Matmul rework

[ADD] support for Multi-dimensional Matrix Multiplication, following numpy MatMul operation specificities.

In the context of Multi-dimensional MatMul, we consider an N-Dim Tensor as a stack of 2-D matrices having as shape the last two dimensions of the Tensor.

From Numpy documentation:

The behavior depends on the arguments in the following way.

• If both arguments are 2-D they are multiplied like conventional matrices.
• If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
• If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
• If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.

include/aidge/operator/MatMul.hpp

@@ -27,34 +27,21 @@
 #include "aidge/utils/Registrar.hpp"
 
 namespace Aidge {
-enum class MatMulAttr { OutChannels };
 
 class MatMul_Op : public OperatorTensor,
               public Registrable<MatMul_Op,
                                  std::string,
-                                 std::unique_ptr<OperatorImpl>(const MatMul_Op &)>,
-              public StaticAttributes<MatMulAttr, DimSize_t> {
+                                 std::unique_ptr<OperatorImpl>(const MatMul_Op &)> {
 public:
     static const std::string Type;
 
-    MatMul_Op() = delete;
-
-    using Attributes_ = StaticAttributes<MatMulAttr, DimSize_t>;
-    template <MatMulAttr e> using attr = typename Attributes_::template attr<e>;
-
-    MatMul_Op(DimSize_t out_channels)
-            : OperatorTensor(Type, 1, 1, 1),
-            Attributes_(
-                attr<MatMulAttr::OutChannels>(out_channels))
-    {}
+    MatMul_Op(): OperatorTensor(Type, 2, 0, 1) {}
 
     /**
      * @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.
      */
-    MatMul_Op(const MatMul_Op& op)
-        : OperatorTensor(op),
-          Attributes_(op)
+    MatMul_Op(const MatMul_Op& op) : OperatorTensor(op)
     {
         mImpl = op.mImpl ? Registrar<MatMul_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
     }
@@ -68,19 +55,7 @@ public:
     }
 
 
-    void computeOutputDims() override final {
-        bool associated = true;
-        for (IOIndex_t i = 0; i < nbInputs(); ++i) {
-            if (!getInput(i)) {
-                AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
-            }
-            associated &= !(getInput(i)->empty());
-        }
-        if (associated) {
-            // <batch, OutChannels>
-            mOutputs[0]->resize({getInput(0)->dims()[0], this->template getAttr<MatMulAttr::OutChannels>()});
-        }
-    }
+    void computeOutputDims() override final;
 
 
     void setBackend(const std::string& name, DeviceIdx_t device = 0) override {
@@ -89,24 +64,16 @@ public:
     }
 
     static const std::vector<std::string> getInputsName(){
-        return {"data_input", "weight"};
+        return {"data_input1", "data_input2"};
     }
     static const std::vector<std::string> getOutputsName(){
         return {"data_output"};
     }
 };
 
-inline std::shared_ptr<Node> MatMul(DimSize_t inChannels, DimSize_t outChannels, const std::string& name = "") {
-    // FIXME: properly handle default w initialization in every cases
-    auto matmul = std::make_shared<Node>(std::make_shared<MatMul_Op>(outChannels), name);
-    addProducer(matmul, 1, {outChannels, inChannels}, "w");
-    return matmul;
+inline std::shared_ptr<Node> MatMul(const std::string& name = "") {
+    return std::make_shared<Node>(std::make_shared<MatMul_Op>(), name);
 }
 } // namespace Aidge
 
-namespace {
-template <>
-const char *const EnumStrings<Aidge::MatMulAttr>::data[] = {"OutChannels"};
-}
-
 #endif /* AIDGE_CORE_OPERATOR__MATMUL_H_ */