Skip to content

Update dependency peft to v0.17.1 #61

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Open
wants to merge 1 commit into
base: konflux-poc
Choose a base branch
from
Open

Update dependency peft to v0.17.1 #61

wants to merge 1 commit into from

Conversation

red-hat-konflux[bot]
Copy link

@red-hat-konflux red-hat-konflux bot commented May 17, 2025 •
edited
Loading

This PR contains the following updates:

Package Change Age Confidence
peft ==0.3.0 -> ==0.17.1 age confidence

Release Notes

huggingface/peft (peft)

v0.17.1: 0.17.1

Compare Source

This patch release contains a few fixes (via #​2710) for the newly introduced target_parameters feature, which allows LoRA to target nn.Parameters directly (useful for mixture of expert layers). Most notably:

  • PEFT no longer removes possibly existing parametrizations from the parameter.
  • Adding multiple adapters (via model.add_adapter or model.load_adapter) did not work correctly. Since a solution is not trivial, PEFT now raises an error to prevent this situation.

v0.17.0: 0.17.0: SHiRA, MiSS, LoRA for MoE, and more

Compare Source

Highlights
peft-v0 17 0
New Methods
SHiRA

@​kkb-code contributed Sparse High Rank Adapters (SHiRA, paper) which promise to offer a potential gain in performance over LoRAs - especially the concept loss when using multiple adapters is improved. Since the adapters only train on 1-2% of the weights and are inherently sparse, switching between adapters may be cheaper than with LoRAs. (#​2584)

MiSS

@​JL-er added a new PEFT method, MiSS (Matrix Shard Sharing) in #​2604. This method is an evolution of Bone, which, according to our PEFT method comparison benchmark, gives excellent results when it comes to performance and memory efficiency. If you haven't tried it, you should do so now.

At the same time, Bone will be deprecated in favor of MiSS and will be removed in PEFT v0.19.0. If you already have a Bone checkpoint, you can use scripts/convert-bone-to-miss.py to convert it into a MiSS checkpoint and proceed with training using MiSS.

Enhancements
LoRA for nn.Parameter

LoRA is now able to target nn.Parameter directly (#​2638, #​2665)! Ever had this complicated nn.Module with promising parameters inside but it was too custom to be supported by your favorite fine-tuning library? No worries, now you can target nn.Parameters directly using the target_parameters config attribute which works similarly to target_modules.

This option can be especially useful for models with Mixture of Expert (MoE) layers, as those often use nn.Parameters directly and cannot be targeted with target_modules. For example, for the Llama4 family of models, use the following config to target the MoE weights:

config = LoraConfig(
    ...,
    target_modules=[],  # <= prevent targeting any modules
    target_parameters=["feed_forward.experts.down_proj", "feed_forward.experts.gate_up_proj"],
)

Note that this feature is still experimental as it comes with a few caveats and therefore might change in the future. Also, MoE weights with many experts can be quite huge, so expect a higher memory usage than compared to targeting normal nn.Linear layers.

Injecting adapters based on a state_dict

Sometimes, it is possible that there is a PEFT adapter checkpoint but the corresponding PEFT config is not known for whatever reason. To inject the PEFT layers for this checkpoint, you would usually have to reverse-engineer the corresponding PEFT config, most notably the target_modules argument, based on the state_dict from the checkpoint. This can be cumbersome and error prone. To avoid this, it is also possible to call inject_adapter_in_model and pass the loaded state_dict as an argument:

from safetensors.torch import load_file
from peft import LoraConfig, inject_adapter_in_model

model = ...
state_dict = load_file(<path-to-safetensors-file>)
lora_config = LoraConfig()  # <= no need to specify further
model = inject_adapter_in_model(lora_config, model, state_dict=state_dict)

Find more on state_dict based injection in the docs.

Changes
Compatibility

A bug in prompt learning methods caused modules_to_save to be ignored. Especially classification tasks are affected since they usually add the classification/score layer to modules_to_save. In consequence, these layers were neither trained nor stored after training. This has been corrected now. (#​2646)

All Changes
New Contributors

Full Changelog: huggingface/peft@v0.16.0...v0.17.0

v0.16.0: 0.16.0: LoRA-FA, RandLoRA, C³A, and much more

Compare Source

Highlights

peft-v0 16 0

New Methods
LoRA-FA

In #​2468, @​AaronZLT added the LoRA-FA optimizer to PEFT. This optimizer is based on AdamW and it increases memory efficiency of LoRA training. This means that you can train LoRA with less memory, or, with the same memory budget, use higher LoRA ranks, potentially getting better results.

RandLoRA

Thanks to @​PaulAlbert31, a new PEFT method called RandLoRA was added to PEFT (#​2464). Similarly to VeRA, it uses non-learnable random low rank matrices that are combined through learnable matrices. This way, RandLoRA can approximate full rank updates of the weights. Training models quantized with bitsandbytes is supported.

C³A

@​Phoveran added Circular Convolution Adaptation, C3A, in #​2577. This new PEFT method can overcome the limit of low rank adaptations as seen e.g. in LoRA while still promising to be fast and memory efficient.

Enhancements

Thanks to @​gslama12 and @​SP1029, LoRA now supports Conv2d layers with groups != 1. This requires the rank r being divisible by groups. See #​2403 and #​2567 for context.

@​dsocek added support for Intel Neural Compressor (INC) quantization to LoRA in #​2499.

DoRA now supports Conv1d layers thanks to @​EskildAndersen (#​2531).

Passing init_lora_weights="orthogonal" now enables orthogonal weight initialization for LoRA (#​2498).

@​gapsong brought us Quantization-Aware LoRA training in #​2571. This can make QLoRA training more efficient, please check the included example. Right now, only GPTQ is supported.

There has been a big refactor of Orthogonal Finetuning, OFT, thanks to @​zqiu24 (#​2575). This makes the PEFT method run more quickly and require less memory. It is, however, incompatible with old OFT checkpoints. If you have old OFT checkpoints, either pin the PEFT version to <0.16.0 or retrain it with the new PEFT version.

Thanks to @​keepdying, LoRA hotswapping with compiled models no longer leads to CUDA graph re-records (#​2611).

Changes
Compatibility
  • #​2481: The value of required_grads_ of modules_to_save is now set to True when used directly with inject_adapter. This is relevant for PEFT integrations, e.g. Transformers or Diffusers.
  • Due to a big refactor of vision language models (VLMs) in Transformers, the model architecture has been slightly adjusted. One consequence of this is that if you use a PEFT prompt learning method that is applied to vlm.language_model, it will no longer work, please apply it to vlm directly (see #​2554 for context). Morever, the refactor results in different checkpoints. We managed to ensure backwards compatability in PEFT, i.e. old checkpoints can be loaded successfully. There is, however, no forward compatibility, i.e. loading checkpoints trained after the refactor is not possible with package versions from before the refactor. In this case, you need to upgrade PEFT and transformers. More context in #​2574.
  • #​2579: There have been bigger refactors in Transformers concerning attention masks. This required some changes on the PEFT side which can affect prompt learning methods. For prefix tuning specifically, this can result in numerical differences but overall performance should be the same. For other prompt learning methods, numerical values should be the same, except if the base model uses 4d attention masks, like Gemma. If you load old prompt learning checkpoints, please double-check that they still perform as expected, especially if they're trained on Gemma or similar models. If not, please re-train them or pin PEFT and transformers to previous versions (<0.16.0 and <4.52.0, respectively).
All Changes
New Contributors

Full Changelog: huggingface/peft@v0.15.2...v0.16.0

v0.15.2

Compare Source

This patch fixes a bug that resulted in prompt learning methods like P-tuning not to work (#​2477).

v0.15.1

Compare Source

This patch includes a fix for #​2450. In this bug modules_to_save was not handled correctly when used in conjunction with DeepSpeed ZeRO stage 3 which resulted in those modules being placeholder values in the saved checkpoints.

Full Changelog: huggingface/peft@v0.15.0...v0.15.1

v0.15.0

Compare Source

Highlights

peft-v0 15 0

New Methods
CorDA: Context-Oriented Decomposition Adaptation

@​iboing and @​5eqn contributed CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning . This task-driven initialization method has two modes, knowledge-preservation and instruction-preservation, both using external data to select ranks intelligently. The former can be used to select those ranks that correspond to weights not affiliated with knowledge from, say, a QA dataset. The latter can be used to select those ranks that correspond most to the task at hand (e.g., a classification task). (#​2231)

Trainable Tokens: Selective token update

The new Trainable Tokens tuner allows for selective training of tokens without re-training the full embedding matrix, e.g. when adding support for reasoning / thinking tokens. This is a lot more memory efficient and the saved checkpoint is much smaller. It can be used standalone or in conjunction with LoRA adapters by passing trainable_token_indices to LoraConfig. (#​2376)

Enhancements

LoRA now supports targeting multihead attention modules (but for now only those with _qkv_same_embed_dim=True). These modules were tricky as they may expose linear submodules but won't use their forward methods, therefore needing explicit support. (#​1324)

Hotswapping now allows different alpha scalings and ranks without recompilation of the model when the model is prepared using a call to prepare_model_for_compiled_hotswap() before compiling the model. (#​2177)

GPTQModel support was added in #​2247 as a replacement for AutoGPTQ which is not maintained anymore.

Changes
  • It's now possible to use all-linear as target_modules for custom (non-transformers) models (#​2267). With this change comes a bugfix where it was possible that non-linear layers were selected when they shared the same name with a linear layer (e.g., bar.foo and baz.foo).
  • The internal tuner API was refactored to make method registration easier. With this change the number of changes to numerous files is reduced to a single register_peft_method() call. (#​2282)
  • PEFT_TYPE_TO_MODEL_MAPPING is now deprecated and should not be relied upon. Use PEFT_TYPE_TO_TUNER_MAPPING instead. (#​2282)
  • Mixed adapter batches can now be used in conjunction with beam search. (#​2287)
  • It was possible that modules_to_save keys wrongly matched parts of the state dict if the key was a substring of another key (e.g., classifier and classifier2). (#​2334)
  • Auto-casting of the input dtype to the LoRA adapter dtype can now be disabled via disable_input_dtype_casting=True. (#​2353)
  • The config parameters rank_pattern and alpha_pattern used by many adapters now supports matching full paths as well by specifying the pattern with a caret in front, for example: ^foo to target model.foo but not model.bar.foo. (#​2419)
  • AutoPeftModels do not reduce the embedding size anymore if the tokenizer size differs from the embedding size. Only if there are more tokens in the tokenizer than in the embedding matrix, the matrix will be resized. This is to prevent resizing of embedding matrices in models that have 'spare' tokens built-in. (#​2427)
What's Changed

Configuration

📅 Schedule: Branch creation - "after 5am on saturday" (UTC), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about these updates again.

  • If you want to rebase/retry this PR, check this box

To execute skipped test pipelines write comment /ok-to-test.

This PR has been generated by MintMaker (powered by Renovate Bot).

@coveralls
Copy link

Pull Request Test Coverage Report for Build 15089424447

Details

  • 0 of 0 changed or added relevant lines in 0 files are covered.
  • No unchanged relevant lines lost coverage.
  • Overall first build on konflux/mintmaker/konflux-poc/peft-0.x at 93.407%
Totals Coverage Status
Change from base Build 15020007478: 93.4%
Covered Lines: 85
Relevant Lines: 91
💛 - Coveralls

@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc/peft-0.x branch from a3dffa8 to 10afd8b Compare July 5, 2025 05:02
@coderabbitai coderabbitai
Copy link

coderabbitai bot commented Jul 5, 2025 •
edited
Loading

Important

Review skipped

Bot user detected.

To trigger a single review, invoke the @coderabbitai review command.

You can disable this status message by setting the reviews.review_status to false in the CodeRabbit configuration file.

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

  • Review comments: Directly reply to a review comment made by CodeRabbit. Example:
    • I pushed a fix in commit <commit_id>, please review it.
    • Open a follow-up GitHub issue for this discussion.
  • Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query.
  • PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
    • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
    • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.

Support

Need help? Join our Discord community for assistance with any issues or questions.

CodeRabbit Commands (Invoked using PR/Issue comments)

Type @coderabbitai help to get the list of available commands.

Other keywords and placeholders

  • Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
  • Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
  • Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

  • You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
  • Please see the configuration documentation for more information.
  • If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Status, Documentation and Community

  • Visit our Status Page to check the current availability of CodeRabbit.
  • Visit our Documentation for detailed information on how to use CodeRabbit.
  • Join our Discord Community to get help, request features, and share feedback.
  • Follow us on X/Twitter for updates and announcements.

@red-hat-konflux red-hat-konflux bot changed the title Update dependency peft to v0.15.2 Update dependency peft to v0.16.0 Jul 5, 2025
@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc/peft-0.x branch from 10afd8b to b908a64 Compare August 9, 2025 08:24
@red-hat-konflux red-hat-konflux bot changed the title Update dependency peft to v0.16.0 Update dependency peft to v0.17.0 Aug 9, 2025
@red-hat-konflux
Update dependency peft to v0.17.1
2f9947a 
Signed-off-by: red-hat-konflux <126015336+red-hat-konflux[bot]@users.noreply.github.com>
@red-hat-konflux red-hat-konflux bot force-pushed the konflux/mintmaker/konflux-poc/peft-0.x branch from b908a64 to 2f9947a Compare August 23, 2025 08:42
@red-hat-konflux red-hat-konflux bot changed the title Update dependency peft to v0.17.0 Update dependency peft to v0.17.1 Aug 23, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers
No reviews
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
1 participant
@coveralls