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Commit c3a2fb53 authored by Wissam Boussella's avatar Wissam Boussella Committed by Maxence Naud
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Update Test_AdaptToBackend.cpp

parent 67d57c1f
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1 merge request!336Draft: Tests adapt to backend
Pipeline #65945 failed
Stage: static_analysis
    Stage: build
      Stage: test
        Stage: coverage
          Stage: release
            This commit is part of merge request !336. Comments created here will be created in the context of that merge request.
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            unit_tests/recipes/Test_AdaptToBackend.cpp
            + 138
            − 138
            Edit View file @ c3a2fb53
            ...@@ -9,100 +9,100 @@ ...@@ -9,100 +9,100 @@
            * *
            ********************************************************************************/ ********************************************************************************/
            #include <catch2/catch_test_macros.hpp> #include <catch2/catch_test_macros.hpp>
            #include <set> #include <set>
            #include "aidge/data/Tensor.hpp" #include "aidge/data/Tensor.hpp"
            #include "aidge/graph/GraphView.hpp" #include "aidge/graph/GraphView.hpp"
            #include "aidge/graph/OpArgs.hpp" #include "aidge/graph/OpArgs.hpp"
            #include "aidge/operator/Conv.hpp" #include "aidge/operator/Conv.hpp"
            #include "aidge/operator/ReLU.hpp" #include "aidge/operator/ReLU.hpp"
            #include "aidge/operator/Transpose.hpp" #include "aidge/operator/Transpose.hpp"
            #include "aidge/operator/Producer.hpp" #include "aidge/operator/Producer.hpp"
            #include "aidge/recipes/Recipes.hpp" #include "aidge/recipes/Recipes.hpp"
            #include "aidge/scheduler/SequentialScheduler.hpp" #include "aidge/scheduler/SequentialScheduler.hpp"
            #include "aidge/operator/MetaOperatorDefs.hpp" #include "aidge/operator/MetaOperatorDefs.hpp"
            namespace Aidge { namespace Aidge {
            //////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
            // Create a dummy implementation // Create a dummy implementation
            template <class Op> template <class Op>
            class OperatorImpl_dummy : public OperatorImpl, class OperatorImpl_dummy : public OperatorImpl,
            public Registrable<OperatorImpl_dummy<Op>, ImplSpec, Impl<void(), void()>> public Registrable<OperatorImpl_dummy<Op>, ImplSpec, Impl<void(), void()>>
            { {
            public: public:
            OperatorImpl_dummy(const Op& op) : OperatorImpl(op, "dummy") {} OperatorImpl_dummy(const Op& op) : OperatorImpl(op, "dummy") {}
            static std::unique_ptr<OperatorImpl_dummy<Op>> create(const Op& op) { static std::unique_ptr<OperatorImpl_dummy<Op>> create(const Op& op) {
            return std::make_unique<OperatorImpl_dummy<Op>>(op); return std::make_unique<OperatorImpl_dummy<Op>>(op);
            } }
            virtual std::shared_ptr<ProdConso> getProdConso() const override { virtual std::shared_ptr<ProdConso> getProdConso() const override {
            const auto impl = Registrar<OperatorImpl_dummy>::create(getBestMatch(getRequiredSpec())); const auto impl = Registrar<OperatorImpl_dummy>::create(getBestMatch(getRequiredSpec()));
            if (impl.prodConso(mOp)==nullptr){ if (impl.prodConso(mOp)==nullptr){
            fmt::println("no prod conso created "); fmt::println("no prod conso created ");
            } }
            return impl.prodConso(mOp); return impl.prodConso(mOp);
            } }
            virtual std::vector<ImplSpec> getAvailableImplSpecs() const override { virtual std::vector<ImplSpec> getAvailableImplSpecs() const override {
            std::set<ImplSpec> implSpecsSet = Registrar<OperatorImpl_dummy>::getKeys(); std::set<ImplSpec> implSpecsSet = Registrar<OperatorImpl_dummy>::getKeys();
            return std::vector<ImplSpec>(implSpecsSet.begin(), implSpecsSet.end()); return std::vector<ImplSpec>(implSpecsSet.begin(), implSpecsSet.end());
            } }
            void forward() override { void forward() override {
            fmt::println("forward: {}", mOp.type()); fmt::println("forward: {}", mOp.type());
            } }
            }; };
            // Register it // Register it
            using Conv2D_Op_Impl_dummy = OperatorImpl_dummy<Conv_Op<2>>; using Conv2D_Op_Impl_dummy = OperatorImpl_dummy<Conv_Op<2>>;
            REGISTRAR(Conv2D_Op_Impl_dummy, REGISTRAR(Conv2D_Op_Impl_dummy,
            {{ // Inputs {{ // Inputs
            {DataType::Any, DataFormat::NHWC}, {DataType::Any, DataFormat::NHWC},
            {DataType::Any, DataFormat::NHWC}, {DataType::Any, DataFormat::NHWC},
            {DataType::Any, DataFormat::Default}}, {DataType::Any, DataFormat::Default}},
            { // Outputs { // Outputs
            {DataType::Float32, DataFormat::NHWC}}}, {DataType::Float32, DataFormat::NHWC}}},
            {ProdConso::inPlaceModel, nullptr, nullptr}); {ProdConso::inPlaceModel, nullptr, nullptr});
            using Conv2D_Op = Conv_Op<2>; using Conv2D_Op = Conv_Op<2>;
            REGISTRAR(Conv2D_Op, "dummy", OperatorImpl_dummy<Conv2D_Op>::create); REGISTRAR(Conv2D_Op, "dummy", OperatorImpl_dummy<Conv2D_Op>::create);
            using ConvRelu = MetaOperator_Op; using ConvRelu = MetaOperator_Op;
            using ConvRelu_Op_Impl_dummy = OperatorImpl_dummy<ConvRelu>; using ConvRelu_Op_Impl_dummy = OperatorImpl_dummy<ConvRelu>;
            REGISTRAR(ConvRelu_Op_Impl_dummy, REGISTRAR(ConvRelu_Op_Impl_dummy,
            {{ // Inputs {{ // Inputs
            {DataType::Any, DataFormat::NHWC}, {DataType::Any, DataFormat::NHWC},
            {DataType::Any, DataFormat::NHWC}, {DataType::Any, DataFormat::NHWC},
            {DataType::Any, DataFormat::Default}}, {DataType::Any, DataFormat::Default}},
            { // Outputs { // Outputs
            {DataType::Float32, DataFormat::NHWC}}}, {DataType::Float32, DataFormat::NHWC}}},
            {ProdConso::inPlaceModel, nullptr, nullptr}); {ProdConso::inPlaceModel, nullptr, nullptr});
            REGISTRAR(ConvRelu, std::array<std::string, 2>({"dummy", "ConvReLU"}), ConvRelu_Op_Impl_dummy::create); REGISTRAR(ConvRelu, std::array<std::string, 2>({"dummy", "ConvReLU"}), ConvRelu_Op_Impl_dummy::create);
            using ReLU_Op_Impl_dummy = OperatorImpl_dummy<ReLU_Op>; using ReLU_Op_Impl_dummy = OperatorImpl_dummy<ReLU_Op>;
            REGISTRAR(ReLU_Op_Impl_dummy, REGISTRAR(ReLU_Op_Impl_dummy,
            {{DataType::Any, DataFormat::Any}}, {{DataType::Any, DataFormat::Any}},
            {ProdConso::inPlaceModel, nullptr, nullptr}); {ProdConso::inPlaceModel, nullptr, nullptr});
            REGISTRAR(ReLU_Op, "dummy", OperatorImpl_dummy<ReLU_Op>::create); REGISTRAR(ReLU_Op, "dummy", OperatorImpl_dummy<ReLU_Op>::create);
            using Transpose_Op_Impl_dummy = OperatorImpl_dummy<Transpose_Op>; using Transpose_Op_Impl_dummy = OperatorImpl_dummy<Transpose_Op>;
            REGISTRAR(Transpose_Op_Impl_dummy, REGISTRAR(Transpose_Op_Impl_dummy,
            {{DataType::Any, DataFormat::Any}}, {{DataType::Any, DataFormat::Any}},
            {ProdConso::inPlaceModel, nullptr, nullptr}); {ProdConso::inPlaceModel, nullptr, nullptr});
            REGISTRAR(Transpose_Op, "dummy", OperatorImpl_dummy<Transpose_Op>::create); REGISTRAR(Transpose_Op, "dummy", OperatorImpl_dummy<Transpose_Op>::create);
            REGISTRAR(Tensor, {"dummy", DataType::Float32}, Registrar<Tensor>::create({"cpu", DataType::Float32})); REGISTRAR(Tensor, {"dummy", DataType::Float32}, Registrar<Tensor>::create({"cpu", DataType::Float32}));
            //////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////
            void applyConstFold(std::shared_ptr<GraphView> &graphView) void applyConstFold(std::shared_ptr<GraphView> &graphView)
            { {
            for (const std::shared_ptr<Node> node : graphView->getNodes()) for (const std::shared_ptr<Node> node : graphView->getNodes())
            { {
            ...@@ -115,45 +115,45 @@ ...@@ -115,45 +115,45 @@
            constantFolding(graphView); constantFolding(graphView);
            } }
            TEST_CASE("[cpu/recipes] AdaptToBackend", "[AdaptToBackend][recipes]") { TEST_CASE("[cpu/recipes] AdaptToBackend", "[AdaptToBackend][recipes]") {
            auto g1 = Sequential({ auto g1 = Sequential({
            Producer({1, 3, 22, 22}, "dataProvider"), Producer({1, 3, 22, 22}, "dataProvider"),
            Conv(3, 4, {3, 3}, "conv1"), Conv(3, 4, {3, 3}, "conv1"),
            ReLU("relu1"), ReLU("relu1"),
            Conv(4, 8, {3, 3}, "conv2"), Conv(4, 8, {3, 3}, "conv2"),
            ReLU("relu2"), ReLU("relu2"),
            Conv(8, 10, {1, 1}, "conv3") Conv(8, 10, {1, 1}, "conv3")
            }); });
            REQUIRE(g1->forwardDims()); REQUIRE(g1->forwardDims());
            g1->setBackend("dummy"); g1->setBackend("dummy");
            auto convOp = std::static_pointer_cast<Conv2D_Op>(g1->getNode("conv1")->getOperator()); auto convOp = std::static_pointer_cast<Conv2D_Op>(g1->getNode("conv1")->getOperator());
            REQUIRE(convOp->getInput(0)->dataFormat() == DataFormat::Default); REQUIRE(convOp->getInput(0)->dataFormat() == DataFormat::Default);
            REQUIRE(convOp->getInput(1)->dataFormat() == DataFormat::Default); REQUIRE(convOp->getInput(1)->dataFormat() == DataFormat::Default);
            REQUIRE(convOp->getOutput(0)->dataFormat() == DataFormat::Default); REQUIRE(convOp->getOutput(0)->dataFormat() == DataFormat::Default);
            g1->save("adapttobackend_before", true); g1->save("adapttobackend_before", true);
            adaptToBackend(g1); adaptToBackend(g1);
            g1->save("adapttobackend_after", true); g1->save("adapttobackend_after", true);
            auto matches = SinglePassGraphMatching(g1).match("Conv2D#<-Transpose<-Producer;Conv2D#<1-Transpose<-Producer;Conv2D#<2-Producer;Conv2D#->Transpose#->ReLU"); auto matches = SinglePassGraphMatching(g1).match("Conv2D#<-Transpose<-Producer;Conv2D#<1-Transpose<-Producer;Conv2D#<2-Producer;Conv2D#->Transpose#->ReLU");
            REQUIRE(matches.size() == 1); REQUIRE(matches.size() == 1);
            convOp = std::static_pointer_cast<Conv2D_Op>(matches.begin()->graph->rootNode()->getOperator()); convOp = std::static_pointer_cast<Conv2D_Op>(matches.begin()->graph->rootNode()->getOperator());
            auto outTransOp = std::static_pointer_cast<Transpose_Op>(matches.begin()->anchors.at("Transpose").at("#")->getOperator()); auto outTransOp = std::static_pointer_cast<Transpose_Op>(matches.begin()->anchors.at("Transpose").at("#")->getOperator());
            REQUIRE(convOp->getInput(0)->dataFormat() == DataFormat::NHWC); REQUIRE(convOp->getInput(0)->dataFormat() == DataFormat::NHWC);
            REQUIRE(convOp->getInput(1)->dataFormat() == DataFormat::NHWC); REQUIRE(convOp->getInput(1)->dataFormat() == DataFormat::NHWC);
            REQUIRE(convOp->getOutput(0)->dataFormat() == DataFormat::NHWC); REQUIRE(convOp->getOutput(0)->dataFormat() == DataFormat::NHWC);
            REQUIRE(outTransOp->getOutput(0)->dataFormat() == DataFormat::Default); REQUIRE(outTransOp->getOutput(0)->dataFormat() == DataFormat::Default);
            // TODO: uncomment when support of NHWC will be implemented in Conv_Op::forwardDims() // TODO: uncomment when support of NHWC will be implemented in Conv_Op::forwardDims()
            REQUIRE(g1->forwardDims()); REQUIRE(g1->forwardDims());
            g1->save("adapttobackend_after_forwarddims", true); g1->save("adapttobackend_after_forwarddims", true);
            SequentialScheduler sched(g1); SequentialScheduler sched(g1);
            sched.forward(); sched.forward();
            } }
            TEST_CASE("[cpu/recipes] AdaptToBackend with MetaOp", "[AdaptToBackend][recipes]") { TEST_CASE("[cpu/recipes] AdaptToBackend with MetaOp", "[AdaptToBackend][recipes]") {
            auto g1 = Sequential({ auto g1 = Sequential({
            Producer({1, 3, 22, 22}, "dataProvider"), Producer({1, 3, 22, 22}, "dataProvider"),
            Conv(3, 4, {3, 3}, "conv1"), Conv(3, 4, {3, 3}, "conv1"),
            ...@@ -161,7 +161,7 @@ ...@@ -161,7 +161,7 @@
            }); });
            g1->forwardDims(); g1->forwardDims();
            g1->setBackend("dummy"); g1->setBackend("dummy");
            fuseToMetaOps(g1, "Conv2D->ReLU", "ConvReLU"); fuseToMetaOps(g1, "Conv2D->ReLU", "ConvReLU");
            g1->save("fuse_meta_op"); g1->save("fuse_meta_op");
            for( auto n : g1->getNodes()){ for( auto n : g1->getNodes()){
            ...@@ -184,10 +184,10 @@ ...@@ -184,10 +184,10 @@
            REQUIRE_NOTHROW(sched.generateMemory()); REQUIRE_NOTHROW(sched.generateMemory());
            REQUIRE_NOTHROW(sched.forward()); REQUIRE_NOTHROW(sched.forward());
            FAIL_CHECK("This test is expected to fail due to known issues."); FAIL_CHECK("This test is expected to fail due to known issues.");
            } }
            // Interesting test because used a lot for export // Interesting test because used a lot for export
            TEST_CASE("[cpu/recipes] AdaptToBackend with MetaOp and constantFolding", "[AdaptToBackend][recipes]") { TEST_CASE("[cpu/recipes] AdaptToBackend with MetaOp and constantFolding", "[AdaptToBackend][recipes]") {
            auto g1 = Sequential({ auto g1 = Sequential({
            Producer({1, 3, 22, 22}, "dataProvider"), Producer({1, 3, 22, 22}, "dataProvider"),
            Conv(3, 4, {3, 3}, "conv1"), Conv(3, 4, {3, 3}, "conv1"),
            ...@@ -195,7 +195,7 @@ ...@@ -195,7 +195,7 @@
            }); });
            g1->forwardDims(); g1->forwardDims();
            g1->setBackend("dummy"); g1->setBackend("dummy");
            fuseToMetaOps(g1, "Conv2D->ReLU", "ConvReLU"); fuseToMetaOps(g1, "Conv2D->ReLU", "ConvReLU");
            g1->save("fuse_meta_op"); g1->save("fuse_meta_op");
            for( auto n : g1->getNodes()){ for( auto n : g1->getNodes()){
            ...@@ -242,7 +242,7 @@ ...@@ -242,7 +242,7 @@
            } }
            REQUIRE(cpt == 2); REQUIRE(cpt == 2);
            FAIL_CHECK("This test is expected to fail due to known issues."); FAIL_CHECK("This test is expected to fail due to known issues.");
            } }
            } // namespace Aidge } // namespace Aidge
            \ No newline at end of file
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