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154 lines
4.2 KiB
154 lines
4.2 KiB
/* 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 <cmath>
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#include <type_traits>
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#include <vector>
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#include "paddle/fluid/operators/jit/kernel_base.h"
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namespace paddle {
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namespace operators {
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namespace jit {
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namespace more {
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namespace mkl {
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template <typename T>
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void MatMul(const T* a, const T* b, T* c, int m, int n, int k);
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template <typename T>
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void VMul(const T* x, const T* y, T* z, int n);
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template <typename T>
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void VAdd(const T* x, const T* y, T* z, int n);
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template <typename T>
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void VScal(const T* a, const T* x, T* y, int n);
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template <typename T>
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void VExp(const T* x, T* y, int n);
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template <typename T>
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void VSquare(const T* x, T* y, int n);
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template <typename T>
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void VCopy(const T* x, T* y, int n);
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template <typename T>
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void VAXPY(T a, const T* x, T* y, int n);
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template <typename T>
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void VSigmoid(const T* x, T* y, int n) {
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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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y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
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y[i] = static_cast<T>(0) - y[i];
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}
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VExp(y, y, n);
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for (int i = 0; i < n; ++i) {
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y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
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}
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}
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template <typename T>
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void VTanh(const T* x, T* y, int n) {
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for (int i = 0; i < n; ++i) {
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y[i] = static_cast<T>(2) * x[i];
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}
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VSigmoid(y, y, n);
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for (int i = 0; i < n; ++i) {
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y[i] = static_cast<T>(2) * y[i] - static_cast<T>(1);
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}
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}
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template <typename T>
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void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
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VCopy<T>(x, y, attr->w);
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for (int h = 1; h != attr->h; ++h) {
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VAXPY<T>(static_cast<T>(1), x + h * attr->w, y, attr->w);
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}
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if (attr->type == SeqPoolType::kAvg || attr->type == SeqPoolType::kSqrt) {
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T scalar = static_cast<T>(1);
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if (attr->type == SeqPoolType::kAvg) {
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scalar = scalar / static_cast<T>(attr->h);
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} else {
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scalar = scalar / std::sqrt(static_cast<T>(attr->h));
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}
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VScal<T>(&scalar, y, y, attr->w);
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}
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}
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template <typename T>
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void ASum(const T* x, T* res, int n);
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template <typename T>
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void Softmax(const T* x, T* y, int n, int bs) {
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std::vector<T> entities(bs);
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for (int i = 0; i < bs; ++i) {
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entities[i] = x[i * n];
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for (int c = 1; c < n; ++c) {
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entities[i] = x[i * n + c] > entities[i] ? x[i * n + c] : entities[i];
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}
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for (int c = 0; c < n; ++c) {
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y[i * n + c] = x[i * n + c] - entities[i];
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}
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}
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VExp(y, y, n * bs);
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for (int i = 0; i < bs; ++i) {
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T sum;
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ASum(&y[i * n], &sum, n);
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sum = static_cast<T>(1) / sum;
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VScal(&sum, &y[i * n], &y[i * n], n);
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}
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}
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#define DECLARE_MKL_KERNEL(name, tuples) \
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template <typename T> \
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class name##Kernel : public KernelMore<tuples<T>> { \
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public: \
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name##Kernel() { this->func = name<T>; } \
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bool UseMe(const typename tuples<T>::attr_type&) const override; \
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const char* ImplType() const override { return "MKL"; } \
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}
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// ABCMNK
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DECLARE_MKL_KERNEL(MatMul, MatMulTuples);
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// XYZN
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DECLARE_MKL_KERNEL(VMul, XYZNTuples);
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DECLARE_MKL_KERNEL(VAdd, XYZNTuples);
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// AXYN
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DECLARE_MKL_KERNEL(VScal, AXYNTuples);
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// XYN
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DECLARE_MKL_KERNEL(VExp, XYNTuples);
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DECLARE_MKL_KERNEL(VSigmoid, XYNTuples);
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DECLARE_MKL_KERNEL(VTanh, XYNTuples);
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DECLARE_MKL_KERNEL(VSquare, XYNTuples);
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DECLARE_MKL_KERNEL(SeqPool, SeqPoolTuples);
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DECLARE_MKL_KERNEL(Softmax, SoftmaxTuples);
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#undef DECLARE_MKL_KERNEL
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} // namespace mkl
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} // namespace more
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} // namespace jit
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} // namespace operators
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} // namespace paddle
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