From e93fe6403d47529d087531899255dcf34c6e9c5c Mon Sep 17 00:00:00 2001
From: Olivier BICHLER <olivier.bichler@cea.fr>
Date: Sun, 30 Jun 2024 17:34:05 +0200
Subject: [PATCH] Initial commit
---
include/aidge/backend/cpu.hpp | 1 +
.../aidge/backend/cpu/operator/FoldImpl.hpp | 50 +++++
.../cpu/operator/FoldImpl_forward_kernels.hpp | 82 ++++++++
src/operator/FoldImpl.cpp | 37 ++++
unit_tests/operator/Test_FoldImpl.cpp | 179 ++++++++++++++++++
5 files changed, 349 insertions(+)
create mode 100644 include/aidge/backend/cpu/operator/FoldImpl.hpp
create mode 100644 include/aidge/backend/cpu/operator/FoldImpl_forward_kernels.hpp
create mode 100644 src/operator/FoldImpl.cpp
create mode 100644 unit_tests/operator/Test_FoldImpl.cpp
diff --git a/include/aidge/backend/cpu.hpp b/include/aidge/backend/cpu.hpp
index 11f9c264..b81145de 100644
--- a/include/aidge/backend/cpu.hpp
+++ b/include/aidge/backend/cpu.hpp
@@ -21,6 +21,7 @@
#include "aidge/backend/cpu/operator/DivImpl.hpp"
#include "aidge/backend/cpu/operator/ErfImpl.hpp"
#include "aidge/backend/cpu/operator/FCImpl.hpp"
+#include "aidge/backend/cpu/operator/FoldImpl.hpp"
#include "aidge/backend/cpu/operator/GlobalAveragePoolingImpl.hpp"
#include "aidge/backend/cpu/operator/LeakyReLUImpl.hpp"
#include "aidge/backend/cpu/operator/MatMulImpl.hpp"
diff --git a/include/aidge/backend/cpu/operator/FoldImpl.hpp b/include/aidge/backend/cpu/operator/FoldImpl.hpp
new file mode 100644
index 00000000..b258745e
--- /dev/null
+++ b/include/aidge/backend/cpu/operator/FoldImpl.hpp
@@ -0,0 +1,50 @@
+/********************************************************************************
+ * 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_FOLDIMPL_H_
+#define AIDGE_CPU_OPERATOR_FOLDIMPL_H_
+
+#include <array>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/OperatorImpl.hpp"
+#include "aidge/operator/Fold.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+namespace Aidge {
+class FoldImpl2DForward_cpu
+ : public Registrable<FoldImpl2DForward_cpu,
+ std::tuple<DataType, DataType>,
+ void(const Fold_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *,
+ void *)> {};
+
+class FoldImpl2D_cpu : public OperatorImpl {
+public:
+ FoldImpl2D_cpu(const Fold_Op<2> &op) : OperatorImpl(op, "cpu") {}
+
+ static std::unique_ptr<FoldImpl2D_cpu> create(const Fold_Op<2> &op) {
+ return std::make_unique<FoldImpl2D_cpu>(op);
+ }
+
+ void forward() override;
+};
+
+namespace {
+// add cpu backend to Fold_Op<2> implementation registry
+static Registrar<Fold_Op<2>> registrarFoldImpl2D_cpu("cpu", Aidge::FoldImpl2D_cpu::create);
+} // namespace
+} // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_FOLDIMPL_H_ */
diff --git a/include/aidge/backend/cpu/operator/FoldImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/FoldImpl_forward_kernels.hpp
new file mode 100644
index 00000000..5caad147
--- /dev/null
+++ b/include/aidge/backend/cpu/operator/FoldImpl_forward_kernels.hpp
@@ -0,0 +1,82 @@
+/********************************************************************************
+ * 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_FOLDIMPL_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_FOLDIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/FoldImpl.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <cmath>
+#include <array>
+#include <algorithm>
+
+namespace Aidge {
+template <class I, class O>
+void FoldImpl2D_cpu_forward_kernel(const Fold_Op<2>::Attrs &attrs, const std::array<DimSize_t, 4> &dims,
+ const void *input_, void *output_)
+{
+ const I *input = static_cast<const I *>(input_);
+ O *output = static_cast<O *>(output_);
+
+ const auto kernelDims = std::get<3>(attrs);
+ const auto dilationDims = std::get<2>(attrs);
+ const auto strideDims = std::get<1>(attrs);
+ const DimSize_t inHeight = std::get<0>(attrs)[0];
+ const DimSize_t inWidth = std::get<0>(attrs)[1];
+
+ const DimSize_t kernelExtentHeight = dilationDims[0] *
+ (kernelDims[0] - 1) + 1;
+ const DimSize_t outHeight = 1 + static_cast<DimSize_t>(
+ floor(static_cast<float>(inHeight - kernelExtentHeight) /
+ static_cast<float>(strideDims[0])));
+ const DimSize_t kernelExtentWidth = dilationDims[1] *
+ (kernelDims[1] - 1) + 1;
+ const DimSize_t outWidth = 1 + static_cast<DimSize_t>(
+ floor(static_cast<float>(inWidth - kernelExtentWidth) /
+ static_cast<float>(strideDims[1])));
+ const DimSize_t outChannels = dims[1];
+
+ std::fill_n(output, outHeight * outWidth * outChannels, O(0));
+
+ for (DimSize_t outC = 0; outC < outChannels; ++outC) {
+ const auto inOffsetH = outC % kernelDims[0];
+ const auto inOffsetW = (outC / kernelDims[0]) % kernelDims[1];
+ const auto inC = outC / kernelDims[0] / kernelDims[1];
+
+ for (DimSize_t outH = 0; outH < outHeight; ++outH) {
+ const auto inH = outH * strideDims[1] + inOffsetH * dilationDims[1];
+
+ for (DimSize_t outW = 0; outW < outWidth; ++outW) {
+ const auto inW = outW * strideDims[0] + inOffsetW * dilationDims[0];
+
+ output[(inC * inHeight + inH) * inWidth + inW] += input[(outC * outHeight + outH) * outWidth + outW];
+ }
+ }
+ }
+}
+
+namespace {
+static Registrar<FoldImpl2DForward_cpu> registrarFoldImpl2DForward_cpu_Float32(
+ {DataType::Float32, DataType::Float32},
+ Aidge::FoldImpl2D_cpu_forward_kernel<float, float>);
+static Registrar<FoldImpl2DForward_cpu> registrarFoldImpl2DForward_cpu_Int32(
+ {DataType::Int32, DataType::Int32},
+ Aidge::FoldImpl2D_cpu_forward_kernel<int, int>);
+static Registrar<FoldImpl2DForward_cpu> registrarFoldImpl2DForward_cpu_Float64(
+ {DataType::Float64, DataType::Float64},
+ Aidge::FoldImpl2D_cpu_forward_kernel<double, double>);
+} // namespace
+} // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_FOLDIMPL_FORWARD_KERNEL_H_ */
diff --git a/src/operator/FoldImpl.cpp b/src/operator/FoldImpl.cpp
new file mode 100644
index 00000000..a885db4c
--- /dev/null
+++ b/src/operator/FoldImpl.cpp
@@ -0,0 +1,37 @@
+/********************************************************************************
+ * 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/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include "aidge/operator/Conv.hpp"
+
+#include "aidge/backend/cpu/operator/FoldImpl.hpp"
+#include "aidge/backend/cpu/operator/FoldImpl_forward_kernels.hpp"
+
+void Aidge::FoldImpl2D_cpu::forward() {
+ assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
+
+ // Find the correct kernel type
+ auto kernelFunc =
+ Registrar<FoldImpl2DForward_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 Fold_Op<2>&>(mOp).getStaticAttributes(),
+ std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<4>(),
+ getCPUPtr(mOp.getRawInput(0)),
+ getCPUPtr(mOp.getRawOutput(0)));
+}
diff --git a/unit_tests/operator/Test_FoldImpl.cpp b/unit_tests/operator/Test_FoldImpl.cpp
new file mode 100644
index 00000000..079fca62
--- /dev/null
+++ b/unit_tests/operator/Test_FoldImpl.cpp
@@ -0,0 +1,179 @@
+/********************************************************************************
+ * 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 <cstdlib>
+#include <memory>
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/graph/GraphView.hpp"
+#include "aidge/scheduler/SequentialScheduler.hpp"
+#include "aidge/operator/Fold.hpp"
+#include "aidge/operator/Unfold.hpp"
+#include "aidge/operator/MatMul.hpp"
+#include "aidge/operator/Reshape.hpp"
+
+#include "aidge/backend/cpu.hpp"
+
+using namespace Aidge;
+
+TEST_CASE("[cpu/operator] Fold(forward)", "[Fold][CPU]") {
+ std::shared_ptr<Node> myUnfold = Unfold({3,3}, "myunfold");
+ std::shared_ptr<Node> myReshape = Reshape({9, 12}, "myreshape");
+ std::shared_ptr<Node> myMatMul = MatMul("mymatmul");
+ std::shared_ptr<Node> myFold = Fold({3,3}, {3,3}, "myfold");
+ myUnfold->addChild(myMatMul, 0, 1);
+ myReshape->addChild(myMatMul, 0, 0);
+ myMatMul->addChild(myFold, 0, 0);
+
+ std::shared_ptr<Tensor> myWeights = std::make_shared<Tensor>(Array4D<int,4,3,3,3> {
+ {
+ {
+ {{ 0, 1, 2},
+ { 3, 4, 5},
+ { 6, 7, 8}},
+ {{ 9, 10, 11},
+ { 12, 13, 14},
+ { 15, 16, 17}},
+ {{ 18, 19, 20},
+ { 21, 22, 23},
+ { 24, 25, 26}}
+ },
+ {
+ {{ 27, 28, 29},
+ { 30, 31, 32},
+ { 33, 34, 35}},
+ {{ 36, 37, 38},
+ { 39, 40, 41},
+ { 42, 43, 44}},
+ {{ 45, 46, 47},
+ { 48, 49, 50},
+ { 51, 52, 53}}
+ },
+ {
+ {{ 54, 55, 56},
+ { 57, 58, 59},
+ { 60, 61, 62}},
+ {{ 63, 64, 65},
+ { 66, 67, 68},
+ { 69, 70, 71}},
+ {{ 72, 73, 74},
+ { 75, 76, 77},
+ { 78, 79, 80}}
+ },
+ {
+ {{ 81, 82, 83},
+ { 84, 85, 86},
+ { 87, 88, 89}},
+ {{ 90, 91, 92},
+ { 93, 94, 95},
+ { 96, 97, 98}},
+ {{ 99, 100, 101},
+ {102, 103, 104},
+ {105, 106, 107}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<int,2,3,5,5> { //NCHW
+ {
+ {
+ {{ 0, 1, 2, 3, 4},
+ { 5, 6, 7, 8, 9},
+ { 10, 11, 12, 13, 14},
+ { 15, 16, 17, 18, 19},
+ { 20, 21, 22, 23, 24}},
+
+ {{ 25, 26, 27, 28, 29},
+ { 30, 31, 32, 33, 34},
+ { 35, 36, 37, 38, 39},
+ { 40, 41, 42, 43, 44},
+ { 45, 46, 47, 48, 49}},
+
+ {{ 50, 51, 52, 53, 54},
+ { 55, 56, 57, 58, 59},
+ { 60, 61, 62, 63, 64},
+ { 65, 66, 67, 68, 69},
+ { 70, 71, 72, 73, 74}}
+ },
+ {
+ {{ 75, 76, 77, 78, 79},
+ { 80, 81, 82, 83, 84},
+ { 85, 86, 87, 88, 89},
+ { 90, 91, 92, 93, 94},
+ { 95, 96, 97, 98, 99}},
+
+ {{100, 101, 102, 103, 104},
+ {105, 106, 107, 108, 109},
+ {110, 111, 112, 113, 114},
+ {115, 116, 117, 118, 119},
+ {120, 121, 122, 123, 124}},
+
+ {{125, 126, 127, 128, 129},
+ {130, 131, 132, 133, 134},
+ {135, 136, 137, 138, 139},
+ {140, 141, 142, 143, 144},
+ {145, 146, 147, 148, 149}}
+ }
+ }
+ });
+ std::shared_ptr<Tensor> myOutput = std::make_shared<Tensor>(Array4D<int,2,4,3,3> {
+ {
+ {
+ {{ 15226, 15577, 15928},
+ { 16981, 17332, 17683},
+ { 18736, 19087, 19438}},
+ {{ 37818, 38898, 39978},
+ { 43218, 44298, 45378},
+ { 48618, 49698, 50778}},
+ {{ 60426, 62235, 64044},
+ { 69471, 71280, 73089},
+ { 78516, 80325, 82134}},
+ {{ 83016, 85554, 88092},
+ { 95706, 98244, 100782},
+ {108396, 110934, 113472}}
+ },
+ {
+ {{ 41551, 41902, 42253},
+ { 43306, 43657, 44008},
+ { 45061, 45412, 45763}},
+ {{118818, 119898, 120978},
+ {124218, 125298, 126378},
+ {129618, 130698, 131778}},
+ {{196101, 197910, 199719},
+ {205146, 206955, 208764},
+ {214191, 216000, 217809}},
+ {{273366, 275904, 278442},
+ {286056, 288594, 291132},
+ {298746, 301284, 303822}}
+ }
+ }
+ });
+
+ auto opUnfold = std::static_pointer_cast<OperatorTensor>(myUnfold -> getOperator());
+ auto opReshape = std::static_pointer_cast<OperatorTensor>(myReshape -> getOperator());
+ auto opMatMul = std::static_pointer_cast<OperatorTensor>(myMatMul -> getOperator());
+ auto opFold = std::static_pointer_cast<OperatorTensor>(myFold -> getOperator());
+ opUnfold->associateInput(0,myInput);
+ opReshape->associateInput(0,myWeights);
+
+ auto g = getConnectedGraphView(myMatMul);
+ g->setDataType(DataType::Int32);
+ g->setBackend("cpu");
+
+ g->save("unfold_matmul_fold");
+ g->forwardDims();
+ g->save("unfold_matmul_fold");
+
+ SequentialScheduler scheduler(g);
+ scheduler.forward();
+ // op->getOutput(0)->print();
+ REQUIRE(*(opFold->getOutput(0)) == *myOutput);
+}
\ No newline at end of file
--
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