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#
# 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 unittest.mock import MagicMock, call, patch
import torch
import torch.nn as nn
from lightning.pytorch.trainer.states import TrainerFn
from nemo.collections.llm import fn
from nemo.lightning.pytorch.callbacks.peft import PEFT, WrappedAdapterIO
from nemo.utils.callbacks.dist_ckpt_io import AsyncFinalizableCheckpointIO
class TestPEFT:
class DummyPEFT(PEFT):
def transform(self, module, name=None, prefix=None):
return module # No-op transform for testing
def freeze_model(self, module):
super().freeze_model(module)
self.is_called = True
return module
class DummyModel(nn.Module, fn.FNMixin):
def __init__(self):
super().__init__()
self.linear = nn.Linear(10, 10)
self.conv = nn.Conv2d(3, 3, 3)
def test_peft_call(self):
model = self.DummyModel()
peft = self.DummyPEFT()
transformed_model = peft(model)
assert (
hasattr(peft, "is_called") and peft.is_called == True
), "peft methods may subclass `freeze_model()`, so it must be called"
assert transformed_model.linear.weight.requires_grad == False
assert transformed_model.conv.weight.requires_grad == False
def test_linear_adapter(self):
from nemo.collections.llm.peft.lora import LinearAdapter
for has_bias in [True, False]:
linear = nn.Linear(10, 10, bias=has_bias)
linear_adapter = LinearAdapter(linear)
bias_in_state_dict = 'bias' in linear.state_dict()
if has_bias:
assert bias_in_state_dict
else:
assert not bias_in_state_dict
# Check if the state-dict keys changed
for key, val in linear.state_dict().items():
assert key in linear_adapter.state_dict(), f"Key {key} not found in LinearAdapter"
assert torch.equal(val, linear_adapter.state_dict()[key]), f"Key {key} diff. val in LinearAdapter"
# Make sure the additional keys are in the allow list
for key, val in linear_adapter.state_dict().items():
if key in linear.state_dict():
continue
assert key in ['lora_a.weight', 'lora_b.weight']
def test_linear_adapter_monkey_patch(self):
from copy import deepcopy
from nemo.collections.llm.peft.lora import patch_linear_module
linear = nn.Linear(10, 10)
state_init = deepcopy(linear.state_dict())
linear_adapter = patch_linear_module(linear)
# Check if the state-dict keys changed
for key, val in state_init.items():
assert key in linear_adapter.state_dict(), f"Key {key} not found in LinearAdapter"
assert torch.equal(val, linear_adapter.state_dict()[key]), f"Key {key} diff. val in LinearAdapter"
# Make sure the additional keys are in the allow list
for key, val in linear_adapter.state_dict().items():
if key in state_init:
continue
assert key in ['lora_a.weight', 'lora_b.weight']
state_dict = linear_adapter.state_dict()
for key in ['lora_a', 'lora_b']:
assert hasattr(linear_adapter, key), f"Expected {key} to be in module"
assert f'{key}.weight' in state_dict, f"Expected {key} to be in state dict"
assert getattr(linear_adapter, key).weight.requires_grad == True, "Expected {key} to require_grad"
def test_peft_setup(self):
peft = self.DummyPEFT()
trainer = MagicMock()
pl_module = MagicMock()
pl_module.model_transform = peft
peft.setup(trainer, pl_module, "fit")
assert isinstance(trainer.strategy._checkpoint_io, AsyncFinalizableCheckpointIO)
assert isinstance(trainer.strategy._checkpoint_io._checkpoint_io, WrappedAdapterIO)
assert peft.model_transform is not None
assert peft._needs_to_call is True
@patch('nemo.lightning.pytorch.callbacks.peft.logging')
def test_peft_on_train_epoch_start_with_adapter(self, mock_logging):
peft = self.DummyPEFT()
trainer = MagicMock()
pl_module = MagicMock()
pl_module.model_transform = peft
trainer.state.fn = TrainerFn.FITTING # Mock the trainer to be in FITTING state
peft.setup(trainer, pl_module, "fit")
assert peft.model_transform is not None
assert peft._needs_to_call is True
peft.wrapped_io = MagicMock()
peft.wrapped_io.adapter_ckpt_path = "dummy_path"
peft.wrapped_io.load_checkpoint.return_value = {"dummy_state": "dummy_value"}
peft.on_train_epoch_start(trainer, pl_module)
# Check for all expected log messages
mock_logging.info.assert_has_calls(
[
call("Loading adapters from dummy_path"),
call("Initializing model parallel"),
call("Setting up optimizers"),
],
any_order=True,
)
# Verify the number of calls
assert mock_logging.info.call_count == 3
trainer.strategy.load_model_state_dict.assert_called_once_with({"dummy_state": "dummy_value"}, strict=False)
trainer.strategy.init_model_parallel.assert_called_once()
trainer.strategy.setup_optimizers.assert_called_once_with(trainer)
def test_params_to_save(self):
# Create model and PEFT instance
model = self.DummyModel()
peft = self.DummyPEFT()
trainer = MagicMock()
trainer.lightning_module = model
# Freeze conv, keep linear trainable
model.conv.requires_grad_(False)
model.linear.requires_grad_(True)
# Call set_params_to_save
peft.set_params_to_save(trainer)
# Expected trainable parameter names
expected_trainable = {'linear.weight', 'linear.bias'}
# Check that params_to_save contains exactly the expected parameters
assert hasattr(peft, 'params_to_save'), "params_to_save not set"
assert (
peft.params_to_save == expected_trainable
), f"Expected trainable params {expected_trainable}, but got {peft.params_to_save}"
# Verify that the parameters marked as trainable actually require gradients
for name, param in model.named_parameters():
if name in peft.params_to_save:
assert param.requires_grad, f"Parameter {name} should require gradients"
else:
assert not param.requires_grad, f"Parameter {name} should not require gradients"
def test_params_to_save_batchnorm(self):
# Create model and PEFT instance
model = self.DummyModel()
model.bn = nn.BatchNorm2d(8)
peft = self.DummyPEFT()
trainer = MagicMock()
trainer.lightning_module = model
# Freeze everything
model.freeze()
# Call set_params_to_save
peft.set_params_to_save(trainer)
# Expect BN buffers to be saved
expected_trainable = {
'bn.running_mean',
'bn.running_var',
'bn.num_batches_tracked',
}
# Check that params_to_save contains exactly the expected parameters
assert hasattr(peft, 'params_to_save'), "params_to_save not set"
assert (
peft.params_to_save == expected_trainable
), f"Expected trainable params {expected_trainable}, but got {peft.params_to_save}"
# Verify that the parameters marked as trainable actually require gradients
for name, param in model.named_parameters():
if name in peft.params_to_save:
assert param.requires_grad, f"Parameter {name} should require gradients"
else:
assert not param.requires_grad, f"Parameter {name} should not require gradients"
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