wehub-resource-sync
105 lines
3.2 KiB
Python
105 lines
3.2 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 unittest
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import numpy as np
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from op_test import get_device_place
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from scipy import special
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import paddle
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def ref_multigammaln(x, p):
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return special.multigammaln(x, p)
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def ref_multigammaln_grad(x, p):
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def single_multigammaln_grad(x, p):
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return special.psi(x - 0.5 * np.arange(0, p)).sum()
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vectorized_multigammaln_grad = np.vectorize(single_multigammaln_grad)
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return vectorized_multigammaln_grad(x, p)
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class TestMultigammalnAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(1024)
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self.x = np.random.rand(10, 20).astype('float32') + 1.0
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self.p = 2
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self.init_input()
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self.place = get_device_place()
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def init_input(self):
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pass
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def test_static_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype)
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out = paddle.multigammaln(x, self.p)
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exe = paddle.static.Executor(self.place)
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res = exe.run(
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feed={
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'x': self.x,
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},
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fetch_list=[out],
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)
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out_ref = ref_multigammaln(self.x, self.p)
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np.testing.assert_allclose(out_ref, res[0], rtol=1e-6, atol=1e-6)
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def test_dygraph_api(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x)
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out = paddle.multigammaln(x, self.p)
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out_ref = ref_multigammaln(self.x, self.p)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-6, atol=1e-6)
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paddle.enable_static()
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class TestMultigammalnAPICase1(TestMultigammalnAPI):
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def init_input(self):
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self.x = np.random.rand(10, 20).astype('float64') + 1.0
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class TestMultigammalnGrad(unittest.TestCase):
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def setUp(self):
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np.random.seed(1024)
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self.dtype = 'float32'
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self.x = np.array([2, 3, 4, 5, 6, 7, 8]).astype(dtype=self.dtype)
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self.p = 3
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self.place = get_device_place()
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def test_backward(self):
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expected_x_grad = ref_multigammaln_grad(self.x, self.p)
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x, dtype=self.dtype, place=self.place)
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x.stop_gradient = False
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out = x.multigammaln(self.p)
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loss = out.sum()
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loss.backward()
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np.testing.assert_allclose(
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x.grad.numpy().astype('float32'),
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expected_x_grad,
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rtol=1e-6,
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atol=1e-6,
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)
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paddle.enable_static()
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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