# 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 unittest import numpy as np from eager_op_test import OpTest import paddle def Heaviside_grad(x, y, dout): tmp = np.zeros(x.shape).astype("float16") dx = np.multiply(tmp, dout) dy = np.multiply(np.equal(x, 0), dout).astype("float16") return dx, dy class TestElementwiseOp(OpTest): def setUp(self): self.op_type = "elementwise_heaviside" x = np.random.random((13, 17)).astype("float64") y = np.random.random((13, 17)).astype("float64") self.python_api = paddle.heaviside self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])} def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out') def test_check_grad_ingore_x(self): self.check_grad(['Y'], 'Out', no_grad_set=set("X")) def test_check_grad_ingore_y(self): self.check_grad(['X'], 'Out', no_grad_set=set('Y')) class TestHeavisideBroadcast(unittest.TestCase): def setUp(self): self.input_1 = np.random.rand(2, 100, 13, 17).astype("float32") self.input_2 = np.random.rand(100, 13, 17).astype("float32") self.input_3 = np.random.rand(100, 13, 1).astype("float32") self.input_4 = np.random.rand(13, 17).astype("float32") self.input_5 = np.random.rand(1).astype("float32") self.np_expected1 = np.heaviside(self.input_1, self.input_2) self.np_expected2 = np.heaviside(self.input_2, self.input_3) self.np_expected3 = np.heaviside(self.input_2, self.input_4) self.np_expected4 = np.heaviside(self.input_4, self.input_5) def test_broadcast(self): paddle.disable_static() self.tensor_1 = paddle.to_tensor(self.input_1) self.tensor_2 = paddle.to_tensor(self.input_2) self.tensor_3 = paddle.to_tensor(self.input_3) self.tensor_4 = paddle.to_tensor(self.input_4) self.tensor_5 = paddle.to_tensor(self.input_5) res = paddle.heaviside(self.tensor_1, self.tensor_2) res = res.numpy() np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05) res = paddle.heaviside(self.tensor_2, self.tensor_3) res = res.numpy() np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05) res = paddle.heaviside(self.tensor_2, self.tensor_4) res = res.numpy() np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05) res = paddle.heaviside(self.tensor_4, self.tensor_5) res = res.numpy() np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05) class TestHeavisideAPI_float64(unittest.TestCase): def setUp(self): self.x_np = np.random.random((13, 17)).astype("float64") self.y_np = np.random.random((13, 17)).astype("float64") self.out_np = np.heaviside(self.x_np, self.y_np) self.dtype = "float64" def test_static(self): for use_cuda in ( [False, True] if paddle.device.is_compiled_with_cuda() else [False] ): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.enable_static() prog = paddle.static.Program() with paddle.static.program_guard(prog): x = paddle.static.data( name=f"x_{self.dtype}", shape=[13, 17], dtype=self.dtype ) y = paddle.static.data( name=f"y_{self.dtype}", shape=[13, 17], dtype=self.dtype ) out = paddle.heaviside(x, y) exe = paddle.static.Executor(place=place) (res,) = exe.run( prog, feed={ f"x_{self.dtype}": self.x_np, f"y_{self.dtype}": self.y_np, }, fetch_list=out, use_prune=True, ) np.testing.assert_allclose(res, self.out_np, rtol=1e-05) def test_dygraph(self): for use_cuda in ( [False, True] if paddle.device.is_compiled_with_cuda() else [False] ): place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace() paddle.disable_static(place=place) result = paddle.heaviside( paddle.to_tensor(self.x_np), paddle.to_tensor(self.y_np) ) np.testing.assert_allclose(result.numpy(), self.out_np, rtol=1e-05) class TestHeavisideAPI_float32(TestHeavisideAPI_float64): def setUp(self): self.x_np = np.random.random((13, 17)).astype("float32") self.y_np = np.random.random((13, 17)).astype("float32") self.out_np = np.heaviside(self.x_np, self.y_np) self.dtype = "float32" class TestHeavisideAPI_int64(TestHeavisideAPI_float64): def setUp(self): self.x_np = np.random.random((13, 17)).astype("int64") self.y_np = np.random.random((13, 17)).astype("int64") self.out_np = np.heaviside(self.x_np, self.y_np) self.dtype = "int64" class TestHeavisideAPI_int32(TestHeavisideAPI_float64): def setUp(self): self.x_np = np.random.random((13, 17)).astype("int32") self.y_np = np.random.random((13, 17)).astype("int32") self.out_np = np.heaviside(self.x_np, self.y_np) self.dtype = "int32" class TestHeavisideAPI_float16(OpTest): def setUp(self): self.dtype = np.float16 self.op_type = "elementwise_heaviside" self.python_api = paddle.heaviside self.inputs = { 'X': np.random.uniform(1, 2, [20, 5]).astype("float16"), 'Y': np.random.uniform(1, 2, [20, 5]).astype("float16"), } self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad( ['X', 'Y'], 'Out', user_defined_grads=Heaviside_grad( self.inputs['X'], self.inputs['Y'], 1 / self.inputs['X'].size ), ) class TestHeavisideError(unittest.TestCase): def test_input(self): paddle.disable_static() def test_input_x(): paddle.heaviside(1, paddle.randn([100])) self.assertRaises(ValueError, test_input_x) def test_input_y(): paddle.heaviside(paddle.randn([100]), 1) self.assertRaises(ValueError, test_input_y) def test_input_xy(): paddle.heaviside( paddle.randn([100], 'float32'), paddle.randn([100], 'float64') ) self.assertRaises(ValueError, test_input_xy) if __name__ == '__main__': unittest.main()