# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""LoRA Callback class."""
from functools import wraps
from typing import List, Union
import torch
from cerebras.modelzoo.common.utils.model.lora import (
LoraConfig,
make_model_lora,
)
from cerebras.modelzoo.trainer.callbacks import Callback
[docs]class Lora(Callback):
"""Callback class that handles lorafying the model."""
def __init__(
self, lora_params: Union[dict, List[dict], LoraConfig, List[LoraConfig]]
):
"""
Args:
lora_params: The parameters to configure LoRA.
"""
self.lora_params = lora_params
[docs] def pre_setup(self, trainer):
model = trainer.callbacks["model"].model
if isinstance(model, torch.nn.Module):
def model_fn():
return make_model_lora(model, self.lora_params)
else:
@wraps(model)
def model_fn():
return make_model_lora(model(), self.lora_params)
trainer.callbacks["model"].model = model_fn