_name]):
+            setattr(par, config.target_modules, cached[i])
+
+    def _remove_adapted_attentions(self, adapter_name: str) -> None:
+        """Remove AdaptedAttention modules from the model and store them in the cache."""
+        config = self.peft_config[adapter_name]
+        adapted_attentions = []
+        for par in self._parents[adapter_name]:
+            attn = getattr(par, config.target_modules)
+            adapted_attentions.append(attn)
+            setattr(par, config.target_modules, attn.model)
+        self._cached_adapters[adapter_name] = adapted_attentions
+
+    def _mark_only_adaption_prompts_as_trainable(self, model: nn.Module) -> None:
+        """Freeze all parameters of the model except the adaption prompts."""
+        for n, p in model.named_parameters():
+            if not is_adaption_prompt_trainable(n):
+                p.requires_grad = False
+
+    def __getattr__(self, name: str):
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            # This is necessary as e.g. causal models have various methods that we
+            # don't want to re-implement here.
+            return getattr(self.model, name)

llmeval-env/lib/python3.10/site-packages/peft/tuners/adaption_prompt/utils.py

@@ -0,0 +1,121 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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 torch
+import torch.nn as nn
+
+
+def llama_rotate_half(x: torch.Tensor) -> torch.Tensor:
+    """
+    Rotate half the hidden dims of the input.
+
+    This function was duplicated verbatim from:
+    https://github.com/huggingface/transformers/blob/1de8ce9ee1191ba761a593ac15d9ccbf5851bfc5/src/transformers/models/llama/modeling_llama.py#L126
+
+    This was done to eliminate the Llama transformers implementation as a dependency of this file. Note that some other
+    functions were also adapted from the transformers implementation but were modified.
+    """
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def llama_apply_rotary_pos_emb(q, cos, sin, position_ids):
+    """
+    Apply rotary position embedding to query states in the Llama model.
+
+    This function was adapted from:
+    https://github.com/huggingface/transformers/blob/1de8ce9ee1191ba761a593ac15d9ccbf5851bfc5/src/transformers/models/llama/modeling_llama.py#L133
+
+    It was modified to remove unnecessary processing of key states. The method is compatible with transformers <=
+    4.34.2 and also with the latest version (>=4.35).
+    """
+    # In previous transformers version cos/sin cached had a shape of 4D
+    if len(cos.shape) == 4:
+        gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+        gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+        cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+        sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    # In the new version, it is 2D so we fall back to the new implementation
+    # https://github.com/huggingface/transformers/blame/eef7ea98c31a333bacdc7ae7a2372bde772be8e4/src/transformers/models/llama/modeling_llama.py#L222-L226
+    else:
+        cos = cos[position_ids].unsqueeze(1)
+        sin = sin[position_ids].unsqueeze(1)
+    q_embed = (q * cos) + (llama_rotate_half(q) * sin)
+    return q_embed
+
+
+def llama_compute_query_states(model: nn.Module, **kwargs) -> torch.Tensor:
+    """
+    Compute query states for Llama models specifically. They need to be recomputed as the forward() method of the
+    original LlamaModel in the transformers library does not return them. See the related discussion in the PR:
+    https://github.com/huggingface/peft/pull/268
+    """
+    hidden_states = kwargs.get("hidden_states")
+    position_ids = kwargs.get("position_ids")
+    past_key_value = kwargs.get("past_key_value")
+    bsz, q_len, _ = hidden_states.size()
+    query_states = model.q_proj(hidden_states).view(bsz, q_len, model.num_heads, model.head_dim).transpose(1, 2)
+
+    factor = model.k_proj.in_features // model.k_proj.out_features
+    value_states = (
+        model.v_proj(hidden_states).view(bsz, q_len, (model.num_heads // factor), model.head_dim).transpose(1, 2)
+    )
+
+    seq_len = q_len
+
+    if past_key_value is not None:
+        if isinstance(past_key_value, tuple):
+            # for transformers <= 4.35
+            seq_len += past_key_value[0].shape[-2]
+        else:
+            # since transformers 4.36, this is a DynamicCache instance
+            seq_len += past_key_value.get_seq_length(model.layer_idx)
+
+    # For transformers > 4.37.2 `position_ids` became a required arguments in the rotary embedding's forward pass.
+    if "position_ids" not in inspect.signature(model.rotary_emb.forward).parameters:
+        # TODO we assume that position_ids is not None here, not sure if that is safe but the old code also did that
+        cos, sin = model.rotary_emb(value_states, seq_len=seq_len)
+        return llama_apply_rotary_pos_emb(query_states, cos, sin, position_ids)
+
+    past_seen_tokens = 0
+    if position_ids is None:
+        # Compute position_ids, since they are required for transformers > 4.37.2
+        if past_key_value is None:
+            new_cache_positions = torch.arange(q_len, q_len + q_len, device=value_states.device)
+        else:
+            past_seen_tokens = past_key_value.get_usable_length(q_len, model.layer_idx)
+            new_cache_positions = torch.arange(past_seen_tokens, past_seen_tokens + q_len, device=value_states.device)
+        position_ids = new_cache_positions.unsqueeze(0)
+
+    rotary_emb_kwargs = {"position_ids": position_ids}
+    # The `seq_len` argument has been officially removed in transformers >= 4.39.0
+    if "seq_len" in inspect.signature(model.rotary_emb.forward).parameters:
+        rotary_emb_kwargs["seq_len"] = q_len + past_seen_tokens
+
+    cos, sin = model.rotary_emb(value_states, **rotary_emb_kwargs)
+
+    # For batched inference unsqueeze it on the correct dim
+    # since: https://github.com/huggingface/transformers/pull/29109
+    if len(cos.shape) == 3:
+        cos = cos.unsqueeze(1)
+        sin = sin.unsqueeze(1)
+
+    return (query_states * cos) + (llama_rotate_half(query_states) * sin)
+
+
+def is_adaption_prompt_trainable(params: str) -> bool:
+    """Return True if module is trainable under adaption prompt fine-tuning."""
+    return params.split(".")[-1].startswith("adaption_")

llmeval-env/lib/python3.10/site-packages/peft/tuners/lokr/__init__.py

@@ -0,0 +1,20 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from .config import LoKrConfig
+from .layer import Conv2d, Linear, LoKrLayer
+from .model import LoKrModel
+
+
+__all__ = ["LoKrConfig", "LoKrModel", "Conv2d", "Linear", "LoKrLayer"]

llmeval-env/lib/python3.10/site-packages/peft/tuners/lokr/config.py

@@ -0,0 +1,127 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from dataclasses import dataclass, field
+from typing import List, Optional, Union
+
+from peft.tuners.lycoris_utils import LycorisConfig
+from peft.utils import PeftType
+
+
+@dataclass
+class LoKrConfig(LycorisConfig):
+    """
+    Configuration class of [`LoKrModel`].
+
+    Args:
+        r (`int`):
+            LoKr rank.
+        alpha (`int`):
+            The alpha parameter for LoKr scaling.
+        rank_dropout (`float`):
+            The dropout probability for rank dimension during training.
+        module_dropout (`float`):
+            The dropout probability for disabling LoKr modules during training.
+        use_effective_conv2d (`bool`):
+            Use parameter effective decomposition for Conv2d with ksize > 1 ("Proposition 3" from FedPara paper).
+        decompose_both (`bool`):
+            Perform rank decomposition of left kronecker product matrix.
+        decompose_factor (`int`):
+            Kronecker product decomposition factor.
+        target_modules (`Optional[Union[List[str], str]]`):
+            The names of the modules to apply the adapter to. If this is specified, only the modules with the specified
+            names will be replaced. When passing a string, a regex match will be performed. When passing a list of
+            strings, either an exact match will be performed or it is checked if the name of the module ends with any
+            of the passed strings. If this is specified as 'all-linear', then all linear/Conv1D modules are chosen,
+            excluding the output layer. If this is not specified, modules will be chosen according to the model
+            architecture. If the architecture is not known, an error will be raised -- in this case, you should specify
+            the target modules manually.
+        init_weights (`bool`):
+            Whether to perform initialization of adapter weights. This defaults to `True`, passing `False` is
+            discouraged.
+        layers_to_transform (`Union[List[int], int]`):
+            The layer indices to transform. If a list of ints is passed, it will apply the adapter to the layer indices
+            that are specified in this list. If a single integer is passed, it will apply the transformations on the
+            layer at this index.
+        layers_pattern (`str`):
+            The layer pattern name, used only if `layers_to_transform` is different from `None`.
+        rank_pattern (`dict`):
+            The mapping from layer names or regexp expression to ranks which are different from the default rank
+            specified by `r`.
+        alpha_pattern (`dict`):
+            The mapping from layer names or regexp expression to alphas which are different from the default alpha
+            specified by `alpha`.
+        modules_to_save (`Optional[List[str]]`):
+            List of modules apart from adapter layers to be set as trainable and saved in the final checkpoint.
+    """
+
+    r: int = field(default=8, metadata={"help": "LoKr rank"})
+    alpha: int = field(default=8, metadata={"help": "LoKr alpha"})
+    rank_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout probability for rank dimension during training"}
+    )
+    module_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout probability for disabling LoKr modules during training"}
+    )
+    use_effective_conv2d: bool = field(
+        default=False,
+        metadata={
+            "help": 'Use parameter effective decomposition for Conv2d 3x3 with ksize > 1 ("Proposition 3" from FedPara paper)'
+        },
+    )
+    decompose_both: bool = field(
+        default=False,
+        metadata={"help": "Perform rank decomposition of left kronecker product matrix."},
+    )
+    decompose_factor: int = field(default=-1, metadata={"help": "Kronecker product decomposition factor."})
+    target_modules: Optional[Union[List[str], str]] = field(
+        default=None,
+        metadata={
+            "help": "List of module names or regex expression of the module names to replace with LoKr."
+            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+            "This can also be a wildcard 'all-linear' which matches all linear/Conv1D layers except the output layer."
+        },
+    )
+    init_weights: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to initialize the weights of the LoKr layers with their default initialization. Don't change "
+                "this setting, except if you know exactly what you're doing."
+            ),
+        },
+    )
+    layers_to_transform: Optional[Union[List[int], int]] = field(
+        default=None,
+        metadata={
+            "help": "The layer indexes to transform, is this argument is specified, PEFT will transform only the layers indexes that are specified inside this list. If a single integer is passed, PEFT will transform only the layer at this index."
+        },
+    )
+    layers_pattern: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The layer pattern name, used only if `layers_to_transform` is different to None and if the layer pattern is not in the common layers pattern."
+        },
+    )
+    modules_to_save: Optional[List[str]] = field(
+        default=None,
+        metadata={
+            "help": "List of modules apart from LoKr layers to be set as trainable and saved in the final checkpoint. "
+            "For example, in Sequence Classification or Token Classification tasks, "
+            "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
+        },
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.LOKR

llmeval-env/lib/python3.10/site-packages/peft/tuners/lokr/layer.py

@@ -0,0 +1,409 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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 math
+from typing import Any, Optional, Set, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from peft.tuners.lycoris_utils import LycorisLayer
+
+
+class LoKrLayer(nn.Module, LycorisLayer):
+    # All names of layers that may contain adapter weights
+    adapter_layer_names = (
+        "lokr_w1",
+        "lokr_w1_a",
+        "lokr_w1_b",
+        "lokr_w2",
+        "lokr_w2_a",
+        "lokr_w2_b",
+        "lokr_t2",
+    )
+    # other_param_names is defined on parent class
+
+    def __init__(self, base_layer: nn.Module) -> None:
+        super().__init__()
+        LycorisLayer.__init__(self, base_layer)
+
+        # LoKr info
+        self.lokr_w1 = nn.ParameterDict({})
+        self.lokr_w1_a = nn.ParameterDict({})
+        self.lokr_w1_b = nn.ParameterDict({})
+        self.lokr_w2 = nn.ParameterDict({})
+        self.lokr_w2_a = nn.ParameterDict({})
+        self.lokr_w2_b = nn.ParameterDict({})
+        self.lokr_t2 = nn.ParameterDict({})
+
+    @property
+    def _available_adapters(self) -> Set[str]:
+        return {
+            *self.lokr_w1,
+            *self.lokr_w1_a,
+            *self.lokr_w1_b,
+            *self.lokr_w2,
+            *self.lokr_w2_a,
+            *self.lokr_w2_b,
+            *self.lokr_t2,
+        }
+
+    def create_adapter_parameters(
+        self,
+        adapter_name: str,
+        r: int,
+        shape,
+        use_w1: bool,
+        use_w2: bool,
+        use_effective_conv2d: bool,
+    ):
+        if use_w1:
+            self.lokr_w1[adapter_name] = nn.Parameter(torch.empty(shape[0][0], shape[1][0]))
+        else:
+            self.lokr_w1_a[adapter_name] = nn.Parameter(torch.empty(shape[0][0], r))
+            self.lokr_w1_b[adapter_name] = nn.Parameter(torch.empty(r, shape[1][0]))
+
+        if len(shape) == 4:
+            # Conv2d
+            if use_w2:
+                self.lokr_w2[adapter_name] = nn.Parameter(torch.empty(shape[0][1], shape[1][1], *shape[2:]))
+            elif use_effective_conv2d:
+                self.lokr_t2[adapter_name] = nn.Parameter(torch.empty(r, r, shape[2], shape[3]))
+                self.lokr_w2_a[adapter_name] = nn.Parameter(torch.empty(r, shape[0][1]))  # b, 1-mode
+                self.lokr_w2_b[adapter_name] = nn.Parameter(torch.empty(r, shape[1][1]))  # d, 2-mode
+            else:
+                self.lokr_w2_a[adapter_name] = nn.Parameter(torch.empty(shape[0][1], r))
+                self.lokr_w2_b[adapter_name] = nn.Parameter(torch.empty(r, shape[1][1] * shape[2] * shape[3]))
+        else:
+            # Linear
+            if use_w2:
+                self.lokr_w2[adapter_name] = nn.Parameter(torch.empty(shape[0][1], shape[1][1]))
+            else:
+                self.lokr_w2_a[adapter_name] = nn.Parameter(torch.empty(shape[0][1], r))
+                self.lokr_w2_b[adapter_name] = nn.Parameter(torch.empty(r, shape[1][1]))
+
+    def reset_adapter_parameters(self, adapter_name: str):
+        if adapter_name in self.lokr_w1:
+            nn.init.zeros_(self.lokr_w1[adapter_name])
+        else:
+            nn.init.zeros_(self.lokr_w1_a[adapter_name])
+            nn.init.kaiming_uniform_(self.lokr_w1_b[adapter_name], a=math.sqrt(5))
+
+        if adapter_name in self.lokr_w2:
+            nn.init.kaiming_uniform_(self.lokr_w2[adapter_name], a=math.sqrt(5))
+        else:
+            nn.init.kaiming_uniform_(self.lokr_w2_a[adapter_name], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.lokr_w2_b[adapter_name], a=math.sqrt(5))
+
+        if adapter_name in self.lokr_t2:
+            nn.init.kaiming_uniform_(self.lokr_t2[adapter_name], a=math.sqrt(5))
+
+    def reset_adapter_parameters_random(self, adapter_name: str):
+        if adapter_name in self.lokr_w1:
+            nn.init.kaiming_uniform_(self.lokr_w1[adapter_name], a=math.sqrt(5))
+        else:
+            nn.init.kaiming_uniform_(self.lokr_w1_a[adapter_name], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.lokr_w1_b[adapter_name], a=math.sqrt(5))
+
+        if adapter_name in self.lokr_w2:
+            nn.init.kaiming_uniform_(self.lokr_w2[adapter_name], a=math.sqrt(5))
+        else:
+            nn.init.kaiming_uniform_(self.lokr_w2_a[adapter_name], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.lokr_w2_b[adapter_name], a=math.sqrt(5))
+
+        if adapter_name in self.lokr_t2:
+            nn.init.kaiming_uniform_(self.lokr_t2[adapter_name], a=math.sqrt(5))
+
+    def update_layer(
+        self,
+        adapter_name: str,
+        r: int,
+        alpha: float,
+        rank_dropout: float,
+        module_dropout: float,
+        init_weights: bool,
+        use_effective_conv2d: bool,
+        decompose_both: bool,
+        decompose_factor: int,
+        **kwargs,
+    ) -> None:
+        """Internal function to create lokr adapter
+
+        Args:
+            adapter_name (`str`): Name for the adapter to add.
+            r (`int`): Rank for the added adapter.
+            alpha (`float`): Alpha for the added adapter.
+            rank_dropout (`float`): The dropout probability for rank dimension during training
+            module_dropout (`float`): The dropout probability for disabling adapter during training.
+            init_weights (`bool`): Whether to initialize adapter weights.
+            use_effective_conv2d (`bool`): Use parameter effective decomposition for Conv2d with ksize > 1.
+            decompose_both (`bool`): Perform rank decomposition of left kronecker product matrix.
+            decompose_factor (`int`): Kronecker product decomposition factor.
+        """
+        if r <= 0:
+            raise ValueError(f"`r` should be a positive integer value but the value passed is {r}")
+
+        self.r[adapter_name] = r
+        self.alpha[adapter_name] = alpha
+        self.scaling[adapter_name] = alpha / r
+        self.rank_dropout[adapter_name] = rank_dropout
+        self.module_dropout[adapter_name] = module_dropout
+        base_layer = self.get_base_layer()
+
+        # Determine shape of LoKr weights
+        if isinstance(base_layer, nn.Linear):
+            in_dim, out_dim = base_layer.in_features, base_layer.out_features
+
+            in_m, in_n = factorization(in_dim, decompose_factor)
+            out_l, out_k = factorization(out_dim, decompose_factor)
+            shape = ((out_l, out_k), (in_m, in_n))  # ((a, b), (c, d)), out_dim = a*c, in_dim = b*d
+
+            use_w1 = not (decompose_both and r < max(shape[0][0], shape[1][0]) / 2)
+            use_w2 = not (r < max(shape[0][1], shape[1][1]) / 2)
+            use_effective_conv2d = False
+        elif isinstance(base_layer, nn.Conv2d):
+            in_dim, out_dim = base_layer.in_channels, base_layer.out_channels
+            k_size = base_layer.kernel_size
+
+            in_m, in_n = factorization(in_dim, decompose_factor)
+            out_l, out_k = factorization(out_dim, decompose_factor)
+            shape = ((out_l, out_k), (in_m, in_n), *k_size)  # ((a, b), (c, d), *k_size)
+
+            use_w1 = not (decompose_both and r < max(shape[0][0], shape[1][0]) / 2)
+            use_w2 = r >= max(shape[0][1], shape[1][1]) / 2
+            use_effective_conv2d = use_effective_conv2d and base_layer.kernel_size != (1, 1)
+        else:
+            raise TypeError(f"LoKr is not implemented for base layers of type {type(base_layer).__name__}")
+
+        # Create weights with provided shape
+        self.create_adapter_parameters(adapter_name, r, shape, use_w1, use_w2, use_effective_conv2d)
+
+        # Initialize weights
+        if init_weights:
+            self.reset_adapter_parameters(adapter_name)
+        else:
+            self.reset_adapter_parameters_random(adapter_name)
+
+        # Move new weights to device
+        weight = getattr(self.get_base_layer(), "weight", None)
+        if weight is not None:
+            # the layer is already completely initialized, this is an update
+            if weight.dtype.is_floating_point or weight.dtype.is_complex:
+                self.to(weight.device, dtype=weight.dtype)
+            else:
+                self.to(weight.device)
+        self.set_adapter(self.active_adapters)
+
+    def get_delta_weight(self, adapter_name: str) -> torch.Tensor:
+        # https://github.com/KohakuBlueleaf/LyCORIS/blob/e4259b870d3354a9615a96be61cb5d07455c58ea/lycoris/modules/lokr.py#L224
+        if adapter_name in self.lokr_w1:
+            w1 = self.lokr_w1[adapter_name]
+        else:
+            w1 = self.lokr_w1_a[adapter_name] @ self.lokr_w1_b[adapter_name]
+
+        if adapter_name in self.lokr_w2:
+            w2 = self.lokr_w2[adapter_name]
+        elif adapter_name in self.lokr_t2:
+            w2 = make_weight_cp(self.lokr_t2[adapter_name], self.lokr_w2_a[adapter_name], self.lokr_w2_b[adapter_name])
+        else:
+            w2 = self.lokr_w2_a[adapter_name] @ self.lokr_w2_b[adapter_name]
+
+        # Make weights with Kronecker product
+        weight = make_kron(w1, w2)
+        weight = weight.reshape(self.get_base_layer().weight.shape)
+
+        # Perform rank dropout during training - drop rows of addition weights
+        rank_dropout = self.rank_dropout[adapter_name]
+        if self.training and rank_dropout:
+            drop = (torch.rand(weight.size(0)) > rank_dropout).float()
+            drop = drop.view(-1, *[1] * len(weight.shape[1:])).to(weight.device)
+            drop /= drop.mean()
+            weight *= drop
+
+        return weight
+
+    def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        previous_dtype = x.dtype
+
+        if self.disable_adapters:
+            if self.merged:
+                self.unmerge()
+            result = self.base_layer(x, *args, **kwargs)
+        elif self.merged:
+            result = self.base_layer(x, *args, **kwargs)
+        else:
+            result = self.base_layer(x, *args, **kwargs)
+
+            # Execute all the adapters
+            for active_adapter in self.active_adapters:
+                if active_adapter not in self._available_adapters:
+                    continue
+
+                module_dropout = self.module_dropout[active_adapter]
+
+                # Modify current execution weights
+                if (not self.training) or (self.training and torch.rand(1) > module_dropout):
+                    result = result + self._get_delta_activations(active_adapter, x, *args, **kwargs)
+
+        result = result.to(previous_dtype)
+        return result
+
+
+class Linear(LoKrLayer):
+    """LoKr implemented in Linear layer"""
+
+    def __init__(
+        self,
+        base_layer: nn.Module,
+        device: Optional[Union[str, torch.device]] = None,
+        dtype: Optional[torch.dtype] = None,
+        adapter_name: str = "default",
+        r: int = 0,
+        alpha: float = 0.0,
+        rank_dropout: float = 0.0,
+        module_dropout: float = 0.0,
+        init_weights: bool = True,
+        **kwargs,
+    ):
+        super().__init__(base_layer)
+
+        # Create adapter and set it active
+        self._active_adapter = adapter_name
+        self.update_layer(adapter_name, r, alpha, rank_dropout, module_dropout, init_weights, **kwargs)
+
+    def _get_delta_activations(
+        self, adapter_name: str, input: torch.Tensor, *args: Any, **kwargs: Any
+    ) -> torch.Tensor:
+        delta_weight = self.get_delta_weight(adapter_name)
+        # don't add bias here, because the bias is already included in the output of the base_layer
+        return F.linear(input, delta_weight)
+
+    def __repr__(self) -> str:
+        rep = super().__repr__()
+        return "lokr." + rep
+
+
+class Conv2d(LoKrLayer):
+    """LoKr implemented in Conv2d layer"""
+
+    def __init__(
+        self,
+        base_layer: nn.Module,
+        device: Optional[Union[str, torch.device]] = None,
+        dtype: Optional[torch.dtype] = None,
+        adapter_name: str = "default",
+        r: int = 0,
+        alpha: float = 0.0,
+        rank_dropout: float = 0.0,
+        module_dropout: float = 0.0,
+        use_effective_conv2d: bool = False,
+        init_weights: bool = True,
+        **kwargs,
+    ):
+        super().__init__(base_layer)
+
+        # Create adapter and set it active
+        self._active_adapter = adapter_name
+        self.update_layer(
+            adapter_name, r, alpha, rank_dropout, module_dropout, init_weights, use_effective_conv2d, **kwargs
+        )
+
+    def _get_delta_activations(
+        self, adapter_name: str, input: torch.Tensor, *args: Any, **kwargs: Any
+    ) -> torch.Tensor:
+        delta_weight = self.get_delta_weight(adapter_name)
+        # don't add bias here, because the bias is already included in the output of the base_layer
+        base_layer = self.get_base_layer()
+        return F.conv2d(
+            input,
+            delta_weight,
+            stride=base_layer.stride,
+            padding=base_layer.padding,
+            dilation=base_layer.dilation,
+            groups=base_layer.groups,
+        )
+
+    def __repr__(self) -> str:
+        rep = super().__repr__()
+        return "lokr." + rep
+
+
+# Below code is a direct copy from https://github.com/KohakuBlueleaf/LyCORIS/blob/eb460098187f752a5d66406d3affade6f0a07ece/lycoris/modules/lokr.py#L11
+
+
+def factorization(dimension: int, factor: int = -1) -> Tuple[int, int]:
+    """Factorizes the provided number into the product of two numbers
+
+    Args:
+        dimension (`int`): The number that needs to be factorized.
+        factor (`int`, optional):
+            Factorization divider. The algorithm will try to output two numbers, one of each will be as close to the
+            factor as possible. If -1 is provided, the decomposition algorithm would try to search dividers near the
+            square root of the dimension. Defaults to -1.
+
+    Returns:
+        Tuple[`int`, `int`]: A tuple of two numbers, whose product is equal to the provided number. The first number is
+        always less than or equal to the second.
+
+    Example:
+        ```py
+        >>> factorization(256, factor=-1)
+        (16, 16)
+
+        >>> factorization(128, factor=-1)
+        (8, 16)
+
+        >>> factorization(127, factor=-1)
+        (1, 127)
+
+        >>> factorization(128, factor=4)
+        (4, 32)
+        ```
+    """
+
+    if factor > 0 and (dimension % factor) == 0:
+        m = factor
+        n = dimension // factor
+        return m, n
+    if factor == -1:
+        factor = dimension
+    m, n = 1, dimension
+    length = m + n
+    while m < n:
+        new_m = m + 1
+        while dimension % new_m != 0:
+            new_m += 1
+        new_n = dimension // new_m
+        if new_m + new_n > length or new_m > factor:
+            break
+        else:
+            m, n = new_m, new_n
+    if m > n:
+        n, m = m, n
+    return m, n
+
+
+def make_weight_cp(t, wa, wb):
+    rebuild2 = torch.einsum("i j k l, i p, j r -> p r k l", t, wa, wb)  # [c, d, k1, k2]
+    return rebuild2
+
+
+def make_kron(w1, w2, scale=1.0):
+    if len(w2.shape) == 4:
+        w1 = w1.unsqueeze(2).unsqueeze(2)
+    w2 = w2.contiguous()
+    rebuild = torch.kron(w1, w2)
+
+    return rebuild * scale

llmeval-env/lib/python3.10/site-packages/peft/tuners/lokr/model.py

@@ -0,0 +1,115 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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 re
+from itertools import chain
+from typing import Dict, Type, Union
+
+import torch
+from torch import nn
+
+from peft.tuners.lycoris_utils import LycorisConfig, LycorisTuner
+
+from .layer import Conv2d, Linear, LoKrLayer
+
+
+class LoKrModel(LycorisTuner):
+    """
+    Creates Low-Rank Kronecker Product model from a pretrained model. The original method is partially described in
+    https://arxiv.org/abs/2108.06098 and in https://arxiv.org/abs/2309.14859 Current implementation heavily borrows
+    from
+    https://github.com/KohakuBlueleaf/LyCORIS/blob/eb460098187f752a5d66406d3affade6f0a07ece/lycoris/modules/lokr.py
+
+    Args:
+        model (`torch.nn.Module`): The model to which the adapter tuner layers will be attached.
+        config ([`LoKrConfig`]): The configuration of the LoKr model.
+        adapter_name (`str`): The name of the adapter, defaults to `"default"`.
+
+    Returns:
+        `torch.nn.Module`: The LoKr model.
+
+    Example:
+        ```py
+        >>> from diffusers import StableDiffusionPipeline
+        >>> from peft import LoKrModel, LoKrConfig
+
+        >>> config_te = LoKrConfig(
+        ...     r=8,
+        ...     lora_alpha=32,
+        ...     target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
+        ...     rank_dropout=0.0,
+        ...     module_dropout=0.0,
+        ...     init_weights=True,
+        ... )
+        >>> config_unet = LoKrConfig(
+        ...     r=8,
+        ...     lora_alpha=32,
+        ...     target_modules=[
+        ...         "proj_in",
+        ...         "proj_out",
+        ...         "to_k",
+        ...         "to_q",
+        ...         "to_v",
+        ...         "to_out.0",
+        ...         "ff.net.0.proj",
+        ...         "ff.net.2",
+        ...     ],
+        ...     rank_dropout=0.0,
+        ...     module_dropout=0.0,
+        ...     init_weights=True,
+        ...     use_effective_conv2d=True,
+        ... )
+
+        >>> model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+        >>> model.text_encoder = LoKrModel(model.text_encoder, config_te, "default")
+        >>> model.unet = LoKrModel(model.unet, config_unet, "default")
+        ```
+
+    **Attributes**:
+        - **model** ([`~torch.nn.Module`]) -- The model to be adapted.
+        - **peft_config** ([`LoKrConfig`]): The configuration of the LoKr model.
+    """
+
+    prefix: str = "lokr_"
+    layers_mapping: Dict[Type[torch.nn.Module], Type[LoKrLayer]] = {
+        torch.nn.Conv2d: Conv2d,
+        torch.nn.Linear: Linear,
+    }
+
+    def _create_and_replace(
+        self,
+        config: LycorisConfig,
+        adapter_name: str,
+        target: Union[LoKrLayer, nn.Module],
+        target_name: str,
+        parent: nn.Module,
+        current_key: str,
+    ) -> None:
+        """
+        A private method to create and replace the target module with the adapter module.
+        """
+
+        # Regexp matching - Find key which matches current target_name in patterns provided
+        pattern_keys = list(chain(config.rank_pattern.keys(), config.alpha_pattern.keys()))
+        target_name_key = next(filter(lambda key: re.match(rf"(.*\.)?{key}$", current_key), pattern_keys), target_name)
+
+        kwargs = config.to_dict()
+        kwargs["r"] = config.rank_pattern.get(target_name_key, config.r)
+        kwargs["alpha"] = config.alpha_pattern.get(target_name_key, config.alpha)
+
+        if isinstance(target, LoKrLayer):
+            target.update_layer(adapter_name, **kwargs)
+        else:
+            new_module = self._create_new_module(config, adapter_name, target, **kwargs)
+            self._replace_module(parent, target_name, new_module, target)

llmeval-env/lib/python3.10/site-packages/peft/tuners/lora/__init__.py

@@ -0,0 +1,37 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from peft.import_utils import is_bnb_4bit_available, is_bnb_available
+
+from .config import LoftQConfig, LoraConfig
+from .gptq import QuantLinear
+from .layer import Conv2d, Embedding, Linear, LoraLayer
+from .model import LoraModel
+
+
+__all__ = ["LoraConfig", "LoftQConfig", "Conv2d", "Embedding", "LoraLayer", "Linear", "LoraModel", "QuantLinear"]
+
+
+def __getattr__(name):
+    if (name == "Linear8bitLt") and is_bnb_available():
+        from .bnb import Linear8bitLt
+
+        return Linear8bitLt
+
+    if (name == "Linear4bit") and is_bnb_4bit_available():
+        from .bnb import Linear4bit
+
+        return Linear4bit
+
+    raise AttributeError(f"module {__name__} has no attribute {name}")

llmeval-env/lib/python3.10/site-packages/peft/tuners/lora/gptq.py

@@ -0,0 +1,114 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from typing import Any, Optional
+
+import torch
+
+from peft.tuners.lora.layer import LoraLayer
+from peft.tuners.tuners_utils import BaseTunerLayer
+from peft.utils import get_auto_gptq_quant_linear
+
+
+class QuantLinear(torch.nn.Module, LoraLayer):
+    def __init__(
+        self,
+        base_layer,
+        adapter_name: str,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        init_lora_weights: bool = True,
+        use_rslora: bool = False,
+        use_dora: bool = False,
+        **kwargs,
+    ):
+        super().__init__()
+        LoraLayer.__init__(self, base_layer)
+
+        if use_dora:
+            raise ValueError(f"{self.__class__.__name__} does not support DoRA yet, please set it to False")
+
+        # self.base_layer and self.quant_linear_module are the same; we need the former for consistency and the latter
+        # for backwards compatibility
+        self.quant_linear_module = base_layer
+        self._active_adapter = adapter_name
+        self.update_layer(
+            adapter_name,
+            r,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            init_lora_weights=init_lora_weights,
+            use_rslora=use_rslora,
+            use_dora=use_dora,
+        )
+
+    def forward(self, x: torch.Tensor):
+        # note: logic differs from default Linear because merging is not supported
+        result = self.quant_linear_module(x)
+
+        if self.disable_adapters:
+            return result
+
+        for active_adapter in self.active_adapters:
+            if active_adapter not in self.lora_A.keys():
+                continue
+            lora_A = self.lora_A[active_adapter]
+            lora_B = self.lora_B[active_adapter]
+            dropout = self.lora_dropout[active_adapter]
+            scaling = self.scaling[active_adapter]
+
+            requires_conversion = not torch.is_autocast_enabled()
+            if requires_conversion:
+                expected_dtype = result.dtype
+                x = x.to(lora_A.weight.dtype)
+
+            output = lora_B(lora_A(dropout(x)))
+            if requires_conversion:
+                output = output.to(expected_dtype)
+            output = output * scaling
+            result += output
+        return result
+
+    def __repr__(self) -> str:
+        rep = super().__repr__()
+        return "lora." + rep
+
+    # TODO: Check if it is better as suggested by users https://github.com/PanQiWei/AutoGPTQ/pull/102
+    # def reset_lora_parameters(self, adapter_name):
+    #     if adapter_name in self.lora_A.keys():
+    #         torch.nn.init.xavier_uniform_(self.lora_A[adapter_name].weight)
+    #         torch.nn.init.zeros_(self.lora_B[adapter_name].weight)
+
+
+def dispatch_gptq(
+    target: torch.nn.Module,
+    adapter_name: str,
+    **kwargs: Any,
+) -> Optional[torch.nn.Module]:
+    new_module = None
+
+    if isinstance(target, BaseTunerLayer):
+        target_base_layer = target.get_base_layer()
+    else:
+        target_base_layer = target
+
+    gptq_quantization_config = kwargs.get("gptq_quantization_config", None)
+    AutoGPTQQuantLinear = get_auto_gptq_quant_linear(gptq_quantization_config)
+
+    if AutoGPTQQuantLinear is not None and isinstance(target_base_layer, AutoGPTQQuantLinear):
+        new_module = QuantLinear(target, adapter_name, **kwargs)
+        target.qweight = target_base_layer.qweight
+
+    return new_module

llmeval-env/lib/python3.10/site-packages/peft/tuners/p_tuning/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from .config import PromptEncoderConfig, PromptEncoderReparameterizationType
+from .model import PromptEncoder
+
+
+__all__ = ["PromptEncoder", "PromptEncoderConfig", "PromptEncoderReparameterizationType"]

llmeval-env/lib/python3.10/site-packages/peft/tuners/p_tuning/config.py

@@ -0,0 +1,59 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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 enum
+from dataclasses import dataclass, field
+from typing import Union
+
+from peft.config import PromptLearningConfig
+from peft.utils import PeftType
+
+
+class PromptEncoderReparameterizationType(str, enum.Enum):
+    MLP = "MLP"
+    LSTM = "LSTM"
+
+
+@dataclass
+class PromptEncoderConfig(PromptLearningConfig):
+    """
+    This is the configuration class to store the configuration of a [`PromptEncoder`].
+
+    Args:
+        encoder_reparameterization_type (Union[[`PromptEncoderReparameterizationType`], `str`]):
+            The type of reparameterization to use.
+        encoder_hidden_size (`int`): The hidden size of the prompt encoder.
+        encoder_num_layers (`int`): The number of layers of the prompt encoder.
+        encoder_dropout (`float`): The dropout probability of the prompt encoder.
+    """
+
+    encoder_reparameterization_type: Union[str, PromptEncoderReparameterizationType] = field(
+        default=PromptEncoderReparameterizationType.MLP,
+        metadata={"help": "How to reparameterize the prompt encoder"},
+    )
+    encoder_hidden_size: int = field(
+        default=None,
+        metadata={"help": "The hidden size of the prompt encoder"},
+    )
+    encoder_num_layers: int = field(
+        default=2,
+        metadata={"help": "The number of layers of the prompt encoder"},
+    )
+    encoder_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout of the prompt encoder"},
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.P_TUNING

llmeval-env/lib/python3.10/site-packages/peft/tuners/p_tuning/model.py

@@ -0,0 +1,130 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+# Based on https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/prompt_encoder.py
+# with some refactor
+import warnings
+
+import torch
+
+from .config import PromptEncoderConfig, PromptEncoderReparameterizationType
+
+
+class PromptEncoder(torch.nn.Module):
+    """
+    The prompt encoder network that is used to generate the virtual token embeddings for p-tuning.
+
+    Args:
+        config ([`PromptEncoderConfig`]): The configuration of the prompt encoder.
+
+    Example:
+
+    ```py
+    >>> from peft import PromptEncoder, PromptEncoderConfig
+
+    >>> config = PromptEncoderConfig(
+    ...     peft_type="P_TUNING",
+    ...     task_type="SEQ_2_SEQ_LM",
+    ...     num_virtual_tokens=20,
+    ...     token_dim=768,
+    ...     num_transformer_submodules=1,
+    ...     num_attention_heads=12,
+    ...     num_layers=12,
+    ...     encoder_reparameterization_type="MLP",
+    ...     encoder_hidden_size=768,
+    ... )
+
+    >>> prompt_encoder = PromptEncoder(config)
+    ```
+
+    **Attributes**:
+        - **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prompt encoder.
+        - **mlp_head** (`torch.nn.Sequential`) -- The MLP head of the prompt encoder if `inference_mode=False`.
+        - **lstm_head** (`torch.nn.LSTM`) -- The LSTM head of the prompt encoder if `inference_mode=False` and
+        `encoder_reparameterization_type="LSTM"`.
+        - **token_dim** (`int`) -- The hidden embedding dimension of the base transformer model.
+        - **input_size** (`int`) -- The input size of the prompt encoder.
+        - **output_size** (`int`) -- The output size of the prompt encoder.
+        - **hidden_size** (`int`) -- The hidden size of the prompt encoder.
+        - **total_virtual_tokens** (`int`): The total number of virtual tokens of the
+        prompt encoder.
+        - **encoder_type** (Union[[`PromptEncoderReparameterizationType`], `str`]): The encoder type of the prompt
+          encoder.
+
+
+    Input shape: (`batch_size`, `total_virtual_tokens`)
+
+    Output shape: (`batch_size`, `total_virtual_tokens`, `token_dim`)
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.token_dim = config.token_dim
+        self.input_size = self.token_dim
+        self.output_size = self.token_dim
+        self.hidden_size = config.encoder_hidden_size
+        self.total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules
+        self.encoder_type = config.encoder_reparameterization_type
+
+        # embedding
+        self.embedding = torch.nn.Embedding(self.total_virtual_tokens, self.token_dim)
+        if not config.inference_mode:
+            if self.encoder_type == PromptEncoderReparameterizationType.LSTM:
+                lstm_dropout = config.encoder_dropout
+                num_layers = config.encoder_num_layers
+                # LSTM
+                self.lstm_head = torch.nn.LSTM(
+                    input_size=self.input_size,
+                    hidden_size=self.hidden_size,
+                    num_layers=num_layers,
+                    dropout=lstm_dropout,
+                    bidirectional=True,
+                    batch_first=True,
+                )
+
+                self.mlp_head = torch.nn.Sequential(
+                    torch.nn.Linear(self.hidden_size * 2, self.hidden_size * 2),
+                    torch.nn.ReLU(),
+                    torch.nn.Linear(self.hidden_size * 2, self.output_size),
+                )
+
+            elif self.encoder_type == PromptEncoderReparameterizationType.MLP:
+                encoder_num_layers_default = PromptEncoderConfig.encoder_num_layers
+                if config.encoder_num_layers != encoder_num_layers_default:
+                    warnings.warn(
+                        f"for {self.encoder_type.value}, the argument `encoder_num_layers` is ignored. "
+                        f"Exactly {encoder_num_layers_default} MLP layers are used."
+                    )
+                layers = [
+                    torch.nn.Linear(self.input_size, self.hidden_size),
+                    torch.nn.ReLU(),
+                    torch.nn.Linear(self.hidden_size, self.hidden_size),
+                    torch.nn.ReLU(),
+                    torch.nn.Linear(self.hidden_size, self.output_size),
+                ]
+                self.mlp_head = torch.nn.Sequential(*layers)
+
+            else:
+                raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.")
+
+    def forward(self, indices):
+        input_embeds = self.embedding(indices)
+        if self.encoder_type == PromptEncoderReparameterizationType.LSTM:
+            output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0])
+        elif self.encoder_type == PromptEncoderReparameterizationType.MLP:
+            output_embeds = self.mlp_head(input_embeds)
+        else:
+            raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.")
+
+        return output_embeds

llmeval-env/lib/python3.10/site-packages/peft/tuners/poly/__init__.py

@@ -0,0 +1,20 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from .config import PolyConfig
+from .layer import Linear, PolyLayer
+from .model import PolyModel
+
+
+__all__ = ["Linear", "PolyConfig", "PolyLayer", "PolyModel"]

llmeval-env/lib/python3.10/site-packages/peft/tuners/poly/config.py

@@ -0,0 +1,89 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from dataclasses import dataclass, field
+from typing import List, Literal, Optional, Union
+
+from peft.config import PeftConfig
+from peft.utils import PeftType
+
+
+@dataclass
+class PolyConfig(PeftConfig):
+    """
+    This is the configuration class to store the configuration of a [`PolyModel`].
+        - [Polytropon (Poly)](https://arxiv.org/abs/2202.13914)
+        - [Multi-Head Routing (MHR)](https://arxiv.org/abs/2211.03831)
+
+    Args:
+        r (`int`): Attention dimension of each Lora in Poly.
+        target_modules (`Union[List[str],str]`): The names of the modules to apply Poly to.
+        modules_to_save (`List[str]`): List of modules apart from Poly layers to be set as trainable
+            and saved in the final checkpoint.
+        init_weights (bool): Whether to perform initialization of Poly weights.
+        poly_type (`Literal["poly"]`): The variant of the Poly module to use. Currently, only "poly"
+            is supported.
+        n_tasks (`int`): The number of tasks in a multitasking scenario.
+        n_skills (`int`): The number of skills (LoRA) in each Poly layer.
+        n_splits (`int`): The number of splits within each LoRA of a Poly layer. A value greater
+            than 1 indicates the use of Multi-Head Routing (MHR).
+    """
+
+    r: int = field(default=8, metadata={"help": "Lora attention dimension"})
+    target_modules: Optional[Union[List[str], str]] = field(
+        default=None,
+        metadata={
+            "help": "List of module names or regex expression of the module names to replace with Poly."
+            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+        },
+    )
+    modules_to_save: Optional[List[str]] = field(
+        default=None,
+        metadata={
+            "help": "List of modules apart from Poly layers to be set as trainable and saved in the final checkpoint. "
+            "For example, in Sequence Classification or Token Classification tasks, "
+            "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
+        },
+    )
+    init_weights: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to initialize the weights of the Poly layers with their default initialization. Don't change "
+                "this setting, except if you know exactly what you're doing."
+            ),
+        },
+    )
+    poly_type: Literal["poly"] = field(
+        default="poly",
+        metadata={"help": 'Type of Poly modules to be used. Currently only "poly" is supported.'},
+    )
+    n_tasks: int = field(
+        default=1,
+        metadata={"help": "Number of tasks in multitasking scenario."},
+    )
+    n_skills: int = field(
+        default=4,
+        metadata={"help": "Number of skills (LoRA) in each Poly layer."},
+    )
+    n_splits: int = field(
+        default=1,
+        metadata={"help": "Number of splits within each LoRA of a Poly layer."},
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.POLY
+        self.target_modules = (
+            set(self.target_modules) if isinstance(self.target_modules, list) else self.target_modules
+        )

llmeval-env/lib/python3.10/site-packages/peft/tuners/poly/layer.py

@@ -0,0 +1,171 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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 math
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from peft.tuners.tuners_utils import BaseTunerLayer
+
+from .config import PolyConfig
+from .router import get_router
+
+
+class PolyLayer(BaseTunerLayer):
+    # All names of layers that may contain (trainable) adapter weights
+    adapter_layer_names = ("poly_lora_A", "poly_lora_B", "poly_router")
+    # All names of other parameters that may contain adapter-related parameters
+    other_param_names = ("r", "n_tasks", "n_skills", "n_splits")
+
+    def __init__(self, base_layer: nn.Module, **kwargs):
+        self.base_layer = base_layer
+        self.r = {}
+        self.n_tasks = {}
+        self.n_skills = {}
+        self.n_splits = {}
+        self.poly_type = {}
+        self.poly_router = nn.ModuleDict()
+        self.poly_lora_A = nn.ParameterDict()
+        self.poly_lora_B = nn.ParameterDict()
+        self.kwargs = kwargs
+
+        base_layer = self.get_base_layer()
+        if isinstance(base_layer, nn.Linear):
+            in_features, out_features = base_layer.in_features, base_layer.out_features
+        else:
+            raise ValueError(f"Unsupported layer type {type(base_layer)}")
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+    def update_layer(self, adapter_name, poly_config):
+        if poly_config.r <= 0:
+            raise ValueError(f"`r` should be a positive integer value but the value passed is {poly_config.r}")
+
+        self.r[adapter_name] = poly_config.r
+        self.n_tasks[adapter_name] = poly_config.n_tasks
+        self.n_skills[adapter_name] = poly_config.n_skills
+        self.n_splits[adapter_name] = poly_config.n_splits
+        self.poly_type[adapter_name] = poly_config.poly_type
+
+        self.poly_lora_A[adapter_name] = nn.Parameter(
+            torch.empty(
+                poly_config.n_splits,
+                poly_config.n_skills,
+                self.in_features // poly_config.n_splits,
+                poly_config.r,
+            )
+        )
+        self.poly_lora_B[adapter_name] = nn.Parameter(
+            torch.empty(
+                poly_config.n_splits,
+                poly_config.n_skills,
+                poly_config.r,
+                self.out_features // poly_config.n_splits,
+            )
+        )
+        self.poly_router[adapter_name] = get_router(poly_config)
+
+        self.reset_poly_parameters(adapter_name, init_weights=poly_config.init_weights)
+
+        weight = getattr(self.get_base_layer(), "weight", None)
+        if weight is not None:
+            # the layer is already completely initialized, this is an update
+            if weight.dtype.is_floating_point or weight.dtype.is_complex:
+                self.to(weight.device, dtype=weight.dtype)
+            else:
+                self.to(weight.device)
+        self.set_adapter(self.active_adapters)
+
+    def reset_poly_parameters(self, adapter_name, init_weights):
+        if adapter_name in self.poly_lora_A.keys():
+            # initialize A the same way as the default for nn.Linear
+            # https://github.com/microsoft/mttl/blob/ce4ca51dbca73be656feb9b3e5233633e3c5dec7/mttl/models/poly.py#L269
+            n_splits, n_skills, d, r = self.poly_lora_A[adapter_name].shape
+            for skill in range(n_skills):
+                for split in range(n_splits):
+                    param = torch.empty((r, d))
+                    torch.nn.init.kaiming_uniform_(param, a=math.sqrt(5))
+                    self.poly_lora_A[adapter_name].data[split, skill, :, :] = param.T
+
+            if init_weights:
+                # initialize B to zero
+                torch.nn.init.zeros_(self.poly_lora_B[adapter_name])
+            else:
+                # initialize B the same way as the default for nn.Linear
+                n_splits, n_skills, r, d = self.poly_lora_B[adapter_name].shape
+                for skill in range(n_skills):
+                    for split in range(n_splits):
+                        param = torch.empty((d, r))
+                        torch.nn.init.kaiming_uniform_(param, a=math.sqrt(5))
+                        self.poly_lora_B[adapter_name].data[split, skill, :, :] = param.T
+
+            # initialized router
+            self.poly_router[adapter_name].reset()
+
+
+class Linear(nn.Module, PolyLayer):
+    # Lora implemented in a dense layer
+    def __init__(
+        self,
+        base_layer,
+        adapter_name: str,
+        poly_config: PolyConfig,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        PolyLayer.__init__(self, base_layer, **kwargs)
+
+        self._active_adapter = adapter_name
+        self.update_layer(adapter_name, poly_config)
+
+    def forward(self, x: torch.Tensor, *args: Any, task_ids: torch.Tensor = None, **kwargs: Any) -> torch.Tensor:
+        previous_dtype = x.dtype
+        if self.disable_adapters:
+            result = self.base_layer(x, *args, **kwargs)
+        else:
+            result = self.base_layer(x, *args, **kwargs)
+            for active_adapter in self.active_adapters:
+                if active_adapter not in self.poly_lora_A.keys():
+                    continue
+
+                r = self.r[active_adapter]
+                poly_router = self.poly_router[active_adapter]
+                poly_lora_A = self.poly_lora_A[active_adapter]
+                poly_lora_B = self.poly_lora_B[active_adapter]
+
+                # Combine the output of LoRAs
+                # https://github.com/microsoft/mttl/blob/ce4ca51dbca73be656feb9b3e5233633e3c5dec7/mttl/models/poly.py#L293
+                mixing_weights = poly_router(task_ids=task_ids, input_ids=x)
+                bs, n_splits, n_skills = mixing_weights.size()
+
+                # A is    n_splits, n_skills, D // n_splits, rank
+                # we want bs,       n_splits, D // n_splits, rank
+                A = torch.einsum("bqs,qsdr->bqdr", (mixing_weights, poly_lora_A))
+                B = torch.einsum("bqs,qsrd->bqrd", (mixing_weights, poly_lora_B))
+
+                A = A.reshape(bs, self.in_features, r)
+                B = B.transpose(1, 2).reshape(bs, r, self.out_features)
+
+                x = x.to(A.dtype)
+                result += x.bmm(A).bmm(B) / r
+
+        result = result.to(previous_dtype)
+        return result
+
+    def __repr__(self) -> str:
+        rep = super().__repr__()
+        return "poly." + rep

llmeval-env/lib/python3.10/site-packages/peft/tuners/poly/router.py

@@ -0,0 +1,83 @@
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# 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.
+
+from abc import ABC, abstractmethod
+
+import torch
+from torch import nn
+from torch.distributions.relaxed_bernoulli import RelaxedBernoulli
+
+from .config import PolyConfig
+
+
+EPS = 1e-12
+
+
+def get_router(poly_config: PolyConfig) -> nn.Module:
+    if poly_config.poly_type == "poly":
+        return PolyRouter(poly_config)
+    else:
+        raise ValueError(
+            f"Unsupported poly_type: {poly_config.poly_type}. "
+            "Currently, only the following types are supported: "
+            "`poly`."
+        )
+
+
+class Router(nn.Module, ABC):
+    @abstractmethod
+    def reset(self):
+        ...
+
+    @abstractmethod
+    def forward(self, task_ids: torch.Tensor, input_ids: torch.Tensor):
+        ...
+
+
+class PolyRouter(Router):
+    # It's a simplified implementation of
+    # https://github.com/microsoft/mttl/blob/ce4ca51dbca73be656feb9b3e5233633e3c5dec7/mttl/models/poly.py#L138
+    def __init__(self, poly_config: PolyConfig):
+        super().__init__()
+
+        self.poly_type = poly_config.poly_type
+        self.n_tasks = poly_config.n_tasks
+        self.n_skills = poly_config.n_skills
+        self.n_splits = poly_config.n_splits
+
+        self.module_logits = nn.Parameter(torch.empty((self.n_tasks, self.n_splits * self.n_skills)))
+
+    def reset(self):
+        torch.nn.init.uniform_(self.module_logits, -1e-3, 1e-3)
+
+    def forward(self, task_ids: torch.Tensor, input_ids: torch.Tensor):
+        if task_ids is None:
+            raise ValueError("task_ids should not be None.")
+        if task_ids.max().item() >= self.n_tasks:
+            raise ValueError(f"Only {self.n_tasks} tasks available. Found task id = {task_ids.max().item()}")
+
+        # move task id to input's device
+        task_ids = task_ids.to(self.module_logits.device)
+
+        module_logits = self.module_logits[task_ids]
+        module_logits = module_logits.view(-1, self.n_splits, self.n_skills)
+
+        if self.training:
+            module_logits = RelaxedBernoulli(temperature=1.0, logits=module_logits).rsample()
+        else:
+            module_logits = torch.sigmoid(module_logits)
+
+        module_weights = module_logits / (module_logits.sum(dim=-1, keepdim=True) + EPS)
+
+        return module_weights