removing unused function

src/axolotl/monkeypatch/lora_kernels.py

@@ -4,13 +4,11 @@ import importlib
 import inspect
 import logging
 import types
-from typing import Type
 
 import torch
 from accelerate.logging import get_logger
 from peft import PeftModelForCausalLM
 from torch import nn
-from transformers import AutoConfig
 from transformers.modeling_utils import PreTrainedModel
 
 from axolotl.kernels.lora import (
@@ -97,45 +95,6 @@ def original_apply_o(self: nn.Module, hidden_states: torch.Tensor) -> torch.Tens
     return attn_output
 
 
-def get_attention_cls_from_config(cfg: DictDefault) -> Type[nn.Module]:
-    """
-    Get the appropriate attention class by inspecting the model config.
-    Uses dynamic import to support any model architecture that follows
-    the standard transformers naming convention.
-
-    Args:
-        cfg: Dictionary mapping `axolotl` config keys to values.
-
-    Returns:
-        The appropriate attention class for the model.
-
-    Raises:
-        ValueError: If `base_model` not specified or attention class cannot be imported
-        ImportError: If the model module or attention class doesn't exist
-    """
-    if "base_model" not in cfg:
-        raise ValueError("base_model must be specified in config")
-
-    # Get model config without loading the model
-    model_config = AutoConfig.from_pretrained(cfg["base_model"])
-    model_type = model_config.model_type
-
-    try:
-        # Dynamically import the module and attention class
-        module_path = f"transformers.models.{model_type}.modeling_{model_type}"
-        module = __import__(
-            module_path, fromlist=[f"{model_type.capitalize()}Attention"]
-        )
-        attention_cls = getattr(module, f"{model_type.capitalize()}Attention")
-
-        return attention_cls
-    except (ImportError, AttributeError) as e:
-        raise ValueError(
-            f"Could not import attention class for model_type: {model_type}. "
-            f"Error: {str(e)}"
-        ) from e
-
-
 # pylint: disable=protected-access
 def patch_self_attn_lora(model: PreTrainedModel):
     """