diff --git a/paddle/fluid/operators/elementwise/elementwise_mod_op_npu.cc b/paddle/fluid/operators/elementwise/elementwise_mod_op_npu.cc new file mode 100644 index 0000000000000000000000000000000000000000..33ac620c774039ed44e825678277a6cf3235df77 --- /dev/null +++ b/paddle/fluid/operators/elementwise/elementwise_mod_op_npu.cc @@ -0,0 +1,77 @@ +/* Copyright (c) 2021 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. */ + +#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h" +#include "paddle/fluid/operators/elementwise/elementwise_npu.h" +#include "paddle/fluid/operators/npu_op_runner.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class ElementwiseModNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto& dev_ctx = + ctx.template device_context(); + auto* x = ctx.Input("X"); + auto* y = ctx.Input("Y"); + auto* out = ctx.Output("Out"); + int axis = ctx.Attr("axis"); + + auto x_dims = x->dims(); + auto y_dims = y->dims(); + + axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis); + + bool direct_compute = false; + if (x_dims.size() >= y_dims.size()) { + direct_compute = + y_dims == framework::slice_ddim(x_dims, axis, x_dims.size()); + } else { + direct_compute = + x_dims == framework::slice_ddim(y_dims, axis, y_dims.size()); + } + + Tensor transformed_x, transformed_y; + if (direct_compute) { + transformed_x.ShareDataWith(*x); + transformed_y.ShareDataWith(*y); + } else { + NpuElementWiseOpBroadcast(dev_ctx, x, y, axis, &transformed_x, + &transformed_y); + } + out->mutable_data(ctx.GetPlace()); + const auto& runner = + NpuOpRunner("FloorMod", {transformed_x, transformed_y}, {*out}, {}); + auto stream = dev_ctx.stream(); + runner.Run(stream); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_NPU_KERNEL( + elementwise_mod, + ops::ElementwiseModNPUKernel, + ops::ElementwiseModNPUKernel, + ops::ElementwiseModNPUKernel, + ops::ElementwiseModNPUKernel, + ops::ElementwiseModNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_elementwise_mod_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_elementwise_mod_op_npu.py new file mode 100644 index 0000000000000000000000000000000000000000..d6551e84080a9a50446d1e79aec0c03eca3984c9 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_elementwise_mod_op_npu.py @@ -0,0 +1,182 @@ +# Copyright (c) 2019 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. + +from __future__ import print_function +import numpy as np +import unittest + +import sys +sys.path.append("..") +from op_test import OpTest + +import paddle +import paddle.fluid as fluid + +import random + +paddle.enable_static() + + +class TestElementwiseModOp(OpTest): + def setUp(self): + self.set_npu() + self.place = paddle.NPUPlace(0) + self.op_type = "elementwise_mod" + self.axis = -1 + self.init_dtype() + self.init_input_output() + self.init_kernel_type() + self.init_axis() + + self.inputs = { + 'X': OpTest.np_dtype_to_fluid_dtype(self.x), + 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) + } + self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn} + self.outputs = {'Out': self.out} + + def init_kernel_type(self): + self.use_mkldnn = False + + def init_dtype(self): + self.dtype = np.int32 + + def init_axis(self): + pass + + def set_npu(self): + self.__class__.use_npu = True + + 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) + + def test_check_output(self): + self.check_output_with_place(self.place) + + +class TestElementwiseModOpInt64(TestElementwiseModOp): + def init_dtype(self): + self.dtype = np.int64 + + +class TestElementwiseModOp_scalar(TestElementwiseModOp): + def init_input_output(self): + scale_x = random.randint(0, 100000000) + scale_y = random.randint(1, 100000000) + self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype) + self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype) + self.out = np.mod(self.x, self.y) + + +class TestElementwiseModOpFloat(TestElementwiseModOp): + def init_dtype(self): + self.dtype = np.float32 + + def init_input_output(self): + self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype) + self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype) + self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y) + + def test_check_output(self): + self.check_output_with_place(self.place, atol=1e-4) + + +class TestElementwiseModOpDouble(TestElementwiseModOpFloat): + def init_dtype(self): + self.dtype = np.float64 + + def test_check_output(self): + self.check_output_with_place(self.place) + + +class TestElementwiseModOpFP16(TestElementwiseModOpFloat): + def init_dtype(self): + self.dtype = np.float16 + + def test_check_output(self): + self.check_output_with_place(self.place, atol=1e-1) + + +class TestElementwiseModOp_broadcast_0(TestElementwiseModOp): + 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 = np.mod(self.x, self.y.reshape(100, 1, 1)) + + def init_axis(self): + self.axis = 0 + + +class TestElementwiseModOp_broadcast_1(TestElementwiseModOp): + 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 = np.mod(self.x, self.y.reshape(1, 100, 1)) + + def init_axis(self): + self.axis = 1 + + +class TestElementwiseModOp_broadcast_2(TestElementwiseModOp): + 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 = np.mod(self.x, self.y.reshape(1, 1, 100)) + + def init_axis(self): + self.axis = 2 + + +class TestRemainderOp(unittest.TestCase): + def test_name(self): + paddle.set_device('npu:0') + with fluid.program_guard(fluid.Program()): + x = fluid.data(name="x", shape=[2, 3], dtype="int64") + y = fluid.data(name='y', shape=[2, 3], dtype='int64') + y_1 = paddle.remainder(x, y, name='div_res') + self.assertEqual(('div_res' in y_1.name), True) + + def test_dygraph(self): + paddle.set_device('npu:0') + with fluid.dygraph.guard(): + np_x = np.array([2, 3, 8, 7]).astype('int64') + np_y = np.array([1, 5, 3, 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.array([0, 3, 2, 1]) + 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.array([-3, 11, -2, 3]) + np_y = np.array([-1, 2, 3, -2]) + x = paddle.to_tensor(np_x, dtype="int64") + y = paddle.to_tensor(np_y, dtype="int64") + z = x % y + z_expected = np.array([0, 1, 1, -1]) + self.assertEqual(np.allclose(z_expected, z.numpy()), True) + + +if __name__ == '__main__': + unittest.main()