mirror of https://github.com/alibaba/MNN.git
605 lines
23 KiB
C++
605 lines
23 KiB
C++
//
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// CPUBinary.cpp
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// MNN
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//
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// Created by MNN on 2018/08/02.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "CPUBinary.hpp"
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#include <math.h>
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#include <algorithm>
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#include "CPUBackend.hpp"
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#include "compute/CommonOptFunction.h"
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#include "compute/ConvOpt.h"
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#include "core/Macro.h"
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#include "core/Concurrency.h"
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#include "core/OpCommonUtils.hpp"
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namespace MNN {
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#define MAX_DIM 6
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CPUBinaryInt::CPUBinaryInt(Backend* b, int32_t type) : MNN::Execution(b), mType(type) {
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// nothing to do
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}
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CPUBinaryFloat::CPUBinaryFloat(Backend* b, int32_t type) : MNN::Execution(b), mType(type) {
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// nothing to do
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}
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ErrorCode CPUBinaryFloat::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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MNN_ASSERT(1 == outputs.size());
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const int input0DataCount = inputs[0]->elementSize();
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const int input1DataCount = inputs[1]->elementSize();
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const int outputDataCount = outputs[0]->elementSize();
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int maxCount = input0DataCount > input1DataCount ? input0DataCount : input1DataCount;
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mElementProc = nullptr;
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mSupportScale = false;
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if (outputs[0]->getType().code != halide_type_float || maxCount < 4 || (outputDataCount > input0DataCount && outputDataCount > input1DataCount)) {
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// Can't optimize
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return NO_ERROR;
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}
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auto eleProc = mElementProc;// Set nullptr for begin
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switch (mType) {
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case BinaryOpOperation_MUL:
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eleProc = MNNMatrixProdCommon;
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break;
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case BinaryOpOperation_ADD:
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eleProc = MNNMatrixAddCommon;
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break;
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case BinaryOpOperation_MAXIMUM:
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eleProc = MNNMatrixMaxCommon;
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break;
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case BinaryOpOperation_SUB:
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eleProc = MNNMatrixSubCommon;
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break;
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default:
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break;
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}
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if (input1DataCount == input0DataCount) {
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mOutside = 1;
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mInside = input0DataCount;
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mElementProc = eleProc;
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return NO_ERROR;
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}
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if (input1DataCount == 1 || input0DataCount == 1) {
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mAxis = 1;
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mOutside = 1;
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switch (mType) {
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case BinaryOpOperation_MUL:
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case BinaryOpOperation_ADD:
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case BinaryOpOperation_SUB:
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mSupportScale = true;
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break;
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default:
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break;
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}
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return NO_ERROR;
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}
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if (nullptr == eleProc) {
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return NO_ERROR;
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}
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// For AddBias / Mul Sqrt
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int dims[MAX_DIM];
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int stride[MAX_DIM];
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int iStride0[MAX_DIM];
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int iStride1[MAX_DIM];
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const Tensor* input0 = inputs[0];
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const Tensor* input1 = inputs[1];
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const Tensor* output = outputs[0];
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if (input0DataCount < input1DataCount) {
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input0 = inputs[1];
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input1 = inputs[0];
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}
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OpCommonUtils::broastCastComputeDim(dims, stride, iStride0, iStride1, input0, input1, output);
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int breakPos = -1;
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for (int i=0; i<MAX_DIM; ++i) {
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if (iStride1[i] > 0) {
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if (breakPos >= 0) {
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// Failed to optmize
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return NO_ERROR;
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}
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breakPos = i;
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}
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}
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MNN_ASSERT(breakPos >= 0);
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//FUNC_PRINT(breakPos);
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mOutside = 1;
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mInside = 1;
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for (int i=0; i<breakPos; ++i) {
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mOutside *= dims[i];
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}
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mAxis = dims[breakPos];
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for (int i=breakPos+1; i<MAX_DIM; ++i) {
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mInside *= dims[i];
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}
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// Serveral Machine need memory 4 * sizeof(float) align
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if (1 == mInside && mAxis >= 4) {
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mElementProc = eleProc;
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//MNN_PRINT("Open Optimize\n");
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} else if (BinaryOpOperation_MAXIMUM != mType && mInside >= 4) {
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mSupportScale = true;
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}
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//MNN_PRINT("%d, %d, %d\n", mInside, mAxis, mOutside);
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return NO_ERROR;
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}
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template <typename Tin, typename Tout, typename Func>
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static ErrorCode _binaryOp(Tensor* input0, Tensor* input1, Tensor* output) {
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Func f;
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const int input0DataCount = input0->elementSize();
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const int input1DataCount = input1->elementSize();
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const Tin* input0Data = input0->host<Tin>();
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const Tin* input1Data = input1->host<Tin>();
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Tout* outputData = output->host<Tout>();
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if (input0DataCount == 1) { // data count == 1, not only mean scalar input, maybe of shape (1, 1, 1, ...,1)
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for (int i = 0; i < input1DataCount; i++) {
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outputData[i] = static_cast<Tout>(f(input0Data[0], input1Data[i]));
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}
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} else if (input1DataCount == 1) {
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for (int i = 0; i < input0DataCount; i++) {
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outputData[i] = static_cast<Tout>(f(input0Data[i], input1Data[0]));
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}
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} else { // both input contains more than one element，which means no scalar input
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bool sameShape = true;
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{
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if (input0->dimensions() == input1->dimensions()) {
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for (int i = 0; i < input0->buffer().dimensions; i++) {
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if (input0->buffer().dim[i].extent != input1->buffer().dim[i].extent) {
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sameShape = false;
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break;
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}
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}
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}
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else {
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sameShape = false;
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}
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}
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if (sameShape) { // two inputs have the same shape, apply element-wise operation
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for (int i = 0; i < input0DataCount; i++) {
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outputData[i] = static_cast<Tout>(f(input0Data[i], input1Data[i]));
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}
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} else { // not the same shape, use broadcast
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MNN_ASSERT(output->dimensions() <= MAX_DIM);
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int dims[MAX_DIM];
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int stride[MAX_DIM];
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int iStride0[MAX_DIM];
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int iStride1[MAX_DIM];
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OpCommonUtils::broastCastComputeDim(dims, stride, iStride0, iStride1, input0, input1, output);
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for (int w = 0; w < dims[5]; ++w) {
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auto ow = outputData + w * stride[5];
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auto i0w = input0Data + w * iStride0[5];
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auto i1w = input1Data + w * iStride1[5];
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#define PTR(x, y, i) \
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auto o##x = o##y + x * stride[i]; \
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auto i0##x = i0##y + x * iStride0[i]; \
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auto i1##x = i1##y + x * iStride1[i]
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for (int v = 0; v < dims[4]; ++v) {
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PTR(v, w, 4);
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for (int u = 0; u < dims[3]; ++u) {
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PTR(u, v, 3);
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for (int z = 0; z < dims[2]; ++z) {
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PTR(z, u, 2);
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for (int y = 0; y < dims[1]; ++y) {
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PTR(y, z, 1);
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for (int x = 0; x < dims[0]; ++x) {
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PTR(x, y, 0);
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*ox = static_cast<Tout>(f(*i0x, *i1x));
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}
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}
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}
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}
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}
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}
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#undef MAX_DIM
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#undef PTR
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}
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// broadcast-capable check is done in compute size
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}
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return NO_ERROR;
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}
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryMax : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return std::max(x, y);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryMin : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return std::min(x, y);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryMul : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return x * y;
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryAdd : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return x + y;
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinarySub : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return x - y;
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryRealDiv : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return x / y;
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryMod : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return x - x / y;
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryGreater : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x > y) ? 1 : 0);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryLess : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x < y) ? 1 : 0);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryGreaterEqual : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x >= y) ? 1 : 0);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryLessEqual : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x <= y) ? 1 : 0);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryEqual : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x == y) ? 1 : 0);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryFloorDiv : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return floor(x / y);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryFloorMod : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return x - floor(x / y) * y;
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinarySquaredDifference : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (x - y) * (x - y);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryPow : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return pow(x, y);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryAtan2 : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return atan(x / y);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryLogicalOr : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x || y) ? 1 : 0);
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}
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};
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template <typename _Arg1, typename _Arg2, typename _ErrorCode>
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struct BinaryNotEqual : std::binary_function<_Arg1, _Arg2, _ErrorCode> {
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_ErrorCode operator()(const _Arg1& x, const _Arg2& y) const {
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return (_ErrorCode)((x != y) ? 1 : 0);
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}
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};
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static void callEleFunc(void(*proc)(float* C, const float* A, const float* B, size_t width, size_t cStride, size_t aStride, size_t bStride, size_t height),
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float* C, const float* A, const float* B, size_t size, bool swap) {
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if (swap) {
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proc(C, B, A, size, 0, 0, 0, 1);
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} else {
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proc(C, A, B, size, 0, 0, 0, 1);
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}
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}
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ErrorCode CPUBinaryFloat::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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auto input = inputs[0];
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auto input1 = inputs[1];
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auto output = outputs[0];
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if (nullptr != mElementProc || mSupportScale) {
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auto numberThread = ((CPUBackend*)backend())->threadNumber();
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auto i1Size = input->elementSize();
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auto i2Size = input1->elementSize();
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bool swap = false;
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if (i1Size < i2Size) {
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auto temp = i2Size;
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i2Size = i1Size;
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i1Size = temp;
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input = inputs[1];
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input1 = inputs[0];
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swap = true;
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}
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auto size = i1Size;
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auto schedule = ((CPUBackend*)backend())->multiThreadDivide(size);
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int sizeDivide = schedule.first;
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int scheduleNumber = schedule.second;
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if (nullptr != mElementProc) {
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if (mOutside == 1) {
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MNN_CONCURRENCY_BEGIN(tId, scheduleNumber) {
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int start = sizeDivide * (int)tId;
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int realSize = sizeDivide;
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if (tId == scheduleNumber -1 ) {
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realSize = size - start;
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}
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if (realSize > 0) {
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mElementProc(output->host<float>() + start, input->host<float>() + start, input1->host<float>() + start, realSize, 0, 0, 0, 1);
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}
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}
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MNN_CONCURRENCY_END();
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} else {
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MNN_CONCURRENCY_BEGIN(tId, numberThread) {
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for (int y = tId; y < mOutside; y+=numberThread) {
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callEleFunc(mElementProc, output->host<float>() + y * mAxis, input->host<float>() + y * mAxis, input1->host<float>(), mAxis, swap);
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}
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}
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MNN_CONCURRENCY_END();
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}
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} else {
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if (mOutside == 1 && mAxis == 1) {
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float* inputPtr = input->host<float>();
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float scalar = input1->host<float>()[0];
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float scale = scalar;
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float bias = 0.0f;
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switch (mType) {
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case BinaryOpOperation_ADD:
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scale = 1.0f;
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bias = scalar;
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break;
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case BinaryOpOperation_SUB:
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if (!swap) {
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scale = 1.0f;
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bias = -scalar;
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} else {
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scale = -1.0f;
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bias = scalar;
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}
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break;
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default:
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break;
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}
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MNN_CONCURRENCY_BEGIN(tId, scheduleNumber) {
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int start = sizeDivide * (int)tId;
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int realSize = sizeDivide;
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if (tId == scheduleNumber -1 ) {
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realSize = size - start;
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}
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if (realSize > 0) {
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MNNScaleAndAddBiasScalar(output->host<float>() + start, inputPtr + start, bias, scale, realSize);
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}
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}
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MNN_CONCURRENCY_END();
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} else {
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float* inputPtr = input->host<float>();
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float* input1Ptr = input1->host<float>();
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auto total = mOutside * mAxis;
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MNN_CONCURRENCY_BEGIN(tId, numberThread) {
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for (int index = tId; index < total; index += numberThread) {
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auto axis = index % mAxis;
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float scalar = input1Ptr[axis];
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float scale = scalar;
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float bias = 0.0f;
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switch (mType) {
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case BinaryOpOperation_ADD:
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scale = 1.0f;
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bias = scalar;
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break;
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case BinaryOpOperation_SUB:
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if (!swap) {
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scale = 1.0f;
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bias = -scalar;
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} else {
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scale = -1.0f;
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bias = scalar;
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}
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break;
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default:
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break;
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}
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MNNScaleAndAddBiasScalar(output->host<float>() + mInside * index, inputPtr + mInside * index, bias, scale, mInside);
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}
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}
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MNN_CONCURRENCY_END();
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}
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}
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return NO_ERROR;
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}
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switch (mType) {
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case BinaryOpOperation_MUL:
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_binaryOp<float, float, BinaryMul<float, float, float>>(input, input1, output);
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break;
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case BinaryOpOperation_ADD:
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_binaryOp<float, float, BinaryAdd<float, float, float>>(input, input1, output);
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break;
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case BinaryOpOperation_SUB:
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_binaryOp<float, float, BinarySub<float, float, float>>(input, input1, output);
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break;
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case BinaryOpOperation_REALDIV:
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_binaryOp<float, float, BinaryRealDiv<float, float, float>>(input, input1, output);
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break;
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case BinaryOpOperation_MINIMUM:
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_binaryOp<float, float, BinaryMin<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_MAXIMUM:
|
||
_binaryOp<float, float, BinaryMax<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_GREATER:
|
||
_binaryOp<float, int32_t, BinaryGreater<float, float, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_LESS:
|
||
_binaryOp<float, int32_t, BinaryLess<float, float, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_LESS_EQUAL:
|
||
_binaryOp<float, int32_t, BinaryLessEqual<float, float, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_GREATER_EQUAL:
|
||
_binaryOp<float, int32_t, BinaryGreaterEqual<float, float, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_EQUAL:
|
||
_binaryOp<float, int32_t, BinaryEqual<float, float, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_FLOORDIV:
|
||
_binaryOp<float, float, BinaryFloorDiv<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_FLOORMOD:
|
||
_binaryOp<float, float, BinaryFloorMod<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_POW:
|
||
_binaryOp<float, float, BinaryPow<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_SquaredDifference:
|
||
_binaryOp<float, float, BinarySquaredDifference<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_ATAN2:
|
||
_binaryOp<float, float, BinaryAtan2<float, float, float>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_NOTEQUAL:
|
||
_binaryOp<float, int32_t, BinaryNotEqual<float, float, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_MOD:
|
||
_binaryOp<float, float, BinaryMod<float, float, float>>(input, input1, output);
|
||
break;
|
||
default:
|
||
MNN_ASSERT(false);
|
||
break;
|
||
}
|
||
return NO_ERROR;
|
||
}
|
||
|
||
ErrorCode CPUBinaryInt::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
|
||
auto input = inputs[0];
|
||
auto input1 = inputs[1];
|
||
auto output = outputs[0];
|
||
switch (mType) {
|
||
case BinaryOpOperation_MUL:
|
||
_binaryOp<int32_t, int32_t, BinaryMul<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_ADD:
|
||
_binaryOp<int32_t, int32_t, BinaryAdd<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_SUB:
|
||
_binaryOp<int32_t, int32_t, BinarySub<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
|
||
case BinaryOpOperation_REALDIV:
|
||
_binaryOp<int32_t, int32_t, BinaryRealDiv<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_MINIMUM:
|
||
_binaryOp<int32_t, int32_t, BinaryMin<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_MAXIMUM:
|
||
_binaryOp<int32_t, int32_t, BinaryMax<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_GREATER:
|
||
_binaryOp<int32_t, int32_t, BinaryGreater<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_LESS:
|
||
_binaryOp<int32_t, int32_t, BinaryLess<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_LESS_EQUAL:
|
||
_binaryOp<int32_t, int32_t, BinaryLessEqual<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_GREATER_EQUAL:
|
||
_binaryOp<int32_t, int32_t, BinaryGreaterEqual<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_EQUAL:
|
||
_binaryOp<int32_t, int32_t, BinaryEqual<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_FLOORDIV:
|
||
_binaryOp<int32_t, int32_t, BinaryFloorDiv<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_FLOORMOD:
|
||
_binaryOp<int32_t, int32_t, BinaryFloorMod<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_SquaredDifference:
|
||
_binaryOp<int32_t, int32_t, BinarySquaredDifference<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_LOGICALOR:
|
||
_binaryOp<int32_t, int32_t, BinaryLogicalOr<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_NOTEQUAL:
|
||
_binaryOp<int32_t, int32_t, BinaryNotEqual<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
case BinaryOpOperation_MOD:
|
||
_binaryOp<int32_t, int32_t, BinaryMod<int32_t, int32_t, int32_t>>(input, input1, output);
|
||
break;
|
||
default:
|
||
MNN_ASSERT(false);
|
||
break;
|
||
}
|
||
return NO_ERROR;
|
||
}
|
||
|
||
class CPUBinaryCreator : public CPUBackend::Creator {
|
||
public:
|
||
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
|
||
const MNN::Op* op, Backend* backend) const override {
|
||
// auto dataType = outputs[0]->getType();
|
||
int32_t type = op->main_as_BinaryOp()->opType();
|
||
// auto dataType = op->main_as_BinaryOp()->T();
|
||
auto dataType = inputs[0]->getType();
|
||
if (dataType.bits == 32) {
|
||
if (dataType.code == halide_type_int) {
|
||
return new CPUBinaryInt(backend, type);
|
||
} else if (dataType.code == halide_type_float) {
|
||
return new CPUBinaryFloat(backend, type);
|
||
}
|
||
}
|
||
MNN_ERROR("CpuBinary: unsupported data type (bits: %d, code: %d)\n",
|
||
dataType.bits, dataType.code);
|
||
return nullptr;
|
||
}
|
||
};
|
||
|
||
REGISTER_CPU_OP_CREATOR(CPUBinaryCreator, OpType_BinaryOp);
|
||
|
||
} // namespace MNN
|