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Update dependency peft to v0.15.2 #61

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This PR contains the following updates:

Package Update Change
peft minor ==0.3.0 -> ==0.15.2

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Release Notes

huggingface/peft (peft)

v0.15.2

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

v0.15.1

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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

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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

New Contributors

Full Changelog: huggingface/peft@v0.14.0...v0.15.0

v0.14.0: Version 0.14.0: EVA, Context-aware Prompt Tuning, Bone, and more

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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.

What's Changed

New Contributors

Full Changelog: huggingface/peft@v0.13.2...v0.14.0

v0.13.2: : Small patch release

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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: huggingface/peft@v0.13.1...v0.13.2

v0.13.1: : Small patch release

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

Full Changelog: huggingface/peft@v0.13.0...v0.13.1

v0.13.0: : LoRA+, VB-LoRA, and more

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peft-v0 13 0

Highlights

New methods

LoRA+

@​kallewoof added LoRA+ to PEFT (#​1915). This is a function that allows to initialize an optimizer with settings that are better suited for training a LoRA adapter.

VB-LoRA

@​leo-yangli added a new method to PEFT called VB-LoRA (#​2039). The idea is to have LoRA layers be composed from a single vector bank (hence "VB") that is shared among all layers. This makes VB-LoRA extremely parameter efficient and the checkpoints especially small (comparable to the VeRA method), while still promising good fine-tuning performance. Check the VB-LoRA docs and example.

Enhancements

New Hugging Face team member @​ariG23498 added the helper function rescale_adapter_scale to PEFT (#​1951). Use this context manager to temporarily increase or decrease the scaling of the LoRA adapter of a model. It also works for PEFT adapters loaded directly into a transformers or diffusers model.

@​ariG23498 also added DoRA support for embedding layers (#​2006). So if you're using the use_dora=True option in the LoraConfig, you can now also target embedding layers.

For some time now, we support inference with batches that are using different adapters for different samples, so e.g. sample 1-5 use "adapter1" and samples 6-10 use "adapter2". However, this only worked for LoRA layers so far. @​saeid93 extended this to also work with layers targeted by modules_to_save (#​1990).

When loading a PEFT adapter, you now have the option to pass low_cpu_mem_usage=True (#​1961). This will initialize the adapter with empty weights ("meta" device) before loading the weights instead of initializing on CPU or GPU. This can speed up loading PEFT adapters. So use this option especially if you have a lot of adapters to load at the same time or if these adapters are very big. Please let us know if you encounter issues with this option, as we may make this the default in the future.

Changes

Safe loading of PyTorch weights

Unless indicated otherwise, PEFT adapters are saved and loaded using the secure safetensors format. However, we also support the PyTorch format for checkpoints, which relies on the inherently insecure pickle protocol from Python. In the future, PyTorch will be more strict when loading these files to improve security by making the option weights_only=True the default. This is generally recommended and should not cause any trouble with PEFT checkpoints, which is why with this release, PEFT will enable this by default. Please open an issue if this causes trouble.

What's Changed

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

@red-hat-konflux
Update dependency peft to v0.15.2
a3dffa8 
Signed-off-by: red-hat-konflux <126015336+red-hat-konflux[bot]@users.noreply.github.com>
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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
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