wehub-resource-sync
109 lines
3.1 KiB
Python
109 lines
3.1 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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import paddle
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def ref_logaddexp_old(x, y):
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y = np.broadcast_to(y, x.shape)
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out = np.log1p(np.exp(-np.absolute(x - y))) + np.maximum(x, y)
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return out
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def ref_logaddexp(x, y):
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return np.logaddexp(x, y)
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class TestLogsumexpAPI(unittest.TestCase):
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def setUp(self):
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self.place = get_device_place()
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def api_case(self):
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self.x = np.random.uniform(-1, 1, self.xshape).astype(self.dtype)
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self.y = np.random.uniform(-1, 1, self.yshape).astype(self.dtype)
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out_ref = ref_logaddexp(self.x, self.y)
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# paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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out = paddle.logaddexp(x, y)
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np.testing.assert_allclose(out.numpy(), out_ref, atol=1e-06)
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def test_api(self):
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self.xshape = [1, 2, 3, 4]
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self.yshape = [1, 2, 3, 4]
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self.dtype = np.float64
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self.api_case()
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def test_api_broadcast(self):
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self.xshape = [1, 2, 3, 4]
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self.yshape = [1, 2, 3, 1]
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self.dtype = np.float32
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self.api_case()
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def test_api_bigdata(self):
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self.xshape = [10, 200, 300]
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self.yshape = [10, 200, 300]
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self.dtype = np.float32
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self.api_case()
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def test_api_int32(self):
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self.xshape = [10, 200, 300]
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self.yshape = [10, 200, 300]
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self.dtype = np.int32
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self.api_case()
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def test_api_int64(self):
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self.xshape = [10, 200, 300]
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self.yshape = [10, 200, 300]
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self.dtype = np.int64
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self.api_case()
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class TestLogsumexpAPI_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.place = get_device_place()
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def api_case(self):
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self.x = np.random.uniform(-1, 1, self.xshape).astype(self.dtype)
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self.y = np.random.uniform(-1, 1, self.yshape).astype(self.dtype)
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out_ref = ref_logaddexp(self.x, self.y)
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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x.stop_gradient = False
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y.stop_gradient = False
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out = paddle.logaddexp(x, y)
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np.testing.assert_allclose(out.numpy(), out_ref, atol=1e-06)
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loss = paddle.sum(out)
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loss.backward()
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np.testing.assert_allclose(x.grad.shape, x.shape)
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def test_api(self):
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self.xshape = [1, 2, 3, 0]
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self.yshape = [1, 2, 3, 1]
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self.dtype = np.float32
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self.api_case()
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if __name__ == '__main__':
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unittest.main()
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