[BUG] Issue with using `rank_pattern` and `alpha_pattern` together in `LoraConfig`

[sirluk]

System Info

peft==0.13.2

Who can help?

@BenjaminBossan

Information

• [X] The official example scripts
• [ ] My own modified scripts

Tasks

• [X] An officially supported task in the examples folder
• [ ] My own task or dataset (give details below)

Reproduction

Threre is an issue in the _create_and_replace method of LoraModel when rank_pattern and alpha_pattern are both set in LoraConfig.

The issue is relating to this line which merges keys from rank_pattern and alpha_pattern to get the target_name_key to retrieve from both dicts.

  # Regexp matching - Find key which matches current target_name in patterns provided
  pattern_keys = list(chain(lora_config.rank_pattern.keys(), lora_config.alpha_pattern.keys()))
  target_name_key = next(filter(lambda key: re.match(rf".*\.{key}$", current_key), pattern_keys), current_key)
  r = lora_config.rank_pattern.get(target_name_key, lora_config.r)
  alpha = lora_config.alpha_pattern.get(target_name_key, lora_config.lora_alpha)

If for example rank_pattern is defined with a more general substring (i.e. matching more layers) than alpha_pattern the appropriate value from alpha_pattern is not retrieved. I assume it is the other way around as well.

You can find a minimal example to reproduce the issue here:

from peft import get_peft_model, LoraConfig
from transformers import AutoModelForCausalLM

model_name = "openai-community/gpt2"
target_modules = ["c_attn"]

model = AutoModelForCausalLM.from_pretrained(model_name)
peft_config = LoraConfig(
    r=8,
    lora_alpha=1,
    target_modules=target_modules,
    alpha_pattern={"h.8.attn.c_attn": 2},
    rank_pattern={"c_attn": 8},
)
peft_model = get_peft_model(model, peft_config)
print("scaling (expected alpha/r=0.25):", peft_model.model.transformer.h[8].attn.c_attn.scaling)

model = AutoModelForCausalLM.from_pretrained(model_name)
peft_config = LoraConfig(
    r=8,
    lora_alpha=1,
    target_modules=target_modules,
    alpha_pattern={"h.8.attn.c_attn": 2},
)
peft_model = get_peft_model(model, peft_config)
print("scaling (expected alpha/r=0.25):", peft_model.model.transformer.h[8].attn.c_attn.scaling)

Expected behavior

I would expect both print statements to print the same value 2/8=0.25. However we see that when rank_pattern is defined, layer "h.8.attn.c_attn" is not assigned the correct scaling value. I would have two suggestions to fix this:

1. Just run next(filter(lambda key: re.match(rf".*\.{key}$", current_key), pattern_keys), current_key)twice, once for rank_pattern keys and once for alpha_pattern keys.
2. Harmonize both dicts in the post_init method of LoraConfig to ensure their keys are both defined at the same granularity.

[BenjaminBossan]

Thanks a lot for investigating this bug, providing a reproducer, and even 2 suggestions to solve this. Option 1 reads like the simpler solution to me. LMK if you are interested in providing a PR to fix this.

[sirluk]

thanks for the feedback! Sounds good, I will create a PR for option 1