deps-py(deps): bump peft from 0.13.2 to 0.19.1 in /apps

[dependabot]

Bumps peft from 0.13.2 to 0.19.1.

Release notes

Sourced from peft's releases.

v0.19.1

A small patch release containing these fixes:

• #3161
• #3165

Full Changelog: huggingface/peft@v0.19.0...v0.19.1

v0.19.0

Highlights

This PEFT release contains no less than nine new PEFT methods, described below. It also contains numerous enhancements that should make PEFT more useful to many users.

New Methods

GraLoRA

@​yeonjoon-jung01 added "GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning" to PEFT (#2851). This method subdivides the base weight into smaller blocks and applies LoRA to those. This more granular adaptation promises to increase expressiveness and improve performance, especially at higher ranks (64+), closing the gap to full fine-tuning.

BD-LoRA

@​Conzel contributed BD-LoRA: "Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving" (#2895). With BD-LoRA, the LoRA weights are implemented in a block-diagonal way. This allows to reduce communication overhead when using tensor parallelism (TP) and thus faster serving.

There is an experiment branch for BD-LoRA support in vLLM: vllm-project/vllm#28136.

Cartridges

Thanks to @​kashif, PEFT now also supports Cartridges (#2953). The main purpose of this method is to train a prefix to compress a long context to a short size and thus save on tokens. On a low level, this is similar to prefix tuning. The PR also added an example recipe to quickly get started.

PVeRA

"PVeRA: Probabilistic Vector-Based Random Matrix Adaptation" was added to PEFT by @​leofillioux in #2952. It is an extension of VeRA, a PEFT method that uses weight sharing between layers to be especially parameter efficient. PVeRA builds on top of that by adding a probabilistic element, sampling from the shared parameters and promising better performance overall.

PSOFT

@​fei407 added PSOFT, "Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation", to PEFT in #3037. Orthogonal fine-tuning techniques like OFT and BOFT are good at preserving the structure and thus capabilities of the underlying base model. PSOFT improves efficiency of this technique by constraining the adaptation to low-rank principal subspace.

Lily

@​yibozhong added Lily: "Low-Rank Interconnected Adaptation across Layers" to PEFT in #2563. Lily is on the surface similar to LoRA but has a sophisticated parameter sharing scheme. The A parameters are shared blockwise (e.g. 4 consecutive q_proj layers share the same A). There is a pool of B parameters that is shared globally, the actual B's are chosen in a data-dependent way through a router. This allows Lily to use higher ranks than LoRA while maintaining a low trainable parameter count.

PEANuT

In #3084, "PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers" was added to PEFT, again by @​yibozhong. PEANuT adds a small, neural net (so called weight-aware neural tweakers) to the base model. Compared to LoRA, this increases expressivity for the same trainable parameter count or allows to greatly lower the parameter count without sacrificing expressivity. This comes at the expensive of a higher memory requirement for the same parameter count and decreased speed.

TinyLoRA

We have another serial contributor in @​kashif, who also contributed TinyLoRA: "Learning to Reason in 13 Parameters" in #3024. This is a PEFT method that allows to train an extremely small number of parameters, much lower than what could be achieved even with LoRA rank 1. The paper shows that in particular with reinforcement learning, it can often be enough to train just a few parameters to achieve good results.

... (truncated)

Commits

• ba6a190 Patch release 0.19.1 (#3168)
• 6d5a6f4 Release 0.19.0 (#3155)
• 076214c FIX Explicit weight conversion map for Mixtral (#3146)
• b386d59 ENH Support models with low precision float dtypes (#3055)
• cf9709c FIX Correct scaling with DARE merging (#3152)
• efe0fe6 Bump the third-party-actions group with 8 updates (#3125)
• 07a1db6 ENH Checkpoint saving with Tensor Parallel (#3096)
• f62f54b TST Enable arrow xpu tests (#3145)
• 9846593 CI Move slow EVA tests to nightly GPU CI (#3108)
• 12d872a FIX CI Remove invalid arg in nightly GPU test call (#3104)
• Additional commits viewable in compare view

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