wehub-resource-sync
152 lines
4.5 KiB
Python
152 lines
4.5 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 unittest
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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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class MyModel(nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(100, 300)
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def forward(self, x):
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return self.linear(x)
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@paddle.no_grad()
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def state_dict(
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self,
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destination=None,
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include_sublayers=True,
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structured_name_prefix="",
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use_hook=True,
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keep_vars=True,
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):
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st = super().state_dict(
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destination=destination,
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include_sublayers=include_sublayers,
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structured_name_prefix=structured_name_prefix,
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use_hook=use_hook,
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keep_vars=keep_vars,
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)
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st["linear.new_weight"] = paddle.transpose(
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st.pop("linear.weight"), [1, 0]
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)
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return st
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@paddle.no_grad()
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def set_state_dict(self, state_dict, use_structured_name=True):
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state_dict["linear.weight"] = paddle.transpose(
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state_dict.pop("linear.new_weight"), [1, 0]
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)
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return super().set_state_dict(state_dict)
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class MyModel2(nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(100, 300)
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def forward(self, x):
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return self.linear(x)
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def is_state_dict_equal(model1, model2):
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st1 = model1.state_dict()
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st2 = model2.state_dict()
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assert set(st1.keys()) == set(st2.keys())
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for k, v1 in st1.items():
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v2 = st2[k]
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if not np.array_equal(v1.numpy(), v2.numpy()):
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return False
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return True
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class MyModel3(nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(100, 300)
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buffer = paddle.to_tensor([0.0])
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self.register_buffer("model_buffer", buffer, persistable=True)
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def forward(self, x):
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return self.linear(x)
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class TestStateDictConvert(unittest.TestCase):
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def test_main(self):
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model1 = MyModel()
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model2 = MyModel()
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self.assertFalse(is_state_dict_equal(model1, model2))
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model2.set_state_dict(model1.state_dict())
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self.assertTrue(is_state_dict_equal(model1, model2))
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class TestStateDictReturn(unittest.TestCase):
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def test_missing_keys_and_unexpected_keys(self):
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model1 = MyModel2()
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tmp_dict = {}
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tmp_dict["unexpected_keys"] = paddle.to_tensor([1])
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missing_keys, unexpected_keys = model1.set_state_dict(tmp_dict)
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self.assertEqual(len(missing_keys), 2)
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self.assertEqual(missing_keys[0], "linear.weight")
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self.assertEqual(missing_keys[1], "linear.bias")
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self.assertEqual(len(unexpected_keys), 1)
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self.assertEqual(unexpected_keys[0], "unexpected_keys")
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def test_missing_keys_and_unexpected_keys_attr(self):
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model1 = MyModel2()
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tmp_dict = {}
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tmp_dict["unexpected_keys"] = paddle.to_tensor([1])
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result = model1.set_state_dict(tmp_dict)
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self.assertIsInstance(result, tuple)
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self.assertIs(result[0], result.missing_keys)
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self.assertIs(result[1], result.unexpected_keys)
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class TestStateKeepVars(unittest.TestCase):
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def test_true(self):
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model = MyModel3()
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x = paddle.randn([5, 100])
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y = model(x)
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y.backward()
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st = model.state_dict()
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has_grad = (
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(st["linear.weight"].grad == model.linear.weight.grad).all()
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and (st["linear.bias"].grad == model.linear.bias.grad).all()
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and st["model_buffer"].grad == model.model_buffer.grad
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)
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self.assertEqual(has_grad, True)
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def test_false(self):
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model = MyModel3()
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x = paddle.randn([5, 100])
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y = model(x)
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y.backward()
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st = model.state_dict(keep_vars=False)
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has_grad = (
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(st["linear.weight"].grad is not None)
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and (st["linear.bias"].grad is not None)
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and (st["model_buffer"].grad is not None)
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)
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self.assertEqual(has_grad, False)
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if __name__ == "__main__":
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unittest.main()
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