Merge branch 'fix/add_missing_attr' into 'dev'

Add missing attributes to operators

See merge request eclipse/aidge/aidge_backend_cpu!65

include/aidge/backend/cpu.hpp

@@ -32,6 +32,7 @@
 #include "aidge/backend/cpu/operator/ScalingImpl.hpp"
 #include "aidge/backend/cpu/operator/SigmoidImpl.hpp"
 #include "aidge/backend/cpu/operator/SqrtImpl.hpp"
+#include "aidge/backend/cpu/operator/SliceImpl.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/SliceImpl.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_SLICEIMPL_H__
+#define __AIDGE_CPU_OPERATOR_SLICEIMPL_H__
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Slice.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <memory>
+#include <vector>
+#include <array>
+
+namespace Aidge {
+// class Slice_Op;
+
+// compute kernel registry for forward and backward
+class SliceImplForward_cpu
+    : public Registrable<SliceImplForward_cpu, std::tuple<DataType, DataType>, void(const Slice_Op::Attrs&, const std::vector<DimSize_t>&, const void*, void*)> {
+};
+class SliceImplBackward_cpu
+    : public Registrable<SliceImplBackward_cpu, std::tuple<DataType, DataType>, void(const Slice_Op::Attrs&, const std::vector<DimSize_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);
+    }
+
+    Elts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Slice_Op> registrarSliceImpl_cpu("cpu", Aidge::SliceImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* __AIDGE_CPU_OPERATOR_SLICEIMPL_H__ */

include/aidge/backend/cpu/operator/SliceImpl_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_SLICEIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <iterator>
+
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/backend/cpu/operator/SliceImpl.hpp"
+
+namespace Aidge {
+
+template<class I, class O>
+void SliceImpl_cpu_forward_kernel(const Slice_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 nbDims = inputDims.size();
+    std::vector<DimSize_t> dims = inputDims;
+    DimSize_t totalSize = std::accumulate(inputDims.cbegin(), inputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const I* inputAccumulation = input;
+    I* outputAccumulation = nullptr;
+    const std::size_t nbAxes = std::get<0>(attrs).size();
+    for (std::size_t i = 0; i < nbAxes; ++i) {
+        const DimIdx_t axis = std::get<2>(attrs)[i] >= 0 ?
+                                    static_cast<DimIdx_t>(std::get<2>(attrs)[i]) :
+                                    static_cast<DimIdx_t>(std::get<2>(attrs)[i] + static_cast<DimIdx_t>(inputDims.size()));
+        const DimSize_t start = std::min(std::get<0>(attrs)[i] >= 0 ?
+                                                static_cast<DimSize_t>(std::get<0>(attrs)[i]) :
+                                                static_cast<DimSize_t>(std::get<0>(attrs)[i] + static_cast<std::int64_t>(inputDims[axis])),
+                                         dims[axis]-1);
+        const DimSize_t end = std::get<1>(attrs)[i] >= 0 ?
+                                        static_cast<DimSize_t>(std::get<1>(attrs)[i]) :
+                                        static_cast<DimSize_t>(std::get<1>(attrs)[i] + static_cast<std::int64_t>(inputDims[axis]));
+        const std::int64_t step = std::get<3>(attrs)[i];
+
+        const std::size_t sliceSize = static_cast<std::size_t>(std::ceil((static_cast<float>(end) - static_cast<float>(start)) / static_cast<float>(step)));
+
+        outputAccumulation = new I[totalSize];
+        const std::size_t stride_pre = std::accumulate(dims.cbegin(), dims.cbegin() + axis, 1, std::multiplies<std::size_t>());
+        const std::size_t stride_post = std::accumulate(dims.crbegin(), dims.crbegin() + nbDims -1 - axis, 1, std::multiplies<std::size_t>());
+        for (std::size_t outer = 0; outer < stride_pre; ++outer)
+        {
+            const std::size_t idx_in = outer * stride_post * dims[axis] + start * stride_post;
+            const std::size_t idx_out = outer * stride_post * sliceSize;
+            std::size_t addedSlices = 0;
+            for (std::size_t inner = 0; inner < sliceSize; ++inner)
+            {
+                std::copy_n(std::next(inputAccumulation, idx_in + inner * step * stride_post),
+                            stride_post,
+                            std::next(outputAccumulation, idx_out + addedSlices * stride_post));
+                addedSlices++;
+            }
+        }
+        totalSize /= dims[axis];
+        totalSize *= sliceSize;
+        dims[axis] = sliceSize;
+        
+        if (inputAccumulation != input) {
+            delete[] inputAccumulation;
+        }
+        inputAccumulation = outputAccumulation;
+        
+    }
+    // Copy elements from inputAccumulation to output while dividing by divisor
+    std::copy_n(inputAccumulation, totalSize, output);
+    if (outputAccumulation) {
+        delete[] outputAccumulation;
+    }
+}
+
+namespace {
+static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::SliceImpl_cpu_forward_kernel<float, float>);
+static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32}, Aidge::SliceImpl_cpu_forward_kernel<int, int>);
+static Registrar<SliceImplForward_cpu> registrarSliceImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::SliceImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_SLICEIMPL_FORWARD_KERNEL_H_ */

src/operator/SliceImpl.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/Slice.hpp"
+
+#include "aidge/backend/cpu/operator/SliceImpl.hpp"
+#include "aidge/backend/cpu/operator/SliceImpl_forward_kernels.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <vector>
+
+Aidge::Elts_t Aidge::SliceImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return Elts_t::DataElts(0);
+}
+
+void Aidge::SliceImpl_cpu::forward() {
+    assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
+
+    // Find the correct kernel type
+    auto kernelFunc = Registrar<SliceImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(dynamic_cast<const Slice_Op&>(mOp).getStaticAttributes(),
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims(),
+        getCPUPtr(mOp.getRawInput(0)),
+        getCPUPtr(mOp.getRawOutput(0)));
+}

unit_tests/operator/Test_SliceImpl.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 <catch2/catch_test_macros.hpp>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/Slice.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/operator] Slice(forward)", "[Slice][CPU]") {
+    SECTION("1D Tensor") {
+        std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array1D<int,10> {
+            {0, 1, -2,-3, 4,-5,-6, 7, 8, 9}
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array1D<int,3> {
+            {0, 1, -2}
+        });
+        std::shared_ptr<Tensor> starts = std::make_shared<Tensor>(Array1D<int,1>{{0}});
+        std::shared_ptr<Tensor> ends = std::make_shared<Tensor>(Array1D<int,1>{{3}});
+        std::shared_ptr<Tensor> axes = std::make_shared<Tensor>(Array1D<int,1>{{0}});
+
+        std::shared_ptr<Node> mySlice = Slice();
+        auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
+        mySlice->getOperator()->associateInput(0,input0);
+        mySlice->getOperator()->associateInput(1,starts);
+        mySlice->getOperator()->associateInput(2,ends);
+        mySlice->getOperator()->associateInput(3,axes);
+        mySlice->getOperator()->setDataType(DataType::Int32);
+        mySlice->getOperator()->setBackend("cpu");
+        mySlice->forward();
+
+        REQUIRE(*(op->getOutput(0)) == *expectedOutput);
+        REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
+        REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
+    }
+
+    SECTION("2D Tensor") {
+        std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array2D<int,2,10> {
+            {
+                { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+            }
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array2D<int,2,3> {
+            {
+                {-5,-6, 7},
+                {-5,-6, 7}
+            }
+        });
+        std::shared_ptr<Tensor> starts = std::make_shared<Tensor>(Array1D<int,2>{{0,5}});
+        std::shared_ptr<Tensor> ends = std::make_shared<Tensor>(Array1D<int,2>{{2,8}});
+        std::shared_ptr<Tensor> axes = std::make_shared<Tensor>(Array1D<int,2>{{0,1}});
+
+        std::shared_ptr<Node> mySlice = Slice();
+        auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
+        mySlice->getOperator()->associateInput(0,input0);
+        mySlice->getOperator()->associateInput(1,starts);
+        mySlice->getOperator()->associateInput(2,ends);
+        mySlice->getOperator()->associateInput(3,axes);
+        mySlice->getOperator()->setDataType(DataType::Int32);
+        mySlice->getOperator()->setBackend("cpu");
+        mySlice->forward();
+        // op->getOutput(0)->print();
+        REQUIRE(*(op->getOutput(0)) == *expectedOutput);
+        REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
+        REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
+    }
+
+    SECTION("3D Tensor") {
+        std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array3D<int,2,2,10> {
+            {
+                {
+                    { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                    {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                },
+                {
+                    { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                    {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                }
+            }
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array3D<int,1,1,3> {
+            {
+                {
+                    { 4,-5,-6}
+                }
+            }
+        });
+        std::shared_ptr<Tensor> starts = std::make_shared<Tensor>(Array1D<int,3>{{0,1,4}});
+        std::shared_ptr<Tensor> ends = std::make_shared<Tensor>(Array1D<int,3>{{1,2,7}});
+        std::shared_ptr<Tensor> axes = std::make_shared<Tensor>(Array1D<int,3>{{0,1,2}});
+
+        std::shared_ptr<Node> mySlice = Slice();
+        auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
+        mySlice->getOperator()->associateInput(0,input0);
+        mySlice->getOperator()->associateInput(1,starts);
+        mySlice->getOperator()->associateInput(2,ends);
+        mySlice->getOperator()->associateInput(3,axes);
+        mySlice->getOperator()->setDataType(DataType::Int32);
+        mySlice->getOperator()->setBackend("cpu");
+        mySlice->forward();
+        // mySlice->getOperator()->output(0).print();
+        REQUIRE(*(op->getOutput(0)) == *expectedOutput);
+        REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
+        REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
+    }
+
+    SECTION("4D Tensor") {
+        std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array4D<int,2,2,2,10> {
+            {
+                {
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    },
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    }
+                },
+                {
+                    {
+                        { 0, 1, 2,-3, 6,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    },
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3,11,-5,-6, 7,-1,10}
+                    }
+                }
+            }
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array4D<int,2,2,2,10> {
+            {
+                {
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    },
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    }
+                },
+                {
+                    {
+                        { 0, 1, 2,-3, 6,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    },
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3,11,-5,-6, 7,-1,10}
+                    }
+                }
+            }
+        });
+        std::shared_ptr<Tensor> starts = std::make_shared<Tensor>(Array1D<int,4>{{0,0,0,0}});
+        std::shared_ptr<Tensor> ends = std::make_shared<Tensor>(Array1D<int,4>{{2,2,2,10}});
+        std::shared_ptr<Tensor> axes = std::make_shared<Tensor>(Array1D<int,4>{{0,1,2,3}});
+
+        std::shared_ptr<Node> mySlice = Slice();
+        auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
+        mySlice->getOperator()->associateInput(0,input0);
+        mySlice->getOperator()->associateInput(1,starts);
+        mySlice->getOperator()->associateInput(2,ends);
+        mySlice->getOperator()->associateInput(3,axes);
+        mySlice->getOperator()->setDataType(DataType::Int32);
+        mySlice->getOperator()->setBackend("cpu");
+        mySlice->forward();
+        // op->getOutput(0)->print();
+        REQUIRE(*(op->getOutput(0)) == *expectedOutput);
+        REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
+        REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
+    }
+
+    SECTION("Attributes instead of inputs") {
+        std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array4D<int,2,2,2,10> {
+            {
+                {
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    },
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    }
+                },
+                {
+                    {
+                        { 0, 1, 2,-3, 6,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3, 4,-5,-6, 7,-1,10}
+                    },
+                    {
+                        { 0, 1, 2,-3, 4,-5,-6, 7, 8, 9},
+                        {-5, 4, 2,-3,11,-5,-6, 7,-1,10}
+                    }
+                }
+            }
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array4D<int,1,1,1,5> {
+            {
+                {
+                    {
+                        { 0, 1, 2,-3, 4}
+                    }
+                }
+            }
+        });
+
+        std::shared_ptr<Node> mySlice = Slice({0,0,0,0}, {1,1,1,5}, {0,1,2,3}, {1,1,1,1});
+        auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
+        mySlice->getOperator()->associateInput(0,input0);
+        mySlice->getOperator()->setDataType(DataType::Int32);
+        mySlice->getOperator()->setBackend("cpu");
+        mySlice->forward();
+        // op->getOutput(0)->print();
+        REQUIRE(*(op->getOutput(0)) == *expectedOutput);
+        REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
+        REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
+    }
+
+    SECTION("Different Steps") {
+        std::shared_ptr<Tensor> input0 = std::make_shared<Tensor>(Array3D<int,4,2,8> {
+            {
+                {
+                    { 0, 1, 2,-3, 4,-5,-6,7},
+                    {-5, 4, 2,-3, 4,-5,-6,-7}
+                },
+                {
+                    { 10, 11, 12,-13, 14,-15,-16,17},
+                    {-15, 14, 12,-13, 14,-15,-16,-17}
+                },
+                {
+                    { 20, 21, 22,-23, 24,-25,-26,27},
+                    {-25, 24, 22,-23, 24,-25,-26,-27}
+                },
+                {
+                    { 30, 31, 32,-33, 34,-35,-36,37},
+                    {-35, 34, 32,-33, 34,-35,-36,-37}
+                }
+            }
+        });
+        std::shared_ptr<Tensor> expectedOutput = std::make_shared<Tensor>(Array3D<int,2,1,3> {
+            {
+                {
+                    { 7, 4, 1}
+                },
+                {
+                    { 27, 24, 21}
+                }
+            }
+        });
+
+        std::shared_ptr<Node> mySlice = Slice({0,0,7}, {4,1,0}, {0,1,2}, {2,1,-3});
+        // Steps are 2,1,-3 so the slice will be:
+        // on Axis 0: from 0 to 4 by step of 2
+        // on Axis 1: from 0 to 1 by step of 1
+        // on Axis 2: from 7 to 0 by step of -3 (reverse the order of elements)
+        auto op = std::static_pointer_cast<OperatorTensor>(mySlice -> getOperator());
+        mySlice->getOperator()->associateInput(0,input0);
+        mySlice->getOperator()->setDataType(DataType::Int32);
+        mySlice->getOperator()->setBackend("cpu");
+        mySlice->forward();
+        op->getOutput(0)->print();
+        REQUIRE(*(op->getOutput(0)) == *expectedOutput);
+        REQUIRE(op->getOutput(0)->dims() == expectedOutput->dims());
+        REQUIRE(op->getOutput(0)->dataType() == expectedOutput->dataType());
+    }
+}