.gitlab/ci/build.gitlab-ci.yml

@@ -95,60 +95,60 @@ build:ubuntu_python:
     paths:
       - venv/
 
-build:windows_cpp:
-  stage: build
-  needs: []
-  tags:
-    - windows
-
-  image: buildtools
-  before_script:
-    # Install Chocolatey
-    - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-    # Install dependencies
-    - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
-    - choco install git -Y
-    - choco install python -Y
-    # Update PATH
-    - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-  script:
-    - mkdir -p build_cpp
-    - mkdir -p install_cpp
-    - cd build_cpp
-    - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug ..
-    - cmake --build . -j2
-    - cmake --install . --config Debug
-
-  artifacts:
-    expire_in: 1 week
-    paths:
-      - build_cpp/
-      - install_cpp/
-
-build:windows_python:
-  stage: build
-  needs: []
-  tags:
-    - windows
-
-  image: buildtools
-  before_script:
-    # Install Chocolatey
-    - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-    # Install dependencies
-    - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
-    - choco install git -Y
-    - choco install python -Y
-    # Update PATH
-    - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-  script:
-    - python -m pip install virtualenv
-    - virtualenv venv
-    - venv\Scripts\Activate.ps1
-    # Numpy dependancy for unit test
-    - python -m pip install -r requirements.txt
-    - python -m pip install .
-  artifacts:
-    expire_in: 1 week
-    paths:
-      - venv/
+# build:windows_cpp:
+#   stage: build
+#   needs: []
+#   tags:
+#     - windows
+
+#   image: buildtools
+#   before_script:
+#     # Install Chocolatey
+#     - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
+#     # Install dependencies
+#     - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
+#     - choco install git -Y
+#     - choco install python -Y
+#     # Update PATH
+#     - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+#   script:
+#     - mkdir -p build_cpp
+#     - mkdir -p install_cpp
+#     - cd build_cpp
+#     - cmake -DCMAKE_INSTALL_PREFIX:PATH=../install_cpp -DCMAKE_BUILD_TYPE=Debug ..
+#     - cmake --build . -j2
+#     - cmake --install . --config Debug
+
+#   artifacts:
+#     expire_in: 1 week
+#     paths:
+#       - build_cpp/
+#       - install_cpp/
+
+# build:windows_python:
+#   stage: build
+#   needs: []
+#   tags:
+#     - windows
+
+#   image: buildtools
+#   before_script:
+#     # Install Chocolatey
+#     - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
+#     # Install dependencies
+#     - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
+#     - choco install git -Y
+#     - choco install python -Y
+#     # Update PATH
+#     - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+#   script:
+#     - python -m pip install virtualenv
+#     - virtualenv venv
+#     - venv\Scripts\Activate.ps1
+#     # Numpy dependancy for unit test
+#     - python -m pip install -r requirements.txt
+#     - python -m pip install .
+#   artifacts:
+#     expire_in: 1 week
+#     paths:
+#       - venv/

.gitlab/ci/test.gitlab-ci.yml

@@ -26,23 +26,23 @@ test:ubuntu_python:
     reports:
       junit: ${CI_PROJECT_NAME}/xmlrunner-results.xml
 
-test:windows_cpp:
-  stage: test
-  needs: ["build:windows_cpp"]
-  tags:
-    - windows
-  image: buildtools
-  before_script:
-    # Install Chocolatey
-    - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-    # Install dependencies
-    - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
-    - choco install python -Y
-    # Update PATH
-    - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
-  script:
-    - cd build_cpp
-    - ctest --output-junit ctest-results.xml --output-on-failure
-  artifacts:
-    reports:
-      junit: build_cpp/ctest-results.xml
+# test:windows_cpp:
+#   stage: test
+#   needs: ["build:windows_cpp"]
+#   tags:
+#     - windows
+#   image: buildtools
+#   before_script:
+#     # Install Chocolatey
+#     - Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
+#     # Install dependencies
+#     - choco install cmake.install --installargs '"ADD_CMAKE_TO_PATH=System"' -Y
+#     - choco install python -Y
+#     # Update PATH
+#     - $env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
+#   script:
+#     - cd build_cpp
+#     - ctest --output-junit ctest-results.xml --output-on-failure
+#   artifacts:
+#     reports:
+#       junit: build_cpp/ctest-results.xml

CHANGELOG

+# Version 0.1.1 (January 29, 2024)
+
+[Add] Support of a negative value for Reshape Operator shape attribute.
+
 # Version 0.1.0 (January 23, 2024)
 
 Initial release

aidge_core/unit_tests/test_parameters.py

@@ -39,12 +39,6 @@ class test_attributes(unittest.TestCase):
         self.assertEqual(fc_op.get_attr("OutChannels"), out_channels)
         self.assertEqual(fc_op.get_attr("NoBias"), nb_bias)
 
-    def test_matmul(self):
-        in_channels = 4
-        out_channels = 8
-        matmul_op = aidge_core.MatMul(in_channels, out_channels).get_operator()
-        self.assertEqual(matmul_op.get_attr("OutChannels"), out_channels)
-
     def test_producer_1D(self):
         dims = [5]
         producer_op = aidge_core.Producer(dims).get_operator()

aidge_core/unit_tests/test_recipies.py

@@ -45,9 +45,9 @@ class test_recipies(unittest.TestCase):
         self.assertTrue(all([i in old_nodes for i in graph_view.get_nodes()]))
 
     def test_fuse_matmul_add(self):
-        matmul0 = aidge_core.MatMul(1, 1, name="MatMul0")
+        matmul0 = aidge_core.MatMul(name="MatMul0")
         add0 = aidge_core.Add(2, name="Add0")
-        matmul1 = aidge_core.MatMul(1, 1, name="MatMul1")
+        matmul1 = aidge_core.MatMul(name="MatMul1")
         add1 = aidge_core.Add(2, name="Add1")
 
         graph_view = aidge_core.sequential([matmul0, add0, matmul1, add1])

include/aidge/aidge.hpp

@@ -14,13 +14,15 @@
 
 #include "aidge/backend/OperatorImpl.hpp"
 #include "aidge/backend/TensorImpl.hpp"
+#include "aidge/backend/StimulusImpl.hpp"
 
 #include "aidge/backend/cpu/data/TensorImpl.hpp"
 #include "aidge/backend/cpu/data/GetCPUPtr.h"
 
 #include "aidge/data/Data.hpp"
 #include "aidge/data/Tensor.hpp"
-
+#include "aidge/data/Database.hpp"
+#include "aidge/data/DataProvider.hpp"
 #include "aidge/graph/Connector.hpp"
 #include "aidge/graph/GraphView.hpp"
 #include "aidge/graph/Node.hpp"
@@ -61,15 +63,15 @@
 #include "aidge/operator/Sub.hpp"
 #include "aidge/operator/Transpose.hpp"
 #include "aidge/scheduler/Scheduler.hpp"
+#include "aidge/stimuli/Stimulus.hpp"
 
 #include "aidge/recipies/Recipies.hpp"
 
 #include "aidge/utils/Attributes.hpp"
 #include "aidge/utils/StaticAttributes.hpp"
 #include "aidge/utils/DynamicAttributes.hpp"
+#include "aidge/utils/Random.hpp"
 #include "aidge/utils/Registrar.hpp"
 #include "aidge/utils/Types.h"
-//#include "aidge/utilsParsing/AstNode.hpp"
-//#include "aidge/utilsParsing/ParsingToken.hpp"
 
 #endif /* AIDGE_IMPORTS_H_ */

include/aidge/backend/StimulusImpl.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_CORE_BACKEND_STIMULUSIMPL_H_
+#define AIDGE_CORE_BACKEND_STIMULUSIMPL_H_
+
+#include <memory>
+
+#include "aidge/data/Tensor.hpp"
+
+namespace Aidge {
+
+/**
+ * @brief Base class to implement data loading functions.
+ */
+class StimulusImpl {
+public:
+    virtual ~StimulusImpl() noexcept = default;
+
+    virtual std::shared_ptr<Tensor> load() const = 0;
+};
+} // namespace Aidge
+
+#endif /* AIDGE_CORE_BACKEND_STIMULUSIMPL_H_ */

include/aidge/backend/TensorImpl.hpp

@@ -12,8 +12,12 @@
 #ifndef AIDGE_TENSORIMPL_H_
 #define AIDGE_TENSORIMPL_H_
 
-#include <cstddef>
-#include <cstdio>
+#include <numeric>     // std::accumulate
+#include <cstddef>     // std::size_t
+#include <functional>  // std::multiplies
+#include <vector>
+#include <utility>     // std::pair, std::make_pair
+
 #include "aidge/data/Data.hpp"
 #include "aidge/utils/Types.h"
 #include "aidge/utils/ErrorHandling.hpp"
@@ -59,20 +63,42 @@ private:
 */
 
 /**
- * This class manages the raw data storage of a Tensor and provide generic copy
+ * @class TensorImpl
+ * @brief Class to manage the raw data storage of a Tensor and provide generic copy
  * primitives from other devices and from/to host.
- * It can own the data or not (use setRawPtr() to set an external data owner).
- * It only knows the data type and data capacity, but does not handle anything else.
+ * @note It can own the data or not (use ``setRawPtr()`` to set an external data owner).
+ * @note It only knows the data type and data capacity, but does not handle anything else.
 */
 class TensorImpl {
+protected:
+
+    const char *mBackend;
+    /// @brief Device id.
+    const DeviceIdx_t mDevice;
+    /// Number of elements (to be) stored.
+    NbElts_t mNbElts;
+
 public:
     TensorImpl() = delete;
-    TensorImpl(const char *backend, DeviceIdx_t device, NbElts_t length) : mBackend(backend), mDevice(device), mNbElts(length) {};
 
+    TensorImpl(const char *backend, DeviceIdx_t device, std::vector<DimSize_t> dims)
+        : mBackend(backend),
+          mDevice(device)
+    {
+        resize(dims);
+    };
+
+    virtual ~TensorImpl() = default;
+
+    virtual bool operator==(const TensorImpl &othImpl) const = 0;
+
+public:
     /**
      * Return the (backend, device) pair for this implementation.
     */
-    std::pair<std::string, DeviceIdx_t> device() const { return std::make_pair(mBackend, mDevice); }
+    std::pair<std::string, DeviceIdx_t> device() const noexcept {
+        return std::make_pair(std::string(mBackend), mDevice);
+    }
 
     /**
      * Copy data from the same device.
@@ -147,8 +173,8 @@ public:
     /**
      * Set the size, in number of elements, that must be stored.
     */
-    void resize(NbElts_t length) {
-        mNbElts = length;
+    virtual void resize(std::vector<DimSize_t> dims) {
+        mNbElts = std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
     }
 
     /**
@@ -161,23 +187,15 @@ public:
     */
     virtual std::size_t scalarSize() const noexcept = 0;
     constexpr const char *backend() const { return mBackend; }
-    virtual ~TensorImpl() = default;
-    virtual bool operator==(const TensorImpl &othImpl) const = 0;
 
     /**
-     * Copy from another backend.
+     * @brief Copy from another backend.
      * @param srcImpl Source TensorImpl to copy from.
      * @param length Number of elements of size scalarSize() to copy
      * @param srcOffset Source offset (in number of elements).
      * @param dstOffset Destination offset (in number of elements).
     */
     void copyFrom(const TensorImpl& srcImpl, NbElts_t length, NbElts_t srcOffset = 0, NbElts_t dstOffset = 0);
-
-protected:
-    const char *mBackend;
-    const DeviceIdx_t mDevice;
-    /// Number of elements (to be) stored
-    NbElts_t mNbElts;
 };
 
 } // namespace Aidge

include/aidge/backend/cpu/data/GetCPUPtr.h

@@ -12,13 +12,16 @@
 #ifndef AIDGE_CPU_DATA_GETCPUPTR_H_
 #define AIDGE_CPU_DATA_GETCPUPTR_H_
 
+#include <cstddef>
+#include <memory>
+
 #include "aidge/data/Tensor.hpp"
 
 namespace Aidge {
-inline void *getCPUPtr(std::shared_ptr<Aidge::Data> const &data) {
+inline void *getCPUPtr(std::shared_ptr<Aidge::Data> const &data, const std::size_t offset = 0) {
   const auto tensor = std::static_pointer_cast<Tensor>(data);
-  return tensor->getImpl()->hostPtr(tensor->getImplOffset());
+  return tensor->getImpl()->hostPtr(tensor->getImplOffset() + offset);
 }
 } // namespace Aidge
 
-#endif // AIDGE_CPU_DATA_GETCPUPTR_H_
\ No newline at end of file
+#endif // AIDGE_CPU_DATA_GETCPUPTR_H_

include/aidge/backend/cpu/data/TensorImpl.hpp

@@ -33,7 +33,7 @@ private:
 public:
     static constexpr const char *Backend = "cpu";
 
-    TensorImpl_cpu(DeviceIdx_t device, NbElts_t length) : TensorImpl(Backend, device, length) {}
+    TensorImpl_cpu(DeviceIdx_t device, std::vector<DimSize_t> dims) : TensorImpl(Backend, device, dims) {}
 
     bool operator==(const TensorImpl &otherImpl) const override final {
         const auto& typedOtherImpl = reinterpret_cast<const TensorImpl_cpu<T> &>(otherImpl);
@@ -47,8 +47,8 @@ public:
         return i == mNbElts;
     }
 
-    static std::shared_ptr<TensorImpl_cpu> create(DeviceIdx_t device, NbElts_t length) {
-        return std::make_shared<TensorImpl_cpu<T>>(device, length);
+    static std::shared_ptr<TensorImpl_cpu> create(DeviceIdx_t device, std::vector<DimSize_t> dims) {
+        return std::make_shared<TensorImpl_cpu<T>>(device, dims);
     }
 
     inline std::size_t scalarSize() const noexcept override final { return sizeof(T); }
@@ -183,10 +183,18 @@ static Registrar<Tensor> registrarTensorImpl_cpu_Float32(
         {"cpu", DataType::Float32}, Aidge::TensorImpl_cpu<float>::create);
 static Registrar<Tensor> registrarTensorImpl_cpu_Float16(
         {"cpu", DataType::Float16}, Aidge::TensorImpl_cpu<half_float::half>::create);
-static Registrar<Tensor> registrarTensorImpl_cpu_Int32(
-        {"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int>::create);
 static Registrar<Tensor> registrarTensorImpl_cpu_Int64(
         {"cpu", DataType::Int64}, Aidge::TensorImpl_cpu<long>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_Int32(
+        {"cpu", DataType::Int32}, Aidge::TensorImpl_cpu<int>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_Int16(
+        {"cpu", DataType::Int16}, Aidge::TensorImpl_cpu<int16_t>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_UInt16(
+        {"cpu", DataType::UInt16}, Aidge::TensorImpl_cpu<uint16_t>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_Int8(
+        {"cpu", DataType::Int8}, Aidge::TensorImpl_cpu<int8_t>::create);
+static Registrar<Tensor> registrarTensorImpl_cpu_UInt8(
+        {"cpu", DataType::UInt8}, Aidge::TensorImpl_cpu<uint8_t>::create);
 }  // namespace
 }  // namespace Aidge
 

include/aidge/data/DataProvider.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_CORE_DATA_DATAPROVIDER_H_
+#define AIDGE_CORE_DATA_DATAPROVIDER_H_
+
+#include <cstddef>  // std::size_t
+#include <memory>   // std::shared_ptr
+#include <string>
+#include <vector>   // std::vector
+
+#include "aidge/data/Database.hpp"
+#include "aidge/data/Data.hpp"
+
+namespace Aidge {
+
+/**
+ * @brief Data Provider. Takes in a database and compose batches by fetching data from the given database.
+ * @todo Implement Drop last batch option. Currently returns the last batch with less elements in the batch.
+ * @todo Implement readRandomBatch to compose batches from the database with a random sampling startegy. Necessary for training.
+ */
+class DataProvider {
+private:
+    // Dataset providing the data to the dataProvider
+    const Database& mDatabase;
+    
+    // Desired size of the produced batches
+    const std::size_t mBatchSize;
+
+    // Enable random shuffling for learning
+    const bool mShuffle;
+
+    // Drops the last non-full batch
+    const bool mDropLast;
+
+    // Number of modality in one item
+    const std::size_t mNumberModality;
+
+    // mNbItems contains the number of items in the database
+    std::size_t mNbItems;
+    // mBatches contains the call order of each database item
+    std::vector<unsigned int> mBatches; 
+    // mIndex browsing the number of batch
+    std::size_t mIndexBatch;
+
+    // mNbBatch contains the number of batch
+    std::size_t mNbBatch;
+    // Size of the Last batch
+    std::size_t mLastBatchSize;
+
+    // Store each modality dimensions, backend and type
+    std::vector<std::vector<std::size_t>> mDataDims;
+    std::vector<std::string> mDataBackends;
+    std::vector<DataType> mDataTypes; 
+
+public:
+    /**
+     * @brief Constructor of Data Provider.
+     * @param database database from which to load the data.
+     * @param batchSize number of data samples per batch.
+     */
+    DataProvider(const Database& database, const std::size_t batchSize, const bool shuffle = false, const bool dropLast = false);
+
+public:
+    /**
+     * @brief Create a batch for each data modality in the database.
+     * @return a vector of tensors. Each tensor is a batch corresponding to one modality.
+     */
+    std::vector<std::shared_ptr<Tensor>> readBatch() const;
+
+    /**
+     * @brief Get the Number of Batch.
+     * 
+     * @return std::size_t 
+     */
+    inline std::size_t getNbBatch(){
+        return mNbBatch;
+    };
+
+    /**
+     * @brief Get the current Index Batch.
+     * 
+     * @return std::size_t 
+     */
+    inline std::size_t getIndexBatch(){
+        return mIndexBatch;
+    };
+
+    /**
+     * @brief Reset the internal index batch that browses the data of the database to zero.
+     */
+    inline void resetIndexBatch(){
+        mIndexBatch = 0;
+    };
+
+    /**
+     * @brief Increment the internal index batch that browses the data of the database.
+     */
+    inline void incrementIndexBatch(){
+        ++mIndexBatch;
+    };
+
+    /**
+     * @brief Setup the batches for one pass on the database.
+     */
+    void setBatches();
+
+    /**
+     * @brief End condition of dataProvider for one pass on the database.
+     * 
+     * @return true when all batch were fetched, False otherwise
+     */
+    inline bool done(){
+        return (mIndexBatch == mNbBatch);
+    };
+
+
+    // Functions for python iterator iter and next (definition in pybind.cpp)
+    /**
+     * @brief __iter__ method for iterator protocol
+     * 
+     * @return DataProvider* 
+     */
+    DataProvider* iter();
+
+    /**
+     * @brief __next__ method for iterator protocol
+     * 
+     * @return std::vector<std::shared_ptr<Aidge::Tensor>> 
+     */
+    std::vector<std::shared_ptr<Aidge::Tensor>> next();
+};
+
+} // namespace Aidge
+
+#endif /* AIDGE_CORE_DATA_DATAPROVIDER_H_ */

include/aidge/data/Database.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_CORE_DATA_DATABASE_H_
+#define AIDGE_CORE_DATA_DATABASE_H_
+
+#include <cstddef>
+#include <memory>
+#include <vector>
+
+#include "aidge/data/Tensor.hpp"
+
+namespace Aidge {
+
+/**
+ * @brief Abstract class representing a map from a key to data.
+ * All databases should inherit from this class. All subclasses should overwrite
+ * :cpp:function:`Database::getItem` to fetch data from a given index.
+ */
+class Database {
+public:
+    Database() = default;
+    virtual ~Database() noexcept = default;
+
+    /**
+     * @brief Fetch an item of the database.
+     * @param index index of the item.
+     * @return vector of data mapped to index.
+     */
+    virtual std::vector<std::shared_ptr<Tensor>> getItem(const std::size_t index) const = 0;
+
+    /**
+     * @brief Get the number of items in the database
+     *
+     * @return std::size_t
+     */
+    virtual std::size_t getLen() const noexcept = 0;
+
+    /**
+     * @brief Get the number of modalities in one database item
+     *
+     * @return std::size_t
+     */
+    virtual std::size_t getNbModalities() const noexcept = 0;
+
+};
+} // namespace Aidge
+
+#endif /* AIDGE_CORE_DATA_DATABASE_H_ */

include/aidge/data/Tensor.hpp

@@ -17,6 +17,7 @@
 #include <memory>
 #include <numeric>   // std::accumulate
 #include <string>
+#include <type_traits>  // std::is_arithmetic
 #include <vector>
 
 #include "aidge/backend/TensorImpl.hpp"
@@ -32,7 +33,7 @@ namespace Aidge {
  * Contains a pointer to an actual contiguous implementation of data.
  */
 class Tensor : public Data,
-               public Registrable<Tensor, std::tuple<std::string, DataType>, std::shared_ptr<TensorImpl>(DeviceIdx_t device, NbElts_t length)> {
+               public Registrable<Tensor, std::tuple<std::string, DataType>, std::shared_ptr<TensorImpl>(DeviceIdx_t device, std::vector<DimSize_t> dims)> {
    private:
     DataType mDataType; /** enum to specify data type. */
     std::vector<DimSize_t> mDims; /** Dimensions of the tensor. */
@@ -59,11 +60,25 @@ class Tensor : public Data,
         // ctor
     }
 
+    /**
+     * @brief Construct a new Tensor object from dimensions.
+     *
+     * @param dims dimensions of the tensor
+     * @param dataType datatype of the tensor (default = DataType::Float32)
+     */
+    Tensor(const std::vector<DimSize_t>& dims, DataType dataType = DataType::Float32)
+        : Data(Type),
+          mDataType(dataType),
+          mDims(dims)
+    {
+        computeSize();
+    }
+
     /**
      * @brief Construct a new Tensor object from another one (shallow copy).
      * Data memory is not copied, but shared between the new Tensor and the
      * initial one.
-     * 
+     *
      * @param otherTensor
      */
     Tensor(const Tensor&)            = default;
@@ -78,13 +93,24 @@ class Tensor : public Data,
             newTensor.makeContiguous();
         }
         else {
-            std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), mDataType})(mImpl->device().second, mSize);
+            std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), mDataType})(mImpl->device().second, mDims);
             newImpl->copy(mImpl->rawPtr(mImplOffset), mSize);
             newTensor.setImpl(newImpl);
         }
         return newTensor;
     }
 
+    template<typename T,
+            typename VT = std::enable_if_t<std::is_arithmetic<T>::value, std::decay_t<T>>>
+    Tensor(T val)
+        : Data(Type),
+          mDataType(NativeType<VT>::type),
+          mDims({}), mStrides({1}),
+          mImpl(Registrar<Tensor>::create({"cpu", NativeType<VT>::type})(0, std::vector<std::size_t>())),
+          mSize(1) {
+        *static_cast<VT*>(mImpl->rawPtr()) = static_cast<VT>(val);
+    }
+
     /**
      * @brief Construct a new Tensor object from the 1-dimension Array helper.
      * @tparam T datatype
@@ -96,7 +122,7 @@ class Tensor : public Data,
           mDataType(NativeType<T>::type),
           mDims({SIZE_0}),
           mStrides({1}),
-          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0)),
+          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0})),
           mSize(SIZE_0) {
         mImpl->copyFromHost(&arr.data[0], SIZE_0);
     }
@@ -105,7 +131,7 @@ class Tensor : public Data,
     constexpr Tensor &operator=(Array1D<T, SIZE_0> &&arr) {
         resize({SIZE_0});
         if (!mImpl) {
-            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0);
+            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0});
         }
         mImpl->copyFromHost(&arr.data[0], SIZE_0, mImplOffset);
         return *this;
@@ -123,7 +149,7 @@ class Tensor : public Data,
           mDataType(NativeType<T>::type),
           mDims({SIZE_0, SIZE_1}),
           mStrides({SIZE_1, 1}),
-          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1)),
+          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1})),
           mSize(SIZE_0 * SIZE_1) {
         mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1);
     }
@@ -132,7 +158,7 @@ class Tensor : public Data,
     constexpr Tensor &operator=(Array2D<T, SIZE_0, SIZE_1> &&arr) {
         resize({SIZE_0, SIZE_1});
         if (!mImpl) {
-            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1);
+            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1});
         }
         mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1, mImplOffset);
         return *this;
@@ -151,7 +177,7 @@ class Tensor : public Data,
           mDataType(NativeType<T>::type),
           mDims({SIZE_0, SIZE_1, SIZE_2}),
           mStrides({SIZE_1 * SIZE_2, SIZE_2, 1}),
-          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2)),
+          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1, SIZE_2})),
           mSize(SIZE_0 * SIZE_1 * SIZE_2) {
         mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2);
     }
@@ -160,7 +186,7 @@ class Tensor : public Data,
     constexpr Tensor &operator=(Array3D<T, SIZE_0, SIZE_1, SIZE_2> &&arr) {
         resize({SIZE_0, SIZE_1, SIZE_2});
         if (!mImpl) {
-            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2);
+            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1, SIZE_2});
         }
         mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2, mImplOffset);
         return *this;
@@ -180,7 +206,7 @@ class Tensor : public Data,
           mDataType(NativeType<T>::type),
           mDims({SIZE_0, SIZE_1, SIZE_2, SIZE_3}),
           mStrides({SIZE_1 * SIZE_2 * SIZE_3, SIZE_2 * SIZE_3, SIZE_3, 1}),
-          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3)),
+          mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1, SIZE_2, SIZE_3})),
           mSize(SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3) {
         mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
     }
@@ -189,7 +215,7 @@ class Tensor : public Data,
     constexpr Tensor &operator=(Array4D<T, SIZE_0, SIZE_1, SIZE_2, SIZE_3> &&arr) {
         resize({SIZE_0, SIZE_1, SIZE_2, SIZE_3});
         if (!mImpl) {
-            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
+            mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1, SIZE_2, SIZE_3});
         }
         mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3, mImplOffset);
         return *this;
@@ -250,7 +276,7 @@ class Tensor : public Data,
             if (mImpl->device() != std::make_pair(name, device)) {
                 // Backend change: create new impl, copy from old to new and replace
                 // impl
-                std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({name, mDataType})(device, mImpl->size());
+                std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({name, mDataType})(device, mDims);
                 if (copyFrom) {
                     newImpl->copyFrom(*mImpl, mImpl->size(), mImplOffset, 0);
                 }
@@ -258,7 +284,7 @@ class Tensor : public Data,
             }
         }
         else {
-            mImpl = Registrar<Tensor>::create({name, mDataType})(device, mSize);
+            mImpl = Registrar<Tensor>::create({name, mDataType})(device, mDims);
         }
     }
 
@@ -277,7 +303,7 @@ class Tensor : public Data,
      * @brief Get the data type enum.
      * @return constexpr DataType
      */
-    constexpr DataType dataType() const { return mDataType; }
+    constexpr DataType dataType() const noexcept { return mDataType; }
 
     /**
      * @brief Set the DataType of the Tensor and converts data
@@ -288,7 +314,7 @@ class Tensor : public Data,
      */
     void setDataType(const DataType dt, bool copyCast = true) {
         if (mImpl && (dataType() != dt)) {
-            std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), dt})(mImpl->device().second, mImpl->size());
+            std::shared_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), dt})(mImpl->device().second, mDims);
             if (copyCast) {
                 newImpl->copyCast(mImpl->rawPtr(mImplOffset), mDataType, mImpl->size());
             }
@@ -306,7 +332,7 @@ class Tensor : public Data,
 
     /**
      * @brief Set the Impl object
-     * 
+     *
      * @param impl New impl shared pointer
      * @param implOffset Storage offset in this new impl for this Tensor
      */
@@ -320,7 +346,7 @@ class Tensor : public Data,
      * @return true
      * @return false
      */
-    bool hasImpl() const { return (mImpl) ? true : false; }
+    bool hasImpl() const noexcept { return mImpl ? true : false; }
 
     /**
      * @brief Get number of dimensions of the Tensor.
@@ -355,13 +381,13 @@ class Tensor : public Data,
      * @brief Return true if Tensor is contiguous in memory.
      * @return bool
      */
-    constexpr bool isContiguous() const { return mContiguous; }
+    constexpr bool isContiguous() const noexcept { return mContiguous; }
 
     /**
      * @brief Get the number of elements in the Tensor object.
      * @return constexpr std::size_t
      */
-    constexpr std::size_t size() const { return mSize; }
+    constexpr std::size_t size() const noexcept { return mSize; }
 
     /**
      * @brief Change the dimensions of the Tensor object according to the given argument.
@@ -375,7 +401,7 @@ class Tensor : public Data,
      * @param dims New dimensions
      */
     template <std::array<DimSize_t, 1>::size_type DIM> // deducing std::array size_type and declaring DIM accordingly
-    void resize(const std::array<DimSize_t, DIM> &dims) {
+    inline void resize(const std::array<DimSize_t, DIM> &dims) {
         resize(std::vector<DimSize_t>(dims.begin(), dims.end()));
     }
 
@@ -390,48 +416,7 @@ class Tensor : public Data,
      * @param dims New dimensions
      * @param strides Stride of the tensor (if not specified, "nested" stride is used)
      */
-    void resize(const std::vector<DimSize_t> &dims, std::vector<DimSize_t> strides = std::vector<DimSize_t>()) {
-        bool checkContiguous = true;
-        if (strides.empty()) {
-            strides.resize(dims.size());
-            size_t expectedStride = 1;
-            for (int dim = dims.size() - 1; dim >= 0; --dim) {
-                strides[dim] = expectedStride;
-                expectedStride*= dims[dim];
-            }
-            checkContiguous = false;
-        }
-        else {
-            AIDGE_ASSERT(strides.size() == dims.size(), "Number of strides must match number of dims");
-        }
-
-        if (mImpl.use_count() > 1) {
-            // Here we could also create a new storage for this tensor in this case
-            // But, is it more likely that the user really wants this, or that he did a mistake?
-            AIDGE_ASSERT(dims == mDims && strides == mStrides, "Cannot resize Tensor with shared storage");
-        }
-        else {
-            mDims = dims;
-            mStrides = strides;
-
-            mContiguous = true;
-            if (checkContiguous) {
-                size_t expectedStride = 1;
-                for (int dim = dims.size() - 1; dim >= 0; --dim) {
-                    if (strides[dim] != expectedStride) {
-                        mContiguous = false;
-                        break;
-                    }
-                    expectedStride*= dims[dim];
-                }
-            }
-
-            computeSize();
-            if (mImpl) {
-                mImpl->resize(mSize);
-            }
-        }
-    }
+    void resize(const std::vector<DimSize_t> &dims, std::vector<DimSize_t> strides = std::vector<DimSize_t>());
 
     /**
      * @brief Return if the Tensor object has at leastone element.
@@ -465,95 +450,7 @@ class Tensor : public Data,
         set<expectedType>(getStorageIdx(coordIdx), value);
     }
 
-
-
-    std::string toString() const {
-        AIDGE_ASSERT(mImpl && (dims().empty() || (dims() == std::vector<DimSize_t>({0})) || (mImpl->hostPtr() != nullptr)), "tensor should have a valid host pointer");
-
-        // TODO: move lambda elsewhere?
-        auto ptrToString = [](DataType dt, void* ptr, size_t idx) {
-            switch (dt) {
-            case DataType::Float64:
-                return std::to_string(static_cast<double*>(ptr)[idx]);
-            case DataType::Float32:
-                return std::to_string(static_cast<float*>(ptr)[idx]);
-            case DataType::Float16:
-                return std::to_string(static_cast<half_float::half*>(ptr)[idx]);
-            case DataType::Int8:
-                return std::to_string(static_cast<int8_t*>(ptr)[idx]);
-            case DataType::Int16:
-                return std::to_string(static_cast<int16_t*>(ptr)[idx]);
-            case DataType::Int32:
-                return std::to_string(static_cast<int32_t*>(ptr)[idx]);
-            case DataType::Int64:
-                return std::to_string(static_cast<int64_t*>(ptr)[idx]);
-            case DataType::UInt8:
-                return std::to_string(static_cast<uint8_t*>(ptr)[idx]);
-            case DataType::UInt16:
-                return std::to_string(static_cast<uint16_t*>(ptr)[idx]);
-            case DataType::UInt32:
-                return std::to_string(static_cast<uint32_t*>(ptr)[idx]);
-            case DataType::UInt64:
-                return std::to_string(static_cast<uint64_t*>(ptr)[idx]);
-            default:
-                AIDGE_ASSERT(true, "unsupported type to convert to string");
-            }
-            return std::string("?");  // To make Clang happy
-        };
-
-        if (dims().empty()) { return ptrToString(mDataType, mImpl->hostPtr(), 0); }
-        std::string res;
-        std::size_t dim = 0;
-        std::size_t counter = 0;
-        if (nbDims()>=2) {
-            std::vector<std::size_t> dimVals(nbDims(), 0);
-            res += "{\n";
-            while (counter < mSize) {
-                std::string spaceString = std::string((dim+1)<<1,' ');
-                if (dim < nbDims()-2) {
-                    if (dimVals[dim] == 0) {
-                        res += spaceString + "{\n";
-                        ++dim;
-                    } else if (dimVals[dim] < static_cast<std::size_t>(dims()[dim])) {
-                        res += spaceString + "},\n" + spaceString + "{\n";
-                        ++dim;
-                    } else {
-                        res += spaceString + "}\n";
-                        dimVals[dim--] = 0;
-                        dimVals[dim]++;
-                    }
-                } else {
-                    for (; dimVals[dim] < static_cast<std::size_t>(dims()[dim]); ++dimVals[dim]) {
-                        res += spaceString + "{";
-                        for (DimSize_t j = 0; j < dims()[dim + 1] - 1; ++j) {
-                            res += " " + ptrToString(mDataType, mImpl->hostPtr(mImplOffset), counter++) + ",";
-                        }
-                        res += " " + ptrToString(mDataType, mImpl->hostPtr(mImplOffset), counter++) + "}";
-                        if (dimVals[dim] < static_cast<std::size_t>(dims()[dim] - 1)) {
-                            res += ",";
-                        }
-                        res += "\n";
-                    }
-                    if (dim == 0) {
-                        break;
-                    }
-                    dimVals[dim--] = 0;
-                    dimVals[dim]++;
-                }
-            }
-
-            for(int i = static_cast<int>(dim); i > 0; --i) {
-                res += std::string((dim+1)<<1,' ') + "}\n";
-            }
-        } else {
-            res += "{";
-            for (DimSize_t j = 0; j < dims()[0]; ++j) {
-                res += " " + ptrToString(mDataType, mImpl->hostPtr(mImplOffset), j) + ((j < dims()[0]-1) ? "," : " ");
-            }
-        }
-        res += "}";
-        return res;
-    }
+    std::string toString() const;
 
     inline void print() const { printf("%s\n", toString().c_str()); }
 
@@ -621,7 +518,7 @@ class Tensor : public Data,
     }
 
     /**
-     * Returns a sub-tensor with one or more dimension less.
+     * @brief Returns a sub-tensor with one or more dimension less.
      * For instance, t.extract({1}) on a CHW tensor will return the HW tensor
      * of channel #1.
      * Likewise, t.extract({0, 1}) on a NCHW tensor will return the HW tensor
@@ -631,15 +528,15 @@ class Tensor : public Data,
      * tensor is returned.
      * It current tensor was contiguous, the returned tensor is garanteed to be
      * contiguous as well.
-     * 
+     *
      * @param coordIdx Coordinates of the sub-tensor to extract
      * @return Tensor Sub-tensor.
     */
     Tensor extract(const std::vector<std::size_t>& coordIdx) const;
 
     /**
-     * Returns a sub-tensor at some coordinate and with some dimension.
-     * 
+     * @brief Returns a sub-tensor at some coordinate and with some dimension.
+     *
      * @param coordIdx First coordinates of the sub-tensor to extract
      * @param dims Dimensions of the sub-tensor to extract
      * @return Tensor Sub-tensor.
@@ -647,7 +544,7 @@ class Tensor : public Data,
     Tensor extract(const std::vector<std::size_t>& coordIdx, const std::vector<std::size_t>& dims) const;
 
     /**
-     * Make the tensor's storage contiguous, if it is not already the case.
+     * @brief Make the tensor's storage contiguous, if it is not already the case.
      * If not contiguous, a new memory space is allocated.
     */
     void makeContiguous();
@@ -704,7 +601,7 @@ class Tensor : public Data,
      * The data type, backend and device stay the same.
      * @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
      * The shared_ptr does not need to be initialized. No new memory allocation
-     * will occur if fallback has already been allocated with the right 
+     * will occur if fallback has already been allocated with the right
      * type/size/device.
      * @return Reference to either itself or to fallback.
     */
@@ -782,10 +679,10 @@ class Tensor : public Data,
     }
 
     /**
-     * Return a reference to a Tensor on desired data type and backend/device:
+     * @brief Return a reference to a Tensor on desired data type and backend/device:
      * - itself, if already with the right characteristics;
      * - the provided Tensor, overwritten with the right characteristics.
-     * NOTE: no data is copy-casted. If it was so in a previous refCastFrom() on
+     * @note no data is copy-casted. If it was so in a previous refCastFrom() on
      * the same fallback, it remains valid, otherwise, data is invalid.
      * @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
      * The shared_ptr does not need to be initialized. No new memory allocation
@@ -800,11 +697,11 @@ class Tensor : public Data,
     const Tensor& ref(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt, const std::string &backend, DeviceIdx_t device = 0) const;
 
     /**
-     * Return a reference to a Tensor with same characteristics
+     * @brief Return a reference to a Tensor with same characteristics
      * (data type, backend/device) as targetReqs Tensor:
      * - itself, if already with the right characteristics;
      * - the provided Tensor, overwritten with the right characteristics.
-     * NOTE: no data is copy-casted. If it was so in a previous refCastFrom() on
+     * @note no data is copy-casted. If it was so in a previous refCastFrom() on
      * the same fallback, it remains valid, otherwise, data is invalid.
      * @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
      * The shared_ptr does not need to be initialized. No new memory allocation
@@ -819,7 +716,11 @@ class Tensor : public Data,
     }
 
 private:
-    ///\bug not protected against overflow
+    /**
+     * @brief Compute the number of elements in the Tensor.
+     * @note If dimensions are not empty, they are multiplied to get the total number
+     * of elements. Else, the Tensor represents a scalar and contains a single element.
+     */
     void computeSize() {
         mSize = std::accumulate(mDims.begin(), mDims.end(), DimSize_t(1), std::multiplies<DimSize_t>());
     }

include/aidge/graph/GraphView.hpp

@@ -209,7 +209,7 @@ public:
      * @brief Compute dimensions of input/output Tensors for each Operator of the
      * GraphView object's Nodes.
      */
-    void forwardDims();
+    void forwardDims(const std::vector<std::vector<DimSize_t>> dims = {});
 
     /** @brief Set the same backend for each Operator of the GraphView object's Nodes. */
     void setBackend(const std::string &backend, DeviceIdx_t device = 0);

include/aidge/operator/Add.hpp

@@ -68,13 +68,7 @@ public:
     // }
 
 
-    // void checkDims() const override final {
-    //     assert(outputDimsForwarded());
-    //     for (const auto& in : mInputs) {
-    //         assert(in->dims() == mOutputs[0]->dims());
-    //     }
-    // }
-
+    void computeOutputDims() override final;
 
     void setBackend(const std::string& name, DeviceIdx_t device = 0) override {
         mImpl = Registrar<Add_Op>::create(name)(*this);

include/aidge/operator/Div.hpp

@@ -60,7 +60,7 @@ public:
     }
 
     static const std::vector<std::string> getInputsName(){
-        return {"data_input"};
+        return {"data_input_1", "data_input_2"};
     }
     static const std::vector<std::string> getOutputsName(){
         return {"data_output"};

include/aidge/operator/GenericOperator.hpp

@@ -59,7 +59,7 @@ public:
     // Helper functions that can be used with setComputeOutputDims():
     static const ComputeDimsFunc Identity;
 
-    void setComputeOutputDims(ComputeDimsFunc func) {
+    inline void setComputeOutputDims(ComputeDimsFunc func) {
         mComputeOutputDims = func;
     }
 

include/aidge/operator/MatMul.hpp

@@ -12,49 +12,32 @@
 #ifndef AIDGE_CORE_OPERATOR_MATMUL_H_
 #define AIDGE_CORE_OPERATOR_MATMUL_H_
 
-#include <array>
-#include <cmath>
-#include <numeric>
 #include <memory>
+#include <string>
 #include <vector>
 
 #include "aidge/utils/Types.h"
 #include "aidge/data/Tensor.hpp"
 #include "aidge/graph/Node.hpp"
 #include "aidge/operator/OperatorTensor.hpp"
-#include "aidge/operator/Producer.hpp"
-#include "aidge/utils/StaticAttributes.hpp"
 #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;
     }
@@ -63,50 +46,40 @@ public:
      * @brief Clone the operator using its copy-constructor.
      * @see Operator::MatMul_Op
      */
-    std::shared_ptr<Operator> clone() const override {
+    std::shared_ptr<Operator> clone() const override final {
         return std::make_shared<MatMul_Op>(*this);
     }
 
-
-    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>()});
-        }
-    }
+    /**
+     * @brief Compute dimensions for the output Tensor following the same rules as
+     * numpy.matmul.
+     * @note - Both inputs are 2-D Tensors: classic matrix multiplication
+     * @note - Either input is N-D with N > 2: it is treated as a stack of matrices residing
+     * in the last two indexes and broadcast accordingly.
+     * @note - First input is 1-D: it is promoted to a matrix by prepending a 1 to its
+     * dimensions (D) -> (1,D). The prepended 1 is removed after computation.
+     * @note - Second input is 1-D: it is promoted to a matrix by appending a 1 to its
+     * dimensions (D) -> (D,1). The appended 1 is removed after computation.
+     */
+    void computeOutputDims() override final;
 
 
-    void setBackend(const std::string& name, DeviceIdx_t device = 0) override {
+    void setBackend(const std::string& name, DeviceIdx_t device = 0) override final {
         mImpl = Registrar<MatMul_Op>::create(name)(*this);
         mOutputs[0]->setBackend(name, device);
     }
 
-    static const std::vector<std::string> getInputsName(){
-        return {"data_input", "weight"};
+    static const std::vector<std::string> getInputsName() {
+        return {"data_input1", "data_input2"};
     }
-    static const std::vector<std::string> getOutputsName(){
+    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_ */

include/aidge/operator/Mul.hpp

@@ -62,7 +62,7 @@ public:
     }
 
     static const std::vector<std::string> getInputsName(){
-        return {"data_input"};
+        return {"data_input_1", "data_input_2"};
     }
     static const std::vector<std::string> getOutputsName(){
         return {"data_output"};

include/aidge/operator/Operator.hpp

@@ -118,9 +118,7 @@ public:
      * @brief Set the a new OperatorImpl to the Operator
      *
      */
-    void setImpl(std::shared_ptr<OperatorImpl> impl){
-        mImpl = impl;
-    }
+    inline void setImpl(std::shared_ptr<OperatorImpl> impl) { mImpl = impl; }
 
     /**
      * @brief Minimum amount of data from a specific input for one computation pass.