wehub-resource-sync
233 lines
10 KiB
Python
233 lines
10 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import os
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import re
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import unittest
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from tempfile import TemporaryDirectory
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import numpy as np
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import paddle
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from parameterized import parameterized
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from paddlenlp.peft.dislora import DisLoRAConfig, DisLoRALinear, DisLoRAModel
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from paddlenlp.transformers import AutoModel, BertModel
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class TestDisLoRALayer(unittest.TestCase):
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def test_r_raise_exception(self):
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with self.assertRaises(ValueError):
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DisLoRALinear(in_features=16, out_features=8, r=0, dislora_alpha=8)
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def test_forward(self):
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# r=8, dislora_alpha=12 (1.5 * 8)
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dislora_layer = DisLoRALinear(in_features=16, out_features=8, r=8, dislora_dropout=0.1, dislora_alpha=12)
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x = paddle.randn([2, 4, 16], "float32")
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output = dislora_layer(x)
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# Check the trainable DisLoRA parameters (related to W_res)
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self.assertFalse(dislora_layer.Direc_Ur.weight.stop_gradient)
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self.assertFalse(dislora_layer.Direc_Vhr.weight.stop_gradient)
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self.assertFalse(dislora_layer.Direc_Sr.stop_gradient)
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self.assertFalse(dislora_layer.Direc_Stsd.stop_gradient)
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# Check the frozen TSD parameters
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self.assertTrue(dislora_layer.Direc_Utsd.weight.stop_gradient)
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self.assertTrue(dislora_layer.Direc_Vhtsd.weight.stop_gradient)
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# Check the frozen main branch weights W_prin
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self.assertTrue(dislora_layer.weight.stop_gradient)
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# Check the bias parameters (by default, they should be trainable, but this depends on the configuration)
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if dislora_layer.bias is not None:
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self.assertFalse(dislora_layer.bias.stop_gradient)
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self.assertEqual(output.shape, [2, 4, 8])
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def test_train_eval(self):
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x = paddle.randn([2, 4, 16], "float32")
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dislora_layer = DisLoRALinear(in_features=16, out_features=8, r=8, dislora_alpha=12)
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dislora_layer.train()
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train_result = dislora_layer(x)
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train_weight = copy.deepcopy(dislora_layer.weight)
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dislora_layer.eval()
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eval_result = dislora_layer(x)
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eval_weight = dislora_layer.weight
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self.assertTrue(paddle.allclose(train_result, eval_result))
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self.assertTrue(paddle.allclose(train_weight, eval_weight))
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def test_save_load(self):
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with TemporaryDirectory() as tempdir:
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dislora_layer = DisLoRALinear(in_features=16, out_features=8, r=8, dislora_alpha=12)
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weights_path = os.path.join(tempdir, "model.pdparams")
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paddle.save(dislora_layer.state_dict(), weights_path)
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new_dislora_layer = DisLoRALinear(in_features=16, out_features=8, r=8, dislora_alpha=12)
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state_dict = paddle.load(weights_path)
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new_dislora_layer.set_dict(state_dict)
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x = paddle.randn([2, 4, 16], "float32")
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self.assertTrue(paddle.allclose(new_dislora_layer(x), dislora_layer(x)))
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def test_load_regular_linear(self):
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with TemporaryDirectory() as tempdir:
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regular_linear = paddle.nn.Linear(in_features=16, out_features=12)
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weights_path = os.path.join(tempdir, "model.pdparams")
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paddle.save(regular_linear.state_dict(), weights_path)
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state_dict = paddle.load(weights_path)
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# should be identical to regular linear
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dislora_layer_r8 = DisLoRALinear(
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in_features=16, out_features=12, r=8, dislora_alpha=12, init_lora_weights=False
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)
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dislora_layer_r10 = DisLoRALinear(
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in_features=16, out_features=12, r=10, dislora_alpha=15, init_lora_weights=False
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)
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# Load regular linear weights first
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filtered_state_dict = {k: v for k, v in state_dict.items() if k in ["weight", "bias"]}
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dislora_layer_r8.set_dict(filtered_state_dict)
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dislora_layer_r10.set_dict(filtered_state_dict)
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# Then perform SVD initialization
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dislora_layer_r8._init_lora_weights()
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dislora_layer_r10._init_lora_weights()
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x = paddle.randn([2, 4, 16], "float32")
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diff_r8 = paddle.abs(dislora_layer_r8(x) - regular_linear(x))
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print(f"R8 - Max diff: {paddle.max(diff_r8).item():.6e}, Mean diff: {paddle.mean(diff_r8).item():.6e}")
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self.assertTrue(paddle.allclose(dislora_layer_r8(x), regular_linear(x), atol=2e-3))
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# Update variable name
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self.assertTrue(paddle.allclose(dislora_layer_r10(x), regular_linear(x), atol=2e-3))
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class TestDisLoRAModel(unittest.TestCase):
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def test_dislora_model_restore(self):
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dislora_config = DisLoRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"],
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r=8,
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dislora_alpha=12,
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base_model_name_or_path="__internal_testing__/tiny-random-bert",
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)
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model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 20]))
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model.eval()
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original_results_1 = model(input_ids)
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dislora_model = DisLoRAModel(model, dislora_config)
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restored_model = dislora_model.restore_original_model()
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restored_model.eval()
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original_results_2 = restored_model(input_ids)
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self.assertIsNotNone(original_results_1)
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self.assertIsNotNone(original_results_2)
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self.assertIsInstance(restored_model, BertModel)
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self.assertTrue(paddle.allclose(original_results_1[0], original_results_2[0]))
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@parameterized.expand([(None,), ("all",), ("dislora",)])
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def test_dislora_model_constructor(self, bias):
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dislora_config = DisLoRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"],
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r=8,
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dislora_alpha=12,
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trainable_bias=bias,
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base_model_name_or_path="__internal_testing__/tiny-random-bert",
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)
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model = AutoModel.from_pretrained(
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"__internal_testing__/tiny-random-bert", hidden_dropout_prob=0, attention_probs_dropout_prob=0
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)
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dislora_model = DisLoRAModel(model, dislora_config)
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dislora_model.mark_only_dislora_as_trainable()
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for name, weight in dislora_model.state_dict().items():
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if any([re.fullmatch(target_module, name) for target_module in dislora_config.target_modules]):
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if any(
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[dislora_param in name for dislora_param in ["Direc_Ur", "Direc_Sr", "Direc_Vhr", "Direc_Stsd"]]
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):
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self.assertFalse(weight.stop_gradient)
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elif any([tsd_param in name for tsd_param in ["Direc_Utsd", "Direc_Vhtsd"]]):
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self.assertTrue(weight.stop_gradient)
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elif "bias" in name and bias in ["dislora", "all"]:
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self.assertFalse(weight.stop_gradient)
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else:
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self.assertTrue(weight.stop_gradient)
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else:
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if "bias" in name and bias == "all":
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self.assertFalse(weight.stop_gradient)
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else:
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self.assertTrue(weight.stop_gradient)
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input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 20]))
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dislora_model.train()
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train_forward_results = dislora_model(input_ids)
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self.assertIsNotNone(train_forward_results)
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dislora_model.eval()
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eval_forward_results = dislora_model(input_ids)
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self.assertIsNotNone(eval_forward_results)
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self.assertTrue(paddle.allclose(train_forward_results[0], eval_forward_results[0]))
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def test_dislora_model_save_load(self):
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with TemporaryDirectory() as tempdir:
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input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 20]))
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dislora_config = DisLoRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"],
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r=8,
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dislora_alpha=12,
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base_model_name_or_path="__internal_testing__/tiny-random-bert",
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)
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model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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dislora_model = DisLoRAModel(model, dislora_config)
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dislora_model.eval()
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original_results = dislora_model(input_ids)
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dislora_model.save_pretrained(tempdir)
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loaded_dislora_model = DisLoRAModel.from_pretrained(model, tempdir)
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loaded_dislora_model.eval()
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loaded_results = loaded_dislora_model(input_ids)
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self.assertTrue(paddle.allclose(original_results[0], loaded_results[0]))
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config_loaded_dislora_model = DisLoRAModel.from_pretrained(model, tempdir, dislora_config=dislora_config)
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config_loaded_dislora_model.eval()
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config_loaded_results = config_loaded_dislora_model(input_ids)
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self.assertTrue(paddle.allclose(original_results[0], config_loaded_results[0]))
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def test_dislora_module_raise_exception(self):
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dislora_config = DisLoRAConfig(
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target_modules=[".*norm1.*"],
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r=8,
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dislora_alpha=12,
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base_model_name_or_path="__internal_testing__/tiny-random-bert",
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)
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model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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with self.assertRaises(ValueError):
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DisLoRAModel(model, dislora_config)
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class TestDisLoRAConfig(unittest.TestCase):
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def test_save_load(self):
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with TemporaryDirectory() as tempdir:
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# Set r and dislora_alpha explicitly
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dislora_config = DisLoRAConfig(target_modules=["test"], r=8, dislora_alpha=12)
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dislora_config.save_pretrained(tempdir)
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loaded_dislora_config = DisLoRAConfig.from_pretrained(tempdir)
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self.assertEqual(dislora_config.r, loaded_dislora_config.r)
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self.assertEqual(dislora_config.dislora_alpha, loaded_dislora_config.dislora_alpha)
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self.assertEqual(dislora_config.dash_flag, loaded_dislora_config.dash_flag)
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self.assertEqual(dislora_config.s_tsd, loaded_dislora_config.s_tsd)
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