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# Copyright 2024-present the HuggingFace Inc. team.
#
# 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.
from __future__ import annotations
import torch
from torch import nn
from peft.import_utils import is_bnb_available
from peft.optimizers import create_loraplus_optimizer
from .testing_utils import require_bitsandbytes, torch_device
if is_bnb_available():
import bitsandbytes as bnb
class SimpleNet(nn.Module):
def __init__(self, bias=True):
super().__init__()
self.embedding = nn.Embedding(100, 20)
self.layer_norm = nn.LayerNorm(20)
self.lin0 = nn.Linear(20, 20, bias=bias)
self.relu = nn.ReLU()
self.lin1 = nn.Linear(20, 16, bias=bias)
def forward(self, X):
X = self.lin0(self.layer_norm(self.embedding(X)))
X = self.relu(X)
X = self.lin1(X)
return X
@require_bitsandbytes
def test_lora_plus_helper_sucess():
model = SimpleNet()
optimizer_cls = bnb.optim.Adam8bit
lr = 5e-5
optim_config = {
"eps": 1e-6,
"betas": (0.9, 0.999),
"loraplus_weight_decay": 0.0,
}
loraplus_lr_ratio = 1.2
loraplus_lr_embedding = 1e-6
optim = create_loraplus_optimizer(
model=model,
optimizer_cls=optimizer_cls,
lr=lr,
loraplus_lr_ratio=loraplus_lr_ratio,
loraplus_lr_embedding=loraplus_lr_embedding,
**optim_config,
)
assert optim is not None
assert len(optim.param_groups) == 4
assert optim.param_groups[0]["lr"] == lr
assert optim.param_groups[1]["lr"] == loraplus_lr_embedding
assert optim.param_groups[2]["lr"] == optim.param_groups[3]["lr"] == (lr * loraplus_lr_ratio)
@require_bitsandbytes
def test_lora_plus_optimizer_sucess():
"""
Test if the optimizer is correctly created and step function runs without any exception
"""
optimizer_cls = bnb.optim.Adam8bit
optim_config = {
"eps": 1e-6,
"betas": (0.9, 0.999),
"loraplus_weight_decay": 0.0,
}
model: SimpleNet = SimpleNet().to(torch_device)
optim = create_loraplus_optimizer(
model=model,
optimizer_cls=optimizer_cls,
lr=5e-5,
loraplus_lr_ratio=1.2,
loraplus_lr_embedding=1e-6,
**optim_config,
)
loss = torch.nn.CrossEntropyLoss()
bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())
x = torch.randint(100, (2, 4, 10)).to(torch_device)
output = model(x).permute(0, 3, 1, 2)
label = torch.randint(16, (2, 4, 10)).to(torch_device)
loss_value = loss(output, label)
loss_value.backward()
optim.step()
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