wehub-resource-sync
75 lines
1.9 KiB
Python
75 lines
1.9 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 os
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os.environ["FLAGS_prim_vjp_skip_default_ops"] = "False"
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import unittest
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import paddle
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from paddle import jit, nn
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paddle.core._set_prim_all_enabled(True)
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x = paddle.randn([4, 1])
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y = paddle.randn([4, 1])
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x.stop_gradient = False
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y.stop_gradient = False
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model = nn.Sequential(nn.Linear(1, 1), nn.Tanh())
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model2 = nn.Sequential(
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nn.Linear(1, 1),
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)
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class TestPaddleSciencemodel(unittest.TestCase):
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def test_concat(self):
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@jit.to_static(full_graph=True)
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def concat(x, y):
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"""abc"""
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z = paddle.concat([x, y], 0)
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out = model(z)
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out0, out1 = paddle.split(out, 2, axis=0)
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g0 = paddle.grad(out0, x)[0]
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g1 = paddle.grad(out1, y)[0]
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return g0, g1
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g0, g1 = concat(x, y)
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loss = g0.sum() + g1.sum()
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loss.backward()
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class TestEularBeam(unittest.TestCase):
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def test_eular_beam(self):
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@jit.to_static(full_graph=True)
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def eular_beam(x):
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"""abc"""
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z_ = model(x)
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out = model2(z_)
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g0 = paddle.grad(out, x)[0]
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g1 = paddle.grad(g0, x)[0]
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g2 = paddle.grad(g1, x)[0]
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g3 = paddle.grad(g2, x)[0]
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return g3
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g3 = eular_beam(x)
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loss = g3.sum()
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loss.backward()
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if __name__ == '__main__':
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unittest.main()
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