wehub-resource-sync
204 lines
8.6 KiB
Python
204 lines
8.6 KiB
Python
# Copyright (c) 2022 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 NamedTemporaryFile, TemporaryDirectory
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import numpy as np
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import paddle
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from paddle import nn
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from parameterized import parameterized
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from paddlenlp.peft.vera import VeRAConfig, VeRALinear, VeRAModel
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from paddlenlp.transformers import AutoModel
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class TestVeraLayer(unittest.TestCase):
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def test_r_raise_exception(self):
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with self.assertRaises(ValueError):
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VeRALinear(
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in_features=16,
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out_features=16,
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r=0,
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vera_dropout=0.1,
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vera_alpha=4,
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base_linear_module=nn.Linear(in_features=16, out_features=16),
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)
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def test_forward(self):
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vera_layer = VeRALinear(
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in_features=16,
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out_features=16,
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r=4,
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vera_dropout=0.1,
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vera_alpha=4,
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base_linear_module=nn.Linear(16, 16),
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pissa_init=True,
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)
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x = paddle.randn([2, 4, 16], "float32")
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output = vera_layer(x)
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self.assertFalse(vera_layer.vera_b.stop_gradient)
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self.assertFalse(vera_layer.vera_d.stop_gradient)
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self.assertTrue(vera_layer.weight.stop_gradient)
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self.assertFalse(vera_layer.bias.stop_gradient)
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self.assertEqual(output.shape, [2, 4, 16])
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def test_train_eval(self):
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x = paddle.randn([2, 4, 16], "float32")
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vera_layer = VeRALinear(
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in_features=16, out_features=16, r=4, base_linear_module=nn.Linear(in_features=16, out_features=16)
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)
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vera_layer.train()
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train_result = vera_layer(x)
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train_weight = copy.deepcopy(vera_layer.weight) # deep copy since this is a pointer
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vera_layer.eval()
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eval_result = vera_layer(x)
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eval_weight = vera_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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vera_layer = VeRALinear(
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in_features=16, out_features=16, r=4, base_linear_module=nn.Linear(in_features=16, out_features=16)
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)
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weights_path = os.path.join(tempdir, "model.pdparams")
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paddle.save(vera_layer.state_dict(), weights_path)
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new_vera_layer = VeRALinear(
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in_features=16, out_features=16, r=4, base_linear_module=nn.Linear(in_features=16, out_features=16)
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)
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state_dict = paddle.load(weights_path)
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new_vera_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_vera_layer(x), vera_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=16)
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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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vera_layer_r8 = VeRALinear(
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in_features=16, out_features=16, r=8, base_linear_module=nn.Linear(in_features=16, out_features=16)
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)
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vera_layer_r4 = VeRALinear(
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in_features=16, out_features=16, r=4, base_linear_module=nn.Linear(in_features=16, out_features=16)
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)
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vera_layer_r8.set_dict(state_dict)
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vera_layer_r4.set_dict(state_dict)
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x = paddle.randn([2, 4, 16], "float32")
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self.assertTrue(paddle.allclose(vera_layer_r8(x), regular_linear(x)))
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self.assertTrue(paddle.allclose(vera_layer_r4(x), regular_linear(x)))
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class TestVeraModel(unittest.TestCase):
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@parameterized.expand([(None,), ("all",), ("vera",)])
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def test_vera_model_constructor(self, bias):
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vera_config = VeRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"], r=4, vera_alpha=4, head_dim=2, pissa_init=True
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)
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# turn off plm dropout for to test train vs test
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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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vera_model = VeRAModel(model, vera_config)
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vera_model.mark_only_vera_as_trainable()
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for name, weight in vera_model.state_dict().items():
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if any([re.fullmatch(target_module, name) for target_module in vera_config.target_modules]):
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if "vera_b" in name or "vera_d" in name:
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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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vera_model.train()
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train_forward_results = vera_model(input_ids)
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self.assertIsNotNone(train_forward_results)
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vera_model.eval()
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eval_forward_results = vera_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_vera_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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vera_config = VeRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"],
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r=4,
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vera_alpha=4,
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)
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model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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vera_model = VeRAModel(model, vera_config)
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vera_model.eval()
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original_results = vera_model(input_ids)
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vera_model.save_pretrained(tempdir)
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loaded_vera_model = VeRAModel.from_pretrained(model, tempdir)
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loaded_vera_model.eval()
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loaded_results = loaded_vera_model(input_ids)
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self.assertTrue(paddle.allclose(original_results[0], loaded_results[0]))
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config_loaded_vera_model = VeRAModel.from_pretrained(model, tempdir, vera_config=vera_config)
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config_loaded_vera_model.eval()
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config_loaded_results = config_loaded_vera_model(input_ids)
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self.assertTrue(paddle.allclose(original_results[0], config_loaded_results[0]))
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def test_restore_original_model(self):
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vera_config = VeRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"],
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r=4,
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vera_alpha=4,
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)
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model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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vera_model = VeRAModel(model, vera_config)
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with self.assertRaises(NotImplementedError):
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vera_model.restore_original_model()
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def test_vera_module_raise_exception(self):
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vera_config = VeRAConfig(target_modules=[".*norm1.*"], r=4, vera_alpha=4)
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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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VeRAModel(model, vera_config)
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def test_pissa_raise_exception(self):
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vera_config = VeRAConfig(target_modules=[".*q_proj.*"], r=4, vera_alpha=8, pissa_init=True)
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model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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with self.assertRaises(AssertionError):
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VeRAModel(model, vera_config)
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class TestVeRAConfig(unittest.TestCase):
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def test_save_load(self):
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with TemporaryDirectory() as tempdir:
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vera_config = VeRAConfig()
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vera_config.save_pretrained(tempdir)
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loaded_vera_config = VeRAConfig.from_pretrained(tempdir)
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self.assertEqual(vera_config, loaded_vera_config)
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def test_save_load_err(self):
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with NamedTemporaryFile("w+t") as f:
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with self.assertRaises(ValueError):
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VeRAConfig.from_pretrained(f.name)
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def test_save_pretrained_file_error(self):
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with NamedTemporaryFile("w+t") as f:
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vera_config = VeRAConfig()
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with self.assertRaises(AssertionError):
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vera_config.save_pretrained(f.name)
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