diff --git a/paddle/fluid/operators/elementwise/elementwise_mod_op_xpu.cc b/paddle/fluid/operators/elementwise/elementwise_mod_op_xpu.cc new file mode 100644 index 0000000000000..156589384c0dd --- /dev/null +++ b/paddle/fluid/operators/elementwise/elementwise_mod_op_xpu.cc @@ -0,0 +1,49 @@ +/* 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. */ + +#ifdef PADDLE_WITH_XPU +#include +#include +#include "paddle/fluid/framework/data_layout.h" +#include "paddle/fluid/framework/op_version_registry.h" +#include "paddle/fluid/operators/common_infer_shape_functions.h" +#include "paddle/fluid/operators/elementwise/elementwise_op_function.h" + +#include "paddle/fluid/operators/elementwise/elementwise_xpu.h" +#include "paddle/fluid/platform/device/device_wrapper.h" + +namespace paddle { +namespace operators { + +template +class ElementwiseModXPUKernel : public framework::OpKernel { + using XPUType = typename XPUTypeTrait::Type; + + public: + void Compute(const framework::ExecutionContext& ctx) const override { + XPUElementwise(ctx, xpu::broadcast_mod); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_XPU_KERNEL(elementwise_mod, ops::ElementwiseModXPUKernel, + ops::ElementwiseModXPUKernel, + ops::ElementwiseModXPUKernel, + ops::ElementwiseModXPUKernel); + +#endif diff --git a/paddle/fluid/platform/device/xpu/xpu2_op_list.h b/paddle/fluid/platform/device/xpu/xpu2_op_list.h index 583014b6f4773..77019a0192312 100644 --- a/paddle/fluid/platform/device/xpu/xpu2_op_list.h +++ b/paddle/fluid/platform/device/xpu/xpu2_op_list.h @@ -130,6 +130,11 @@ XPUOpMap& get_kl2_ops() { {"elementwise_sub", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()), pOpKernelType(vartype::FP16, XPUPlace())})}, + {"elementwise_mod", + XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()), + pOpKernelType(vartype::FP16, XPUPlace()), + pOpKernelType(vartype::INT64, XPUPlace()), + pOpKernelType(vartype::INT32, XPUPlace())})}, {"equal", XPUKernelSet({pOpKernelType(vartype::INT64, XPUPlace()), pOpKernelType(vartype::INT32, XPUPlace()), pOpKernelType(vartype::FP32, XPUPlace())})}, diff --git a/python/paddle/fluid/tests/unittests/xpu/test_elementwise_mod_op_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_elementwise_mod_op_xpu.py new file mode 100644 index 0000000000000..9ef2c093604b0 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/xpu/test_elementwise_mod_op_xpu.py @@ -0,0 +1,133 @@ +# 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 sys +sys.path.append("..") +import unittest +import numpy as np +from op_test import OpTest +import paddle.fluid as fluid +from paddle.fluid import compiler, Program, program_guard + +import paddle +from op_test_xpu import XPUOpTest +from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper + +paddle.enable_static() + + +class XPUTestElementwiseModOp(XPUOpTestWrapper): + def __init__(self) -> None: + self.op_name = 'elementwise_mod' + self.use_dynamic_create_class = False + + class ElementwiseModOp(XPUOpTest): + def init_kernel_type(self): + self.use_mkldnn = False + + def init_input_output(self): + self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype) + self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype) + self.out = np.mod(self.x, self.y) + self.inputs = { + 'X': OpTest.np_dtype_to_fluid_dtype(self.x), + 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) + } + self.outputs = {'Out': self.out} + self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn} + + def init_dtype(self): + pass + + def init_axis(self): + pass + + def setUp(self): + self.op_type = 'elementwise_mod' + self.use_xpu = True + self.dtype = self.in_type + self.axis = -1 + self.init_dtype() + self.init_input_output() + self.init_kernel_type() + self.init_axis() + + def test_check_output(self): + if paddle.is_compiled_with_xpu(): + place = paddle.XPUPlace(0) + self.check_output_with_place(place) + + class TestElementwiseModOp_broadcast_1(ElementwiseModOp): + def init_input_output(self): + self.inputs = { + 'X': np.random.rand(2, 100, 3).astype(self.dtype), + 'Y': np.random.rand(2, 100, 3).astype(self.dtype) + } + + self.attrs = {'axis': 1} + self.outputs = {'Out': self.inputs['X'] % self.inputs['Y']} + + class TestElementwiseModOp_broadcast_2(ElementwiseModOp): + def init_input_output(self): + self.inputs = { + 'X': np.random.rand(22, 128, 3).astype(self.dtype), + 'Y': np.random.rand(22, 128, 3).astype(self.dtype) + } + + self.attrs = {'axis': 1} + self.outputs = {'Out': self.inputs['X'] % self.inputs['Y']} + + class TestRemainderOp(unittest.TestCase): + def test_dygraph(self): + with fluid.dygraph.guard(): + np_x = np.random.rand(22, 128, 3).astype('int64') + np_y = np.random.rand(22, 128, 3).astype('int64') + x = paddle.to_tensor(np_x) + y = paddle.to_tensor(np_y) + z = paddle.remainder(x, y) + np_z = z.numpy() + z_expected = np.mod(np_x, np_y) + self.assertEqual((np_z == z_expected).all(), True) + + np_x = np.array([-3.3, 11.5, -2, 3.5]) + np_y = np.array([-1.2, 2., 3.3, -2.3]) + x = paddle.to_tensor(np_x) + y = paddle.to_tensor(np_y) + z = x % y + z_expected = np.array([-0.9, 1.5, 1.3, -1.1]) + self.assertEqual(np.allclose(z_expected, z.numpy()), True) + + np_x = np.random.rand(22, 128, 3).astype('int32') + np_y = np.random.rand(22, 128, 3).astype('int32') + x = paddle.to_tensor(np_x) + y = paddle.to_tensor(np_y) + z = paddle.remainder(x, y) + np_z = z.numpy() + z_expected = np.mod(np_x, np_y) + self.assertEqual((np_z == z_expected).all(), True) + + np_x = np.array([-3, 11, -2, 3]) + np_y = np.array([-1, 2, 3, -2]) + x = paddle.to_tensor(np_x, dtype="float16") + y = paddle.to_tensor(np_y, dtype="float16") + z = x % y + z_expected = np.array([0, 1, 1, -1]) + self.assertEqual(np.allclose(z_expected, z.numpy()), True) + + +support_types = get_xpu_op_support_types('elementwise_mod') +for stype in support_types: + create_test_class(globals(), XPUTestElementwiseModOp, stype) + +if __name__ == '__main__': + unittest.main()