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0.18.0: RoAd, ALoRA, Arrow, WaveFT, DeLoRA, OSF, and more

13 Nov 11:14
@BenjaminBossan BenjaminBossan
v0.18.0
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0.18.0: RoAd, ALoRA, Arrow, WaveFT, DeLoRA, OSF, and more Latest
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Highlights

peft-v0 18 0

FIXME update list of all changes, so some more commits were added

New Methods

RoAd

@ppetrushkov added RoAd: 2D Rotary Adaptation to PEFT in #2678. RoAd learns 2D rotation matrices that are applied using only element-wise multiplication, thus promising very fast inference with adapters in unmerged state.

Remarkably, besides LoRA, RoAd is the only PEFT method that supports mixed adapter batches. This means that when you have loaded a model with multiple RoAd adapters, you can use all of them for different samples in the same batch, which is much more efficient than switching adapters between batches:

model = PeftModel.from_pretrained(base_model, <path-to-road-adapter-A>, adapter_name="adapter-A")
model.add_adapter("adapter-B", <path-to-road-adapter-B>)

inputs = ...  # input with 3 samples
# apply adapter A to sample 0, adapter B to sample 1, and use the base model for sample 2:
adapter_names = ["adapter-A", "adapter-B", "__base__"]
output_mixed = model(**inputs, adapter_names=adapter_names)
gen_mixed = model.generate(**inputs, adapter_names=adapter_names)

ALoRA

Activated LoRA is a technique added by @kgreenewald in #2609 for causal language models, allowing to selectively enable LoRA adapters depending on a specific token invocation sequence in the input. This has the major benefit of being able to re-use most of the KV cache during inference when the adapter is only used to generate part of the response, after which the base model takes over again.

Arrow & GenKnowSub

@TheTahaaa contributed not only support for Arrow, a dynamic routing algorithm between multiple loaded LoRAs in #2644, but also GenKnowSub, a technique built upon Arrow where the 'library' of LoRAs available to Arrow is first modified by subtracting general knowledge adapters (e.g., trained on subsets of Wikipedia) to enhance task-specific performance.

WaveFT

Thanks to @Bilican, Wavelet Fine-Tuning (WaveFT) was added to PEFT in #2560. This method trains sparse updates in the wavelet domain of residual matrices, which is especially parameter efficient. It is very interesting for image generation, as it promises to generate diverse outputs while preserving subject fidelity.

DeLoRA

Decoupled Low-rank Adaptation (DeLoRA) was added by @mwbini in #2780. This new PEFT method is similar to DoRA in so far as it decouples the angle and magnitude of the learned adapter weights. However, DeLoRA implements this in a way that promises to better prevent divergence. Moreover, it constrains the deviation of the learned weight by imposing an upper limit of the norm, which can be adjusted via the delora_lambda parameter.

OSF

Orthogonal Fine-Tuning (OSF) was added by @NikhilNayak-debug in #2685. By freezing the high-rank subspace of the targeted weight matrices and projecting gradient updates to a low-rank subspace, OSF achieves good performance on continual learning tasks. While it is a bit memory intensive for standard fine-tuning processes, it is definitely worth checking out on tasks where performance degradation of previously learned tasks is a concern.

Enhancements

Text generation benchmark

In #2525, @ved1beta added the text generation benchmark to PEFT. This is a framework to determine and compare metrics with regard to text generation of different PEFT methods, e.g. runtime and memory usage. Right now, this benchmark is still lacking experimental settings and a visualization, analogous to what we have in the MetaMathQA benchmark. If this is something that interests you, we encourage you to let us know or, even better, contribute to this benchmark.

Reliable interface for integrations

PEFT has integrations with other libraries like Transformers and Diffusers. To facilitate this integration, PEFT now provides a stable interface of functions that should be used if applicable. For example, the set_adapter function can be used to switch between PEFT adapters on the model, even if the model is not a PeftModel instance. We commit to keeping these functions backwards compatible, so it's safe for other libraries to build on top of those.

Handling of weight tying

Some Transformers models can have tied weights. This is especially prevalent when it comes to the embedding and the LM head. Currently, the way that this is handled in PEFT is not obvious. We thus drafted an issue to illustrate the intended behavior in #2864. This shows what our goal is, although not everything is implemented yet.

In #2803, @romitjain added the ensure_weight_tying argument to LoraConfig. This argument, if set to True, enforces weight tying of the modules targeted with modules_to_save. Thus, if embedding and LM head are tied, they will share weights, which is important to allow, for instance, weight merging. Therefore, for most users, we recommend to enable this setting if they want to fully fine-tune the embedding and LM head. For backwards compatability, the setting is off by default though.

Note that in accordance with #2864, the functionality of ensure_weight_tying=True will be expanded to also include trainable tokens (#2870) and LoRA (tbd.) in the future.

Support Conv1d and 1x1 Conv2 layers in LoHa and LoKr

@grewalsk extended LoHa and LoKr to support nn.Conv1d layers, as well as nn.Conv2d with 1x1 kernels, in #2515.

New prompt tuning initialization

Thanks to @macmacmacmac, we now have a new initialization option for prompt tuning, random discrete initialization (#2815). This option should generally work better than random initialization, as corroborated on our PEFT method comparison suite. Give it a try if you use prompt tuning.

Combining LoRA adapters with negative weights

If you use multiple LoRA adapters, you can merge them into a single adapter using model.add_weighted_adapter. However, so far, this only worked with positive weights per adapter. Thanks to @sambhavnoobcoder and @valteu, it is now possible to pass negative weights too.

Changes

Transformers compatibility

At the time of writing, the Transformers v5 release is imminent. This Transformers version will be incomptabile with PEFT < 0.18.0. If you plan to use Transformers v5 with PEFT, please upgrade PEFT to 0.18.0+.

Python version

This PEFT version no longer supports Python 3.9, which has reached its end of life. Please use Python 3.10+.

Updates to OFT

The OFT method has been updated to make it slightly faster and to stabilize the numerics in #2805. This means, however, that existing checkpoints may give slightly different results after upgrading to PEFT 0.18.0. Therefore, if you use OFT, we recommend to retrain the adapter.

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githubnemo, nirbo, and 31 other contributors
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0.17.1

21 Aug 10:04
@BenjaminBossan BenjaminBossan
v0.17.1
53c25fe
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0.17.1

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

01 Aug 17:08
@githubnemo githubnemo
v0.17.0
48f6493
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0.17.0: SHiRA, MiSS, LoRA for MoE, and more

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)

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Full Changelog: v0.16.0...v0.17.0

Contributors

githubnemo, BenjaminBossan, and 8 other contributors
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0.16.0: LoRA-FA, RandLoRA, C³A, and much more

03 Jul 15:35
@BenjaminBossan BenjaminBossan
v0.16.0
45996a1
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0.16.0: LoRA-FA, RandLoRA, C³A, and much more

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).

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githubnemo, BenjaminBossan, and 25 other contributors
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v0.15.2

15 Apr 15:28
@githubnemo githubnemo
v0.15.2
3c7b6e7
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v0.15.2

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

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v0.15.1

27 Mar 15:46
@githubnemo githubnemo
v0.15.1
c42eb22
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v0.15.1

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: v0.15.0...v0.15.1

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v0.15.0

19 Mar 15:05
@githubnemo githubnemo
v0.15.0
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v0.15.0

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

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githubnemo, Qubitium, and 16 other contributors
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Version 0.14.0: EVA, Context-aware Prompt Tuning, Bone, and more

06 Dec 11:42
@githubnemo githubnemo
v0.14.0
de88c70
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Version 0.14.0: EVA, Context-aware Prompt Tuning, Bone, and more

Highlights

peft-v0 14 0

New Methods

Context-aware Prompt Tuning

@tsachiblau added a new soft prompt method called Context-aware Prompt Tuning (CPT) which is a combination of In-Context Learning and Prompt Tuning in the sense that, for each training sample, it builds a learnable context from training examples in addition to the single training sample. Allows for sample- and parameter-efficient few-shot classification and addresses recency-bias.

Explained Variance Adaptation

@sirluk contributed a new LoRA initialization method called Explained Variance Adaptation (EVA). Instead of randomly initializing LoRA weights, this method uses SVD on minibatches of finetuning data to initialize the LoRA weights and is also able to re-allocate the ranks of the adapter based on the explained variance ratio (derived from SVD). Thus, this initialization method can yield better initial values and better rank distribution.

Bone

@JL-er added an implementation for Block Affine (Bone) Adaptation which utilizes presumed sparsity in the base layer weights to divide them into multiple sub-spaces that share a single low-rank matrix for updates. Compared to LoRA, Bone has the potential to significantly reduce memory usage and achieve faster computation.

Enhancements

PEFT now supports LoRAs for int8 torchao quantized models (check this and this notebook) . In addition, VeRA can now be used with 4 and 8 bit bitsandbytes quantization thanks to @ZiadHelal.

Hot-swapping of LoRA adapters is now possible using the hotswap_adapter function. Now you are able to load one LoRA and replace its weights in-place with the LoRA weights of another adapter which, in general, should be faster than deleting one adapter and loading the other adapter in its place. The feature is built so that no re-compilation of the model is necessary if torch.compile was called on the model (right now, this requires ranks and alphas to be the same for the adapters).

LoRA and IA³ now support Conv3d layers thanks to @jsilter, and @JINO-ROHIT added a notebook showcasing PEFT model evaluation using lm-eval-harness toolkit.

With the target_modules argument, you can specify which layers to target with the adapter (e.g. LoRA). Now you can also specify which modules not to target by using the exclude_modules parameter (thanks @JINO-ROHIT).

Changes

  • There have been made several fixes to the OFT implementation, among other things, to fix merging, which makes adapter weights trained with PEFT versions prior to this release incompatible (see #1996 for details).
  • Adapter configs are now forward-compatible by accepting unknown keys.
  • Prefix tuning was fitted to the DynamicCache caching infrastructure of transformers (see #2096). If you are using this PEFT version and a recent version of transformers with an old prefix tuning checkpoint, you should double check that it still works correctly and retrain it if it doesn't.
  • Added lora_bias parameter to LoRA layers to enable bias on LoRA B matrix. This is useful when extracting LoRA weights from fully fine-tuned parameters with bias vectors so that these can be taken into account.
  • #2180 provided a couple of bug fixes to LoKr (thanks @yaswanth19). If you're using LoKr, your old checkpoints should still work but it's recommended to retrain your adapter.
  • from_pretrained now warns the user if PEFT keys are missing.
  • Attribute access to modules in modules_to_save is now properly and transparently handled.
  • PEFT supports the changes to bitsandbytes 8bit quantization from the recent v0.45.0 release. To benefit from these improvements, we thus recommend to upgrade bitsandbytes if you're using QLoRA. Expect slight numerical differences in model outputs if you're using QLoRA with 8bit bitsandbytes quantization.

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githubnemo, jsilter, and 19 other contributors
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v0.13.2: Small patch release

11 Oct 11:45
@BenjaminBossan BenjaminBossan
v0.13.2
431c0e2

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v0.13.2: Small patch release

This patch release contains a small bug fix for an issue that prevented some LoRA checkpoints to be loaded correctly (mostly concerning stable diffusion checkpoints not trained with PEFT when loaded in diffusers, #2144).

Full Changelog: v0.13.1...v0.13.2

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v0.13.1: Small patch release

08 Oct 12:29
@BenjaminBossan BenjaminBossan
v0.13.1
b8da272

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v0.13.1: Small patch release

This patch release contains a small bug fix for the low_cpu_mem_usage=True option (#2113).

Full Changelog: v0.13.0...v0.13.1

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