wehub-resource-sync
163 lines
5.8 KiB
Python
163 lines
5.8 KiB
Python
# Copyright (c) 2022 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 get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test import OpTest
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from op_test_xpu import XPUOpTest
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from utils import dygraph_guard
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import paddle
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from paddle import base
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paddle.enable_static()
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class XPUTestElementwiseModOp(XPUOpTestWrapper):
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def __init__(self) -> None:
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self.op_name = 'elementwise_mod'
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self.use_dynamic_create_class = False
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class ElementwiseModOp(XPUOpTest):
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def init_kernel_type(self):
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self.use_onednn = False
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
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self.out = np.mod(self.x, self.y)
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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def init_dtype(self):
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pass
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def init_axis(self):
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pass
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def setUp(self):
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self.op_type = 'elementwise_mod'
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self.use_xpu = True
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self.dtype = self.in_type
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self.axis = -1
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place)
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class ElementwiseModOpZeroSize(ElementwiseModOp):
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def init_input_output(self):
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self.x = np.random.uniform(0, 10000, [0, 10]).astype(self.dtype)
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self.y = np.random.uniform(0, 1000, [0, 10]).astype(self.dtype)
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self.out = np.mod(self.x, self.y)
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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class TestRemainderOp(unittest.TestCase):
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def test_dygraph(self):
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with base.dygraph.guard():
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np_x = np.random.rand(22, 128, 3).astype('int64')
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np_y = np.random.rand(22, 128, 3).astype('int64')
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = paddle.remainder(x, y)
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np_z = z.numpy()
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z_expected = np.mod(np_x, np_y)
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self.assertEqual((np_z == z_expected).all(), True)
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np_x = np.array([-3.3, 11.5, -2, 3.5])
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np_y = np.array([-1.2, 2.0, 3.3, -2.3])
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = x % y
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z_expected = np.array([-0.9, 1.5, 1.3, -1.1])
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np.testing.assert_allclose(z_expected, z.numpy(), rtol=1e-05)
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np_x = np.random.rand(22, 128, 3).astype('int32')
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np_y = np.random.rand(22, 128, 3).astype('int32')
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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z = paddle.remainder(x, y)
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np_z = z.numpy()
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z_expected = np.mod(np_x, np_y)
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self.assertEqual((np_z == z_expected).all(), True)
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np_x = np.array([-3, 11, -2, 3])
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np_y = np.array([-1, 2, 3, -2])
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x = paddle.to_tensor(np_x, dtype="float16")
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y = paddle.to_tensor(np_y, dtype="float16")
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z = x % y
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z_expected = np.array([0, 1, 1, -1])
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np.testing.assert_allclose(z_expected, z.numpy(), rtol=1e-05)
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support_types = get_xpu_op_support_types('elementwise_mod')
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real_types = [t for t in support_types if t != 'complex64']
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for stype in real_types:
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create_test_class(globals(), XPUTestElementwiseModOp, stype)
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if 'complex64' in support_types:
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class TestElementwiseModOpComplex64(unittest.TestCase):
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def test_check_output(self):
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with dygraph_guard():
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dtype = "complex64"
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a = np.array([6 + 4j]).astype(dtype)
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b = np.array([3 + 5j]).astype(dtype)
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res = np.array([-2 + 2j]).astype(dtype)
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res_pd = paddle.remainder(
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paddle.to_tensor(a), paddle.to_tensor(b)
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)
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np.testing.assert_allclose(res, res_pd.numpy())
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dtype = "complex64"
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a = np.array([6 + 4j]).astype(dtype)
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b = np.array([3 + 5j]).astype(dtype)
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res = np.array([-2 + 2j]).astype(dtype)
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res_pd = paddle.remainder(
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paddle.to_tensor(a), paddle.to_tensor(b)
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)
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np.testing.assert_allclose(res, res_pd.numpy())
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with base.device_guard("xpu"):
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res_pd = paddle.remainder(
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paddle.to_tensor(a), paddle.to_tensor(b)
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)
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np.testing.assert_allclose(res, res_pd.numpy())
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if __name__ == '__main__':
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unittest.main()
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