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add elementwise floordiv, mod; test=develop
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+386
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‎paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/elementwise/elementwise_floordiv_op.h"
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#include <string>
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#include "paddle/fluid/operators/elementwise/elementwise_op.h"
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namespace paddle {
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namespace operators {
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class ElementwiseFloorDivOpMaker : public ElementwiseOpMaker {
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protected:
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std::string GetName() const override { return "FloorDiv"; }
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std::string GetEquation() const override { return "Out = X % Y"; }
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,
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ops::ElementwiseFloorDivOpMaker);
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REGISTER_OP_CPU_KERNEL(
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elementwise_floordiv,
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ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, int>,
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ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext,
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int64_t>);

‎paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/elementwise/elementwise_floordiv_op.h"
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#include "paddle/fluid/platform/float16.h"
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(
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elementwise_floordiv,
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ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int64_t>);

‎paddle/fluid/operators/elementwise/elementwise_floordiv_op.h

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/operators/elementwise/elementwise_op.h"
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#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
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#include "paddle/fluid/operators/math/blas.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct FloorDivFunctor {
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inline HOSTDEVICE T operator()(T a, T b) const { return a / b; }
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};
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template <typename DeviceContext, typename T>
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void elementwise_floor_div(const framework::ExecutionContext &ctx,
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const framework::Tensor *x,
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const framework::Tensor *y, framework::Tensor *z) {
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int axis = ctx.Attr<int>("axis");
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ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
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ctx, x, y, axis, FloorDivFunctor<T>(), z);
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}
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template <typename DeviceContext, typename T>
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class ElementwiseFloorDivKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *x = ctx.Input<framework::LoDTensor>("X");
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auto *y = ctx.Input<framework::LoDTensor>("Y");
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auto *z = ctx.Output<framework::LoDTensor>("Out");
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z->mutable_data<T>(ctx.GetPlace());
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// dtype of x and y is int64 or int32
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elementwise_floor_div<DeviceContext, T>(ctx, x, y, z);
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}
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};
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} // namespace operators
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} // namespace paddle

‎paddle/fluid/operators/elementwise/elementwise_mod_op.cc

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
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#include <string>
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#include "paddle/fluid/operators/elementwise/elementwise_op.h"
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namespace paddle {
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namespace operators {
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class ElementwiseModOpMaker : public ElementwiseOpMaker {
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protected:
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std::string GetName() const override { return "Mod"; }
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std::string GetEquation() const override { return "Out = X % Y"; }
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(elementwise_mod, ops::ElementwiseOp,
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ops::ElementwiseModOpMaker);
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REGISTER_OP_CPU_KERNEL(
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elementwise_mod,
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ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int>,
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ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int64_t>);

‎paddle/fluid/operators/elementwise/elementwise_mod_op.cu

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/elementwise/elementwise_mod_op.h"
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#include "paddle/fluid/platform/float16.h"
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(
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elementwise_mod, ops::ElementwiseModKernel<plat::CUDADeviceContext, int>,
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ops::ElementwiseModKernel<plat::CUDADeviceContext, int64_t>);

‎paddle/fluid/operators/elementwise/elementwise_mod_op.h

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/operators/elementwise/elementwise_op.h"
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#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
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#include "paddle/fluid/operators/math/blas.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct ModFunctor {
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inline HOSTDEVICE T operator()(T a, T b) const { return a % b; }
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};
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template <typename DeviceContext, typename T>
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void elementwise_mod(const framework::ExecutionContext &ctx,
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const framework::Tensor *x, const framework::Tensor *y,
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framework::Tensor *z) {
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int axis = ctx.Attr<int>("axis");
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ElementwiseComputeEx<ModFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
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ModFunctor<T>(), z);
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}
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template <typename DeviceContext, typename T>
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class ElementwiseModKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *x = ctx.Input<framework::LoDTensor>("X");
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auto *y = ctx.Input<framework::LoDTensor>("Y");
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auto *z = ctx.Output<framework::LoDTensor>("Out");
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z->mutable_data<T>(ctx.GetPlace());
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// dtype of x and y is int64 or int32
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elementwise_mod<DeviceContext, T>(ctx, x, y, z);
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}
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};
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} // namespace operators
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} // namespace paddle

‎python/paddle/fluid/layers/math_op_patch.py

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@@ -174,6 +174,8 @@ def __impl__(self, other_var):
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("__rtruediv__", "elementwise_div", True),
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("__pow__", "elementwise_pow", False),
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("__rpow__", "elementwise_pow", True),
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("__floordiv__", "elementwise_floordiv", False),
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("__mod__", "elementwise_mod", False),
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# for logical compare
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("__eq__", "equal", False),
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("__ne__", "not_equal", False),

‎python/paddle/fluid/layers/nn.py

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@@ -8887,9 +8887,24 @@ def elementwise_pow(x, y, axis=-1, act=None, name=None):
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return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
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def elementwise_mod(x, y, axis=-1, act=None, name=None):
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return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
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def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
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return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))
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for func in [
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elementwise_add, elementwise_div, elementwise_sub, elementwise_mul,
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elementwise_max, elementwise_min, elementwise_pow
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elementwise_add,
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elementwise_div,
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elementwise_sub,
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elementwise_mul,
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elementwise_max,
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elementwise_min,
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elementwise_pow,
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elementwise_mod,
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elementwise_floordiv,
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]:
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op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
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func.__doc__ = _generate_doc_string_(

‎python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py

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# Copyright (c) 2018 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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import random
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class TestElementwiseModOp(OpTest):
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def init_kernel_type(self):
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self.use_mkldnn = False
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def setUp(self):
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self.op_type = "elementwise_floordiv"
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self.dtype = np.int32
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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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self.inputs = {
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'X': OpTest.np_dtype_to_fluid_dtype(self.x),
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'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
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}
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self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
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self.outputs = {'Out': self.out}
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def test_check_output(self):
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self.check_output()
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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.floor_divide(self.x, self.y)
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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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class TestElementwiseModOp_scalar(TestElementwiseModOp):
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def init_input_output(self):
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scale_x = random.randint(0, 100000000)
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scale_y = random.randint(1, 100000000)
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self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
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self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
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self.out = np.floor_divide(self.x, self.y)
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if __name__ == '__main__':
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unittest.main()

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