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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 op_test import OpTest, skip_check_grad_ci
- from op_test_xpu import XPUOpTest
-
- import paddle
- from paddle import base
-
- paddle.enable_static()
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp(XPUOpTest):
- def setUp(self):
- self.op_type = "elementwise_add"
- self.init_dtype()
- self.init_input_output()
- self.init_axis()
- self.init_max_relative_error()
- self.inputs = {
- 'X': OpTest.np_dtype_to_base_dtype(self.x),
- 'Y': OpTest.np_dtype_to_base_dtype(self.y),
- }
- self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
- self.outputs = {'Out': self.out}
-
- 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',
- max_relative_error=self.max_relative_error,
- )
-
- 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',
- no_grad_set=set("X"),
- max_relative_error=self.max_relative_error,
- )
-
- 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',
- no_grad_set=set("Y"),
- max_relative_error=self.max_relative_error,
- )
-
- def init_input_output(self):
- self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
- self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
- self.out = np.add(self.x, self.y)
-
- def init_dtype(self):
- self.dtype = np.float32
-
- def init_axis(self):
- self.axis = -1
-
- def init_max_relative_error(self):
- self.max_relative_error = 0.006
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- @skip_check_grad_ci(
- reason="[skip shape check] Use y_shape(1) to test broadcast."
- )
- class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 3, 4).astype(self.dtype)
- self.y = np.random.rand(1).astype(self.dtype)
- self.out = self.x + self.y
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- @skip_check_grad_ci(
- reason="[skip shape check] Use y_shape(1,1) to test broadcast."
- )
- class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 3, 4).astype(self.dtype)
- self.y = np.random.rand(1, 1).astype(self.dtype)
- self.out = self.x + self.y
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.random((100,)).astype(self.dtype)
- self.y = np.random.random((100,)).astype(self.dtype)
- self.out = np.add(self.x, self.y)
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(100, 2, 3).astype(self.dtype)
- self.y = np.random.rand(100).astype(self.dtype)
- self.out = self.x + self.y.reshape(100, 1, 1)
-
- def init_axis(self):
- self.axis = 0
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 100, 3).astype(self.dtype)
- self.y = np.random.rand(100).astype(self.dtype)
- self.out = self.x + self.y.reshape(1, 100, 1)
-
- def init_axis(self):
- self.axis = 1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 3, 100).astype(self.dtype)
- self.y = np.random.rand(100).astype(self.dtype)
- self.out = self.x + self.y.reshape(1, 1, 100)
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
- self.y = np.random.rand(10, 12).astype(self.dtype)
- self.out = self.x + self.y.reshape(1, 10, 12, 1)
-
- def init_axis(self):
- self.axis = 1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(100, 2, 3, 4).astype(self.dtype)
- self.y = np.random.rand(100, 1).astype(self.dtype)
- self.out = self.x + self.y.reshape(100, 1, 1, 1)
-
- def init_axis(self):
- self.axis = 0
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(10, 3, 12).astype(self.dtype)
- self.y = np.random.rand(10, 1, 12).astype(self.dtype)
- self.out = self.x + self.y
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype)
- self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype)
- self.out = self.x + self.y
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype)
- self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype)
- self.out = self.x + self.y
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 10, 12).astype(self.dtype)
- self.y = np.random.rand(10, 12).astype(self.dtype)
- self.out = self.x + self.y.reshape(1, 10, 12)
-
- def init_axis(self):
- self.axis = 1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- @skip_check_grad_ci(
- reason="[skip shape check] Use y_shape(1) to test broadcast."
- )
- class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(100, 1).astype(self.dtype)
- self.y = np.random.rand(1).astype(self.dtype)
- self.out = self.x + self.y.reshape(1, 1)
-
- def init_axis(self):
- self.axis = 1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(100, 2, 3).astype(self.dtype)
- self.y = np.random.rand(100, 1, 1).astype(self.dtype)
- self.out = self.x + self.y
-
- def init_axis(self):
- self.axis = -1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(2, 3, 100).astype(self.dtype)
- self.y = np.random.rand(1, 1, 100).astype(self.dtype)
- self.out = self.x + self.y
-
- def init_axis(self):
- self.axis = -1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype)
- self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype)
- self.out = self.x + self.y
-
- def init_axis(self):
- self.axis = -1
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp):
- def init_input_output(self):
- self.x = np.random.rand(10, 12).astype(self.dtype)
- self.y = np.random.rand(2, 3, 10, 12).astype(self.dtype)
- self.out = self.x + self.y
-
- def init_axis(self):
- self.axis = 2
-
-
- @unittest.skipIf(
- not paddle.is_compiled_with_xpu(), "core is not compiled with XPU"
- )
- class TestAddOp(unittest.TestCase):
- def test_name(self):
- with base.program_guard(base.Program()):
- x = paddle.static.data(name="x", shape=[2, 3], dtype="float32")
- y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')
-
- y_1 = paddle.add(x, y, name='add_res')
- self.assertEqual(('add_res' in y_1.name), True)
-
- def test_declarative(self):
- with base.program_guard(base.Program()):
-
- def gen_data():
- return {
- "x": np.array([2, 3, 4]).astype('float32'),
- "y": np.array([1, 5, 2]).astype('float32'),
- }
-
- x = paddle.static.data(name="x", shape=[3], dtype='float32')
- y = paddle.static.data(name="y", shape=[3], dtype='float32')
- z = paddle.add(x, y)
-
- place = base.XPUPlace(0)
- exe = base.Executor(place)
- z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
- z_expected = np.array([3.0, 8.0, 6.0])
- self.assertEqual((z_value == z_expected).all(), True)
-
- def test_dygraph(self):
- with base.dygraph.guard():
- np_x = np.array([2, 3, 4]).astype('float32')
- np_y = np.array([1, 5, 2]).astype('float32')
- x = paddle.to_tensor(np_x)
- y = paddle.to_tensor(np_y)
- z = paddle.add(x, y)
- np_z = z.numpy()
- z_expected = np.array([3.0, 8.0, 6.0])
- self.assertEqual((np_z == z_expected).all(), True)
-
-
- if __name__ == '__main__':
- unittest.main()
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