wehub-resource-sync
79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
# Copyright (c) 2020 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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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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class MyLayer(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.linear = paddle.nn.Linear(1, 1)
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def forward(self, x):
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return self.linear(x)
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class TestBackward(Dy2StTestBase):
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def test_order_0(self):
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"""
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loss = 1 * w * 1 + 2 * w * 2
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delta_w = 5
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"""
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model = paddle.jit.to_static(
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function=MyLayer(),
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, None], dtype=paddle.float32
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)
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],
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)
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model.clear_gradients()
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inp = paddle.ones([1, 1])
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out1 = model(inp * 1)
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out2 = model(inp * 2)
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loss = out2 * 2 + out1 * 1
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loss.backward()
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self.assertEqual(model.linear.weight.grad, 5)
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def test_order_1(self):
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"""
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loss = 2 * w * 2 + 1 * w * 1
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delta_w = 5
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"""
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model = paddle.jit.to_static(
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function=MyLayer(),
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, None], dtype=paddle.float32
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)
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],
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)
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model.clear_gradients()
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inp = paddle.ones([1, 1])
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out1 = model(inp * 1)
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out2 = model(inp * 2)
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loss = out1 * 1 + out2 * 2
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loss.backward()
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self.assertEqual(model.linear.weight.grad, 5)
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if __name__ == '__main__':
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unittest.main()
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