wehub-resource-sync
66 lines
1.8 KiB
Python
66 lines
1.8 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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from dygraph_to_static_utils import (
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Dy2StTestBase,
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)
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import paddle
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SEED = 2020
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np.random.seed(SEED)
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear1 = paddle.nn.Linear(10, 3)
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self.linear2 = paddle.nn.Linear(3, 1)
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def forward(self, x):
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out1 = self.linear1(x)
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out2 = self.linear2(out1)
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return [out1, out2] # 梯度为0
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# return [out1] # 梯度正常
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# return [out2, out1] # 梯度正常
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class TestGradientAggregationInDy2Static(Dy2StTestBase):
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def test_to_static(self):
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def simplenet_grad(inp, to_static=False):
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net = SimpleNet()
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if to_static:
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net = paddle.jit.to_static(net)
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loss = net(inp)
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loss[0].backward()
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return net.linear1.weight.grad
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inp = paddle.to_tensor(
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np.random.randn(
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10,
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)
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).astype("float32")
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np.testing.assert_allclose(
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simplenet_grad(inp, True).numpy(),
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simplenet_grad(inp, False).numpy(),
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rtol=1e-05,
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)
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if __name__ == '__main__':
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unittest.main()
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