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include/aidge/backend/version.h.in 0 → 100644
View file @ 99af25ca
#ifndef VERSION_H
#define VERSION_H
namespace Aidge {
static constexpr const int PROJECT_VERSION_MAJOR = @PROJECT_VERSION_MAJOR@;
static constexpr const int PROJECT_VERSION_MINOR = @PROJECT_VERSION_MINOR@;
static constexpr const int PROJECT_VERSION_PATCH = @PROJECT_VERSION_PATCH@;
static constexpr const char * PROJECT_VERSION = "@PROJECT_VERSION_MAJOR@.@PROJECT_VERSION_MINOR@.@PROJECT_VERSION_PATCH@";
static constexpr const char * PROJECT_GIT_HASH = "@GIT_COMMIT_HASH@";
}
#endif // VERSION_H
include/aidge/data/TensorImpl.hpp deleted 100644 → 0
View file @ 61307e84
#ifndef AIDGE_CPU_DATA_TENSORIMPL_H_
#define AIDGE_CPU_DATA_TENSORIMPL_H_
#include "aidge/backend/TensorImpl.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
template <class T>
class TensorImpl_cpu : public TensorImpl {
private:
const Tensor &mTensor; // Impl needs to access Tensor information, but is not
// supposed to change it!
std::vector<T> mData;
public:
static constexpr const char *Backend = "cpu";
TensorImpl_cpu(const Tensor &tensor) : TensorImpl(Backend), mTensor(tensor) {}
bool operator==(const TensorImpl &otherImpl) const override final {
std::size_t i = 0;
for (; i < mTensor.size() &&
mData[i] == reinterpret_cast<const TensorImpl_cpu<T> &>(otherImpl).data()[i];
++i) {
}
return i == mTensor.size();
}
static std::unique_ptr<TensorImpl_cpu> create(const Tensor &tensor) {
return std::make_unique<TensorImpl_cpu<T>>(tensor);
}
// native interface
const std::vector<T> &data() const { return mData; }
std::size_t scalarSize() const override { return sizeof(T); }
void copy(const void *src, NbElts_t length) override {
std::copy(static_cast<const T *>(src), static_cast<const T *>(src) + length,
static_cast<T *>(rawPtr()));
}
void *rawPtr() override {
lazyInit(mData);
return mData.data();
};
virtual ~TensorImpl_cpu() = default;
void setRawPtr(void *ptr) override final {
T *newPtr = static_cast<T *>(ptr);
mData = std::vector<T>(newPtr, newPtr + mTensor.size());
};
private:
void lazyInit(std::vector<T> &data) {
assert(mTensor.dataType() == NativeType<T>::type);
if (data.size() != mTensor.size()) data.resize(mTensor.size());
}
};
namespace {
static Registrar<Tensor> registrarTensorImpl_cpu_Float64(
{"cpu", DataType::Float64}, Aidge::TensorImpl_cpu<double>::create);
static Registrar<Tensor> registrarTensorImpl_cpu_Float32(
{"cpu", DataType::Float32}, Aidge::TensorImpl_cpu<float>::create);
static Registrar<Tensor> registrarTensorImpl_cpu_Int32(
{"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int>::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_DATA_TENSORIMPL_H_ */
include/aidge/operator/AddImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_ADDIMPL_H_
#define AIDGE_CPU_OPERATOR_ADDIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Add.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
namespace Aidge {
// class Add_Op<2>;
// compute kernel registry for forward and backward
template <DimIdx_t NUM>
class AddImplForward_cpu;
template <DimIdx_t NUM>
class AddImplBackward_cpu;
template <>
class AddImplForward_cpu<1>
: public Registrable<AddImplForward_cpu<1>, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {};
template <>
class AddImplBackward_cpu<1>
: public Registrable<AddImplBackward_cpu<1>, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {};
template <>
class AddImplForward_cpu<2> : public Registrable<AddImplForward_cpu<2>, std::tuple<DataType, DataType, DataType>,
void(const std::size_t, const void*, const void*, void*)> {};
template <>
class AddImplBackward_cpu<2> : public Registrable<AddImplBackward_cpu<2>, std::tuple<DataType, DataType, DataType>,
void(const std::size_t, const void*, const void*, void*)> {};
template <>
class AddImplForward_cpu<3> : public Registrable<AddImplForward_cpu<3>, std::tuple<DataType, DataType, DataType, DataType>,
void(const std::size_t, const void*, const void*, const void*, void*)> {
};
template <>
class AddImplBackward_cpu<3>
: public Registrable<AddImplBackward_cpu<3>, std::tuple<DataType, DataType, DataType, DataType>,
void(const std::size_t, const void*, const void*, const void*, void*)> {};
template <DimIdx_t NUM>
class AddImpl_cpu : public OperatorImpl {
private:
const Add_Op<NUM>& mOp;
std::array<NbElts_t, NUM> mNbConsumedData = {};
std::array<NbElts_t, 1> mNbProducedData = {};
public:
AddImpl_cpu(const Add_Op<NUM>& op) : mOp(op) {}
static std::unique_ptr<AddImpl_cpu<NUM>> create(const Add_Op<NUM>& op) {
return std::make_unique<AddImpl_cpu<NUM>>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final {
assert(mOp.getInput(inputIdx) && "requires valid input");
// Requires the whole tensors
const auto& inputDims = std::static_pointer_cast<Tensor>(mOp.getInput(inputIdx))->dims();
return std::accumulate(inputDims.begin(), inputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
}
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final {
// for the direct convolution algorithm, convolutions can be in-place, if there is no padding!
return 0;
}
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, const std::vector<DimSize_t>& inputsSize) const override final {
// Requires the whole tensors, regardless of available data on inputs
assert(outputIdx == 0 && "operator has only one output");
const auto& outputDims = std::static_pointer_cast<Tensor>(mOp.getOutput(0))->dims();
return std::accumulate(outputDims.begin(), outputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
}
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final {
assert(inputIdx < mNbConsumedData.size());
return mNbConsumedData[inputIdx];
}
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final {
assert(outputIdx < mNbProducedData.size());
return mNbProducedData[outputIdx];
}
void forward() {
// nothing
}
void backward() { printf("Not implemented yet.\n"); }
};
template <>
class AddImpl_cpu<1> : public OperatorImpl {
private:
const Add_Op<1>& mOp;
std::array<NbElts_t, 1> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
AddImpl_cpu(const Add_Op<1>& op) : mOp(op), mNbConsumedData({0}), mNbProducedData({0}) {}
static std::unique_ptr<AddImpl_cpu<1>> create(const Add_Op<1>& op) {
return std::make_unique<AddImpl_cpu<1>>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t /*inputIdx*/) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx,
__attribute__((unused)) const std::vector<DimSize_t> &inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t /*inputIdx*/) const override final;
NbElts_t getNbProducedData(const IOIndex_t /*outputIdx*/) const override final;
void forward();
void backward();
};
template <>
class AddImpl_cpu<2> : public OperatorImpl {
private:
const Add_Op<2>& mOp;
std::array<NbElts_t, 2> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
AddImpl_cpu(const Add_Op<2>& op) : mOp(op), mNbConsumedData({0, 0}), mNbProducedData({0}) {}
static std::unique_ptr<AddImpl_cpu<2>> create(const Add_Op<2>& op) {
return std::make_unique<AddImpl_cpu<2>>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx,
__attribute__((unused)) const std::vector<DimSize_t>& inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t /*outputIdx*/) const override final;
void forward();
void backward();
};
template <>
class AddImpl_cpu<3> : public OperatorImpl {
private:
const Add_Op<3>& mOp;
std::array<NbElts_t, 3> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
AddImpl_cpu(const Add_Op<3>& op) : mOp(op), mNbConsumedData({0, 0, 0}), mNbProducedData({0}) {}
static std::unique_ptr<AddImpl_cpu<3>> create(const Add_Op<3>& op) {
return std::make_unique<AddImpl_cpu<3>>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t /*inputIdx*/) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, const std::vector<DimSize_t>& /*inputsSize*/) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
static Registrar<Add_Op<1>> registrarAddImpl1I_cpu("cpu", Aidge::AddImpl_cpu<1>::create);
static Registrar<Add_Op<2>> registrarAddImpl2I_cpu("cpu", Aidge::AddImpl_cpu<2>::create);
static Registrar<Add_Op<3>> registrarAddImpl3I_cpu("cpu", Aidge::AddImpl_cpu<3>::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_ADDIMPL_H_ */
\ No newline at end of file
include/aidge/operator/AddImpl_forward_kernels.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_ADDIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_ADDIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include "aidge/operator/AddImpl.hpp"
namespace Aidge {
template <class I1, class O>
void AddImpl1I_cpu_forward_kernel(const std::size_t inputLength, const void* input1_, void* output_) {
// FIXME: missing Add parameters as arguments
const I1* input1 = static_cast<const I1*>(input1_);
O* output = static_cast<O*>(output_);
for (std::size_t oIndex = 0; oIndex < inputLength; ++oIndex) {
output[oIndex] = input1[oIndex];
}
}
template <class I1, class I2, class O>
void AddImpl2I_cpu_forward_kernel(const std::size_t inputLength, const void* input1_, const void* input2_,
void* output_) {
// FIXME: missing Add parameters as arguments
const I1* input1 = static_cast<const I1*>(input1_);
const I2* input2 = static_cast<const I2*>(input2_);
O* output = static_cast<O*>(output_);
for (std::size_t oIndex = 0; oIndex < inputLength; ++oIndex) {
output[oIndex] = input1[oIndex] + input2[oIndex];
}
}
template <class I1, class I2, class I3, class O>
void AddImpl3I_cpu_forward_kernel(const std::size_t inputLength, const void* input1_, const void* input2_,
const void* input3_, void* output_) {
// FIXME: missing Add parameters as arguments
const I1* input1 = static_cast<const I1*>(input1_);
const I2* input2 = static_cast<const I2*>(input2_);
const I3* input3 = static_cast<const I3*>(input3_);
O* output = static_cast<O*>(output_);
for (std::size_t oIndex = 0; oIndex < inputLength; ++oIndex) {
output[oIndex] = input1[oIndex] + input2[oIndex] + input3[oIndex];
}
}
namespace {
static Registrar<AddImplForward_cpu<1>> registrarAddImpl1IForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::AddImpl1I_cpu_forward_kernel<float, float>);
static Registrar<AddImplForward_cpu<1>> registrarAddImpl1IForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::AddImpl1I_cpu_forward_kernel<int, int>);
static Registrar<AddImplForward_cpu<1>> registrarAddImpl1IForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::AddImpl1I_cpu_forward_kernel<double, double>);
static Registrar<AddImplForward_cpu<2>> registrarAddImpl2IForward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::AddImpl2I_cpu_forward_kernel<float, float, float>);
static Registrar<AddImplForward_cpu<2>> registrarAddImpl2IForward_cpu_Int32(
{DataType::Int32, DataType::Int32, DataType::Int32}, Aidge::AddImpl2I_cpu_forward_kernel<int, int, int>);
static Registrar<AddImplForward_cpu<2>> registrarAddImpl2IForward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64}, Aidge::AddImpl2I_cpu_forward_kernel<double, double, double>);
static Registrar<AddImplForward_cpu<3>> registrarAddImpl3IForward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::AddImpl3I_cpu_forward_kernel<float, float, float, float>);
static Registrar<AddImplForward_cpu<3>> registrarAddImpl3IForward_cpu_Int32(
{DataType::Int32, DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::AddImpl3I_cpu_forward_kernel<int, int, int, int>);
static Registrar<AddImplForward_cpu<3>> registrarAddImpl3IForward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::AddImpl3I_cpu_forward_kernel<double, double, double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_ADDIMPL_CPU_FORWARD_KERNEL_H_ */
include/aidge/operator/AvgPoolingImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_AVGPOOLINGIMPL_H_
#define AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_H_
#include <array>
#include <memory>
#include <tuple>
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/AvgPooling.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
// class AvgPooling_Op;
// compute kernel registry for forward and backward
class AvgPoolingImpl2DForward_cpu
: public Registrable<AvgPoolingImpl2DForward_cpu,
std::tuple<DataType, DataType>,
void(const AvgPooling_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
class AvgPoolingImpl2DBackward_cpu
: public Registrable<AvgPoolingImpl2DBackward_cpu,
std::tuple<DataType, DataType>,
void(const AvgPooling_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
class AvgPoolingImpl2D_cpu : public OperatorImpl {
private:
const AvgPooling_Op<2> &mOp;
std::array<NbElts_t, 1> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
AvgPoolingImpl2D_cpu(const AvgPooling_Op<2> &op) : mOp(op), mNbConsumedData({0}), mNbProducedData({0}) {}
static std::unique_ptr<AvgPoolingImpl2D_cpu> create(const AvgPooling_Op<2> &op) {
return std::make_unique<AvgPoolingImpl2D_cpu>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, const std::vector<DimSize_t> &inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
// add cpu backend to AvgPooling_Op<2> implementation registry
static Registrar<AvgPooling_Op<2>> registrarAvgPoolingImpl2D_cpu("cpu", Aidge::AvgPoolingImpl2D_cpu::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_H_ */
\ No newline at end of file
include/aidge/operator/AvgPoolingImpl_forward_kernels.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_AVGPOOLINGIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include "aidge/operator/AvgPoolingImpl.hpp"
#include "aidge/utils/Types.h"
#include "aidge/data/Data.hpp"
#include <array>
#include <tuple>
#include <cmath>
namespace Aidge {
/**
* @brief Forward kernel for 2D AvgPoolingolution on CPU backend.
* @tparam I Input data type.
* @tparam O Output data type.
* @param params tuple of Parameters from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param output_ Output Tensor.
*/
template <class I, class O>
void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Parameters &params,
const std::array<DimSize_t, 4> &dims,
const void *input_,
void *output_) {
// FIXME: missing convolution parameters as arguments
const I *input = static_cast<const I *>(input_);
O *output = static_cast<O *>(output_);
// output H size
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<2>(params)[0] + std::get<2>(params)[2] - std::get<1>(params)[0] + std::get<0>(params)[0]) /
static_cast<float>(std::get<0>(params)[0])));
// output W size
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<2>(params)[1] + std::get<2>(params)[3] - std::get<1>(params)[1] + std::get<0>(params)[1]) /
static_cast<float>(std::get<0>(params)[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, ch, Xin, Yin)
// weight (outCh, ch, kernelX, kernelY)
// does not take Dilation parameter into account
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
for (std::size_t ch = 0; ch < dims[1]; ++ch) {
const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
const signedsize difx = static_cast<signedsize>(std::get<2>(params)[0] - ox * std::get<0>(params)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (dims[2] + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<1>(params)[0] ? std::get<1>(params)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
const signedsize dify = static_cast<signedsize>(std::get<2>(params)[1] - oy * std::get<0>(params)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (dims[3] + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<1>(params)[1] ? std::get<1>(params)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
const std::size_t ix = ox * std::get<0>(params)[0];
const std::size_t iy = oy * std::get<0>(params)[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += static_cast<O>(
input[iIndex + (ix+0)*dims[3] + (iy+0)] +
input[iIndex + (ix+0)*dims[3] + (iy+1)] +
input[iIndex + (ix+0)*dims[3] + (iy+2)] +
input[iIndex + (ix+1)*dims[3] + (iy+0)] +
input[iIndex + (ix+1)*dims[3] + (iy+1)] +
input[iIndex + (ix+1)*dims[3] + (iy+2)] +
input[iIndex + (ix+2)*dims[3] + (iy+0)] +
input[iIndex + (ix+2)*dims[3] + (iy+1)] +
input[iIndex + (ix+2)*dims[3] + (iy+2)]) / O(9);
} else {
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
output[oIndexFull] += input[iIndex + (ix+sx)*dims[3] + (iy+sy)];
}
}
// padding not used
output[oIndexFull] /= (sxMax - sxMin) * (syMax - syMin);
}
}
}
}
}
}
namespace {
static Registrar<AvgPoolingImpl2DForward_cpu> registrarAvgPoolingImpl2DForward_cpu_Float32(
std::tuple<DataType, DataType>({DataType::Float32, DataType::Float32}),
Aidge::AvgPoolingImpl2D_cpu_forward_kernel<float, float>);
static Registrar<AvgPoolingImpl2DForward_cpu> registrarAvgPoolingImpl2DForward_cpu_Int32(
{DataType::Int32, DataType::Int32},
Aidge::AvgPoolingImpl2D_cpu_forward_kernel<int, int>);
static Registrar<AvgPoolingImpl2DForward_cpu> registrarAvgPoolingImpl2DForward_cpu_Float64(
{DataType::Float64, DataType::Float64},
Aidge::AvgPoolingImpl2D_cpu_forward_kernel<double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_FORWARD_KERNEL_H_ */
include/aidge/operator/BatchNormImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_BATCHNORMIMPL_H_
#define AIDGE_CPU_OPERATOR_BATCHNORMIMPL_H_
#include <array>
#include <memory>
#include <tuple>
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/BatchNorm.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
// class BatchNorm_Op;
// compute kernel registry for forward and backward
class BatchNormImpl2DForward_cpu
: public Registrable<BatchNormImpl2DForward_cpu,
std::tuple<DataType, DataType, DataType>,
void(const BatchNorm_Op<2>::Parameters &,
const std::array<DimSize_t, 4> &,
const void *,
const void *,
const void *,
void *,
void *,
void *,
const bool)> {};
class BatchNormImpl2DBackward_cpu
: public Registrable<BatchNormImpl2DBackward_cpu,
std::tuple<DataType, DataType, DataType>,
void(const BatchNorm_Op<2>::Parameters &,
const std::array<DimSize_t, 4> &,
const void *,
const void *,
const void *,
void *,
void *,
void *)> {};
class BatchNormImpl2D_cpu : public OperatorImpl {
private:
const BatchNorm_Op<2> &mOp;
std::array<NbElts_t, 5> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
BatchNormImpl2D_cpu(const BatchNorm_Op<2> &op) : mOp(op), mNbConsumedData({0, 0, 0, 0, 0}), mNbProducedData({0}) {}
static std::unique_ptr<BatchNormImpl2D_cpu> create(const BatchNorm_Op<2> &op) {
return std::make_unique<BatchNormImpl2D_cpu>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, const std::vector<DimSize_t> &inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
// add cpu backend to BatchNorm_Op<2> implementation registry
static Registrar<BatchNorm_Op<2>> registrarBatchNormImpl2D_cpu("cpu", Aidge::BatchNormImpl2D_cpu::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_BATCHNORMIMPL_H_ */
\ No newline at end of file
include/aidge/operator/ConvDepthWiseImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_CONVDEPTHWISEIMPL_H_
#define AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMPL_H_
#include <array>
#include <memory>
#include <tuple>
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/ConvDepthWise.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
// class ConvDepthWise_Op;
// compute kernel registry for forward and backward
class ConvDepthWiseImpl2DForward_cpu
: public Registrable<ConvDepthWiseImpl2DForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const ConvDepthWise_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvDepthWiseImpl2DBackward_cpu
: public Registrable<ConvDepthWiseImpl2DBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const ConvDepthWise_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvDepthWiseImpl2D_cpu : public OperatorImpl {
private:
const ConvDepthWise_Op<2> &mOp;
std::array<NbElts_t, 3> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
ConvDepthWiseImpl2D_cpu(const ConvDepthWise_Op<2> &op) : mOp(op), mNbConsumedData({0, 0, 0}), mNbProducedData({0}) {}
static std::unique_ptr<ConvDepthWiseImpl2D_cpu> create(const ConvDepthWise_Op<2> &op) {
return std::make_unique<ConvDepthWiseImpl2D_cpu>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, __attribute__((unused)) const std::vector<DimSize_t> &inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
// add cpu backend to ConvDepthWise_Op<2> implementation registry
static Registrar<ConvDepthWise_Op<2>> registrarConvDepthWiseImpl2D_cpu("cpu", Aidge::ConvDepthWiseImpl2D_cpu::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMPL_H_ */
\ No newline at end of file
include/aidge/operator/ConvDepthWiseImpl_forward_kernels.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_CONVDEPTHWISEIMP_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include "aidge/operator/ConvDepthWiseImpl.hpp"
#include "aidge/utils/Types.h"
#include <cmath>
#include <array>
#include <algorithm>
namespace Aidge {
/**
* @brief Forward kernel for 2D ConvDepthWiseolution on CPU backend.
* @tparam I Input data type.
* @tparam W Weight data type.
* @tparam B Bias data type.
* @tparam O Output data type.
* @param params tuple of Parameters from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param weights_ const weight Tensor.
* @param biases_ const Biais Tensor.
* @param output_ Output Tensor.
*/
template <class I, class W, class B, class O>
void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Parameters &params, const std::array<DimSize_t, 4> &dims,
const void *input_, const void *weights_, const void *biases_, void *output_) {
// FIXME: missing convolution parameters as arguments
const I *input = static_cast<const I *>(input_);
const W *weights = static_cast<const W *>(weights_);
const B *biases = static_cast<const B *>(biases_);
O *output = static_cast<O *>(output_);
// output H size
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<4>(params)[0] + std::get<4>(params)[2] - std::get<3>(params)[0] + std::get<0>(params)[0]) /
static_cast<float>(std::get<0>(params)[0])));
// output W size
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<4>(params)[1] + std::get<4>(params)[3] - std::get<3>(params)[1] + std::get<0>(params)[1]) /
static_cast<float>(std::get<0>(params)[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, ch, Xin, Yin)
// weight (outCh, ch, kernelX, kernelY)
// does not take Dilation parameter into account
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
for (std::size_t ch = 0; ch < std::get<2>(params); ++ch) {
const std::size_t oIndex = (ch + batch*std::get<2>(params)) * oxSize * oySize;
B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
const std::size_t wIndex = ch * std::get<3>(params)[0] * std::get<3>(params)[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
const signedsize difx = static_cast<signedsize>(std::get<4>(params)[0] - ox * std::get<0>(params)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (dims[2] + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<3>(params)[0] ? std::get<3>(params)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
const signedsize dify = static_cast<signedsize>(std::get<4>(params)[1] - oy * std::get<0>(params)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (dims[3] + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<3>(params)[1] ? std::get<3>(params)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
const signedsize ix = static_cast<signedsize>(ox * std::get<0>(params)[0]) - std::get<4>(params)[0];
const signedsize iy = static_cast<signedsize>(oy * std::get<0>(params)[1]) - std::get<4>(params)[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += (weights[wIndex + 0*std::get<3>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 0*std::get<3>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 0*std::get<3>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 1*std::get<3>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 1*std::get<3>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 1*std::get<3>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 2*std::get<3>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 2*std::get<3>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 2*std::get<3>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+2)]);
} else {
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
output[oIndexFull] += weights[wIndex + sx*std::get<3>(params)[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*dims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy))];
}
}
}
}
}
}
}
}
namespace {
static Registrar<ConvDepthWiseImpl2DForward_cpu> registrarConvDepthWiseImpl2DForward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::ConvDepthWiseImpl2D_cpu_forward_kernel<float, float, float, float>);
static Registrar<ConvDepthWiseImpl2DForward_cpu> registrarConvDepthWiseImpl2DForward_cpu_Int32(
{DataType::Int32, DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::ConvDepthWiseImpl2D_cpu_forward_kernel<int, int, int, int>);
static Registrar<ConvDepthWiseImpl2DForward_cpu> registrarConvDepthWiseImpl2DForward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::ConvDepthWiseImpl2D_cpu_forward_kernel<double, double, double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONVDEPTHWISEIMPL_FORWARD_KERNEL_H_ */
include/aidge/operator/ConvImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_CONVIMPL_H_
#define AIDGE_CPU_OPERATOR_CONVIMPL_H_
#include <array>
#include <memory>
#include <tuple>
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Conv.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
// class Conv_Op;
// compute kernel registry for forward and backward
class ConvImpl2DForward_cpu
: public Registrable<ConvImpl2DForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const Conv_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvImpl2DBackward_cpu
: public Registrable<ConvImpl2DBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const Conv_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *,
const void *, const void *, void *)> {};
class ConvImpl2D_cpu : public OperatorImpl {
private:
const Conv_Op<2> &mOp;
std::array<NbElts_t, 3> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
ConvImpl2D_cpu(const Conv_Op<2> &op) : mOp(op), mNbConsumedData({0, 0, 0}), mNbProducedData({0}) {}
static std::unique_ptr<ConvImpl2D_cpu> create(const Conv_Op<2> &op) {
return std::make_unique<ConvImpl2D_cpu>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, __attribute__((unused)) const std::vector<DimSize_t> &inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
// add cpu backend to Conv_Op<2> implementation registry
static Registrar<Conv_Op<2>> registrarConvImpl2D_cpu("cpu", Aidge::ConvImpl2D_cpu::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONVIMPL_H_ */
\ No newline at end of file
include/aidge/operator/ConvImpl_forward_kernels.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_CONVIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_CONVIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include "aidge/operator/ConvImpl.hpp"
#include "aidge/utils/Types.h"
#include <cmath>
#include <array>
#include <algorithm>
namespace Aidge {
/**
* @brief Forward kernel for 2D Convolution on CPU backend.
* @tparam I Input data type.
* @tparam W Weight data type.
* @tparam B Bias data type.
* @tparam O Output data type.
* @param params tuple of Parameters from the Operator
* @param dims Array of input dimensions.
* @param input_ const input Tensor.
* @param weights_ const weight Tensor.
* @param biases_ const Biais Tensor.
* @param output_ Output Tensor.
*/
template <class I, class W, class B, class O>
void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Parameters &params, const std::array<DimSize_t, 4> &dims,
const void *input_, const void *weights_, const void *biases_, void *output_) {
// FIXME: missing convolution parameters as arguments
const I *input = static_cast<const I *>(input_);
const W *weights = static_cast<const W *>(weights_);
const B *biases = static_cast<const B *>(biases_);
O *output = static_cast<O *>(output_);
/*
// output H size
const std::size_t oxSize =
static_cast<std::size_t>(static_cast<float>(dims[0] - std::get<4>(params)[0] + std::get<0>(params)[0]) /
static_cast<float>(std::get<0>(params)[0]));
// output W size
const std::size_t oySize =
static_cast<std::size_t>(static_cast<float>(dims[1] - std::get<4>(params)[1] + std::get<0>(params)[1]) /
static_cast<float>(std::get<0>(params)[1]));
// TODO: kernel computation
// output (Xout, Yout, outCh, batch)
// input (Xin, Yin, inCh, batch)
// weight (kernelX, kernelY, inCh, outCh)
// does not take Dilation parameter into account
for (std::size_t ox = 0; ox < oxSize; ++ox) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
const std::size_t ix = ox * std::get<0>(params)[0];
const std::size_t iy = oy * std::get<0>(params)[1];
for (std::size_t outCh = 0; outCh < std::get<3>(params); ++outCh) {
const std::size_t oIndex = dims[3] * (outCh + std::get<3>(params) * (oy + oySize * ox));
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
for (std::size_t batch = 0; batch < dims[3]; ++batch) {
output[oIndex + batch] = biasVal;
}
for (std::size_t inCh = 0; inCh < dims[2]; ++inCh) {
for (std::size_t sx = 0; sx < std::get<4>(params)[0]; ++sx) {
for (std::size_t sy = 0; sy < std::get<4>(params)[1]; ++sy) {
const std::size_t wIndex =
outCh + std::get<3>(params) * (inCh + dims[2] * (sy + std::get<4>(params)[1] * sx));
std::size_t iIndex = dims[3] * (inCh + dims[2] * ((iy + sy) + dims[1] * (ix + sx)));
for (std::size_t batch = 0; batch < dims[3]; ++batch) {
output[oIndex + batch] += weights[wIndex] * input[iIndex + batch];
}
}
}
}
}
}
}
*/
// output H size
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<5>(params)[0] + std::get<5>(params)[2] - std::get<4>(params)[0] + std::get<0>(params)[0]) /
static_cast<float>(std::get<0>(params)[0])));
// output W size
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<5>(params)[1] + std::get<5>(params)[3] - std::get<4>(params)[1] + std::get<0>(params)[1]) /
static_cast<float>(std::get<0>(params)[1])));
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
// input (batch, inCh, Xin, Yin)
// weight (outCh, inCh, kernelX, kernelY)
// does not take Dilation parameter into account
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
for (std::size_t outCh = 0; outCh < std::get<3>(params); ++outCh) {
const std::size_t oIndex = (outCh + batch*std::get<3>(params)) * oxSize * oySize;
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
for (std::size_t inCh = 0; inCh < dims[1]; ++inCh) {
const std::size_t iIndex = (inCh + batch*dims[1]) * dims[2] * dims[3];
const std::size_t wIndex = (inCh + outCh*dims[1]) * std::get<4>(params)[0] * std::get<4>(params)[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
const signedsize difx = static_cast<signedsize>(std::get<5>(params)[0] - ox * std::get<0>(params)[0]);
const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
const std::size_t sxMax = (dims[2] + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<4>(params)[0] ? std::get<4>(params)[0] : dims[2] + difx);
for (std::size_t oy = 0; oy < oySize; ++oy) {
const signedsize dify = static_cast<signedsize>(std::get<5>(params)[1] - oy * std::get<0>(params)[1]);
const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
const std::size_t syMax = (dims[3] + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<4>(params)[1] ? std::get<4>(params)[1] : dims[3] + dify);
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
const signedsize ix = static_cast<signedsize>(ox * std::get<0>(params)[0]) - std::get<5>(params)[0];
const signedsize iy = static_cast<signedsize>(oy * std::get<0>(params)[1]) - std::get<5>(params)[1];
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += (weights[wIndex + 0*std::get<4>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 0*std::get<4>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 0*std::get<4>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*dims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 1*std::get<4>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 1*std::get<4>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 1*std::get<4>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*dims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 2*std::get<4>(params)[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 2*std::get<4>(params)[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 2*std::get<4>(params)[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*dims[3] + static_cast<std::size_t>(iy+2)]);
} else {
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
output[oIndexFull] += weights[wIndex + sx*std::get<4>(params)[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*dims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy))];
}
}
}
}
}
}
}
}
}
namespace {
static Registrar<ConvImpl2DForward_cpu> registrarConvImpl2DForward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::ConvImpl2D_cpu_forward_kernel<float, float, float, float>);
static Registrar<ConvImpl2DForward_cpu> registrarConvImpl2DForward_cpu_Int32(
{DataType::Int32, DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::ConvImpl2D_cpu_forward_kernel<int, int, int, int>);
static Registrar<ConvImpl2DForward_cpu> registrarConvImpl2DForward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::ConvImpl2D_cpu_forward_kernel<double, double, double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONVIMPL_FORWARD_KERNEL_H_ */
include/aidge/operator/FCImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_FCIMPL_H_
#define AIDGE_CPU_OPERATOR_FCIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/FC.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
#include <array>
namespace Aidge {
// class FC_Op;
// compute kernel registry for forward and backward
class FCImplForward_cpu : public Registrable<FCImplForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const FC_Op::Parameters &, const DimSize_t, const DimSize_t,
const void *, const void *, const void *, void *)> {};
class FCImplBackward_cpu : public Registrable<FCImplBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const FC_Op::Parameters &, const DimSize_t, const DimSize_t,
const void *, const void *, const void *, void *)> {};
class FCImpl_cpu : public OperatorImpl {
private:
const FC_Op &mOp;
std::array<NbElts_t, 3> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
FCImpl_cpu(const FC_Op &op) : mOp(op), mNbConsumedData({0, 0, 0}), mNbProducedData({0}) {}
static std::unique_ptr<FCImpl_cpu> create(const FC_Op &op) { return std::make_unique<FCImpl_cpu>(op); }
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, __attribute__((unused)) const std::vector<DimSize_t> &inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
static Registrar<FC_Op> registrarFCImpl_cpu("cpu", Aidge::FCImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_FCIMPL_H_ */
\ No newline at end of file
include/aidge/operator/LeakyReLUImpl.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_LEAKYRELUIMPL_H_
#define AIDGE_CPU_OPERATOR_LEAKYRELUIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/LeakyReLU.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
namespace Aidge {
// class LeakyReLU_Op;
// compute kernel registry for forward and backward
class LeakyReLUImplForward_cpu
: public Registrable<LeakyReLUImplForward_cpu, std::tuple<DataType, DataType>, void(const LeakyReLU_Op::Parameters&, std::size_t, const void*, void*)> {
};
class LeakyReLUImplBackward_cpu
: public Registrable<LeakyReLUImplBackward_cpu, std::tuple<DataType, DataType>, void(const LeakyReLU_Op::Parameters&, std::size_t, const void*, void*)> {
};
class LeakyReLUImpl_cpu : public OperatorImpl {
private:
const LeakyReLU_Op& mOp;
std::array<NbElts_t, 1> mNbConsumedData;
std::array<NbElts_t, 1> mNbProducedData;
public:
LeakyReLUImpl_cpu(const LeakyReLU_Op& op) : mOp(op), mNbConsumedData({0}), mNbProducedData({0}) {}
static std::unique_ptr<LeakyReLUImpl_cpu> create(const LeakyReLU_Op& op) {
return std::make_unique<LeakyReLUImpl_cpu>(op);
}
public:
NbElts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
NbElts_t getRequiredMemory(__attribute__((unused)) const IOIndex_t outputIdx, __attribute__((unused)) const std::vector<DimSize_t>& inputsSize) const override final;
NbElts_t getNbConsumedData(const IOIndex_t inputIdx) const override final;
NbElts_t getNbProducedData(const IOIndex_t outputIdx) const override final;
void forward();
void backward();
};
namespace {
static Registrar<LeakyReLU_Op> registrarLeakyReLUImpl_cpu("cpu", Aidge::LeakyReLUImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_LEAKYRELUIMPL_H_ */
\ No newline at end of file
include/aidge/operator/SoftmaxImpl_forward_kernels.hpp deleted 100644 → 0
View file @ 61307e84
/********************************************************************************
* 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_CPU_OPERATOR_SOFTMAXIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_SOFTMAXIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include <cstddef>
#include <cmath>
#include "aidge/data/Data.hpp"
#include "aidge/utils/Types.h"
#include "aidge/operator/SoftmaxImpl.hpp"
namespace Aidge {
template <class I, class O>
void SoftmaxImpl_cpu_forward_kernel(const DimSize_t batchSize,
const DimSize_t channelSize,
const DimSize_t featureSize,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
for (std::size_t batch = 0; batch < batchSize; ++batch) {
for (std::size_t feature = 0; feature < featureSize; ++feature) {
std::size_t ioIndex = batch*channelSize*featureSize + feature;
I sum(0.0);
for (std::size_t ch = 0; ch < channelSize; ++ch) {
output[ioIndex] = std::exp(input[ioIndex]);
sum += output[ioIndex];
ioIndex+=featureSize;
}
ioIndex = batch*channelSize*featureSize + feature;
for (std::size_t ch = 0; ch < channelSize; ++ch) {
output[ioIndex] /= sum;
ioIndex += featureSize;
}
}
}
}
namespace {
static Registrar<SoftmaxImplForward_cpu> registrarSoftmaxImplForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::SoftmaxImpl_cpu_forward_kernel<float, float>);
static Registrar<SoftmaxImplForward_cpu> registrarSoftmaxImplForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::SoftmaxImpl_cpu_forward_kernel<int, int>);
static Registrar<SoftmaxImplForward_cpu> registrarSoftmaxImplForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::SoftmaxImpl_cpu_forward_kernel<double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_SOFTMAXIMPL_FORWARD_KERNEL_H_ */
include/aidge/utils/sys_info/CpuVersionInfo.hpp 0 → 100644
View file @ 99af25ca
#ifndef AIDGE_UTILS_SYS_INFO_CPU_VERSION_INFO_H
#define AIDGE_UTILS_SYS_INFO_CPU_VERSION_INFO_H
#include "aidge/utils/Log.hpp"
#include "aidge/backend/cpu_version.h"
namespace Aidge {
constexpr inline const char * getBackendCPUProjectVersion(){
return PROJECT_VERSION;
}
constexpr inline const char * getBackendCPUGitHash(){
return PROJECT_GIT_HASH;
}
void showBackendCpuVersion() {
Log::info("Aidge backend CPU: {} ({}), {} {}", getBackendCPUProjectVersion(), getBackendCPUGitHash(), __DATE__, __TIME__);
// Compiler version
#if defined(__clang__)
/* Clang/LLVM. ---------------------------------------------- */
Log::info("Clang/LLVM compiler version: {}.{}.{}\n", __clang_major__ , __clang_minor__, __clang_patchlevel__);
#elif defined(__ICC) || defined(__INTEL_COMPILER)
/* Intel ICC/ICPC. ------------------------------------------ */
Log::info("Intel ICC/ICPC compiler version: {}\n", __INTEL_COMPILER);
#elif defined(__GNUC__) || defined(__GNUG__)
/* GNU GCC/G++. --------------------------------------------- */
Log::info("GNU GCC/G++ compiler version: {}.{}.{}", __GNUC__, __GNUC_MINOR__, __GNUC_PATCHLEVEL__);
#elif defined(_MSC_VER)
/* Microsoft Visual Studio. --------------------------------- */
Log::info("Microsoft Visual Studio compiler version: {}\n", _MSC_VER);
#else
Log::info("Unknown compiler\n");
#endif
}
} // namespace Aidge
#endif // AIDGE_UTILS_SYS_INFO_CPU_VERSION_INFO_H
project_name.txt
View file @ 99af25ca
aidge_backend_cpu
\ No newline at end of file
aidge_backend_cpu
.pylintrc → pyproject.toml
View file @ 99af25ca
[MASTER]
[project]
name = "aidge_backend_cpu"
description="CPU implementation of operators of the AIDGE framework"
dependencies = [
"numpy",
]
requires-python = ">= 3.10"
readme = "README.md"
license = { file = "LICENSE" }
classifiers = [
"Development Status :: 2 - Pre-Alpha",
"Programming Language :: Python :: 3"
]
dynamic = ["version"] # defined by pbr
[project.urls]
Homepage = "https://www.deepgreen.ai/en/platform"
Documentation = "https://eclipse-aidge.readthedocs.io/en/latest/"
Repository = "https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cpu"
Issues = "https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cpu/-/issues"
Changelog = "https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cpu/-/releases"
[build-system]
requires = [
"setuptools>=64",
"cmake>=3.18.4.post1",
"pbr"
]
build-backend = "setuptools.build_meta"
#####################################################
# SETUPTOOLS
[tool.setuptools]
[tool.setuptools.packages.find]
where = ["."] # list of folders that contain the packages (["."] by default)
include = ["aidge_backend_cpu*"] # package names should match these glob patterns (["*"] by default)
exclude = ["aidge_backend_cpu.unit_tests*"] # exclude packages matching these glob patterns (empty by default)
namespaces = false # to disable scanning PEP 420 namespaces (true by default)
#####################################################
# CIBUILDWHEEL
[tool.cibuildwheel]
build-frontend = "build"
test-requires = "pytest"
test-command = "pytest {project}/aidge_backend_cpu/unit_tests"
# uncomment to run cibuildwheel locally on selected distros
#build=[
# "cp38-manylinux_x86_64",
# "cp39-manylinux_x86_64",
# "cp310-manylinux_x86_64"
# "cp38-win_amd64",
#]
manylinux-x86_64-image = "manylinux_2_28"
### AIDGE DEPENDENCIES DECLARATION
[tool.cibuildwheel.environment]
AIDGE_DEPENDENCIES = "aidge_core" # format => "dep_1 dep_2 ... dep_n"
AIDGE_INSTALL="/AIDGE_INSTALL_CIBUILDWHEEL"
[tool.cibuildwheel.linux]
before-build = [
"bash .gitlab/ci/cibuildwheel_build_deps_before_build_wheel.sh /host"
]
before-test = [
"pip install aidge-core"
]
[tool.cibuildwheel.windows]
before-build = [
"powershell -File .\\.gitlab\\ci\\cibuildwheel_build_deps_before_build_wheel.ps1"
]
before-test = [
"powershell -File .\\.gitlab\\ci\\cibuildwheel_build_deps_before_build_wheel.ps1"
]
#####################################################
# PYLINT
[tool.pylint.main]
# Analyse import fallback blocks. This can be used to support both Python 2 and 3
# compatible code, which means that the block might have code that exists only in
# one or another interpreter, leading to false positives when analysed.
# analyse-fallback-blocks =
# Clear in-memory caches upon conclusion of linting. Useful if running pylint in
# a server-like mode.
# clear-cache-post-run =
# Always return a 0 (non-error) status code, even if lint errors are found. This
# is primarily useful in continuous integration scripts.
# exit-zero =
# A comma-separated list of package or module names from where C extensions may
# be loaded. Extensions are loading into the active Python interpreter and may
# run arbitrary code.
extension-pkg-allow-list= aidge_core, aidge_backend_cpu, torch, tensorflow
extension-pkg-allow-list = ["aidge_core", "aidge_backend_cpu", "torch", "tensorflow"]
# A comma-separated list of package or module names from where C extensions may
# be loaded. Extensions are loading into the active Python interpreter and may
# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
# for backward compatibility.)
extension-pkg-whitelist=
# extension-pkg-whitelist =
# Return non-zero exit code if any of these messages/categories are detected,
# even if score is above --fail-under value. Syntax same as enable. Messages
# specified are enabled, while categories only check already-enabled messages.
fail-on=
# fail-on =
# Specify a score threshold to be exceeded before program exits with error.
fail-under=0.0
# Specify a score threshold under which the program will exit with error.
# fail-under =
# Interpret the stdin as a python script, whose filename needs to be passed as
# the module_or_package argument.
# from-stdin =
# Files or directories to be skipped. They should be base names, not paths.
ignore=CVS
ignore = ["CVS"]
# Add files or directories matching the regular expressions patterns to the
# ignore-list. The regex matches against paths and can be in Posix or Windows
# format. Because '\\' represents the directory delimiter on Windows systems, it
# can't be used as an escape character.
# ignore-paths =
# Add files or directories matching the regex patterns to the ignore-list. The
# regex matches against paths.
ignore-paths=
# Files or directories matching the regular expression patterns are skipped. The
# regex matches against base names, not paths. The default value ignores Emacs
# file locks
# ignore-patterns =
# Files or directories matching the regex patterns are skipped. The regex
# matches against base names, not paths.
ignore-patterns=
# List of module names for which member attributes should not be checked (useful
# for modules/projects where namespaces are manipulated during runtime and thus
# existing member attributes cannot be deduced by static analysis). It supports
# qualified module names, as well as Unix pattern matching.
ignored-modules = ["aidge_core", "aidge_backend_cpu"]
# Python code to execute, usually for sys.path manipulation such as
# pygtk.require().
#init-hook=
# init-hook =
# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
# number of processors available to use.
jobs=1
# Control the amount of potential inferred values when inferring a single
# object. This can help the performance when dealing with large functions or
# complex, nested conditions.
limit-inference-results=100
# number of processors available to use, and will cap the count on Windows to
# avoid hangs.
jobs = 1
# Control the amount of potential inferred values when inferring a single object.
# This can help the performance when dealing with large functions or complex,
# nested conditions.
limit-inference-results = 100
# List of plugins (as comma separated values of python module names) to load,
# usually to register additional checkers.
load-plugins=
# load-plugins =
# Pickle collected data for later comparisons.
persistent=yes
persistent = true
# Minimum Python version to use for version dependent checks. Will default to the
# version used to run pylint.
py-version = "3.11"
# Discover python modules and packages in the file system subtree.
# recursive =
# Add paths to the list of the source roots. Supports globbing patterns. The
# source root is an absolute path or a path relative to the current working
# directory used to determine a package namespace for modules located under the
# source root.
# source-roots =
# When enabled, pylint would attempt to guess common misconfiguration and emit
# user-friendly hints instead of false-positive error messages.
suggestion-mode=yes
suggestion-mode = true
# Allow loading of arbitrary C extensions. Extensions are imported into the
# active Python interpreter and may run arbitrary code.
unsafe-load-any-extension=no
[MESSAGES CONTROL]
# Only show warnings with the listed confidence levels. Leave empty to show
# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
confidence=
# Disable the message, report, category or checker with the given id(s). You
# can either give multiple identifiers separated by comma (,) or put this
# option multiple times (only on the command line, not in the configuration
# file where it should appear only once). You can also use "--disable=all" to
# disable everything first and then reenable specific checks. For example, if
# you want to run only the similarities checker, you can use "--disable=all
# --enable=similarities". If you want to run only the classes checker, but have
# no Warning level messages displayed, use "--disable=all --enable=classes
# --disable=W".
disable=print-statement,
parameter-unpacking,
unpacking-in-except,
old-raise-syntax,
backtick,
long-suffix,
old-ne-operator,
old-octal-literal,
import-star-module-level,
non-ascii-bytes-literal,
raw-checker-failed,
bad-inline-option,
locally-disabled,
file-ignored,
suppressed-message,
useless-suppression,
deprecated-pragma,
use-symbolic-message-instead,
apply-builtin,
basestring-builtin,
buffer-builtin,
cmp-builtin,
coerce-builtin,
execfile-builtin,
file-builtin,
long-builtin,
raw_input-builtin,
reduce-builtin,
standarderror-builtin,
unicode-builtin,
xrange-builtin,
coerce-method,
delslice-method,
getslice-method,
setslice-method,
no-absolute-import,
old-division,
dict-iter-method,
dict-view-method,
next-method-called,
metaclass-assignment,
indexing-exception,
raising-string,
reload-builtin,
oct-method,
hex-method,
nonzero-method,
cmp-method,
input-builtin,
round-builtin,
intern-builtin,
unichr-builtin,
map-builtin-not-iterating,
zip-builtin-not-iterating,
range-builtin-not-iterating,
filter-builtin-not-iterating,
using-cmp-argument,
eq-without-hash,
div-method,
idiv-method,
rdiv-method,
exception-message-attribute,
invalid-str-codec,
sys-max-int,
bad-python3-import,
deprecated-string-function,
deprecated-str-translate-call,
deprecated-itertools-function,
deprecated-types-field,
next-method-defined,
dict-items-not-iterating,
dict-keys-not-iterating,
dict-values-not-iterating,
deprecated-operator-function,
deprecated-urllib-function,
xreadlines-attribute,
deprecated-sys-function,
exception-escape,
comprehension-escape,
c-extension-no-member,
too-many-locals,
missing-class-docstring,
missing-function-docstring,
too-many-ancestor,
too-many-arguments,
protected-access,
too-many-branches,
too-many-ancestors,
wrong-import-order,
wrong-import-position,
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where
# it should appear only once). See also the "--disable" option for examples.
enable=c-extension-no-member
[REPORTS]
# Python expression which should return a score less than or equal to 10. You
# have access to the variables 'error', 'warning', 'refactor', and 'convention'
# which contain the number of messages in each category, as well as 'statement'
# which is the total number of statements analyzed. This score is used by the
# global evaluation report (RP0004).
evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details.
#msg-template=
# Set the output format. Available formats are text, parseable, colorized, json
# and msvs (visual studio). You can also give a reporter class, e.g.
# mypackage.mymodule.MyReporterClass.
output-format=text
# Tells whether to display a full report or only the messages.
reports=no
# Activate the evaluation score.
score=yes
[REFACTORING]
# Maximum number of nested blocks for function / method body
max-nested-blocks=5
# Complete name of functions that never returns. When checking for
# inconsistent-return-statements if a never returning function is called then
# it will be considered as an explicit return statement and no message will be
# printed.
never-returning-functions=sys.exit,argparse.parse_error
[BASIC]
# unsafe-load-any-extension =
[tool.pylint.basic]
# Naming style matching correct argument names.
argument-naming-style=snake_case
argument-naming-style = "snake_case"
# Regular expression matching correct argument names. Overrides argument-
# naming-style.
#argument-rgx=
# Regular expression matching correct argument names. Overrides argument-naming-
# style. If left empty, argument names will be checked with the set naming style.
# argument-rgx =
# Naming style matching correct attribute names.
attr-naming-style=snake_case
attr-naming-style = "snake_case"
# Regular expression matching correct attribute names. Overrides attr-naming-
# style. If left empty, attribute names will be checked with the set naming
# style.
#attr-rgx=
# attr-rgx =
# Bad variable names which should always be refused, separated by a comma.
bad-names=foo,
bar,
baz,
toto,
tutu,
tata
bad-names = ["foo", "bar", "baz", "toto", "tutu", "tata"]
# Bad variable names regexes, separated by a comma. If names match any regex,
# they will always be refused
bad-names-rgxs=
# bad-names-rgxs =
# Naming style matching correct class attribute names.
class-attribute-naming-style=any
class-attribute-naming-style = "any"
# Regular expression matching correct class attribute names. Overrides class-
# attribute-naming-style.
#class-attribute-rgx=
# attribute-naming-style. If left empty, class attribute names will be checked
# with the set naming style.
# class-attribute-rgx =
# Naming style matching correct class constant names.
class-const-naming-style=UPPER_CASE
class-const-naming-style = "UPPER_CASE"
# Regular expression matching correct class constant names. Overrides class-
# const-naming-style.
#class-const-rgx=
# const-naming-style. If left empty, class constant names will be checked with
# the set naming style.
# class-const-rgx =
# Naming style matching correct class names.
class-naming-style=PascalCase
class-naming-style = "PascalCase"
# Regular expression matching correct class names. Overrides class-naming-
# style.
#class-rgx=
# Regular expression matching correct class names. Overrides class-naming-style.
# If left empty, class names will be checked with the set naming style.
# class-rgx =
# Naming style matching correct constant names.
const-naming-style=UPPER_CASE
const-naming-style = "UPPER_CASE"
# Regular expression matching correct constant names. Overrides const-naming-
# style.
#const-rgx=
# style. If left empty, constant names will be checked with the set naming style.
# const-rgx =
# Minimum line length for functions/classes that require docstrings, shorter
# ones are exempt.
docstring-min-length=-1
# Minimum line length for functions/classes that require docstrings, shorter ones
# are exempt.
docstring-min-length = -1
# Naming style matching correct function names.
function-naming-style=snake_case
function-naming-style = "snake_case"
# Regular expression matching correct function names. Overrides function-
# naming-style.
#function-rgx=
# Regular expression matching correct function names. Overrides function-naming-
# style. If left empty, function names will be checked with the set naming style.
# function-rgx =
# Good variable names which should always be accepted, separated by a comma.
good-names=i,
j,
k,
ex,
Run,
_,
good-names = ["i", "j", "k", "ex", "Run", "_"]
# Good variable names regexes, separated by a comma. If names match any regex,
# they will always be accepted
good-names-rgxs=
# good-names-rgxs =
# Include a hint for the correct naming format with invalid-name.
include-naming-hint=no
# include-naming-hint =
# Naming style matching correct inline iteration names.
inlinevar-naming-style=any
inlinevar-naming-style = "any"
# Regular expression matching correct inline iteration names. Overrides
# inlinevar-naming-style.
#inlinevar-rgx=
# inlinevar-naming-style. If left empty, inline iteration names will be checked
# with the set naming style.
# inlinevar-rgx =
# Naming style matching correct method names.
method-naming-style=snake_case
method-naming-style = "snake_case"
# Regular expression matching correct method names. Overrides method-naming-
# style.
#method-rgx=
# style. If left empty, method names will be checked with the set naming style.
# method-rgx =
# Naming style matching correct module names.
module-naming-style=snake_case
module-naming-style = "snake_case"
# Regular expression matching correct module names. Overrides module-naming-
# style.
#module-rgx=
# style. If left empty, module names will be checked with the set naming style.
# module-rgx =
# Colon-delimited sets of names that determine each other's naming style when
# the name regexes allow several styles.
name-group=
# Colon-delimited sets of names that determine each other's naming style when the
# name regexes allow several styles.
# name-group =
# Regular expression which should only match function or class names that do
# not require a docstring.
no-docstring-rgx=^_
# Regular expression which should only match function or class names that do not
# require a docstring.
no-docstring-rgx = "^_"
# List of decorators that produce properties, such as abc.abstractproperty. Add
# to this list to register other decorators that produce valid properties.
# These decorators are taken in consideration only for invalid-name.
property-classes=abc.abstractproperty
# to this list to register other decorators that produce valid properties. These
# decorators are taken in consideration only for invalid-name.
property-classes = ["abc.abstractproperty"]
# Regular expression matching correct type alias names. If left empty, type alias
# names will be checked with the set naming style.
# typealias-rgx =
# Regular expression matching correct type variable names. If left empty, type
# variable names will be checked with the set naming style.
# typevar-rgx =
# Naming style matching correct variable names.
variable-naming-style=snake_case
variable-naming-style = "snake_case"
# Regular expression matching correct variable names. Overrides variable-naming-
# style. If left empty, variable names will be checked with the set naming style.
# variable-rgx =
[tool.pylint.classes]
# Warn about protected attribute access inside special methods
# check-protected-access-in-special-methods =
# Regular expression matching correct variable names. Overrides variable-
# naming-style.
#variable-rgx=
# List of method names used to declare (i.e. assign) instance attributes.
defining-attr-methods = ["__init__", "__new__", "setUp", "__post_init__"]
# List of member names, which should be excluded from the protected access
# warning.
exclude-protected = ["_asdict", "_fields", "_replace", "_source", "_make"]
# List of valid names for the first argument in a class method.
valid-classmethod-first-arg = ["cls"]
# List of valid names for the first argument in a metaclass class method.
valid-metaclass-classmethod-first-arg = ["cls"]
[tool.pylint.design]
# List of regular expressions of class ancestor names to ignore when counting
# public methods (see R0903)
# exclude-too-few-public-methods =
# List of qualified class names to ignore when counting class parents (see R0901)
# ignored-parents =
# Maximum number of arguments for function / method.
max-args = 5
# Maximum number of attributes for a class (see R0902).
max-attributes = 7
# Maximum number of boolean expressions in an if statement (see R0916).
max-bool-expr = 5
# Maximum number of branch for function / method body.
max-branches = 12
# Maximum number of locals for function / method body.
max-locals = 15
# Maximum number of parents for a class (see R0901).
max-parents = 7
# Maximum number of public methods for a class (see R0904).
max-public-methods = 20
# Maximum number of return / yield for function / method body.
max-returns = 6
[FORMAT]
# Maximum number of statements in function / method body.
max-statements = 50
# Minimum number of public methods for a class (see R0903).
min-public-methods = 2
[tool.pylint.exceptions]
# Exceptions that will emit a warning when caught.
overgeneral-exceptions = ["BaseException", "Exception"]
[tool.pylint.format]
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
expected-line-ending-format=
# expected-line-ending-format =
# Regexp for a line that is allowed to be longer than the limit.
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
ignore-long-lines = "^\\s*(# )?<?https?://\\S+>?$"
# Number of spaces of indent required inside a hanging or continued line.
indent-after-paren=4
indent-after-paren = 4
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
# tab).
indent-string=' '
indent-string = " "
# Maximum number of characters on a single line.
max-line-length=200
max-line-length = 200
# Maximum number of lines in a module.
max-module-lines=1000
max-module-lines = 1000
# Allow the body of a class to be on the same line as the declaration if body
# contains single statement.
single-line-class-stmt=no
# single-line-class-stmt =
# Allow the body of an if to be on the same line as the test if there is no else.
# single-line-if-stmt =
[tool.pylint.imports]
# List of modules that can be imported at any level, not just the top level one.
# allow-any-import-level =
# Allow explicit reexports by alias from a package __init__.
# allow-reexport-from-package =
# Allow wildcard imports from modules that define __all__.
# allow-wildcard-with-all =
# Deprecated modules which should not be used, separated by a comma.
# deprecated-modules =
# Allow the body of an if to be on the same line as the test if there is no
# else.
single-line-if-stmt=no
# Output a graph (.gv or any supported image format) of external dependencies to
# the given file (report RP0402 must not be disabled).
# ext-import-graph =
# Output a graph (.gv or any supported image format) of all (i.e. internal and
# external) dependencies to the given file (report RP0402 must not be disabled).
# import-graph =
# Output a graph (.gv or any supported image format) of internal dependencies to
# the given file (report RP0402 must not be disabled).
# int-import-graph =
[LOGGING]
# Force import order to recognize a module as part of the standard compatibility
# libraries.
# known-standard-library =
# Force import order to recognize a module as part of a third party library.
known-third-party = ["enchant"]
# Couples of modules and preferred modules, separated by a comma.
# preferred-modules =
[tool.pylint.logging]
# The type of string formatting that logging methods do. `old` means using %
# formatting, `new` is for `{}` formatting.
logging-format-style=old
logging-format-style = "old"
# Logging modules to check that the string format arguments are in logging
# function parameter format.
logging-modules=logging
logging-modules = ["logging"]
[tool.pylint."messages control"]
# Only show warnings with the listed confidence levels. Leave empty to show all.
# Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
confidence = ["HIGH", "CONTROL_FLOW", "INFERENCE", "INFERENCE_FAILURE", "UNDEFINED"]
# Disable the message, report, category or checker with the given id(s). You can
# either give multiple identifiers separated by comma (,) or put this option
# multiple times (only on the command line, not in the configuration file where
# it should appear only once). You can also use "--disable=all" to disable
# everything first and then re-enable specific checks. For example, if you want
# to run only the similarities checker, you can use "--disable=all
# --enable=similarities". If you want to run only the classes checker, but have
# no Warning level messages displayed, use "--disable=all --enable=classes
# --disable=W".
disable = ["raw-checker-failed", "bad-inline-option", "locally-disabled", "file-ignored", "suppressed-message", "useless-suppression", "deprecated-pragma", "use-symbolic-message-instead", "use-implicit-booleaness-not-comparison-to-string", "use-implicit-booleaness-not-comparison-to-zero", "too-many-locals", "missing-class-docstring", "missing-function-docstring", "too-many-arguments", "protected-access", "too-many-branches", "too-many-ancestors", "wrong-import-order", "wrong-import-position"]
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where it
# should appear only once). See also the "--disable" option for examples.
enable = ["c-extension-no-member"]
[tool.pylint.method_args]
# List of qualified names (i.e., library.method) which require a timeout
# parameter e.g. 'requests.api.get,requests.api.post'
timeout-methods = ["requests.api.delete", "requests.api.get", "requests.api.head", "requests.api.options", "requests.api.patch", "requests.api.post", "requests.api.put", "requests.api.request"]
[tool.pylint.miscellaneous]
# List of note tags to take in consideration, separated by a comma.
notes = ["FIXME", "XXX", "TODO"]
# Regular expression of note tags to take in consideration.
# notes-rgx =
[MISCELLANEOUS]
[tool.pylint.refactoring]
# Maximum number of nested blocks for function / method body
max-nested-blocks = 5
# List of note tags to take in consideration, separated by a comma.
notes=FIXME,
XXX,
TODO
# Complete name of functions that never returns. When checking for inconsistent-
# return-statements if a never returning function is called then it will be
# considered as an explicit return statement and no message will be printed.
never-returning-functions = ["sys.exit", "argparse.parse_error"]
# Regular expression of note tags to take in consideration.
#notes-rgx=
# Let 'consider-using-join' be raised when the separator to join on would be non-
# empty (resulting in expected fixes of the type: ``"- " + " - ".join(items)``)
suggest-join-with-non-empty-separator = true
[tool.pylint.reports]
# Python expression which should return a score less than or equal to 10. You
# have access to the variables 'fatal', 'error', 'warning', 'refactor',
# 'convention', and 'info' which contain the number of messages in each category,
# as well as 'statement' which is the total number of statements analyzed. This
# score is used by the global evaluation report (RP0004).
evaluation = "10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)"
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details.
# msg-template =
# Set the output format. Available formats are: text, parseable, colorized, json2
# (improved json format), json (old json format) and msvs (visual studio). You
# can also give a reporter class, e.g. mypackage.mymodule.MyReporterClass.
# output-format =
# Tells whether to display a full report or only the messages.
# reports =
[SIMILARITIES]
# Activate the evaluation score.
score = true
[tool.pylint.similarities]
# Comments are removed from the similarity computation
ignore-comments=yes
ignore-comments = true
# Docstrings are removed from the similarity computation
ignore-docstrings=yes
ignore-docstrings = true
# Imports are removed from the similarity computation
ignore-imports=no
# ignore-imports =
# Signatures are removed from the similarity computation
ignore-signatures=no
# ignore-signatures =
# Minimum lines number of a similarity.
min-similarity-lines=4
[SPELLING]
min-similarity-lines = 4
[tool.pylint.spelling]
# Limits count of emitted suggestions for spelling mistakes.
max-spelling-suggestions=4
max-spelling-suggestions = 4
# Spelling dictionary name. Available dictionaries: none. To make it work,
# install the 'python-enchant' package.
spelling-dict=
# Spelling dictionary name. No available dictionaries : You need to install both
# the python package and the system dependency for enchant to work.
# spelling-dict =
# List of comma separated words that should be considered directives if they
# appear and the beginning of a comment and should not be checked.
spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
# appear at the beginning of a comment and should not be checked.
spelling-ignore-comment-directives = "fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:"
# List of comma separated words that should not be checked.
spelling-ignore-words=
# spelling-ignore-words =
# A path to a file that contains the private dictionary; one word per line.
spelling-private-dict-file=
# spelling-private-dict-file =
# Tells whether to store unknown words to the private dictionary (see the
# --spelling-private-dict-file option) instead of raising a message.
spelling-store-unknown-words=no
[STRING]
# This flag controls whether inconsistent-quotes generates a warning when the
# character used as a quote delimiter is used inconsistently within a module.
check-quote-consistency=no
# This flag controls whether the implicit-str-concat should generate a warning
# on implicit string concatenation in sequences defined over several lines.
check-str-concat-over-line-jumps=no
[TYPECHECK]
# spelling-store-unknown-words =
[tool.pylint.typecheck]
# List of decorators that produce context managers, such as
# contextlib.contextmanager. Add to this list to register other decorators that
# produce valid context managers.
contextmanager-decorators=contextlib.contextmanager
contextmanager-decorators = ["contextlib.contextmanager"]
# List of members which are set dynamically and missed by pylint inference
# system, and so shouldn't trigger E1101 when accessed. Python regular
# expressions are accepted.
generated-members=
# generated-members =
# Tells whether missing members accessed in mixin class should be ignored. A
# mixin class is detected if its name ends with "mixin" (case insensitive).
ignore-mixin-members=yes
# Tells whether to warn about missing members when the owner of the attribute
# is inferred to be None.
ignore-none=yes
# class is considered mixin if its name matches the mixin-class-rgx option.
# Tells whether to warn about missing members when the owner of the attribute is
# inferred to be None.
ignore-none = true
# This flag controls whether pylint should warn about no-member and similar
# checks whenever an opaque object is returned when inferring. The inference
# can return multiple potential results while evaluating a Python object, but
# some branches might not be evaluated, which results in partial inference. In
# that case, it might be useful to still emit no-member and other checks for
# the rest of the inferred objects.
ignore-on-opaque-inference=yes
# checks whenever an opaque object is returned when inferring. The inference can
# return multiple potential results while evaluating a Python object, but some
# branches might not be evaluated, which results in partial inference. In that
# case, it might be useful to still emit no-member and other checks for the rest
# of the inferred objects.
ignore-on-opaque-inference = true
# List of symbolic message names to ignore for Mixin members.
ignored-checks-for-mixins = ["no-member", "not-async-context-manager", "not-context-manager", "attribute-defined-outside-init"]
# List of class names for which member attributes should not be checked (useful
# for classes with dynamically set attributes). This supports the use of
# qualified names.
ignored-classes=optparse.Values,
thread._local,
_thread._local,
aidge.global_variables,
aidge.cells.abstract_cell.Trainable,
torch,
tensorflow,
# List of module names for which member attributes should not be checked
# (useful for modules/projects where namespaces are manipulated during runtime
# and thus existing member attributes cannot be deduced by static analysis). It
# supports qualified module names, as well as Unix pattern matching.
ignored-modules= aidge_core, aidge_backend_cpu
# Show a hint with possible names when a member name was not found. The aspect
# of finding the hint is based on edit distance.
missing-member-hint=yes
ignored-classes = ["optparse.Values", "thread._local", "_thread._local", "aidge.global_variables", "aidge.cells.abstract_cell.Trainable", "torch", "tensorflow"]
# Show a hint with possible names when a member name was not found. The aspect of
# finding the hint is based on edit distance.
missing-member-hint = true
# The minimum edit distance a name should have in order to be considered a
# similar match for a missing member name.
missing-member-hint-distance=1
missing-member-hint-distance = 1
# The total number of similar names that should be taken in consideration when
# showing a hint for a missing member.
missing-member-max-choices=1
# List of decorators that change the signature of a decorated function.
signature-mutators=
missing-member-max-choices = 1
# Regex pattern to define which classes are considered mixins.
mixin-class-rgx = ".*[Mm]ixin"
[VARIABLES]
# List of decorators that change the signature of a decorated function.
# signature-mutators =
# List of additional names supposed to be defined in builtins. Remember that
# you should avoid defining new builtins when possible.
additional-builtins=
[tool.pylint.variables]
# List of additional names supposed to be defined in builtins. Remember that you
# should avoid defining new builtins when possible.
# additional-builtins =
# Tells whether unused global variables should be treated as a violation.
allow-global-unused-variables=yes
allow-global-unused-variables = true
# List of names allowed to shadow builtins
allowed-redefined-builtins=
# allowed-redefined-builtins =
# List of strings which can identify a callback function by name. A callback
# name must start or end with one of those strings.
callbacks=cb_,
_cb
# List of strings which can identify a callback function by name. A callback name
# must start or end with one of those strings.
callbacks = ["cb_", "_cb"]
# A regular expression matching the name of dummy variables (i.e. expected to
# not be used).
dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
# A regular expression matching the name of dummy variables (i.e. expected to not
# be used).
dummy-variables-rgx = "_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_"
# Argument names that match this expression will be ignored. Default to name
# with leading underscore.
ignored-argument-names=_.*|^ignored_|^unused_
# Argument names that match this expression will be ignored.
ignored-argument-names = "_.*|^ignored_|^unused_"
# Tells whether we should check for unused import in __init__ files.
init-import=no
# init-import =
# List of qualified module names which can have objects that can redefine
# builtins.
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
[CLASSES]
# Warn about protected attribute access inside special methods
check-protected-access-in-special-methods=no
# List of method names used to declare (i.e. assign) instance attributes.
defining-attr-methods=__init__,
__new__,
setUp,
__post_init__
# List of member names, which should be excluded from the protected access
# warning.
exclude-protected=_asdict,
_fields,
_replace,
_source,
_make
# List of valid names for the first argument in a class method.
valid-classmethod-first-arg=cls
# List of valid names for the first argument in a metaclass class method.
valid-metaclass-classmethod-first-arg=cls
[DESIGN]
# List of qualified class names to ignore when countint class parents (see
# R0901)
ignored-parents=
# Maximum number of arguments for function / method.
max-args=5
# Maximum number of attributes for a class (see R0902).
max-attributes=7
# Maximum number of boolean expressions in an if statement (see R0916).
max-bool-expr=5
# Maximum number of branch for function / method body.
max-branches=12
# Maximum number of locals for function / method body.
max-locals=15
# Maximum number of parents for a class (see R0901).
max-parents=7
# Maximum number of public methods for a class (see R0904).
max-public-methods=20
# Maximum number of return / yield for function / method body.
max-returns=6
# Maximum number of statements in function / method body.
max-statements=50
# Minimum number of public methods for a class (see R0903).
min-public-methods=2
[IMPORTS]
# List of modules that can be imported at any level, not just the top level
# one.
allow-any-import-level=
# Allow wildcard imports from modules that define __all__.
allow-wildcard-with-all=no
# Analyse import fallback blocks. This can be used to support both Python 2 and
# 3 compatible code, which means that the block might have code that exists
# only in one or another interpreter, leading to false positives when analysed.
analyse-fallback-blocks=no
# Deprecated modules which should not be used, separated by a comma.
deprecated-modules=
# Output a graph (.gv or any supported image format) of external dependencies
# to the given file (report RP0402 must not be disabled).
ext-import-graph=
# Output a graph (.gv or any supported image format) of all (i.e. internal and
# external) dependencies to the given file (report RP0402 must not be
# disabled).
import-graph=
# Output a graph (.gv or any supported image format) of internal dependencies
# to the given file (report RP0402 must not be disabled).
int-import-graph=
# Force import order to recognize a module as part of the standard
# compatibility libraries.
known-standard-library=
# Force import order to recognize a module as part of a third party library.
known-third-party=enchant
# Couples of modules and preferred modules, separated by a comma.
preferred-modules=
[EXCEPTIONS]
# Exceptions that will emit a warning when being caught. Defaults to
# "BaseException, Exception".
overgeneral-exceptions=BaseException,
Exception
\ No newline at end of file
redefining-builtins-modules = ["six.moves", "past.builtins", "future.builtins", "builtins", "io"]
python_binding/pybind_cpu.cpp
View file @ 99af25ca
#include <pybind11/pybind11.h>
// Need to call this header to register every impl
#include "aidge/aidge_backend_cpu.hpp"
#include "aidge/backend/cpu.hpp"
namespace py = pybind11;
namespace Aidge {
void init_Aidge(py::module& /*m*/){
void init_CpuVersionInfo(py::module& m);
void init_Aidge(py::module& m){
init_CpuVersionInfo(m);
}
PYBIND11_MODULE(aidge_backend_cpu, m) {
init_Aidge(m);
}
......
python_binding/utils/sys_info/pybind_CpuVersionInfo.cpp 0 → 100644
View file @ 99af25ca
#include <pybind11/pybind11.h>
#include "aidge/utils/sys_info/CpuVersionInfo.hpp"
namespace py = pybind11;
namespace Aidge {
void init_CpuVersionInfo(py::module& m){
m.def("show_version", &showBackendCpuVersion);
m.def("get_project_version", &getBackendCPUProjectVersion);
m.def("get_git_hash", &getBackendCPUGitHash);
}
}
setup.cfg 0 → 100644
View file @ 99af25ca
# pbr file
[metadata]
version = file: version.txt

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