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MNN/source/core/Tensor.cpp

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//
// Tensor.cpp
// MNN
//
// Created by MNN on 2018/07/06.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <complex.h>
#include <string.h>
#include <MNN/Tensor.hpp>
#include "MNN_generated.h"
#include "core/Backend.hpp"
#include "core/MNNMemoryUtils.h"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
using namespace std;
namespace MNN {
Tensor::Tensor(int dimSize, DimensionType type) {
MNN_ASSERT(dimSize <= MNN_MAX_TENSOR_DIM);
mDescribe = new InsideDescribe;
mBuffer.dimensions = dimSize;
mBuffer.type = halide_type_of<float>();
mBuffer.device = 0;
mBuffer.host = nullptr;
mBuffer.dim = &mDescribe->dims[0];
switch (type) {
case CAFFE:
mDescribe->dimensionFormat = MNN_DATA_FORMAT_NCHW;
break;
case TENSORFLOW:
mDescribe->dimensionFormat = MNN_DATA_FORMAT_NHWC;
break;
case CAFFE_C4:
mDescribe->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
break;
default:
break;
}
}
Tensor::Tensor(const Tensor* tensor, DimensionType type, bool allocMemory) {
MNN_ASSERT(tensor != nullptr);
auto buffer = tensor->buffer();
mDescribe = new InsideDescribe;
mBuffer.dimensions = buffer.dimensions;
mBuffer.type = buffer.type;
mBuffer.device = 0;
mBuffer.host = nullptr;
mBuffer.dim = &mDescribe->dims[0];
for (int i = 0; i < buffer.dimensions; ++i) {
mBuffer.dim[i].extent = buffer.dim[i].extent;
}
switch (type) {
case CAFFE:
mDescribe->dimensionFormat = MNN_DATA_FORMAT_NCHW;
break;
case TENSORFLOW:
mDescribe->dimensionFormat = MNN_DATA_FORMAT_NHWC;
break;
case CAFFE_C4:
mDescribe->dimensionFormat = MNN_DATA_FORMAT_NC4HW4;
type = CAFFE;
break;
default:
break;
}
// format mapping
auto originType = tensor->getDimensionType();
if (originType != type && buffer.dimensions >= 4) {
std::vector<int> axisMap;
// NCHW -> NHWC
if (originType == CAFFE) {
axisMap.push_back(0);
for (int i = 2; i < buffer.dimensions; ++i) {
axisMap.push_back(i);
}
axisMap.push_back(1);
}
// NHWC -> NCHW
else {
axisMap.push_back(0);
axisMap.push_back(buffer.dimensions - 1);
for (int i = 1; i < buffer.dimensions - 1; ++i) {
axisMap.push_back(i);
}
}
for (int i = 0; i < buffer.dimensions; ++i) {
mBuffer.dim[i].extent = buffer.dim[axisMap[i]].extent;
}
}
TensorUtils::setLinearLayout(this);
if (allocMemory) {
auto memorySize = size();
if (memorySize > 0) {
mDescribe->memoryType = Tensor::InsideDescribe::MEMORY_HOST;
mBuffer.host = (uint8_t*)MNNMemoryAllocAlign(size(), MNN_MEMORY_ALIGN_DEFAULT);
MNN_ASSERT(mBuffer.host != nullptr);
}
}
}
Tensor::~Tensor() {
if (mBuffer.type.code == halide_type_handle) {
auto handles = (void**)mBuffer.host;
for (int i = 0; i < elementSize(); ++i) {
if (nullptr != handles[i]) {
mDescribe->extra.handleFreeFunction(handles[i]);
}
}
}
if (mDescribe->memoryType == InsideDescribe::MEMORY_HOST) {
if (nullptr != mBuffer.host) {
MNNMemoryFreeAlign(mBuffer.host);
}
}
delete mDescribe;
}
Tensor* Tensor::createDevice(const std::vector<int>& dims, halide_type_t type, DimensionType dimType) {
auto shapeTensor = new Tensor((int)dims.size(), dimType);
for (int i = 0; i < dims.size(); ++i) {
shapeTensor->setLength(i, dims[i]);
}
shapeTensor->buffer().type = type;
TensorUtils::setLinearLayout(shapeTensor);
return shapeTensor;
}
Tensor* Tensor::create(const std::vector<int>& dims, halide_type_t type, void* userData, DimensionType dimType) {
Tensor shapeTensor((int)dims.size(), dimType);
for (int i = 0; i < dims.size(); ++i) {
shapeTensor.setLength(i, dims[i]);
}
shapeTensor.buffer().type = type;
bool ownData = userData == nullptr;
auto result = new Tensor(&shapeTensor, dimType, ownData);
if (nullptr != userData) {
result->buffer().host = (uint8_t*)userData;
}
return result;
}
bool Tensor::copyFromHostTensor(const Tensor* hostTensor) {
auto bn = mDescribe->backend;
if (nullptr == bn) {
return false;
}
bn->onCopyBuffer(hostTensor, this);
return true;
}
bool Tensor::copyToHostTensor(Tensor* hostTensor) const {
auto bn = mDescribe->backend;
if (nullptr == bn) {
return false;
}
bn->onCopyBuffer(this, hostTensor);
return true;
}
static Tensor::DimensionType getDimType(const Tensor* origin) {
auto dimformat = TensorUtils::getDescribe(origin)->dimensionFormat;
switch (dimformat) {
case MNN_DATA_FORMAT_NHWC:
return Tensor::TENSORFLOW;
case MNN_DATA_FORMAT_NCHW:
return Tensor::CAFFE;
case MNN_DATA_FORMAT_NC4HW4:
return Tensor::CAFFE_C4;
default:
break;
}
return Tensor::CAFFE;
}
Tensor* Tensor::createHostTensorFromDevice(const Tensor* device, bool copyContent) {
auto tensor = Tensor::create(device->shape(), device->getType(), nullptr, getDimType(device));
if (copyContent) {
device->copyToHostTensor(tensor);
}
return tensor;
}
Tensor::DimensionType Tensor::getDimensionType() const {
if (mDescribe->dimensionFormat == MNN_DATA_FORMAT_NHWC) {
return Tensor::TENSORFLOW;
}
return Tensor::CAFFE;
}
Tensor::HandleDataType Tensor::getHandleDataType() const {
if (halide_type_handle != mBuffer.type.code) {
return HANDLE_NONE;
}
return HANDLE_STRING;
}
void Tensor::setType(int type) {
switch (type) {
case DataType_DT_DOUBLE:
case DataType_DT_FLOAT:
mBuffer.type = halide_type_of<float>();
break;
case DataType_DT_BFLOAT16:
mBuffer.type = halide_type_t(halide_type_float, 16);
break;
case DataType_DT_QINT32:
case DataType_DT_INT32:
case DataType_DT_BOOL:
case DataType_DT_INT64:
mBuffer.type = halide_type_of<int32_t>();
break;
case DataType_DT_QINT8:
case DataType_DT_INT8:
mBuffer.type = halide_type_of<int8_t>();
break;
case DataType_DT_QUINT8:
case DataType_DT_UINT8:
mBuffer.type = halide_type_of<uint8_t>();
break;
case DataType_DT_QUINT16:
case DataType_DT_UINT16:
mBuffer.type = halide_type_of<uint16_t>();
break;
case DataType_DT_QINT16:
case DataType_DT_INT16:
mBuffer.type = halide_type_of<int16_t>();
break;
case DataType_DT_STRING:
mBuffer.type = halide_type_t(halide_type_handle, sizeof(void*) * 8);
mDescribe->extra.handleFreeFunction = (void (*)(void*))::free;
break;
default:
MNN_PRINT("Unsupported data type!");
MNN_ASSERT(false);
break;
}
}
std::vector<int> Tensor::shape() const {
std::vector<int> result;
for (int i = 0; i < mBuffer.dimensions; ++i) {
result.push_back(mBuffer.dim[i].extent);
}
return result;
}
template <typename T>
void printData(const Tensor* tensor, const void* data, const char* fmt) {
const T* buffer = (const T*)data;
if (tensor->dimensions() != 4) {
auto size = tensor->elementSize();
for (int i = 0; i < size; i++) {
MNN_PRINT(fmt, buffer[i]);
}
MNN_PRINT("\n");
return;
}
auto tf = tensor->getDimensionType() == Tensor::TENSORFLOW;
auto batch = tensor->batch();
auto channel = tensor->channel();
auto height = tensor->height();
auto width = tensor->width();
auto unit = sizeof(T);
if (tf) {
auto bytesPerRow = channel * unit;
auto bytesPerImage = width * bytesPerRow;
auto bytesPerBatch = height * bytesPerImage;
for (int b = 0; b < batch; b++) {
auto bytes = buffer + b * bytesPerBatch / unit;
MNN_PRINT("batch %d:\n", b);
for (int h = 0; h < height; h++) {
for (int w = 0; w < width; w++) {
for (int c = 0; c < channel; c++) {
MNN_PRINT(fmt, bytes[h * width * channel + w * channel + c]);
}
MNN_PRINT("\n");
}
MNN_PRINT("--------------\n");
}
}
} else if (TensorUtils::getDescribe(tensor)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4) { // NC/4HW4
auto components = 4;
auto bytesPerRow = width * components * unit;
auto bytesPerImage = height * bytesPerRow;
auto bytesPerBatch = UP_DIV(channel, 4) * bytesPerImage;
for (int b = 0; b < batch; b++) {
auto bytes = buffer + b * bytesPerBatch / unit;
MNN_PRINT("batch %d:\n", b);
for (int c = 0; c < channel; c++) {
for (int h = 0; h < height; h++) {
for (int w = 0; w < width; w++) {
auto n = c / components, r = c % components;
MNN_PRINT(fmt, bytes[(n * width * height + h * width + w) * components + r]);
}
MNN_PRINT("\n");
}
MNN_PRINT("--------------\n");
}
}
} else { // NCHW
auto bytesPerRow = width * unit;
auto bytesPerImage = height * bytesPerRow;
auto bytesPerBatch = channel * bytesPerImage;
for (int b = 0; b < batch; b++) {
auto bytes = buffer + b * bytesPerBatch / unit;
MNN_PRINT("batch %d:\n", b);
for (int c = 0; c < channel; c++) {
for (int h = 0; h < height; h++) {
for (int w = 0; w < width; w++) {
MNN_PRINT(fmt, bytes[c * width * height + h * width + w]);
}
MNN_PRINT("\n");
}
MNN_PRINT("--------------\n");
}
}
}
}
void Tensor::print() const {
// print dimensions
MNN_PRINT("====== Tensor %p ======", this);
MNN_PRINT("\nDimension: ");
for (int i = 0; i < mBuffer.dimensions; i++) {
MNN_PRINT("%d, ", mBuffer.dim[i].extent);
}
// convert to host if needed
auto printee = this;
bool device = this->buffer().host == NULL && this->buffer().device != 0;
if (device) {
printee = this->createHostTensorFromDevice(this, true);
}
auto buffer = printee->buffer().host;
MNN_PRINT("\nData: ");
if (printee->getType().code == halide_type_int) {
if (printee->getType().bits == 8) { // int8
printData<int8_t>(printee, buffer, "%d, ");
} else if (printee->getType().bits == 16) { // int16
printData<int16_t>(printee, buffer, "%d, ");
} else if (printee->getType().bits == 32) { // int32
printData<int32_t>(printee, buffer, "%d, ");
} else {
MNN_PRINT("\nunsupported data type");
}
} else if (printee->getType().code == halide_type_uint) {
if (printee->getType().bits == 8) { // uint8
printData<uint8_t>(printee, buffer, "%d, ");
} else {
MNN_PRINT("\nunsupported data type");
}
} else if (printee->getType().code == halide_type_float) {
if (printee->getType().bits == 32) { // float32
printData<float>(printee, buffer, "%f, ");
} else {
MNN_PRINT("\nunsupported data type\n");
}
} else {
MNN_PRINT("\nunsupported data type");
}
// clean up
if (printee != this) {
delete printee;
}
}
void Tensor::printShape() const {
const int dims = this->dimensions();
MNN_PRINT("\t**Tensor shape**: ");
if (dims == 0) {
MNN_PRINT("\t*Scalar*");
}
for (int i = 0; i < dims; ++i) {
MNN_PRINT("%d, ", this->length(i));
}
MNN_PRINT("\n");
}
int Tensor::size() const {
auto dataSize = mBuffer.type.bytes();
MNN_ASSERT(dataSize >= 1);
for (int i = 0; i < this->buffer().dimensions; i++) {
int currentDimSize = mBuffer.dim[i].extent;
if (mDescribe->dimensionFormat == MNN_DATA_FORMAT_NC4HW4 && 1 == i) {
currentDimSize = ALIGN_UP4(currentDimSize);
}
dataSize *= currentDimSize;
}
return dataSize;
}
} // namespace MNN
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