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Commit a7666da2 authored by Cyril Moineau's avatar Cyril Moineau
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Merge branch 'dev' into 'main'

0.2.2

Closes #12

See merge request eclipse/aidge/aidge_backend_cpu!61
parents 43e9a1c1 566878e6
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with 82 additions and 847 deletions
# Version 0.2.2 (May 14, 2024)
* Remove implmentation for Operators soly handling memory and format
- Concat
- Gather
- Memorize
- Pop
- Reshape
- Slice
- Transpose
* Fix ReLU backward kernel
* Add `showCpuVersion()` function to show which compiler was used
# Version 0.2.1 (April 11, 2024)
Fix: explicit linkage with fmt
......
cmake_minimum_required(VERSION 3.15)
file(READ "${CMAKE_SOURCE_DIR}/version.txt" version)
file(READ "${CMAKE_SOURCE_DIR}/project_name.txt" project)
file(STRINGS "${CMAKE_SOURCE_DIR}/version.txt" version)
add_definitions(-DPROJECT_VERSION="${version}")
file(STRINGS "${CMAKE_SOURCE_DIR}/project_name.txt" project)
message(STATUS "Project name: ${project}")
message(STATUS "Project version: ${version}")
execute_process(
COMMAND git rev-parse --short HEAD
WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}
OUTPUT_VARIABLE GIT_COMMIT_HASH
OUTPUT_STRIP_TRAILING_WHITESPACE
)
message(STATUS "Latest git commit: ${GIT_COMMIT_HASH}")
# Define a preprocessor macro with the Git commit version
add_definitions(-DGIT_COMMIT_HASH="${GIT_COMMIT_HASH}")
# Note : project name is {project} and python module name is also {project}
set(module_name _${project}) # target name
......
......@@ -40,7 +40,7 @@ class test_recipes(unittest.TestCase):
graph_view.set_backend("cpu")
np_weights = np.arange(9).reshape([1, 1, 3, 3]).astype(np.float32)
np_bias = np.arange(1).reshape([1, 1]).astype(np.float32)
np_bias = np.arange(1).reshape([1]).astype(np.float32)
np_scale = np.array([0.05]).astype(np.float32)
np_shift = np.array([0.05]).astype(np.float32)
......
......@@ -16,32 +16,25 @@
#include "aidge/backend/cpu/operator/AvgPoolingImpl.hpp"
#include "aidge/backend/cpu/operator/MaxPoolingImpl.hpp"
#include "aidge/backend/cpu/operator/BatchNormImpl.hpp"
#include "aidge/backend/cpu/operator/ConcatImpl.hpp"
#include "aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp"
#include "aidge/backend/cpu/operator/ConvImpl.hpp"
#include "aidge/backend/cpu/operator/DivImpl.hpp"
#include "aidge/backend/cpu/operator/ErfImpl.hpp"
#include "aidge/backend/cpu/operator/FCImpl.hpp"
#include "aidge/backend/cpu/operator/GatherImpl.hpp"
#include "aidge/backend/cpu/operator/GlobalAveragePoolingImpl.hpp"
#include "aidge/backend/cpu/operator/LeakyReLUImpl.hpp"
#include "aidge/backend/cpu/operator/MatMulImpl.hpp"
#include "aidge/backend/cpu/operator/MemorizeImpl.hpp"
#include "aidge/backend/cpu/operator/MulImpl.hpp"
#include "aidge/backend/cpu/operator/PadImpl.hpp"
#include "aidge/backend/cpu/operator/PopImpl.hpp"
#include "aidge/backend/cpu/operator/PowImpl.hpp"
#include "aidge/backend/cpu/operator/ReduceMeanImpl.hpp"
#include "aidge/backend/cpu/operator/ReLUImpl.hpp"
#include "aidge/backend/cpu/operator/ReshapeImpl.hpp"
#include "aidge/backend/cpu/operator/ScalingImpl.hpp"
#include "aidge/backend/cpu/operator/SigmoidImpl.hpp"
#include "aidge/backend/cpu/operator/SliceImpl.hpp"
#include "aidge/backend/cpu/operator/SqrtImpl.hpp"
#include "aidge/backend/cpu/operator/SoftmaxImpl.hpp"
#include "aidge/backend/cpu/operator/SubImpl.hpp"
#include "aidge/backend/cpu/operator/TanhImpl.hpp"
#include "aidge/backend/cpu/operator/TransposeImpl.hpp"
#include "aidge/backend/cpu/data/TensorImpl.hpp"
......
/********************************************************************************
* 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_CONCATIMPL_H_
#define AIDGE_CPU_OPERATOR_CONCATIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Concat.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/data/GetCPUPtr.h"
#include <memory>
#include <vector>
namespace Aidge {
// class Concat_Op<2>;
// compute kernel registry for forward and backward
class ConcatImplForward_cpu
: public Registrable<ConcatImplForward_cpu, std::tuple<DataType, DataType>, void(const Concat_Op::Attrs&,
const std::vector<DimSize_t>,
const std::vector<DimSize_t>&,
const std::vector<const void*>,
void*)> {};
class ConcatImplBackward_cpu
: public Registrable<ConcatImplBackward_cpu, std::tuple<DataType, DataType>, void(const Concat_Op::Attrs&,
const std::vector<DimSize_t>,
const std::vector<DimSize_t>&,
const std::vector<const void*>,
void*)> {};
class ConcatImpl_cpu : public OperatorImpl {
public:
ConcatImpl_cpu(const Concat_Op& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<ConcatImpl_cpu> create(const Concat_Op& op) {
return std::make_unique<ConcatImpl_cpu>(op);
}
public:
void forward() override;
void backward() override;
};
namespace {
static Registrar<Concat_Op> registrarConcatImpl_cpu("cpu", Aidge::ConcatImpl_cpu::create);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONCATIMPL_H_ */
/********************************************************************************
* 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_CONCATIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_CONCATIMPL_FORWARD_KERNEL_H_
#include <algorithm>
#include <numeric>
#include <cstddef>
#include <vector>
#include "aidge/backend/cpu/operator/ConcatImpl.hpp"
#include "aidge/data/Data.hpp"
#include "aidge/operator/Concat.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/data/GetCPUPtr.h"
namespace Aidge {
template <class I, class O>
void ConcatImpl_cpu_forward_kernel(const Concat_Op::Attrs& attrs,
const std::vector<DimSize_t>& dimsFirstInput,
const std::vector<DimSize_t>& concatAxisValues,
const std::vector<const void*>& inputs_,
void* output_)
{
// FIXME: missing Concat attributes as arguments
std::vector<const I*> inputs;
for (const auto& input_ : inputs_) {
inputs.push_back(static_cast<const I*>(input_));
}
O* output = static_cast<O*>(output_);
DimSize_t outputAxisValue = std::accumulate(concatAxisValues.begin(), concatAxisValues.end(), 0);
DimSize_t prodDimLower = 1;
for (DimIdx_t i = 0; i < std::get<0>(attrs); ++i) {
prodDimLower *= dimsFirstInput[i];
}
DimSize_t prodDimHigher = 1;
for (DimIdx_t i = std::get<0>(attrs) + 1; static_cast<std::size_t>(i) < dimsFirstInput.size();
++i) {
prodDimHigher *= dimsFirstInput[i];
}
std::size_t oIndexStart = 0;
std::size_t oIndex = 0;
for (std::size_t inputId = 0; inputId < inputs.size(); ++inputId) {
oIndex = oIndexStart;
const DimSize_t iOffset = prodDimHigher*concatAxisValues[inputId];
for (std::size_t iIndex = 0; iIndex < prodDimLower; ++iIndex) {
std::copy(inputs[inputId] + iIndex*iOffset, inputs[inputId] + (iIndex+1)*iOffset, output + oIndex);
oIndex += prodDimHigher*outputAxisValue;
}
oIndexStart += concatAxisValues[inputId]*prodDimHigher;
}
}
namespace {
static Registrar<ConcatImplForward_cpu> registrarConcatImplForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::ConcatImpl_cpu_forward_kernel<float, float>);
static Registrar<ConcatImplForward_cpu> registrarConcatImplForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::ConcatImpl_cpu_forward_kernel<int, int>);
static Registrar<ConcatImplForward_cpu> registrarConcatImplForward_cpu_Float64(
{DataType::Float64, DataType::Float64},
Aidge::ConcatImpl_cpu_forward_kernel<double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_CONCATIMPL_CPU_FORWARD_KERNEL_H_ */
/********************************************************************************
* 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_GATHERIMPL_H_
#define AIDGE_CPU_OPERATOR_GATHERIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Gather.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
namespace Aidge {
// class Gather_Op;
// compute kernel registry for forward and backward
class GatherImplForward_cpu
: public Registrable<GatherImplForward_cpu, std::tuple<DataType, DataType>, void(const typename Gather_Op::Attrs&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class GatherImplBackward_cpu
: public Registrable<GatherImplBackward_cpu, std::tuple<DataType, DataType>, void(const typename Gather_Op::Attrs&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class GatherImpl_cpu : public OperatorImpl {
public:
GatherImpl_cpu(const Gather_Op& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<GatherImpl_cpu> create(const Gather_Op& op) {
return std::make_unique<GatherImpl_cpu>(op);
}
void forward() override;
};
namespace {
static Registrar<Gather_Op> registrarGatherImpl_cpu("cpu", Aidge::GatherImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_GATHERIMPL_H_ */
/********************************************************************************
* 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_GATHERIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_GATHERIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include <cstddef>
#include <cmath>
#include "aidge/data/Data.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/operator/GatherImpl.hpp"
namespace Aidge {
template <class I, class O>
void GatherImpl_cpu_forward_kernel(const typename Gather_Op::Attrs& attrs, const std::vector<DimSize_t>& inputDims, const void* input_, void* output_)
{
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
const std::size_t axisIdx = std::get<2>(attrs)>=0 ?
std::get<2>(attrs) :
static_cast<std::size_t>(std::get<2>(attrs)) + inputDims.size();
std::size_t postAxisElems = 1;
for (std::size_t i = axisIdx + 1; i < inputDims.size(); ++i) {
postAxisElems *= inputDims[i];
}
std::size_t preAxisElems = 1;
for (std::size_t i = 0; i < axisIdx; ++i) {
preAxisElems *= inputDims[i];
}
const std::vector<std::int64_t> indices = std::get<0>(attrs);
for (std::size_t i=0; i<preAxisElems; ++i)
{
for(std::size_t j=0; j<indices.size(); ++j)
{
const std::size_t idx = indices[j] >= 0 ? indices[j] : static_cast<std::size_t>(indices[j]) + inputDims[axisIdx];
const I* startPtr = std::next(input, i * postAxisElems * inputDims[axisIdx] + idx * postAxisElems);
std::copy_n(startPtr, postAxisElems, output);
output += postAxisElems;
}
}
}
namespace {
static Registrar<GatherImplForward_cpu> registrarGatherImplForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::GatherImpl_cpu_forward_kernel<float, float>);
static Registrar<GatherImplForward_cpu> registrarGatherImplForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::GatherImpl_cpu_forward_kernel<int, int>);
static Registrar<GatherImplForward_cpu> registrarGatherImplForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::GatherImpl_cpu_forward_kernel<double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_GATHERIMPL_FORWARD_KERNEL_H_ */
/********************************************************************************
* 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_MEMORIZEIMPL_H_
#define AIDGE_CPU_OPERATOR_MEMORIZEIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Memorize.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/data/GetCPUPtr.h"
#include <memory>
#include <vector>
namespace Aidge {
class MemorizeImpl_cpu : public OperatorImpl {
public:
MemorizeImpl_cpu(const Memorize_Op& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<MemorizeImpl_cpu> create(const Memorize_Op& op) {
return std::make_unique<MemorizeImpl_cpu>(op);
}
Elts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
Elts_t getRequiredMemory(const Aidge::IOIndex_t outputIdx,
const std::vector<Aidge::DimSize_t> &/*inputsSize*/) const override final;
void updateConsummerProducer() override final;
void forward() override;
};
namespace {
static Registrar<Memorize_Op> registrarMemorizeImpl_cpu("cpu", Aidge::MemorizeImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_MEMORIZEIMPL_H_ */
/********************************************************************************
* 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_POPIMPL_H_
#define AIDGE_CPU_OPERATOR_POPIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Pop.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/data/GetCPUPtr.h"
#include <memory>
#include <vector>
namespace Aidge {
// class Pop_Op;
// compute kernel registry for forward and backward
class PopImplForward_cpu
: public Registrable<PopImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
};
class PopImplBackward_cpu
: public Registrable<PopImplBackward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
};
class PopImpl_cpu : public OperatorImpl {
public:
PopImpl_cpu(const Pop_Op& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<PopImpl_cpu> create(const Pop_Op& op) {
return std::make_unique<PopImpl_cpu>(op);
}
Elts_t getNbRequiredData(const IOIndex_t inputIdx) const override final;
void forward() override;
};
namespace {
static Registrar<Pop_Op> registrarPopImpl_cpu("cpu", Aidge::PopImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_POPIMPL_H_ */
......@@ -30,7 +30,7 @@ class ReLUImplForward_cpu
: public Registrable<ReLUImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
};
class ReLUImplBackward_cpu
: public Registrable<ReLUImplBackward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
: public Registrable<ReLUImplBackward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, void*)> {
};
class ReLUImpl_cpu : public OperatorImpl {
......
......@@ -14,31 +14,32 @@
#include <cstddef> // std::size_t
#include "aidge/utils/Registrar.hpp"
#include "aidge/backend/cpu/operator/ReLUImpl.hpp"
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I, class O>
template <class O, class GI, class GO>
void ReLUImpl_cpu_backward_kernel(const std::size_t inputLenght,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
const void* output_, const void* grad_output_,
void* grad_input_) {
const O* output = static_cast<const O*>(output_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
for (std::size_t i = 0; i < inputLenght; ++i) {
output[i] = (input[i] > I(0)) ? static_cast<O>(input[i]) : O(0);
grad_input[i] = (output[i] > GO(0)) ? GI(grad_output[i]) : GI(0);
}
}
namespace {
static Registrar<ReLUImplBackward_cpu> registrarReLUImplBackward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::ReLUImpl_cpu_backward_kernel<float, float>);
{DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::ReLUImpl_cpu_backward_kernel<float, float, float>);
static Registrar<ReLUImplBackward_cpu> registrarReLUImplBackward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::ReLUImpl_cpu_backward_kernel<int, int>);
{DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::ReLUImpl_cpu_backward_kernel<int, int, int>);
static Registrar<ReLUImplBackward_cpu> registrarReLUImplBackward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::ReLUImpl_cpu_backward_kernel<double, double>);
{DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::ReLUImpl_cpu_backward_kernel<double, double, double>);
} // namespace
} // namespace Aidge
......
/********************************************************************************
* 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_RESHAPEIMPL_H_
#define AIDGE_CPU_OPERATOR_RESHAPEIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Reshape.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
namespace Aidge {
// class Reshape_Op;
// compute kernel registry for forward and backward
class ReshapeImplForward_cpu
: public Registrable<ReshapeImplForward_cpu, std::tuple<DataType, DataType>, void(std::size_t, const void*, void*)> {
};
class ReshapeImplBackward_cpu
: public Registrable<ReshapeImplBackward_cpu, std::tuple<DataType, DataType>, void(std::size_t, const void*, void*)> {
};
class ReshapeImpl_cpu : public OperatorImpl {
public:
ReshapeImpl_cpu(const Reshape_Op& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<ReshapeImpl_cpu> create(const Reshape_Op& op) {
return std::make_unique<ReshapeImpl_cpu>(op);
}
Elts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
void forward() override;
};
namespace {
static Registrar<Reshape_Op> registrarReshapeImpl_cpu("cpu", Aidge::ReshapeImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_RESHAPEIMPL_H_ */
/********************************************************************************
* 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_RESHAPEIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_RESHAPEIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include <cmath>
#include "aidge/backend/cpu/operator/ReshapeImpl.hpp"
namespace Aidge {
template <class I, class O>
void ReshapeImpl_cpu_forward_kernel(std::size_t inputLength,
const void* input_,
void* output_) {
const I* input = static_cast<const I*>(input_);
O* output = static_cast<O*>(output_);
std::copy_n(input, inputLength, output);
}
namespace {
static Registrar<ReshapeImplForward_cpu> registrarReshapeImplForward_cpu_Float32(
{DataType::Float32, DataType::Float32},
Aidge::ReshapeImpl_cpu_forward_kernel<float, float>);
static Registrar<ReshapeImplForward_cpu> registrarReshapeImplForward_cpu_Int32(
{DataType::Int32, DataType::Int32},
Aidge::ReshapeImpl_cpu_forward_kernel<int, int>);
static Registrar<ReshapeImplForward_cpu> registrarReshapeImplForward_cpu_Float64(
{DataType::Float64, DataType::Float64},
Aidge::ReshapeImpl_cpu_forward_kernel<double, double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_RESHAPEIMPL_FORWARD_KERNEL_H_ */
/********************************************************************************
* 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_SLICEIMPL_H_
#define AIDGE_CPU_OPERATOR_SLICEIMPL_H_
#include <memory>
#include <vector>
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Slice.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
// class Slice_Op;
// compute kernel registry for forward and backward
class SliceImplForward_cpu
: public Registrable<SliceImplForward_cpu, std::tuple<DataType>,
void(const typename Slice_Op::Attrs&,
const std::vector<std::size_t>,
const void*,
void*)> {};
class SliceImplBackward_cpu
: public Registrable<SliceImplBackward_cpu, std::tuple<DataType>,
void(const typename Slice_Op::Attrs&,
const std::vector<std::size_t>,
const void*,
void*)> {};
class SliceImpl_cpu : public OperatorImpl {
public:
SliceImpl_cpu(const Slice_Op& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<SliceImpl_cpu> create(const Slice_Op& op) {
return std::make_unique<SliceImpl_cpu>(op);
}
void forward() override;
void backward() override;
};
namespace {
static Registrar<Slice_Op> registrarSliceImpl_cpu("cpu", Aidge::SliceImpl_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_SLICEIMPL_H_ */
/********************************************************************************
* 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_SLICEIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_
#include <cstddef>
#include <vector>
#include "aidge/backend/cpu/operator/SliceImpl.hpp"
#include "aidge/data/Data.hpp"
#include "aidge/operator/Slice.hpp"
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I>
void SliceImpl_cpu_forward_kernel(const typename Slice_Op::Attrs& attrs,
const std::vector<std::size_t> inputDims,
const void* input_,
void* output_) {
std::vector<std::size_t> slicedDims = inputDims;
std::size_t beginning = 0;
DimSize_t nbAxes = std::get<2>(attrs).size();
for (std::size_t i = 0; i < nbAxes; ++i) {
// For each slice operation get the params and cast them to size_t
const std::int64_t axis_ = std::get<2>(attrs)[i];
const std::int64_t start_ = std::get<0>(attrs)[i];
const std::int64_t end_ = std::get<1>(attrs)[i];
const std::size_t axis = axis_ >= 0 ? axis_ : static_cast<std::size_t>(axis_) + inputDims.size();
const std::size_t start = start_ >= 0 ? start_ : start_ + inputDims[axis];
const std::size_t end = end_ >= 0 ? end_ : end_ + inputDims[axis];
std::size_t stride = 1;
for (std::size_t j = inputDims.size() - 1; j > axis; --j) stride *= inputDims[j];
beginning += start * stride;
const std::size_t sliceLength = end - start + 1;
slicedDims[axis] = sliceLength;
}
const I* input = static_cast<const I*>(input_) + beginning;
I* output = static_cast<I*>(output_);
const std::size_t nbDims = slicedDims.size();
// for inputDims = {4,5,5,3} & slicedDims = {3,2,2,1}, substractDims = {1,5,5,3}
std::vector<std::size_t> substractedDims = std::vector<std::size_t>(nbDims);
for (std::size_t i = 0; i < nbDims; ++i) {
substractedDims[i] = inputDims[i] - slicedDims[i];
}
// for slicedDims = {3,2,2,1}, prodSlicedDims = {12,4,2,1}
std::vector<std::size_t> prodSlicedDims = std::vector<std::size_t>(nbDims);
std::vector<std::size_t> prodInputDims = std::vector<std::size_t>(nbDims + 1);
prodSlicedDims[nbDims - 1] = slicedDims[nbDims - 1];
prodInputDims[nbDims - 1] = inputDims[nbDims - 1];
prodInputDims[nbDims] = 1;
for (std::size_t i = 2; i <= nbDims; ++i) {
prodSlicedDims[nbDims - i] = prodSlicedDims[nbDims - i + 1] * slicedDims[nbDims - i];
prodInputDims[nbDims - i] = prodInputDims[nbDims - i + 1] * inputDims[nbDims - i];
}
std::size_t j = 0;
std::size_t i = 0;
for (; j < prodSlicedDims[0];) {
output[j] = input[i++];
++j;
for (std::size_t idx = nbDims - 1; idx > 0; --idx) {
i += j % prodSlicedDims[idx] == 0 ? substractedDims[idx] * prodInputDims[idx + 1] : 0;
}
}
}
namespace {
static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float32(
{DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float>);
static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Int32(
{DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int>);
static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float64(
{DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_ */
/********************************************************************************
* 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_TransposeIMPL_H_
#define AIDGE_CPU_OPERATOR_TransposeIMPL_H_
#include "aidge/backend/OperatorImpl.hpp"
#include "aidge/operator/Transpose.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
#include <memory>
#include <vector>
namespace Aidge {
// class Transpose_Op;
// compute kernel registry for forward and backward
class TransposeImpl2DForward_cpu
: public Registrable<TransposeImpl2DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<2>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl3DForward_cpu
: public Registrable<TransposeImpl3DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<3>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl4DForward_cpu
: public Registrable<TransposeImpl4DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<4>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl5DForward_cpu
: public Registrable<TransposeImpl5DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<5>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl6DForward_cpu
: public Registrable<TransposeImpl6DForward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<6>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl2DBackward_cpu
: public Registrable<TransposeImpl2DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<2>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl3DBackward_cpu
: public Registrable<TransposeImpl3DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<3>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl4DBackward_cpu
: public Registrable<TransposeImpl4DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<4>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl5DBackward_cpu
: public Registrable<TransposeImpl5DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<5>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl6DBackward_cpu
: public Registrable<TransposeImpl6DBackward_cpu, std::tuple<DataType, DataType>, void( const typename Transpose_Op<6>::Attrs& attrs, const std::vector<DimSize_t>&, const std::vector<DimSize_t>&, const void*, void*)> {
};
class TransposeImpl2D_cpu : public OperatorImpl {
public:
TransposeImpl2D_cpu(const Transpose_Op<2>& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<TransposeImpl2D_cpu> create(const Transpose_Op<2>& op) {
return std::make_unique<TransposeImpl2D_cpu>(op);
}
void forward() override;
};
class TransposeImpl3D_cpu : public OperatorImpl {
public:
TransposeImpl3D_cpu(const Transpose_Op<3>& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<TransposeImpl3D_cpu> create(const Transpose_Op<3>& op) {
return std::make_unique<TransposeImpl3D_cpu>(op);
}
void forward() override;
};
class TransposeImpl4D_cpu : public OperatorImpl {
public:
TransposeImpl4D_cpu(const Transpose_Op<4>& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<TransposeImpl4D_cpu> create(const Transpose_Op<4>& op) {
return std::make_unique<TransposeImpl4D_cpu>(op);
}
void forward() override;
};
class TransposeImpl5D_cpu : public OperatorImpl {
public:
TransposeImpl5D_cpu(const Transpose_Op<5>& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<TransposeImpl5D_cpu> create(const Transpose_Op<5>& op) {
return std::make_unique<TransposeImpl5D_cpu>(op);
}
void forward() override;
};
class TransposeImpl6D_cpu : public OperatorImpl {
public:
TransposeImpl6D_cpu(const Transpose_Op<6>& op) : OperatorImpl(op, "cpu") {}
static std::unique_ptr<TransposeImpl6D_cpu> create(const Transpose_Op<6>& op) {
return std::make_unique<TransposeImpl6D_cpu>(op);
}
void forward() override;
};
namespace {
static Registrar<Transpose_Op<2>> registrarTransposeImpl2D_cpu("cpu", Aidge::TransposeImpl2D_cpu::create);
static Registrar<Transpose_Op<3>> registrarTransposeImpl3D_cpu("cpu", Aidge::TransposeImpl3D_cpu::create);
static Registrar<Transpose_Op<4>> registrarTransposeImpl4D_cpu("cpu", Aidge::TransposeImpl4D_cpu::create);
static Registrar<Transpose_Op<5>> registrarTransposeImpl5D_cpu("cpu", Aidge::TransposeImpl5D_cpu::create);
static Registrar<Transpose_Op<6>> registrarTransposeImpl6D_cpu("cpu", Aidge::TransposeImpl6D_cpu::create);
}
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_TransposeIMPL_H_ */
/********************************************************************************
* 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_TRANSPOSEIMPL_FORWARD_KERNEL_H_
#define AIDGE_CPU_OPERATOR_TRANSPOSEIMPL_FORWARD_KERNEL_H_
#include "aidge/utils/Registrar.hpp"
#include <cstddef>
#include <cmath>
#include "aidge/data/Data.hpp"
#include "aidge/utils/Types.h"
#include "aidge/backend/cpu/operator/TransposeImpl.hpp"
namespace Aidge {
template <class I, class O, DimSize_t DIM>
void TransposeImpl_cpu_forward_kernel( const typename Transpose_Op<DIM>::Attrs& attrs, const std::vector<DimSize_t>& inputDims, const std::vector<DimSize_t>& outputDims, const void* input_, void* output_)
{
O* output = static_cast<O*>(output_);
const I* input = static_cast<const I*>(input_);
// Compute total number of elements in the input array
size_t totalElements = 1;
for (size_t dimSize : inputDims) {
totalElements *= dimSize;
}
std::vector<std::size_t> outStrides(DIM, 1);
for (size_t i = 0; i < DIM; ++i) {
for (size_t j = i+1; j < DIM; ++j)
{
outStrides[i] *= outputDims[j];
}
}
std::vector<size_t> indices(outputDims.size(), 0);
for (size_t i = 0; i < totalElements; ++i) {
size_t idx = 0;
// Permute indices based on OutputDimsOrder attr
std::vector<size_t> permutedIndices(DIM);
for (size_t j = 0; j < DIM; ++j) {
permutedIndices[j] = indices[std::get<0>(attrs)[j]];
}
for (int j = DIM -1; j >=0; --j) {
idx += permutedIndices[j] * outStrides[j];
}
// Copy the value in output
output[idx] = input[i];
// Update indices for the next iteration
for (int j = DIM - 1; j >= 0; --j) {
if (indices[j] < inputDims[j] - 1) {
indices[j]++;
break;
} else {
indices[j] = 0;
}
}
}
}
namespace {
// DIM = 2
static Registrar<TransposeImpl2DForward_cpu> registrarTransposeImpl2DForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 2>);
static Registrar<TransposeImpl2DForward_cpu> registrarTransposeImpl2DForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 2>);
static Registrar<TransposeImpl2DForward_cpu> registrarTransposeImpl2DForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 2>);
// DIM = 3
static Registrar<TransposeImpl3DForward_cpu> registrarTransposeImpl3DForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 3>);
static Registrar<TransposeImpl3DForward_cpu> registrarTransposeImpl3DForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 3>);
static Registrar<TransposeImpl3DForward_cpu> registrarTransposeImpl3DForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 3>);
// DIM = 4
static Registrar<TransposeImpl4DForward_cpu> registrarTransposeImpl4DForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 4>);
static Registrar<TransposeImpl4DForward_cpu> registrarTransposeImpl4DForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 4>);
static Registrar<TransposeImpl4DForward_cpu> registrarTransposeImpl4DForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 4>);
// DIM = 5
static Registrar<TransposeImpl5DForward_cpu> registrarTransposeImpl5DForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 5>);
static Registrar<TransposeImpl5DForward_cpu> registrarTransposeImpl5DForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 5>);
static Registrar<TransposeImpl5DForward_cpu> registrarTransposeImpl5DForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 5>);
// DIM = 6
static Registrar<TransposeImpl6DForward_cpu> registrarTransposeImpl6DForward_cpu_Float32(
{DataType::Float32, DataType::Float32}, Aidge::TransposeImpl_cpu_forward_kernel<float, float, 6>);
static Registrar<TransposeImpl6DForward_cpu> registrarTransposeImpl6DForward_cpu_Int32(
{DataType::Int32, DataType::Int32}, Aidge::TransposeImpl_cpu_forward_kernel<int, int, 6>);
static Registrar<TransposeImpl6DForward_cpu> registrarTransposeImpl6DForward_cpu_Float64(
{DataType::Float64, DataType::Float64}, Aidge::TransposeImpl_cpu_forward_kernel<double, double, 6>);
} // namespace
} // namespace Aidge
#endif /* AIDGE_CPU_OPERATOR_TRANSPOSEIMPL_FORWARD_KERNEL_H_ */
#ifndef AIDGE_UTILS_SYS_INFO_CPU_VERSION_INFO_H
#define AIDGE_UTILS_SYS_INFO_CPU_VERSION_INFO_H
#include "aidge/utils/Log.hpp"
namespace Aidge {
#ifndef PROJECT_VERSION // Normally defined in CMakeLists.txt
#define PROJECT_VERSION "Unknown version"
#endif
#ifndef GIT_COMMIT_HASH
#define GIT_COMMIT_HASH ""
#endif
void showCpuVersion() {
Log::info("Aidge backend CPU: {} ({}), {} {}", PROJECT_VERSION, GIT_COMMIT_HASH, __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
......@@ -6,10 +6,13 @@ namespace py = pybind11;
namespace Aidge {
void init_Aidge(py::module& /*m*/){
void init_cpu_sys_info(py::module& m);
void init_Aidge(py::module& m){
init_cpu_sys_info(m);
}
PYBIND11_MODULE(aidge_backend_cpu, m) {
init_Aidge(m);
}
......
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