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- # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import os
- import unittest
-
- import numpy as np
- from numpy.testing import assert_allclose
-
- import paddle
-
- os.environ['NVIDIA_TF32_OVERRIDE'] = "0"
-
-
- class Test0DCase0(unittest.TestCase):
- def setUp(self):
- paddle.disable_static()
-
- def tearDown(self):
- paddle.enable_static()
-
- def test_func(self):
- x = paddle.rand([])
- x.stop_gradient = False
- y = paddle.rand([])
- y.stop_gradient = False
- z = paddle.einsum("...,...->...", x, y)
- assert_allclose(
- z.numpy(),
- np.einsum('...,...->...', x.numpy(), y.numpy()),
- atol=1e-6,
- )
- z.mean().backward()
- assert z.shape == []
- assert x.grad.shape == []
- assert y.grad.shape == []
-
-
- class Test0DCase1(Test0DCase0):
- def test_func(self):
- x = paddle.rand([])
- x.stop_gradient = False
- y = paddle.rand([2, 2])
- y.stop_gradient = False
- z = paddle.einsum("...,ij->...", x, y)
- assert_allclose(
- z.numpy(), np.einsum('...,ij->...', x.numpy(), y.numpy()), atol=1e-6
- )
- z.mean().backward()
- assert z.shape == []
- assert x.grad.shape == []
- assert y.grad.shape == [2, 2]
-
-
- class Test0DCase2(Test0DCase0):
- def test_func(self):
- x = paddle.rand([2, 2])
- x.stop_gradient = False
- y = paddle.rand([2, 2])
- y.stop_gradient = False
- z = paddle.einsum("ij,ij->", x, y)
- assert_allclose(
- z.numpy(), np.einsum('ij,ij->', x.numpy(), y.numpy()), atol=1e-6
- )
- z.mean().backward()
- assert z.shape == []
- assert x.grad.shape == [2, 2]
- assert y.grad.shape == [2, 2]
-
-
- class Test0DCase3(Test0DCase0):
- def test_func(self):
- x = paddle.rand([2, 2])
- x.stop_gradient = True
- y = paddle.rand([2, 2])
- y.stop_gradient = False
- z = paddle.einsum("ij,ij->", x, y)
- assert_allclose(
- z.numpy(), np.einsum('ij,ij->', x.numpy(), y.numpy()), atol=1e-6
- )
- z.mean().backward()
- assert z.shape == []
- assert x.grad is None
- assert y.grad.shape == [2, 2]
-
-
- class Test0DCase4(Test0DCase0):
- def test_func(self):
- x = paddle.rand([])
- x.stop_gradient = False
- z = paddle.einsum("...->...", x)
- assert_allclose(z.numpy(), np.einsum('...->...', x.numpy()), atol=1e-6)
- z.mean().backward()
- assert z.shape == []
- assert x.grad.shape == []
- assert x.grad.numpy() == 1.0
-
-
- class Test0DCase5(Test0DCase0):
- def test_func(self):
- x = paddle.rand([2, 2])
- x.stop_gradient = False
- y = paddle.rand([2, 2])
- y.stop_gradient = False
- z = paddle.einsum("i...j, i...j->...", x, y)
- assert_allclose(
- z.numpy(),
- np.einsum('i...j, i...j->...', x.numpy(), y.numpy()),
- atol=1e-6,
- )
- z.mean().backward()
- assert z.shape == []
- assert x.grad.shape == [2, 2]
- assert y.grad.shape == [2, 2]
-
-
- class Test0DCase6(Test0DCase0):
- def test_func(self):
- x = paddle.rand([2, 2])
- x.stop_gradient = False
- z = paddle.einsum("ij->", x)
- assert_allclose(z.numpy(), np.einsum('ij->', x.numpy()), atol=1e-6)
- z.mean().backward()
- assert z.shape == []
- assert x.grad.shape == [2, 2]
-
-
- class Test0DCase7(Test0DCase0):
- def test_func(self):
- """
- 3 operands.
- """
- x = paddle.rand([2, 2])
- y = paddle.rand([])
- z = paddle.rand([])
- x.stop_gradient = False
- y.stop_gradient = False
- z.stop_gradient = False
- o = paddle.einsum("ij...,...,...->...", x, y, z)
- assert_allclose(
- o.numpy(),
- np.einsum("ij...,...,...->...", x.numpy(), y.numpy(), z.numpy()),
- atol=1e-6,
- )
- o.mean().backward()
- assert o.shape == []
- assert x.grad.shape == [2, 2]
- assert y.grad.shape == []
- assert z.grad.shape == []
-
-
- class Test0DCase8(Test0DCase0):
- def test_func(self):
- """
- 3 operands.
- """
- x = paddle.rand([2, 2])
- y = paddle.rand([])
- z = paddle.rand([])
- e = paddle.rand([3, 1])
- x.stop_gradient = False
- y.stop_gradient = False
- z.stop_gradient = False
- e.stop_gradient = False
- o = paddle.einsum("ij...,...,..., km->...", x, y, z, e)
- assert_allclose(
- o.numpy(),
- np.einsum(
- "ij...,...,...,km->...",
- x.numpy(),
- y.numpy(),
- z.numpy(),
- e.numpy(),
- ),
- atol=1e-6,
- )
- o.mean().backward()
- assert o.shape == []
- assert x.grad.shape == [2, 2]
- assert y.grad.shape == []
- assert z.grad.shape == []
- assert e.grad.shape == [3, 1]
-
-
- if __name__ == "__main__":
- unittest.main()
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