Added Tanh and Sigmoid operators

include/aidge/backend/cpu.hpp

@@ -31,9 +31,11 @@
 #include "aidge/backend/cpu/operator/ProducerImpl.hpp"
 #include "aidge/backend/cpu/operator/ReLUImpl.hpp"
 #include "aidge/backend/cpu/operator/ScalingImpl.hpp"
+#include "aidge/backend/cpu/operator/SigmoidImpl.hpp"
 #include "aidge/backend/cpu/operator/SliceImpl.hpp"
 #include "aidge/backend/cpu/operator/SqrtImpl.hpp"
 #include "aidge/backend/cpu/operator/SoftmaxImpl.hpp"
 #include "aidge/backend/cpu/operator/SubImpl.hpp"
+#include "aidge/backend/cpu/operator/TanhImpl.hpp"
 
 #endif /* AIDGE_CPU_IMPORTS_H_ */
\ No newline at end of file

include/aidge/backend/cpu/operator/ReLUImpl_forward_kernels.hpp

@@ -25,6 +25,7 @@ void ReLUImpl_cpu_forward_kernel(std::size_t inputLenght,
     const I* input = static_cast<const I*>(input_);
     O* output = static_cast<O*>(output_);
 
+#pragma omp parallel for if (inputLenght > 1024)
     for (std::size_t i = 0; i < inputLenght; ++i) {
         output[i] = input[i] > 0 ? input[i] : 0;
     }

include/aidge/backend/cpu/operator/SigmoidImpl.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_SIGMOIDIMPL_H_
+#define AIDGE_CPU_OPERATOR_SIGMOIDIMPL_H_
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Sigmoid.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Sigmoid_Op;
+
+// compute kernel registry for forward and backward
+class SigmoidImplForward_cpu
+    : public Registrable<SigmoidImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+};
+class SigmoidImplBackward_cpu
+    : public Registrable<SigmoidImplBackward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+};
+
+class SigmoidImpl_cpu : public OperatorImpl {
+public:
+    SigmoidImpl_cpu(const Sigmoid_Op& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<SigmoidImpl_cpu> create(const Sigmoid_Op& op) {
+        return std::make_unique<SigmoidImpl_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Sigmoid_Op> registrarSigmoidImpl_cpu("cpu", Aidge::SigmoidImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_SIGMOIDIMPL_H_ */

include/aidge/backend/cpu/operator/SigmoidImpl_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_SIGMOIDIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_SIGMOIDIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/SigmoidImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void SigmoidImpl_cpu_forward_kernel(std::size_t inputLenght,
+                                     const void* input_,
+                                     void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+#pragma omp parallel for if (inputLenght > 1024)
+    for (std::size_t i = 0; i < inputLenght; ++i) {
+        output[i] = static_cast<O>(1.0) / (static_cast<O>(1.0) + std::exp(-input[i]));
+    }
+}
+
+namespace {
+static Registrar<SigmoidImplForward_cpu> registrarSigmoidImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::SigmoidImpl_cpu_forward_kernel<float, float>);
+static Registrar<SigmoidImplForward_cpu> registrarSigmoidImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::SigmoidImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_SIGMOIDIMPL_FORWARD_KERNEL_H_ */

include/aidge/backend/cpu/operator/TanhImpl.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_TANHIMPL_H_
+#define AIDGE_CPU_OPERATOR_TANHIMPL_H_
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Tanh.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// class Tanh_Op;
+
+// compute kernel registry for forward and backward
+class TanhImplForward_cpu
+    : public Registrable<TanhImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+};
+class TanhImplBackward_cpu
+    : public Registrable<TanhImplBackward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
+};
+
+class TanhImpl_cpu : public OperatorImpl {
+public:
+    TanhImpl_cpu(const Tanh_Op& op) : OperatorImpl(op) {}
+
+    static std::unique_ptr<TanhImpl_cpu> create(const Tanh_Op& op) {
+        return std::make_unique<TanhImpl_cpu>(op);
+    }
+
+    NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<Tanh_Op> registrarTanhImpl_cpu("cpu", Aidge::TanhImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_TANHIMPL_H_ */

include/aidge/backend/cpu/operator/TanhImpl_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_TANHIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_TANHIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/TanhImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void TanhImpl_cpu_forward_kernel(std::size_t inputLenght,
+                                     const void* input_,
+                                     void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+#pragma omp parallel for if (inputLenght > 1024)
+    for (std::size_t i = 0; i < inputLenght; ++i) {
+        output[i] = std::tanh(input[i]);
+    }
+}
+
+namespace {
+static Registrar<TanhImplForward_cpu> registrarTanhImplForward_cpu_Float32(
+        {DataType::Float32, DataType::Float32}, Aidge::TanhImpl_cpu_forward_kernel<float, float>);
+static Registrar<TanhImplForward_cpu> registrarTanhImplForward_cpu_Float64(
+        {DataType::Float64, DataType::Float64}, Aidge::TanhImpl_cpu_forward_kernel<double, double>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_TANHIMPL_FORWARD_KERNEL_H_ */

src/operator/SigmoidImpl.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 <chrono>  // std::chrono::milliseconds
+#include <numeric> // std::accumulate
+#include <thread>  // std::this_thread::sleep_for
+#include <vector>
+
+#include "aidge/operator/Sigmoid.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+#include "aidge/backend/cpu/operator/SigmoidImpl.hpp"
+#include "aidge/backend/cpu/operator/SigmoidImpl_forward_kernels.hpp"
+
+Aidge::NbElts_t Aidge::SigmoidImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+
+void Aidge::SigmoidImpl_cpu::forward() {
+    assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
+
+    // Find the correct kernel type
+    auto kernelFunc = Registrar<SigmoidImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size(),
+        getCPUPtr(mOp.getRawInput(0)),
+        getCPUPtr(mOp.getRawOutput(0)));
+}

src/operator/TanhImpl.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 <chrono>  // std::chrono::milliseconds
+#include <numeric> // std::accumulate
+#include <thread>  // std::this_thread::sleep_for
+#include <vector>
+
+#include "aidge/operator/Tanh.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+#include "aidge/backend/cpu/operator/TanhImpl.hpp"
+#include "aidge/backend/cpu/operator/TanhImpl_forward_kernels.hpp"
+
+Aidge::NbElts_t Aidge::TanhImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return 0;
+}
+
+void Aidge::TanhImpl_cpu::forward() {
+    assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
+
+    // Find the correct kernel type
+    auto kernelFunc = Registrar<TanhImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    // Call kernel
+    kernelFunc(std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->size(),
+        getCPUPtr(mOp.getRawInput(0)),
+        getCPUPtr(mOp.getRawOutput(0)));
+}

unit_tests/scheduler/Test_Scheduler.cpp

@@ -225,11 +225,7 @@ TEST_CASE("[cpu/scheduler] SequentialScheduler(forward)") {
         bias->addChild(add2, 0, 1);
         add1->getOperator()->setInput(0, in);
         // Update GraphView inputs/outputs following previous connections:
-        g->add(mem);
-        g->add(add1);
-        g->add(add2);
-        g->add(init);
-        g->add(bias);
+        g->add({mem, add1, add2, init, bias});
 
         g->setBackend("cpu");
         g->setDataType(Aidge::DataType::Int32);