README.md

@@ -14,9 +14,11 @@ So far be sure to have the correct requirements to use this library
 
 ## Pip installation
 
-You will need to install first the aidge_core library before installing aidge_cpu.
-Also, make sure that the install path was set before installing aidge_core library.
-Then run in your python environnement : 
+You will need to install first the ``aidge_core`` library before installing ``aidge_backend_cpu``.
+
+If you have set a custom install path for the ``aidge_core`` library, make sure to use the same one here.
+
+Then run in your python environnement :
 ``` bash
 pip install . -v
 ```
@@ -46,4 +48,4 @@ Important: this command can also be run with `make`.
 To compile the CPU library with the python binding + the associated unitary tests, run
 ```
 make cpu_with_pybind_tests
-```
\ No newline at end of file
+```

include/aidge/backend/cpu.hpp

@@ -23,5 +23,6 @@
 #include "aidge/backend/cpu/operator/ProducerImpl.hpp"
 #include "aidge/backend/cpu/operator/ReLUImpl.hpp"
 #include "aidge/backend/cpu/operator/SoftmaxImpl.hpp"
+#include "aidge/backend/cpu/operator/ScalingImpl.hpp"
 
 #endif /* AIDGE_CPU_IMPORTS_H_ */
\ No newline at end of file

include/aidge/backend/cpu/operator/ScalingImpl.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_ScalingIMPL_H__
+#define __AIDGE_CPU_OPERATOR_ScalingIMPL_H__
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Scaling.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Scaling_Op;
+
+// compute kernel registry for forward and backward
+class ScalingImplForward_cpu
+    : public Registrable<ScalingImplForward_cpu, std::tuple<DataType, DataType>, void(const Scaling_Op::Parameters&, std::size_t, const void*, void*)> {
+};
+class ScalingImplBackward_cpu
+    : public Registrable<ScalingImplBackward_cpu, std::tuple<DataType, DataType>, void(const Scaling_Op::Parameters&, std::size_t, const void*, void*)> {
+};
+
+class ScalingImpl_cpu : public OperatorImpl {
+   private:
+    const Scaling_Op& mOp;
+    std::array<NbElts_t, 1> mNbConsumedData;
+    std::array<NbElts_t, 1> mNbProducedData;
+
+   public:
+    ScalingImpl_cpu(const Scaling_Op& op) : mOp(op), mNbConsumedData({0}), mNbProducedData({0}) {}
+
+    static std::unique_ptr<ScalingImpl_cpu> create(const Scaling_Op& op) {
+        //std::cout << "ScalingImpl_cpu create" << std::endl;
+        return std::make_unique<ScalingImpl_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(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 updateConsummerProducer() override final;
+
+    void forward();
+
+    void backward();
+};
+
+namespace {
+static Registrar<Scaling_Op> registrarScalingImpl_cpu("cpu", Aidge::ScalingImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* __AIDGE_CPU_OPERATOR_ScalingIMPL_H__ */
\ No newline at end of file

include/aidge/backend/cpu/operator/ScalingImpl_forward_kernels.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_ScalingIMPL_FORWARD_KERNEL_H__
+#define __AIDGE_CPU_OPERATOR_ScalingIMPL_FORWARD_KERNEL_H__
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/ScalingImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void ScalingImpl_cpu_forward_kernel(const Scaling_Op::Parameters& params,
+                                     std::size_t inputLenght,
+                                     const void* input_,
+                                     void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+    I scalingFactor = static_cast<I>(std::get<0>(params));
+
+    for (std::size_t i = 0; i < inputLenght; ++i) {
+        output[i] = input[i] * scalingFactor;
+    }
+}
+
+namespace {
+static Registrar<ScalingImplForward_cpu> registrarScalingImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::ScalingImpl_cpu_forward_kernel<float, float>);
+static Registrar<ScalingImplForward_cpu> registrarScalingImplForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::ScalingImpl_cpu_forward_kernel<int, int>);
+static Registrar<ScalingImplForward_cpu> registrarScalingImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::ScalingImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* __AIDGE_CPU_OPERATOR_ScalingIMPL_FORWARD_KERNEL_H__ */

setup.py

@@ -62,11 +62,11 @@ class CMakeBuild(build_ext):
 
         os.chdir(str(build_temp))
 
-        # Impose to use the executable of the python 
+        # Impose to use the executable of the python
         # used to launch setup.py to setup PythonInterp
         param_py = "-DPYTHON_EXECUTABLE=" + sys.executable
-        
-        install_path = f"{build_temp}/install" if "AIDGE_INSTALL" not in os.environ else os.environ["AIDGE_INSTALL"]
+
+        install_path = os.path.join(sys.prefix, "lib", "libAidge")  if "AIDGE_INSTALL" not in os.environ else os.environ["AIDGE_INSTALL"]
 
         self.spawn(['cmake', str(cwd), param_py, '-DTEST=OFF', f'-DCMAKE_INSTALL_PREFIX:PATH={install_path}'])
         if not self.dry_run:
@@ -83,11 +83,11 @@ class CMakeBuild(build_ext):
             for file in files:
                 if file.endswith('.so') and (root != str(aidge_package.absolute())):
                     currentFile=os.path.join(root, file)
-                    shutil.copy(currentFile, str(aidge_package.absolute())) 
+                    shutil.copy(currentFile, str(aidge_package.absolute()))
 
         # Copy version.txt in aidge_package
         os.chdir(os.path.dirname(__file__))
-        shutil.copy("version.txt", str(aidge_package.absolute()))    
+        shutil.copy("version.txt", str(aidge_package.absolute()))
 
 
 if __name__ == '__main__':

src/operator/ScalingImpl.cpp

+/********************************************************************************
+ * 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
+ *
+ ********************************************************************************/
+
+#include <cassert>
+#include <numeric>    // std::accumulate
+#include <functional> // std::multiplies
+
+#include "aidge/operator/Scaling.hpp"
+
+#include "aidge/backend/cpu/operator/ScalingImpl.hpp"
+#include "aidge/backend/cpu/operator/ScalingImpl_forward_kernels.hpp"
+#include "aidge/utils/Types.h"
+#include <vector>
+
+// FIXME: replace whole Tensor with minimum needed data quantity
+Aidge::NbElts_t Aidge::ScalingImpl_cpu::getNbRequiredData(Aidge::IOIndex_t /*inputIdx*/) const {
+    assert(mOp.getInput(0) && "requires valid input");
+
+    // Requires the whole tensors
+    const auto& inputDims = mOp.getInput(0)->dims();
+
+    return std::accumulate(inputDims.begin(), inputDims.end(),
+                        static_cast<NbElts_t>(1), std::multiplies<NbElts_t>());
+}
+
+Aidge::NbElts_t Aidge::ScalingImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // for the direct convolution algorithm, convolutions can be in-place, if there is no padding!
+    return 0;
+}
+
+Aidge::NbElts_t Aidge::ScalingImpl_cpu::getRequiredMemory(const Aidge::IOIndex_t outputIdx, const std::vector<Aidge::DimSize_t> &inputsSize) const {
+    (void) outputIdx;
+    (void) inputsSize;
+    const auto& outputDims = mOp.getOutput(0)->dims();
+    return std::accumulate(outputDims.begin(), outputDims.end(),
+                        static_cast<NbElts_t>(1), std::multiplies<NbElts_t>());
+}
+
+Aidge::NbElts_t Aidge::ScalingImpl_cpu::getNbConsumedData(Aidge::IOIndex_t /*inputIdx*/) const {
+    return mNbConsumedData[0];
+}
+
+Aidge::NbElts_t Aidge::ScalingImpl_cpu::getNbProducedData(Aidge::IOIndex_t /*outputIdx*/) const {
+    return mNbProducedData[0];
+}
+
+void Aidge::ScalingImpl_cpu::updateConsummerProducer(){
+    mNbConsumedData[0]+= getNbRequiredData(0); // each input is consumed by the minimum amount for a forward pass
+
+    mNbProducedData[0]+= getRequiredMemory(0, {});
+}
+
+void Aidge::ScalingImpl_cpu::forward() {
+    // FIXME: uncomment the following code once memory handling will work
+    assert(mOp.getInput(0) && "missing input #0");
+
+    // Find the correct kernel type
+    auto kernelFunc = Registrar<ScalingImplForward_cpu>::create({
+        mOp.getInput(0)->dataType(),
+        mOp.getOutput(0)->dataType()});
+
+    // Call kernel
+    kernelFunc(mOp.getParams(),
+        std::static_pointer_cast<Tensor>(mOp.getInput(0))->size(),
+        mOp.getInput(0)->getImpl()->rawPtr(),
+        mOp.getOutput(0)->getImpl()->rawPtr());
+
+
+    mNbConsumedData[0]+= getNbRequiredData(0); // each input is consumed by the minimum amount for a forward pass
+
+    mNbProducedData[0]+= getRequiredMemory(0, {});
+}
+
+void Aidge::ScalingImpl_cpu::backward() {
+    printf("Not implemented yet.\n");
+}