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7 years ago
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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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#pragma once
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#include <cstdint>
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#include "paddle/fluid/platform/hostdevice.h"
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namespace paddle {
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namespace framework {
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template <typename T, size_t N>
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class Array {
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static_assert(N > 0, "The size of array must be larger than 0");
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public:
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HOSTDEVICE Array() {}
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HOSTDEVICE explicit Array(const T &val) {
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for (size_t i = 0; i < N; ++i) data_[i] = val;
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}
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HOSTDEVICE const T *Get() const { return data_; }
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HOSTDEVICE T *GetMutable() { return data_; }
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HOSTDEVICE T &operator[](size_t index) { return data_[index]; }
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HOSTDEVICE const T &operator[](size_t index) const { return data_[index]; }
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HOSTDEVICE constexpr size_t size() const { return N; }
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private:
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T data_[N];
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};
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} // namespace framework
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} // namespace paddle
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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/op_registry.h"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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using Tensor = framework::Tensor;
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class AttentionLSTMOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override;
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override;
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};
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class AttentionLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override;
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};
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} // namespace operators
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} // namespace paddle
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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 <string>
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#include "paddle/fluid/platform/cpu_info.h"
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namespace paddle {
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namespace operators {
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namespace math {
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#define SIGMOID_THRESHOLD_MIN -40.0
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#define SIGMOID_THRESHOLD_MAX 13.0
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#define EXP_MAX_INPUT 40.0
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template <typename T>
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inline T sigmoid(T x) {
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return 1. / (1. + exp(-x));
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}
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template <typename T>
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inline T tanh(T x) {
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return 2. * sigmoid(2. * x) - 1.;
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_identity(const int n, const T* x, T* y) {
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// do nothing
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return;
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_sigmoid(const int n, const T* x, T* y) {
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const T min = SIGMOID_THRESHOLD_MIN;
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const T max = SIGMOID_THRESHOLD_MAX;
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for (int i = 0; i < n; ++i) {
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T tmp = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
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y[i] = 1.0 / (1.0 + std::exp(-tmp));
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}
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_tanh(const int n, const T* x, T* y) {
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for (int i = 0; i < n; ++i) {
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y[i] = tanh<T>(x[i]);
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}
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_relu(const int n, const T* x, T* y) {
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for (int i = 0; i < n; ++i) {
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y[i] = x[i] > 0 ? x[i] : 0;
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}
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}
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template <>
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inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
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float* y) {
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// TODO(TJ): complete me
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for (int i = 0; i < n; ++i) {
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y[i] = x[i] > 0 ? x[i] : 0;
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}
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}
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template <>
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inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
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float* y) {
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// TODO(TJ): complete me
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for (int i = 0; i < n; ++i) {
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y[i] = x[i] > 0 ? x[i] : 0;
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}
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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class VecActivations {
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public:
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std::function<void(const int, const T*, T*)> operator()(
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const std::string& type) {
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if (type == "sigmoid") {
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return vec_sigmoid<T, isa>;
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} else if (type == "relu") {
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return vec_relu<T, isa>;
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} else if (type == "tanh") {
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return vec_tanh<T, isa>;
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} else if (type == "identity" || type == "") {
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return vec_identity<T, isa>;
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}
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PADDLE_THROW("Not support type %s.", type);
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}
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,28 @@
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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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#include "paddle/fluid/operators/stack_op.h"
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namespace plat = paddle::platform;
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(stack, ops::StackOp, ops::StackOpMaker,
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ops::StackGradOpDescMaker);
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REGISTER_OPERATOR(stack_grad, ops::StackOpGrad);
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REGISTER_OP_CPU_KERNEL(stack, ops::StackKernel<plat::CPUDeviceContext, float>,
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ops::StackKernel<plat::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(stack_grad,
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ops::StackGradKernel<plat::CPUDeviceContext, float>,
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ops::StackGradKernel<plat::CPUDeviceContext, double>);
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@ -0,0 +1,25 @@
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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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#include "paddle/fluid/operators/stack_op.h"
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namespace plat = paddle::platform;
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(stack, ops::StackKernel<plat::CUDADeviceContext, float>,
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ops::StackKernel<plat::CUDADeviceContext, double>);
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REGISTER_OP_CUDA_KERNEL(stack_grad,
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ops::StackGradKernel<plat::CUDADeviceContext, float>,
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ops::StackGradKernel<plat::CUDADeviceContext, double>);
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