Moved transpose default impl to core

include/aidge/operator/Transpose.hpp

@@ -26,40 +26,47 @@
 #include "aidge/utils/Types.h"
 
 namespace Aidge {
+class Transpose_OpImpl : public OperatorImpl {
+public:
+    Transpose_OpImpl(const Operator& op, const std::string& backend = ""): OperatorImpl(op, backend) {}
+    void forward() override;
+};
+
 enum class TransposeAttr { OutputDimsOrder };
 
-template <DimIdx_t DIM>
 class Transpose_Op : public OperatorTensor,
-                public Registrable<Transpose_Op<DIM>, std::string, std::shared_ptr<OperatorImpl>(const Transpose_Op<DIM> &)>,
-                public StaticAttributes<TransposeAttr,
-                                       std::array<DimSize_t, DIM>> {
+                public Registrable<Transpose_Op, std::string, std::shared_ptr<OperatorImpl>(const Transpose_Op&)>,
+                public StaticAttributes<TransposeAttr, std::vector<DimSize_t>> {
 
    public:
     static const std::string Type;
 
     Transpose_Op() = delete;
 
-    using Attributes_ = StaticAttributes<TransposeAttr,
-                                             std::array<DimSize_t, DIM>>;
+    using Attributes_ = StaticAttributes<TransposeAttr, std::vector<DimSize_t>>;
     template <TransposeAttr e>
     using attr = typename Attributes_::template attr<e>;
 
-    constexpr Transpose_Op(const std::array<DimSize_t, DIM> &output_dims_order)
+    Transpose_Op(const std::vector<DimSize_t> &output_dims_order)
         : OperatorTensor(Type, 1, 0, 1),
-          Attributes_(attr<TransposeAttr::OutputDimsOrder>(output_dims_order)) { }
+          Attributes_(attr<TransposeAttr::OutputDimsOrder>(output_dims_order))
+    {
+        mImpl = std::make_shared<Transpose_OpImpl>(*this);
+    }
 
     /**
      * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
      * @param op Operator to copy.
      */
-    Transpose_Op(const Transpose_Op<DIM>& op)
+    Transpose_Op(const Transpose_Op& op)
         : OperatorTensor(op),
           Attributes_(op)
     {
-        if (op.mImpl){
-            SET_IMPL_MACRO(Transpose_Op<DIM>, *this, op.backend());
-        }else{
-            mImpl = nullptr;
+        if (!op.backend().empty()) {
+            SET_IMPL_MACRO(Transpose_Op, *this, op.backend());
+        }
+        else {
+            mImpl = std::make_shared<Transpose_OpImpl>(*this);
         }
     }
 
@@ -68,27 +75,12 @@ class Transpose_Op : public OperatorTensor,
      * @see Operator::Transpose_Op
      */
     std::shared_ptr<Operator> clone() const override {
-        return std::make_shared<Transpose_Op<DIM>>(*this);
+        return std::make_shared<Transpose_Op>(*this);
     }
 
-    bool forwardDims(bool /*allowDataDependency*/ = false) override final {
-        if (!getInput(0)->empty()) {
-            auto attr = (this)->getStaticAttributes();
-            const std::array<DimSize_t, DIM>& outDimsOrder = static_cast<const std::array<DimSize_t, DIM>&>(std::get<0>(attr));
-            std::vector<DimSize_t> outputDims;
-            for (std::size_t i = 0; i < DIM; ++i) {
-                outputDims.push_back(getInput(0)->dims()[outDimsOrder[i]]);
-            }
-            mOutputs[0]->resize(outputDims);
-            return true;
-        }
-        return false;
-    }
+    bool forwardDims(bool /*allowDataDependency*/ = false) override final;
 
-    void setBackend(const std::string &name, DeviceIdx_t device = 0) override {
-        SET_IMPL_MACRO(Transpose_Op<DIM>, *this, name);
-        mOutputs[0]->setBackend(name, device);
-    }
+    void setBackend(const std::string &name, DeviceIdx_t device = 0) override;
 
     static const std::vector<std::string> getInputsName(){
         return {"data_input"};
@@ -98,26 +90,10 @@ class Transpose_Op : public OperatorTensor,
     }
 };
 
-template <std::array<DimSize_t, 1>::size_type DIM>
-inline std::shared_ptr<Node> Transpose(const std::array<DimSize_t, DIM> &output_dims_order,
+inline std::shared_ptr<Node> Transpose(const std::vector<DimSize_t> &output_dims_order,
                                            const std::string& name = "") {
-    // FIXME: properly handle default w&b initialization in every cases
-    static_assert(DIM<=MaxDim,"Too many kernel dimensions required by Transpose, not supported");
-    return std::make_shared<Node>(std::make_shared<Transpose_Op<static_cast<DimIdx_t>(DIM)>>(output_dims_order), name);
-}
-
-// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
-template <DimSize_t DIM>
-inline std::shared_ptr<Node> Transpose(
-    DimSize_t const (&output_dims_order)[DIM],
-    const std::string& name = "") {
-    static_assert(DIM<=MaxDim,"Too many kernel dimensions required by Transpose, not supported");
-    return Transpose(to_array(output_dims_order), name);
+    return std::make_shared<Node>(std::make_shared<Transpose_Op>(output_dims_order), name);
 }
-
-template <DimIdx_t DIM>
-const std::string Transpose_Op<DIM>::Type = "Transpose";
-
 }  // namespace Aidge
 
 namespace {

python_binding/operator/pybind_Transpose.cpp

@@ -25,32 +25,19 @@
 namespace py = pybind11;
 namespace Aidge {
 
-template <DimIdx_t DIM>
 void declare_Transpose(py::module &m) {
-  const std::string pyClassName("TransposeOp" + std::to_string(DIM) + "D");
-  py::class_<Transpose_Op<DIM>, std::shared_ptr<Transpose_Op<DIM>>, Attributes, OperatorTensor>(
-    m, ("TransposeOp" + std::to_string(DIM) + "D").c_str(), py::multiple_inheritance())
-  .def("get_inputs_name", &Transpose_Op<DIM>::getInputsName)
-  .def("get_outputs_name", &Transpose_Op<DIM>::getOutputsName)
-  .def("attributes_name", &Transpose_Op<DIM>::staticGetAttrsName);
-
-  declare_registrable<Transpose_Op<DIM>>(m, pyClassName);
-
-  m.def(("Transpose" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& output_dims_order,
-                                                                  const std::string& name) {
-        AIDGE_ASSERT(output_dims_order.size() == DIM, "output_dims_order size [{}] does not match DIM [{}]", output_dims_order.size(), DIM);
-        return Transpose<DIM>(to_array<DIM>(output_dims_order.begin()), name);
-    }, py::arg("output_dims_order"),
-       py::arg("name") = "");
-
+  const std::string pyClassName("TransposeOp");
+  py::class_<Transpose_Op, std::shared_ptr<Transpose_Op>, Attributes, OperatorTensor>(
+    m, ("TransposeOp").c_str(), py::multiple_inheritance())
+  .def("get_inputs_name", &Transpose_Op::getInputsName)
+  .def("get_outputs_name", &Transpose_Op::getOutputsName)
+  .def("attributes_name", &Transpose_Op::staticGetAttrsName);
+  declare_registrable<Transpose_Op>(m, pyClassName);
+  m.def("Transpose", &Transpose, py::arg("output_dims_order"), py::arg("name") = "");
 }
 
 void init_Transpose(py::module &m) {
-  declare_Transpose<2>(m);
-  declare_Transpose<3>(m);
-  declare_Transpose<4>(m);
-  declare_Transpose<5>(m);
-  declare_Transpose<6>(m);
+  declare_Transpose(m);
 
 }
 } // namespace Aidge

src/operator/Transpose.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 "aidge/operator/Transpose.hpp"
+
+#include <cstddef>    // std::size_t
+#include <cstdint>    // std::int64_t
+#include <memory>
+#include <stdexcept>  // std::runtime_error
+#include <string>
+#include <vector>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/utils/ErrorHandling.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+void Aidge::Transpose_OpImpl::forward() {
+    const Transpose_Op& op = dynamic_cast<const Transpose_Op&>(mOp);
+    const auto inputDims = op.getInput(0)->dims();
+    const auto outputDims = op.getOutput(0)->dims();
+
+    std::vector<std::size_t> outStrides(outputDims.size(), 1);
+    for (size_t i = 0; i < outputDims.size(); ++i) {
+        for (size_t j = i+1; j < outputDims.size(); ++j)
+        {
+            outStrides[i] *= outputDims[j];
+        }
+    }
+
+    std::vector<size_t> indices(outputDims.size(), 0);
+    for (size_t i = 0; i < op.getInput(0)->size(); ++i) {
+        size_t idx = 0;
+        // Permute indices based on OutputDimsOrder attr
+        for (int j = outputDims.size() -1; j >=0; --j) {
+            idx += indices[op.getAttr<std::vector<DimSize_t>>(0)[j]] * outStrides[j];
+        }
+        // Copy the value in output
+        op.getOutput(0)->getImpl()->copy(op.getInput(0)->getImpl()->rawPtr(i), 1, idx);
+
+        // Update indices for the next iteration
+        for (int j = outputDims.size() - 1; j >= 0; --j) {
+            if (indices[j] < inputDims[j] - 1) {
+                indices[j]++;
+                break;
+            } else {
+                indices[j] = 0;
+            }
+        }
+    }
+}
+
+const std::string Aidge::Transpose_Op::Type = "Transpose";
+
+bool Aidge::Transpose_Op::forwardDims(bool /*allowDataDependency*/) {
+    // check input has been associated
+    if (!getInput(0)) {
+        AIDGE_THROW_OR_ABORT(std::runtime_error, "Input was not connected");
+    }
+
+    if (!getInput(0)->empty()) {
+        const auto& outDimsOrder = getAttr<std::vector<DimSize_t>>(0);
+        std::vector<DimSize_t> outputDims;
+        for (std::size_t i = 0; i < outDimsOrder.size(); ++i) {
+            outputDims.push_back(getInput(0)->dims()[outDimsOrder[i]]);
+        }
+        mOutputs[0]->resize(outputDims);
+        return true;
+    }
+    return false;
+}
+
+void Aidge::Transpose_Op::setBackend(const std::string& name, Aidge::DeviceIdx_t device) {
+    if (Registrar<Transpose_Op>::exists({name})){
+        SET_IMPL_MACRO(Transpose_Op, *this, name);
+    }
+    else {
+        mImpl = std::make_shared<Transpose_OpImpl>(*this);
+    }
+    mOutputs[0]->setBackend(name, device);
+}

unit_tests/operator/Test_TransposeImpl.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 <memory>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/Transpose.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/operator] Transpose(forward)") {
+    SECTION("3D Tensor") {
+        std::shared_ptr<Tensor> input = std::make_shared<Tensor>(Array3D<float,2,3,4> {
+            {
+                {{0.42507452, 0.11244237, 0.43243718, 0.62354952},
+                {0.90250170, 0.48719984, 0.45781207, 0.92536664},
+                {0.06348717, 0.91678733, 0.64452291, 0.00484818}},
+
+                {{0.66873497, 0.99508536, 0.55714869, 0.84887981},
+                {0.41666120, 0.92365038, 0.80034822, 0.38721532},
+                {0.52037925, 0.53937608, 0.66380072, 0.36330253}}
+            }
+        });
+        std::shared_ptr<Tensor> output = std::make_shared<Tensor>(Array3D<float,2,4,3> { 
+            {
+                {{0.42507452, 0.90250170, 0.06348717},
+                {0.11244237, 0.48719984, 0.91678733},
+                {0.43243718, 0.45781207, 0.64452291},
+                {0.62354952, 0.92536664, 0.00484818}},
+
+                {{0.66873497, 0.41666120, 0.52037925},
+                {0.99508536, 0.92365038, 0.53937608},
+                {0.55714869, 0.80034822, 0.66380072},
+                {0.84887981, 0.38721532, 0.36330253}}
+            }
+        });
+        std::shared_ptr<Node> myTranspose = Transpose({0,2,1});
+        auto op = std::static_pointer_cast<OperatorTensor>(myTranspose -> getOperator());
+        op->associateInput(0,input);
+        op->setDataType(DataType::Float32);
+        op->setBackend("cpu");
+        myTranspose->forward();
+
+        REQUIRE(*(op->getOutput(0)) == *output);
+    }
+    SECTION("4D Tensor") {
+        std::shared_ptr<Tensor> input = std::make_shared<Tensor>(Array4D<int,2,3,1,4> {
+            {
+                {
+                    {
+                        {1, 2, 3, 4}
+                    },
+                    {
+                        {5, 6, 7, 8}
+                    },
+                    {
+                        {9, 10, 11, 12}
+                    }
+                },
+                {
+                    {
+                        {13, 14, 15, 16}
+                    },
+                    {
+                        {17, 18, 19, 20}
+                    },
+                    {
+                        {21, 22, 23, 24}
+                    }
+                }
+            }
+        });
+        std::shared_ptr<Tensor> output = std::make_shared<Tensor>(Array4D<int,2,4,1,3> { 
+            {
+                {
+                    {
+                        {1, 5, 9}
+                    },
+                    {
+                        {2, 6, 10}
+                    },
+                    {
+                        {3, 7, 11}
+                    },
+                    {
+                        {4, 8, 12}
+                    }
+                },
+                {
+                    {
+                        {13, 17, 21}
+                    },
+                    {
+                        {14, 18, 22}
+                    },
+                    {
+                        {15, 19, 23}
+                    },
+                    {
+                        {16, 20, 24}
+                    }
+                }
+            }
+        });
+        std::shared_ptr<Node> myTranspose = Transpose({0,3,2,1});
+        auto op = std::static_pointer_cast<OperatorTensor>(myTranspose -> getOperator());
+        op->associateInput(0,input);
+        op->setDataType(DataType::Int32);
+        op->setBackend("cpu");
+        myTranspose->forward();
+
+        REQUIRE(*(op->getOutput(0)) == *output);
+    }
+}
\ No newline at end of file