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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 unittest
-
- import numpy as np
- from get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op_test import skip_check_grad_ci
- from op_test_xpu import XPUOpTest
-
- import paddle
-
- paddle.enable_static()
-
-
- class XPUTestElementwiseMaxOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'elementwise_max'
- self.use_dynamic_create_class = False
-
- class TestElementwiseOp(XPUOpTest):
- def setUp(self):
- self.use_xpu = True
- self.op_type = "elementwise_max"
- self.dtype = self.in_type
- self.init_input_output()
- # If x and y have the same value, the max() is not differentiable.
- # So we generate test data by the following method
- # to avoid them being too close to each other.
-
- def init_input_output(self):
- x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
- sgn = np.random.choice([-1, 1], [13, 17]).astype(self.dtype)
- y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
- self.inputs = {'X': x, 'Y': y}
- self.outputs = {
- 'Out': np.maximum(self.inputs['X'], self.inputs['Y'])
- }
-
- def test_check_output(self):
- if paddle.is_compiled_with_xpu():
- place = paddle.XPUPlace(0)
- self.check_output_with_place(place)
-
- def test_check_grad_normal(self):
- if paddle.is_compiled_with_xpu():
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(place, ['X', 'Y'], 'Out')
-
- def test_check_grad_ignore_x(self):
- if paddle.is_compiled_with_xpu():
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(
- place,
- ['Y'],
- 'Out',
- max_relative_error=0.006,
- no_grad_set=set("X"),
- )
-
- def test_check_grad_ignore_y(self):
- if paddle.is_compiled_with_xpu():
- place = paddle.XPUPlace(0)
- self.check_grad_with_place(
- place,
- ['X'],
- 'Out',
- max_relative_error=0.006,
- no_grad_set=set('Y'),
- )
-
- @skip_check_grad_ci(
- reason="[skip shape check] Use y_shape(1) to test broadcast."
- )
- class TestElementwiseMaxOp_scalar(TestElementwiseOp):
- def init_input_output(self):
- x = np.random.random_integers(-5, 5, [2, 3, 20]).astype(self.dtype)
- y = np.array([0.5]).astype(self.dtype)
- self.inputs = {'X': x, 'Y': y}
- self.outputs = {
- 'Out': np.maximum(self.inputs['X'], self.inputs['Y'])
- }
-
- class TestElementwiseMaxOp_Vector(TestElementwiseOp):
- def init_input_output(self):
- x = np.random.random((100,)).astype(self.dtype)
- sgn = np.random.choice([-1, 1], (100,)).astype(self.dtype)
- y = x + sgn * np.random.uniform(0.1, 1, (100,)).astype(self.dtype)
- self.inputs = {'X': x, 'Y': y}
- self.outputs = {
- 'Out': np.maximum(self.inputs['X'], self.inputs['Y'])
- }
-
- class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
- def init_input_output(self):
- x = np.random.uniform(0.5, 1, (1, 3, 100)).astype(self.dtype)
- sgn = np.random.choice([-1, 1], (100,)).astype(self.dtype)
- y = x[0, 0, :] + sgn * np.random.uniform(1, 2, (100,)).astype(
- self.dtype
- )
- self.inputs = {'X': x, 'Y': y}
-
- self.outputs = {
- 'Out': np.maximum(
- self.inputs['X'], self.inputs['Y'].reshape(1, 1, 100)
- )
- }
-
- class TestElementwiseMaxOp_broadcast_4(TestElementwiseOp):
- def init_input_output(self):
- x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(self.dtype)
- sgn = np.random.choice([-1, 1], (2, 3, 1, 5)).astype(self.dtype)
- y = x + sgn * np.random.uniform(1, 2, (2, 3, 1, 5)).astype(
- self.dtype
- )
- self.inputs = {'X': x, 'Y': y}
-
- self.outputs = {
- 'Out': np.maximum(self.inputs['X'], self.inputs['Y'])
- }
-
-
- support_types = get_xpu_op_support_types('elementwise_max')
- for stype in support_types:
- create_test_class(globals(), XPUTestElementwiseMaxOp, stype)
-
- if __name__ == '__main__':
- unittest.main()
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