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119 lines
4.0 KiB
119 lines
4.0 KiB
/* 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/framework/op_registry.h"
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#include "paddle/fluid/operators/math/selected_rows_functor.h"
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#include "paddle/fluid/platform/transform.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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using platform::Transform;
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template <typename T>
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class ClipFunctor {
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public:
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explicit ClipFunctor(const T min, const T max) : min_(min), max_(max) {}
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HOSTDEVICE T operator()(const T& x) const {
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if (x < min_)
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return min_;
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else if (x > max_)
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return max_;
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else
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return x;
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}
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private:
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T min_;
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T max_;
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};
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template <typename T>
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class ClipGradFunctor {
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public:
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explicit ClipGradFunctor(const T min, const T max) : min_(min), max_(max) {}
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HOSTDEVICE T operator()(const T& x, const T& y) const {
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return (y > min_ && y < max_) ? x : 0;
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}
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private:
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T min_;
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T max_;
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};
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template <typename DeviceContext, typename T>
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class ClipKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto max = context.Attr<T>("max");
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auto min = context.Attr<T>("min");
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auto* x_var = context.InputVar("X");
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if (x_var->IsType<framework::LoDTensor>()) {
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auto* x = context.Input<framework::LoDTensor>("X");
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auto* out = context.Output<framework::LoDTensor>("Out");
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T* out_data = out->mutable_data<T>(context.GetPlace());
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const T* x_data = x->data<T>();
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int64_t numel = x->numel();
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Transform<DeviceContext> trans;
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trans(context.template device_context<DeviceContext>(), x_data,
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x_data + numel, out_data, ClipFunctor<T>(min, max));
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} else if (x_var->IsType<framework::SelectedRows>()) {
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auto* x = context.Input<framework::SelectedRows>("X");
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auto* out = context.Output<framework::SelectedRows>("Out");
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PADDLE_ENFORCE_NE(x, out,
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"Inplace clip is not allowed when x is SelectedRows");
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math::scatter::MergeAdd<DeviceContext, T> merge_func;
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merge_func(context.template device_context<DeviceContext>(), *x, out);
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auto* out_tensor = out->mutable_value();
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auto* out_data = out_tensor->data<T>();
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int64_t numel = out_tensor->numel();
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Transform<DeviceContext> trans;
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trans(context.template device_context<DeviceContext>(), out_data,
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out_data + numel, out_data, ClipFunctor<T>(min, max));
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} else {
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PADDLE_THROW("ClipOp only supports LoDTensor and SelectedRows");
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}
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}
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};
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template <typename DeviceContext, typename T>
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class ClipGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto max = context.Attr<T>("max");
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auto min = context.Attr<T>("min");
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auto* d_out =
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context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
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auto* d_x =
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context.Output<framework::LoDTensor>(framework::GradVarName("X"));
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if (d_x != nullptr) {
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auto* x = context.Input<framework::LoDTensor>("X");
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int64_t numel = d_out->numel();
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auto* d_x_data = d_x->mutable_data<T>(context.GetPlace());
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const T* d_out_data = d_out->data<T>();
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const T* x_data = x->data<T>();
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Transform<DeviceContext> trans;
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trans(context.template device_context<DeviceContext>(), d_out_data,
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d_out_data + numel, x_data, d_x_data, ClipGradFunctor<T>(min, max));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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