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ml_model.hpp
/*
Copyright (c) 2016 Robert W. Rose
Copyright (c) 2018 Paul Maevskikh
Copyright (c) 2024 NORCE
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Note: This file is based on kerasify/keras_model.hh
*/
#ifndef ML_MODEL_H_
#define ML_MODEL_H_
#include <fmt/format.h>
#include <algorithm>
#include <chrono>
#include <cmath>
#include <cstdio>
#include <numeric>
#include <opm/common/ErrorMacros.hpp>
#include <opm/material/densead/Math.hpp>
#include <string>
#include <vector>
namespace Opm
{
namespace ML
{
// NN layer
// ---------------------
template <class T>
class Tensor
{
public:
Tensor()
{
}
explicit Tensor(int i)
{
resizeI<std::vector<int>>({i});
}
Tensor(int i, int j)
{
resizeI<std::vector<int>>({i, j});
}
Tensor(int i, int j, int k)
{
resizeI<std::vector<int>>({i, j, k});
}
Tensor(int i, int j, int k, int l)
{
resizeI<std::vector<int>>({i, j, k, l});
}
template <typename Sizes>
void resizeI(const Sizes& sizes)
{
if (sizes.size() == 1)
dims_ = {(int)sizes[0]};
if (sizes.size() == 2)
dims_ = {(int)sizes[0], (int)sizes[1]};
if (sizes.size() == 3)
dims_ = {(int)sizes[0], (int)sizes[1], (int)sizes[2]};
if (sizes.size() == 4)
dims_ = {(int)sizes[0], (int)sizes[1], (int)sizes[2], (int)sizes[3]};
data_.resize(std::accumulate(begin(dims_), end(dims_), 1.0, std::multiplies<>()));
}
void flatten()
{
OPM_ERROR_IF(dims_.size() == 0, "Invalid tensor");
int elements = dims_[0];
for (unsigned int i = 1; i < dims_.size(); i++) {
elements *= dims_[i];
}
dims_ = {elements};
}
T& operator()(int i)
{
OPM_ERROR_IF(dims_.size() != 1, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
return data_[i];
}
T& operator()(int i, int j)
{
OPM_ERROR_IF(dims_.size() != 2, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
OPM_ERROR_IF(!(j < dims_[1] && j >= 0),
fmt::format(" Invalid j: "
"{}"
" max: "
"{}",
j,
dims_[1]));
return data_[dims_[1] * i + j];
}
const T& operator()(int i, int j) const
{
OPM_ERROR_IF(dims_.size() != 2, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
OPM_ERROR_IF(!(j < dims_[1] && j >= 0),
fmt::format(" Invalid j: "
"{}"
" max: "
"{}",
j,
dims_[1]));
return data_[dims_[1] * i + j];
}
T& operator()(int i, int j, int k)
{
OPM_ERROR_IF(dims_.size() != 3, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
OPM_ERROR_IF(!(j < dims_[1] && j >= 0),
fmt::format(" Invalid j: "
"{}"
" max: "
"{}",
j,
dims_[1]));
OPM_ERROR_IF(!(k < dims_[2] && k >= 0),
fmt::format(" Invalid k: "
"{}"
" max: "
"{}",
k,
dims_[2]));
return data_[dims_[2] * (dims_[1] * i + j) + k];
}
const T& operator()(int i, int j, int k) const
{
OPM_ERROR_IF(dims_.size() != 3, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
OPM_ERROR_IF(!(j < dims_[1] && j >= 0),
fmt::format(" Invalid j: "
"{}"
" max: "
"{}",
j,
dims_[1]));
OPM_ERROR_IF(!(k < dims_[2] && k >= 0),
fmt::format(" Invalid k: "
"{}"
" max: "
"{}",
k,
dims_[2]));
return data_[dims_[2] * (dims_[1] * i + j) + k];
}
T& operator()(int i, int j, int k, int l)
{
OPM_ERROR_IF(dims_.size() != 4, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
OPM_ERROR_IF(!(j < dims_[1] && j >= 0),
fmt::format(" Invalid j: "
"{}"
" max: "
"{}",
j,
dims_[1]));
OPM_ERROR_IF(!(k < dims_[2] && k >= 0),
fmt::format(" Invalid k: "
"{}"
" max: "
"{}",
k,
dims_[2]));
OPM_ERROR_IF(!(l < dims_[3] && l >= 0),
fmt::format(" Invalid l: "
"{}"
" max: "
"{}",
l,
dims_[3]));
return data_[dims_[3] * (dims_[2] * (dims_[1] * i + j) + k) + l];
}
const T& operator()(int i, int j, int k, int l) const
{
OPM_ERROR_IF(dims_.size() != 4, "Invalid indexing for tensor");
OPM_ERROR_IF(!(i < dims_[0] && i >= 0),
fmt::format(" Invalid i: "
"{}"
" max: "
"{}",
i,
dims_[0]));
OPM_ERROR_IF(!(j < dims_[1] && j >= 0),
fmt::format(" Invalid j: "
"{}"
" max: "
"{}",
j,
dims_[1]));
OPM_ERROR_IF(!(k < dims_[2] && k >= 0),
fmt::format(" Invalid k: "
"{}"
" max: "
"{}",
k,
dims_[2]));
OPM_ERROR_IF(!(l < dims_[3] && l >= 0),
fmt::format(" Invalid l: "
"{}"
" max: "
"{}",
l,
dims_[3]));
return data_[dims_[3] * (dims_[2] * (dims_[1] * i + j) + k) + l];
}
void fill(const T& value)
{
std::fill(data_.begin(), data_.end(), value);
}
// Tensor addition
Tensor operator+(const Tensor& other)
{
OPM_ERROR_IF(dims_.size() != other.dims_.size(),
"Cannot add tensors with different dimensions");
Tensor result;
result.dims_ = dims_;
result.data_.resize(data_.size());
std::transform(data_.begin(),
data_.end(),
other.data_.begin(),
result.data_.begin(),
[](const T& x, const T& y) { return x + y; });
return result;
}
// Tensor multiplication
Tensor multiply(const Tensor& other)
{
OPM_ERROR_IF(dims_.size() != other.dims_.size(),
"Cannot multiply elements with different dimensions");
Tensor result;
result.dims_ = dims_;
result.data_.resize(data_.size());
std::transform(data_.begin(),
data_.end(),
other.data_.begin(),
result.data_.begin(),
[](const T& x, const T& y) { return x * y; });
return result;
}
// Tensor dot for 2d tensor
Tensor dot(const Tensor& other)
{
OPM_ERROR_IF(dims_.size() != 2, "Invalid tensor dimensions");
OPM_ERROR_IF(other.dims_.size() != 2, "Invalid tensor dimensions");
OPM_ERROR_IF(dims_[1] != other.dims_[0],
"Cannot multiply with different inner dimensions");
Tensor tmp(dims_[0], other.dims_[1]);
for (int i = 0; i < dims_[0]; i++) {
for (int j = 0; j < other.dims_[1]; j++) {
for (int k = 0; k < dims_[1]; k++) {
tmp(i, j) += (*this)(i, k) * other(k, j);
}
}
}
return tmp;
}
void swap(Tensor& other)
{
dims_.swap(other.dims_);
data_.swap(other.data_);
}
std::vector<int> dims_;
std::vector<T> data_;
};
// NN layer
// ---------------------
template <class Evaluation>
class NNLayer
{
public:
NNLayer()
{
}
virtual ~NNLayer()
{
}
// Loads the ML trained file, returns true if the file exists
virtual bool loadLayer(std::ifstream& file) = 0;
// Apply the NN layers
virtual bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out) = 0;
};
template <class Evaluation>
class NNLayerActivation : public NNLayer<Evaluation>
{
public:
enum class ActivationType {
kLinear = 1,
kRelu = 2,
kSoftPlus = 3,
kSigmoid = 4,
kTanh = 5,
kHardSigmoid = 6
};
NNLayerActivation()
: activation_type_(ActivationType::kLinear)
{
}
bool loadLayer(std::ifstream& file) override;
bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out) override;
private:
ActivationType activation_type_;
};
template <class Evaluation>
class NNLayerScaling : public NNLayer<Evaluation>
{
public:
NNLayerScaling()
: data_min(1.0f)
, data_max(1.0f)
, feat_inf(1.0f)
, feat_sup(1.0f)
{
}
bool loadLayer(std::ifstream& file) override;
bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out) override;
private:
Tensor<float> weights_;
Tensor<float> biases_;
float data_min;
float data_max;
float feat_inf;
float feat_sup;
};
template <class Evaluation>
class NNLayerUnScaling : public NNLayer<Evaluation>
{
public:
NNLayerUnScaling()
: data_min(1.0f)
, data_max(1.0f)
, feat_inf(1.0f)
, feat_sup(1.0f)
{
}
bool loadLayer(std::ifstream& file) override;
bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out) override;
private:
Tensor<float> weights_;
Tensor<float> biases_;
float data_min;
float data_max;
float feat_inf;
float feat_sup;
};
template <class Evaluation>
class NNLayerDense : public NNLayer<Evaluation>
{
public:
bool loadLayer(std::ifstream& file) override;
bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out) override;
private:
Tensor<float> weights_;
Tensor<float> biases_;
NNLayerActivation<Evaluation> activation_;
};
template <class Evaluation>
class NNLayerEmbedding : public NNLayer<Evaluation>
{
public:
bool loadLayer(std::ifstream& file) override;
bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out) override;
private:
Tensor<float> weights_;
};
template <class Evaluation>
class NNModel
{
public:
enum class LayerType { kScaling = 1, kUnScaling = 2, kDense = 3, kActivation = 4 };
virtual ~NNModel() = default;
// loads models (.model files) generated by Kerasify
virtual bool loadModel(const std::string& filename);
virtual bool apply(const Tensor<Evaluation>& in, Tensor<Evaluation>& out);
private:
std::vector<std::unique_ptr<NNLayer<Evaluation>>> layers_;
};
class NNTimer
{
public:
void start()
{
start_ = std::chrono::high_resolution_clock::now();
}
float stop()
{
std::chrono::time_point<std::chrono::high_resolution_clock> now
= std::chrono::high_resolution_clock::now();
std::chrono::duration<double> diff = now - start_;
return diff.count();
}
private:
std::chrono::time_point<std::chrono::high_resolution_clock> start_;
};
} // namespace ML
} // namespace Opm
#endif // ML_MODEL_H_
Math.hpp
A number of commonly used algebraic functions for the localized OPM automatic differentiation (AD) fr...
Opm::ML::NNLayerActivation
Definition ml_model.hpp:392
Opm::ML::NNLayerDense
Definition ml_model.hpp:477
Opm::ML::NNLayerEmbedding
Definition ml_model.hpp:495
Opm::ML::NNModel
Definition ml_model.hpp:510
Opm::ML::NNTimer
Definition ml_model.hpp:528
Opm::ML::Tensor
Implements mathematical tensor (Max 4d).
Definition ml_model.hpp:55
Opm
This class implements a small container which holds the transmissibility mulitpliers for all the face...
Definition Exceptions.hpp:30