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Tensor.hpp 28.48 KiB
/********************************************************************************
* Copyright (c) 2023 CEA-List
*
* This program and the accompanying materials are made available under the
* terms of the Eclipse Public License 2.0 which is available at
* http://www.eclipse.org/legal/epl-2.0.
*
* SPDX-License-Identifier: EPL-2.0
*
********************************************************************************/
#ifndef AIDGE_CORE_DATA_TENSOR_H_
#define AIDGE_CORE_DATA_TENSOR_H_
#include <cstring>
#include <set>
#include <memory>
#include <numeric> // std::accumulate
#include <string>
#include <vector>
#include "aidge/backend/TensorImpl.hpp"
#include "aidge/data/Data.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include "aidge/utils/ArrayHelpers.hpp"
namespace Aidge {
/**
* @brief Description for the tensor data structure.
* @details Sets the properties of the tensor without actually containing any data.
* Contains a pointer to an actual contiguous implementation of data.
*/
class Tensor : public Data,
public Registrable<Tensor, std::tuple<std::string, DataType>, std::shared_ptr<TensorImpl>(DeviceIdx_t device, NbElts_t length)> {
private:
DataType mDataType; /** enum to specify data type. */
std::vector<DimSize_t> mDims; /** Dimensions of the tensor. */
std::vector<DimSize_t> mStrides; /** Stride dimensions of the tensor. */
std::shared_ptr<TensorImpl> mImpl; /** Pointer to the actual data implementation. */
std::size_t mImplOffset = 0;
std::shared_ptr<Tensor> mGrad; /** Pointer to the associated gradient Tensor instance. */
// Cached data
std::size_t mSize = 0; /** Number of elements in the Tensor. */
bool mContiguous = true;
public:
static constexpr const char *Type = "Tensor";
/**
* @brief Construct a new empty Tensor object.
* @param dataType Sets the type of inserted data.
*/
Tensor(DataType dataType = DataType::Float32)
: Data(Type),
mDataType(dataType)
{
// ctor
}
/**
* @brief Construct a new Tensor object from another one (shallow copy).
* Data memory is not copied, but shared between the new Tensor and the
* initial one.
*
* @param otherTensor
*/
Tensor(const Tensor&) = default;
Tensor(Tensor&&) = default;
/**
* Perform a deep copy of the tensor.
*/
Tensor clone() const {
Tensor newTensor(*this);
if (!newTensor.isContiguous()) {
newTensor.makeContiguous();
}
else {
std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), mDataType})(mImpl->device().second, mSize);
newImpl->copy(mImpl->rawPtr(mImplOffset), mSize);
newTensor.setImpl(newImpl);
}
return newTensor;
}
/**
* @brief Construct a new Tensor object from the 1-dimension Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
*/
template <typename T, std::size_t SIZE_0>
constexpr Tensor(Array1D<T, SIZE_0> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0}),
mStrides({1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0)),
mSize(SIZE_0) {
mImpl->copyFromHost(&arr.data[0], SIZE_0);
}
template <typename T, std::size_t SIZE_0>
constexpr Tensor &operator=(Array1D<T, SIZE_0> &&arr) {
resize({SIZE_0});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0);
}
mImpl->copyFromHost(&arr.data[0], SIZE_0, mImplOffset);
return *this;
}
/**
* @brief Construct a new Tensor object from the 2-dimensions Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
* @tparam SIZE_1 second array dimension.
*/
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1>
constexpr Tensor(Array2D<T, SIZE_0, SIZE_1> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0, SIZE_1}),
mStrides({SIZE_1, 1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1)),
mSize(SIZE_0 * SIZE_1) {
mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1);
}
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1>
constexpr Tensor &operator=(Array2D<T, SIZE_0, SIZE_1> &&arr) {
resize({SIZE_0, SIZE_1});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1);
}
mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1, mImplOffset);
return *this;
}
/**
* @brief Construct a new Tensor object from the 3-dimensions Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
* @tparam SIZE_1 second array dimension.
* @tparam SIZE_2 third array dimension.
*/
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2>
constexpr Tensor(Array3D<T, SIZE_0, SIZE_1, SIZE_2> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0, SIZE_1, SIZE_2}),
mStrides({SIZE_1 * SIZE_2, SIZE_2, 1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2)),
mSize(SIZE_0 * SIZE_1 * SIZE_2) {
mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2);
}
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2>
constexpr Tensor &operator=(Array3D<T, SIZE_0, SIZE_1, SIZE_2> &&arr) {
resize({SIZE_0, SIZE_1, SIZE_2});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2);
}
mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2, mImplOffset);
return *this;
}
/**
* @brief Construct a new Tensor object from the 4-dimensions Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
* @tparam SIZE_1 second array dimension.
* @tparam SIZE_2 third array dimension.
* @tparam SIZE_3 fourth array dimension.
*/
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2, std::size_t SIZE_3>
constexpr Tensor(Array4D<T, SIZE_0, SIZE_1, SIZE_2, SIZE_3> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0, SIZE_1, SIZE_2, SIZE_3}),
mStrides({SIZE_1 * SIZE_2 * SIZE_3, SIZE_2 * SIZE_3, SIZE_3, 1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3)),
mSize(SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3) {
mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
}
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2, std::size_t SIZE_3>
constexpr Tensor &operator=(Array4D<T, SIZE_0, SIZE_1, SIZE_2, SIZE_3> &&arr) {
resize({SIZE_0, SIZE_1, SIZE_2, SIZE_3});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
}
mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3, mImplOffset);
return *this;
}
/**
* @brief Copy dimensions, datatype and data from another Tensor.
* If current Tensor already has an implementation, data is copied to the
* existing implementation. Tensor backend/device remain untouched.
* If current Tensor does not have an implementation, only a shallow copy
* is performed and the Tensor will share data with t.
* @param t other Tensor object.
* @return Tensor&
*/
Tensor &operator=(const Tensor &t) {
resize(t.dims(), t.strides());
setDataType(t.dataType(), false); // do not convert existing data
if (t.hasImpl()) { if (hasImpl()) {
copyFrom(t);
}
else {
// Perform a shallow copy only
setImpl(t.mImpl, t.mImplOffset);
}
}
else {
setImpl(nullptr);
}
return *this;
}
/**
* @brief Assess data type, dimensions, backend and data are the same.
* @param otherTensor
*/
bool operator==(const Tensor &otherTensor) const {
if ((!mImpl && !otherTensor.mImpl) || (dataType() != otherTensor.dataType()) ||
(dims() != otherTensor.dims()) || (mImpl->backend() != otherTensor.mImpl->backend())) {
return false;
}
return *mImpl == *(otherTensor.mImpl);
}
/**
* @brief Set the backend of the Tensor associated implementation. If there
* was no previous implementation set, data will be allocated, but it will
* not be initialized to any particular value.
* If data was already initialized in a previous backend, it will be moved
* to the new one except if copyFrom is false.
* @param name Backend name
* @param device Backend device
* @param copyFrom If true (default), move data from previous backend/device
* to the new one. Previous data is lost otherwise.
*/
inline void setBackend(const std::string &name, DeviceIdx_t device = 0, bool copyFrom = true) {
if (mImpl) {
if (mImpl->device() != std::make_pair(name, device)) {
// Backend change: create new impl, copy from old to new and replace
// impl
std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({name, mDataType})(device, mImpl->size());
if (copyFrom) {
newImpl->copyFrom(*mImpl, mImpl->size(), mImplOffset, 0);
}
setImpl(newImpl);
}
}
else {
mImpl = Registrar<Tensor>::create({name, mDataType})(device, mSize);
}
}
/**
* @brief Get a list of available backends.
* @return std::set<std::string>
*/
static std::set<std::string> getAvailableBackends(){
std::set<std::string> backendsList;
for(std::tuple<std::string, DataType> tupleKey : Registrar<Tensor>::getKeys())
backendsList.insert(std::get<0>(tupleKey));
return backendsList;
}
/**
* @brief Get the data type enum.
* @return constexpr DataType
*/
constexpr DataType dataType() const { return mDataType; }
/**
* @brief Set the DataType of the Tensor and converts data
* if the Tensor has already been initialized and copyCast is true.
* @param dt DataType
* @param copyCast If true (default), previous data is copy-casted. Otherwise
* previous data is lost.
*/
void setDataType(const DataType dt, bool copyCast = true) {
if (mImpl && (dataType() != dt)) {
std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), dt})(mImpl->device().second, mImpl->size());
if (copyCast) {
newImpl->copyCast(mImpl->rawPtr(mImplOffset), mDataType, mImpl->size());
}
setImpl(newImpl);
}
mDataType = dt;
}
/**
* @brief Get the Impl object
* @return constexpr const std::shared_ptr<TensorImpl>&
*/
constexpr const std::shared_ptr<TensorImpl> &getImpl() const { return mImpl; }
constexpr std::size_t getImplOffset() const { return mImplOffset; }
/**
* @brief Set the Impl object
*
* @param impl New impl shared pointer
* @param implOffset Storage offset in this new impl for this Tensor
*/
void setImpl(std::shared_ptr<TensorImpl> impl, std::size_t implOffset = 0) {
mImpl = impl;
mImplOffset = implOffset;
}
/**
* @brief Return if an implementaiton has been associated.
* @return true
* @return false
*/
bool hasImpl() const { return (mImpl) ? true : false; }
/**
* @brief Get number of dimensions of the Tensor.
* @return std::size_t
*/
inline std::size_t nbDims() const { return mDims.size(); }
/**
* @brief Get dimensions of the Tensor object.
* @tparam DIM number of dimensions.
* @return constexpr std::array<DimSize_t, DIM>
*/
template <DimIdx_t DIM>
constexpr std::array<DimSize_t, DIM> dims() const {
assert(DIM == mDims.size() && "wrong number of dimensions");
return to_array<DIM>(mDims.cbegin());
}
/**
* @brief Get dimensions of the Tensor object.
* @return constexpr const std::vector<DimSize_t>&
*/
constexpr const std::vector<DimSize_t> &dims() const { return mDims; }
/**
* @brief Get strides of the Tensor object.
* @return constexpr const std::vector<DimSize_t>& */
constexpr const std::vector<DimSize_t> &strides() const { return mStrides; }
/**
* @brief Return true if Tensor is contiguous in memory.
* @return bool
*/
constexpr bool isContiguous() const { return mContiguous; }
/**
* @brief Get the number of elements in the Tensor object.
* @return constexpr std::size_t
*/
constexpr std::size_t size() const { return mSize; }
/**
* @brief Change the dimensions of the Tensor object according to the given argument.
* If the overall size is not changed (meaning we actually only performed a
* reshape), data is garanteed to remain valid.
* Otherwise, no garantee is provided regarding the validy of previous data
* (unlike std::vector). If the new overall size is larger than the previous
* one, all previous data is invalided. Otherwise, previous data may or may
* not remain valid, depending on the backend implementation.
* @tparam DIM Number of dimensions.
* @param dims New dimensions
*/
template <std::array<DimSize_t, 1>::size_type DIM> // deducing std::array size_type and declaring DIM accordingly
inline void resize(const std::array<DimSize_t, DIM> &dims) {
resize(std::vector<DimSize_t>(dims.begin(), dims.end()));
}
/**
* @brief Change the dimensions of the Tensor object according to the given argument.
* If the overall size is not changed (meaning we actually only performed a
* reshape), data is garanteed to remain valid.
* Otherwise, no garantee is provided regarding the validy of previous data
* (unlike std::vector). If the new overall size is larger than the previous
* one, all previous data is invalided. Otherwise, previous data may or may
* not remain valid, depending on the backend implementation.
* @param dims New dimensions
* @param strides Stride of the tensor (if not specified, "nested" stride is used)
*/
void resize(const std::vector<DimSize_t> &dims, std::vector<DimSize_t> strides = std::vector<DimSize_t>());
/**
* @brief Return if the Tensor object has at leastone element.
* @return true
* @return false
*/
bool empty() const { return mDims.empty(); }
template <typename expectedType>
const expectedType& get(std::size_t idx) const {
AIDGE_ASSERT(NativeType<expectedType>::type == mDataType, "wrong data type");
AIDGE_ASSERT(idx < mSize, "idx out of range");
return *reinterpret_cast<expectedType *>(mImpl->hostPtr(mImplOffset + idx));
}
template <typename expectedType>
const expectedType& get(std::vector<std::size_t> coordIdx) const {
return get<expectedType>(getStorageIdx(coordIdx));
}
template <typename expectedType>
void set(std::size_t idx, expectedType value){
AIDGE_ASSERT(NativeType<expectedType>::type == mDataType, "wrong data type");
AIDGE_ASSERT(idx < mSize, "idx out of range");
expectedType* dataPtr = static_cast<expectedType*>(mImpl->hostPtr(mImplOffset + idx));
*dataPtr = value;
}
template <typename expectedType>
void set(std::vector<std::size_t> coordIdx, expectedType value){
set<expectedType>(getStorageIdx(coordIdx), value);
}
std::string toString() const;
inline void print() const { printf("%s\n", toString().c_str()); }
std::shared_ptr<Tensor> grad() {
if (!mGrad) {
mGrad = std::make_shared<Tensor>(mDataType);
mGrad->resize(mDims);
if (mImpl) mGrad->setBackend(mImpl->backend());
}
return mGrad;
}
/**
* @brief From the the 1D contiguous index, return the coordinate of an element in the tensor.
* Beware: do not use this function with the storage index!
*
* @param flatIdx 1D contiguous index of the value considering a flatten, contiguous, tensor.
* @return std::vector<DimSize_t>
*/
std::vector<std::size_t> getCoord(std::size_t flatIdx) const {
std::vector<std::size_t> coordIdx = std::vector<std::size_t>(mDims.size());
std::size_t idx = flatIdx;
for (std::size_t i = mDims.size() - 1; i > 0; --i){
coordIdx[i] = (idx % mDims[i]);
idx/=mDims[i];
}
coordIdx[0] = idx % mDims[0];
return coordIdx;
}
/**
* @brief From the coordinate returns the 1D contiguous index of an element in the tensor.
* If the number of coordinates is inferior to the number of dimensions,
* the remaining coordinates are assumed to be 0.
* Beware: the contiguous index will only correspond to the storage index
* if the tensor is contiguous!
*
* @param coordIdx Coordinate to an element in the tensor
* @return DimSize_t Contiguous index
*/
std::size_t getIdx(const std::vector<std::size_t>& coordIdx) const {
AIDGE_ASSERT(coordIdx.size() <= mDims.size(), "Coordinates does not match number of dimensions");
std::size_t flatIdx = 0;
std::size_t i = 0;
for(; i < coordIdx.size() - 1; ++i){
AIDGE_ASSERT(coordIdx[i] < mDims[i], "Coordinates dimensions does not fit the dimensions of the tensor");
flatIdx = (flatIdx + coordIdx[i]) * mDims[i + 1];
}
return flatIdx + coordIdx[i];
}
/**
* @brief From the coordinate returns the 1D storage index of an element in the tensor.
* If the number of coordinates is inferior to the number of dimensions,
* the remaining coordinates are assumed to be 0.
*
* @param coordIdx Coordinate to an element in the tensor
* @return DimSize_t Storage index
*/
std::size_t getStorageIdx(const std::vector<std::size_t>& coordIdx) const {
AIDGE_ASSERT(coordIdx.size() <= mDims.size(), "Coordinates does not match number of dimensions"); return std::inner_product(coordIdx.begin(), coordIdx.end(), mStrides.begin(), DimSize_t(0));
}
/**
* @brief Returns a sub-tensor with one or more dimension less.
* For instance, t.extract({1}) on a CHW tensor will return the HW tensor
* of channel #1.
* Likewise, t.extract({0, 1}) on a NCHW tensor will return the HW tensor
* of batch #0 and channel #1.
* No memory copy is performed, the returned tensor does not own the memory.
* If the number of coordinates matches the number of dimensions, an empty
* tensor is returned.
* It current tensor was contiguous, the returned tensor is garanteed to be
* contiguous as well.
*
* @param coordIdx Coordinates of the sub-tensor to extract
* @return Tensor Sub-tensor.
*/
Tensor extract(const std::vector<std::size_t>& coordIdx) const;
/**
* @brief Returns a sub-tensor at some coordinate and with some dimension.
*
* @param coordIdx First coordinates of the sub-tensor to extract
* @param dims Dimensions of the sub-tensor to extract
* @return Tensor Sub-tensor.
*/
Tensor extract(const std::vector<std::size_t>& coordIdx, const std::vector<std::size_t>& dims) const;
/**
* @brief Make the tensor's storage contiguous, if it is not already the case.
* If not contiguous, a new memory space is allocated.
*/
void makeContiguous();
/**
* Copy-cast data from a Tensor on the same device.
* If current tensor backend/device is set and is different from src, an
* assertion is raised.
* @param src Source tensor to copy-cast from.
*/
void copyCast(const Tensor& src);
/**
* Copy data from a Tensor from another backend/device.
* If current tensor data type is set and is different from src, an
* assertion is raised.
* @param src Source tensor to copy from.
*/
void copyFrom(const Tensor& src);
/**
* Copy-cast data from a Tensor.
* @param src Source tensor to copy-cast from.
* @param movedSrc shared_ptr to an indermediate Tensor that will
* contain the moved data if a device change should occur AND a type
* conversion is necessary (otherwise it remains unused).
* Any data already present will be overwritten. No new memory allocation
* will occur if movedSrc has already been allocated with the right
* type/size/device.
* If required, memory is always allocated on current (destination)
* Tensor's device.
*/
void copyCastFrom(const Tensor& src, std::shared_ptr<Tensor>& movedSrc);
/**
* Copy-cast data from a Tensor.
* In case of both a device change AND a data type conversion, an
* intermediate buffer on will be allocated and deallocated each time.
* If required, buffer's memory is always allocated on current (destination) * Tensor's device.
* @param src Source tensor to copy-cast from.
*/
void copyCastFrom(const Tensor& src) {
// Internal buffer will be allocated and deallocated at each call
// (only if needed)
std::shared_ptr<Tensor> movedSrc;
copyCastFrom(src, movedSrc);
}
/**
* Return a reference to a Tensor that is garanteed to be contiguous:
* - itself, if already contiguous;
* - the provided Tensor, overwritten with the copied data.
* The data type, backend and device stay the same.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @return Reference to either itself or to fallback.
*/
Tensor& refContiguous(std::shared_ptr<Tensor>& fallback);
const Tensor& refContiguous(std::shared_ptr<Tensor>& fallback) const;
/**
* Return a reference to a Tensor casted to the desired data type:
* - itself, if already at the right data type;
* - the provided Tensor, overwritten with the copy-casted data.
* The backend stays the same.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param dt The desired data type.
* @return Reference to either itself or to fallback.
*/
Tensor& refCast(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt);
const Tensor& refCast(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt) const;
/**
* Return a reference to a Tensor on the desired backend/device:
* - itself, if already on the right device;
* - the provided Tensor, overwritten with the copied data.
* The data type stays the same.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param backend The desired backend.
* @param device The desired device.
* @return Reference to either itself or to fallback.
*/
Tensor& refFrom(std::shared_ptr<Tensor>& fallback, const std::string &backend, DeviceIdx_t device = 0);
const Tensor& refFrom(std::shared_ptr<Tensor>& fallback, const std::string &backend, DeviceIdx_t device = 0) const;
/**
* Return a reference to a Tensor on desired data type and backend/device:
* - itself, if already with the right characteristics;
* - the provided Tensor, overwritten with the copy-casted data.
* If required, fallback is always allocated on desired (destination)
* device.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param dt The desired data type.
* @param backend The desired backend.
* @param device The desired device.
* @return Reference to either itself or to fallback.
*/ Tensor& refCastFrom(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt, const std::string &backend, DeviceIdx_t device = 0) {
// First refFrom, to ensure that fallback, if required, is also on desired device
return refFrom(fallback, backend, device).refCast(fallback, dt);
}
/**
* Return a reference to a Tensor with same characteristics
* (data type, backend/device) as targetReqs Tensor:
* - itself, if already with the right characteristics;
* - the provided Tensor, overwritten with the copy-casted data.
* If required, fallback is always allocated on current (destination)
* Tensor's device.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param targetReqs Tensor with the desired target characteristics.
* @return Reference to either itself or to fallback.
*/
Tensor& refCastFrom(std::shared_ptr<Tensor>& fallback, const Tensor& targetReqs) {
const auto& device = targetReqs.getImpl()->device();
return refCastFrom(fallback, targetReqs.dataType(), device.first, device.second);
}
/**
* @brief Return a reference to a Tensor on desired data type and backend/device:
* - itself, if already with the right characteristics;
* - the provided Tensor, overwritten with the right characteristics.
* @note no data is copy-casted. If it was so in a previous refCastFrom() on
* the same fallback, it remains valid, otherwise, data is invalid.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param dt The desired data type.
* @param backend The desired backend.
* @param device The desired device.
* @return Reference to either itself or to fallback.
*/
Tensor& ref(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt, const std::string &backend, DeviceIdx_t device = 0);
const Tensor& ref(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt, const std::string &backend, DeviceIdx_t device = 0) const;
/**
* @brief Return a reference to a Tensor with same characteristics
* (data type, backend/device) as targetReqs Tensor:
* - itself, if already with the right characteristics;
* - the provided Tensor, overwritten with the right characteristics.
* @note no data is copy-casted. If it was so in a previous refCastFrom() on
* the same fallback, it remains valid, otherwise, data is invalid.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param targetReqs Tensor with the desired target characteristics.
* @return Reference to either itself or to fallback.
*/
Tensor& ref(std::shared_ptr<Tensor>& fallback, const Tensor& targetReqs) {
const auto& device = targetReqs.getImpl()->device();
return ref(fallback, targetReqs.dataType(), device.first, device.second);
}
private:
/**
* @brief Compute the number of elements in the Tensor.
* @note If dimensions are not empty, they are multiplied to get the total number
* of elements. Else, the Tensor represents a scalar and contains a single element.
*/
void computeSize() {
mSize = std::accumulate(mDims.begin(), mDims.end(), DimSize_t(1), std::multiplies<DimSize_t>());
}};
} // namespace Aidge
#endif /* AIDGE_CORE_DATA_TENSOR_H_ */