mirror of https://github.com/alibaba/MNN.git
1095 lines
48 KiB
C++
1095 lines
48 KiB
C++
//
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// CPURaster.cpp
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// MNN
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//
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// Created by MNN on b'2020/04/02'.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "CPURaster.hpp"
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#include "compute/CommonOptFunction.h"
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#include "CPUTensorConvert.hpp"
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#include "math/Vec.hpp"
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#include "core/OpCommonUtils.hpp"
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#include "core/Concurrency.h"
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#include "compute/ConvOpt.h"
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#include "CPUMatMul.hpp"
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#include "CPUUnary.hpp"
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#include "core/BufferAllocator.hpp"
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#include "CPUResizeCache.hpp"
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using Vec4 = MNN::Math::Vec<float, 4>;
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namespace MNN {
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static void getBatchChannelArea(const Tensor* t, int& batch, int& channel, int& area) {
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batch = t->batch();
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if (t->dimensions() == 4) {
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channel = t->channel();
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area = t->width() * t->height();
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} else if (t->dimensions() == 3) {
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auto format = TensorUtils::getDescribe(t)->dimensionFormat;
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if (format == MNN_DATA_FORMAT_NHWC) {
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channel = t->length(2);
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area = t->length(1);
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} else {
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channel = t->length(1);
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area = t->length(2);
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}
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} else if (t->dimensions() == 5) {
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auto format = TensorUtils::getDescribe(t)->dimensionFormat;
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if (format == MNN_DATA_FORMAT_NHWC) {
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channel = t->length(4);
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area = t->length(1) * t->length(2) * t->length(3);
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} else {
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channel = t->length(1);
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area = t->length(2) * t->length(3) * t->length(4);
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}
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} else {
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auto format = TensorUtils::getDescribe(t)->dimensionFormat;
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if (format == MNN_DATA_FORMAT_NHWC) {
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for (int i = t->dimensions() - 1; i > 0; i--) {
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int len = t->length(i);
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if (len > 1) {
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if (channel == 1) {
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channel = len;
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} else {
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area *= len;
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}
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}
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}
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} else {
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for (int i = 1; i < t->dimensions(); i++) {
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int len = t->length(i);
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if (len > 1) {
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if (channel == 1) {
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channel = len;
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} else {
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area *= len;
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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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// Detect if the region is a transpose
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static bool _transpose(const Tensor::InsideDescribe::Region& region) {
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int srcOne = -1, dstOne = -1;
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for (int i = 0; i < 3; i++) {
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if (region.src.stride[i] == 1 && region.size[i] != 1) {
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if (srcOne >= 0 || region.size[i] < 4) {
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return false;
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}
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srcOne = i;
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}
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if (region.dst.stride[i] == 1 && region.size[i] != 1) {
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if (dstOne >= 0 || region.size[i] < 4) {
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return false;
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}
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dstOne = i;
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}
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}
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return srcOne >= 0 && dstOne >= 0 && srcOne != dstOne;
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}
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static int _singleConvert(const Tensor::InsideDescribe::Region& region, const Tensor* dest) {
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auto origin = region.origin;
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auto srcFormat = TensorUtils::getDescribe(origin)->dimensionFormat;
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auto dstFormat = TensorUtils::getDescribe(dest)->dimensionFormat;
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if (srcFormat == dstFormat) {
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return 0;
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}
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if (0 != region.src.offset || 0 != region.dst.offset) {
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return 0;
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}
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int dstBatch = 1, dstChannel = 1, dstArea = 1,
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srcBatch = 1, srcChannel = 1, srcArea = 1;
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getBatchChannelArea(origin, srcBatch, srcChannel, srcArea);
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getBatchChannelArea(dest, dstBatch, dstChannel, dstArea);
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if (dstBatch != srcBatch) {
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return 0;
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}
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if (dstChannel != srcChannel) {
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return 0;
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}
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if (dstArea != srcArea) {
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return 0;
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}
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// just support : [N,C,A] => [N,A,C] or [N,A,C] => [N,C,A]
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// not support : [N, X, Y, A] => [N, A, Y, X]
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if (region.src.stride[1] != 1 && region.size[0] > 1 && region.size[1] > 1 && region.size[2] > 1) {
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return 0;
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}
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auto totalSize = dstBatch * dstChannel * dstArea;
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int srcSize = 1;
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int dstSize = 1;
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int res = 1;
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for (int i=0; i<3; ++i) {
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if (region.size[i] == 1) {
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continue;
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}
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if (region.src.stride[i] != region.dst.stride[i]) {
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if (dstArea == 1) {
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// Batch / Channel transpose
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return 0;
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}
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res = 2;
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}
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srcSize += (region.size[i] - 1) * region.src.stride[i];
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dstSize += (region.size[i] - 1) * region.dst.stride[i];
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}
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if (srcSize != totalSize || dstSize != totalSize ) {
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return 0;
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}
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// Check If it can be described as NHWC <-> NC4HW4 transpose
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if (2 == res) {
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int srcChannelStride;
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int dstChannelStride;
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int srcAreaStride;
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int dstAreaStride;
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if (MNN_DATA_FORMAT_NC4HW4 == srcFormat) {
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srcChannelStride = srcArea;
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srcAreaStride = 1;
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dstChannelStride = 1;
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dstAreaStride = srcChannel;
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} else {
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srcChannelStride = 1;
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srcAreaStride = srcChannel;
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dstAreaStride = 1;
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dstChannelStride = srcArea;
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}
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for (int i=0; i<3; ++i) {
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if (region.size[i] == 1) {
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continue;
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}
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if (region.size[i] == dstBatch) {
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if (region.src.stride[i] != region.dst.stride[i]) {
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return 0;
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}
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continue;
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}
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if (region.size[i] == srcChannel) {
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if (region.src.stride[i] != srcChannelStride || region.dst.stride[i] != dstChannelStride) {
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return 0;
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}
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}
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if (region.size[i] == srcArea) {
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if (region.src.stride[i] != srcAreaStride || region.dst.stride[i] != dstAreaStride) {
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return 0;
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}
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}
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}
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return 2;
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}
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return 1;
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}
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ErrorCode CPURaster::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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MNN_ASSERT(inputs.size() == 1);
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MNN_ASSERT(outputs.size() == 1);
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auto input = inputs[0];
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auto output = outputs[0];
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auto des = TensorUtils::getDescribe(input);
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auto outputDes = TensorUtils::getDescribe(output);
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mNeedZero = !TensorUtils::regionIsFull(input);
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mZeroPoint = 0;
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if (outputDes->quantAttr != nullptr && outputDes->type == DataType_DT_INT8) {
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#ifdef MNN_USE_SSE
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mZeroPoint = (int)outputDes->quantAttr->zero + 128;
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#else
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mZeroPoint = (int)outputDes->quantAttr->zero;
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#endif
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}
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mTempInput.clear();
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mFastBlit.clear();
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mCacheRegions.clear();
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mTempOutput = nullptr;
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auto midFormat = MNN_DATA_FORMAT_NCHW;
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mTempInputCopy.clear();
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mOutputPtr = output->host<void>();
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mFast = false;
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auto core = static_cast<CPUBackend*>(backend())->functions();
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// all_srcFormat == dstFormat == NC4HW4 : Fast Exe
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if (outputDes->dimensionFormat == MNN_DATA_FORMAT_NC4HW4) {
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mFast = true;
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for (int i=0; i< des->regions.size(); ++i) {
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auto& slice = des->regions[i];
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if (TensorUtils::getDescribe(slice.origin)->dimensionFormat != MNN_DATA_FORMAT_NC4HW4) {
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mFast = false;
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break;
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}
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if (!OpCommonUtils::canBlitFast(slice, output, core->pack, true)) {
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mFast = false;
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break;
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}
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}
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if (mFast) {
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for (int i=0; i< des->regions.size(); ++i) {
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auto& slice = des->regions[i];
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if (slice.origin == nullptr) {
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continue;
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}
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Tensor::InsideDescribe::Region newRegion;
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OpCommonUtils::turnToPackRegion(slice, newRegion, output, core->pack, true);
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mFastBlit.emplace_back(std::make_pair(slice.origin->host<void>(), std::move(newRegion)));
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}
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return NO_ERROR;
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}
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}
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mSingleConvert = 0;
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// srcNum == 1 && srcFormat != dstFormat : Single Convert
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if (des->regions.size() == 1) {
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mSingleConvert = _singleConvert(des->regions[0], output);
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if (mSingleConvert > 0) {
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return NO_ERROR;
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}
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}
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// Acquire Buffer for temp output
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// TODO: optimize it
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if (MNN_DATA_FORMAT_NC4HW4 == outputDes->dimensionFormat) {
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mTempOutput.reset(new Tensor);
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TensorUtils::setupTensorInfo(output, mTempOutput.get(), midFormat);
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}
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if (nullptr != mTempOutput) {
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auto res = backend()->onAcquireBuffer(mTempOutput.get(), Backend::DYNAMIC);
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if (!res) {
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return OUT_OF_MEMORY;
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}
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mOutputPtr = mTempOutput->host<void>();
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}
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// input is NC4HW4 add Convert
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std::vector<Tensor*> forRelease;
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for (int i=0; i< des->regions.size(); ++i) {
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auto& slice = des->regions[i];
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auto origin = slice.origin;
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if (nullptr == origin || nullptr == origin->host<void>()) {
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continue;
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}
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if (slice.mask != 0) {
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mTempInputCopy.emplace_back(std::make_pair(origin->host<void>(), &slice));
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continue;
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}
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// if tensor is not NC4HW4 or has been merged, don't need deal
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if (TensorUtils::getDescribe(origin)->dimensionFormat != MNN_DATA_FORMAT_NC4HW4) {
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mTempInputCopy.emplace_back(std::make_pair(origin->host<void>(), &slice));
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continue;
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}
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// if NC4HW4's C%4 == 0, change convert to transpose and fuse it
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if (origin->batch() == 1 && origin->channel() % core->pack == 0) {
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int channel = origin->channel();
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int area = 1;
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// conv3d/pool3d will has 5 dims, area = depth * width * height, otherwise area = width * height
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for (int d = 2; d < origin->dimensions(); d++) {
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area *= origin->length(d);
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}
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Tensor::InsideDescribe::Region regionTmp;
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regionTmp.src.offset = 0;
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regionTmp.src.stride[0] = area * core->pack;
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regionTmp.src.stride[1] = 1;
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regionTmp.src.stride[2] = core->pack;
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regionTmp.dst.offset = 0;
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regionTmp.dst.stride[0] = area * core->pack;
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regionTmp.dst.stride[1] = area;
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regionTmp.dst.stride[2] = 1;
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regionTmp.size[0] = channel / core->pack;
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regionTmp.size[1] = core->pack;
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regionTmp.size[2] = area;
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regionTmp.origin = slice.origin;
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bool merge = TensorUtils::fuseRegion(regionTmp, slice);
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if (merge) {
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// cache the merged tensor
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slice.mask = 1;
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mTempInputCopy.emplace_back(std::make_pair(origin->host<void>(), &slice));
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continue;
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}
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}
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auto cache = static_cast<CPUBackend*>(backend())->getCache();
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auto tempTensor = cache->findCacheTensor(origin, midFormat);
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if (nullptr == tempTensor) {
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std::shared_ptr<Tensor> newTensor(new Tensor);
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TensorUtils::copyShape(origin, newTensor.get());
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TensorUtils::getDescribe(newTensor.get())->dimensionFormat = midFormat;
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newTensor->buffer().type = origin->getType();
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TensorUtils::setLinearLayout(newTensor.get());
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mTempInput.insert(std::make_pair(origin, newTensor.get()));
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auto res = backend()->onAcquireBuffer(newTensor.get(), Backend::DYNAMIC);
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if (!res) {
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return OUT_OF_MEMORY;
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}
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tempTensor = newTensor.get();
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TensorUtils::getDescribe(tempTensor)->useCount = TensorUtils::getDescribe(origin)->useCount;
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cache->pushCacheTensor(newTensor, origin, midFormat);
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}
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if (--TensorUtils::getDescribe(tempTensor)->useCount == 0) {
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forRelease.emplace_back(tempTensor);
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}
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mTempInputCopy.emplace_back(std::make_pair(tempTensor->host<void>(), &slice));
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}
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for (auto t : forRelease) {
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backend()->onReleaseBuffer(t, Backend::DYNAMIC);
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}
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if (nullptr != mTempOutput) {
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backend()->onReleaseBuffer(mTempOutput.get(), Backend::DYNAMIC);
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}
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auto threadNumber = static_cast<CPUBackend*>(backend())->threadNumber();
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if (mTempInputCopy.size() == 1 && threadNumber > 1) {
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// Split to multi region
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auto region = mTempInputCopy[0].second;
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const int thredHold = 100;//TODO: Find better way to determine it
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if (region->size[0] * region->size[1] * region->size[2] < thredHold) {
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return NO_ERROR;
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}
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auto ptr = mTempInputCopy[0].first;
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int pos = -1;
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for (int i=0; i<3; ++i) {
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if (region->size[i] > 1) {
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pos = i;
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break;
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}
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}
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if (-1 == pos) {
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// Don't need divide
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return NO_ERROR;
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}
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mTempInputCopy.clear();
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int divSize = UP_DIV(region->size[pos], threadNumber);
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mCacheRegions.resize(threadNumber);
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for (int i=0; i<threadNumber; ++i) {
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int sta = i * divSize;
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int fin = sta + divSize;
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fin = std::min(fin, region->size[pos]);
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if (fin <= sta) {
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break;
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}
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for (int v=0; v<3; ++v) {
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mCacheRegions[i].src.stride[v] = region->src.stride[v];
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mCacheRegions[i].dst.stride[v] = region->dst.stride[v];
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}
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int curSize = fin - sta;
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for (int v=0; v<pos; ++v) {
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mCacheRegions[i].size[v] = region->size[v];
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}
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mCacheRegions[i].size[pos] = curSize;
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mCacheRegions[i].src.offset = region->src.offset + sta * region->src.stride[pos];
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mCacheRegions[i].dst.offset = region->dst.offset + sta * region->dst.stride[pos];
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for (int v=pos+1; v<3; ++v) {
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mCacheRegions[i].size[v] = region->size[v];
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}
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mTempInputCopy.emplace_back(std::make_pair(ptr, mCacheRegions.data() + i));
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}
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}
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return NO_ERROR;
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}
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static void _transpose4Bit(int32_t* dstO, const int32_t* srcO, const Tensor::InsideDescribe::Region& region) {
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int dims[4], keepDim = -1;
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for (int i = 0; i < 3; i++) {
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if (region.src.stride[i] == 1 && region.size[i] != 1) {
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dims[1] = region.size[i];
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dims[3] = region.dst.stride[i];
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}else if (region.dst.stride[i] == 1 && region.size[i] != 1) {
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dims[0] = region.size[i];
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dims[2] = region.src.stride[i];
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} else {
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keepDim = i;
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}
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}
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for (int z=0; z<region.size[keepDim]; ++z) {
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auto srcZ = srcO + region.src.stride[keepDim] * z;
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auto dstZ = dstO + region.dst.stride[keepDim] * z;
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MNNTranspose32Bit(dstZ, srcZ, dims);
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}
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}
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typedef void (*BlitProc)(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds);
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static void _4BitcopyWithStride(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
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auto src = (uint32_t*)srcO;
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auto dst = (uint32_t*)dstO;
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for (int i=0; i<size; ++i) {
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*dst = *src;
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src+=stride;
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dst+=ds;
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}
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}
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static void _2BitcopyWithStride(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
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auto src = (uint16_t*)srcO;
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auto dst = (uint16_t*)dstO;
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for (int i=0; i<size; ++i) {
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*dst = *src;
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src+=stride;
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dst+=ds;
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}
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}
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static void _1BitcopyWithStride(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
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auto src = (uint8_t*)srcO;
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auto dst = (uint8_t*)dstO;
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for (int i=0; i<size; ++i) {
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*dst = *src;
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src+=stride;
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dst+=ds;
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}
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}
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static void _4BitcopyWithStrideC4(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
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auto src = (float*)srcO;
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auto dst = (float*)dstO;
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for (int i=0; i<size; ++i) {
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Vec4::save(dst, Vec4::load(src));
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src+= (4 * stride);
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dst+= (4 * ds);
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}
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}
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static void _2BitcopyWithStrideC4(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds) {
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auto src = (uint64_t*)srcO;
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auto dst = (uint64_t*)dstO;
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for (int i=0; i<size; ++i) {
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*dst = *src;
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src+=stride;
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dst+=ds;
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}
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}
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void CPURaster::executeFaster(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) const {
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auto input = inputs[0];
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auto output = outputs[0];
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auto bytes = CPUBackend::getBytes(backend(), output);
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auto core = static_cast<const CPUBackend*>(backend())->functions();
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auto threadNum = static_cast<CPUBackend*>(backend())->threadNumber();
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if (mNeedZero) {
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::memset(output->host<void>(), mZeroPoint, static_cast<CPUBackend*>(backend())->getTensorSize(output) * bytes);
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}
|
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auto byteC4 = bytes * core->pack;
|
|
auto C4proc = _4BitcopyWithStride;
|
|
switch (byteC4) {
|
|
case 16:
|
|
C4proc = _4BitcopyWithStrideC4;
|
|
break;
|
|
case 8:
|
|
C4proc = _2BitcopyWithStrideC4;
|
|
break;
|
|
case 4:
|
|
C4proc = _4BitcopyWithStride;
|
|
break;
|
|
default:
|
|
C4proc = core->MNNSelectBlitFunction(byteC4);
|
|
break;
|
|
}
|
|
MNN_CONCURRENCY_BEGIN(tId, threadNum) {
|
|
for (int u=(int)tId; u<mFastBlit.size(); u+=threadNum) {
|
|
auto& iter = mFastBlit[u];
|
|
auto& slice = iter.second;
|
|
//Offset use byte
|
|
auto srcPtr = (uint8_t*)iter.first + slice.src.offset * bytes;
|
|
auto dstPtr = (uint8_t*)mOutputPtr + slice.dst.offset * bytes;
|
|
if (slice.src.stride[1] == slice.size[2] && slice.dst.stride[1] == slice.size[2] && slice.src.stride[2] == 1) {
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto srcZ = srcPtr + z * slice.src.stride[0] * byteC4;
|
|
auto dstZ = dstPtr + z * slice.dst.stride[0] * byteC4;
|
|
::memcpy(dstZ, srcZ, slice.size[1] * slice.src.stride[1] * byteC4);
|
|
}
|
|
continue;
|
|
}
|
|
if (1 == slice.src.stride[2] && 1 == slice.dst.stride[2]) {
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto srcZ = srcPtr + z * slice.src.stride[0] * byteC4;
|
|
auto dstZ = dstPtr + z * slice.dst.stride[0] * byteC4;
|
|
for (int y=0; y<slice.size[1]; ++y) {
|
|
auto srcY = srcZ + y * slice.src.stride[1] * byteC4;
|
|
auto dstY = dstZ + y * slice.dst.stride[1] * byteC4;
|
|
::memcpy(dstY, srcY, slice.size[2] * byteC4);
|
|
}
|
|
}
|
|
continue;
|
|
}
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto srcZ = srcPtr + z * slice.src.stride[0] * byteC4;
|
|
auto dstZ = dstPtr + z * slice.dst.stride[0] * byteC4;
|
|
for (int y=0; y<slice.size[1]; ++y) {
|
|
auto srcY = srcZ + y * slice.src.stride[1] * byteC4;
|
|
auto dstY = dstZ + y * slice.dst.stride[1] * byteC4;
|
|
C4proc(dstY, srcY, slice.size[2], slice.src.stride[2], slice.dst.stride[2]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
MNN_CONCURRENCY_END();
|
|
}
|
|
|
|
static BlitProc _selectUnitProc(int bytes) {
|
|
auto proc = _1BitcopyWithStride;
|
|
switch (bytes) {
|
|
case 4:
|
|
proc = _4BitcopyWithStride;
|
|
break;
|
|
case 2:
|
|
proc = _2BitcopyWithStride;
|
|
break;
|
|
case 1:
|
|
proc = _1BitcopyWithStride;
|
|
break;
|
|
default:
|
|
MNN_ASSERT(false);
|
|
break;
|
|
}
|
|
return proc;
|
|
}
|
|
static void _zero(const Tensor::InsideDescribe::Region& slice, int bytes, uint8_t* dstPtr) {
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto dstZ = dstPtr + (z) * slice.dst.stride[0] * bytes;
|
|
for (int y=0; y<slice.size[1]; ++y) {
|
|
auto dstY = dstZ + y * slice.dst.stride[1] * bytes;
|
|
::memset(dstY, 0, slice.size[2] * bytes);
|
|
}
|
|
}
|
|
}
|
|
static void _blit(const Tensor::InsideDescribe::Region& slice, int bytes, const uint8_t* srcPtr, uint8_t* dstPtr, void(*proc)(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds)) {
|
|
if (slice.src.stride[1] == slice.size[2] && slice.dst.stride[1] == slice.size[2] && slice.src.stride[2] == 1) {
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto srcZ = srcPtr + z * slice.src.stride[0] * bytes;
|
|
auto dstZ = dstPtr + z * slice.dst.stride[0] * bytes;
|
|
::memcpy(dstZ, srcZ, slice.size[1] * slice.src.stride[1] * bytes);
|
|
}
|
|
return;
|
|
}
|
|
if (_transpose(slice) && 4 == bytes) {
|
|
_transpose4Bit((int32_t*)dstPtr, (const int32_t*)srcPtr, slice);
|
|
return;
|
|
}
|
|
if (1 == slice.src.stride[2] && 1 == slice.dst.stride[2]) {
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto srcZ = srcPtr + z * slice.src.stride[0] * bytes;
|
|
auto dstZ = dstPtr + z * slice.dst.stride[0] * bytes;
|
|
for (int y=0; y<slice.size[1]; ++y) {
|
|
auto srcY = srcZ + y * slice.src.stride[1] * bytes;
|
|
auto dstY = dstZ + y * slice.dst.stride[1] * bytes;
|
|
::memcpy(dstY, srcY, slice.size[2] * bytes);
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
for (int z=0; z<slice.size[0]; ++z) {
|
|
auto srcZ = srcPtr + z * slice.src.stride[0] * bytes;
|
|
auto dstZ = dstPtr + (z) * slice.dst.stride[0] * bytes;
|
|
for (int y=0; y<slice.size[1]; ++y) {
|
|
auto srcY = srcZ + y * slice.src.stride[1] * bytes;
|
|
auto dstY = dstZ + y * slice.dst.stride[1] * bytes;
|
|
proc(dstY, srcY, slice.size[2], slice.src.stride[2], slice.dst.stride[2]);
|
|
}
|
|
}
|
|
}
|
|
void CPURaster::tensorConvert(Tensor* input, Tensor* output, int bytes) {
|
|
auto& subIb = input->buffer();
|
|
auto& subOb = output->buffer();
|
|
auto source = TensorUtils::getDescribe(input)->dimensionFormat;
|
|
auto dest = TensorUtils::getDescribe(output)->dimensionFormat;
|
|
if (subIb.dimensions <= 1 || source == dest) {
|
|
::memcpy(subOb.host, subIb.host, input->elementSize() * bytes);
|
|
return;
|
|
}
|
|
auto tup = CPUTensorConverter::splitDimensions(subIb, source);
|
|
int area = std::get<1>(tup), batch = std::get<0>(tup), channel = std::get<2>(tup);
|
|
const int bitLength = bytes;
|
|
auto core = static_cast<CPUBackend*>(backend())->functions();
|
|
auto threadNumber = static_cast<CPUBackend*>(backend())->threadNumber();
|
|
MNN_CONCURRENCY_BEGIN(tId, threadNumber) {
|
|
CPUTensorConverter::convert(subIb.host, subOb.host, source, dest, batch, area, channel, bitLength, core, tId, threadNumber);
|
|
};
|
|
MNN_CONCURRENCY_END();
|
|
}
|
|
|
|
|
|
ErrorCode CPURaster::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
|
|
if (mFast) {
|
|
executeFaster(inputs, outputs);
|
|
return NO_ERROR;
|
|
}
|
|
auto core = static_cast<CPUBackend*>(backend())->functions();
|
|
auto input = inputs[0];
|
|
auto output = outputs[0];
|
|
auto bytes = CPUBackend::getBytes(backend(), output);
|
|
auto outputEleSize = static_cast<CPUBackend*>(backend())->getTensorSize(output);
|
|
auto threadNum = static_cast<CPUBackend*>(backend())->threadNumber();
|
|
if (mSingleConvert > 0) {
|
|
auto realInput = TensorUtils::getDescribe(input)->regions[0].origin;
|
|
int srcBatch = 1, srcChannel = 1, srcArea = 1;
|
|
getBatchChannelArea(realInput, srcBatch, srcChannel, srcArea);
|
|
auto sourceFormat = TensorUtils::getDescribe(realInput)->dimensionFormat;
|
|
auto destFormat = TensorUtils::getDescribe(output)->dimensionFormat;
|
|
auto channelC4 = UP_DIV(srcChannel, core->pack);
|
|
int batchStrideC4 = channelC4 * core->pack * srcArea * bytes;
|
|
int batchStride = srcChannel * srcArea * bytes;
|
|
int inputBatchStride = batchStride;
|
|
int outputBatchStride = batchStride;
|
|
if (MNN_DATA_FORMAT_NC4HW4 == sourceFormat) {
|
|
if (realInput->dimensions() <= 1) {
|
|
::memcpy(output->host<uint8_t>(), realInput->host<uint8_t>(), realInput->elementSize() * bytes);
|
|
return NO_ERROR;
|
|
}
|
|
inputBatchStride = batchStrideC4;
|
|
if (2 == mSingleConvert) {
|
|
destFormat = MNN_DATA_FORMAT_NHWC;
|
|
}
|
|
}
|
|
if (MNN_DATA_FORMAT_NC4HW4 == destFormat) {
|
|
if (output->dimensions() <= 1) {
|
|
::memcpy(output->host<uint8_t>(), realInput->host<uint8_t>(), realInput->elementSize() * bytes);
|
|
return NO_ERROR;
|
|
}
|
|
outputBatchStride = batchStrideC4;
|
|
if (2 == mSingleConvert) {
|
|
sourceFormat = MNN_DATA_FORMAT_NHWC;
|
|
}
|
|
}
|
|
MNN_CONCURRENCY_BEGIN(tId, threadNum) {
|
|
CPUTensorConverter::convert(realInput->host<uint8_t>(), output->host<uint8_t>(), sourceFormat, destFormat, srcBatch, srcArea, srcChannel, bytes, core, tId, threadNum);
|
|
};
|
|
MNN_CONCURRENCY_END();
|
|
return NO_ERROR;
|
|
}
|
|
if (mNeedZero) {
|
|
if (mTempOutput == nullptr) {
|
|
::memset(output->host<void>(), mZeroPoint, outputEleSize * bytes);
|
|
} else {
|
|
::memset(mTempOutput->host<void>(), mZeroPoint, mTempOutput->elementSize() * bytes);
|
|
}
|
|
}
|
|
for (auto& iter : mTempInput) {
|
|
tensorConvert(iter.first, iter.second, bytes);
|
|
}
|
|
auto proc = _selectUnitProc(bytes);
|
|
MNN_CONCURRENCY_BEGIN(tId, threadNum) {
|
|
for (int u=tId; u<mTempInputCopy.size(); u+=threadNum) {
|
|
auto& iter = mTempInputCopy[u];
|
|
auto& slice = *(iter.second);
|
|
auto srcPtr = (uint8_t*)iter.first + slice.src.offset * bytes;
|
|
auto dstPtr = (uint8_t*)mOutputPtr + slice.dst.offset * bytes;
|
|
_blit(slice, bytes, srcPtr, dstPtr, proc);
|
|
}
|
|
}
|
|
MNN_CONCURRENCY_END();
|
|
if (nullptr != mTempOutput) {
|
|
tensorConvert(mTempOutput.get(), output, bytes);
|
|
}
|
|
return NO_ERROR;
|
|
}
|
|
class CPULoop : public Execution {
|
|
public:
|
|
struct ThreadContainer {
|
|
std::vector<std::shared_ptr<Execution>> exe;
|
|
std::vector<uint8_t*> stackPtr;
|
|
};
|
|
CPULoop(Backend* bn, const LoopParam* loop) : Execution(bn) {
|
|
// The LoopParam is created by geometry, won't be released
|
|
mLoop = loop;
|
|
mStack.resize(loop->tensorNumber());
|
|
int numberThread = mLoop->parallel() ? static_cast<CPUBackend*>(backend())->threadNumber() : 1;
|
|
mContainer.resize(numberThread);
|
|
for (int i=0; i<numberThread; ++i) {
|
|
mContainer[i].stackPtr.resize(mLoop->tensorNumber());
|
|
mContainer[i].exe.resize(mLoop->commands()->size());
|
|
}
|
|
}
|
|
virtual ~ CPULoop() {
|
|
// Do nothing
|
|
}
|
|
virtual ErrorCode onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) override {
|
|
int inputIndexSize = mLoop->inputIndexes()->size();
|
|
MNN_ASSERT(inputIndexSize == inputs.size());
|
|
for (int i=0; i<inputIndexSize; ++i) {
|
|
mStack[mLoop->inputIndexes()->data()[i]] = inputs[i];
|
|
}
|
|
int outputIndexSize = mLoop->outputIndexes()->size();
|
|
MNN_ASSERT(outputIndexSize == outputs.size());
|
|
for (int i=0; i<outputIndexSize; ++i) {
|
|
mStack[mLoop->outputIndexes()->data()[i]] = outputs[i];
|
|
}
|
|
int numberThread = mLoop->parallel() ? static_cast<CPUBackend*>(backend())->threadNumber() : 1;
|
|
mMaxCacheSize = 0;
|
|
auto bytes = static_cast<CPUBackend*>(backend())->functions()->bytes;
|
|
for (int i=0; i<mLoop->commands()->size(); ++i) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(i);
|
|
auto op = cmd->op();
|
|
if (OpType_UnaryOp == op->type()) {
|
|
if (nullptr != op->main_as_UnaryOp()) {
|
|
auto view0 = cmd->view()->GetAs<View>(0);
|
|
auto view1 = cmd->view()->GetAs<View>(1);
|
|
MNN_ASSERT(view0->stride()->data()[2] == 1);
|
|
if (view1->stride()->data()[2] != 1) {
|
|
mMaxCacheSize = std::max(mMaxCacheSize, cmd->size()->data()[2] * bytes);
|
|
}
|
|
}
|
|
continue;
|
|
}
|
|
if (OpType_BinaryOp == op->type()) {
|
|
auto view0 = cmd->view()->GetAs<View>(0);
|
|
auto view1 = cmd->view()->GetAs<View>(1);
|
|
if (cmd->fuse() >= 0) {
|
|
if (view1->stride()->data()[2] != 1 || view0->stride()->data()[2] != 1) {
|
|
mMaxCacheSize = std::max(mMaxCacheSize, cmd->size()->data()[2] * bytes);
|
|
}
|
|
continue;
|
|
}
|
|
auto view2 = cmd->view()->GetAs<View>(2);
|
|
MNN_ASSERT(view0->stride()->data()[2] == 1);
|
|
if (view1->stride()->data()[2] != 1 || view2->stride()->data()[2] != 1) {
|
|
mMaxCacheSize = std::max(mMaxCacheSize, 2 * cmd->size()->data()[2] * bytes);
|
|
}
|
|
continue;
|
|
}
|
|
if (OpType_MatMul == op->type()) {
|
|
bool transposeC = true;
|
|
int e = cmd->size()->data()[0];
|
|
int l = cmd->size()->data()[1];
|
|
int h = cmd->size()->data()[2];
|
|
std::shared_ptr<Tensor> A, B, C, Bias;
|
|
C.reset(Tensor::createDevice<float>({e, h}));
|
|
if (op->main_as_MatMul()->transposeA()) {
|
|
A.reset(Tensor::createDevice<float>({l, e}));
|
|
} else {
|
|
A.reset(Tensor::createDevice<float>({e, l}));
|
|
}
|
|
if (op->main_as_MatMul()->transposeB()) {
|
|
B.reset(Tensor::createDevice<float>({h, l}));
|
|
} else {
|
|
B.reset(Tensor::createDevice<float>({l, h}));
|
|
}
|
|
auto view = cmd->view()->GetAs<View>(0);
|
|
if (view->stride()->data()[0] == 1) {
|
|
transposeC = false;
|
|
}
|
|
std::vector<Tensor*> inputs, outputs;
|
|
if (cmd->indexes()->size() > 3) {
|
|
Bias.reset(Tensor::createDevice<float>({h}));
|
|
inputs = {A.get(), B.get(), Bias.get()};
|
|
} else {
|
|
inputs = {A.get(), B.get()};
|
|
}
|
|
outputs = {C.get()};
|
|
auto bufferPool = static_cast<CPUBackend*>(backend())->getBufferAllocator();
|
|
auto code = NO_ERROR;
|
|
if (numberThread > 1) {
|
|
bufferPool->barrierBegin();
|
|
}
|
|
for (int v=0; v<numberThread; ++v) {
|
|
if (numberThread > 1) {
|
|
bufferPool->beginGroup();
|
|
}
|
|
do {
|
|
// If not loop parallel, parallel inside
|
|
bool needParallel = numberThread == 1;
|
|
mContainer[v].exe[i].reset(new CPUMatMul(backend(), op->main_as_MatMul()->transposeA(), op->main_as_MatMul()->transposeB(), transposeC, needParallel));
|
|
if (nullptr == mContainer[v].exe[i]) {
|
|
code = OUT_OF_MEMORY;
|
|
break;
|
|
}
|
|
code = mContainer[v].exe[i]->onResize(inputs, outputs);
|
|
} while (false);
|
|
if (numberThread > 1) {
|
|
bufferPool->endGroup();
|
|
}
|
|
if (NO_ERROR != code) {
|
|
break;
|
|
}
|
|
}
|
|
if (numberThread > 1) {
|
|
bufferPool->barrierEnd();
|
|
}
|
|
if (NO_ERROR != code) {
|
|
return code;
|
|
}
|
|
continue;
|
|
}
|
|
}
|
|
auto threadNumber = static_cast<CPUBackend*>(backend())->threadNumber();
|
|
if (mMaxCacheSize > 0) {
|
|
auto buffer = static_cast<CPUBackend*>(backend())->getBufferAllocator()->alloc(threadNumber * mMaxCacheSize);
|
|
if (nullptr == buffer.first) {
|
|
return OUT_OF_MEMORY;
|
|
}
|
|
mCacheBuffer = (uint8_t*)buffer.first + buffer.second;
|
|
static_cast<CPUBackend*>(backend())->getBufferAllocator()->free(buffer);
|
|
}
|
|
return NO_ERROR;
|
|
}
|
|
|
|
virtual ErrorCode onExecute(const std::vector<Tensor *> &originInputs, const std::vector<Tensor *> &originOutputs) override {
|
|
auto cpubackend = static_cast<CPUBackend*>(backend());
|
|
auto precision = cpubackend->precisionMode();
|
|
auto threadNumber = cpubackend->threadNumber();
|
|
if (mLoop->initCommand() != nullptr) {
|
|
auto cmd = mLoop->initCommand();
|
|
if (cmd->op() == nullptr) {
|
|
::memset(originOutputs[0]->host<void>(), 0, cpubackend->getTensorSize(originOutputs[0]) * cpubackend->functions()->bytes);
|
|
} else {
|
|
Tensor::InsideDescribe::Region reg;
|
|
auto srcView = cmd->view()->GetAs<View>(1);
|
|
auto dstView = cmd->view()->GetAs<View>(0);
|
|
::memcpy(reg.size, cmd->size()->data(), 3 * sizeof(int32_t));
|
|
::memcpy(reg.src.stride, srcView->stride()->data(), 3 * sizeof(int32_t));
|
|
::memcpy(reg.dst.stride, dstView->stride()->data(), 3 * sizeof(int32_t));
|
|
auto input = mStack[cmd->indexes()->data()[1]];
|
|
auto inputSize = input->elementSize();
|
|
auto output = mStack[cmd->indexes()->data()[0]];
|
|
auto bytes = input->getType().bytes();
|
|
if (halide_type_float == input->getType().code) {
|
|
bytes = cpubackend->functions()->bytes;
|
|
}
|
|
auto proc = _selectUnitProc(bytes);
|
|
_blit(reg, bytes, input->host<uint8_t>(), output->host<uint8_t>(), proc);
|
|
}
|
|
}
|
|
if (1 == mLoop->commands()->size()) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(0);
|
|
auto op = cmd->op();
|
|
if (OpType_UnaryOp == op->type() && nullptr == op->main()) {
|
|
// For Gather / Single Unary
|
|
auto index0 = cmd->iterIndexes()->data()[0];
|
|
auto index1 = cmd->iterIndexes()->data()[1];
|
|
int32_t iter = 0;
|
|
int32_t* iter0 = &iter;
|
|
int32_t* iter1 = &iter;
|
|
int32_t iter0Stride = 0;
|
|
int32_t iter1Stride = 0;
|
|
if (index0 >= 0) {
|
|
iter0 = originInputs[index0]->host<int32_t>();
|
|
iter0Stride = 1;
|
|
}
|
|
if (index1 >= 0) {
|
|
iter1 = originInputs[index1]->host<int32_t>();
|
|
iter1Stride = 1;
|
|
}
|
|
Tensor::InsideDescribe::Region reg;
|
|
auto srcView = cmd->view()->GetAs<View>(1);
|
|
auto dstView = cmd->view()->GetAs<View>(0);
|
|
::memcpy(reg.size, cmd->size()->data(), 3 * sizeof(int32_t));
|
|
::memcpy(reg.src.stride, srcView->stride()->data(), 3 * sizeof(int32_t));
|
|
::memcpy(reg.dst.stride, dstView->stride()->data(), 3 * sizeof(int32_t));
|
|
auto input = mStack[cmd->indexes()->data()[1]];
|
|
auto inputSize = input->elementSize();
|
|
auto output = mStack[cmd->indexes()->data()[0]];
|
|
auto bytes = input->getType().bytes();
|
|
if (halide_type_float == input->getType().code) {
|
|
bytes = static_cast<CPUBackend*>(backend())->functions()->bytes;
|
|
}
|
|
auto proc = _selectUnitProc(bytes);
|
|
auto step0 = cmd->steps()->data()[0];
|
|
auto step1 = cmd->steps()->data()[1];
|
|
auto loopNumber = mLoop->loopNumber();
|
|
for (; iter<loopNumber; ++iter) {
|
|
auto srcIter = *(iter1 + iter1Stride * iter);
|
|
auto dstIter = *(iter0 + iter0Stride * iter);
|
|
auto srcOffset = srcIter * step1 + srcView->offset();
|
|
auto dstOffset = dstIter * step0 + dstView->offset();
|
|
if (srcOffset >= 0 && srcOffset < inputSize) {
|
|
_blit(reg, bytes, input->host<uint8_t>() + bytes * srcOffset, output->host<uint8_t>() + bytes * dstOffset, proc);
|
|
} else {
|
|
_zero(reg, bytes, output->host<uint8_t>() + bytes * dstOffset);
|
|
}
|
|
}
|
|
return NO_ERROR;
|
|
}
|
|
}
|
|
auto bytes = static_cast<CPUBackend*>(backend())->functions()->bytes;
|
|
auto func = [&](int iter, int tId) {
|
|
auto blit = _selectUnitProc(bytes);
|
|
for (int index=0; index<mLoop->commands()->size(); ++index) {
|
|
auto cmd = mLoop->commands()->GetAs<RegionCommand>(index);
|
|
auto op = cmd->op();
|
|
int size = cmd->iterIndexes()->size();
|
|
for (int v=0; v<size; ++v) {
|
|
auto tensorIndex = cmd->indexes()->data()[v];
|
|
auto tensor = mStack[tensorIndex];
|
|
auto iterIndex = cmd->iterIndexes()->data()[v];
|
|
auto offset = iter;
|
|
if (iterIndex >= 0) {
|
|
offset = mStack[iterIndex]->host<int32_t>()[iter];
|
|
}
|
|
auto view = cmd->view()->GetAs<View>(v);
|
|
offset = offset * cmd->steps()->data()[v] + view->offset();
|
|
mContainer[tId].stackPtr[tensorIndex] = tensor->host<uint8_t>() + offset * bytes;
|
|
MNN_ASSERT(nullptr != tensor->host<uint8_t>());
|
|
}
|
|
if (OpType_UnaryOp == op->type()) {
|
|
auto src = (uint8_t*)mContainer[tId].stackPtr[cmd->indexes()->data()[1]];
|
|
auto dst = (uint8_t*)mContainer[tId].stackPtr[cmd->indexes()->data()[0]];
|
|
if (nullptr == op->main()) {
|
|
// Copy
|
|
Tensor::InsideDescribe::Region reg;
|
|
auto srcView = cmd->view()->GetAs<View>(1);
|
|
auto dstView = cmd->view()->GetAs<View>(0);
|
|
::memcpy(reg.size, cmd->size()->data(), 3 * sizeof(int32_t));
|
|
::memcpy(reg.src.stride, srcView->stride()->data(), 3 * sizeof(int32_t));
|
|
::memcpy(reg.dst.stride, dstView->stride()->data(), 3 * sizeof(int32_t));
|
|
auto proc = _selectUnitProc(bytes);
|
|
auto step0 = cmd->steps()->data()[0];
|
|
auto step1 = cmd->steps()->data()[1];
|
|
auto loopNumber = mLoop->loopNumber();
|
|
_blit(reg, bytes, (const uint8_t*)src, (uint8_t*)dst, proc);
|
|
continue;
|
|
}
|
|
auto proc = static_cast<CPUBackend*>(backend())->functions()->MNNSelectUnaryFunctionForFloat(op->main_as_UnaryOp()->opType(), static_cast<CPUBackend*>(backend())->precisionMode());
|
|
auto lastS = cmd->size()->data()[2];
|
|
if (lastS == 1 || cmd->view()->GetAs<View>(1)->stride()->data()[2] == 1) {
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto srcZ = src + z * cmd->view()->GetAs<View>(1)->stride()->data()[0] * bytes;
|
|
auto dstZ = dst + z * cmd->view()->GetAs<View>(0)->stride()->data()[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto srcY = srcZ + y * cmd->view()->GetAs<View>(1)->stride()->data()[1] * bytes;
|
|
auto dstY = dstZ + y * cmd->view()->GetAs<View>(0)->stride()->data()[1] * bytes;
|
|
proc(dstY, srcY, lastS);
|
|
}
|
|
}
|
|
} else {
|
|
// Blit to cache
|
|
auto srcCache = mCacheBuffer + mMaxCacheSize * tId;
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto srcZ = src + z * cmd->view()->GetAs<View>(1)->stride()->data()[0] * bytes;
|
|
auto dstZ = dst + z * cmd->view()->GetAs<View>(0)->stride()->data()[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto srcY = srcZ + y * cmd->view()->GetAs<View>(1)->stride()->data()[1] * bytes;
|
|
auto dstY = dstZ + y * cmd->view()->GetAs<View>(0)->stride()->data()[1] * bytes;
|
|
blit(srcCache, srcY, lastS, cmd->view()->GetAs<View>(1)->stride()->data()[2], 1);
|
|
proc(dstY, srcCache, lastS);
|
|
}
|
|
}
|
|
}
|
|
continue;
|
|
}
|
|
if (OpType_MatMul == op->type()) {
|
|
const float* APtr = nullptr;
|
|
const float* BPtr = nullptr;
|
|
const float* BiasPtr = nullptr;
|
|
float* CPtr = nullptr;
|
|
auto exe = static_cast<CPUMatMul*>(mContainer[tId].exe[index].get());
|
|
APtr = (const float*)mContainer[tId].stackPtr[cmd->indexes()->data()[1]];
|
|
BPtr = (const float*)mContainer[tId].stackPtr[cmd->indexes()->data()[2]];
|
|
CPtr = (float*)mContainer[tId].stackPtr[cmd->indexes()->data()[0]];
|
|
if (size > 3) {
|
|
BiasPtr = (const float*)mContainer[tId].stackPtr[cmd->indexes()->data()[3]];
|
|
}
|
|
exe->execute(APtr, BPtr, CPtr, BiasPtr);
|
|
continue;
|
|
}
|
|
if (OpType_BinaryOp == op->type()) {
|
|
auto src0 = mContainer[tId].stackPtr[cmd->indexes()->data()[1]];
|
|
auto dst = mContainer[tId].stackPtr[cmd->indexes()->data()[0]];
|
|
auto proc = static_cast<CPUBackend*>(backend())->functions()->MNNSelectBinaryFunctionForFloat(op->main_as_BinaryOp()->opType());
|
|
auto lastS = cmd->size()->data()[2];
|
|
auto stride0 = cmd->view()->GetAs<View>(0)->stride()->data();
|
|
auto stride1 = cmd->view()->GetAs<View>(1)->stride()->data();
|
|
if (cmd->fuse() >= 0) {
|
|
if (stride0[2] != 1) {
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto src0Z = src0 + z * stride1[0] * bytes;
|
|
auto dstZ = dst + z * stride0[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto src0Y = src0Z + y * stride1[1] * bytes;
|
|
auto dstY = dstZ + y * stride0[1] * bytes;
|
|
for (int x=0; x<cmd->size()->data()[2]; ++x) {
|
|
auto src0X = src0Y + x * stride1[2] * bytes;
|
|
auto dstX = dstY + x * stride0[2] * bytes;
|
|
proc(dstX, dstX, src0X, 1, -1);
|
|
}
|
|
}
|
|
}
|
|
} else if (cmd->size()->data()[2] == 1 || (stride1[2] == 1 && stride0[2] == 1)) {
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto src0Z = src0 + z * stride1[0] * bytes;
|
|
auto dstZ = dst + z * stride0[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto src0Y = src0Z + y * stride1[1] * bytes;
|
|
auto dstY = dstZ + y * stride0[1] * bytes;
|
|
proc(dstY, dstY, src0Y, cmd->size()->data()[2], -1);
|
|
}
|
|
}
|
|
} else {
|
|
auto cache0 = mCacheBuffer + mMaxCacheSize * tId;
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto src0Z = src0 + z * stride1[0] * bytes;
|
|
auto dstZ = dst + z * stride0[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto src0Y = src0Z + y * stride1[1] * bytes;
|
|
auto dstY = dstZ + y * stride0[1] * bytes;
|
|
blit(cache0, src0Y, cmd->size()->data()[2], stride1[2], 1);
|
|
proc(dstY, dstY, cache0, cmd->size()->data()[2], -1);
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
MNN_ASSERT(stride0[2] == 1);
|
|
auto src1 = mContainer[tId].stackPtr[cmd->indexes()->data()[2]];
|
|
auto stride2 = cmd->view()->GetAs<View>(2)->stride()->data();
|
|
if (cmd->size()->data()[2] == 1 || (stride1[2] == 1 && stride2[2] == 1)) {
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto src0Z = src0 + z * stride1[0] * bytes;
|
|
auto src1Z = src1 + z * stride2[0] * bytes;
|
|
auto dstZ = dst + z * stride0[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto src0Y = src0Z + y * stride1[1] * bytes;
|
|
auto src1Y = src1Z + y * stride2[1] * bytes;
|
|
auto dstY = dstZ + y * stride0[1] * bytes;
|
|
proc(dstY, src0Y, src1Y, cmd->size()->data()[2], -1);
|
|
}
|
|
}
|
|
} else {
|
|
auto cache0 = mCacheBuffer + mMaxCacheSize * tId;
|
|
auto cache1 = cache0 + cmd->size()->data()[2] * bytes;
|
|
for (int z=0; z<cmd->size()->data()[0]; ++z) {
|
|
auto src0Z = src0 + z * stride1[0] * bytes;
|
|
auto src1Z = src1 + z * stride2[0] * bytes;
|
|
auto dstZ = dst + z * stride0[0] * bytes;
|
|
for (int y=0; y<cmd->size()->data()[1]; ++y) {
|
|
auto src0Y = src0Z + y * stride1[1] * bytes;
|
|
auto src1Y = src1Z + y * stride2[1] * bytes;
|
|
auto dstY = dstZ + y * stride0[1] * bytes;
|
|
blit(cache0, src0Y, cmd->size()->data()[2], stride1[2], 1);
|
|
blit(cache1, src1Y, cmd->size()->data()[2], stride2[2], 1);
|
|
proc(dstY, cache0, cache1, cmd->size()->data()[2], -1);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
if (mLoop->parallel()) {
|
|
MNN_CONCURRENCY_BEGIN(tId, threadNumber) {
|
|
for (int iter=tId; iter < mLoop->loopNumber(); iter+=threadNumber) {
|
|
func(iter, tId);
|
|
}
|
|
}
|
|
MNN_CONCURRENCY_END();
|
|
} else {
|
|
for (int iter=0; iter < mLoop->loopNumber(); ++iter) {
|
|
func(iter, 0);
|
|
}
|
|
}
|
|
return NO_ERROR;
|
|
}
|
|
private:
|
|
const LoopParam* mLoop;
|
|
std::vector<Tensor*> mStack;
|
|
std::vector<ThreadContainer> mContainer;
|
|
uint8_t* mCacheBuffer = nullptr;
|
|
int mMaxCacheSize = 0;
|
|
};
|
|
|
|
class CPURasterFactory : public CPUBackend::Creator {
|
|
public:
|
|
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
|
|
const MNN::Op* op, Backend* backend) const {
|
|
if (op->type() == OpType_While) {
|
|
if (op->main_type() != OpParameter_LoopParam) {
|
|
return nullptr;
|
|
}
|
|
return new CPULoop(backend, op->main_as_LoopParam());
|
|
}
|
|
return new CPURaster(backend);
|
|
}
|
|
};
|
|
|
|
REGISTER_CPU_OP_CREATOR(CPURasterFactory, OpType_Raster);
|
|
REGISTER_CPU_OP_CREATOR(CPURasterFactory, OpType_While);
|
|
|
|
}
|