# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # 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. import copy import os import re import unittest from tempfile import TemporaryDirectory import numpy as np import paddle from parameterized import parameterized from paddlenlp.peft.lora import LoRAConfig, LoRALinear, LoRAModel from paddlenlp.transformers import AutoModel, BertModel class TestMosLoraLayer(unittest.TestCase): def test_r_raise_exception(self): with self.assertRaises(ValueError): LoRALinear(in_features=16, out_features=8, r=0, lora_dropout=0.1, lora_alpha=8, lora_use_mixer=True) def test_forward(self): lora_layer = LoRALinear( in_features=16, out_features=8, r=4, lora_dropout=0.1, lora_alpha=8, lora_use_mixer=True ) x = paddle.randn([2, 4, 16], "float32") output = lora_layer(x) self.assertFalse(lora_layer.lora_A.stop_gradient) self.assertFalse(lora_layer.lora_B.stop_gradient) self.assertTrue(lora_layer.weight.stop_gradient) self.assertFalse(lora_layer.bias.stop_gradient) self.assertEqual(output.shape, [2, 4, 8]) def test_train_eval(self): x = paddle.randn([2, 4, 16], "float32") lora_layer = LoRALinear(in_features=16, out_features=8, r=4, lora_use_mixer=True) lora_layer.train() train_result = lora_layer(x) train_weight = copy.deepcopy(lora_layer.weight) # deep copy since this is a pointer lora_layer.eval() eval_result = lora_layer(x) eval_weight = lora_layer.weight self.assertTrue(paddle.allclose(train_result, eval_result)) self.assertTrue(paddle.allclose(train_weight, eval_weight)) def test_save_load(self): with TemporaryDirectory() as tempdir: lora_layer = LoRALinear(in_features=16, out_features=8, r=4, lora_use_mixer=True) weights_path = os.path.join(tempdir, "model.pdparams") paddle.save(lora_layer.state_dict(), weights_path) new_lora_layer = LoRALinear(in_features=16, out_features=8, r=4, lora_use_mixer=True) state_dict = paddle.load(weights_path) new_lora_layer.set_dict(state_dict) x = paddle.randn([2, 4, 16], "float32") self.assertTrue(paddle.allclose(new_lora_layer(x), lora_layer(x))) def test_load_regular_linear(self): with TemporaryDirectory() as tempdir: regular_linear = paddle.nn.Linear(in_features=16, out_features=8) weights_path = os.path.join(tempdir, "model.pdparams") paddle.save(regular_linear.state_dict(), weights_path) state_dict = paddle.load(weights_path) # should be identical to regular linear lora_layer_r8 = LoRALinear(in_features=16, out_features=8, r=8, lora_use_mixer=True) lora_layer_r4 = LoRALinear(in_features=16, out_features=8, r=4, lora_use_mixer=True) lora_layer_r8.set_dict(state_dict) lora_layer_r4.set_dict(state_dict) x = paddle.randn([2, 4, 16], "float32") self.assertTrue(paddle.allclose(lora_layer_r8(x), regular_linear(x))) self.assertTrue(paddle.allclose(lora_layer_r4(x), regular_linear(x))) def test_merge(self): lora_layer_r8 = LoRALinear(in_features=16, out_features=8, r=8, lora_use_mixer=True) lora_layer_r8.merge() def test_unmerge(self): lora_layer_r8 = LoRALinear(in_features=16, out_features=8, r=8, lora_use_mixer=True) lora_layer_r8.merged = True lora_layer_r8.unmerge() lora_layer_r8 = LoRALinear(in_features=16, out_features=8, r=8) lora_layer_r8.merged = True lora_layer_r8.unmerge() class TestMosLoraModel(unittest.TestCase): def test_lora_model_restore(self): lora_config = LoRAConfig( target_modules=[".*q_proj.*", ".*v_proj.*"], r=4, lora_alpha=8, enable_lora_list=[None, [True, False]], head_dim=2, lora_use_mixer=True, ) model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert") input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 20])) model.eval() original_results_1 = model(input_ids) lora_model = LoRAModel(model, lora_config) restored_model = lora_model.restore_original_model() restored_model.eval() original_results_2 = restored_model(input_ids) self.assertIsNotNone(original_results_1) self.assertIsNotNone(original_results_2) self.assertIsInstance(restored_model, BertModel) self.assertTrue(paddle.allclose(original_results_1[0], original_results_2[0])) def test_parallel_support(self): lora_config = LoRAConfig( target_modules=[".*q_proj.*", ".*v_proj.*"], r=4, lora_alpha=8, enable_lora_list=[None, [True, False]], head_dim=2, lora_use_mixer=True, tensor_parallel_degree=2, ) model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert") model.eval() with self.assertRaises(NotImplementedError): LoRAModel(model, lora_config) @parameterized.expand([(None,), ("all",), ("lora",)]) def test_lora_model_constructor(self, bias): lora_config = LoRAConfig( target_modules=[".*q_proj.*", ".*v_proj.*"], r=4, lora_alpha=8, enable_lora_list=[None, [True, False]], trainable_bias=bias, head_dim=2, lora_use_mixer=True, ) # turn off plm dropout for to test train vs test model = AutoModel.from_pretrained( "__internal_testing__/tiny-random-bert", hidden_dropout_prob=0, attention_probs_dropout_prob=0 ) lora_model = LoRAModel(model, lora_config) lora_model.mark_only_lora_as_trainable() for name, weight in lora_model.state_dict().items(): if any([re.fullmatch(target_module, name) for target_module in lora_config.target_modules]): if "lora" in name: self.assertFalse(weight.stop_gradient) elif "bias" in name and bias in ["lora", "all"]: self.assertFalse(weight.stop_gradient) else: self.assertTrue(weight.stop_gradient) else: if "bias" in name and bias == "all": self.assertFalse(weight.stop_gradient) else: self.assertTrue(weight.stop_gradient) input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 20])) lora_model.train() train_forward_results = lora_model(input_ids) self.assertIsNotNone(train_forward_results) lora_model.eval() eval_forward_results = lora_model(input_ids) self.assertIsNotNone(eval_forward_results) self.assertTrue(paddle.allclose(train_forward_results[0], eval_forward_results[0])) def test_lora_model_save_load(self): with TemporaryDirectory() as tempdir: input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 20])) lora_config = LoRAConfig( target_modules=[".*q_proj.*", ".*v_proj.*"], r=4, lora_alpha=8, lora_use_mixer=True ) model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert") lora_model = LoRAModel(model, lora_config) lora_model.eval() original_results = lora_model(input_ids) lora_model.save_pretrained(tempdir) loaded_lora_model = LoRAModel.from_pretrained(model, tempdir) loaded_lora_model.eval() loaded_results = loaded_lora_model(input_ids) self.assertTrue(paddle.allclose(original_results[0], loaded_results[0])) config_loaded_lora_model = LoRAModel.from_pretrained(model, tempdir, lora_config=lora_config) config_loaded_lora_model.eval() config_loaded_results = config_loaded_lora_model(input_ids) self.assertTrue(paddle.allclose(original_results[0], config_loaded_results[0])) def test_lora_module_raise_exception(self): lora_config = LoRAConfig( target_modules=[".*norm1.*"], r=4, lora_alpha=8, enable_lora_list=None, lora_use_mixer=True ) model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert") with self.assertRaises(ValueError): LoRAModel(model, lora_config) class TestMosLoRAConfig(unittest.TestCase): def test_save_load(self): with TemporaryDirectory() as tempdir: lora_config = LoRAConfig() lora_config.save_pretrained(tempdir) loaded_lora_config = LoRAConfig.from_pretrained(tempdir) self.assertEqual(lora_config, loaded_lora_config) if __name__ == "__main__": unittest.main()